Articles | Volume 23, issue 7
https://doi.org/10.5194/nhess-23-2419-2023
© 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-23-2419-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Indicator-to-impact links to help improve agricultural drought preparedness in Thailand
UK Centre for Ecology & Hydrology (UKCEH), Wallingford, United
Kingdom
Michael Eastman
UK Centre for Ecology & Hydrology (UKCEH), Wallingford, United
Kingdom
Met Office, Exeter, United Kingdom
Eugene Magee
UK Centre for Ecology & Hydrology (UKCEH), Wallingford, United
Kingdom
Lucy J. Barker
UK Centre for Ecology & Hydrology (UKCEH), Wallingford, United
Kingdom
Thomas Chitson
UK Centre for Ecology & Hydrology (UKCEH), Wallingford, United
Kingdom
Chaiwat Ekkawatpanit
Department of Civil Engineering, King Mongkut's University of
Technology Thonburi, Bangkok, Thailand
Daniel Goodwin
School of Social Sciences, University of Tasmania, Hobart, Australia
School of Water, Energy and Environment, Cranfield University, Cranfield, United Kingdom
Jamie Hannaford
UK Centre for Ecology & Hydrology (UKCEH), Wallingford, United
Kingdom
Irish Climate Analysis and Research UnitS (ICARUS), Maynooth
University, Maynooth, Ireland
Ian Holman
School of Water, Energy and Environment, Cranfield University, Cranfield, United Kingdom
Liwa Pardthaisong
Department of Geography, Faculty of Social Sciences, Chiang Mai
University, Chiang Mai, Thailand
Simon Parry
UK Centre for Ecology & Hydrology (UKCEH), Wallingford, United
Kingdom
Dolores Rey Vicario
School of Water, Energy and Environment, Cranfield University, Cranfield, United Kingdom
Supattra Visessri
Department of Water Resources Engineering, Faculty of Engineering,
Chulalongkorn University, Bangkok, Thailand
Disaster and Risk Management Information Systems Research Unit,
Chulalongkorn University, Bangkok, Thailand
Related authors
Jamie Hannaford, Stephen Turner, Amulya Chevuturi, Wilson Chan, Lucy J. Barker, Maliko Tanguy, Simon Parry, and Stuart Allen
Hydrol. Earth Syst. Sci., 29, 4371–4394, https://doi.org/10.5194/hess-29-4371-2025, https://doi.org/10.5194/hess-29-4371-2025, 2025
Short summary
Short summary
This extended review asks whether hydrological (river flow) droughts have become more severe over time in the UK based on literature review and original analyses. The UK is a good international exemplar, given the richness of available data. We find that there is little compelling evidence for a trend towards worsening river flow droughts, at odds with future climate change projections. We outline reasons for this discrepancy and make recommendations to guide researchers and policymakers.
Srinidhi Jha, Lucy J. Barker, Jamie Hannaford, and Maliko Tanguy
EGUsphere, https://doi.org/10.5194/egusphere-2025-4096, https://doi.org/10.5194/egusphere-2025-4096, 2025
This preprint is open for discussion and under review for Hydrology and Earth System Sciences (HESS).
Short summary
Short summary
The influence of climate change on drought in the UK has gained attention recently. However, a probabilistic assessment of temperature’s nonstationary influences on hydrological drought characteristics, which could provide key insights into future risks and uncertainties, has not been conducted. This study evaluates changes across seasons and warming scenarios, finding that rare droughts may become more severe, while frequent summer droughts are shorter but more intense.
Burak Bulut, Eugene Magee, Rachael Armitage, Opeyemi E. Adedipe, Maliko Tanguy, Lucy J. Barker, and Jamie Hannaford
EGUsphere, https://doi.org/10.5194/egusphere-2025-3176, https://doi.org/10.5194/egusphere-2025-3176, 2025
This preprint is open for discussion and under review for Natural Hazards and Earth System Sciences (NHESS).
Short summary
Short summary
This study developed a generic machine learning model to forecast drought impacts, with the UK as the main focus. The same model was successfully validated in Germany, showing potential for use in other regions. It captured local patterns of past drought impacts, matching observed events. Using weather and soil data, the model supports early warning and drought risk management. Results are promising, though testing in more climates and conditions would strengthen confidence.
Wilson Chan, Katie Facer-Childs, Maliko Tanguy, Eugene Magee, Burak Bulut, Nicky Stringer, Jeff Knight, and Jamie Hannaford
EGUsphere, https://doi.org/10.5194/egusphere-2025-2369, https://doi.org/10.5194/egusphere-2025-2369, 2025
Short summary
Short summary
The UK Hydrological Outlook river flow forecasting system recently implemented the Historic Weather Analogues method. The method improves winter river flow forecast skill across the UK, especially in upland, fast-responding catchments with low catchment storage. Forecast skill is highest in winter due to accurate prediction of atmospheric circulation patterns like the North Atlantic Oscillation. The Ensemble Streamflow prediction method remains a robust benchmark, especially for other seasons.
Maliko Tanguy, Michael Eastman, Amulya Chevuturi, Eugene Magee, Elizabeth Cooper, Robert H. B. Johnson, Katie Facer-Childs, and Jamie Hannaford
Hydrol. Earth Syst. Sci., 29, 1587–1614, https://doi.org/10.5194/hess-29-1587-2025, https://doi.org/10.5194/hess-29-1587-2025, 2025
Short summary
Short summary
Our research compares two techniques, bias correction (BC) and data assimilation (DA), for improving river flow forecasts across 316 UK catchments. BC, which corrects errors after simulation, showed broad improvements, while DA, adjusting model states before forecast, excelled under specific conditions like snowmelt and high baseflows. Each method's unique strengths suit different scenarios. These insights can enhance forecasting systems, offering reliable and user-friendly hydrological predictions.
Iván Noguera, Jamie Hannaford, and Maliko Tanguy
Hydrol. Earth Syst. Sci., 29, 1295–1317, https://doi.org/10.5194/hess-29-1295-2025, https://doi.org/10.5194/hess-29-1295-2025, 2025
Short summary
Short summary
The study provides a detailed characterisation of flash drought in the UK for 1969–2021. The spatio-temporal distribution and trends of flash droughts are highly variable, with important regional and seasonal contrasts. In the UK, flash drought development responds primarily to precipitation variability, while the atmospheric evaporative demand plays a secondary role. We also found that the North Atlantic Oscillation is the main circulation pattern controlling flash drought development.
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
Short summary
Short summary
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.
Simon Parry, Jonathan D. Mackay, Thomas Chitson, Jamie Hannaford, Eugene Magee, Maliko Tanguy, Victoria A. Bell, Katie Facer-Childs, Alison Kay, Rosanna Lane, Robert J. Moore, Stephen Turner, and John Wallbank
Hydrol. Earth Syst. Sci., 28, 417–440, https://doi.org/10.5194/hess-28-417-2024, https://doi.org/10.5194/hess-28-417-2024, 2024
Short summary
Short summary
We studied drought in a dataset of possible future river flows and groundwater levels in the UK and found different outcomes for these two sources of water. Throughout the UK, river flows are likely to be lower in future, with droughts more prolonged and severe. However, whilst these changes are also found in some boreholes, in others, higher levels and less severe drought are indicated for the future. This has implications for the future balance between surface water and groundwater below.
Jamie Hannaford, Stephen Turner, Amulya Chevuturi, Wilson Chan, Lucy J. Barker, Maliko Tanguy, Simon Parry, and Stuart Allen
Hydrol. Earth Syst. Sci., 29, 4371–4394, https://doi.org/10.5194/hess-29-4371-2025, https://doi.org/10.5194/hess-29-4371-2025, 2025
Short summary
Short summary
This extended review asks whether hydrological (river flow) droughts have become more severe over time in the UK based on literature review and original analyses. The UK is a good international exemplar, given the richness of available data. We find that there is little compelling evidence for a trend towards worsening river flow droughts, at odds with future climate change projections. We outline reasons for this discrepancy and make recommendations to guide researchers and policymakers.
Srinidhi Jha, Lucy J. Barker, Jamie Hannaford, and Maliko Tanguy
EGUsphere, https://doi.org/10.5194/egusphere-2025-4096, https://doi.org/10.5194/egusphere-2025-4096, 2025
This preprint is open for discussion and under review for Hydrology and Earth System Sciences (HESS).
Short summary
Short summary
The influence of climate change on drought in the UK has gained attention recently. However, a probabilistic assessment of temperature’s nonstationary influences on hydrological drought characteristics, which could provide key insights into future risks and uncertainties, has not been conducted. This study evaluates changes across seasons and warming scenarios, finding that rare droughts may become more severe, while frequent summer droughts are shorter but more intense.
Burak Bulut, Eugene Magee, Rachael Armitage, Opeyemi E. Adedipe, Maliko Tanguy, Lucy J. Barker, and Jamie Hannaford
EGUsphere, https://doi.org/10.5194/egusphere-2025-3176, https://doi.org/10.5194/egusphere-2025-3176, 2025
This preprint is open for discussion and under review for Natural Hazards and Earth System Sciences (NHESS).
Short summary
Short summary
This study developed a generic machine learning model to forecast drought impacts, with the UK as the main focus. The same model was successfully validated in Germany, showing potential for use in other regions. It captured local patterns of past drought impacts, matching observed events. Using weather and soil data, the model supports early warning and drought risk management. Results are promising, though testing in more climates and conditions would strengthen confidence.
Wilson Chan, Katie Facer-Childs, Maliko Tanguy, Eugene Magee, Burak Bulut, Nicky Stringer, Jeff Knight, and Jamie Hannaford
EGUsphere, https://doi.org/10.5194/egusphere-2025-2369, https://doi.org/10.5194/egusphere-2025-2369, 2025
Short summary
Short summary
The UK Hydrological Outlook river flow forecasting system recently implemented the Historic Weather Analogues method. The method improves winter river flow forecast skill across the UK, especially in upland, fast-responding catchments with low catchment storage. Forecast skill is highest in winter due to accurate prediction of atmospheric circulation patterns like the North Atlantic Oscillation. The Ensemble Streamflow prediction method remains a robust benchmark, especially for other seasons.
Bailey J. Anderson, Eduardo Muñoz-Castro, Lena M. Tallaksen, Alessia Matano, Jonas Götte, Rachael Armitage, Eugene Magee, and Manuela I. Brunner
EGUsphere, https://doi.org/10.5194/egusphere-2025-1391, https://doi.org/10.5194/egusphere-2025-1391, 2025
Short summary
Short summary
When flood happen during, or shortly after, droughts, the impacts of can be magnified. In hydrological research, defining these events can be challenging. Here we have tried to address some of the challenges defining these events using real-world examples. We show how different methodological approaches differ in their results, make suggestions on when to use which approach, and outline some pitfalls of which researchers should be aware.
Maliko Tanguy, Michael Eastman, Amulya Chevuturi, Eugene Magee, Elizabeth Cooper, Robert H. B. Johnson, Katie Facer-Childs, and Jamie Hannaford
Hydrol. Earth Syst. Sci., 29, 1587–1614, https://doi.org/10.5194/hess-29-1587-2025, https://doi.org/10.5194/hess-29-1587-2025, 2025
Short summary
Short summary
Our research compares two techniques, bias correction (BC) and data assimilation (DA), for improving river flow forecasts across 316 UK catchments. BC, which corrects errors after simulation, showed broad improvements, while DA, adjusting model states before forecast, excelled under specific conditions like snowmelt and high baseflows. Each method's unique strengths suit different scenarios. These insights can enhance forecasting systems, offering reliable and user-friendly hydrological predictions.
Iván Noguera, Jamie Hannaford, and Maliko Tanguy
Hydrol. Earth Syst. Sci., 29, 1295–1317, https://doi.org/10.5194/hess-29-1295-2025, https://doi.org/10.5194/hess-29-1295-2025, 2025
Short summary
Short summary
The study provides a detailed characterisation of flash drought in the UK for 1969–2021. The spatio-temporal distribution and trends of flash droughts are highly variable, with important regional and seasonal contrasts. In the UK, flash drought development responds primarily to precipitation variability, while the atmospheric evaporative demand plays a secondary role. We also found that the North Atlantic Oscillation is the main circulation pattern controlling flash drought development.
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.
Riccardo Biella, Ansastasiya Shyrokaya, Monica Ionita, Raffaele Vignola, Samuel Sutanto, Andrijana Todorovic, Claudia Teutschbein, Daniela Cid, Maria Carmen Llasat, Pedro Alencar, Alessia Matanó, Elena Ridolfi, Benedetta Moccia, Ilias Pechlivanidis, Anne van Loon, Doris Wendt, Elin Stenfors, Fabio Russo, Jean-Philippe Vidal, Lucy Barker, Mariana Madruga de Brito, Marleen Lam, Monika Bláhová, Patricia Trambauer, Raed Hamed, Scott J. McGrane, Serena Ceola, Sigrid Jørgensen Bakke, Svitlana Krakovska, Viorica Nagavciuc, Faranak Tootoonchi, Giuliano Di Baldassarre, Sandra Hauswirth, Shreedhar Maskey, Svitlana Zubkovych, Marthe Wens, and Lena Merete Tallaksen
EGUsphere, https://doi.org/10.5194/egusphere-2024-2069, https://doi.org/10.5194/egusphere-2024-2069, 2024
Short summary
Short summary
This research by the Drought in the Anthropocene (DitA) network highlights gaps in European drought management exposed by the 2022 drought and proposes a new direction. Using a Europe-wide survey of water managers, we examine four areas: increasing drought risk, impacts, drought management strategies, and their evolution. Despite growing risks, management remains fragmented and short-term. However, signs of improvement suggest readiness for change. We advocate for a European Drought Directive.
Ed Hawkins, Nigel Arnell, Jamie Hannaford, and Rowan Sutton
Geosci. Commun., 7, 161–165, https://doi.org/10.5194/gc-7-161-2024, https://doi.org/10.5194/gc-7-161-2024, 2024
Short summary
Short summary
Climate change can often seem rather remote, especially when the discussion is about global averages which appear to have little relevance to local experiences. But those global changes are already affecting people, even if they do not fully realise it, and effective communication of this issue is critical. We use long observations and well-understood physical principles to visually highlight how global emissions influence local flood risk in one river basin in the UK.
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
Short summary
Short summary
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.
Simon Parry, Jonathan D. Mackay, Thomas Chitson, Jamie Hannaford, Eugene Magee, Maliko Tanguy, Victoria A. Bell, Katie Facer-Childs, Alison Kay, Rosanna Lane, Robert J. Moore, Stephen Turner, and John Wallbank
Hydrol. Earth Syst. Sci., 28, 417–440, https://doi.org/10.5194/hess-28-417-2024, https://doi.org/10.5194/hess-28-417-2024, 2024
Short summary
Short summary
We studied drought in a dataset of possible future river flows and groundwater levels in the UK and found different outcomes for these two sources of water. Throughout the UK, river flows are likely to be lower in future, with droughts more prolonged and severe. However, whilst these changes are also found in some boreholes, in others, higher levels and less severe drought are indicated for the future. This has implications for the future balance between surface water and groundwater below.
Jamie Hannaford, Jonathan D. Mackay, Matthew Ascott, Victoria A. Bell, Thomas Chitson, Steven Cole, Christian Counsell, Mason Durant, Christopher R. Jackson, Alison L. Kay, Rosanna A. Lane, Majdi Mansour, Robert Moore, Simon Parry, Alison C. Rudd, Michael Simpson, Katie Facer-Childs, Stephen Turner, John R. Wallbank, Steven Wells, and Amy Wilcox
Earth Syst. Sci. Data, 15, 2391–2415, https://doi.org/10.5194/essd-15-2391-2023, https://doi.org/10.5194/essd-15-2391-2023, 2023
Short summary
Short summary
The eFLaG dataset is a nationally consistent set of projections of future climate change impacts on hydrology. eFLaG uses the latest available UK climate projections (UKCP18) run through a series of computer simulation models which enable us to produce future projections of river flows, groundwater levels and groundwater recharge. These simulations are designed for use by water resource planners and managers but could also be used for a wide range of other purposes.
Veit Blauhut, Michael Stoelzle, Lauri Ahopelto, Manuela I. Brunner, Claudia Teutschbein, Doris E. Wendt, Vytautas Akstinas, Sigrid J. Bakke, Lucy J. Barker, Lenka Bartošová, Agrita Briede, Carmelo Cammalleri, Ksenija Cindrić Kalin, Lucia De Stefano, Miriam Fendeková, David C. Finger, Marijke Huysmans, Mirjana Ivanov, Jaak Jaagus, Jiří Jakubínský, Svitlana Krakovska, Gregor Laaha, Monika Lakatos, Kiril Manevski, Mathias Neumann Andersen, Nina Nikolova, Marzena Osuch, Pieter van Oel, Kalina Radeva, Renata J. Romanowicz, Elena Toth, Mirek Trnka, Marko Urošev, Julia Urquijo Reguera, Eric Sauquet, Aleksandra Stevkov, Lena M. Tallaksen, Iryna Trofimova, Anne F. Van Loon, Michelle T. H. van Vliet, Jean-Philippe Vidal, Niko Wanders, Micha Werner, Patrick Willems, and Nenad Živković
Nat. Hazards Earth Syst. Sci., 22, 2201–2217, https://doi.org/10.5194/nhess-22-2201-2022, https://doi.org/10.5194/nhess-22-2201-2022, 2022
Short summary
Short summary
Recent drought events caused enormous damage in Europe. We therefore questioned the existence and effect of current drought management strategies on the actual impacts and how drought is perceived by relevant stakeholders. Over 700 participants from 28 European countries provided insights into drought hazard and impact perception and current management strategies. The study concludes with an urgent need to collectively combat drought risk via a European macro-level drought governance approach.
William Rust, John P. Bloomfield, Mark Cuthbert, Ron Corstanje, and Ian Holman
Hydrol. Earth Syst. Sci., 26, 2449–2467, https://doi.org/10.5194/hess-26-2449-2022, https://doi.org/10.5194/hess-26-2449-2022, 2022
Short summary
Short summary
We highlight the importance of the North Atlantic Oscillation in controlling droughts in the UK. Specifically, multi-year cycles in the NAO are shown to influence the frequency of droughts and this influence changes considerably over time. We show that the influence of these varying controls is similar to the projected effects of climate change on water resources. We also show that these time-varying behaviours have important implications for water resource forecasts used for drought planning.
William Rust, Mark Cuthbert, John Bloomfield, Ron Corstanje, Nicholas Howden, and Ian Holman
Hydrol. Earth Syst. Sci., 25, 2223–2237, https://doi.org/10.5194/hess-25-2223-2021, https://doi.org/10.5194/hess-25-2223-2021, 2021
Short summary
Short summary
In this paper, we find evidence for the cyclical behaviour (on a 7-year basis) in UK streamflow records that match the main cycle of the North Atlantic Oscillation. Furthermore, we find that the strength of these 7-year cycles in streamflow is dependent on proportional contributions from groundwater and the response times of the underlying groundwater systems. This may allow for improvements to water management practices through better understanding of long-term streamflow behaviour.
Calum Brown, Ian Holman, and Mark Rounsevell
Earth Syst. Dynam., 12, 211–231, https://doi.org/10.5194/esd-12-211-2021, https://doi.org/10.5194/esd-12-211-2021, 2021
Short summary
Short summary
The variety of human and natural processes in the land system can be modelled in many different ways. However, little is known about how and why basic model assumptions affect model results. We compared two models that represent land use in completely distinct ways and found several results that differed greatly. We identify the main assumptions that caused these differences and therefore key issues that need to be addressed for more robust model development.
Gemma Coxon, Nans Addor, John P. Bloomfield, Jim Freer, Matt Fry, Jamie Hannaford, Nicholas J. K. Howden, Rosanna Lane, Melinda Lewis, Emma L. Robinson, Thorsten Wagener, and Ross Woods
Earth Syst. Sci. Data, 12, 2459–2483, https://doi.org/10.5194/essd-12-2459-2020, https://doi.org/10.5194/essd-12-2459-2020, 2020
Short summary
Short summary
We present the first large-sample catchment hydrology dataset for Great Britain. The dataset collates river flows, catchment attributes, and catchment boundaries for 671 catchments across Great Britain. We characterise the topography, climate, streamflow, land cover, soils, hydrogeology, human influence, and discharge uncertainty of each catchment. The dataset is publicly available for the community to use in a wide range of environmental and modelling analyses.
Lucy J. Barker, Jamie Hannaford, and Miaomiao Ma
Proc. IAHS, 383, 273–279, https://doi.org/10.5194/piahs-383-273-2020, https://doi.org/10.5194/piahs-383-273-2020, 2020
Short summary
Short summary
Drought monitoring and early warning are critical aspects of drought preparedness and can help mitigate impacts on society and the environment. We reviewed academic literature in England and Chinese on the topic of drought monitoring and early warning in China. The number of papers on this topic has increased substantially but the most recent advances have not been operationalised. We identify the methods that can be translated from the experimental to national, operational systems.
Miaomiao Ma, Juan Lv, Zhicheng Su, Jamie Hannaford, Hongquan Sun, Yanping Qu, Zikang Xing, Lucy Barker, and Yaxu Wang
Proc. IAHS, 383, 267–272, https://doi.org/10.5194/piahs-383-267-2020, https://doi.org/10.5194/piahs-383-267-2020, 2020
Kerstin Stahl, Jean-Philippe Vidal, Jamie Hannaford, Erik Tijdeman, Gregor Laaha, Tobias Gauster, and Lena M. Tallaksen
Proc. IAHS, 383, 291–295, https://doi.org/10.5194/piahs-383-291-2020, https://doi.org/10.5194/piahs-383-291-2020, 2020
Short summary
Short summary
Numerous indices exist for the description of hydrological drought, some are based on absolute thresholds of overall streamflows or water levels and some are based on relative anomalies with respect to the season. This article discusses paradigms and experiences with such index uses in drought monitoring and drought analysis to raise awareness of the different interpretations of drought severity.
Chaiwat Ekkawatpanit, Weerayuth Pratoomchai, Chatchapol Khemngoen, and Patchanok Srivihok
Proc. IAHS, 383, 355–365, https://doi.org/10.5194/piahs-383-355-2020, https://doi.org/10.5194/piahs-383-355-2020, 2020
Short summary
Short summary
This study focused on a climate change impact assessment on water resources in the Klong Yai River basin in Thailand using multiple Global Climate Models (GCMs). According to the projections, maximum surface air temperature is projected to increase around 0.3–1.4°. Precipitation in the near future (2017–2026 and 2027–2036) shows an increasing trend. The SWAT model was used in Klong Yai River basin in order to assess the amount of inflow into reservoirs under the climate change conditions.
Cited articles
Anyamba, A. and Tucker, C. J.: Historical perspectives on AVHRR NDVI and vegetation drought monitoring, Remote Sensing of Drought: Innovative Monitoring Approaches, edited by: Wardlow, B. D., Anderson, M. C., Verdin, J. P., CRC Press, New York, United States of America, 2023–2051, https://doi.org/10.1201/b11863, 2012.
Arunrat, N., Sereenonchai, S., Chaowiwat, W., and Wang, C.: Climate change
impact on major crop yield and water footprint under CMIP6 climate
projections in repeated drought and flood areas in Thailand, Sci. Total
Environ., 807, 150741, https://doi.org/10.1016/j.scitotenv.2021.150741, 2022.
Bachmair, S., Stahl, K., Collins, K., Hannaford, J., Acreman, M., Svoboda,
M., Knutson, C., Smith, K. H., Wall, N., Fuchs, B., Crossman, N. D., and
Overton, I. C.: Drought indicators revisited: the need for a wider
consideration of environment and society, WIREs Water, 3, 516–536,
https://doi.org/10.1002/wat2.1154, 2016a.
Bachmair, S., Svensson, C., Hannaford, J., Barker, L. J., and Stahl, K.: A quantitative analysis to objectively appraise drought indicators and model drought impacts, Hydrol. Earth Syst. Sci., 20, 2589–2609, https://doi.org/10.5194/hess-20-2589-2016, 2016b.
Bachmair, S., Tanguy, M., Hannaford, J., and Stahl, K.: How well do
meteorological indicators represent agricultural and forest drought across
Europe?, Environ. Res. Lett., 13, 034042, https://doi.org/10.1088/1748-9326/aaafda, 2018.
Blair, G. S., Henrys, P., Leeson, A., Watkins, J., Eastoe, E., Jarvis, S.,
and Young, P. J.: Data Science of the Natural Environment: A Research
Roadmap, Front. Environ. Sci., 7, 121, https://doi.org/10.3389/fenvs.2019.00121,
2019.
Bolton, D. K. and Friedl, M. A.: Forecasting crop yield using remotely
sensed vegetation indices and crop phenology metrics, Agr. Forest Meteorol.,
173, 74–84, https://doi.org/10.1016/j.agrformet.2013.01.007, 2013.
Bouras, E. H., Jarlan, L., Er-Raki, S., Balaghi, R., Amazirh, A., Richard,
B., and Khabba, S.: Cereal Yield Forecasting with Satellite Drought-Based
Indices, Weather Data and Regional Climate Indices Using Machine Learning in
Morocco, Remote Sens., 13, 3101, https://doi.org/10.3390/rs13163101, 2021.
Bréda, N., Huc, R., Granier, A., and Dreyer, E.: Temperate forest trees
and stands under severe drought: a review of ecophysiological responses,
adaptation processes and long-term consequences, Ann. For. Sci., 63,
625–644, 2006.
Buckley, B. M., Barbetti, M., Watanasak, M., Arrigo, R. D., Boonchirdchoo,
S., and Sarutanon, S.: Dendrochronological Investigations in Thailand, IAWA
J., 16, 393–409, https://doi.org/10.1163/22941932-90001429, 1995.
Byer, S. and Jin, Y.: Detecting Drought-Induced Tree Mortality in Sierra
Nevada Forests with Time Series of Satellite Data, Remote Sens., 9, 929, https://doi.org/10.3390/rs9090929, 2017.
CFE-DMHA: THAILAND Disaster Management Reference Handbook, Center for
Excellence in Disaster Management & Humanitarian Assistance, ISBN 978-971-955429-955433-955427, https://reliefweb.int/report/thailand/disaster-management-reference-handbook-thailand-january-2022 (last access: 22 June 2023), 2022.
Coelho, A. P., de Faria, R. T., Leal, F. T., Barbosa, J. D. A., and Rosalen,
D. L.: Validation of white oat yield estimation models using vegetation
indices. Basic areas, Bragantia, 79, 2, https://doi.org/10.1590/1678-4499.20190387, 2020.
Connor, D. J., Cock, J. H., and Parra, G. E.: Response of cassava to water
shortage I. Growth and yield, Field Crop. Res., 4, 181–200, https://doi.org/10.1016/0378-4290(81)90071-X, 1981.
Didan, K.: MYD13A1 MODIS/Aqua Vegetation Indices 16-day L3 Global 500m SIN
Grid V006, NASA EOSDIS Land Processes DAAC [data set], https://doi.org/10.5067/MODIS/MYD13A1.006, 2015a.
Didan, K.: MOD13A1 MODIS/Terra Vegetation Indices 16-Day L3 Global 500m SIN
Grid V006, NASA EOSDIS Land Processes DAAC [data set], https://doi.org/10.5067/MODIS/MOD13A1.006, 2015b.
Dubey, S. K., Gavli, A. S., Yadav, S. K., Sehgal, S., and Ray, S. S.: Remote
Sensing-Based Yield Forecasting for Sugarcane (Saccharum officinarum L.)
Crop in India, J. Indian Soc. Remot., 46, 1823–1833, https://doi.org/10.1007/s12524-018-0839-2, 2018.
FAO: Irrigation in Asia in Figures, Water reports, Food and Agriculture
Organization of the United Nations, https://www.fao.org/1023/I9275EN/i9275en.pdf (last access: 5 January 2023), 1999.
FAO: The impact of disasters and crises on agriculture and food security:
2021, Food and Agriculture Association of the United Nations, Rome,
https://doi.org/10.4060/cb3673en, 2021.
Farooq, M., Hussain, M., Wahid, A., and Siddique, K. H. M.: Drought Stress
in Plants: An Overview, in: Plant Responses to Drought Stress: From
Morphological to Molecular Features, edited by: Aroca, R., Springer Berlin
Heidelberg, Berlin, Heidelberg, 1–33, https://doi.org/10.1007/978-3-642-32653-0_1, 2012.
FFTC: Fruit Production, Marketing and Research and Development System in
Thailand, Food and Fertilizer Technology Cente for the Asian and Pacific
Region, https://www.fftc.org.tw/en/publications/main/1912 (last access:
19 January 2023), 2015.
Friedl, M. and Sulla-Menashe, D.: MCD12Q1 MODIS/Terra+Aqua Land Cover Type
Yearly L3 Global 500m SIN Grid V006, NASA EOSDIS Land Processes
DAAC [data set], https://doi.org/10.5067/MODIS/MCD12Q1.006, 2019.
García-León, D., Contreras, S., and Hunink, J.: Comparison of
meteorological and satellite-based drought indices as yield predictors of
Spanish cereals, Agr. Water Manage., 213, 388–396, https://doi.org/10.1016/j.agwat.2018.10.030, 2019.
Gheewala, S. H., Silalertruksa, T., Nilsalab, P., Mungkung, R., Perret, S.
R., and Chaiyawannakarn, N.: Water Footprint and Impact of Water Consumption
for Food, Feed, Fuel Crops Production in Thailand, Water, 6, 1698–1718,
2014.
Hariadi, M. H., van der Schrier, G., Steeneveld, G.-J., Sutanto, S., Sutanudjaja, E., Ratri, D. N., Sopaheluwakan, A., and Klein Tank, A.: A high-resolution perspective of extreme rainfall and river flow under extreme climate change in Southeast Asia, Hydrol. Earth Syst. Sci. Discuss. [preprint], https://doi.org/10.5194/hess-2023-14, in review, 2023.
Hobeichi, S., Abramowitz, G., Evans, J. P., and Ukkola, A.: Toward a Robust,
Impact-Based, Predictive Drought Metric, Water Resour. Res., 58,
e2021WR031829, https://doi.org/10.1029/2021WR031829, 2022.
ICID: Thailand, International Commission on Irrigation & Drainage, Thai National Committee on Irrigation and Drainage (THAICID), https://www.icid.org/v_thailand.pdf (last access: 5 June 2022),
2020.
Ikeda, M. and Palakhamarn, T.: Economic Damage from Natural Hazards and
Local Disaster Management Plans in Japan and Thailand, ERIA Discussion Paper
Series, No. 346, ERIA-DP-2020-2019, Economic Research Institute for ASEAN and
East Asia, https://www.eria.org/research/economic-damage-from-natural-hazards-and-local-disaster-management-plans-in-japan-and-thailand/
(last access: 22 June 2023), 2020.
IPCC: Climate Change 2021: The Physical Science Basis. 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, United Kingdom and New York, NY, USA, 2391 pp., https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf (last access: 4 July 2023), 2021.
IPCC: Climate Change 2022: Impacts, Adaptation and Vulnerability.
Contribution of Working Group II to the Sixth Assessment Report of the
Intergovernmental Panel on Climate Change, edited by: Pörtner, H.-O., Roberts, D. C., Tignor, M., Poloczanska, E. S., Mintenbeck, K., Alegría, A., Craig, M., Langsdorf, S., Löschke, S., Möller, V., Okem, A., and Rama, B., Cambridge University Press. Cambridge University Press, Cambridge, UK and New York, NY, USA, 3056 pp., https://doi.org/10.1017/9781009325844, 2022.
Jain, S. K., Keshri, R., Goswami, A., Sarkar, A., and Chaudhry, A.:
Identification of drought-vulnerable areas using NOAA AVHRR data, Int. J.
Remote Sens., 30, 2653–2668, https://doi.org/10.1080/01431160802555788, 2009.
Jiao, W., Zhang, L., Chang, Q., Fu, D., Cen, Y., and Tong, Q.: Evaluating an
Enhanced Vegetation Condition Index (VCI) Based on VIUPD for Drought
Monitoring in the Continental United States, Remote Sens., 8, 224, https://doi.org/10.3390/rs8030224, 2016.
Kadam, N. N., Tamilselvan, A., Lawas, L. M. F., Quinones, C., Bahuguna, R.
N., Thomson, M. J., Dingkuhn, M., Muthurajan, R., Struik, P. C., Yin, X.,
and Jagadish, S. V. K.: Genetic Control of Plasticity in Root Morphology and
Anatomy of Rice in Response to Water Deficit, Plant Physiol., 174,
2302–2315, https://doi.org/10.1104/pp.17.00500, 2017.
Khadka, D., Babel, M. S., Shrestha, S., Virdis, S. G. P., and Collins, M.:
Multivariate and multi-temporal analysis of meteorological drought in the
northeast of Thailand, Weather and Climate Extremes, 34, 100399, https://doi.org/10.1016/j.wace.2021.100399, 2021.
Kogan, F., Salazar, L., and Roytman, L.: Forecasting crop production using
satellite-based vegetation health indices in Kansas, USA, Int. J. Remote
Sens., 33, 2798–2814, https://doi.org/10.1080/01431161.2011.621464, 2012.
Kogan, F. N.: Global Drought Watch from Space, B. Am. Meteorol. Soc., 78,
621–636, https://doi.org/10.1175/1520-0477(1997)078<0621:GDWFS>2.0.CO;2, 1997.
Lacombe, G., Polthanee, A., and Trébuil, G.: Long-term change in
rainfall distribution in Northeast Thailand: will cropping systems be able
to adapt?, Cah. Agric., 26, 25001, https://doi.org/10.1051/cagri/2017006, 2017.
LePoer, B. L.: Thailand: a country study, Federal Research
Division, Washington, D.C., USA, 60–65, OCLC 44366465, https://www.loc.gov/item/88600485/ (last access: 4 July 2023), 1987.
Liu, W. T. and Kogan, F. N.: Monitoring regional drought using the
Vegetation Condition Index, Int. J. Remote Sens., 17, 2761–2782, https://doi.org/10.1080/01431169608949106, 1996.
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.
Martin, S. A. and Ritchie, R. J.: Sourcing Thai geography literature for
ASEAN and international education, Singapore J. Trop. Geo., 41, 61–85,
https://doi.org/10.1111/sjtg.12296, 2020.
Maselli, F., Romanelli, S., Bottai, L., and Maracchi, G.: Processing of GAC
NDVI data for yield forecasting in the Sahelian region, Int. J. Remote
Sens., 21, 3509–3523, https://doi.org/10.1080/014311600750037525, 2000.
McKee, T. B., Doesken, N. J., and Kleist, J.: The relationship of drought
frequency and duration to time scales, Eighth Conference on Applied
Climatology, 17–22 January 1993, Anaheim, California, American Meteorological Society, https://www.droughtmanagement.info/literature/AMS_Relationship_Drought_Frequency_Duration_Time_Scales_1993.pdf (last access: 4 July 2023), 1993.
Menzel, C. M. and Waite, G. K.: Litchi and longan, botany, production and
uses, CABI Publishing, Oxfordshire/Cambridge, MA, ISBN 9780851996967, 2005.
Mishra, S. S. and Panda, D.: Leaf Traits and Antioxidant Defense for Drought
Tolerance During Early Growth Stage in Some Popular Traditional Rice
Landraces from Koraput, India, Rice Science, 24, 207–217, https://doi.org/10.1016/j.rsci.2017.04.001, 2017.
Mongkolsawat, C., Thirangoon, P., Suwanwerakamtorn, R., Karladee, N.,
Paiboonsak, S., and Champathet, P.: An evaluation of drought risk area in
Northeast Thailand using remotely sensed data and GIS, Asian Journal of
Geoinformatics, 1, 33–43, 2001.
NESDC: Statistics on Thailand's drought situation for the period 1989–2021,
National Economic and Social Development Council, Data collated from annual
report of the Disaster Data Center DDPM, https://www.nesdc.go.th/ewt_dl_link.php?nid=9787 (last access:
23 June 2023), 2021.
OAE: Agricultural production data, Office of Agricultural Economics,
https://www.ceicdata.com/en/thailand/agricultural-production-index-office-of-agricultural-economics (last access: 23 June 2023), 2021.
OAE: Agricultural Statistics of Thailand 2021, Office of Agricultural
Economics, Ministry of Agriculture and Cooperatives, Bangkok, Thailand, https://www.oae.go.th/assets/portals/1/files/jounal/2565/yearbook2564.pdf (last access: 23 June 2023), 2022.
OECD: OECD Economic Surveys Economic Assessment: Thailand, Organisation for
Economic Co-operation and Development, https://www.oecd.org/economy/thailand-economic-snapshot/ (last access: 23 June 2023), 2020.
Okogbenin, E., Setter, T., Ferguson, M., Mutegi, R., Ceballos, H., Olasanmi,
B., and Fregene, M.: Phenotypic approaches to drought in cassava: review,
Front. Physiol., 4, https://doi.org/10.3389/fphys.2013.00093, 2013.
Oliveira, S. L., Macedo, M. M. C., and Porto, M. C. M.: Effects of water
stress on cassava root production, Pesquia Agropecuria Brasil, 17, 121–124,
https://agris.fao.org/agris-search/search.do?recordID=US201302182137 (last access: 31 January 2023), 1982.
Parsons, D. J., Rey, D., Tanguy, M., and Holman, I. P.: Regional variations
in the link between drought indices and reported agricultural impacts of
drought, Agr. Syst., 173, 119–129, https://doi.org/10.1016/j.agsy.2019.02.015, 2019.
Pearson, K.: Notes on the history of correlation, Biometrika, 13, 25–45, https://doi.org/10.1093/biomet/13.1.25, 1920.
Prabnakorn, S., Maskey, S., Suryadi, F. X., and de Fraiture, C.: Rice yield
in response to climate trends and drought index in the Mun River Basin,
Thailand, Sci. Total Environ., 621, 108–119, https://doi.org/10.1016/j.scitotenv.2017.11.136, 2018.
Pradawet, C., Khongdee, N., Pansak, W., Spreer, W., Hilger, T., and Cadisch,
G.: Thermal imaging for assessment of maize water stress and yield
prediction under drought conditions, J. Agron. Crop Sci., 209, 56–70,
https://doi.org/10.1111/jac.12582, 2023.
Pyper, B. J. and Peterman, R. M.: Comparison of methods to account for
autocorrelation in correlation analyses of fish data, Can. J. Fish. Aquat.
Sci., 55, 2127–2140, https://doi.org/10.1139/f98-104, 1998.
Rakthai, S., Fu, P.-L., Fan, Z.-X., Gaire, N. P., Pumijumnong, N.,
Eiadthong, W., and Tangmitcharoen, S.: Increased Drought Sensitivity Results
in a Declining Tree Growth of Pinus latteri in Northeastern Thailand,
Forests, 11, 361, https://doi.org/10.3390/f11030361, 2020.
RFD: Forest area of Thailand, 1973–2018, Royal Forest Department (RFD),
http://forestinfo.forest.go.th/Content.aspx?id=72 (last access: 5 June 2022), 2022.
Roebroek, C. T. J., Melsen, L. A., Hoek van Dijke, A. J., Fan, Y., and Teuling, A. J.: Global distribution of hydrologic controls on forest growth, Hydrol. Earth Syst. Sci., 24, 4625–4639, https://doi.org/10.5194/hess-24-4625-2020, 2020.
Running, S., Mu, Q., and Zhao, M.: MOD16A2 MODIS/Terra Net
Evapotranspiration 8-Day L4 Global 500m SIN Grid V006, NASA
EOSDIS Land Processes DAAC [data set], https://doi.org/10.5067/MODIS/MOD16A2.006, 2017.
Salakpi, E. E., Hurley, P. D., Muthoka, J. M., Bowell, A., Oliver, S., and Rowhani, P.: A dynamic hierarchical Bayesian approach for forecasting vegetation condition, Nat. Hazards Earth Syst. Sci., 22, 2725–2749, https://doi.org/10.5194/nhess-22-2725-2022, 2022.
Sa-nguansilp, C., Wijitkosum, S., and Sriprachote, A.: Agricultural Drought
Risk Assessment in Lam Ta Kong Watershed, International Journal of
Geoinformatics, 13, 46, https://journals.sfu.ca/ijg/index.php/journal/article/view/1090 (last access: 23 June 2023), 2017.
Sano, M., Buckley, B. M., and Sweda, T.: Tree-ring based hydroclimate
reconstruction over northern Vietnam from Fokienia hodginsii: eighteenth
century mega-drought and tropical Pacific influence, Clim. Dynam., 33, 331–340, https://doi.org/10.1007/s00382-008-0454-y, 2008.
Sanoamuang, L. and Dabseepai, P.: Diversity, Distribution, and Habitat
Occurrence of the Diaptomid Copepods (Crustacea: Copepoda: Diaptomidae) in
Freshwater Ecosystems of Thailand, Water, 13, 2381, https://doi.org/10.3390/w13172381, 2021.
Schenk, H. J. and Jackson, R. B.: The Global Biogeography Of Roots, Ecol.
Monogr., 72, 311–328, https://doi.org/10.1890/0012-9615(2002)072[0311:TGBOR]2.0.CO;2, 2002.
Shams Esfandabadi, H., Ghamary Asl, M., Shams Esfandabadi, Z., Gautam, S.,
and Ranjbari, M.: Drought assessment in paddy rice fields using remote
sensing technology towards achieving food security and SDG2, Brit. Food J.,
124, 4219–4233, https://doi.org/10.1108/BFJ-08-2021-0872, 2022.
Shen, R., Huang, A., Li, B., and Guo, J.: Construction of a drought
monitoring model using deep learning based on multi-source remote sensing
data, Int. J. Appl. Earth Obs., 79, 48–57, https://doi.org/10.1016/j.jag.2019.03.006, 2019.
Singh, R. P., Roy, S., and Kogan, F.: Vegetation and temperature condition
indices from NOAA AVHRR data for drought monitoring over India, Int. J.
Remote Sens., 24, 4393–4402, https://doi.org/10.1080/0143116031000084323, 2003.
Smith, K. H., Svoboda, M., Hayes, M., Reges, H., Doesken, N., Lackstrom, K.,
Dow, K., and Brennan, A.: Local Observers Fill In the Details on Drought
Impact Reporter Maps, B. Am. Meteorol. Soc., 95, 1659–1662, https://doi.org/10.1175/1520-0477-95.11.1659, 2014.
Smith, R., Adams, J., Stephens, D., and Hick, P.: Forecasting wheat yield in
a Mediterranean-type environment from the NOAA satellite, Aust. J. Agr.
Res., 46, 113–125, https://doi.org/10.1071/AR9950113, 1995.
Sowcharoensuk, C. and Marknual, C.: Severe drought: Agriculture sector takes
direct hit and spillover effects on manufacturing supply chains, Bank of
Ayudhya's Krungsri Research Intellingence Report, https://www.krungsri.com/getmedia/dc6db8a2-00d2-4c3b-bbd4-ebad9275193b/RI_Drought_200207_EN.pdf.aspx (last access: 23 June 2023), 2020.
Spreer, W., Schulze, K., Ongprasert, S., Wiriya-Alongkorn, W., and
Müller, J.: Mango and Longan Production in Northern Thailand: The Role
of Water Saving Irrigation and Water Stress Monitoring, in: Sustainable Land
Use and Rural Development in Southeast Asia: Innovations and Policies for
Mountainous Areas, edited by: Fröhlich, H. L., Schreinemachers, P.,
Stahr, K., and Clemens, G., Springer Berlin Heidelberg, Berlin, Heidelberg,
215–228, https://doi.org/10.1007/978-3-642-33377-4_6, 2013.
Stahl, K., Kohn, I., Blauhut, V., Urquijo, J., De Stefano, L., Acácio, V., Dias, S., Stagge, J. H., Tallaksen, L. M., Kampragou, E., Van Loon, A. F., Barker, L. J., Melsen, L. A., Bifulco, C., Musolino, D., de Carli, A., Massarutto, A., Assimacopoulos, D., and Van Lanen, H. A. J.: Impacts of European drought events: insights from an international database of text-based reports, Nat. Hazards Earth Syst. Sci., 16, 801–819, https://doi.org/10.5194/nhess-16-801-2016, 2016.
Sutanto, S. J., van der Weert, M., Wanders, N., Blauhut, V., and Van Lanen,
H. A. J.: Moving from drought hazard to impact forecasts, Nat. Commun., 10,
4945, https://doi.org/10.1038/s41467-019-12840-z, 2019.
Thammachote, P. and Trichim, J. I.: The Impact of the COVID-19 Pandemic on
Thailand's Agricultural Export Flows, Feed the Future report, The U.S.
Government's Global Hunger & Food Security Initiative, https://www.canr.msu.edu/prci/PRCI-Research-Paper-4-Thailand_updated.pdf (last access: 29 January 2023), 2021.
Thavorntam, W. and Shahnawaz, S.: Evaluation of Drought in the North of
Thailand using Meteorological and Satellite-Based Drought Indices,
International Journal of Geoinformatics, 18, 13–26, https://doi.org/10.52939/ijg.v18i5.2367, 2022.
Thavorntam, W., Tantemsapya, N., and Armstrong, L.: A combination of
meteorological and satellite-based drought indices in a better drought
assessment and forecasting in Northeast Thailand, Nat. Hazards, 77,
1453–1474, https://doi.org/10.1007/s11069-014-1501-0, 2015.
Torres, P., Rodes-Blanco, M., Viana-Soto, A., Nieto, H., and García,
M.: The Role of Remote Sensing for the Assessment and Monitoring of Forest
Health: A Systematic Evidence Synthesis, Forests, 12, 1134, https://doi.org/10.3390/f12081134, 2021.
Tucker, C. J.: Red and photographic infrared linear combinations for
monitoring vegetation, Remote Sens. Environ., 8, 127–150, https://doi.org/10.1016/0034-4257(79)90013-0, 1979.
UNDRR: The Sendai Framework for Disaster Risk Reduction 2015–2030, United
Nations Office for Disaster Risk Reduction, 32 pp., https://www.undrr.org/publication/sendai-framework-disaster-risk-reduction-2015-2030 (last access: 23 June 2023), 2015.
UNDRR and ADCP: Disaster Risk Reduction in Thailand: Status Report 2020,
UNDRR (United Nations Office for Disaster Risk Reduction) and ADCP (Asian
Disaster Preparedness Center), Climate Change and Climate Risk Management,
DRR Report, http://www.adpc.net/Igo/contents/Publications/publications-Details.asp?pid=1681#sthash.JBSoZQWU.dpbs (last access: 23 June 2023), 2020.
Unganai, L. S. and Kogan, F. N.: Southern Africa's recent droughts from
space, Adv. Space Res.-Series, 21, 507–511, https://doi.org/10.1016/S0273-1177(97)00888-0, 1998.
Varawoot, V.: Historical Irrigation Development of Thailand, Irrigation
Technology Research, Development Laboratory, Department of Irrigation
Engineering, Kasetsart University, Kamphaengsaen campus, https://eng.kps.ku.ac.th/irre/slideshow/pdf/4.pdf (last access:
18 February 2023), 2016.
Venkatappa, M., Sasaki, N., Han, P., and Abe, I.: Impacts of droughts and
floods on croplands and crop production in Southeast Asia – An application
of Google Earth Engine, Sci. Total Environ., 795, 148829, https://doi.org/10.1016/j.scitotenv.2021.148829, 2021.
Vicente-Serrano, S. 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, 2010.
Wan, Z., Hook, S., and Hulley, G.: MOD11A2 MODIS/Terra Land Surface
Temperature/Emissivity 8-Day L3 Global 1km SIN Grid V006, NASA
EOSDIS Land Processes DAAC [data set], https://doi.org/10.5067/MODIS/MOD11A2.061, 2015.
Wang, Y., Lv, J., Hannaford, J., Wang, Y., Sun, H., Barker, L. J., Ma, M., Su, Z., and Eastman, M.: Linking drought indices to impacts to support drought risk assessment in Liaoning province, China, Nat. Hazards Earth Syst. Sci., 20, 889–906, https://doi.org/10.5194/nhess-20-889-2020, 2020.
WBG and ADB: Climate Risk Country Profile: Thailand (2021), The World Bank
Group and the Asian Development Bank, https://www.adb.org/sites/default/files/publication/722251/climate-risk-country-profile-thailand.pdf
(last access: 23 June 2023), 2021.
Wijitkosum, S.: Fuzzy AHP for drought risk assessment in Lam Ta Kong
watershed, the north-eastern region of Thailand, Soil Water Res.,
13, 218–225, 2018.
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.
WMO: WMO Atlas of Mortality and Economic Losses from Weather, Climate and
Water Extremes (1970–2019), WMO-No. 1267, World Meteorological
Organization (WMO), ISBN 978-992-963-11267-11265, https://library.wmo.int/index.php?lvl=notice_display&id=21930#.Y11294o_11263bP11262Ul (last access: 23 June 2023), 2014.
Yang, X., Wang, B., Chen, L., Li, P., and Cao, C.: The different influences
of drought stress at the flowering stage on rice physiological traits, grain
yield, and quality, Sci. Rep., 9, 3742, https://doi.org/10.1038/s41598-019-40161-0, 2019.
Yatagai, A., Kamiguchi, K., Arakawa, O., Hamada, A., Yasutomi, N., and
Kitoh, A.: APHRODITE: Constructing a Long-Term Daily Gridded Precipitation
Dataset for Asia Based on a Dense Network of Rain Gauges, B. Am. Meteorol.
Soc., 93, 1401–1415, https://doi.org/10.1175/bams-d-11-00122.1, 2012.
Yoshida, K., Srisutham, M., Sritumboon, S., Suanburi, D., and
Janjirauttikul, N.: Weather-induced economic damage to upland crops and the
impact on farmer household income in Northeast Thailand, Paddy Water
Environ., 17, 341–349, https://doi.org/10.1007/s10333-019-00729-y, 2019.
Short summary
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.
Droughts in Thailand are becoming more severe due to climate change. Understanding the link...
Altmetrics
Final-revised paper
Preprint