Articles | Volume 23, issue 11
https://doi.org/10.5194/nhess-23-3543-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-23-3543-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Review article: Towards improved drought prediction in the Mediterranean region – modeling approaches and future directions
School of Computer Science, Mohammed VI Polytechnic University, Ben Guerir, 43150, Morocco
Nabil El Moçayd
International Water Research Institute, Mohammed VI Polytechnic University, Ben Guerir, 43150, Morocco
Institute of Applied Physics, Mohammed VI Polytechnic University, Ben Guerir, 43150, Morocco
El Houcine Bergou
School of Computer Science, Mohammed VI Polytechnic University, Ben Guerir, 43150, Morocco
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Alexandre Tuel, Nabil El Moçayd, Moulay Driss Hasnaoui, and Elfatih A. B. Eltahir
Hydrol. Earth Syst. Sci., 26, 571–588, https://doi.org/10.5194/hess-26-571-2022, https://doi.org/10.5194/hess-26-571-2022, 2022
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Snowmelt in the High Atlas is critical for irrigation in Morocco but is threatened by climate change. We assess future trends in High Atlas snowpack by modelling it under historical and future climate scenarios and estimate their impact on runoff. We find that the combined warming and drying will result in a roughly 80 % decline in snowpack, a 5 %–30 % decrease in runoff efficiency and 50 %–60 % decline in runoff under a business-as-usual scenario.
Narjiss Satour, Otmane Raji, Nabil El Moçayd, Ilias Kacimi, and Nadia Kassou
Nat. Hazards Earth Syst. Sci., 21, 1101–1118, https://doi.org/10.5194/nhess-21-1101-2021, https://doi.org/10.5194/nhess-21-1101-2021, 2021
Nabil El Moçayd, Suchul Kang, and Elfatih A. B. Eltahir
Hydrol. Earth Syst. Sci., 24, 1467–1483, https://doi.org/10.5194/hess-24-1467-2020, https://doi.org/10.5194/hess-24-1467-2020, 2020
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The present work addresses the impact of climate change on the Water Highway project in Morocco. This project aims to transfer 860 × 106 m3 yr−1 of water from the north to the south. As the project is very sensitive to the availability of water in the northern regions, we evaluate its feasibility under different future climate change scenarios: under a pessimistic climate scenario, the project is infeasible; however, under an optimistic scenario a rescaled version might be feasible.
Related subject area
Hydrological Hazards
Precursors and pathways: dynamically informed extreme event forecasting demonstrated on the historic Emilia-Romagna 2023 flood
Demonstrating the use of UNSEEN climate data for hydrological applications: case studies for extreme floods and droughts in England
Exploring the use of seasonal forecasts to adapt flood insurance premiums
Are 2D shallow-water solvers fast enough for early flood warning? A comparative assessment on the 2021 Ahr valley flood event
Water depth estimate and flood extent enhancement for satellite-based inundation maps
Probabilistic flood inundation mapping through copula Bayesian multi-modeling of precipitation products
Flood occurrence and impact models for socioeconomic applications over Canada and the United States
Model-based assessment of climate change impact on inland flood risk at the German North Sea coast caused by compounding storm tide and precipitation events
An improved dynamic bidirectional coupled hydrologic–hydrodynamic model for efficient flood inundation prediction
Quantifying hazard resilience by modeling infrastructure recovery as a resource-constrained project scheduling problem
Hydrometeorological controls of and social response to the 22 October 2019 catastrophic flash flood in Catalonia, north-eastern Spain
A downward-counterfactual analysis of flash floods in Germany
Hyper-resolution flood hazard mapping at the national scale
Compound droughts under climate change in Switzerland
Brief communication: SWM – stochastic weather model for precipitation-related hazard assessments using ERA5-Land data
Text mining uncovers the unique dynamics of socio-economic impacts of the 2018–2022 multi-year drought in Germany
The value of multi-source data for improved flood damage modelling with explicit input data uncertainty treatment: INSYDE 2.0
Risk of compound flooding substantially increases in the future Mekong River delta
Limited effect of the confluence angle and tributary gradient on Alpine confluence morphodynamics under intense sediment loads
Coupling WRF with HEC-HMS and WRF-Hydro for flood forecasting in typical mountainous catchments of northern China
Does a convection-permitting regional climate model bring new perspectives on the projection of Mediterranean floods?
Added value of seasonal hindcasts to create UK hydrological drought storylines
Flash flood detection via copula-based intensity–duration–frequency curves: evidence from Jamaica
Algorithmically Detected Rain-on-Snow Flood Events in Different Climate Datasets: A Case Study of the Susquehanna River Basin
Seasonal forecasting of local-scale soil moisture droughts with Global BROOK90: a case study of the European drought of 2018
How to mitigate flood events similar to the 1979 catastrophic floods in the lower Tagus
Review article: Drought as a continuum: memory effects in interlinked hydrological, ecological, and social systems
Assessing LISFLOOD-FP with the next-generation digital elevation model FABDEM using household survey and remote sensing data in the Central Highlands of Vietnam
CRHyME (Climatic Rainfall Hydrogeological Modelling Experiment): a new model for geo-hydrological hazard assessment at the basin scale
Current and future rainfall-driven flood risk from hurricanes in Puerto Rico under 1.5 and 2 °C climate change
Modelling hazards impacting the flow regime in the Hranice Karst due to the proposed Skalička Dam
Using integrated hydrological–hydraulic modelling and global data sources to analyse the February 2023 floods in the Umbeluzi Catchment (Mozambique)
Impact-based flood forecasting in the Greater Horn of Africa
Floods in the Pyrenees: A global view through a regional database
Brief communication: A first hydrological investigation of extreme August 2023 floods in Slovenia, Europe
Multivariate regression trees as an “explainable machine learning” approach to explore relationships between hydroclimatic characteristics and agricultural and hydrological drought severity: case of study Cesar River basin
Assessing typhoon-induced compound flood drivers: a case study in Ho Chi Minh City, Vietnam
Assessing the ability of a new seamless short-range ensemble rainfall product to anticipate flash floods in the French Mediterranean area
Sentinel-1-based analysis of the severe flood over Pakistan 2022
Sensitivity analysis of erosion on the landward slope of an earthen flood defense located in southern France submitted to wave overtopping
Transferability of machine learning-based modeling frameworks across flood events for hindcasting maximum river flood depths in coastal watersheds
Better prepared but less resilient: the paradoxical impact of frequent flood experience on adaptive behavior and resilience
Assessing the spatial spread–skill of ensemble flood maps with remote-sensing observations
An integrated modeling approach to evaluate the impacts of nature-based solutions of flood mitigation across a small watershed in the southeast United States
Indicator-to-impact links to help improve agricultural drought preparedness in Thailand
The potential of open-access data for flood estimations: uncovering inundation hotspots in Ho Chi Minh City, Vietnam, through a normalized flood severity index
Analyzing the informative value of alternative hazard indicators for monitoring drought hazard for human water supply and river ecosystems at the global scale
A methodological framework for the evaluation of short-range flash-flood hydrometeorological forecasts at the event scale
Hydrological drought forecasting under a changing environment in the Luanhe River basin
A multi-disciplinary analysis of the exceptional flood event of July 2021 in central Europe – Part 2: Historical context and relation to climate change
Joshua Dorrington, Marta Wenta, Federico Grazzini, Linus Magnusson, Frederic Vitart, and Christian M. Grams
Nat. Hazards Earth Syst. Sci., 24, 2995–3012, https://doi.org/10.5194/nhess-24-2995-2024, https://doi.org/10.5194/nhess-24-2995-2024, 2024
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Extreme rainfall is the leading weather-related source of damages in Europe, but it is still difficult to predict on long timescales. A recent example of this was the devastating floods in the Italian region of Emiglia Romagna in May 2023. We present perspectives based on large-scale dynamical information that allows us to better understand and predict such events.
Alison L. Kay, Nick Dunstone, Gillian Kay, Victoria A. Bell, and Jamie Hannaford
Nat. Hazards Earth Syst. Sci., 24, 2953–2970, https://doi.org/10.5194/nhess-24-2953-2024, https://doi.org/10.5194/nhess-24-2953-2024, 2024
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Hydrological hazards affect people and ecosystems, but extremes are not fully understood due to limited observations. A large climate ensemble and simple hydrological model are used to assess unprecedented but plausible floods and droughts. The chain gives extreme flows outside the observed range: summer 2022 ~ 28 % lower and autumn 2023 ~ 42 % higher. Spatial dependence and temporal persistence are analysed. Planning for such events could help water supply resilience and flood risk management.
Viet Dung Nguyen, Jeroen Aerts, Max Tesselaar, Wouter Botzen, Heidi Kreibich, Lorenzo Alfieri, and Bruno Merz
Nat. Hazards Earth Syst. Sci., 24, 2923–2937, https://doi.org/10.5194/nhess-24-2923-2024, https://doi.org/10.5194/nhess-24-2923-2024, 2024
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Our study explored how seasonal flood forecasts could enhance insurance premium accuracy. Insurers traditionally rely on historical data, yet climate fluctuations influence flood risk. We employed a method that predicts seasonal floods to adjust premiums accordingly. Our findings showed significant year-to-year variations in flood risk and premiums, underscoring the importance of adaptability. Despite limitations, this research aids insurers in preparing for evolving risks.
Shahin Khosh Bin Ghomash, Heiko Apel, and Daniel Caviedes-Voullième
Nat. Hazards Earth Syst. Sci., 24, 2857–2874, https://doi.org/10.5194/nhess-24-2857-2024, https://doi.org/10.5194/nhess-24-2857-2024, 2024
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Early warning is essential to minimise the impact of flash floods. We explore the use of highly detailed flood models to simulate the 2021 flood event in the lower Ahr valley (Germany). Using very high-resolution models resolving individual streets and buildings, we produce detailed, quantitative, and actionable information for early flood warning systems. Using state-of-the-art computational technology, these models can guarantee very fast forecasts which allow for sufficient time to respond.
Andrea Betterle and Peter Salamon
Nat. Hazards Earth Syst. Sci., 24, 2817–2836, https://doi.org/10.5194/nhess-24-2817-2024, https://doi.org/10.5194/nhess-24-2817-2024, 2024
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The study proposes a new framework, named FLEXTH, to estimate flood water depth and improve satellite-based flood monitoring using topographical data. FLEXTH is readily available as a computer code, offering a practical and scalable solution for estimating flood depth quickly and systematically over large areas. The methodology can reduce the impacts of floods and enhance emergency response efforts, particularly where resources are limited.
Francisco Javier Gomez, Keighobad Jafarzadegan, Hamed Moftakhari, and Hamid Moradkhani
Nat. Hazards Earth Syst. Sci., 24, 2647–2665, https://doi.org/10.5194/nhess-24-2647-2024, https://doi.org/10.5194/nhess-24-2647-2024, 2024
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This study utilizes the global copula Bayesian model averaging technique for accurate and reliable flood modeling, especially in coastal regions. By integrating multiple precipitation datasets within this framework, we can effectively address sources of error in each dataset, leading to the generation of probabilistic flood maps. The creation of these probabilistic maps is essential for disaster preparedness and mitigation in densely populated areas susceptible to extreme weather events.
Manuel Grenier, Mathieu Boudreault, David A. Carozza, Jérémie Boudreault, and Sébastien Raymond
Nat. Hazards Earth Syst. Sci., 24, 2577–2595, https://doi.org/10.5194/nhess-24-2577-2024, https://doi.org/10.5194/nhess-24-2577-2024, 2024
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Modelling floods at the street level for large countries like Canada and the United States is difficult and very costly. However, many applications do not necessarily require that level of detail. As a result, we present a flood modelling framework built with artificial intelligence for socioeconomic studies like trend and scenarios analyses. We find for example that an increase of 10 % in average precipitation yields an increase in displaced population of 18 % in Canada and 14 % in the US.
Helge Bormann, Jenny Kebschull, Lidia Gaslikova, and Ralf Weisse
Nat. Hazards Earth Syst. Sci., 24, 2559–2576, https://doi.org/10.5194/nhess-24-2559-2024, https://doi.org/10.5194/nhess-24-2559-2024, 2024
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Inland flooding is threatening coastal lowlands. If rainfall and storm surges coincide, the risk of inland flooding increases. We examine how such compound events are influenced by climate change. Data analysis and model-based scenario analysis show that climate change induces an increasing frequency and intensity of compounding precipitation and storm tide events along the North Sea coast. Overload of inland drainage systems will also increase if no timely adaptation measures are taken.
Yanxia Shen, Zhenduo Zhu, Qi Zhou, and Chunbo Jiang
Nat. Hazards Earth Syst. Sci., 24, 2315–2330, https://doi.org/10.5194/nhess-24-2315-2024, https://doi.org/10.5194/nhess-24-2315-2024, 2024
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We present an improved Multigrid Dynamical Bidirectional Coupled hydrologic–hydrodynamic Model (IM-DBCM) with two major improvements: (1) automated non-uniform mesh generation based on the D-infinity algorithm was implemented to identify flood-prone areas where high-resolution inundation conditions are needed, and (2) ghost cells and bilinear interpolation were implemented to improve numerical accuracy in interpolating variables between the coarse and fine grids. The improved model was reliable.
Taylor Glen Johnson, Jorge Leandro, and Divine Kwaku Ahadzie
Nat. Hazards Earth Syst. Sci., 24, 2285–2302, https://doi.org/10.5194/nhess-24-2285-2024, https://doi.org/10.5194/nhess-24-2285-2024, 2024
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Reliance on infrastructure creates vulnerabilities to disruptions caused by natural hazards. To assess the impacts of natural hazards on the performance of infrastructure, we present a framework for quantifying resilience and develop a model of recovery based upon an application of project scheduling under resource constraints. The resilience framework and recovery model were applied in a case study to assess the resilience of building infrastructure to flooding hazards in Accra, Ghana.
Arnau Amengual, Romu Romero, María Carmen Llasat, Alejandro Hermoso, and Montserrat Llasat-Botija
Nat. Hazards Earth Syst. Sci., 24, 2215–2242, https://doi.org/10.5194/nhess-24-2215-2024, https://doi.org/10.5194/nhess-24-2215-2024, 2024
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On 22 October 2019, the Francolí River basin experienced a heavy precipitation event, resulting in a catastrophic flash flood. Few studies comprehensively address both the physical and human dimensions and their interrelations during extreme flash flooding. This research takes a step forward towards filling this gap in knowledge by examining the alignment among all these factors.
Paul Voit and Maik Heistermann
Nat. Hazards Earth Syst. Sci., 24, 2147–2164, https://doi.org/10.5194/nhess-24-2147-2024, https://doi.org/10.5194/nhess-24-2147-2024, 2024
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To identify flash flood potential in Germany, we shifted the most extreme rainfall events from the last 22 years systematically across Germany and simulated the consequent runoff reaction. Our results show that almost all areas in Germany have not seen the worst-case scenario of flood peaks within the last 22 years. With a slight spatial change of historical rainfall events, flood peaks of a factor of 2 or more would be achieved for most areas. The results can aid disaster risk management.
Günter Blöschl, Andreas Buttinger-Kreuzhuber, Daniel Cornel, Julia Eisl, Michael Hofer, Markus Hollaus, Zsolt Horváth, Jürgen Komma, Artem Konev, Juraj Parajka, Norbert Pfeifer, Andreas Reithofer, José Salinas, Peter Valent, Roman Výleta, Jürgen Waser, Michael H. Wimmer, and Heinz Stiefelmeyer
Nat. Hazards Earth Syst. Sci., 24, 2071–2091, https://doi.org/10.5194/nhess-24-2071-2024, https://doi.org/10.5194/nhess-24-2071-2024, 2024
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A methodology of regional flood hazard mapping is proposed, based on data in Austria, which combines automatic methods with manual interventions to maximise efficiency and to obtain estimation accuracy similar to that of local studies. Flood discharge records from 781 stations are used to estimate flood hazard patterns of a given return period at a resolution of 2 m over a total stream length of 38 000 km. The hazard maps are used for civil protection, risk awareness and insurance purposes.
Christoph Nathanael von Matt, Regula Muelchi, Lukas Gudmundsson, and Olivia Martius
Nat. Hazards Earth Syst. Sci., 24, 1975–2001, https://doi.org/10.5194/nhess-24-1975-2024, https://doi.org/10.5194/nhess-24-1975-2024, 2024
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The simultaneous occurrence of meteorological (precipitation), agricultural (soil moisture), and hydrological (streamflow) drought can lead to augmented impacts. By analysing drought indices derived from the newest climate scenarios for Switzerland (CH2018, Hydro-CH2018), we show that with climate change the concurrence of all drought types will increase in all studied regions of Switzerland. Our results stress the benefits of and need for both mitigation and adaptation measures at early stages.
Melody Gwyneth Whitehead and Mark Stephen Bebbington
Nat. Hazards Earth Syst. Sci., 24, 1929–1935, https://doi.org/10.5194/nhess-24-1929-2024, https://doi.org/10.5194/nhess-24-1929-2024, 2024
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Precipitation-driven hazards including floods, landslides, and lahars can be catastrophic and difficult to forecast due to high uncertainty around future weather patterns. This work presents a stochastic weather model that produces statistically similar (realistic) rainfall over long time periods at minimal computational cost. These data provide much-needed inputs for hazard simulations to support long-term, time and spatially varying risk assessments.
Jan Sodoge, Christian Kuhlicke, Miguel D. Mahecha, and Mariana Madruga de Brito
Nat. Hazards Earth Syst. Sci., 24, 1757–1777, https://doi.org/10.5194/nhess-24-1757-2024, https://doi.org/10.5194/nhess-24-1757-2024, 2024
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We delved into the socio-economic impacts of the 2018–2022 drought in Germany. We derived a dataset covering the impacts of droughts in Germany between 2000 and 2022 on sectors such as agriculture and forestry based on newspaper articles. Notably, our study illustrated that the longer drought had a wider reach and more varied effects. We show that dealing with longer droughts requires different plans compared to shorter ones, and it is crucial to be ready for the challenges they bring.
Mario Di Bacco, Daniela Molinari, and Anna Rita Scorzini
Nat. Hazards Earth Syst. Sci., 24, 1681–1696, https://doi.org/10.5194/nhess-24-1681-2024, https://doi.org/10.5194/nhess-24-1681-2024, 2024
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INSYDE 2.0 is a tool for modelling flood damage to residential buildings. By incorporating ultra-detailed survey and desk-based data, it improves the reliability and informativeness of damage assessments while addressing input data uncertainties.
Melissa Wood, Ivan D. Haigh, Quan Quan Le, Hung Nghia Nguyen, Hoang Tran Ba, Stephen E. Darby, Robert Marsh, Nikolaos Skliris, and Joël J.-M. Hirschi
EGUsphere, https://doi.org/10.5194/egusphere-2024-949, https://doi.org/10.5194/egusphere-2024-949, 2024
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We look at how compound flooding from the combination of river flooding and storm tide (storm surge plus astronomical tide) may be changing over time due to climate change, with a case study of the Mekong River delta. We found that future compound flooding has potential to flood the region more extensively and be longer lasting than compound floods today. This is useful to know because it means that managers of deltas such as the Mekong can assess options for improving existing flood defences.
Théo St. Pierre Ostrander, Thomé Kraus, Bruno Mazzorana, Johannes Holzner, Andrea Andreoli, Francesco Comiti, and Bernhard Gems
Nat. Hazards Earth Syst. Sci., 24, 1607–1634, https://doi.org/10.5194/nhess-24-1607-2024, https://doi.org/10.5194/nhess-24-1607-2024, 2024
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Mountain river confluences are hazardous during localized flooding events. A physical model was used to determine the dominant controls over mountain confluences. Contrary to lowland confluences, in mountain regions, the channel discharges and (to a lesser degree) the tributary sediment concentration control morphological patterns. Applying conclusions drawn from lowland confluences could misrepresent depositional and erosional patterns and the related flood hazard at mountain river confluences.
Sheik Umar Jam-Jalloh, Jia Liu, Yicheng Wang, and Yuchen Liu
Nat. Hazards Earth Syst. Sci. Discuss., https://doi.org/10.5194/nhess-2024-20, https://doi.org/10.5194/nhess-2024-20, 2024
Revised manuscript accepted for NHESS
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Explore our paper on improving flood prediction using advanced weather models. We coupled the WRF model with WRF-Hydro and HEC-HMS to enhance accuracy. Discover how our findings contribute to adaptive atmospheric-hydrologic systems for effective flood forecasting.
Nils Poncet, Philippe Lucas-Picher, Yves Tramblay, Guillaume Thirel, Humberto Vergara, Jonathan Gourley, and Antoinette Alias
Nat. Hazards Earth Syst. Sci., 24, 1163–1183, https://doi.org/10.5194/nhess-24-1163-2024, https://doi.org/10.5194/nhess-24-1163-2024, 2024
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High-resolution convection-permitting climate models (CPMs) are now available to better simulate rainstorm events leading to flash floods. In this study, two hydrological models are compared to simulate floods in a Mediterranean basin, showing a better ability of the CPM to reproduce flood peaks compared to coarser-resolution climate models. Future projections are also different, with a projected increase for the most severe floods and a potential decrease for the most frequent events.
Wilson C. H. Chan, Nigel W. Arnell, Geoff Darch, Katie Facer-Childs, Theodore G. Shepherd, and Maliko Tanguy
Nat. Hazards Earth Syst. Sci., 24, 1065–1078, https://doi.org/10.5194/nhess-24-1065-2024, https://doi.org/10.5194/nhess-24-1065-2024, 2024
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The most recent drought in the UK was declared in summer 2022. We pooled a large sample of plausible winters from seasonal hindcasts and grouped them into four clusters based on their atmospheric circulation configurations. Drought storylines representative of what the drought could have looked like if winter 2022/23 resembled each winter circulation storyline were created to explore counterfactuals of how bad the 2022 drought could have been over winter 2022/23 and beyond.
Dino Collalti, Nekeisha Spencer, and Eric Strobl
Nat. Hazards Earth Syst. Sci., 24, 873–890, https://doi.org/10.5194/nhess-24-873-2024, https://doi.org/10.5194/nhess-24-873-2024, 2024
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The risk of extreme rainfall events causing floods is likely increasing with climate change. Flash floods, which follow immediately after extreme rainfall, are particularly difficult to forecast and assess. We develop a decision rule for flash flood classification with data on all incidents between 2001 and 2018 in Jamaica with the statistical copula method. This decision rule tells us for any rainfall event of a certain duration how intense it has to be to likely trigger a flash flood.
Colin M. Zarzycki, Benjamin D. Ascher, Alan M. Rhoades, and Rachel R. McCrary
EGUsphere, https://doi.org/10.5194/egusphere-2023-3094, https://doi.org/10.5194/egusphere-2023-3094, 2024
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We developed an automated workflow to detect rain-on-snow events, which cause flooding in the northeastern U.S., in climate data. Analyzing the Susquehanna River Basin, this technique identified known events affecting river flow. Comparing four gridded datasets revealed variations in event frequency and severity, driven by different snowmelt and runoff estimates. This highlights the need for accurate climate data in flood management and risk prediction for these compound extremes.
Ivan Vorobevskii, Thi Thanh Luong, and Rico Kronenberg
Nat. Hazards Earth Syst. Sci., 24, 681–697, https://doi.org/10.5194/nhess-24-681-2024, https://doi.org/10.5194/nhess-24-681-2024, 2024
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This study presents a new version of a framework which allows us to model water balance components at any site on a local scale. Compared with the first version, the second incorporates new datasets used to set up and force the model. In particular, we highlight the ability of the framework to provide seasonal forecasts. This gives potential stakeholders (farmers, foresters, policymakers, etc.) the possibility to forecast, for example, soil moisture drought and thus apply the necessary measures.
Diego Fernández-Nóvoa, Alexandre M. Ramos, José González-Cao, Orlando García-Feal, Cristina Catita, Moncho Gómez-Gesteira, and Ricardo M. Trigo
Nat. Hazards Earth Syst. Sci., 24, 609–630, https://doi.org/10.5194/nhess-24-609-2024, https://doi.org/10.5194/nhess-24-609-2024, 2024
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The present study focuses on an in-depth analysis of floods in the lower section of the Tagus River from a hydrodynamic perspective by means of the Iber+ numerical model and on the development of dam operating strategies to mitigate flood episodes using the exceptional floods of February 1979 as a benchmark. The results corroborate the model's capability to evaluate floods in the study area and confirm the effectiveness of the proposed strategies to reduce flood impact in the lower Tagus valley.
Anne F. Van Loon, Sarra Kchouk, Alessia Matanó, Faranak Tootoonchi, Camila Alvarez-Garreton, Khalid E. A. Hassaballah, Minchao Wu, Marthe L. K. Wens, Anastasiya Shyrokaya, Elena Ridolfi, Riccardo Biella, Viorica Nagavciuc, Marlies H. Barendrecht, Ana Bastos, Louise Cavalcante, Franciska T. de Vries, Margaret Garcia, Johanna Mård, Ileen N. Streefkerk, Claudia Teutschbein, Roshanak Tootoonchi, Ruben Weesie, Valentin Aich, Juan P. Boisier, Giuliano Di Baldassarre, Yiheng Du, Mauricio Galleguillos, René Garreaud, Monica Ionita, Sina Khatami, Johanna K. L. Koehler, Charles H. Luce, Shreedhar Maskey, Heidi D. Mendoza, Moses N. Mwangi, Ilias G. Pechlivanidis, Germano G. Ribeiro Neto, Tirthankar Roy, Robert Stefanski, Patricia Trambauer, Elizabeth A. Koebele, Giulia Vico, and Micha Werner
EGUsphere, https://doi.org/10.5194/egusphere-2024-421, https://doi.org/10.5194/egusphere-2024-421, 2024
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Drought is a creeping phenomenon, but it is often still analysed and managed like an event without taking into consideration what happened before and after. In this paper we review the literature and discuss five cases, where drought, its impacts and responses develop differently over time. We look at the hydrological, ecological and social system and their connections. And we provide suggestions for further research and for monitoring, modelling and management.
Laurence Hawker, Jeffrey Neal, James Savage, Thomas Kirkpatrick, Rachel Lord, Yanos Zylberberg, Andre Groeger, Truong Dang Thuy, Sean Fox, Felix Agyemang, and Pham Khanh Nam
Nat. Hazards Earth Syst. Sci., 24, 539–566, https://doi.org/10.5194/nhess-24-539-2024, https://doi.org/10.5194/nhess-24-539-2024, 2024
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We present a global flood model built using a new terrain data set and evaluated in the Central Highlands of Vietnam.
Andrea Abbate, Leonardo Mancusi, Francesco Apadula, Antonella Frigerio, Monica Papini, and Laura Longoni
Nat. Hazards Earth Syst. Sci., 24, 501–537, https://doi.org/10.5194/nhess-24-501-2024, https://doi.org/10.5194/nhess-24-501-2024, 2024
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CRHyME (Climatic Rainfall Hydrogeological Modelling Experiment) is a new physically based and spatially distributed rainfall-runoff model. The main novelties consist of reproducing rainfall-induced geo-hydrological hazards such as shallow landslide, debris flow and watershed erosion through a multi-hazard approach. CRHyME was written in Python, works at a high spatial and temporal resolution, and is a tool suitable for quantifying extreme rainfall consequences at the basin scale.
Leanne Archer, Jeffrey Neal, Paul Bates, Emily Vosper, Dereka Carroll, Jeison Sosa, and Daniel Mitchell
Nat. Hazards Earth Syst. Sci., 24, 375–396, https://doi.org/10.5194/nhess-24-375-2024, https://doi.org/10.5194/nhess-24-375-2024, 2024
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We model hurricane-rainfall-driven flooding to assess how the number of people exposed to flooding changes in Puerto Rico under the 1.5 and 2 °C Paris Agreement goals. Our analysis suggests 8 %–10 % of the population is currently exposed to flooding on average every 5 years, increasing by 2 %–15 % and 1 %–20 % at 1.5 and 2 °C. This has implications for adaptation to more extreme flooding in Puerto Rico and demonstrates that 1.5 °C climate change carries a significant increase in risk.
Miroslav Spano and Jaromir Riha
Nat. Hazards Earth Syst. Sci. Discuss., https://doi.org/10.5194/nhess-2024-21, https://doi.org/10.5194/nhess-2024-21, 2024
Revised manuscript accepted for NHESS
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Our study examines how building the Skalička Dam near the Hranice Karst affects local groundwater. We used advanced modeling to analyze two dam layouts: lateral and through-flow reservoirs. Results show the through-flow variant significantly alters water levels and mineral water discharge, while the lateral layout has less impact.
Luis Cea, Manuel Álvarez, and Jerónimo Puertas
Nat. Hazards Earth Syst. Sci., 24, 225–243, https://doi.org/10.5194/nhess-24-225-2024, https://doi.org/10.5194/nhess-24-225-2024, 2024
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Mozambique is highly exposed to the impact of floods. To reduce flood damage, it is necessary to develop mitigation measures. Hydrological software is a very useful tool for that purpose, since it allows for a precise quantification of flood hazard in different scenarios. We present a methodology to quantify flood hazard in data-scarce regions, using freely available data and software, and we show its potential by analysing the flood event that took place in the Umbeluzi Basin in February 2023.
Lorenzo Alfieri, Andrea Libertino, Lorenzo Campo, Francesco Dottori, Simone Gabellani, Tatiana Ghizzoni, Alessandro Masoero, Lauro Rossi, Roberto Rudari, Nicola Testa, Eva Trasforini, Ahmed Amdihun, Jully Ouma, Luca Rossi, Yves Tramblay, Huan Wu, and Marco Massabò
Nat. Hazards Earth Syst. Sci., 24, 199–224, https://doi.org/10.5194/nhess-24-199-2024, https://doi.org/10.5194/nhess-24-199-2024, 2024
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This work describes Flood-PROOFS East Africa, an impact-based flood forecasting system for the Greater Horn of Africa. It is based on hydrological simulations, inundation mapping, and estimation of population and assets exposed to upcoming river floods. The system supports duty officers in African institutions in the daily monitoring of hydro-meteorological disasters. A first evaluation shows the system performance for the catastrophic floods in the Nile River basin in summer 2020.
María Carmen Llasat, Montserrat Llasat-Botija, Erika Pardo, Raül Marcos-Matamoros, and Marc Lemus-Canovas
Nat. Hazards Earth Syst. Sci. Discuss., https://doi.org/10.5194/nhess-2023-206, https://doi.org/10.5194/nhess-2023-206, 2024
Revised manuscript accepted for NHESS
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Climate change is leading in the Pyrenees Massif to a change in socioeconomic increasing their sensitivity to natural risks such as floods. However, until now, no systematic study like this one had been carried out that would allow evaluating the frequency, distribution and main meteorological features of these events on a massif scale. In 35 years there have been 181 flood events that have produced 154 fatalities.
Nejc Bezak, Panos Panagos, Leonidas Liakos, and Matjaž Mikoš
Nat. Hazards Earth Syst. Sci., 23, 3885–3893, https://doi.org/10.5194/nhess-23-3885-2023, https://doi.org/10.5194/nhess-23-3885-2023, 2023
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Extreme flooding occurred in Slovenia in August 2023. This brief communication examines the main causes, mechanisms and effects of this event. The flood disaster of August 2023 can be described as relatively extreme and was probably the most extreme flood event in Slovenia in recent decades. The economic damage was large and could amount to well over 5 % of Slovenia's annual gross domestic product; the event also claimed three lives.
Ana Paez-Trujilo, Jeffer Cañon, Beatriz Hernandez, Gerald Corzo, and Dimitri Solomatine
Nat. Hazards Earth Syst. Sci., 23, 3863–3883, https://doi.org/10.5194/nhess-23-3863-2023, https://doi.org/10.5194/nhess-23-3863-2023, 2023
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This study uses a machine learning technique, the multivariate regression tree approach, to assess the hydroclimatic characteristics that govern agricultural and hydrological drought severity. The results show that the employed technique successfully identified the primary drivers of droughts and their critical thresholds. In addition, it provides relevant information to identify the areas most vulnerable to droughts and design strategies and interventions for drought management.
Francisco Rodrigues do Amaral, Nicolas Gratiot, Thierry Pellarin, and Tran Anh Tu
Nat. Hazards Earth Syst. Sci., 23, 3379–3405, https://doi.org/10.5194/nhess-23-3379-2023, https://doi.org/10.5194/nhess-23-3379-2023, 2023
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We propose an in-depth analysis of typhoon-induced compound flood drivers in the megacity of Ho Chi Minh, Vietnam. We use in situ and satellite measurements throughout the event to form a holistic overview of its impact. No evidence of storm surge was found, and peak precipitation presents a 16 h time lag to peak river discharge, which evacuates only 1.5 % of available water. The astronomical tide controls the river level even during the extreme event, and it is the main urban flood driver.
Juliette Godet, Olivier Payrastre, Pierre Javelle, and François Bouttier
Nat. Hazards Earth Syst. Sci., 23, 3355–3377, https://doi.org/10.5194/nhess-23-3355-2023, https://doi.org/10.5194/nhess-23-3355-2023, 2023
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This article results from a master's research project which was part of a natural hazards programme developed by the French Ministry of Ecological Transition. The objective of this work was to investigate a possible way to improve the operational flash flood warning service by adding rainfall forecasts upstream of the forecasting chain. The results showed that the tested forecast product, which is new and experimental, has a real added value compared to other classical forecast products.
Florian Roth, Bernhard Bauer-Marschallinger, Mark Edwin Tupas, Christoph Reimer, Peter Salamon, and Wolfgang Wagner
Nat. Hazards Earth Syst. Sci., 23, 3305–3317, https://doi.org/10.5194/nhess-23-3305-2023, https://doi.org/10.5194/nhess-23-3305-2023, 2023
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In August and September 2022, millions of people were impacted by a severe flood event in Pakistan. Since many roads and other infrastructure were destroyed, satellite data were the only way of providing large-scale information on the flood's impact. Based on the flood mapping algorithm developed at Technische Universität Wien (TU Wien), we mapped an area of 30 492 km2 that was flooded at least once during the study's time period. This affected area matches about the total area of Belgium.
Clément Houdard, Adrien Poupardin, Philippe Sergent, Abdelkrim Bennabi, and Jena Jeong
Nat. Hazards Earth Syst. Sci., 23, 3111–3124, https://doi.org/10.5194/nhess-23-3111-2023, https://doi.org/10.5194/nhess-23-3111-2023, 2023
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We developed a system able to to predict, knowing the appropriate characteristics of the flood defense structure and sea state, the return periods of potentially dangerous events as well as a ranking of parameters by order of uncertainty.
The model is a combination of statistical and empirical methods that have been applied to a Mediterranean earthen dike. This shows that the most important characteristics of the dyke are its geometrical features, such as its height and slope angles.
Maryam Pakdehi, Ebrahim Ahmadisharaf, Behzad Nazari, and Eunsaem Cho
Nat. Hazards Earth Syst. Sci. Discuss., https://doi.org/10.5194/nhess-2023-152, https://doi.org/10.5194/nhess-2023-152, 2023
Revised manuscript accepted for NHESS
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Machine learning (ML) models have growingly received attention for predicting flood events. However, there has been concerns about the transferability of these models (their capability in predicting out-of-sample events). Here, we showed that ML models can be transferable for hindcasting maximum river flood depths across major events (Hurricanes Ida, Isaias, Sandy, and Irene) in coastal watersheds when informed by the spatial distribution of pertinent features and underlying physical processes.
Lisa Köhler, Torsten Masson, Sabrina Köhler, and Christian Kuhlicke
Nat. Hazards Earth Syst. Sci., 23, 2787–2806, https://doi.org/10.5194/nhess-23-2787-2023, https://doi.org/10.5194/nhess-23-2787-2023, 2023
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We analyzed the impact of flood experience on adaptive behavior and self-reported resilience. The outcomes draw a paradoxical picture: the most experienced people are the most adapted but the least resilient. We find evidence for non-linear relationships between the number of floods experienced and resilience. We contribute to existing knowledge by focusing specifically on the number of floods experienced and extending the rare scientific literature on the influence of experience on resilience.
Helen Hooker, Sarah L. Dance, David C. Mason, John Bevington, and Kay Shelton
Nat. Hazards Earth Syst. Sci., 23, 2769–2785, https://doi.org/10.5194/nhess-23-2769-2023, https://doi.org/10.5194/nhess-23-2769-2023, 2023
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Ensemble forecasts of flood inundation produce maps indicating the probability of flooding. A new approach is presented to evaluate the spatial performance of an ensemble flood map forecast by comparison against remotely observed flooding extents. This is important for understanding forecast uncertainties and improving flood forecasting systems.
Betina I. Guido, Ioana Popescu, Vidya Samadi, and Biswa Bhattacharya
Nat. Hazards Earth Syst. Sci., 23, 2663–2681, https://doi.org/10.5194/nhess-23-2663-2023, https://doi.org/10.5194/nhess-23-2663-2023, 2023
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We used an integrated model to evaluate the impacts of nature-based solutions (NBSs) on flood mitigation across the Little Pee Dee and Lumber River watershed, the Carolinas, US. This area is strongly affected by climatic disasters, which are expected to increase due to climate change and urbanization, so exploring an NBS approach is crucial for adapting to future alterations. Our research found that NBSs can have visible effects on the reduction in hurricane-driven flooding.
Maliko Tanguy, Michael Eastman, Eugene Magee, Lucy J. Barker, Thomas Chitson, Chaiwat Ekkawatpanit, Daniel Goodwin, Jamie Hannaford, Ian Holman, Liwa Pardthaisong, Simon Parry, Dolores Rey Vicario, and Supattra Visessri
Nat. Hazards Earth Syst. Sci., 23, 2419–2441, https://doi.org/10.5194/nhess-23-2419-2023, https://doi.org/10.5194/nhess-23-2419-2023, 2023
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Droughts in Thailand are becoming more severe due to climate change. Understanding the link between drought impacts on the ground and drought indicators used in drought monitoring systems can help increase a country's preparedness and resilience to drought. With a focus on agricultural droughts, we derive crop- and region-specific indicator-to-impact links that can form the basis of targeted mitigation actions and an improved drought monitoring and early warning system in Thailand.
Leon Scheiber, Mazen Hoballah Jalloul, Christian Jordan, Jan Visscher, Hong Quan Nguyen, and Torsten Schlurmann
Nat. Hazards Earth Syst. Sci., 23, 2313–2332, https://doi.org/10.5194/nhess-23-2313-2023, https://doi.org/10.5194/nhess-23-2313-2023, 2023
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Numerical models are increasingly important for assessing urban flooding, yet reliable input data are oftentimes hard to obtain. Taking Ho Chi Minh City as an example, this paper explores the usability and reliability of open-access data to produce preliminary risk maps that provide first insights into potential flooding hotspots. As a key novelty, a normalized flood severity index is presented which combines flood depth and duration to enhance the interpretation of hydro-numerical results.
Claudia Herbert and Petra Döll
Nat. Hazards Earth Syst. Sci., 23, 2111–2131, https://doi.org/10.5194/nhess-23-2111-2023, https://doi.org/10.5194/nhess-23-2111-2023, 2023
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This paper presents a new method for selecting streamflow drought hazard indicators for monitoring drought hazard for human water supply and river ecosystems in large-scale drought early warning systems. Indicators are classified by their inherent assumptions about the habituation of people and ecosystems to the streamflow regime and their level of drought characterization, namely drought magnitude (water deficit at a certain point in time) and severity (cumulated magnitude since drought onset).
Maryse Charpentier-Noyer, Daniela Peredo, Axelle Fleury, Hugo Marchal, François Bouttier, Eric Gaume, Pierre Nicolle, Olivier Payrastre, and Maria-Helena Ramos
Nat. Hazards Earth Syst. Sci., 23, 2001–2029, https://doi.org/10.5194/nhess-23-2001-2023, https://doi.org/10.5194/nhess-23-2001-2023, 2023
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This paper proposes a methodological framework designed for event-based evaluation in the context of an intense flash-flood event. The evaluation adopts the point of view of end users, with a focus on the anticipation of exceedances of discharge thresholds. With a study of rainfall forecasts, a discharge evaluation and a detailed look at the forecast hydrographs, the evaluation framework should help in drawing robust conclusions about the usefulness of new rainfall ensemble forecasts.
Min Li, Mingfeng Zhang, Runxiang Cao, Yidi Sun, and Xiyuan Deng
Nat. Hazards Earth Syst. Sci., 23, 1453–1464, https://doi.org/10.5194/nhess-23-1453-2023, https://doi.org/10.5194/nhess-23-1453-2023, 2023
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It is an important disaster reduction strategy to forecast hydrological drought. In order to analyse the impact of human activities on hydrological drought, we constructed the human activity factor based on the method of restoration. With the increase of human index (HI) value, hydrological droughts tend to transition to more severe droughts. The conditional distribution model involving of human activity factor can further improve the forecasting accuracy of drought in the Luanhe River basin.
Patrick Ludwig, Florian Ehmele, Mário J. Franca, Susanna Mohr, Alberto Caldas-Alvarez, James E. Daniell, Uwe Ehret, Hendrik Feldmann, Marie Hundhausen, Peter Knippertz, Katharina Küpfer, Michael Kunz, Bernhard Mühr, Joaquim G. Pinto, Julian Quinting, Andreas M. Schäfer, Frank Seidel, and Christina Wisotzky
Nat. Hazards Earth Syst. Sci., 23, 1287–1311, https://doi.org/10.5194/nhess-23-1287-2023, https://doi.org/10.5194/nhess-23-1287-2023, 2023
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Heavy precipitation in July 2021 led to widespread floods in western Germany and neighboring countries. The event was among the five heaviest precipitation events of the past 70 years in Germany, and the river discharges exceeded by far the statistical 100-year return values. Simulations of the event under future climate conditions revealed a strong and non-linear effect on flood peaks: for +2 K global warming, an 18 % increase in rainfall led to a 39 % increase of the flood peak in the Ahr river.
Cited articles
Aas, K. and Berg, D.: Models for construction of multivariate dependence – a comparison study, Eur. J. Finance, 15, 639–659, https://doi.org/10.1080/13518470802588767, 2009.
Abbaszadeh, P., Moradkhani, H., and Yan, H.: Enhancing hydrologic data assimilation by evolutionary Particle Filter and Markov Chain Monte Carlo, Adv. Water Resour., 111, 192–204, https://doi.org/10.1016/j.advwatres.2017.11.011, 2018.
Abhishek, A., Das, N. N., Ines, A. V. M., Andreadis, K. M., Jayasinghe, S., Granger, S., Ellenburg, W. L., Dutta, R., Hanh Quyen, N., Markert, A. M., Mishra, V., and Phanikumar, M. S.: Evaluating the impacts of drought on rice productivity over Cambodia in the Lower Mekong Basin, J. Hydrol., 599, 126291, https://doi.org/10.1016/j.jhydrol.2021.126291, 2021.
Achite, M., Bazrafshan, O., Azhdari, Z., Wałęga, A., Krakauer, N., and Caloiero, T.: Forecasting of SPI and SRI Using Multiplicative ARIMA under Climate Variability in a Mediterranean Region: Wadi Ouahrane Basin, Algeria, Climate, 10, 36, https://doi.org/10.3390/cli10030036, 2022.
Achour, K., Meddi, M., Zeroual, A., Bouabdelli, S., Maccioni, P., and Moramarco, T.: Spatio-temporal analysis and forecasting of drought in the plains of northwestern Algeria using the standardized precipitation index, J. Earth Syst. Sci., 129, 42, https://doi.org/10.1007/s12040-019-1306-3, 2020.
Agana, N. A. and Homaifar, A.: A deep learning based approach for long-term drought prediction, in: IEEE SoutheastCon 2017, 30 March–2 April 2017, Concord, NC, USA, 1–8, https://doi.org/10.1109/SECON.2017.7925314, 2017.
AghaKouchak, A.: Entropy–Copula in Hydrology and Climatology, J. Hydrometeorol., 15, 2176–2189, https://doi.org/10.1175/JHM-D-13-0207.1, 2014.
AghaKouchak, A., Cheng, L., Mazdiyasni, O., and Farahmand, A.: Global warming and changes in risk of concurrent climate extremes: Insights from the 2014 California drought, Geophys. Res. Lett., 41, 8847–8852, 2014.
AghaKouchak, A., Farahmand, A., Melton, F. S., Teixeira, J., Anderson, M. C., Wardlow, B. D., and Hain, C. R.: Remote sensing of drought: Progress, challenges and opportunities: Remote Sensing Of Drought, Rev. Geophys., 53, 452–480, https://doi.org/10.1002/2014RG000456, 2015.
AghaKouchak, A., Mirchi, A., Madani, K., Di Baldassarre, G., Nazemi, A., Alborzi, A., Anjileli, H., Azarderakhsh, M., Chiang, F., Hassanzadeh, E., Huning, L. S., Mallakpour, I., Martinez, A., Mazdiyasni, O., Moftakhari, H., Norouzi, H., Sadegh, M., Sadeqi, D., Van Loon, A. F., and Wanders, N.: Anthropogenic Drought: Definition, Challenges, and Opportunities, Rev. Geophys., 59, e2019RG000683, https://doi.org/10.1029/2019RG000683, 2021.
AghaKouchak, A., Pan, B., Mazdiyasni, O., Sadegh, M., Jiwa, S., Zhang, W., Love, C. A., Madadgar, S., Papalexiou, S. M., Davis, S. J., Hsu, K., and Sorooshian, S.: Status and prospects for drought forecasting: opportunities in artificial intelligence and hybrid physical–statistical forecasting, Philos. T. Roy. Soc. A, 380, 20210288, https://doi.org/10.1098/rsta.2021.0288, 2022.
Aghelpour, P., Mohammadi, B., Biazar, S. M., Kisi, O., and Sourmirinezhad, Z.: A Theoretical Approach for Forecasting Different Types of Drought Simultaneously, Using Entropy Theory and Machine-Learning Methods, ISPRS Int. J. Geo-Inf., 9, 701, https://doi.org/10.3390/ijgi9120701, 2020.
Aguirre-Gutiérrez, J., Oliveras, I., Rifai, S., Fauset, S., Adu-Bredu, S., Affum‐Baffoe, K., Baker, T. R., Feldpausch, T. R., Gvozdevaite, A., and Hubau, W.: Drier tropical forests are susceptible to functional changes in response to a long‐term drought, Ecol. Lett., 22, 855–865, https://doi.org/10.1111/ele.13243, 2019.
Ahmed, K., Sachindra, D. A., Shahid, S., Demirel, M. C., and Chung, E.-S.: Selection of multi-model ensemble of general circulation models for the simulation of precipitation and maximum and minimum temperature based on spatial assessment metrics, Hydrol. Earth Syst. Sci., 23, 4803–4824, https://doi.org/10.5194/hess-23-4803-2019, 2019.
Akyuz, D. E., Bayazit, M., and Onoz, B.: Markov Chain Models for Hydrological Drought Characteristics, J. Hydrometeorol., 13, 298–309, https://doi.org/10.1175/JHM-D-11-019.1, 2012.
Al Sayah, M. J., Abdallah, C., Khouri, M., Nedjai, R., and Darwich, T.: A framework for climate change assessment in Mediterranean data-sparse watersheds using remote sensing and ARIMA modeling, Theor. Appl. Climatol., 143, 639–658, https://doi.org/10.1007/s00704-020-03442-7, 2021.
Arnold, J. G., Srinivasan, R., Muttiah, R. S., and Williams, J. R.: Large area hydrologic modeling and assessment part I: model development, J. Am. Water Resour. Assoc., 34, 73–89, 1998.
Ayugi, B., Eresanya, E. O., Onyango, A. O., Ogou, F. K., Okoro, E. C., Okoye, C. O., Anoruo, C. M., Dike, V. N., Ashiru, O. R., Daramola, M. T., Mumo, R., and Ongoma, V.: Review of Meteorological Drought in Africa: Historical Trends, Impacts, Mitigation Measures, and Prospects, Pure Appl. Geophys., 179, 1365–1386, https://doi.org/10.1007/s00024-022-02988-z, 2022.
Babre, A., Bikse, J., Popovs, K., Kalvans, A., and Delina, A.: Differences in the ERA5-Land reanalysis and real observation datasets for calculation of drought indices from two distinct points, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-18404, https://doi.org/10.5194/egusphere-egu2020-18404, 2020.
Bağçaci, S. Ç., Yucel, I., Duzenli, E., and Yilmaz, M. T.: Intercomparison of the expected change in the temperature and the precipitation retrieved from CMIP6 and CMIP5 climate projections: A Mediterranean hot spot case, Turkey, Atmos. Res., 256, 105576, https://doi.org/10.1016/j.atmosres.2021.105576, 2021.
Balting, D. F., AghaKouchak, A., Lohmann, G., and Ionita, M.: Northern Hemisphere drought risk in a warming climate, Npj Clim. Atmos. Sci., 4, 1–13, https://doi.org/10.1038/s41612-021-00218-2, 2021.
Band, S. S., Karami, H., Jeong, Y.-W., Moslemzadeh, M., Farzin, S., Chau, K.-W., Bateni, S. M., and Mosavi, A.: Evaluation of Time Series Models in Simulating Different Monthly Scales of Drought Index for Improving Their Forecast Accuracy, Front. Earth Sci., 10, 839527, https://doi.org/10.3389/feart.2022.839527, 2022.
Bannister, R.: A review of operational methods of variational and ensemble-variational data assimilation, Q. J. Roy. Meteorol. Soc., 143, 607–633, 2017.
Baronetti, A., Dubreuil, V., Provenzale, A., and Fratianni, S.: Future droughts in northern Italy: high-resolution projections using EURO-CORDEX and MED-CORDEX ensembles, Climatic Change, 172, 22, https://doi.org/10.1007/s10584-022-03370-7, 2022.
Başakın, E. E., Ekmekcioğlu, Ö., and Özger, M.: Drought prediction using hybrid soft-computing methods for semi-arid region, Model. Earth Syst. Environ., 7, 2363–2371, https://doi.org/10.1007/s40808-020-01010-6, 2021.
Bazrkar, M. H. and Chu, X.: Ensemble stationary-based support vector regression for drought prediction under changing climate, J. Hydrol., 603, 127059, https://doi.org/10.1016/j.jhydrol.2021.127059, 2021.
Beck, H. E., Pan, M., Roy, T., Weedon, G. P., Pappenberger, F., van Dijk, A. I. J. M., Huffman, G. J., Adler, R. F., and Wood, E. F.: Daily evaluation of 26 precipitation datasets using Stage-IV gauge-radar data for the CONUS, Hydrol. Earth Syst. Sci., 23, 207–224. https://doi.org/10.5194/hess-23-207-2019, 2019.
Belal, A.-A., El-Ramady, H. R., Mohamed, E. S., and Saleh, A. M.: Drought risk assessment using remote sensing and GIS techniques, Arab. J. Geosci., 7, 35–53, https://doi.org/10.1007/s12517-012-0707-2, 2014.
Ben Abdelmalek, M. and Nouiri, I.: Study of trends and mapping of drought events in Tunisia and their impacts on agricultural production, Sci. Total Environ., 734, 139311, https://doi.org/10.1016/j.scitotenv.2020.139311, 2020.
Ben Mhenni, N., Shinoda, M., and Nandintsetseg, B.: Assessment of drought frequency, severity, and duration and its impacts on vegetation greenness and agriculture production in Mediterranean dryland: A case study in Tunisia, Nat. Hazards, 105, 2755–2776, https://doi.org/10.1007/s11069-020-04422-w, 2021.
Bergou, E., Gratton, S., and Vicente, L. N.: Levenberg–Marquardt Methods Based on Probabilistic Gradient Models and Inexact Subproblem Solution, with Application to Data Assimilation, SIAMASA J. Uncertain. Quantif., 4, 924–951, 2016.
Bergou, E., Diouane, Y., and Kungurtsev, V.: Convergence and Complexity Analysis of a Levenberg-Marquardt Algorithm for Inverse Problems, J. Optimiz. Theory Appl., 185, 927–944, https://doi.org/10.1007/s10957-020-01666-1, 2020.
Bonaccorso, B., Cancelliere, A., and Rossi, G.: Probabilistic forecasting of drought class transitions in Sicily (Italy) using Standardized Precipitation Index and North Atlantic Oscillation Index, J. Hydrol., 526, 136–150, https://doi.org/10.1016/j.jhydrol.2015.01.070, 2015.
Bouabdelli, S., Meddi, M., Zeroual, A., and Alkama, R.: Hydrological drought risk recurrence under climate change in the karst area of Northwestern Algeria, J. Water Clim. Change, 11, 164–188, https://doi.org/10.2166/wcc.2020.207, 2020.
Bouabdelli, S., Zeroual, A., Meddi, M., and Assani, A.: Impact of temperature on agricultural drought occurrence under the effects of climate change, Theor. Appl. Climatol., 148, 191–209, https://doi.org/10.1007/s00704-022-03935-7, 2022.
Bouznad, I.-E., Guastaldi, E., Zirulia, A., Brancale, M., Barbagli, A., and Bengusmia, D.: Trend analysis and spatiotemporal prediction of precipitation, temperature, and evapotranspiration values using the ARIMA models: case of the Algerian Highlands, Arab. J. Geosci., 13, 1281, https://doi.org/10.1007/s12517-020-06330-6, 2021.
Box, G. E., Jenkins, G. M., Reinsel, G. C., and Ljung, G. M.: Time series analysis: forecasting and control, John Wiley & Sons, ISBN 0139051007, ISBN 9780139051005, 2015.
Bradford, J. B., Schlaepfer, D. R., Lauenroth, W. K., and Palmquist, K. A.: Robust ecological drought projections for drylands in the 21st century, Global Change Biol., 26, 3906–3919, https://doi.org/10.1111/gcb.15075, 2020.
Briassoulis, H. (Ed.): Policy Integration for Complex Environmental Problems: The Example of Mediterranean Desertification, Routledge, London, https://doi.org/10.4324/9781315246598, 2017.
Brönnimann, S., Xoplaki, E., Casty, C., Pauling, A., and Luterbacher, J.: ENSO influence on Europe during the last centuries, Clim. Dynam., 28, 181–197, https://doi.org/10.1007/s00382-006-0175-z, 2007.
Brouziyne, Y., Abouabdillah, A., Chehbouni, A., Hanich, L., Bergaoui, K., McDonnell, R., and Benaabidate, L.: Assessing Hydrological Vulnerability to Future Droughts in a Mediterranean Watershed: Combined Indices-Based and Distributed Modeling Approaches, Water, 12, 2333, https://doi.org/10.3390/w12092333, 2020.
Cancelliere, A., Mauro, G. D., Bonaccorso, B., and Rossi, G.: Drought forecasting using the Standardized Precipitation Index, Water Resour. Manage., 21, 801–819, https://doi.org/10.1007/s11269-006-9062-y, 2007.
Carrão, H., Russo, S., Sepulcre-Canto, G., and Barbosa, P.: An empirical standardized soil moisture index for agricultural drought assessment from remotely sensed data, Int. J. Appl. Earth Obs. Geoinform., 48, 74–84, https://doi.org/10.1016/j.jag.2015.06.011, 2016.
Carvalho, D., Pereira, S. C., Silva, R., and Rocha, A.: Aridity and desertification in the Mediterranean under EURO-CORDEX future climate change scenarios, Climatic Change, 174, 28, https://doi.org/10.1007/s10584-022-03454-4, 2022.
Chaqdid, A., Tuel, A., El Fatimy, A., and El Moçayd, N.: Extreme Rainfall Events in Morocco: Spatial Dependence and Climate Drivers, Weather Clim. Extrem., 40, 100556, https://doi.org/10.1016/j.wace.2023.100556, 2023.
Ceglar, A., Turco, M., Toreti, A., and Doblas-Reyes, F. J.: Linking crop yield anomalies to large-scale atmospheric circulation in Europe, Agr. Forest Meteorol., 240–241, 35–45, https://doi.org/10.1016/j.agrformet.2017.03.019, 2017.
Chen, L., Singh, V. P., Guo, S., Mishra, A. K., and Guo, J.: Drought Analysis Using Copulas, J. Hydrol. Eng., 18, 797–808, https://doi.org/10.1061/(ASCE)HE.1943-5584.0000697, 2013.
Cook, B. I., Anchukaitis, K. J., Touchan, R., Meko, D. M., and Cook, E. R.: Spatiotemporal drought variability in the Mediterranean over the last 900 years, J. Geophys. Res.-Atmos., 121, 2060–2074. https://doi.org/10.1002/2015JD023929, 2016.
Cos, J., Doblas-Reyes, F., Jury, M., Marcos, R., Bretonnière, P.-A., and Samsó, M. : The Mediterranean climate change hotspot in the CMIP5 and CMIP6 projections, Earth Syst. Dynam., 13, 321–340, https://doi.org/10.5194/esd-13-321-2022, 2022.
Crausbay, S. D., Ramirez, A. R., Carter, S. L., Cross, M. S., Hall, K. R., Bathke, D. J., Betancourt, J. L., Colt, S., Cravens, A. E., Dalton, M. S., Dunham, J. B., Hay, L. E., Hayes, M. J., McEvoy, J., McNutt, C. A., Moritz, M. A., Nislow, K. H., Raheem, N., and Sanford, T.: Defining Ecological Drought for the Twenty-First Century, B. Am. Meteorol. Soc., 98, 2543–2550, https://doi.org/10.1175/BAMS-D-16-0292.1, 2017.
Czaja, A. and Frankignoul, C.: Influence of the North Atlantic SST on the atmospheric circulation, Geophys. Res. Lett., 26, 2969–2972, https://doi.org/10.1029/1999GL900613, 1999.
Dai, A.: Drought under global warming: a review, Wiley Interdisciplin. Rev.: Clim. Change, 2, 45–65, 2011.
Danandeh Mehr, A., Rikhtehgar Ghiasi, A., Yaseen, Z. M., Sorman, A. U., and Abualigah, L.: A novel intelligent deep learning predictive model for meteorological drought forecasting, J. Ambient Intel. Humaniz. Comput., 14, 10441-10455, https://doi.org/10.1007/s12652-022-03701-7, 2032.
Danielson, J. J. and Gesch, D. B.: Global multi-resolution terrain elevation data 2010 (GMTED2010), US Geological Survey Open-File Report 2011-1073, p. 26, https://pubs.usgs.gov/of/2011/1073/pdf/of2011-1073.pdf (last access: 20 November 2023), 2011.
Das, J., Jha, S., and Goyal, M. K.: Non-stationary and copula-based approach to assess the drought characteristics encompassing climate indices over the Himalayan states in India, J. Hydrol., 580, 124356, https://doi.org/10.1016/j.jhydrol.2019.124356, 2020.
Day, G. N.: Extended streamflow forecasting using NWSRFS, J. Water Resour. Pl. Manage., 111, 157–170, 1985.
Dehghani, M., Saghafian, B., Nasiri Saleh, F., Farokhnia, A., and Noori, R.: Uncertainty analysis of streamflow drought forecast using artificial neural networks and Monte-Carlo simulation, Int. J. Climatol., 34, 1169–1180, 2014.
De Luca, D. L., Apollonio, C., and Petroselli, A.: The Benefit of Continuous Hydrological Modelling for Drought Hazard Assessment in Small and Coastal Ungauged Basins: A Case Study in Southern Italy, Climate, 10, 34, https://doi.org/10.3390/cli10030034, 2022.
Derdous, O., Bouamrane, A., and Mrad, D.: Spatiotemporal analysis of meteorological drought in a Mediterranean dry land: case of the Cheliff Basin–Algeria, Model. Earth Syst. Environ., 7, 135–143, https://doi.org/10.1007/s40808-020-00951-2, 2021.
Dikshit, A., Pradhan, B., and Santosh, M.: Artificial neural networks in drought prediction in the 21st century – A scientometric analysis, Appl. Soft Comput., 114, 108080, https://doi.org/10.1016/j.asoc.2021.108080, 2022.
Di Nunno, F., Granata, F., Gargano, R., and de Marinis, G.: Prediction of spring flows using nonlinear autoregressive exogenous (NARX) neural network models, Environ. Monit. Assess., 193, 350, https://doi.org/10.1007/s10661-021-09135-6, 2021.
Djerbouai, S. and Souag-Gamane, D.: Drought Forecasting Using Neural Networks, Wavelet Neural Networks, and Stochastic Models: Case of the Algerois Basin in North Algeria, Water Resour. Manage., 30, 2445–2464, https://doi.org/10.1007/s11269-016-1298-6, 2016.
Doblas-Reyes, F. J., García-Serrano, J., Lienert, F., Biescas, A. P., and Rodrigues, L. R. L.: Seasonal climate predictability and forecasting: status and prospects, WIREs Clim. Change, 4, 245–268, https://doi.org/10.1002/wcc.217, 2013.
D'Odorico, P., Carr, J., Dalin, C., Dell'Angelo, J., Konar, M., Laio, F., Ridolfi, L., Rosa, L., Suweis, S., Tamea, S., and Tuninetti, M.: Global virtual water trade and the hydrological cycle: patterns, drivers, and socio-environmental impacts, Environ. Res. Lett., 14, 053001, https://doi.org/10.1088/1748-9326/ab05f4, 2019.
Dubrovský, M., Hayes, M., Duce, P., Trnka, M., Svoboda, M., and Zara, P.: Multi-GCM projections of future drought and climate variability indicators for the Mediterranean region, Reg. Environ. Change, 14, 1907–1919, https://doi.org/10.1007/s10113-013-0562-z, 2014.
Dünkeloh, A. and Jacobeit, J.: Circulation dynamics of Mediterranean precipitation variability 1948–98, Int. J. Climatol., 23, 1843–1866, https://doi.org/10.1002/joc.973, 2003.
Dunstone, N., Smith, D., Scaife, A., Hermanson, L., Eade, R., Robinson, N., Andrews, M., and Knight, J.: Skilful predictions of the winter North Atlantic Oscillation one year ahead, Nat. Geosci., 9, 809–814, https://doi.org/10.1088/1748-9326/ab9f7d, 2016.
Dutra, E., Viterbo, P., and Miranda, P. M. A.: ERA-40 reanalysis hydrological applications in the characterization of regional drought, Geophys. Res. Lett., 35, 679, https://doi.org/10.1029/2008GL035381, 2008.
Eberle, C. and Higuera Roa, O.: Technical Report: Mediterranean wildfires, Interconnected Disaster Risks 2021/2022, UNU-EHS – United Nations University – Institute for Environment and Human Security, https://doi.org/10.53324/VCEB1752, 2022.
El Aissaoui, K., Ousmana, H., El Hmaidi, A., Bekri, M. H., El Faleh, E. M., Essahlaoui, A., El Ouali, A., and Berrada, M.: Weather drought index prediction using the support vector regression in the Ansegmir Watershed, Upper Moulouya, Morocco, J. Water Land Dev., 50, 187–194, 2021.
El Alaoui El Fels, A., Saidi, M. E. M., Bouiji, A., and Benrhanem, M.: Rainfall regionalization and variability of extreme precipitation using artificial neural networks: a case study from western central Morocco, J. Water Clim. Change, 12, 1107–1122, https://doi.org/10.2166/wcc.2020.217, 2020.
El Ibrahimi, A. and Baali, A.: Application of several artificial intelligence models for forecasting meteorological drought using the standardized precipitation index in the saïss plain (Northern Morocco), Int. J. Intel. Eng. Syst., 11, 267–275, 2018.
Elkharrim, M. and Bahi, L.: Using Statistical Downscaling of GCM Simulations to Assess Climate Change Impacts on Drought Conditions in the Northwest of Morocco, Mod. Appl. Sci., 9, 1–11, https://doi.org/10.5539/mas.v9n2p1, 2014.
Entekhabi, D., Njoku, E. G., O'Neill, P. E., Kellogg, K. H., Crow, W. T., Edelstein, W. N., Entin, J. K., Goodman, S. D., Jackson, T. J., and Johnson, J.: The soil moisture active passive (SMAP) mission, Proc. IEEE, 98, 704–716, 2010.
Esit, M. and Yuce, M. I.: Copula-Based Bivariate Drought Severity and Duration Frequency Analysis Considering Spatial-Temporal Variability in the Ceyhan Basin, Turkey, Theor. Appl. Climatol., 151, 1113–1131, https://doi.org/10.1007/s00704-022-04317-9, 2023.
Evensen, G.: Inverse methods and data assimilation in nonlinear ocean models, Physica D, 77, 108–129, https://doi.org/10.1016/0167-2789(94)90130-9, 1994.
Feng, P., Wang, B., Liu, D. L., and Yu, Q.: Machine learning-based integration of remotely-sensed drought factors can improve the estimation of agricultural drought in South-Eastern Australia, Agric. Syst., 173, 303–316, https://doi.org/10.1016/j.agsy.2019.03.015, 2019.
Fereres, E. and Soriano, M. A.: Deficit irrigation for reducing agricultural water use, J. Exp. Bot., 58, 147–159, https://doi.org/10.1093/jxb/erl165, 2007.
Fung, K. F., Huang, Y. F., Koo, C. H., and Soh, Y. W.: Drought forecasting: A review of modelling approaches 2007–2017, J. Water Clim. Change, 11, 771–799, https://doi.org/10.2166/wcc.2019.236, 2019.
Funk, C., Peterson, P., Landsfeld, M., Pedreros, D., Verdin, J., Shukla, S., Husak, G., Rowland, J., Harrison, L., and Hoell, A.: The climate hazards infrared precipitation with stations – a new environmental record for monitoring extremes, Sci. Data, 2, 1–21, 2015.
Gamelin, B. L., Feinstein, J., Wang, J., Bessac, J., Yan, E., Kotamarthi, and V. R.: Projected U.S. drought extremes through the twenty-first century with vapor pressure deficit, Sci. Rep., 12, 8615, https://doi.org/10.1038/s41598-022-12516-7, 2022.
Genest, C. and Favre, A.-C.: Everything You Always Wanted to Know about Copula Modeling but Were Afraid to Ask, J. Hydrol. Eng., 12, 347–368, 2007.
Giorgi, F. and Gutowski, W. J.: Regional Dynamical Downscaling and the CORDEX Initiative, Annu. Rev. Environ. Resour., 40, 467–490, https://doi.org/10.1146/annurev-environ-102014-021217, 2015.
Giorgi, F. and Lionello, P.: Climate change projections for the Mediterranean region, Global Planet. Change, 63, 90–104, https://doi.org/10.1016/j.gloplacha.2007.09.005, 2008.
Gneiting, T., Raftery, A. E., Westveld, A. H., and Goldman, T.: Calibrated probabilistic forecasting using ensemble model output statistics and minimum CRPS estimation, Mon. Weather Rev., 133, 1098–1118, 2005.
Gouveia, C. M., Trigo, R. M., Beguería, S., and Vicente-Serrano, S. M.: Drought impacts on vegetation activity in the Mediterranean region: An assessment using remote sensing data and multi-scale drought indicators, Global Planet. Change, 151, 15–27, https://doi.org/10.1016/j.gloplacha.2016.06.011, 2017.
Grönquist, P., Yao, C., Ben-Nun, T., Dryden, N., Dueben, P., Li, S., and Hoefler, T.: Deep learning for post-processing ensemble weather forecasts, Philos. T. Roy. Soc. A, 379, 20200092, https://doi.org/10.1098/rsta.2020.0092, 2021.
Gruber, A. and Peng, J.: Remote sensing of soil moisture, in: Reference Module in Earth Systems and Environmental Sciences, Elsevier, https://doi.org/10.1016/B978-0-12-822974-3.00019-7, 2022.
Guion, A., Turquety, S., Polcher, J., Pennel, R., Bastin, S., and Arsouze, T.: Droughts and heatwaves in the Western Mediterranean: impact on vegetation and wildfires using the coupled WRF-ORCHIDEE regional model (RegIPSL), Clim. Dynam., 58, 2881–2903, https://doi.org/10.1007/s00382-021-05938-y, 2022.
Gumus, V., El Moçayd, N., Seker, M., and Seaid, M.: Evaluation of future temperature and precipitation projections in Morocco using the ANN-based multi-model ensemble from CMIP6, Atmos. Res., 292, 106880, https://doi.org/10.1016/j.atmosres.2023.106880, 2023.
Habibi, B., Meddi, M., Torfs, P. J. J. F., Remaoun, M., and Van Lanen, H. A. J.: Characterisation and prediction of meteorological drought using stochastic models in the semi-arid Chéliff–Zahrez basin (Algeria), J. Hydrol.: Reg. Stud., 16, 15–31, https://doi.org/10.1016/j.ejrh.2018.02.005, 2018.
Hadri, A., Saidi, M. E. M., and Boudhar, A.: Multiscale drought monitoring and comparison using remote sensing in a Mediterranean arid region: a case study from west-central Morocco, Arab. J. Geosci., 14, 118, https://doi.org/10.1007/s12517-021-06493-w, 2021.
Haile, G. G., Tang, Q., Li, W., Liu, X., and Zhang, X.: Drought: Progress in broadening its understanding, WIREs Water, 7, e1407, https://doi.org/10.1002/wat2.1407, 2020.
Hamdi, Y., Chebana, F., and Ouarda, T.: Bivariate drought frequency analysis in the Medjerda River Basin Tunisia, J. Civ. Environ. Eng., 6, 1–11, 2016.
Han, J. and Singh, V. P.: Forecasting of droughts and tree mortality under global warming: a review of causative mechanisms and modeling methods, J. Water Clim. Change, 11, 600–632, https://doi.org/10.2166/wcc.2020.239, 2020.
Hansen, J. W., Mason, S. J., Sun, L., and Tall, A.: Rview Of Seasonal Climate Forecasting For Agriculture In Sub-Saharan Africe, Exp. Agricult., 47, 205–240, https://doi.org/10.1017/S0014479710000876, 2011.
Hao, Z., Hao, F., Singh, V. P., Sun, A. Y., and Xia, Y.: Probabilistic prediction of hydrologic drought using a conditional probability approach based on the meta-Gaussian model, J. Hydrol., 542, 772–780, 2016.
Hao, Z., Singh, V. P., and Xia, Y.: Seasonal Drought Prediction: Advances, Challenges, and Future Prospects, Rev. Geophys., 56, 108–141, https://doi.org/10.1002/2016RG000549, 2018.
Harrigan, S., Prudhomme, C., Parry, S., Smith, K., and Tanguy, M.: Benchmarking ensemble streamflow prediction skill in the UK, Hydrol. Earth Syst. Sci., 22, 2023–2039, https://doi.org/10.5194/hess-22-2023-2018, 2018.
Hoell, A., Funk, C., and Barlow, M.: The regional forcing of Northern hemisphere drought during recent warm tropical west Pacific Ocean La Niña events, Clim. Dynam., 42, 3289–3311, 2014.
Hoerling, M. and Kumar, A.: The Perfect Ocean for Drought, Science, 299, 691–694, https://doi.org/10.1126/science.1079053, 2003.
Hosmer Jr., D. W., Lemeshow, S., and Sturdivant, R. X.: Applied logistic regression, John Wiley & Sons, ISBN 0471356328,9780471356325, 2013.
Huffman, G. J., Bolvin, D. T., Nelkin, E. J., and Tan, J.: Integrated Multi-satellitE Retrievals for GPM (IMERG) technical documentation, Nasa/Gsfc Code 612, NASA, https://docserver.gesdisc.eosdis.nasa.gov/public/project/GPM/IMERG_doc.06.pdf (last access: 16 November 2023), 2015.
Ionita, M. and Nagavciuc, V.: Changes in drought features at the European level over the last 120 years, Nat. Hazards Earth Syst. Sci., 21, 1685–1701, https://doi.org/10.5194/nhess-21-1685-2021, 2021.
IPCC: Climate Change 2021: The Physical Science Basis, in: Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, edited by: Masson-Delmotte, V., Zhai, P., Pirani, A., Connors, S. L., Péan, C., Berger, S., Caud, N., Chen, Y., Goldfarb, L., Gomis, M. I., Huang, M., Leitzell, K., Lonnoy, E., Matthews, J. B. R., Maycock, T. K., Waterfield, T., Yelekçi, O., Yu, R., and Zhou, B., Cambridge University Press, Cambridge, UK and New York, NY, USA, 2391 pp., https://doi.org/10.1017/9781009157896, 2021.
Isendahl, N.: Drought in the Mediterranean: WWF Policy Proposals, WWF-World Wide Fund for Nature, 45 pp., http://awsassets.wwf.es/downloads/wwf_med_drought_report_jul061.pdf (last access: 16 November 2023), 2006.
Jehanzaib, M., Yoo, J., Kwon, H.-H., and Kim, T.-W.: Reassessing the frequency and severity of meteorological drought considering non-stationarity and copula-based bivariate probability, J. Hydrol., 603, 126948, https://doi.org/10.1016/j.jhydrol.2021.126948, 2021.
Jiang, T., Su, X., Zhang, G., Zhang, T., and Wu, H.: Estimating propagation probability from meteorological to ecological droughts using a hybrid machine learning copula method, Hydrol. Earth Syst. Sci., 27, 559–576, https://doi.org/10.5194/hess-27-559-2023, 2023.
Jiménez-Donaire, M. P., Tarquis, A., and Giráldez, J. V.: Evaluation of a combined drought indicator and its potential for agricultural drought prediction in southern Spain, Nat. Hazards Earth Syst. Sci., 20, 21–33, https://doi.org/10.5194/nhess-20-21-2020, 2020.
Joe, H.: Multivariate Models and Multivariate Dependence Concepts, in: Chapman & Hall/CRC Monographs on Statistics & Applied Probability, Taylor & Francis, ISBN 0412073315, ISBN 9780412073311, 1997.
Joe, H.: Dependence modeling with copulas, in: Chapman & Hall/CRC Monographs on Statistics & Applied Probability, Taylor and Francis, Hoboken, NJ, ISBN 1466583223, ISBN 9781466583221, 2014.
Johnson, N. C. and Xie, S.-P.: Changes in the sea surface temperature threshold for tropical convection, Nat. Geosci., 3, 842–845, 2010.
Junqueira, R., Viola, M. R., Amorim, J. S., Wongchuig, S. C., de Mello, C. R., Vieira-Filho, M., and Coelho, G.: Hydrological Retrospective and Historical Drought Analysis in a Brazilian Savanna Basin, Water, 14, 2178, https://doi.org/10.3390/w14142178, 2022.
Kahya, E.: The Impacts of NAO on the Hydrology of the Eastern Mediterranean, in: Hydrological, Socioeconomic and Ecological Impacts of the North Atlantic Oscillation in the Mediterranean Region, Advances in Global Change Research, edited by: Vicente-Serrano, S. M. and Trigo, R. M., Springer Netherlands, Dordrecht, 57–71, https://doi.org/10.1007/978-94-007-1372-7_5, 2011.
Kang, S., Zhang, L., Liang, Y., Hu, X., Cai, H., and Gu, B.: Effects of limited irrigation on yield and water use efficiency of winter wheat in the Loess Plateau of China, Agr. Water Manage., 45, 203–216, 2000.
Karabulut, M.: Drought analysis in Antakya-Kahramanmaraş Graben, Turkey, J. Arid Land, 7, 741–754, https://doi.org/10.1007/s40333-015-0011-6, 2015.
Kiem, A. S., Johnson, F., Westra, S., van Dijk, A., Evans, J. P., O'Donnell, A., Rouillard, A., Barr, C., Tyler, J., Thyer, M., Jakob, D., Woldemeskel, F., Sivakumar, B., and Mehrotra, R.: Natural hazards in Australia: droughts, Climatic Change, 139, 37–54, https://doi.org/10.1007/s10584-016-1798-7, 201
Kim, W. M. and Raible, C. C.: Dynamics of the Mediterranean droughts from 850 to 2099 CE in the Community Earth System Model, Clim. Past, 17, 887–911, https://doi.org/10.5194/cp-17-887-2021, 2021.
Krishnamurti, T., Kishtawal, C. M., LaRow, T. E., Bachiochi, D. R., Zhang, Z., Williford, C. E., Gadgil, S., and Surendran, S.: Improved weather and seasonal climate forecasts from multimodel superensemble, Science, 285, 1548–1550, 1999.
Kuśmierek-Tomaszewska, R. and Żarski, J.: Assessment of Meteorological and Agricultural Drought Occurrence in Central Poland in 1961–2020 as an Element of the Climatic Risk to Crop Production, Agriculture, 11, 855, https://doi.org/10.3390/agriculture11090855, 2021.
KyungHwan, S. and DegHyo, B.: Applicability assessment of hydrological drought outlook using ESP method, J. Korea Water Resour. Assoc., 48, 581–595, 2015.
Lazri, M., Ameur, S., Brucker, J. M., Lahdir, M., and Sehad, M.: Analysis of drought areas in northern Algeria using Markov chains, J. Earth Syst. Sci., 124, 61–70, https://doi.org/10.1007/s12040-014-0500-6, 2015.
Le Page, M. and Zribi, M.: Analysis and predictability of drought in Northwest Africa using optical and microwave satellite remote sensing products, Sci. Rep., 9, 1–13, 2019.
Li, L., She, D., Zheng, H., Lin, P., and Yang, Z.-L.: Elucidating Diverse Drought Characteristics from Two Meteorological Drought Indices (SPI and SPEI) in China, J. Hydrometeorol., 21, 1513–1530, https://doi.org/10.1175/JHM-D-19-0290.1, 2020.
Li, X., Fang, G., Wei, J., Arnault, J., Laux, P., Wen, X., and Kunstmann, H.: Evaluation and projection of precipitation and temperature in a coastal climatic transitional zone in China based on CMIP6 GCMs, Clim. Dynam., https://doi.org/10.1007/s00382-023-06781-z, in press, 2023.
Li, Y., Wang, B., and Gong, Y.: Drought Assessment Based on Data Fusion and Deep Learning, Comput. Intel. Neurosci., 2022, e4429286, https://doi.org/10.1155/2022/4429286, 2022.
Liang, X., Lettenmaier, D. P., Wood, E. F., and Burges, S. J.: A simple hydrologically based model of land surface water and energy fluxes for general circulation models, J. Geophys. Res.-Atmos., 99, 14415–14428, https://doi.org/10.1029/94JD00483, 1994.
Lionello, P.: The Climate of the Mediterranean Region: From the Past to the Future, in: Elsevier insights, Elsevier Science, ISBN 0124160425, ISBN 9780124160422, 2012.
Lionello, P., Giorgi, F., Rohling, E., and Seager, R.: Chapter 3 – Mediterranean climate: past, present and future, in: Oceanography of the Mediterranean Sea, edited by: Schroeder, K. and Chiggiato, J., Elsevier, 41–91, https://doi.org/10.1016/B978-0-12-823692-5.00011-X, 2023.
Liu, D., Mishra, A. K., and Yu, Z.: Evaluation of hydroclimatic variables for maize yield estimation using crop model and remotely sensed data assimilation, Stoch. Environ. Res. Risk A., 33, 1283–1295, https://doi.org/10.1007/s00477-019-01700-3, 2019.
Liu, S., Fu, G., Liu, C., Zhang, Y., and Zhou, Y.: Ensemble of machine learning models for real-time probabilistic forecasting of hydrological drought, J. Hydro., 583, 124610, https://doi.org/10.1016/j.jhydrol.2020.124610, 2020.
Livada, I. and Assimakopoulos, V. D.: Spatial and temporal analysis of drought in greece using the Standardized Precipitation Index (SPI), Theor. Appl. Climatol., 89, 143–153, https://doi.org/10.1007/s00704-005-0227-z, 2007.
Livezey, R. E. and Smith, T. M.: Covariability of aspects of North American climate with global sea surface temperatures on interannual to interdecadal timescales, J. Climate, 12, 289–302, 1999.
Lloyd-Hughes, B.: The impracticality of a universal drought definition, Theor. Appl. Climatol., 117, 607–611, https://doi.org/10.1007/s00704-013-1025-7, 2014.
Lorenz, E. N.: Deterministic nonperiodic flow, J. Atmos. Sci., 20, 130–141, 1963.
Madadgar, S. and Moradkhani, H.: Drought Analysis under Climate Change Using Copula, J. Hydrol. Eng., 18, 746–759, https://doi.org/10.1061/(ASCE)HE.1943-5584.0000532, 2013.
Madadgar, S. and Moradkhani, H.: Improved Bayesian multimodeling: Integration of copulas and Bayesian model averaging, Water Resour. Res., 50, 9586–9603, https://doi.org/10.1002/2014WR015965, 2014.
Madadgar, S., AghaKouchak, A., Shukla, S., Wood, A .W., Cheng, L., Hsu, K.-L., and Svoboda, M.: A hybrid statistical-dynamical framework for meteorological drought prediction: Application to the southwestern United States, Water Resour. Res., 52, 5095–5110, https://doi.org/10.1002/2015WR018547, 2016.
Maloney, K. O., Schmid, M., and Weller, D. E.: Applying additive modelling and gradient boosting to assess the effects of watershed and reach characteristics on riverine assemblages, Meth. Ecol. Evol., 3, 116–128, https://doi.org/10.1111/j.2041-210X.2011.00124.x, 2012.
Manatsa, D., Mushore, T., and Lenouo, A.: Improved predictability of droughts over southern Africa using the standardized precipitation evapotranspiration index and ENSO, Theor. Appl. Climatol., 127, 259–274, https://doi.org/10.1007/s00704-015-1632-6, 2017.
Mandel, J., Bergou, E., Gürol, S., Gratton, S., and Kasanický, I.: Hybrid Levenberg–Marquardt and weak-constraint ensemble Kalman smoother method, Nonlin. Processes Geophys. 23, 59–73, https://doi.org/10.5194/npg-23-59-2016, 2016.
Maraun, D.: Bias Correcting Climate Change Simulations – a Critical Review, Curr. Clim. Change Rep., 2, 211–220, https://doi.org/10.1007/s40641-016-0050-x, 2016.
Marcos-Garcia, P., Lopez-Nicolas, A., and Pulido-Velazquez, M.: Combined use of relative drought indices to analyze climate change impact on meteorological and hydrological droughts in a Mediterranean basin, J. Hydrol., 554, 292–305, https://doi.org/10.1016/j.jhydrol.2017.09.028, 2017.
Mariotti, A., Zeng, N., and Lau, K.-M.: Euro-Mediterranean rainfall and ENSO – a seasonally varying relationship, Geophys. Res. Lett., 29, 59-1–59-4, https://doi.org/10.1029/2001GL014248, 2002.
Mariotti, A., Zeng, N., Yoon, J.-H., Artale, V., Navarra, A., Alpert, P., and Li, L. Z. X.: Mediterranean water cycle changes: transition to drier 21st century conditions in observations and CMIP3 simulations, Environ. Res. Lett., 3, 044001, https://doi.org/10.1088/1748-9326/3/4/044001, 2008.
Martens, B., Miralles, D. G., Lievens, H., van der Schalie, R., de Jeu, R. A. M., Fernández-Prieto, D., Beck, H. E., Dorigo, W. A., and Verhoest, N. E. C.: GLEAM v3: satellite-based land evaporation and root-zone soil moisture, Geosci. Model Dev., 10, 1903–1925, https://doi.org/10.5194/gmd-10-1903-2017, 2017.
Martínez-Fernández, J., González-Zamora, A., Sánchez, N., Gumuzzio, A., and Herrero-Jiménez, C. M.: Satellite soil moisture for agricultural drought monitoring: Assessment of the SMOS derived Soil Water Deficit Index, Remote Sens. Environ., 177, 277–286, https://doi.org/10.1016/j.rse.2016.02.064, 2016.
Marx, A., Kumar, R., Thober, S., Rakovec, O., Wanders, N., Zink, M., Wood, E. F., Pan, M., Sheffield, J., and Samaniego, L.: Climate change alters low flows in Europe under global warming of 1.5, 2, and 3 ∘C, Hydrol. Earth Syst. Sci., 22, 1017–1032, https://doi.org/10.5194/hess-22-1017-2018, 2018.
Mathbout, S., Lopez-Bustins, J. A., Royé, D., and Martin-Vide, J.: Mediterranean-Scale Drought: Regional Datasets for Exceptional Meteorological Drought Events during 1975–2019, Atmosphere, 12, 941, https://doi.org/10.3390/atmos12080941, 2021.
McKee, T. B., Doesken, N. J., and Kleist, J.: The Relationship Of Drought Frequency And Duration To Time Scales, https://climate.colostate.edu/pdfs/relationshipofdroughtfrequency.pdf (last access: 16 Novembver 2023), 1993.
Mehran, A., AghaKouchak, A., and Phillips, T. J.: Evaluation of CMIP5 continental precipitation simulations relative to satellite‐based gauge‐adjusted observations, J. Geophys. Res.-Atmos., 119, 1695–1707, https://doi.org/10.1002/2013JD021152, 2014.
Mehran, A., AghaKouchak, A., Nakhjiri, N., Stewardson, M. J., Peel, M. C., Phillips, T. J., Wada, Y., and Ravalico, J. K.: Compounding impacts of human-induced water stress and climate change on water availability, Sci. Rep., 7, 6282, https://doi.org/10.1038/s41598-017-06765-0, 2017.
Mendes, M. P., Rodriguez-Galiano, V., and Aragones, D.: Evaluating the BFAST method to detect and characterise changing trends in water time series: A case study on the impact of droughts on the Mediterranean climate, Sci. Total Environ., 846, 157428, https://doi.org/10.1016/j.scitotenv.2022.157428, 2022.
Mendicino, G., Senatore, A., and Versace, P.: A Groundwater Resource Index (GRI) for drought monitoring and forecasting in a mediterranean climate, J. Hydrol., 357, 282–302, https://doi.org/10.1016/j.jhydrol.2008.05.005, 2008.
Mesbahzadeh, T., Mirakbari, M., Mohseni Saravi, M., Soleimani Sardoo, F., and Miglietta, M. M.: Meteorological drought analysis using copula theory and drought indicators under climate change scenarios (RCP), Meteorol. Appl., 27, e1856, https://doi.org/10.1002/met.1856, 2020.
Michaelides, S., Karacostas, T., Sánchez, J. L., Retalis, A., Pytharoulis, I., Homar, V., Romero, R., Zanis, P., Giannakopoulos, C., Bühl, J., Ansmann, A., Merino, A., Melcón, P., Lagouvardos, K., Kotroni, V., Bruggeman, A., López-Moreno, J. I., Berthet, C., Katragkou, E., Tymvios, F., Hadjimitsis, D. G., Mamouri, R.-E., and Nisantzi, A.: Reviews and perspectives of high impact atmospheric processes in the Mediterranean, Atmos. Res., 208, 4–44, https://doi.org/10.1016/j.atmosres.2017.11.022, 2018.
Milano, M., Ruelland, D., Dezetter, A., Fabre, J., Ardoin-Bardin, S., and Servat, E.: Modeling the current and future capacity of water resources to meet water demands in the Ebro basin, J. Hydrol., 500, 114–126, https://doi.org/10.1016/j.jhydrol.2013.07.010, 2013.
Mimeau, L., Tramblay, Y., Brocca, L., Massari, C., Camici, S., and Finaud-Guyot, P.: Modeling the response of soil moisture to climate variability in the Mediterranean region, Hydrol. Earth Syst. Sci., 25, 653–669, https://doi.org/10.5194/hess-25-653-2021, 2021.
Miralles, D. G., Gash, J. H., Holmes, T. R. H., de Jeu, R. A. M., and Dolman, A. J.: Global canopy interception from satellite observations, J. Geophys. Res.-Atmos., 115, D16122, https://doi.org/10.1029/2009JD013530, 2010.
Mishra, A. K. and Desai, V. R.: Drought forecasting using feed-forward recursive neural network, Ecol. Model., 198, 127–138, https://doi.org/10.1016/j.ecolmodel.2006.04.017, 2006.
Mishra, A. K. and Singh, V. P.: A review of drought concepts, J. Hydrol., 391, 202–216, 2010.
Mishra, A. K. and Singh, V. P.: Drought modeling – A review, J. Hydrol., 403, 157–175, https://doi.org/10.1016/j.jhydrol.2011.03.049, 2011.
Mo, K. C. and Lettenmaier, D. P.: Heat wave flash droughts in decline, Geophys. Res. Lett., 42, 2823–2829, https://doi.org/10.1002/2015GL064018, 2015.
Mo, K. C. and Lyon, B.: Global Meteorological Drought Prediction Using the North American Multi-Model Ensemble, J. Hydrometeorol., 16, 1409–1424, https://doi.org/10.1175/JHM-D-14-0192.1, 2015.
Mohammed, S., Elbeltagi, A., Bashir, B., Alsafadi, K., Alsilibe, F., Alsalman, A., Zeraatpisheh, M., Széles, A., and Harsányi, E.: A comparative analysis of data mining techniques for agricultural and hydrological drought prediction in the eastern Mediterranean, Comput. Electron. Agric., 197, 106925, https://doi.org/10.1016/j.compag.2022.106925, 2022.
Mohanty, B. P., Cosh, M. H., Lakshmi, V., and Montzka, C.: Soil Moisture Remote Sensing: State-of-the-Science, Vadose Zone J., 16, vzj2016.10.0105, https://doi.org/10.2136/vzj2016.10.0105, 2017.
Mokhtarzad, M., Eskandari, F., Jamshidi Vanjani, N., and Arabasadi, A.: Drought forecasting by ANN, ANFIS, and SVM and comparison of the models, Environ. Earth Sci., 76, 729, https://doi.org/10.1007/s12665-017-7064-0, 2017.
Morid, S., Smakhtin, V., and Bagherzadeh, K.: Drought forecasting using artificial neural networks and time series of drought indices, Int. J. Climatol., 27, 2103–2111, https://doi.org/10.1002/joc.1498, 2007.
Mortuza, M. R., Moges, E., and Demissie, Y., Li, H.-Y.: Historical and future drought in Bangladesh using copula-based bivariate regional frequency analysis, Theor. Appl. Climatol., 135, 855–871, https://doi.org/10.1007/s00704-018-2407-7, 2019.
Murray, V. and Ebi, K. L.: IPCC Special Report on Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation (SREX), J. Epidemiol. Commun. Health, 66, 759–760, https://doi.org/10.1136/jech-2012-201045, 2012.
Myronidis, D., Stathis, D., Ioannou, K., and Fotakis, D.: An Integration of Statistics Temporal Methods to Track the Effect of Drought in a Shallow Mediterranean Lake, Water Resour. Manage., 26, 4587–4605, https://doi.org/10.1007/s11269-012-0169-z, 2012.
Nalbantis, I.: Evaluation of a hydrological drought index, https://www.ewra.net/ew/pdf/EW_2008_23-24_06.pdf (last access: 16 November 2023), 2008.
Nalbantis, I. and Tsakiris, G.: Assessment of Hydrological Drought Revisited, Water Resour. Manage., 23, 881–897, https://doi.org/10.1007/s11269-008-9305-1, 2009.
Narasimhan, B. and Srinivasan, R.: Development and evaluation of Soil Moisture Deficit Index (SMDI) and Evapotranspiration Deficit Index (ETDI) for agricultural drought monitoring, Agr. Forest Meteorol., 133, 69–88, https://doi.org/10.1016/j.agrformet.2005.07.012, 2005.
NASA: GMTED2010, https://www.usgs.gov/coastal-changes-and-impacts/gmted2010 (last access: 10 November 2023), 2023.
Nelsen, R. B.: An Introduction to Copulas, in: Springer Series in Statistics, Springer, New York, ISBN 978-0-387-28678-5, 2007.
Oleson, K., Dai, Y., Bonan, B., Bosilovichm, M., Dickinson, R., Dirmeyer, P., Hoffman, F., Houser, P., Levis, S., and Niu, G.-Y.: Technical description of the community land model (CLM), No. NCAR/TN-461+STR), University Corporation for Atmospheric Research, https://doi.org/10.5065/D6N877R0, 2004.
Ozga-Zielinski, B., Ciupak, M., Adamowski, J., Khalil, B., and Malard, J.: Snow-melt flood frequency analysis by means of copula based 2D probability distributions for the Narew River in Poland, J. Hydrol.: Reg. Stud., 6, 26–51, https://doi.org/10.1016/j.ejrh.2016.02.001, 2016.
Özger, M., Başakın, E. E., Ekmekcioğlu, Ö., and Hacısüleyman, V.: Comparison of wavelet and empirical mode decomposition hybrid models in drought prediction, Comput. Electron. Agric., 179, 105851, https://doi.org/10.1016/j.compag.2020.105851, 2020.
Pablos, M., Martínez-Fernández, J., Sánchez, N., and González-Zamora, Á.: Temporal and Spatial Comparison of Agricultural Drought Indices from Moderate Resolution Satellite Soil Moisture Data over Northwest Spain, Remote Sens., 9, 1168, https://doi.org/10.3390/rs9111168, 2017.
Palmer, T. N., Alessandri, A., Andersen, U., Cantelaube, P., Davey, M., Delécluse, P., Déqué, M., Diez, E., Doblas-Reyes, F. J., and Feddersen, H.: Development of a European multimodel ensemble system for seasonal-to-interannual prediction (DEMETER), B. Am. Meteorol. Soc., 85, 853–872, 2004.
Palmer, W. C.: Meteorological Drought, US Department of Commerce, Weather Bureau, https://books.googleusercontent.com/books/content?req=AKW5 (last access: 16 November 2023), 1965.
Palmer, W. C.: Keeping Track of Crop Moisture Conditions, Nationwide: The New Crop Moisture Index, Weatherwise, 21, 156–161, https://doi.org/10.1080/00431672.1968.9932814, 1968.
Papadopoulos, C., Spiliotis, M., Gkiougkis, I., Pliakas, F., and Papadopoulos, B.: Fuzzy linear regression analysis for groundwater response to meteorological drought in the aquifer system of Xanthi plain, NE Greece, J. Hydroinform.,23, 1112–1129, https://doi.org/10.2166/hydro.2021.025, 2021.
Papaioannou, G., Loukas, A., Vasiliades, L., and Aronica, G. T.: Flood inundation mapping sensitivity to riverine spatial resolution and modelling approach, Nat. Hazards, 83, 117–132, https://doi.org/10.1007/s11069-016-2382-1, 2016.
Pappenberger, F. and Beven, K. J.: Ignorance is bliss: Or seven reasons not to use uncertainty analysis, Water Resour. Res., 42, W05302, https://doi.org/10.1029/2005WR004820, 2006.
Parker, T., Gallant, A., Hobbins, M., and Hoffmann, D.: Flash drought in Australia and its relationship to evaporative demand, Environ. Res. Lett., 16, 064033, https://doi.org/10.1088/1748-9326/abfe2c, 2021.
Paulo, A. A. and Pereira, L. S.: Prediction of SPI Drought Class Transitions Using Markov Chains, Water Resour. Manage., 21, 1813–1827, https://doi.org/10.1007/s11269-006-9129-9, 2007.
Paulo, A. A., Rosa, R. D., and Pereira, L. S.: Climate trends and behaviour of drought indices based on precipitation and evapotranspiration in Portugal, Nat. Hazards Earth Syst. Sci., 12, 1481–1491, https://doi.org/10.5194/nhess-12-1481-2012, 2012.
Paz, S., Tourre, Y. M., and Planton, S.: North Africa-West Asia (NAWA) sea-level pressure patterns and their linkages with the Eastern Mediterranean (EM) climate, Geophys. Res. Lett., 30, 1999, https://doi.org/10.1029/2003GL017862, 2003.
Peng, Y.: Decadal–centennial hydroclimate variability over eastern China during the last millennium: Results from the product of Paleo Hydrodynamics Data Assimilation, Atmos. Ocean Sci. Lett., 14, 100038, https://doi.org/10.1016/j.aosl.2021.100038, 2021.
Planton, S., Lionello, P., Vincenzo, A., Aznar, R., Carrillo, A., Colin, J., Congedi, L., Dubois, C., Elizalde, A., Gualdi, S., Hertig, E., Jacobeit, J., Jorda, G., Li, L., Mariotti, A., Piani, C., Ruti, P., Sanchez-Gomez, E., Sannino, G., Sevault, F., Somot, S., and Tsimplis, M.: The climate of the Mediterranean region in future climate projections, The climate of the Mediterranean Region, Elsevier, https://doi.org/10.1016/B978-0-12-416042-2.00008-2, 2012.
Pontes Filho, J. D., Souza Filho, F. A., Martins, E. S. P. R., and Studart, T. M. C.: Copula-Based Multivariate Frequency Analysis of the 2012–2018 Drought in Northeast Brazil, Water 12, 834, https://doi.org/10.3390/w12030834, 2020.
Pozzi, W., Sheffield, J., Stefanski, R., Cripe, D., Pulwarty, R., Vogt, J. V., Heim, R. R., Brewer, M. J., Svoboda, M., and Westerhoff, R.: Toward global drought early warning capability: Expanding international cooperation for the development of a framework for monitoring and forecasting, B. of the Am. Meteorol. Soc., 94, 776–785, https://doi.org/10.1175/BAMS-D-11-00176.1, 2013.
Prabhakar, K. and Rama, S. V.: Implications of Regional Droughts and Transboundary Drought Risks on Drought Monitoring and Early Warning: A Review, Climate, 10, 124, https://doi.org/10.3390/cli10090124, 2022.
Prodhan, F. A., Zhang, J., Hasan, S. S., Pangali Sharma, T. P., and Mohana, H. P.: A review of machine learning methods for drought hazard monitoring and forecasting: Current research trends, challenges, and future research directions, Environ. Model. Softw., 149, 105327, https://doi.org/10.1016/j.envsoft.2022.105327, 2022.
Pulwarty, S. R. and Sivakumar, M. V. K.: Information systems in a changing climate: Early warnings and drought risk management, Weather Clim. Extrem., 3, 14–21, https://doi.org/10.1016/j.wace.2014.03.005, 2014.
Rafiei-Sardooi, E., Mohseni-Saravi, M., Barkhori, S., Azareh, A., Choubin, B., and Jafari-Shalamzar, M.: Drought modeling: a comparative study between time series and neuro-fuzzy approaches, Arab. J. Geosci., 11, 487, https://doi.org/10.1007/s12517-018-3835-5, 2018.
Raftery, A. E., Gneiting, T., Balabdaoui, F., and Polakowski, M.: Using Bayesian Model Averaging to Calibrate Forecast Ensembles, Mon. Weather Rev., 133, 1155–1174, https://doi.org/10.1175/MWR2906.1, 2005.
Rahali, H., Elaryf, S., Amar, H., and Zellou, B.: Integrated Ensemble Weight of Evidence and Logistic Regression for Potential Groundwater Mapping: An Application to the Northern Piedmont of High Atlas Mountains (Morocco), in: Recent Advances in Environmental Science from the Euro-Mediterranean and Surrounding Regions, 2nd Edn., Environmental Science and Engineering, Springer International Publishing, Cham, 1703–1710, https://doi.org/10.1007/978-3-030-51210-1_270, 2021.
Redolat, D., Monjo, R., Lopez-Bustins, J. A., and Martin-Vide, J.: Upper-Level Mediterranean Oscillation index and seasonal variability of rainfall and temperature, Theor. Appl. Climatol., 135, 1059–1077, https://doi.org/10.1007/s00704-018-2424-6, 2019.
Rhee, J. and Im, J.: Meteorological drought forecasting for ungauged areas based on machine learning: Using long-range climate forecast and remote sensing data, Agr. Forest Meteorol., 237–238, 105–122, https://doi.org/10.1016/j.agrformet.2017.02.011, 2017.
Ribeiro, A. F. S. and Pires, C. A. L.: Seasonal drought predictability in Portugal using statistical–dynamical techniques, Phys. Chem. Earth Pt. ABC, 94, 155–166, https://doi.org/10.1016/j.pce.2015.04.003, 2016.
Rodrigo-Comino, J., Senciales-González, J. M., Yu, Y., Salvati, L., Giménez-Morera, A., and Cerdà, A,: Long-term changes in rainfed olive production, rainfall and farmer's income in Bailén (Jaén, Spain), Euro-Mediterr. J. Environ. Integr., 6, 58, https://doi.org/10.1007/s41207-021-00268-1, 2021.
Rodrigues, M., Cunill Camprubí, À., Balaguer-Romano, R., Coco Megía, C. J., Castañares, F., Ruffault, J., Fernandes, P. M., and Resco de Dios, V.: Drivers and implications of the extreme 2022 wildfire season in Southwest Europe, Sci. Total Environ., 859, 160320, https://doi.org/10.1016/j.scitotenv.2022.160320, 2023.
Ropelewski, C. F. and Halpert, M. S.: Global and Regional Scale Precipitation Patterns Associated with the El Niño/Southern Oscillation, Mon. Weather Rev., 115, 1606–1626, https://doi.org/10.1175/1520-0493(1987)115<1606:GARSPP>2.0.CO;2, 1987.
Ruffault, J., Martin-StPaul, N. K., Duffet, C., Goge, F., and Mouillot, F.: Projecting future drought in Mediterranean forests: bias correction of climate models matters!, Theor. Appl. Climatol., 117, 113–122, https://doi.org/10.1007/s00704-013-0992-z, 2014.
Russo, A., Gouveia, C. M., Dutra, E., Soares, P. M. M., and Trigo, R. M.: The synergy between drought and extremely hot summers in the Mediterranean, Environ. Res. Lett., 14, 014011, https://doi.org/10.1088/1748-9326/aaf09e, 2019.
Ruti, P. M., Somot, S., Giorgi, F., Dubois, C., Flaounas, E., Obermann, A., Dell'Aquila, A., Pisacane, G., Harzallah, A., Lombardi, E., Ahrens, B., Akhtar, N., Alias, A., Arsouze, T., Aznar, R., Bastin, S., Bartholy, J., Béranger, K., Beuvier, J., Bouffies-Cloché, S., Brauch, J., Cabos, W., Calmanti, S., Calvet, J.-C., Carillo, A., Conte, D., Coppola, E., Djurdjevic, V., Drobinski, P., Elizalde-Arellano, A., Gaertner, M., Galàn, P., Gallardo, C., Gualdi, S., Goncalves, M., Jorba, O., Jordà, G., L'Heveder, B., Lebeaupin-Brossier, C., Li, L., Liguori, G., Lionello, P., Maciàs, D., Nabat, P., Önol, B., Raikovic, B., Ramage, K., Sevault, F., Sannino, G., Struglia, M. V., Sanna, A., Torma, C., and Vervatis, V.: Med-CORDEX Initiative for Mediterranean Climate Studies, B. Am. Meteorol. Soc., 97, 1187–1208, https://doi.org/10.1175/BAMS-D-14-00176.1, 2016.
Sadeghi, M., Nguyen, P., Naeini, M. R., Hsu, K., Braithwaite, D., and Sorooshian, S.: PERSIANN-CCS-CDR, a 3-hourly 0.04 global precipitation climate data record for heavy precipitation studies, Sci. Data, 8, 157, https://doi.org/10.1038/s41597-021-00940-9, 2021.
Saltelli, A., Ratto, M., Andres, T., Campolongo, F., Cariboni, J., Gatelli, D., Saisana, M., and Tarantola, S.: Global sensitivity analysis: the primer, John Wiley & Sons, ISBN 0470725176, ISBN 9780470725177, 2008.
Salvadori, G. and De Michele, C.: Frequency analysis via copulas: Theoretical aspects and applications to hydrological events, Water Resour. Res., 40, W12511, https://doi.org/10.1029/2004WR003133, 2004.
Sanchis-Ibor, C., Molle, F., and Kuper, M.: Chapter 4 – Irrigation and water governance, in: Water Resources in the Mediterranean Region, edited by: Zribi, M., Brocca, L., Tramblay, Y., and Molle, F., Elsevier, 77–106, https://doi.org/10.1016/B978-0-12-818086-0.00004-2, 2020.
Santos, J. F., Portela, M. M., and Pulido-Calvo, I.:Spring drought prediction based on winter NAO and global SST in Portugal, Hydrol. Process., 28, 1009–1024, https://doi.org/10.1002/hyp.9641, 2014.
Satour, N., Raji, O., El Moçayd, N., Kacimi, I., and Kassou, N.: Spatialized flood resilience measurement in rapidly urbanized coastal areas with a complex semi-arid environment in northern Morocco, Nat. Hazards Earth Syst. Sci., 21, 1101–1118, https://doi.org/10.5194/nhess-21-1101-2021, 2021.
Saunders, M. A. and Qian, B.: Seasonal predictability of the winter NAO from north Atlantic sea surface temperatures, Geophys. Res. Lett., 29, 6-1–6-4, https://doi.org/10.1029/2002GL014952, 2002.
Savu, C. and Trede, M.: Hierarchies of Archimedean copulas, Quant. Finance, 10, 295–304, https://doi.org/10.1080/14697680902821733, 2010.
Scaife, A., Arribas, A., Blockley, E., Brookshaw, A., Clark, R., Dunstone, N., Eade, R., Fereday, D., Folland, C., and Gordon, M.: Skillful long‐range prediction of European and North American winters, Geophys. Res. Lett., 41, 2514–2519, https://doi.org/10.1002/2014GL059637, 2014.
Scanlon, B. R., Keese, K. E., Flint, A. L., Flint, L. E., Gaye, C. B., Edmunds, W. M., and Simmers, I.: Global synthesis of groundwater recharge in semiarid and arid regions, Hydrol. Process., 20, 3335–3370, 2006.
Schepen, A., Wang, Q. J., and Robertson, D. E.: Seasonal Forecasts of Australian Rainfall through Calibration and Bridging of Coupled GCM Outputs, Mon. Weather Rev., 142, 1758–1770, https://doi.org/10.1175/MWR-D-13-00248.1, 2014.
Schepen, A., Wang, Q. J., and Everingham, Y.: Calibration, Bridging, and Merging to Improve GCM Seasonal Temperature Forecasts in Australia, Mon. Weather Rev. 144, 2421–2441, https://doi.org/10.1175/MWR-D-15-0384.1, 2016.
Seifi, A., Ehteram, M., Soroush, F., and Torabi Haghighi, A.: Multi-model ensemble prediction of pan evaporation based on the Copula Bayesian Model Averaging approach, Eng. Appl. Artif. Intel., 114, 105124, https://doi.org/10.1016/j.engappai.2022.105124, 2022.
Seker, M. and Gumus, V.: Projection of temperature and precipitation in the Mediterranean region through multi-model ensemble from CMIP6, Atmos. Res., 280, 106440, https://doi.org/10.1016/j.atmosres.2022.106440, 2022.
Seneviratne, S., Nicholls, N., Easterling, D., Goodess, C., Kanae, S., Kossin, J., Luo, Y., Marengo, J., McInnes, K., Rahimi, M., Reichstein, M., Sorteberg, A., Vera, C., Zhang, X., Alexander, L. V., Allen, S., Benito, G., Cavazos, T., Clague, J., Conway, D., Della-Marta, P. M., Gerber, M., Gong, S., Goswami, B. N., Hemer, M., Huggel, C., van den Hurk, B., Kharin, V. V., Kitoh, A., Klein Tank, A. M. G., Li, G., Mason, S. J., McGuire, W., van Oldenborgh, G. J., Orlowsky, B., Smith, S., Thiaw, W., Velegrakis, A., Yiou, P., Zhang, T., Zhou, T., and Zwiers, F. W.: Changes in climate extremes and their impacts on the natural physical environment, Cambridge University Press, 109–230, https://doi.org/10.7916/d8-6nbt-s431, 2012.
Seneviratne, S. I., Donat, M. G., Pitman, A. J., Knutti, R., and Wilby, R. L.: Allowable CO2 emissions based on regional and impact-related climate targets, Nature, 529, 477–483, https://doi.org/10.1038/nature16542, 2016.
Serinaldi, F., Bonaccorso, B., Cancelliere, A., and Grimaldi, S.: Probabilistic characterization of drought properties through copulas, Recent Dev. Stat. Tools Hydrol. Appl., 34, 596–605, https://doi.org/10.1016/j.pce.2008.09.004, 2009.
Shabbar, A. and Skinner, W.: Summer Drought Patterns in Canada and the Relationship toGlobal Sea Surface Temperatures, J. Climate, 17, 2866–2880, https://doi.org/10.1175/1520-0442(2004)017<2866:SDPICA>2.0.CO;2, 2004.
Shah, D. and Mishra, V.: Integrated Drought Index (IDI) for Drought Monitoring and Assessment in India, Water Resour. Res., 56, e2019WR026284, https://doi.org/10.1029/2019WR026284, 2020.
Shahzaman, M., Zhu, W., Bilal, M., Habtemicheal, B. A., Mustafa, F., Arshad, M., Ullah, I., Ishfaq, S., and Iqbal, R.: Remote Sensing Indices for Spatial Monitoring of Agricultural Drought in South Asian Countries, Remote Sens., 13, 2059, https://doi.org/10.3390/rs13112059, 2021a.
Shahzaman, M., Zhu, W., Ullah, I., Mustafa, F., Bilal, M., Ishfaq, S., Nisar, S., Arshad, M., Iqbal, R., and Aslam, R. W.: Comparison of multi-year reanalysis, models, and satellite remote sensing products for agricultural drought monitoring over south asian countries, Remote Sens., 13, 3294, https://doi.org/10.3390/rs13163294, 2021b.
Sharma, A., Wasko, C., and Lettenmaier, D. P.: If precipitation extremes are increasing, why aren't floods?, Water Resour. Res., 54, 8545–8551, 2018.
Sheffield, J. and Wood, E. F.: Drought: Past problems and future scenarios, Routledge, ISBN 1136540415, ISBN 9781136540417, 2011.
Sheffield, J., Wood, E. F., and Roderick, M. L.: Little change in global drought over the past 60 years, Nature, 491, 435–438, https://doi.org/10.1038/nature11575, 2012.
Shi, C., Xie, Z., Qian, H., Liang, M., and Yang, X.: China land soil moisture EnKF data assimilation based on satellite remote sensing data, Sci. China Earth Sci., 54, 1430–1440, 2011.
Shukla, S. and Wood, A. W.: Use of a standardized runoff index for characterizing hydrologic drought, Geophys. Res. Lett., 35, L02405, https://doi.org/10.1029/2007GL032487, 2008.
Sklar, M.: Fonctions de repartition an dimensions et leurs marges, Publ. Inst. Stat. Univ. Paris, 8, 229–231, 1959.
Slater, L. J., Arnal, L., Boucher, M.-A., Chang, A. Y.-Y., Moulds, S., Murphy, C., Nearing, G., Shalev, G., Shen, C., Speight, L., Villarini, G., Wilby, R. L., Wood, A., and Zappa, M.: Hybrid forecasting: blending climate predictions with AI models, Hydrol. Earth Syst. Sci., 27, 1865–1889, https://doi.org/10.5194/hess-27-1865-2023, 2023.
Sousa, P. M., Trigo, R. M., Aizpurua, P., Nieto, R., Gimeno, L., and Garcia-Herrera, R.: Trends and extremes of drought indices throughout the 20th century in the Mediterranean, Nat. Hazards Earth Syst. Sci., 11, 33–51, https://doi.org/10.5194/nhess-11-33-2011, 2011.
Stagge, J. H., Tallaksen, L. M., Xu, C. Y., and Lanen, H. A. J. V.: Standardized precipitation-evapotranspiration index (SPEI): Sensitivity to potential evapotranspiration model and parameters, in: Hydrology in a Changing World, Environmental and Human Dimensions Proceedings of FRIEND-Water 2014, October 2014, Montpellier, France, 367–373, ISBN 9781907161414, 2014.
Steiger, N. J. and Smerdon, J. E.: A pseudoproxy assessment of data assimilation for reconstructing the atmosphere–ocean dynamics of hydroclimate extremes, Clim. Past, 13, 1435–1449, https://doi.org/10.5194/cp-13-1435-2017, 2017.
Steiger, N. J., Smerdon, J. E., Cook, E. R., and Cook, B. I.: A reconstruction of global hydroclimate and dynamical variables over the Common Era, Sci. Data, 5, 180086, https://doi.org/10.1038/sdata.2018.86, 2018.
Strazzo, S., Collins, D .C., Schepen, A., Wang, Q. J., Becker, E., and Jia, L.: Application of a Hybrid Statistical–Dynamical System to Seasonal Prediction of North American Temperature and Precipitation, Mon. Weather Rev., 147, 607–625, https://doi.org/10.1175/MWR-D-18-0156.1, 2019.
Sutanto, S. J., Wetterhall, F., and Lanen, H. A. J. V.: Hydrological drought forecasts outperform meteorological drought forecasts, Environ. Res. Lett., 15, 084010, https://doi.org/10.1088/1748-9326/ab8b13, 2020.
Svoboda, M., Hayes, M., and Wood, D.: Standardized Precipitation Index: User Guide, Faculty Publications, Drought Mitigation Center, ISBN 978-92-63-11091-6, 2012.
Tang, Q., Zhang, X., Duan, Q., Huang, S., Yuan, X., Cui, H., Li, Z., and Liu, X.: Hydrological monitoring and seasonal forecasting: Progress and perspectives, J. Geogr. Sci., 26, 904–920, https://doi.org/10.1007/s11442-016-1306-z, 2016.
Tatlhego, M., Chiarelli, D. D., Rulli, M. C., and D'Odorico, P.: The value generated by irrigation in the command areas of new agricultural dams in Africa, Agr. Water Manage., 264, 107517, https://doi.org/10.1016/j.agwat.2022.107517, 2022.
Tatli, H.: Detecting persistence of meteorological drought via the Hurst exponent, Meteorol. Appl., 22, 763–769, https://doi.org/10.1002/met.1519, 2015.
Tian, M., Fan, H., Xiong, Z., and Li, L.: Data-driven ensemble model for probabilistic prediction of debris-flow volume using Bayesian model averaging, Bull. Eng. Geol. Environ., 82, 34, https://doi.org/10.1007/s10064-022-03050-x, 2023.
Tigkas, D. and Tsakiris, G.: Early Estimation of Drought Impacts on Rainfed Wheat Yield in Mediterranean Climate, Environ. Process., 2, 97–114, https://doi.org/10.1007/s40710-014-0052-4, 2015.
Torres-Vázquez, M.Á., Halifa-Marín, A., Montávez, J. P., and Turco, M.: High resolution monitoring and probabilistic prediction of meteorological drought in a Mediterranean environment, Weather Clim. Extrem., 40, 100558, https://doi.org/10.1016/j.wace.2023.100558, 2023.
Tosunoglu, F. and Can, I.: Application of copulas for regional bivariate frequency analysis of meteorological droughts in Turkey, Nat. Hazards, 82, 1457–1477, https://doi.org/10.1007/s11069-016-2253-9, 2016.
Tramblay, Y., Feki, H., Quintana‐Seguí, P., and Guijarro, J. A.: The SAFRAN daily gridded precipitation product in Tunisia (1979–2015), Int. J. Climatol., 39, 5830–5838, https://doi.org/10.1002/joc.6181, 2019.
Tramblay, Y., Koutroulis, A., Samaniego, L., Vicente-Serrano, S. M., Volaire, F., Boone, A., Le Page, M., Llasat, M. C., Albergel, C., Burak, S., Cailleret, M., Kalin, K. C., Davi, H., Dupuy, J.-L., Greve, P., Grillakis, M., Hanich, L., Jarlan, L., Martin-StPaul, N., Martínez-Vilalta, J., Mouillot, F., Pulido-Velazquez, D., Quintana-Seguí, P., Renard, D., Turco, M., Türkeş, M., Trigo, R., Vidal, J.-P., Vilagrosa, A., Zribi, M., and Polcher, J.: Challenges for drought assessment in the Mediterranean region under future climate scenarios, Earth-Sci. Rev., 210, 103348, https://doi.org/10.1016/j.earscirev.2020.103348, 2020.
Trenberth, K. E., Dai, A., Van Der Schrier, G., Jones, P. D., Barichivich, J., Briffa, K. R., and Sheffield, J.: Global warming and changes in drought, Nat. Clim. Change, 4, 17–22, 2014.
Troin, M., Arsenault, R., Wood, A. W., Brissette, F., and Martel, J.-L.: Generating Ensemble Streamflow Forecasts: A Review of Methods and Approaches Over the Past 40 Years, Water Resour. Res., 57, e2020WR028392, https://doi.org/10.1029/2020WR028392, 2021.
Tsakiris, G. and Vangelis, H.: Establishing a drought index incorporating evapotranspiration, Eur. Water J., 9, 3–11, 2005.
Tuel, A. and El Moçayd, N.: Evaluating extreme precipitation in gridded datasets with a novel station database in Morocco, Stoch. Environ. Res. Risk A., 37, 3085–3097, https://doi.org/10.1007/s00477-023-02437-w, 2023.
Tuel, A. and Eltahir, E. A. B.: Why Is the Mediterranean a Climate Change Hot Spot?, J. Climate, 33, 5829–5843, https://doi.org/10.1175/JCLI-D-19-0910.1, 2020.
Tuel, A., Kang, S., and Eltahir, E. A. B.: Understanding climate change over the southwestern Mediterranean using high-resolution simulations, Clim. Dynam., 56, 985–1001, https://doi.org/10.1007/s00382-020-05516-8, 2021.
Turco, M., von Hardenberg, J., AghaKouchak, A., Llasat, M. C., Provenzale, A., and Trigo, R. M.: On the key role of droughts in the dynamics of summer fires in Mediterranean Europe, Sci. Rep., 7, 1–10, https://doi.org/10.1038/s41598-017-00116-9, 2017a.
Turco, M., Ceglar, A., Prodhomme, C., Soret, A., Toreti, A., and Francisco, J. D.-R.: Summer drought predictability over Europe: empirical versus dynamical forecasts, Environ. Res. Lett., 12, 084006, https://doi.org/10.1088/1748-9326/aa7859, 2017b.
Turco, M., Jerez, S., Donat, M. G., Toreti, A., Vicente-Serrano, S. M., and Doblas-Reyes, F. J.: DROP: A Probabilistic Drought Monitoring Tool, B. Am. Meteorol. Soc., 101, 991–994, https://doi.org/10.1175/BAMS-D-19-0192.1, 2020.
Ulbrich, U. and Christoph, M.: A shift of the NAO and increasing storm track activity over Europe due to anthropogenic greenhouse gas forcing, Clim. Dynam., 15, 551–559, https://doi.org/10.1007/s003820050299, 1999.
Valentini, R., Matteucci, G., Dolman, A. J., Schulze, E.-D., Rebmann, C., Moors, E. J., Granier, A., Gross, P., Jensen, N. O., Pilegaard, K., Lindroth, A., Grelle, A., Bernhofer, C., Grünwald, T., Aubinet, M., Ceulemans, R., Kowalski, A. S., Vesala, T., Rannik, Ü., Berbigier, P., Loustau, D., Guðmundsson, J., Thorgeirsson, H., Ibrom, A., Morgenstern, K., Clement, R., Moncrieff, J., Montagnani, L., Minerbi, S., and Jarvis, P. G.: Respiration as the main determinant of carbon balance in European forests, Nature, 404, 861–865, https://doi.org/10.1038/35009084, 2000.
Van Loon, A. F. and Laaha, G.: Hydrological drought severity explained by climate and catchment characteristics, J. Hydrol., 526, 3–14, https://doi.org/10.1016/j.jhydrol.2014.10.059, 2015.
Vasiliades, L. and Loukas, A.: Hydrological response to meteorological drought using the Palmer drought indices in Thessaly, Greece, Water Resour. Manage., 237, 3–21, https://doi.org/10.1016/j.desal.2007.12.019, 2009.
Vicente-Serrano, Sergio M., Beguería, S., and López-Moreno, J. I.: A Multiscalar Drought Index Sensitive to Global Warming: The Standardized Precipitation Evapotranspiration Index, J. Climate, 23, 1696–1718, https://doi.org/10.1175/2009JCLI2909.1, 2010a.
Vicente-Serrano, S. M., Beguería, S., López-Moreno, J. I., Angulo, M., and Kenawy, A. E.: A New Global 0.5∘ Gridded Dataset (1901–2006) of a Multiscalar Drought Index: Comparison with Current Drought Index Datasets Based on the Palmer Drought Severity Index, J. Hydrometeorol., 11, 1033–1043, https://doi.org/10.1175/2010JHM1224.1, 2010b.
Vicente-Serrano, S. M., López-Moreno, J. I., Lorenzo-Lacruz, J., Kenawy, A. E., Azorin-Molina, C., Morán-Tejeda, E., Pasho, E., Zabalza, J., Beguería, S., and Angulo-Martínez, M.: The NAO Impact on Droughts in the Mediterranean Region, in: Hydrological, Socioeconomic and Ecological Impacts of the North Atlantic Oscillation in the Mediterranean Region, Advances in Global Change Research, edited by: Vicente-Serrano, S. M. and Trigo, R. M., Springer Netherlands, Dordrecht, 23–40, https://doi.org/10.1007/978-94-007-1372-7_3, 2011.
Vicente-Serrano, S. M., Beguería, S., Lorenzo-Lacruz, J., Camarero, J. J., López-Moreno, J. I., Azorin-Molina, C., Revuelto, J., Morán-Tejeda, E., and Sanchez-Lorenzo, A.: Performance of Drought Indices for Ecological, Agricultural, and Hydrological Applications, Earth Interact., 16, 1–27, https://doi.org/10.1175/2012EI000434.1, 2012.
Vicente-Serrano, S. M., Aguilar, E., Martínez, R., Martín-Hernández, N., Azorin-Molina, C., Sanchez-Lorenzo, A., El Kenawy, A., Tomás-Burguera, M., Moran-Tejeda, E., López-Moreno, J. I., Revuelto, J., Beguería, S., Nieto, J. J., Drumond, A., Gimeno, L., and Nieto, R.: The complex influence of ENSO on droughts in Ecuador, Clim. Dynam., 48, 405–427, https://doi.org/10.1007/s00382-016-3082-y, 2017.
Vicente-Serrano, S. M., Quiring, S. M., Peña-Gallardo, M., Yuan, S., and Domínguez-Castro, F.: A review of environmental droughts: Increased risk under global warming?, Earth-Sci. Rev., 201, 102953, https://doi.org/10.1016/j.earscirev.2019.102953, 2020.
Vogel, J., Paton, E., Aich, V., and Bronstert, A.: Increasing compound warm spells and droughts in the Mediterranean Basin, Weather Clim. Extrem., 32, 100312, https://doi.org/10.1016/j.wace.2021.100312, 2021.
Vrugt, J. A., Diks, C. G. H., Gupta, H. V., Bouten, W., and Verstraten, J. M.: Improved treatment of uncertainty in hydrologic modeling: Combining the strengths of global optimization and data assimilation: Treatment Of Uncertainty In Hydrologic Modeling, Water Resour. Res., 41, W01017, https://doi.org/10.1029/2004WR003059, 2005.
Wanders, N. and Wood, E. F.: Improved sub-seasonal meteorological forecast skill using weighted multi-model ensemble simulations, Environ. Res. Lett., 11, 094007, https://doi.org/10.1088/1748-9326/11/9/094007, 2016.
Wells, N., Goddard, S., and Hayes, M. J.: A Self-Calibrating Palmer Drought Severity Index, J. Climate, 17, 2335–2351, https://doi.org/10.1175/1520-0442(2004)017<2335:ASPDSI>2.0.CO;2, 2004.
Wilby, R., Charles, S., Zorita, E., Timbal, B., Whetton, P., and Mearns, L.: Guidelines for Use of Climate Scenarios Developed from Statistical Downscaling Methods, Supporting material of the Intergovernmental Panel on Climate Change, 2004.
Wilby, R. L., Wigley, T., Conway, D., Jones, P., Hewitson, B., Main, J., and Wilks, D.: Statistical downscaling of general circulation model output: A comparison of methods, Water Resour. Res., 34, 2995–3008, 1998.
Wilhite, D. A. and Glantz, M. H.: Understanding: the Drought Phenomenon: The Role of Definitions, Water Int., 10, 111–120, https://doi.org/10.1080/02508068508686328, 1985.
Wilhite, D. A. and Pulwarty, R. S.: Drought and Water Crises: Lessons Drawn, Some Lessons Learned, and the Road Ahead, in: Drought and Water Crises, CRC Press, ISBN 9781315265551, 2017.
Wilhite, D. A., Sivakumar, M. V., and Pulwarty, R.: Managing drought risk in a changing climate: The role of national drought policy, Weather Clim. Extrem., 3, 4–13, https://doi.org/10.1016/j.wace.2014.01.002, 2014.
WMO: Guide to hydrological practices: data aquisition and processing, analysis, forecasting and other applications, https://www.innovativehydrology.com/WMO-No.168-1994.pdf (last access: 16 November 2023), 1994.
Wood, A. W., Maurer, E. P., Kumar, A., and Lettenmaier, D. P.: Long-range experimental hydrologic forecasting for the eastern United States, J. Geophys. Res.-Atmos., 107, ACL 6-1–ACL 6-15, https://doi.org/10.1029/2001JD000659, 2002.
Wood, A. W., Hopson, T., Newman, A., Brekke, L., Arnold, J., and Clark, M.: Quantifying Streamflow Forecast Skill Elasticity to Initial Condition and Climate Prediction Skill, J. Hydrometeorol., 17, 651–668, https://doi.org/10.1175/JHM-D-14-0213.1, 2016.
Wood, E. F., Schubert, S. D., Wood, A. W., Peters-Lidard, C. D., Mo, K. C., Mariotti, A., and Pulwarty, R. S: Prospects for Advancing Drought Understanding, Monitoring, and Prediction, J. Hydrometeorol., 16, 1636–1657, https://doi.org/10.1175/JHM-D-14-0164.1, 2015.
Wu, T., Bai, J., and Han, H.: Short-term agricultural drought prediction based on D-vine copula quantile regression in snow-free unfrozen surface area, China, Geocarto Int., 37, 9320–9338, https://doi.org/10.1080/10106049.2021.2017015, 2022.
Xu, K., Yang, D., Xu, X., and Lei, H.: Copula based drought frequency analysis considering the spatio-temporal variability in Southwest China, J. Hydrol., 527, 630–640, https://doi.org/10.1016/j.jhydrol.2015.05.030, 2015.
Xu, L., Chen, N., Zhang, X., and Chen, Z.: An evaluation of statistical, NMME and hybrid models for drought prediction in China, J. Hydrol., 566, 235–249, https://doi.org/10.1016/j.jhydrol.2018.09.020, 2018.
Xu, L., Abbaszadeh, P., Moradkhani, H., Chen, N., and Zhang, X.: Continental drought monitoring using satellite soil moisture, data assimilation and an integrated drought index, Remote Sens. Environ., 250, 112028, https://doi.org/10.1016/j.rse.2020.112028, 2020.
Yevjevich, V. M.: Objective approach to definitions and investigations of continental hydrologic droughts, https://api.mountainscholar.org/server/api/core/bitstreams/5f26da05-d712-49bc-acc0-397ec0f70fef/content (last access: 16 November 2023), 1967.
Yilmaz, O. S., Acar, U., Sanli, F. B., Gulgen, F., and Ates, A. M.: Mapping burn severity and monitoring CO content in Türkiye's 2021 Wildfires, using Sentinel-2 and Sentinel-5P satellite data on the GEE platform, Earth Sci. Inform., 16, 221–240, https://doi.org/10.1007/s12145-023-00933-9, 2023.
Yoo, C., Im, J., Park, J., and Noh, H. J.: Drought forecasting using an integration of wavelet analysis and kernel-based extreme learning machine, J. Hydrol., 531, 1031–1040, https://doi.org/10.1016/j.jhydrol.2015.10.067, 2015.
Yuan, X. and Wood, E. F.: Multimodel seasonal forecasting of global drought onset, Geophys. Res. Lett., 40, 4900–4905, https://doi.org/10.1002/grl.50949, 2013.
Yuan, X., Ma, Z., Pan, M., and Shi, C.: Microwave remote sensing of short-term droughts during crop growing seasons, Geophys. Res. Lett., 42, 4394–4401, 2015.
Zarei, A. and Mahmoudi, M.: Ability Assessment of the Stationary and Cyclostationary Time Series Models to Predict Drought Indices, Water Resour. Manage., 34, 5009–5029, https://doi.org/10.1007/s11269-020-02710-5, 2020.
Zellou, B. and Rahali, H.: Assessment of the joint impact of extreme rainfall and storm surge on the risk of flooding in a coastal area, J. Hydrol., 569, 647–665, https://doi.org/10.1016/j.jhydrol.2018.12.028, 2019.
Zeng, H., Wu, B., Zhang, M., Zhang, N., Elnashar, A., Zhu, L., Zhu, W., Wu, F., Yan, N., and Liu, W.: Dryland ecosystem dynamic change and its drivers in Mediterranean region, Curr. Opin. Environ. Sustainabil., 48, 59–67, https://doi.org/10.1016/j.cosust.2020.10.013, 2021.
Zhang, G. P.: Time series forecasting using a hybrid ARIMA and neural network model, Neurocomputing, 50, 159–175, https://doi.org/10.1016/S0925-2312(01)00702-0, 2003.
Zhang, J., Mu, Q., and Huang, J.: Assessing the remotely sensed Drought Severity Index for agricultural drought monitoring and impact analysis in North China, Ecol. Indic., 63, 296–309, 2016.
Zhang, Z., Lai, H., Wang, F., Feng, K., Qi, Q., and Li, Y.: Spatial–Temporal Patterns and Propagation Dynamics of Ecological Drought in the North China Plain, Water, 14, 1542, https://doi.org/10.3390/w14101542, 2022.
Zhou, H., Geng, G., Yang, J., Hu, H., Sheng, L., and Lou, W.: Improving Soil Moisture Estimation via Assimilation of Remote Sensing Product into the DSSAT Crop Model and Its Effect on Agricultural Drought Monitoring, Remote Sens., 14, 3187, https://doi.org/10.3390/rs14133187, 2022.
Zhou, Y., Zaitchik, B. F., Kumar, S. V., Arsenault, K. R., Matin, M. A., Qamer, F. M., Zamora, R. A., and Shakya, K.: Developing a hydrological monitoring and sub-seasonal to seasonal forecasting system for South and Southeast Asian river basins, Hydrol. Earth Syst. Sci., 25, 41–61, https://doi.org/10.5194/hess-25-41-2021, 2021.
Zhu, S., Luo, X., Chen, S., Xu, Z., Zhang, H., and Xiao, Z.: Improved Hidden Markov Model Incorporated with Copula for Probabilistic Seasonal Drought Forecasting, J. Hydrol. Eng., 25, 04020019, https://doi.org/10.1061/(ASCE)HE.1943-5584.0001901, 2020.
Short summary
In this study, we underscore the critical importance of strengthening drought prediction capabilities in the Mediterranean region. We present an in-depth evaluation of current drought forecasting approaches, encompassing statistical, dynamical, and hybrid statistical–dynamical models, and highlight unexplored research opportunities. Additionally, we suggest viable directions to enhance drought prediction and early warning systems within the area.
In this study, we underscore the critical importance of strengthening drought prediction...
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