Articles | Volume 21, issue 7
https://doi.org/10.5194/nhess-21-2215-2021
© Author(s) 2021. 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-21-2215-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
23 Jul 2021
Research article |
| 23 Jul 2021
| 23 Jul 2021
Leveraging multi-model season-ahead streamflow forecasts to trigger advanced flood preparedness in Peru
Colin Keating
CORRESPONDING AUTHOR
Department of Civil and Environmental Engineering, University of
Wisconsin–Madison, Madison, Wisconsin, USA
Nelson Institute for Environmental Studies, University of
Wisconsin–Madison, Madison, Wisconsin, USA
Donghoon Lee
Department of Civil and Environmental Engineering, University of
Wisconsin–Madison, Madison, Wisconsin, USA
Climate Hazards Center, Department of Geography, University of
California, Santa Barbara, Santa Barbara, California, USA
Juan Bazo
Red Cross Red Crescent Climate Centre, The Hague, 2521 CV, the
Netherlands
Universidad Tecnológica del Perú (UTP), Lima, Peru
Paul Block
Department of Civil and Environmental Engineering, University of
Wisconsin–Madison, Madison, Wisconsin, USA
Related authors
No articles found.
Donghoon Lee, Jia Yi Ng, Stefano Galelli, and Paul Block
Hydrol. Earth Syst. Sci., 26, 2431–2448, https://doi.org/10.5194/hess-26-2431-2022, https://doi.org/10.5194/hess-26-2431-2022, 2022
Short summary
Short summary
To fully realize the potential of seasonal streamflow forecasts in the hydropower industry, we need to understand the relationship between reservoir design specifications, forecast skill, and value. Here, we rely on realistic forecasts and simulated hydropower operations for 753 dams worldwide to unfold such relationship. Our analysis shows how forecast skill affects hydropower production, what type of dams are most likely to benefit from seasonal forecasts, and where these dams are located.
Jamie Towner, Andrea Ficchí, Hannah L. Cloke, Juan Bazo, Erin Coughlan de Perez, and Elisabeth M. Stephens
Hydrol. Earth Syst. Sci., 25, 3875–3895, https://doi.org/10.5194/hess-25-3875-2021, https://doi.org/10.5194/hess-25-3875-2021, 2021
Short summary
Short summary
We examine whether several climate indices alter the magnitude, timing and duration of floods in the Amazon. We find significant changes in both flood magnitude and duration, particularly in the north-eastern Amazon for negative SST years in the central Pacific Ocean. This response is not repeated when the negative anomaly is positioned further east. These results have important implications for both social and physical sectors working towards the improvement of flood early warning systems.
Donghoon Lee, Hassan Ahmadul, Jonathan Patz, and Paul Block
Nat. Hazards Earth Syst. Sci., 21, 1807–1823, https://doi.org/10.5194/nhess-21-1807-2021, https://doi.org/10.5194/nhess-21-1807-2021, 2021
Short summary
Short summary
This article assesses the thematic and composite social and health vulnerability of Bangladesh to floods. Tailored vulnerability, weighted by flood forecast and satellite inundation, can be used to predict the massive impacts of the August 2017 flood event. This approach has several advantages and practical implications, including the potential to promote targeted and coordinated disaster management and health practices.
Jamie Towner, Hannah L. Cloke, Ervin Zsoter, Zachary Flamig, Jannis M. Hoch, Juan Bazo, Erin Coughlan de Perez, and Elisabeth M. Stephens
Hydrol. Earth Syst. Sci., 23, 3057–3080, https://doi.org/10.5194/hess-23-3057-2019, https://doi.org/10.5194/hess-23-3057-2019, 2019
Short summary
Short summary
This study presents an intercomparison analysis of eight global hydrological models (GHMs), assessing their ability to simulate peak river flows in the Amazon basin. Results indicate that the meteorological input is the most influential component of the hydrological modelling chain, with the recent ERA-5 reanalysis dataset significantly improving the ability to simulate flood peaks in the Peruvian Amazon. In contrast, calibration of the Lisflood routing model was found to have no impact.
Eric Mortensen, Shu Wu, Michael Notaro, Stephen Vavrus, Rob Montgomery, José De Piérola, Carlos Sánchez, and Paul Block
Hydrol. Earth Syst. Sci., 22, 287–303, https://doi.org/10.5194/hess-22-287-2018, https://doi.org/10.5194/hess-22-287-2018, 2018
Short summary
Short summary
Some do not realize the intrinsic importance of water until there is no water left. This is the reality faced by people in southern Peru, a dry area of the world where several economic activities and cities vie for scarce water resources. With the developed season-ahead precipitation prediction model, stakeholders and decision makers in this region will have another tool in their belt to respond to and plan for the negative impacts brought on by drought.
Ying Zhang, Semu Moges, and Paul Block
Hydrol. Earth Syst. Sci., 22, 143–157, https://doi.org/10.5194/hess-22-143-2018, https://doi.org/10.5194/hess-22-143-2018, 2018
Short summary
Short summary
The study proposes advancing local-level seasonal rainfall predictions by first conditioning on regional-level predictions, as defined through cluster analysis. This statistical approach is applied to western Ethiopia, where lives and livelihoods are vulnerable to its high spatial–temporal rainfall variability, particularly given the high reliance on rain-fed agriculture. The statistical model improves in skills versus the non-clustered case or dynamical models for some critical regions.
Justin Delorit, Edmundo Cristian Gonzalez Ortuya, and Paul Block
Hydrol. Earth Syst. Sci., 21, 4711–4725, https://doi.org/10.5194/hess-21-4711-2017, https://doi.org/10.5194/hess-21-4711-2017, 2017
Short summary
Short summary
This work provides forecasts of water supply for the semi-arid Elqui River Valley, Chile, at periods prior to the October–January growing season. Forecasts are constructed provide water rights holders, whose allocations are subject to annual change, with an advanced indication of expected allocations. Forecasts, based on global and local indicators, are best suited to provide an initial indication of allocation category (above, near, or below normal) in May and are quantified in September.
D. Lee, P. Ward, and P. Block
Hydrol. Earth Syst. Sci., 19, 4689–4705, https://doi.org/10.5194/hess-19-4689-2015, https://doi.org/10.5194/hess-19-4689-2015, 2015
Short summary
Short summary
This paper presents a global approach to defining high-flow seasons by identifying temporal patterns of streamflow. Simulations of streamflow from the PCR-GLOBWB model are evaluated to define dominant and minor high-flow seasons globally, and verified with GRDC observations and flood records from Dartmouth Flood Observatory.
Related subject area
Hydrological Hazards
Drought propagation in high-latitude catchments: insights from a 60-year analysis using standardized indices
Brief communication: Hydrological and hydraulic investigation of the extreme September 2024 flood on the Lamone River in Emilia-Romagna, Italy
Improving pluvial flood simulations with a multi-source digital elevation model super-resolution method
It could have been much worse: spatial counterfactuals of the July 2021 flood in the Ahr Valley, Germany
Rapid high-resolution impact-based flood early warning is possible with RIM2D: a showcase for the 2023 pluvial flood in Braunschweig
The 2018–2023 drought in Berlin: impacts and analysis of the perspective of water resources management
Recent large-inland-lake outbursts on the Tibetan Plateau: processes, causes, and mechanisms
Modelling urban stormwater drainage overflows for assessing flood hazards: application to the urban area of Dakar (Senegal)
Dynamics and impacts of monsoon-induced geological hazards: a 2022 flood study along the Swat River in Pakistan
Monte Carlo-based sensitivity analysis of the RIM2D hydrodynamic model for the 2021 flood event in western Germany
Climate change impacts on floods in West Africa: New insight from two large-scale hydrological models
Mind the gap: misalignment between drought monitoring and community realities
Forecasting agricultural drought: the Australian Agriculture Drought Indicators
Post-wildfire sediment source and transport modeling, empirical observations, and applied mitigation: an Arizona, USA, case study
Causes of the exceptionally high number of fatalities in the Ahr valley, Germany, during the 2021 flood
Groundwater recharge in Brandenburg is declining – but why?
Large-scale flood risk assessment in data-scarce areas: an application to Central Asia
Multi-scale hydraulic graph neural networks for flood modelling
The role of antecedent conditions in translating precipitation events into extreme floods at the catchment scale and in a large-basin context
Brief communication: Stay local or go global? On the construction of plausible counterfactual scenarios to assess flash flood hazards
Integrating susceptibility maps of multiple hazards and building exposure distribution: a case study of wildfires and floods for the province of Quang Nam, Vietnam
Tangible and intangible ex post assessment of flood-induced damage to cultural heritage
A multivariate statistical framework for mixed storm types in compound flood analysis
Invited perspectives: safeguarding the usability and credibility of flood hazard and risk assessments
Influence of building collapse on pluvial and fluvial flood inundation of metro stations in central Shanghai
Impact of drought hazards on flow regimes in anthropogenically impacted streams: an isotopic perspective on climate stress
The effect of wildfires on flood risk: a multi-hazard flood risk approach for the Ebro River basin, Spain
Modelling hazards impacting the flow regime in the Hranice Karst due to the proposed Skalička Dam
Spatiotemporal variability of flash floods and their human impacts in the Czech Republic during the 2001–2023 period
Risk of compound flooding substantially increases in the future Mekong River delta
Transferability of machine-learning-based modeling frameworks across flood events for hindcasting maximum river water depths in coastal watersheds
Floods in the Pyrenees: a global view through a regional database
Algorithmically detected rain-on-snow flood events in different climate datasets: a case study of the Susquehanna River basin
Disentangling Atmospheric, Hydrological, and Coupling Uncertainties in Compound Flood Modeling within a Coupled Earth System Model
Review article: Drought as a continuum – memory effects in interlinked hydrological, ecological, and social systems
Coupling WRF with HEC-HMS and WRF-Hydro for flood forecasting in typical mountainous catchments of northern China
Temporal persistence of postfire flood hazards under present and future climate conditions in southern Arizona, USA
Evaluating Yangtze River Delta Urban Agglomeration flood risk using hybrid method of AutoML and AHP
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
Hail events in Germany, rare or frequent natural hazards?
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
Claudia Teutschbein, Thomas Grabs, Markus Giese, Andrijana Todorović, and Roland Barthel
Nat. Hazards Earth Syst. Sci., 25, 2541–2564, https://doi.org/10.5194/nhess-25-2541-2025, https://doi.org/10.5194/nhess-25-2541-2025, 2025
Short summary
Short summary
This study is an exploration of how droughts develop and spread in high-latitude regions, focusing on the unique conditions found in areas like Scandinavia. It reveals that droughts affect soil, rivers, and groundwater differently, depending on such factors as land cover, water availability, and soil properties. The findings highlight the importance of tailored water management strategies to protect resources and ecosystems in these regions, especially as climate change continues to affect weather patterns.
Alessia Ferrari, Giulia Passadore, Renato Vacondio, Luca Carniello, Mattia Pivato, Elena Crestani, Francesco Carraro, Francesca Aureli, Sara Carta, Francesca Stumpo, and Paolo Mignosa
Nat. Hazards Earth Syst. Sci., 25, 2473–2479, https://doi.org/10.5194/nhess-25-2473-2025, https://doi.org/10.5194/nhess-25-2473-2025, 2025
Short summary
Short summary
Between 17 and 20 September 2024, the Lamone River basin in northern Italy was hit by extreme precipitation. This study adopts the hydrological model Rhyme and the hydrodynamic model PARFLOOD to simulate the hydrological processes in the watershed and the levee-breach-induced inundation affecting the village of Traversara. The close match between the resulting flooded areas and the observed ones shows the capability of these numerical models to support the preparedness for at-risk populations.
Yue Zhu, Paolo Burlando, Puay Yok Tan, Christian Geiß, and Simone Fatichi
Nat. Hazards Earth Syst. Sci., 25, 2271–2286, https://doi.org/10.5194/nhess-25-2271-2025, https://doi.org/10.5194/nhess-25-2271-2025, 2025
Short summary
Short summary
This study addresses the challenge of accurately predicting floods in regions with limited terrain data. By utilising a deep learning model, we developed a method that improves the resolution of digital elevation data by fusing low-resolution elevation data with high-resolution satellite imagery. This approach not only substantially enhances flood prediction accuracy, but also holds potential for broader applications in simulating natural hazards that require terrain information.
Sergiy Vorogushyn, Li Han, Heiko Apel, Viet Dung Nguyen, Björn Guse, Xiaoxiang Guan, Oldrich Rakovec, Husain Najafi, Luis Samaniego, and Bruno Merz
Nat. Hazards Earth Syst. Sci., 25, 2007–2029, https://doi.org/10.5194/nhess-25-2007-2025, https://doi.org/10.5194/nhess-25-2007-2025, 2025
Short summary
Short summary
The July 2021 flood in central Europe was one of the deadliest floods in Europe in the recent decades and the most expensive flood in Germany. In this paper, we show that the hydrological impact of this event in the Ahr valley could have been even worse if the rainfall footprint trajectory had been only slightly different. The presented methodology of spatial counterfactuals generates plausible unprecedented events and helps to better prepare for future extreme floods.
Shahin Khosh Bin Ghomash, Heiko Apel, Kai Schröter, and Max Steinhausen
Nat. Hazards Earth Syst. Sci., 25, 1737–1749, https://doi.org/10.5194/nhess-25-1737-2025, https://doi.org/10.5194/nhess-25-1737-2025, 2025
Short summary
Short summary
This work introduces RIM2D (Rapid Inundation Model 2D), a hydrodynamic model for precise and rapid flood predictions that is ideal for early warning systems. We demonstrate RIM2D's ability to deliver detailed and localized flood forecasts using the June 2023 flood in Braunschweig, Germany, as a case study. This research highlights the readiness of RIM2D and the required hardware for integration into operational flood warning and impact-based forecasting systems.
Ina Pohle, Sarah Zeilfelder, Johannes Birner, and Benjamin Creutzfeldt
Nat. Hazards Earth Syst. Sci., 25, 1293–1313, https://doi.org/10.5194/nhess-25-1293-2025, https://doi.org/10.5194/nhess-25-1293-2025, 2025
Short summary
Short summary
Climate change, the lignite mining phase-out and structural changes challenge water resources management of the German capital Berlin. Reduced water availability and rising demand are creating latent water quality problems. The 2018–2023 drought uniquely impacted temperature, precipitation, groundwater and surface water. Analysing the impacts of the 2018–2023 drought helps to address water-related challenges and implement effective measures in Berlin and its surrounding areas.
Fenglin Xu, Yong Liu, Guoqing Zhang, Ping Zhao, R. Iestyn Woolway, Yani Zhu, Jianting Ju, Tao Zhou, Xue Wang, and Wenfeng Chen
Nat. Hazards Earth Syst. Sci., 25, 1187–1206, https://doi.org/10.5194/nhess-25-1187-2025, https://doi.org/10.5194/nhess-25-1187-2025, 2025
Short summary
Short summary
Glacial lake outbursts have been widely studied, but large-inland-lake outbursts have received less attention. Recently, with the rapid expansion of inland lakes, signs of potential outbursts have increased. However, their processes, causes, and mechanisms are still not well understood. Here, the outburst processes of two inland lakes were investigated using a combination of field surveys, remote sensing mapping, and hydrodynamic modeling. Their causes and mechanisms were also investigated.
Laurent Pascal Malang Diémé, Christophe Bouvier, Ansoumana Bodian, and Alpha Sidibé
Nat. Hazards Earth Syst. Sci., 25, 1095–1112, https://doi.org/10.5194/nhess-25-1095-2025, https://doi.org/10.5194/nhess-25-1095-2025, 2025
Short summary
Short summary
We propose a decision support tool that detect the occurrence of flooding by drainage overflow, with sufficiently short calculation times. The simulations are based on a drainage topology on 5 m grids, incorporating changes to surface flows induced by urbanization. The method can be used for flood mapping in project mode and in real time. It applies to the present situation as well as to any scenario involving climate change or urban growth.
Nazir Ahmed Bazai, Mehtab Alam, Peng Cui, Wang Hao, Adil Poshad Khan, Muhammad Waseem, Yao Shunyu, Muhammad Ramzan, Li Wanhong, and Tashfain Ahmed
Nat. Hazards Earth Syst. Sci., 25, 1071–1093, https://doi.org/10.5194/nhess-25-1071-2025, https://doi.org/10.5194/nhess-25-1071-2025, 2025
Short summary
Short summary
The 2022 monsoon in Pakistan's Swat River basin brought record rainfall, exceeding averages by 7–8%, triggering catastrophic debris flows and floods. Key factors include extreme rainfall, deforestation, and steep slopes. Fieldwork, remote sensing, and simulations highlight land degradation's role in intensifying floods. Recommendations include reforestation, early warning systems, and land use reforms to protect communities and reduce future risks
Shahin Khosh Bin Ghomash, Patricio Yeste, Heiko Apel, and Viet Dung Nguyen
Nat. Hazards Earth Syst. Sci., 25, 975–990, https://doi.org/10.5194/nhess-25-975-2025, https://doi.org/10.5194/nhess-25-975-2025, 2025
Short summary
Short summary
Hydrodynamic models are vital for predicting floods, like those in Germany's Ahr region in July 2021. We refine the RIM2D model for the Ahr region, analyzing the impact of various factors using Monte Carlo simulations. Accurate parameter assignment is crucial, with channel roughness and resolution playing key roles. Coarser resolutions are suitable for flood extent predictions, aiding early-warning systems. Our work provides guidelines for optimizing hydrodynamic models in the Ahr region.
Serigne Bassirou Diop, Job Ekolu, Yves Tramblay, Bastien Dieppois, Stefania Grimaldi, Ansoumana Bodian, Juliette Blanchet, Ponnambalam Rameshwaran, Peter Salamon, and Benjamin Sultan
EGUsphere, https://doi.org/10.5194/egusphere-2025-130, https://doi.org/10.5194/egusphere-2025-130, 2025
Short summary
Short summary
West Africa is very vulnerable to rivers floods. Current flood hazards are poorly understood due to limited data. This study is filling this knowledge gap using recent databases and two regional hydrological models to analyze changes in flood risk under two climate scenarios. Results show that most areas will see more frequent and severe floods, with some increasing by over 45 %. These findings stress the urgent need for climate-resilient strategies to protect communities and infrastructure.
Sarra Kchouk, Louise Cavalcante, Lieke A. Melsen, David W. Walker, Germano Ribeiro Neto, Rubens Gondim, Wouter J. Smolenaars, and Pieter R. van Oel
Nat. Hazards Earth Syst. Sci., 25, 893–912, https://doi.org/10.5194/nhess-25-893-2025, https://doi.org/10.5194/nhess-25-893-2025, 2025
Short summary
Short summary
Droughts impact water and people, yet monitoring often overlooks impacts on people. In northeastern Brazil, we compare official data to local experiences, finding data mismatches and blind spots. Mismatches occur due to the data's broad scope missing finer details. Blind spots arise from ignoring diverse community responses and vulnerabilities to droughts. We suggest enhanced monitoring by technical extension officers for both severe and mild droughts.
Andrew Schepen, Andrew Bolt, Dorine Bruget, John Carter, Donald Gaydon, Mihir Gupta, Zvi Hochman, Neal Hughes, Chris Sharman, Peter Tan, and Peter Taylor
EGUsphere, https://doi.org/10.5194/egusphere-2024-4129, https://doi.org/10.5194/egusphere-2024-4129, 2025
Short summary
Short summary
The success of agricultural enterprises is affected by climate variability and other important factors like soil conditions and market prices. We have developed an agricultural drought forecasting system to help drought analysts and policymakers more accurately identify communities that are enduring financial stress. By coupling climate forecasts and agricultural models, we can skillfully predict crop yields and farm profits for the coming seasons, which will support proactive responses.
Edward R. Schenk, Alex Wood, Allen Haden, Gabriel Baca, Jake Fleishman, and Joe Loverich
Nat. Hazards Earth Syst. Sci., 25, 727–745, https://doi.org/10.5194/nhess-25-727-2025, https://doi.org/10.5194/nhess-25-727-2025, 2025
Short summary
Short summary
Post-wildfire flooding and debris are dangerous and damaging. This study used three different sediment models to predict post-wildfire sediment sources and transport amounts downstream of the 2019 Museum Fire in northern Arizona, USA. The predictions were compared with real-world measurements of sediment that was cleaned out of the city of Flagstaff after four large floods in 2021. Results provide avenues for continued model refinement and an example of potential mitigation strategies.
Belinda Rhein and Heidi Kreibich
Nat. Hazards Earth Syst. Sci., 25, 581–589, https://doi.org/10.5194/nhess-25-581-2025, https://doi.org/10.5194/nhess-25-581-2025, 2025
Short summary
Short summary
In July 2021, flooding killed 190 people in Germany, 134 of them in the Ahr valley, making it the deadliest flood in recent German history. The flash flood was extreme in terms of water levels, flow velocities and flood extent, and early warning and evacuation were inadequate. Many died on the ground floor or in the street, with older and impaired individuals especially vulnerable. Clear warnings should urge people to seek safety rather than save belongings, and timely evacuations are essential.
Till Francke and Maik Heistermann
EGUsphere, https://doi.org/10.5194/egusphere-2025-222, https://doi.org/10.5194/egusphere-2025-222, 2025
Short summary
Short summary
Brandenburg is among the driest federal states in Germany. The low ground water recharge (GWR) is fundamental to both water supply and the support of natural ecosystems. In this study, we show that the decline of observed discharge and groundwater tables since 1980 can be explained by climate change in combination with an increasing leaf area index. Still, simulated GWR rates remain highly uncertain due to the uncertainty of precipitation trends.
Paola Ceresa, Gianbattista Bussi, Simona Denaro, Gabriele Coccia, Paolo Bazzurro, Mario Martina, Ettore Fagà, Carlos Avelar, Mario Ordaz, Benjamin Huerta, Osvaldo Garay, Zhanar Raimbekova, Kanatbek Abdrakhmatov, Sitora Mirzokhonova, Vakhitkhan Ismailov, and Vladimir Belikov
Nat. Hazards Earth Syst. Sci., 25, 403–428, https://doi.org/10.5194/nhess-25-403-2025, https://doi.org/10.5194/nhess-25-403-2025, 2025
Short summary
Short summary
A fully probabilistic flood risk assessment was carried out for five Central Asian countries to support regional and national risk financing and insurance applications. The paper presents the first high-resolution regional-scale transboundary flood risk assessment study in the area aiming to provide tools for decision-making.
Roberto Bentivoglio, Elvin Isufi, Sebastiaan Nicolas Jonkman, and Riccardo Taormina
Nat. Hazards Earth Syst. Sci., 25, 335–351, https://doi.org/10.5194/nhess-25-335-2025, https://doi.org/10.5194/nhess-25-335-2025, 2025
Short summary
Short summary
Deep learning methods are increasingly used as surrogates for spatio-temporal flood models but struggle with generalization and speed. Here, we propose a multi-resolution approach using graph neural networks that predicts dike breach floods across different meshes, topographies, and boundary conditions with high accuracy and up to 1000× speed-ups. The model also generalizes to larger more complex case studies with just one additional simulation for fine-tuning.
Maria Staudinger, Martina Kauzlaric, Alexandre Mas, Guillaume Evin, Benoit Hingray, and Daniel Viviroli
Nat. Hazards Earth Syst. Sci., 25, 247–265, https://doi.org/10.5194/nhess-25-247-2025, https://doi.org/10.5194/nhess-25-247-2025, 2025
Short summary
Short summary
Various combinations of antecedent conditions and precipitation result in floods of varying degrees. Antecedent conditions played a crucial role in generating even large ones. The key predictors and spatial patterns of antecedent conditions leading to flooding at the basin's outlet were distinct. Precipitation and soil moisture from almost all sub-catchments were important for more frequent floods. For rarer events, only the predictors of specific sub-catchments were important.
Paul Voit and Maik Heistermann
Nat. Hazards Earth Syst. Sci., 24, 4609–4615, https://doi.org/10.5194/nhess-24-4609-2024, https://doi.org/10.5194/nhess-24-4609-2024, 2024
Short summary
Short summary
Floods have caused significant damage in the past. To prepare for such events, we rely on historical data but face issues due to rare rainfall events, lack of data and climate change. Counterfactuals, or
what ifscenarios, simulate historical rainfall in different locations to estimate flood levels. Our new study refines this by deriving more-plausible local scenarios, using the June 2024 Bavaria flood as a case study. This method could improve preparedness for future floods.
Chinh Luu, Giuseppe Forino, Lynda Yorke, Hang Ha, Quynh Duy Bui, Hanh Hong Tran, Dinh Quoc Nguyen, Hieu Cong Duong, and Matthieu Kervyn
Nat. Hazards Earth Syst. Sci., 24, 4385–4408, https://doi.org/10.5194/nhess-24-4385-2024, https://doi.org/10.5194/nhess-24-4385-2024, 2024
Short summary
Short summary
This study presents a novel and integrated approach to assessing the climate hazards of floods and wildfires. We explore multi-hazard assessment and risk through a machine learning modeling approach. The process includes collecting a database of topography, climate, geology, environment, and building data; developing models for multi-hazard assessment and coding in the Google Earth Engine; and producing credible multi-hazard susceptibility and building exposure maps.
Claudia De Lucia, Michele Amaddii, and Chiara Arrighi
Nat. Hazards Earth Syst. Sci., 24, 4317–4339, https://doi.org/10.5194/nhess-24-4317-2024, https://doi.org/10.5194/nhess-24-4317-2024, 2024
Short summary
Short summary
This work describes the flood damage to cultural heritage (CH) that occurred in September 2022 in central Italy. Datasets related to flood impacts on cultural heritage are rare, and this work aims at highlighting both tangible and intangible aspects and their correlation with physical characteristics of flood (i.e. water depth and flow velocity). The results show that current knowledge and datasets are inadequate for risk assessment of CH.
Pravin Maduwantha, Thomas Wahl, Sara Santamaria-Aguilar, Robert Jane, James F. Booth, Hanbeen Kim, and Gabriele Villarini
Nat. Hazards Earth Syst. Sci., 24, 4091–4107, https://doi.org/10.5194/nhess-24-4091-2024, https://doi.org/10.5194/nhess-24-4091-2024, 2024
Short summary
Short summary
When assessing the likelihood of compound flooding, most studies ignore that it can arise from different storm types with distinct statistical characteristics. Here, we present a new statistical framework that accounts for these differences and shows how neglecting these can impact the likelihood of compound flood potential.
Bruno Merz, Günter Blöschl, Robert Jüpner, Heidi Kreibich, Kai Schröter, and Sergiy Vorogushyn
Nat. Hazards Earth Syst. Sci., 24, 4015–4030, https://doi.org/10.5194/nhess-24-4015-2024, https://doi.org/10.5194/nhess-24-4015-2024, 2024
Short summary
Short summary
Flood risk assessments help us decide how to reduce the risk of flooding. Since these assessments are based on probabilities, it is hard to check their accuracy by comparing them to past data. We suggest a new way to validate these assessments, making sure they are practical for real-life decisions. This approach looks at both the technical details and the real-world situations where decisions are made. We demonstrate its practicality by applying it to flood emergency planning.
Zhi Li, Hanqi Li, Zhibo Zhang, Chaomeng Dai, and Simin Jiang
Nat. Hazards Earth Syst. Sci., 24, 3977–3990, https://doi.org/10.5194/nhess-24-3977-2024, https://doi.org/10.5194/nhess-24-3977-2024, 2024
Short summary
Short summary
This study used advanced computer simulations to investigate how earthquake-induced building collapse affects flooding of the metro stations in Shanghai. Results show that the influences of building collapse on rainfall-driven and river-driven floods are different because these two types of floods have different origination and propagation mechanisms.
Maria Magdalena Warter, Dörthe Tetzlaff, Christian Marx, and Chris Soulsby
Nat. Hazards Earth Syst. Sci., 24, 3907–3924, https://doi.org/10.5194/nhess-24-3907-2024, https://doi.org/10.5194/nhess-24-3907-2024, 2024
Short summary
Short summary
Streams are increasingly impacted by droughts and floods. Still, the amount of water needed for sustainable flows remains unclear and contested. A comparison of two streams in the Berlin–Brandenburg region of northeast Germany, using stable water isotopes, shows strong groundwater dependence with seasonal rainfall contributing to high/low flows. Understanding streamflow variability can help us assess the impacts of climate change on future water resource management.
Samuel Jonson Sutanto, Matthijs Janssen, Mariana Madruga de Brito, and Maria del Pozo Garcia
Nat. Hazards Earth Syst. Sci., 24, 3703–3721, https://doi.org/10.5194/nhess-24-3703-2024, https://doi.org/10.5194/nhess-24-3703-2024, 2024
Short summary
Short summary
A conventional flood risk assessment only evaluates flood hazard in isolation without considering wildfires. This study, therefore, evaluates the effect of wildfires on flood risk, considering both current and future conditions for the Ebro River basin in Spain. Results show that extreme climate change increases the risk of flooding, especially when considering the effect of wildfires, highlighting the importance of adopting a multi-hazard risk management approach.
Miroslav Spano and Jaromir Riha
Nat. Hazards Earth Syst. Sci., 24, 3683–3701, https://doi.org/10.5194/nhess-24-3683-2024, https://doi.org/10.5194/nhess-24-3683-2024, 2024
Short summary
Short summary
The study examines the effects of hydrogeological hazard due to construction of the Skalička Dam near the Hranice Karst on groundwater discharges and water levels in the local karst formations downstream. A simplified pipe model was used to analyze the impact of two dam layouts: lateral and through-flow reservoirs. Results show that the through-flow scheme more significantly influences water levels and the discharge of mineral water, while the lateral layout has only negligible impact.
Rudolf Brázdil, Dominika Faturová, Monika Šulc Michalková, Jan Řehoř, Martin Caletka, and Pavel Zahradníček
Nat. Hazards Earth Syst. Sci., 24, 3663–3682, https://doi.org/10.5194/nhess-24-3663-2024, https://doi.org/10.5194/nhess-24-3663-2024, 2024
Short summary
Short summary
Flash floods belong to natural hazards that can be enhanced in frequency, intensity, and impact during recent climate change. This paper presents a complex analysis of spatiotemporal variability and human impacts (including material damage and fatalities) of flash floods in the Czech Republic for the 2001–2023 period. The analysis generally shows no statistically significant trends in the characteristics analyzed.
Melissa Wood, Ivan D. Haigh, Quan Quan Le, Hung Nghia Nguyen, Hoang Ba Tran, Stephen E. Darby, Robert Marsh, Nikolaos Skliris, and Joël J.-M. Hirschi
Nat. Hazards Earth Syst. Sci., 24, 3627–3649, https://doi.org/10.5194/nhess-24-3627-2024, https://doi.org/10.5194/nhess-24-3627-2024, 2024
Short summary
Short summary
We look at how compound flooding from the combination of river flooding and storm tides (storm surge and 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 the potential to flood the region more extensively and be longer lasting than compound floods today. This is useful to know because it means managers of deltas such as the Mekong can assess options for improving existing flood defences.
Maryam Pakdehi, Ebrahim Ahmadisharaf, Behzad Nazari, and Eunsaem Cho
Nat. Hazards Earth Syst. Sci., 24, 3537–3559, https://doi.org/10.5194/nhess-24-3537-2024, https://doi.org/10.5194/nhess-24-3537-2024, 2024
Short summary
Short summary
Machine learning (ML) algorithms have increasingly received attention for modeling flood events. However, there are concerns about the transferability of these models (their capability in predicting out-of-sample and unseen events). Here, we show that ML models can be transferable for hindcasting maximum river flood depths across extreme events (four hurricanes) in a large coastal watershed (HUC6) when informed by the spatial distribution of pertinent features and underlying physical processes.
María Carmen Llasat, Montserrat Llasat-Botija, Erika Pardo, Raül Marcos-Matamoros, and Marc Lemus-Canovas
Nat. Hazards Earth Syst. Sci., 24, 3423–3443, https://doi.org/10.5194/nhess-24-3423-2024, https://doi.org/10.5194/nhess-24-3423-2024, 2024
Short summary
Short summary
This paper shows the first public and systematic dataset of flood episodes referring to the entire Pyrenees massif, at municipal scale, named PIRAGUA_flood. Of the 181 flood events (1981–2015) that produced 154 fatalities, 36 were transnational, with the eastern part of the massif most affected. Dominant weather types show a southern component flow, with a talweg on the Iberian Peninsula and a depression in the vicinity. A positive and significant trend was found in Nouvelle-Aquitaine.
Colin M. Zarzycki, Benjamin D. Ascher, Alan M. Rhoades, and Rachel R. McCrary
Nat. Hazards Earth Syst. Sci., 24, 3315–3335, https://doi.org/10.5194/nhess-24-3315-2024, https://doi.org/10.5194/nhess-24-3315-2024, 2024
Short summary
Short summary
We developed an automated workflow to detect rain-on-snow events, which cause flooding in the northeastern United States, 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.
Dongyu Feng, Zeli Tan, Darren Engwirda, Jonathan D. Wolfe, Donghui Xu, Chang Liao, Gautam Bisht, James J. Benedict, Tian Zhou, Mithun Deb, Hong-Yi Li, and L. Ruby Leung
EGUsphere, https://doi.org/10.5194/egusphere-2024-2785, https://doi.org/10.5194/egusphere-2024-2785, 2024
Short summary
Short summary
Our study explores how riverine and coastal flooding during hurricanes is influenced by the interaction of atmosphere, land, river and ocean conditions. Using an advanced Earth system model, we simulate Hurricane Irene to evaluate how meteorological and hydrological uncertainties affect flood modeling. Our findings reveal the importance of a multi-component modeling system, how hydrological conditions play critical roles in flood modeling, and greater flood risks if multiple factors are present.
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
Nat. Hazards Earth Syst. Sci., 24, 3173–3205, https://doi.org/10.5194/nhess-24-3173-2024, https://doi.org/10.5194/nhess-24-3173-2024, 2024
Short summary
Short summary
Drought is a creeping phenomenon but is often still analysed and managed like an isolated event, without taking into account what happened before and after. Here, we review the literature and analyse five cases to discuss how droughts and their impacts develop over time. We find that the responses of hydrological, ecological, and social systems can be classified into four types and that the systems interact. We provide suggestions for further research and monitoring, modelling, and management.
Sheik Umar Jam-Jalloh, Jia Liu, Yicheng Wang, and Yuchen Liu
Nat. Hazards Earth Syst. Sci., 24, 3155–3172, https://doi.org/10.5194/nhess-24-3155-2024, https://doi.org/10.5194/nhess-24-3155-2024, 2024
Short summary
Short summary
Our paper explores improving flood forecasting using advanced weather and hydrological models. By coupling the WRF model with WRF-Hydro and HEC-HMS, we achieved more accurate forecasts. WRF–WRF-Hydro excels for short, intense storms, while WRF–HEC-HMS is better for longer, evenly distributed storms. Our research shows how these models provide insights for adaptive atmospheric–hydrologic systems and aims to boost flood preparedness and response with more reliable, timely predictions.
Tao Liu, Luke A. McGuire, Ann M. Youberg, Charles J. Abolt, and Adam L. Atchley
Nat. Hazards Earth Syst. Sci. Discuss., https://doi.org/10.5194/nhess-2024-151, https://doi.org/10.5194/nhess-2024-151, 2024
Revised manuscript accepted for NHESS
Short summary
Short summary
After a fire, soil infiltration decreases, increasing flash flood risks, worsened by intense rainfall from climate change. Using data from a burned watershed in Arizona and a hydrological model, we examined postfire soil changes under medium and high emissions scenarios. Results showed soil infiltration increased sixfold from the first to third postfire year. Both scenarios suggest that rainfall intensification will extend high flood risks after fires by late century.
Yu Gao, Haipeng Lu, Yaru Zhang, Hengxu Jin, Shuai Wu, Yixuan Gao, and Shuliang Zhang
Nat. Hazards Earth Syst. Sci. Discuss., https://doi.org/10.5194/nhess-2024-144, https://doi.org/10.5194/nhess-2024-144, 2024
Revised manuscript accepted for NHESS
Short summary
Short summary
This study focuses on the Yangtze River Delta Urban Agglomeration (YRDUA), where we determined flood risk assessment indices across different dimensions, including hazard, exposure, vulnerability, and resilience. We constructed a flood risk assessment model using AutoML and AHP to examine the spatial and temporal changes in flood risk in the region over the past 30 years (1990 to 2020), aiming to provide a scientific basis for flood prevention and resilience strategies in the YRDUA.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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.
Tabea Wilke, Katharina Lengfeld, and Markus Schultze
EGUsphere, https://doi.org/10.5194/egusphere-2024-2507, https://doi.org/10.5194/egusphere-2024-2507, 2024
Short summary
Short summary
Hail in Germany is a natural hazard that is not in everyone's focus, even though it can cause great damage. In this study we focus on hail frequency, sizes and spatial distribution in Germany based on crowd sourcing and weather radar data. We compare different algorithms based on weather radar data with crowd sourced data and show the annual and diurnal cycle of hail in Germany.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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.
Cited articles
Aguirre, J., De La Torre Ugarte, D., Bazo, J., Quequezana, P., and Collado,
M.: Evaluation of early action mechanisms in Peru regarding preparedness for
El Niño, Int. J. Disaster Risk Sci., 10, 493–510,
https://doi.org/10.1007/s13753-019-00245-x, 2019.
Ali, M., Prasad, R., Xiang, Y., and Mundher Yaseen, Z.: Complete ensemble
empirical mode decomposition hybridized with random forest and kernel ridge
regression model for monthly rainfall forecasts, J. Hydrol., 584, 1–15,
https://doi.org/10.1016/j.jhydrol.2020.124647, 2020.
Asefa, T., Kemblowski, M., Mckee, M., and Khalil, A.: Multi-time scale stream
flow predictions: The support vector machines approach, J. Hydrol., 318,
7–16, https://doi.org/10.1016/j.jhydrol.2005.06.001, 2006.
Aybar, C., Fernández, C., Huerta, A., Lavado, W., Vega, F., and
Felipe-Obando, O.: Construction of a high-resolution gridded rainfall
dataset for Peru from 1981 to the present day, Hydrol. Sci. J., 65,
770–785, https://doi.org/10.1080/02626667.2019.1649411, 2020.
Badr, H. S., Zaitchik, B. F., and Guikema, S. D.: Application of statistical
models to the prediction of seasonal rainfall anomalies over the Sahel, J.
Appl. Meteorol. Climatol., 53, 614–636, https://doi.org/10.1175/JAMC-D-13-0181.1, 2013.
Bayer, A. M., Danysh, H. E., Garvich, M., Gonzálvez, G., Checkley, W.,
Álvarez, M., and Gilman, R. H.: An unforgettable event: a qualitative
study of the 1997–98 El Niño in northern Peru, Disasters, 38,
351–375, https://doi.org/10.1111/disa.12046, 2014.
Bazo, J., de las Nieves Lorenzo, M., and Porfirio da Rocha, R.: Relationship
between monthly rainfall in NW Peru and tropical sea surface temperature,
Adv. Meteorol., 2013, 1–9, https://doi.org/10.1155/2013/152875, 2013.
Bazo, J., Singh, R., Destrooper, M., and Coughlan de Perez, E.: Pilot
experiences in using seamless forecasts for early action: The
“ready-set-go!” approach in the Red Cross, in: Sub-seasonal to Seasonal
Prediction, edited by: Robertson, A. W. and Vitart, F., Elsevier, Amsterdam, the Netherlands, 387–398, 2019.
BBC News: Peru floods: Four killed as Piura bursts its banks, BBC News, 27 March, available at:
https://www.bbc.com/news/world-latin-america-39418314 (last access: 21 May 2020), 2017.
Bischiniotis, K., van den Hurk, B., Zsoter, E., Coughlan De Perez, E.,
Grillakis, M., and Aerts, J. C. J. H.: Evaluation of a global ensemble flood
prediction system in Peru, Hydrol. Sci. J., 64, 1171–1189,
https://doi.org/10.1080/02626667.2019.1617868, 2019.
Block, P. and Rajagopalan, B.: Statistical – dynamical approach for
streamflow modeling at Malakal, Sudan, on the White Nile River, J. Hydrol.
Eng., 14, 185–196, https://doi.org/10.1061/(ASCE)1084-0699(2009)14:2(185), 2009.
Block, P. J., Assis, F., Filho, S., Sun, L., and Kwon, H.: A streamflow
forecasting framework using multiple climate and hydrological models, J. Am.
Water Resour. Assoc., 45, 828–843, https://doi.org/10.1111/j.1752-1688.2009.00327.x,
2009.
Bohn, T., Sonessa, M., and Lettenmaier, D.: Seasonal hydrologic forecasting:
Do multimodel ensemble averages always yield improvements in forecast
skill?, J. Hydrometeorol., 11, 1358–1372, https://doi.org/10.1175/2010JHM1267.1, 2010.
Braman, L. M., Aalst, M. K. Van, Mason, S. J., Suarez, P., Ait-Chellouche,
Y., and Tall, A.: Climate forecasts in disaster management: Red Cross flood
operations in West Africa, 2008, Disasters, 37, 144–164,
https://doi.org/10.1111/j.1467-7717.2012.01297.x, 2013.
Cabot Venton, C., Fitzgibbon, C., Shitarek, T., Coulter, L., and Dooley, O.:
The economics of early response and disaster resilience: Lessons from Kenya
and Ethiopia, Department for International Development, London, UK, 2012.
Caviedes, C. N.: El Niño 1982–83, Geogr. Rev., 74, 267–290, 1984.
Cohen, J., Coumou, D., Hwang, J., Mackey, L., Orenstein, P., Totz, S., and
Tziperman, E.: S2S reboot: An argument for greater inclusion of machine
learning in subseasonal to seasonal forecasts, Wiley Interdiscip. Rev. Clim.
Chang., 10, 1–15, https://doi.org/10.1002/wcc.567, 2019.
Coughlan de Perez, E., van den Hurk, B., van Aalst, M. K., Amuron, I., Bamanya, D., Hauser, T., Jongma, B., Lopez, A., Mason, S., Mendler de Suarez, J., Pappenberger, F., Rueth, A., Stephens, E., Suarez, P., Wagemaker, J., and Zsoter, E.: Action-based flood forecasting for triggering humanitarian action, Hydrol. Earth Syst. Sci., 20, 3549–3560, https://doi.org/10.5194/hess-20-3549-2016, 2016.
Doocy, S., Daniels, A., Murray, S., and Kirsch, T. D.: The human impact of
floods: a historical review of events 1980–2009 and systematic literature
review, PLOS Curr. Disasters, 16 April 2013, Edition 1,
https://doi.org/10.1371/currents.dis.f4deb457904936b07c09daa98ee8171a, 2013.
EM-DAT: Emergency Events Database (EM-DAT), available at:
https://www.emdat.be/ (last access: 3 March 2020), 1988.
Emerton, R., Zsoter, E., Arnal, L., Cloke, H. L., Muraro, D., Prudhomme, C., Stephens, E. M., Salamon, P., and Pappenberger, F.: Developing a global operational seasonal hydro-meteorological forecasting system: GloFAS-Seasonal v1.0, Geosci. Model Dev., 11, 3327–3346, https://doi.org/10.5194/gmd-11-3327-2018, 2018.
Espinoza Villar, J. C., Guyot, J. L., Ronchail, J., Cochonneau, G.,
Filizola, N., Fraizy, P., Labat, D., de Oliveira, E., Ordoñez, J. J., and
Vauchel, P.: Contrasting regional discharge evolutions in the Amazon basin
(1974–2004), J. Hydrol., 375, 297–311, https://doi.org/10.1016/j.jhydrol.2009.03.004, 2009.
Fan, Y. and van den Dool, H.: Climate Prediction Center global monthly soil
moisture data set at 0.5∘ resolution for 1948 to present, J.
Geophys. Res., 109, 1–8, https://doi.org/10.1029/2003JD004345, 2004.
French, A. and Mechler, R.: Managing El Niño Risks Under Uncertainty in
Peru: Learning from the past for a more disaster-resilient future, International Institute for Applied Systems Analysis, Laxenburg, Austria, 2017.
Funk, C., Peterson, P., Landsfeld, M., Pedreros, D., Verdin, J., Shukla, S.,
Husak, G., Rowland, J., Harrison, L., Hoell, A., and Michaelsen, J.: The
climate hazards infrared precipitation with stations – a new environmental
record for monitoring extremes, Sci. Data, 2, 1–21, https://doi.org/10.1038/sdata.2015.66, 2015.
Gámiz-Fortis, S. R., Esteban-Parra, M. J., Trigo, R. M., and
Castro-Díez, Y.: Potential predictability of an Iberian river flow
based on its relationship with previous winter global SST, J. Hydrol., 385,
143–149, https://doi.org/10.1016/j.jhydrol.2010.02.010, 2010.
Garreaud, R. D., Vuille, M., Compagnucci, R., and Marengo, J.: Present-day
South American climate, Palaeogeogr. Palaeocl., 281,
180–195, https://doi.org/10.1016/j.palaeo.2007.10.032, 2009.
Giuliani, M., Zaniolo, M., Castelletti, A., Davoli, G., and Block, P.:
Detecting the state of the climate system via artificial intelligence to
improve seasonal forecasts and inform reservoir operations, Water Resour.
Res., 55, 9133–9147, https://doi.org/10.1029/2019WR025035, 2019.
Gneiting, T. and Raftery, A. E.: Weather forecasting with ensemble methods,
Science, 310, 248–249, https://doi.org/10.1126/science.1115255, 2005.
Golnaraghi, M.: Institutional Partnerships in Multi-Hazard Early Warning
Systems: A Compilation of Seven National Good Practices and Guiding
Principles, Springer, New York, USA, 2012.
Gros, C., Bailey, M., Schwager, S., Hassan, A., Zingg, R., Mamtaz Uddin, M.,
Shahjahan, M., Islam, H., Lux, S., Jaime, C., and Coughlan de Perez, E.:
Household-level effects of providing forecast-based cash in anticipation of
extreme weather events: Quasi-experimental evidence from humanitarian
interventions in the 2017 floods in Bangladesh, Int. J. Disaster Risk
Reduct., 41, 1–11, https://doi.org/10.1016/j.ijdrr.2019.101275, 2019.
Harriman, L.: Cyclone Phailin in India: Early warning and timely actions
saved lives, Environ. Dev., 9, 93–100, https://doi.org/10.1016/j.envdev.2013.12.001,
2014.
Hartmann, H., Pagano, T., Sorooshian, S., and Bales, R.: Confidence builders:
evaluating seasonal climate forecasts from user perspectives, Bull. Am.
Meteorol. Soc., 83, 683–698, https://doi.org/10.1175/1520-0477(2002)083<0683:CBESCF>2.3.CO;2, 2002.
IFRC: World Disasters Report 2009: Focus on early warning, early action, Geneva, Switzerland, 2009.
IFRC: DREF operation update Peru: Floods, Geneva, Switzerland, available at: https://reliefweb.int/sites/reliefweb.int/files/resources/MDRPE005du1.pdf (last access: 17 December 2020), 2012.
IFRC: Emergency Plan of Action (EPoA) Peru: Flood, Geneva, Switzerland, available at: http://adore.ifrc.org/Download.aspx?FileId=160680 (last access: 15 May 2019), 2015.
IFRC: Peru: Floods in the lower Amazon jungle early action protocol
summary, Geneva, Switzerland, available at: https://reliefweb.int/report/peru/peru-floods-lower-amazon-jungle-early-action-protocol-summary (last access: 26 February 2020), 2019.
IFRC: World disasters report 2020, Geneva, Switzerland, 2020.
Infanti, J. and Kirtman, B.: Southeastern U.S. rainfall prediction in the
North American Multi-Model Ensemble, J. Hydrometeorol., 15, 529–550,
https://doi.org/10.1175/JHM-D-13-072.1, 2014.
Ionita, M., Dima, M., Lohmann, G., Scholz, P., and Rimbu, N.: Predicting the
June 2013 European flooding based on precipitation, soil moisture, and sea
level pressure, J. Hydrometeorol., 16, 598–614,
https://doi.org/10.1175/JHM-D-14-0156.1, 2015.
Kalnay, E., Kanamitsu, M., Kistler, R., Collins, W., Deaven, D., Gandin, L.,
Iredell, M., Saha, S., White, G., Woollen, J., Zhu, Y., Chelliah, M.,
Ebisuzaki, W., Higgins, W., Janowiak, J., Mo, K. C., Ropelewski, C., Wang,
J., Leetmaa, A., Reynolds, R., Jenne, R., and Joseph, D.: The NCEP/NCAR
40-Year Reanalysis Project, Bull. Am. Meteorol. Soc., 77, 437–472, 1996.
Keating, C.: Peru Streamflow Prediction, available at: https://gitlab.com/ckeating/peru_streamflow_prediction, last access: 29 March 2021.
Kirtman, B., Min, D., Infanti, J., Kinter III, J., Paolino, D., Zhang, Q.,
Dool, H., Saha, S., Mendez, M. P., Becker, E., Peng, P., Tripp, P.,
Merryfield, W., Denis, B., and Wood, E.: The North American Multimodel
Ensemble, Bull. Am. Meteorol. Soc., 95, 585–602,
https://doi.org/10.1175/BAMS-D-12-00050.1, 2014.
Kvist, L. P. and Nebel, G.: A review of Peruvian flood plain forests:
ecosystems, inhabitants and resource use, For. Ecol. Manage., 150, 3–26,
https://doi.org/10.1016/S0378-1127(00)00679-4, 2001.
Lagos, P., Silva, Y., Nickl, E., and Mosquera, K.: El Niño? related
precipitation variability in Perú, Adv. Geosci., 231–237, available at: https://hal.archives-ouvertes.fr/hal-00297103 (last access: 21 December 2020), 2008.
Lala, J., Tilahun, S., and Block, P.: Predicting Rainy Season Onset in the
Ethiopian Highlands for Agricultural Planning, J. Hydrometeorol., 21,
1675–1689, https://doi.org/10.1175/JHM-D-20-0058.1, 2020.
Lee, D., Ward, P., and Block, P.: Defining high-flow seasons using temporal streamflow patterns from a global model, Hydrol. Earth Syst. Sci., 19, 4689–4705, https://doi.org/10.5194/hess-19-4689-2015, 2015.
Lee, D., Ward, P. J., and Block, P. J.: Attribution of large-scale climate
patterns to seasonal peak-flow and prospects for prediction globally, Water
Resour. Res., 54, 1–23, https://doi.org/10.1002/2017WR021205, 2018.
Lopez, A., Coughlan de Perez, E., Bazo, J., Suarez, P., van den Hurk, B., and
van Aalst, M.: Bridging forecast verification and humanitarian decisions: A
valuation approach for setting up action-oriented early warnings, Weather
Clim. Extrem., 27, 1–8, https://doi.org/10.1016/j.wace.2018.03.006, 2017.
Marengo, J. A.: Interdecadal variability and trends of rainfall across the
Amazon basin, Theor. Appl. Climatol., 78, 79–96, https://doi.org/10.1007/s00704-004-0045-8, 2004.
Moradkhani, H. and Meier, M.: Long-Lead Water Supply Forecast Using
Large-Scale Climate Predictors and Independent Component Analysis, J.
Hydrol. Eng., 15, 744–762, https://doi.org/10.1061/(ASCE)HE.1943-5584.0000246,
2010.
Munich RE: Natural catastrophes 2011: Analyses, assessments, positions, Munich, Germany, 2012.
Munich RE: Natural catastrophes 2017: A stormy year, Munich, Germany, 2018.
NOAA: Equatorial Pacific Sea Surface Temperatures, NOAA Natl. Centers
Environ. Inf., available at:
https://www.ncdc.noaa.gov/teleconnections/enso/indicators/sst/, last access: 10 May 2020.
North, G., Bell, T., Cahalan, R., and Moeng, F.: Sampling errors in the
estimation of empirical orthoginal functions, Mon. Weather Rev., 110,
699–706, https://doi.org/10.1175/1520-0493(1982)110<0699:SEITEO>2.0.CO;2, 1982.
Public Health London: Heatwave plan for England, available at:
https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/888668/Heatwave_plan_for_England_2020.pdf (last access: 3 July 2020), 2018.
Ramirez, I. J. and Briones, F.: Understanding the El Niño Costero of
2017: The definition problem and challenges of climate forecast and disaster
responses, Int. J. Disaster Risk Sci., 8, 489–492,
https://doi.org/10.1007/s13753-017-0151-8, 2017.
Regonda, S. K., Rajagopalan, B., Clark, M., and Zagona, E.: A multimodel
ensemble forecast framework: Application to spring seasonal flows in the
Gunnison River Basin, Water Resour. Res., 42, 1–14,
https://doi.org/10.1029/2005WR004653, 2006.
Rodriguez, R., Mabres, A., Luckman, B., Evans, M., Masiokas, M., and Ektvedt,
T. M.: “El Niño” events recorded in dry-forest species of the lowlands
of northwest Peru, Dendrochronologia, 22, 181–186,
https://doi.org/10.1016/j.dendro.2005.05.002, 2005.
Rodríguez-Morata, C., Ballesteros-canovas, J., Rohrer, M., Espinoza, J.
C., Beniston, M., and Stoffel, M.: Linking atmospheric circulation patterns
with hydro-geomorphic disasters in Peru, Int. J. Climatol., 38, 3388–3404,
https://doi.org/10.1002/joc.5507, 2018.
Shabri, A. and Suhartono: Streamflow forecasting using least-squares support
vector machines, Hydrol. Sci. J., 57, 1275–1293,
https://doi.org/10.1080/02626667.2012.714468, 2012.
Smith, T. M., Reynolds, R. W., Peterson, T. C., and Lawrimore, J.:
Improvements to NOAA's historical merged land – ocean surface temperature
analysis (1880–2006), J. Clim., 21, 2283–2296, https://doi.org/10.1175/2007JCLI2100.1,
2008.
Stephens, E., Day, J. J., Pappenberger, F., and Cloke, H.: Precipitation and
floodiness, Geophys. Res. Lett., 42, 316–323, https://doi.org/10.1002/2015GL066779, 2015.
Takahashi, K. and Martínez, A. G.: The very strong coastal El Niño
in 1925 in the far-eastern Pacific, Clim. Dyn., 52, 7389–7415,
https://doi.org/10.1007/s00382-017-3702-1, 2017.
Tanner, T., Gray, B., Guigma, K., Iqbal, J., Levine, S., Macleod, D., Nahar,
K., Rejve, K., and Venton, C. C.: Scaling up early action. Lessons,
challenges and future potential in Bangladesh, Overseas Development Institute, London, UK, 2019.
Towner, J., Cloke, H. L., Lavado, W., Santini, W., Bazo, J., Coughlan de
Perez, E., and Stephens, E. M.: Attribution of Amazon floods to modes of
climate variability: A review, Meteorol. Appl., 27, e1949, https://doi.org/10.1002/met.1949, 2020.
USAID: Peru – Floods Fact Sheet #1, Fiscal Year (FY) 1998, available at:
https://reliefweb.int/report/peru/peru-floods-fact-sheet-1-fiscal-year-fy-1998
(last access: 6 May 2020), 1998.
Venkateswaran, K., MacClune, K., and Enriquez, M.: Learning from El
Niño Costero 2017: Opportunities for building resilience in Peru, Institute for Social and Environmental Transition (ISET-International), Boulder, CO, USA, 2017.
Wang, F., Vavrus, S., and Block, P.: Rainy season precipitation forecasts in
coastal Peru from the North American Multi-Model Ensemble (NMME), Int. J. Climatol., in review, 2021.
Wei, W. and Watkins, D. W.: Probabilistic streamflow forecasts based on
hydrologic persistence and large-scale climate signals in central Texas, J.
Hydroinformatics, 13, 760–774, https://doi.org/10.2166/hydro.2010.133, 2011.
Wilkinson, E., Weingärtner, L., Choularton, R., Bailey, M., Todd, M.,
Kniveton, D., and Venton, C. C.: Forecasting hazards, averting disasters.
Implementing forecast-based early action at scale, Overseas Development Institute, London, UK, 2018.
Wilks, D.: Statistical methods in the atmospheric sciences, Academic Press, San Diego, California, USA, 2011.
Wolter, K. and Timlin, M. S.: El Niño/Southern Oscillation behaviour
since 1871 as diagnosed in an extended multivariate ENSO index (MEI.ext),
Int. J. Climatol., 31, 1074–1087, https://doi.org/10.1002/joc.2336, 2011.
Wu, S., Notaro, M., Vavrus, S., Mortensen, E., Montgomery, R., de Piérola,
J. and Block, P.: Efficacy of tendency and linear inverse models to
predict southern Peru's rainy season precipitation, Int. J. Climatol., 38,
2590–2604, https://doi.org/10.1002/joc.5442, 2018.
Zealand, C. M., Burn, D. H., and Simonovic, S. P.: Short term streamflow
forecasting using artificial neural networks, J. Hydrol., 214, 32–48,
https://doi.org/10.1016/S0022-1694(98)00242-X, 1999.
Zhou, J. and Lau, K.-M.: Does a monsoon climate exist over South America?,
J. Clim., 11, 1020–1040, https://doi.org/10.1175/1520-0442(1998)011<1020:DAMCEO>2.0.CO;2, 1998.
Zimmerman, B. G., Vimont, D. J., and Block, P. J.: Utilizing the state of
ENSO as a means for season-ahead predictor selection, Water Resour. Res., 52,
3761–3774, https://doi.org/10.1002/2015WR017644, 2016.
Short summary
Disaster planning has historically underallocated resources for flood preparedness, but evidence supports reduced vulnerability via early actions. We evaluate the ability of multiple season-ahead streamflow prediction models to appropriately trigger early actions for the flood-prone Marañón River and Piura River in Peru. Our findings suggest that locally tailored statistical models may offer improved performance compared to operational physically based global models in low-data environments.
Disaster planning has historically underallocated resources for flood preparedness, but evidence...
Altmetrics
Final-revised paper
Preprint