Articles | Volume 24, issue 4
https://doi.org/10.5194/hess-24-1939-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-24-1939-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Quantifying the impacts of human water use and climate variations on recent drying of Lake Urmia basin: the value of different sets of spaceborne and in situ data for calibrating a global hydrological model
Seyed-Mohammad Hosseini-Moghari
CORRESPONDING AUTHOR
Key Laboratory of Water Cycle and Related Land Surface Processes,
Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China
Department of Irrigation and Reclamation Engineering, University of
Tehran, Karaj, Iran
Shahab Araghinejad
Department of Irrigation and Reclamation Engineering, University of
Tehran, Karaj, Iran
Mohammad J. Tourian
University of Stuttgart, Institute of Geodesy (GIS), Stuttgart,
Germany
Kumars Ebrahimi
Department of Irrigation and Reclamation Engineering, University of
Tehran, Karaj, Iran
Petra Döll
Institute of Physical Geography, Goethe University Frankfurt,
Frankfurt am Main, Germany
Senckenberg Leibniz Biodiversity and Climate Research Centre (SBiK-F), Frankfurt am Main, Germany
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Seyed-Mohammad Hosseini-Moghari and Petra Döll
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-291, https://doi.org/10.5194/hess-2024-291, 2024
Preprint under review for HESS
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Modeling reservoir outflow and storage is challenging due to limited publicly available data and human decision-making. For 100 reservoirs in the USA, we examined how calibrating reservoir algorithms against outflow and storage-related variables affects performance. We found that calibration notably improves storage simulations, while outflow simulations are more influenced by the quality of inflow data. We recommend using remotely sensed storage anomalies to calibrate reservoir algorithms.
Petra Döll, Howlader Mohammad Mehedi Hasan, Kerstin Schulze, Helena Gerdener, Lara Börger, Somayeh Shadkam, Sebastian Ackermann, Seyed-Mohammad Hosseini-Moghari, Hannes Müller Schmied, Andreas Güntner, and Jürgen Kusche
Hydrol. Earth Syst. Sci., 28, 2259–2295, https://doi.org/10.5194/hess-28-2259-2024, https://doi.org/10.5194/hess-28-2259-2024, 2024
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Currently, global hydrological models do not benefit from observations of model output variables to reduce and quantify model output uncertainty. For the Mississippi River basin, we explored three approaches for using both streamflow and total water storage anomaly observations to adjust the parameter sets in a global hydrological model. We developed a method for considering the observation uncertainties to quantify the uncertainty of model output and provide recommendations.
H. M. Mehedi Hasan, Petra Döll, Seyed-Mohammad Hosseini-Moghari, Fabrice Papa, and Andreas Güntner
EGUsphere, https://doi.org/10.5194/egusphere-2023-2324, https://doi.org/10.5194/egusphere-2023-2324, 2023
Short summary
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We calibrate a global hydrological model using multiple observations to analyse the benefits and trade-offs of multi-variable calibration. We found such an approach to be very important for understanding the real-world system. However, some observations are very essential to the system, in particular streamflow. We also showed uncertainties in the calibration results, which is often useful for making informed decisions. We emphasis to consider observation uncertainty in model calibration.
Seyed-Mohammad Hosseini-Moghari and Petra Döll
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-291, https://doi.org/10.5194/hess-2024-291, 2024
Preprint under review for HESS
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Modeling reservoir outflow and storage is challenging due to limited publicly available data and human decision-making. For 100 reservoirs in the USA, we examined how calibrating reservoir algorithms against outflow and storage-related variables affects performance. We found that calibration notably improves storage simulations, while outflow simulations are more influenced by the quality of inflow data. We recommend using remotely sensed storage anomalies to calibrate reservoir algorithms.
Peyman Saemian, Omid Elmi, Molly Stroud, Ryan Riggs, Benjamin M. Kitambo, Fabrice Papa, George H. Allen, and Mohammad J. Tourian
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-406, https://doi.org/10.5194/essd-2024-406, 2024
Preprint under review for ESSD
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Our study addresses the need for better river discharge data, crucial for water management, by expanding global gauge networks with satellite data. We used satellite altimetry to estimate river discharge for over 8,700 stations worldwide, filling gaps in existing records. Our data set, SAEM supports a better understanding of water systems, helping to manage water resources more effectively, especially in regions with limited monitoring infrastructure.
Emmanuel Nyenah, Petra Döll, Daniel S. Katz, and Robert Reinecke
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-97, https://doi.org/10.5194/gmd-2024-97, 2024
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Research software is crucial for scientific progress but is often developed by scientists with limited training, time, and funding, leading to software that is hard to understand, (re)use, modify, and maintain. Our study across 10 research sectors highlights strengths in version control, open-source licensing, and documentation while emphasizing the need for containerization and code quality. Recommendations include workshops, code quality metrics, funding, and adherence to FAIR standards.
Petra Döll, Howlader Mohammad Mehedi Hasan, Kerstin Schulze, Helena Gerdener, Lara Börger, Somayeh Shadkam, Sebastian Ackermann, Seyed-Mohammad Hosseini-Moghari, Hannes Müller Schmied, Andreas Güntner, and Jürgen Kusche
Hydrol. Earth Syst. Sci., 28, 2259–2295, https://doi.org/10.5194/hess-28-2259-2024, https://doi.org/10.5194/hess-28-2259-2024, 2024
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Currently, global hydrological models do not benefit from observations of model output variables to reduce and quantify model output uncertainty. For the Mississippi River basin, we explored three approaches for using both streamflow and total water storage anomaly observations to adjust the parameter sets in a global hydrological model. We developed a method for considering the observation uncertainties to quantify the uncertainty of model output and provide recommendations.
Laura Müller and Petra Döll
Geosci. Commun., 7, 121–144, https://doi.org/10.5194/gc-7-121-2024, https://doi.org/10.5194/gc-7-121-2024, 2024
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To be able to adapt to climate change, stakeholders need to be informed about future uncertain climate change hazards. Using freely available output of global hydrological models, we quantified future local changes in water resources and their uncertainty. To communicate these in participatory processes, we propose using "percentile boxes" to support the development of flexible strategies for climate risk management worldwide, involving stakeholders and scientists.
Hannes Müller Schmied, Tim Trautmann, Sebastian Ackermann, Denise Cáceres, Martina Flörke, Helena Gerdener, Ellen Kynast, Thedini Asali Peiris, Leonie Schiebener, Maike Schumacher, and Petra Döll
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-213, https://doi.org/10.5194/gmd-2023-213, 2023
Revised manuscript accepted for GMD
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Assessing water availability and water use at the global scale is challenging but essential for a range of purposes. We describe the newest version of the global hydrological model WaterGAP which has been used for numerous water resources assessments since 1996. We show the effects of new model features and model evaluations against observed streamflow and water storage anomalies as well as water abstractions statistics. The publically available model output for several variants is described.
H. M. Mehedi Hasan, Petra Döll, Seyed-Mohammad Hosseini-Moghari, Fabrice Papa, and Andreas Güntner
EGUsphere, https://doi.org/10.5194/egusphere-2023-2324, https://doi.org/10.5194/egusphere-2023-2324, 2023
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We calibrate a global hydrological model using multiple observations to analyse the benefits and trade-offs of multi-variable calibration. We found such an approach to be very important for understanding the real-world system. However, some observations are very essential to the system, in particular streamflow. We also showed uncertainties in the calibration results, which is often useful for making informed decisions. We emphasis to consider observation uncertainty in model calibration.
Thedini Asali Peiris and Petra Döll
Hydrol. Earth Syst. Sci., 27, 3663–3686, https://doi.org/10.5194/hess-27-3663-2023, https://doi.org/10.5194/hess-27-3663-2023, 2023
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Hydrological models often overlook vegetation's response to CO2 and climate, impairing their ability to forecast impacts on evapotranspiration and water resources. To address this, we suggest involving two model variants: (1) the standard method and (2) a modified approach (proposed here) based on the Priestley–Taylor equation (PT-MA). While not universally applicable, a dual approach helps consider uncertainties related to vegetation responses to climate change, enhancing model representation.
Benjamin M. Kitambo, Fabrice Papa, Adrien Paris, Raphael M. Tshimanga, Frederic Frappart, Stephane Calmant, Omid Elmi, Ayan Santos Fleischmann, Melanie Becker, Mohammad J. Tourian, Rômulo A. Jucá Oliveira, and Sly Wongchuig
Earth Syst. Sci. Data, 15, 2957–2982, https://doi.org/10.5194/essd-15-2957-2023, https://doi.org/10.5194/essd-15-2957-2023, 2023
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The surface water storage (SWS) in the Congo River basin (CB) remains unknown. In this study, the multi-satellite and hypsometric curve approaches are used to estimate SWS in the CB over 1992–2015. The results provide monthly SWS characterized by strong variability with an annual mean amplitude of ~101 ± 23 km3. The evaluation of SWS against independent datasets performed well. This SWS dataset contributes to the better understanding of the Congo basin’s surface hydrology using remote sensing.
Claudia Herbert and Petra Döll
Nat. Hazards Earth Syst. Sci., 23, 2111–2131, https://doi.org/10.5194/nhess-23-2111-2023, https://doi.org/10.5194/nhess-23-2111-2023, 2023
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This paper presents a new method for selecting streamflow drought hazard indicators for monitoring drought hazard for human water supply and river ecosystems in large-scale drought early warning systems. Indicators are classified by their inherent assumptions about the habituation of people and ecosystems to the streamflow regime and their level of drought characterization, namely drought magnitude (water deficit at a certain point in time) and severity (cumulated magnitude since drought onset).
Mohammad J. Tourian, Omid Elmi, Yasin Shafaghi, Sajedeh Behnia, Peyman Saemian, Ron Schlesinger, and Nico Sneeuw
Earth Syst. Sci. Data, 14, 2463–2486, https://doi.org/10.5194/essd-14-2463-2022, https://doi.org/10.5194/essd-14-2463-2022, 2022
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HydroSat as a global water cycle database provides estimates of and uncertainty in geometric quantities of the water cycle: (1) surface water extent of lakes and rivers, (2) water level time series of lakes and rivers, (3) terrestrial water storage anomaly, (4) water storage anomaly of lakes and reservoirs, and (5) river discharge estimates for large and small rivers.
Benjamin Kitambo, Fabrice Papa, Adrien Paris, Raphael M. Tshimanga, Stephane Calmant, Ayan Santos Fleischmann, Frederic Frappart, Melanie Becker, Mohammad J. Tourian, Catherine Prigent, and Johary Andriambeloson
Hydrol. Earth Syst. Sci., 26, 1857–1882, https://doi.org/10.5194/hess-26-1857-2022, https://doi.org/10.5194/hess-26-1857-2022, 2022
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This study presents a better characterization of surface hydrology variability in the Congo River basin, the second largest river system in the world. We jointly use a large record of in situ and satellite-derived observations to monitor the spatial distribution and different timings of the Congo River basin's annual flood dynamic, including its peculiar bimodal pattern.
Martin Horwath, Benjamin D. Gutknecht, Anny Cazenave, Hindumathi Kulaiappan Palanisamy, Florence Marti, Ben Marzeion, Frank Paul, Raymond Le Bris, Anna E. Hogg, Inès Otosaka, Andrew Shepherd, Petra Döll, Denise Cáceres, Hannes Müller Schmied, Johnny A. Johannessen, Jan Even Øie Nilsen, Roshin P. Raj, René Forsberg, Louise Sandberg Sørensen, Valentina R. Barletta, Sebastian B. Simonsen, Per Knudsen, Ole Baltazar Andersen, Heidi Ranndal, Stine K. Rose, Christopher J. Merchant, Claire R. Macintosh, Karina von Schuckmann, Kristin Novotny, Andreas Groh, Marco Restano, and Jérôme Benveniste
Earth Syst. Sci. Data, 14, 411–447, https://doi.org/10.5194/essd-14-411-2022, https://doi.org/10.5194/essd-14-411-2022, 2022
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Global mean sea-level change observed from 1993 to 2016 (mean rate of 3.05 mm yr−1) matches the combined effect of changes in water density (thermal expansion) and ocean mass. Ocean-mass change has been assessed through the contributions from glaciers, ice sheets, and land water storage or directly from satellite data since 2003. Our budget assessments of linear trends and monthly anomalies utilise new datasets and uncertainty characterisations developed within ESA's Climate Change Initiative.
Tom Gleeson, Thorsten Wagener, Petra Döll, Samuel C. Zipper, Charles West, Yoshihide Wada, Richard Taylor, Bridget Scanlon, Rafael Rosolem, Shams Rahman, Nurudeen Oshinlaja, Reed Maxwell, Min-Hui Lo, Hyungjun Kim, Mary Hill, Andreas Hartmann, Graham Fogg, James S. Famiglietti, Agnès Ducharne, Inge de Graaf, Mark Cuthbert, Laura Condon, Etienne Bresciani, and Marc F. P. Bierkens
Geosci. Model Dev., 14, 7545–7571, https://doi.org/10.5194/gmd-14-7545-2021, https://doi.org/10.5194/gmd-14-7545-2021, 2021
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Groundwater is increasingly being included in large-scale (continental to global) land surface and hydrologic simulations. However, it is challenging to evaluate these simulations because groundwater is
hiddenunderground and thus hard to measure. We suggest using multiple complementary strategies to assess the performance of a model (
model evaluation).
Camelia-Eliza Telteu, Hannes Müller Schmied, Wim Thiery, Guoyong Leng, Peter Burek, Xingcai Liu, Julien Eric Stanislas Boulange, Lauren Seaby Andersen, Manolis Grillakis, Simon Newland Gosling, Yusuke Satoh, Oldrich Rakovec, Tobias Stacke, Jinfeng Chang, Niko Wanders, Harsh Lovekumar Shah, Tim Trautmann, Ganquan Mao, Naota Hanasaki, Aristeidis Koutroulis, Yadu Pokhrel, Luis Samaniego, Yoshihide Wada, Vimal Mishra, Junguo Liu, Petra Döll, Fang Zhao, Anne Gädeke, Sam S. Rabin, and Florian Herz
Geosci. Model Dev., 14, 3843–3878, https://doi.org/10.5194/gmd-14-3843-2021, https://doi.org/10.5194/gmd-14-3843-2021, 2021
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We analyse water storage compartments, water flows, and human water use sectors included in 16 global water models that provide simulations for the Inter-Sectoral Impact Model Intercomparison Project phase 2b. We develop a standard writing style for the model equations. We conclude that even though hydrologic processes are often based on similar equations, in the end these equations have been adjusted, or the models have used different values for specific parameters or specific variables.
Eklavyya Popat and Petra Döll
Nat. Hazards Earth Syst. Sci., 21, 1337–1354, https://doi.org/10.5194/nhess-21-1337-2021, https://doi.org/10.5194/nhess-21-1337-2021, 2021
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Two drought hazard indices are presented that combine drought deficit and anomaly aspects: one for soil moisture drought (SMDAI) where we simplified the DSI and the other for streamflow drought (QDAI), which is to our knowledge the first ever deficit anomaly drought index including surface water demand. Both indices are tested at the global scale with WaterGAP 2.2d outputs, providing more differentiated spatial and temporal patterns distinguishing the actual degree of respective drought hazard.
Hannes Müller Schmied, Denise Cáceres, Stephanie Eisner, Martina Flörke, Claudia Herbert, Christoph Niemann, Thedini Asali Peiris, Eklavyya Popat, Felix Theodor Portmann, Robert Reinecke, Maike Schumacher, Somayeh Shadkam, Camelia-Eliza Telteu, Tim Trautmann, and Petra Döll
Geosci. Model Dev., 14, 1037–1079, https://doi.org/10.5194/gmd-14-1037-2021, https://doi.org/10.5194/gmd-14-1037-2021, 2021
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In a globalized world with large flows of virtual water between river basins and international responsibilities for the sustainable development of the Earth system and its inhabitants, quantitative estimates of water flows and storages and of water demand by humans are required. Global hydrological models such as WaterGAP are developed to provide this information. Here we present a thorough description, evaluation and application examples of the most recent model version, WaterGAP v2.2d.
Robert Reinecke, Hannes Müller Schmied, Tim Trautmann, Lauren Seaby Andersen, Peter Burek, Martina Flörke, Simon N. Gosling, Manolis Grillakis, Naota Hanasaki, Aristeidis Koutroulis, Yadu Pokhrel, Wim Thiery, Yoshihide Wada, Satoh Yusuke, and Petra Döll
Hydrol. Earth Syst. Sci., 25, 787–810, https://doi.org/10.5194/hess-25-787-2021, https://doi.org/10.5194/hess-25-787-2021, 2021
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Billions of people rely on groundwater as an accessible source of drinking water and for irrigation, especially in times of drought. Groundwater recharge is the primary process of regenerating groundwater resources. We find that groundwater recharge will increase in northern Europe by about 19 % and decrease by 10 % in the Amazon with 3 °C global warming. In the Mediterranean, a 2 °C warming has already lead to a reduction in recharge by 38 %. However, these model predictions are uncertain.
Denise Cáceres, Ben Marzeion, Jan Hendrik Malles, Benjamin Daniel Gutknecht, Hannes Müller Schmied, and Petra Döll
Hydrol. Earth Syst. Sci., 24, 4831–4851, https://doi.org/10.5194/hess-24-4831-2020, https://doi.org/10.5194/hess-24-4831-2020, 2020
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We analysed how and to which extent changes in water storage on continents had an effect on global ocean mass over the period 1948–2016. Continents lost water to oceans at an accelerated rate, inducing sea level rise. Shrinking glaciers explain 81 % of the long-term continental water mass loss, while declining groundwater levels, mainly due to sustained groundwater pumping for irrigation, is the second major driver. This long-term decline was partly offset by the impoundment of water in dams.
Tom Gleeson, Thorsten Wagener, Petra Döll, Samuel C. Zipper, Charles West, Yoshihide Wada, Richard Taylor, Bridget Scanlon, Rafael Rosolem, Shams Rahman, Nurudeen Oshinlaja, Reed Maxwell, Min-Hui Lo, Hyungjun Kim, Mary Hill, Andreas Hartmann, Graham Fogg, James S. Famiglietti, Agnès Ducharne, Inge de Graaf, Mark Cuthbert, Laura Condon, Etienne Bresciani, and Marc F. P. Bierkens
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2020-378, https://doi.org/10.5194/hess-2020-378, 2020
Revised manuscript not accepted
Isabel Meza, Stefan Siebert, Petra Döll, Jürgen Kusche, Claudia Herbert, Ehsan Eyshi Rezaei, Hamideh Nouri, Helena Gerdener, Eklavyya Popat, Janna Frischen, Gustavo Naumann, Jürgen V. Vogt, Yvonne Walz, Zita Sebesvari, and Michael Hagenlocher
Nat. Hazards Earth Syst. Sci., 20, 695–712, https://doi.org/10.5194/nhess-20-695-2020, https://doi.org/10.5194/nhess-20-695-2020, 2020
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The paper presents, for the first time, a global-scale drought risk assessment for both irrigated and rainfed agricultural systems while considering drought hazard indicators, exposure and expert-weighted vulnerability indicators. We identify global patterns of drought risk and, by disaggregating risk into its underlying components and factors, provide entry points for risk reduction.
Robert Reinecke, Laura Foglia, Steffen Mehl, Jonathan D. Herman, Alexander Wachholz, Tim Trautmann, and Petra Döll
Hydrol. Earth Syst. Sci., 23, 4561–4582, https://doi.org/10.5194/hess-23-4561-2019, https://doi.org/10.5194/hess-23-4561-2019, 2019
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Recently, the first global groundwater models were developed to better understand surface-water–groundwater interactions and human water use impacts. However, the reliability of model outputs is limited by a lack of data as well as model assumptions required due to the necessarily coarse spatial resolution. In this study we present the first global maps of model sensitivity according to their parameterization and build a foundation to improve datasets, model design, and model understanding.
Robert Reinecke, Laura Foglia, Steffen Mehl, Tim Trautmann, Denise Cáceres, and Petra Döll
Geosci. Model Dev., 12, 2401–2418, https://doi.org/10.5194/gmd-12-2401-2019, https://doi.org/10.5194/gmd-12-2401-2019, 2019
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G³M is a new global groundwater model (http://globalgroundwatermodel.org) that simulates lateral and vertical flows as well as exchanges with surface water bodies like rivers, lakes, and wetlands for the whole globe except Antarctica and Greenland. The newly developed model framework enables an efficient integration into established global hydrological models. This paper presents the G³M concept and specific model design decisions together with first results under a naturalized equilibrium.
Nizar Abou Zaki, Ali Torabi Haghighi, Pekka M. Rossi, Mohammad J. Tourian, and Bjørn Klove
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2018-471, https://doi.org/10.5194/hess-2018-471, 2018
Preprint withdrawn
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Groundwater is considered a main source of fresh water in semi-arid climatic zones, especially for agricultural usage. This study compares in-situ groundwater volume variation measurements with GRACE derived water mass data. The study concludes the possibility of using GRACE data to monitor groundwater depletion in catchments that lack measured data. GRACE data can here help in drawing general conclusions for integrated water resources management, and sustainable usage of this resources.
Zhongwei Huang, Mohamad Hejazi, Xinya Li, Qiuhong Tang, Chris Vernon, Guoyong Leng, Yaling Liu, Petra Döll, Stephanie Eisner, Dieter Gerten, Naota Hanasaki, and Yoshihide Wada
Hydrol. Earth Syst. Sci., 22, 2117–2133, https://doi.org/10.5194/hess-22-2117-2018, https://doi.org/10.5194/hess-22-2117-2018, 2018
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This study generate a historical global monthly gridded water withdrawal data (0.5 × 0.5 degrees) for the period 1971–2010, distinguishing six water use sectors (irrigation, domestic, electricity generation, livestock, mining, and manufacturing). This dataset is the first reconstructed global water withdrawal data product at sub-annual and gridded resolution that is derived from different models and data sources, and was generated by spatially and temporally downscaling country-scale estimates.
Tingju Zhu, Petra Döll, Hannes Müller Schmied, Claudia Ringler, and Mark W. Rosegrant
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2017-216, https://doi.org/10.5194/gmd-2017-216, 2017
Revised manuscript has not been submitted
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The global hydrological model IGHM was developed to simulate water availability over global land areas month by month. The simulated water availability is for analyzing irrigation water supply and crop production in a global water and food projections model, IMPACT. Water availability simulated by another global hydrological model, WGHM, was used to determine parameter values in IGHM. This paper describes the structure of IGHM, the method of its parameter determination, and its performance.
Hannes Müller Schmied, Linda Adam, Stephanie Eisner, Gabriel Fink, Martina Flörke, Hyungjun Kim, Taikan Oki, Felix Theodor Portmann, Robert Reinecke, Claudia Riedel, Qi Song, Jing Zhang, and Petra Döll
Proc. IAHS, 374, 53–62, https://doi.org/10.5194/piahs-374-53-2016, https://doi.org/10.5194/piahs-374-53-2016, 2016
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We analyzed simulated water balance components on global and continental scale as impacted by the uncertainty of climate forcing datasets. On average, around 62 % of precipitation on global land area evapotranspires and 38 % is discharge to oceans and inland sinks. Human water use increased during the 20th century by a factor of 5. Uncertainty of precipitation variable has most impact on model results, followed by shortwave downward radiation. Model calibration reduces this uncertainty.
Hannes Müller Schmied, Linda Adam, Stephanie Eisner, Gabriel Fink, Martina Flörke, Hyungjun Kim, Taikan Oki, Felix Theodor Portmann, Robert Reinecke, Claudia Riedel, Qi Song, Jing Zhang, and Petra Döll
Hydrol. Earth Syst. Sci., 20, 2877–2898, https://doi.org/10.5194/hess-20-2877-2016, https://doi.org/10.5194/hess-20-2877-2016, 2016
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The assessment of water balance components of the global land surface by means of hydrological models is affected by large uncertainties, in particular related to meteorological forcing. We analyze the effect of five state-of-the-art forcings on water balance components at different spatial and temporal scales modeled with WaterGAP. Furthermore, the dominant effect (precipitation/human alteration) for long-term changes in river discharge is assessed.
K. Frieler, A. Levermann, J. Elliott, J. Heinke, A. Arneth, M. F. P. Bierkens, P. Ciais, D. B. Clark, D. Deryng, P. Döll, P. Falloon, B. Fekete, C. Folberth, A. D. Friend, C. Gellhorn, S. N. Gosling, I. Haddeland, N. Khabarov, M. Lomas, Y. Masaki, K. Nishina, K. Neumann, T. Oki, R. Pavlick, A. C. Ruane, E. Schmid, C. Schmitz, T. Stacke, E. Stehfest, Q. Tang, D. Wisser, V. Huber, F. Piontek, L. Warszawski, J. Schewe, H. Lotze-Campen, and H. J. Schellnhuber
Earth Syst. Dynam., 6, 447–460, https://doi.org/10.5194/esd-6-447-2015, https://doi.org/10.5194/esd-6-447-2015, 2015
S. Siebert, M. Kummu, M. Porkka, P. Döll, N. Ramankutty, and B. R. Scanlon
Hydrol. Earth Syst. Sci., 19, 1521–1545, https://doi.org/10.5194/hess-19-1521-2015, https://doi.org/10.5194/hess-19-1521-2015, 2015
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We developed the historical irrigation data set (HID) depicting the spatio-temporal development of the area equipped for irrigation (AEI) between 1900 and 2005 at 5arcmin resolution.
The HID reflects very well the spatial patterns of irrigated land as shown on two historical maps for 1910 and 1960.
Global AEI increased from 63 million ha (Mha) in 1900 to 111 Mha in 1950 and 306 Mha in 2005. Mean aridity on irrigated land increased and mean natural river discharge decreased from 1900 to 1950.
H. Müller Schmied, S. Eisner, D. Franz, M. Wattenbach, F. T. Portmann, M. Flörke, and P. Döll
Hydrol. Earth Syst. Sci., 18, 3511–3538, https://doi.org/10.5194/hess-18-3511-2014, https://doi.org/10.5194/hess-18-3511-2014, 2014
H. Hoff, P. Döll, M. Fader, D. Gerten, S. Hauser, and S. Siebert
Hydrol. Earth Syst. Sci., 18, 213–226, https://doi.org/10.5194/hess-18-213-2014, https://doi.org/10.5194/hess-18-213-2014, 2014
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Evolution of river regimes in the Mekong River basin over 8 decades and the role of dams in recent hydrological extremes
Skill of seasonal flow forecasts at catchment scale: an assessment across South Korea
To what extent do flood-inducing storm events change future flood hazards?
When ancient numerical demons meet physics-informed machine learning: adjoint-based gradients for implicit differentiable modeling
Assessing the impact of climate change on high return levels of peak flows in Bavaria applying the CRCM5 large ensemble
Impacts of climate and land surface change on catchment evapotranspiration and runoff from 1951 to 2020 in Saxony, Germany
Quantifying and reducing flood forecast uncertainty by the CHUP-BMA method
Developing a tile drainage module for the Cold Regions Hydrological Model: lessons from a farm in southern Ontario, Canada
To bucket or not to bucket? Analyzing the performance and interpretability of hybrid hydrological models with dynamic parameterization
Widespread flooding dynamics under climate change: characterising floods using grid-based hydrological modelling and regional climate projections
HESS Opinions: The sword of Damocles of the impossible flood
Metamorphic testing of machine learning and conceptual hydrologic models
The influence of human activities on streamflow reductions during the megadrought in central Chile
Elevational control of isotopic composition and application in understanding hydrologic processes in the mid Merced River catchment, Sierra Nevada, California, USA
Enhancing long short-term memory (LSTM)-based streamflow prediction with a spatially distributed approach
Broadleaf afforestation impacts on terrestrial hydrology insignificant compared to climate change in Great Britain
Impacts of spatiotemporal resolutions of precipitation on flood event simulation based on multimodel structures – a case study over the Xiang River basin in China
A network approach for multiscale catchment classification using traits
Multi-model approach in a variable spatial framework for streamflow simulation
Advancing understanding of lake–watershed hydrology: a fully coupled numerical model illustrated by Qinghai Lake
Technical note: Testing the connection between hillslope-scale runoff fluctuations and streamflow hydrographs at the outlet of large river basins
Empirical stream thermal sensitivity cluster on the landscape according to geology and climate
Deep learning for monthly rainfall–runoff modelling: a large-sample comparison with conceptual models across Australia
On optimization of calibrations of a distributed hydrological model with spatially distributed information on snow
Toward interpretable LSTM-based modeling of hydrological systems
Flow intermittence prediction using a hybrid hydrological modelling approach: influence of observed intermittence data on the training of a random forest model
What controls the tail behaviour of flood series: rainfall or runoff generation?
Learning Landscape Features from Streamflow with Autoencoders
Seasonal prediction of end-of-dry-season watershed behavior in a highly interconnected alluvial watershed in northern California
Glaciers determine the sensitivity of hydrological processes to perturbed climate in a large mountainous basin on the Tibetan Plateau
Leveraging gauge networks and strategic discharge measurements to aid the development of continuous streamflow records
On the need for physical constraints in deep learning rainfall–runoff projections under climate change: a sensitivity analysis to warming and shifts in potential evapotranspiration
Evaluation of hydrological models on small mountainous catchments: impact of the meteorological forcings
Projecting sediment export from two highly glacierized alpine catchments under climate change: exploring non-parametric regression as an analysis tool
Simulation-Based Inference for Parameter Estimation of Complex Watershed Simulators
Spatio-temporal patterns and trends of streamflow in water-scarce Mediterranean basins
A framework for parameter estimation, sensitivity analysis, and uncertainty analysis for holistic hydrologic modeling using SWAT+
On understanding mountainous carbonate basins of the Mediterranean using parsimonious modeling solutions
Comparing quantile regression forest and mixture density long short-term memory models for probabilistic post-processing of satellite precipitation-driven streamflow simulations
Jari-Pekka Nousu, Kersti Leppä, Hannu Marttila, Pertti Ala-aho, Giulia Mazzotti, Terhikki Manninen, Mika Korkiakoski, Mika Aurela, Annalea Lohila, and Samuli Launiainen
Hydrol. Earth Syst. Sci., 28, 4643–4666, https://doi.org/10.5194/hess-28-4643-2024, https://doi.org/10.5194/hess-28-4643-2024, 2024
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We used hydrological models, field measurements, and satellite-based data to study the soil moisture dynamics in a subarctic catchment. The role of groundwater was studied with different ways to model the groundwater dynamics and via comparisons to the observational data. The choice of groundwater model was shown to have a strong impact, and representation of lateral flow was important to capture wet soil conditions. Our results provide insights for ecohydrological studies in boreal regions.
Nienke Tempel, Laurène Bouaziz, Riccardo Taormina, Ellis van Noppen, Jasper Stam, Eric Sprokkereef, and Markus Hrachowitz
Hydrol. Earth Syst. Sci., 28, 4577–4597, https://doi.org/10.5194/hess-28-4577-2024, https://doi.org/10.5194/hess-28-4577-2024, 2024
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This study explores the impact of climatic variability on root zone water storage capacities and, thus, on hydrological predictions. Analysing data from 286 areas in Europe and the US, we found that, despite some variations in root zone storage capacity due to changing climatic conditions over multiple decades, these changes are generally minor and have a limited effect on water storage and river flow predictions.
Bu Li, Ting Sun, Fuqiang Tian, Mahmut Tudaji, Li Qin, and Guangheng Ni
Hydrol. Earth Syst. Sci., 28, 4521–4538, https://doi.org/10.5194/hess-28-4521-2024, https://doi.org/10.5194/hess-28-4521-2024, 2024
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This paper developed hybrid semi-distributed hydrological models by employing a process-based model as the backbone and utilizing deep learning to parameterize and replace internal modules. The main contribution is to provide a high-performance tool enriched with explicit hydrological knowledge for hydrological prediction and to improve understanding about the hydrological sensitivities to climate change in large alpine basins.
Dan Elhanati, Nadine Goeppert, and Brian Berkowitz
Hydrol. Earth Syst. Sci., 28, 4239–4249, https://doi.org/10.5194/hess-28-4239-2024, https://doi.org/10.5194/hess-28-4239-2024, 2024
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A continuous time random walk framework was developed to allow modeling of a karst aquifer discharge response to measured rainfall. The application of the numerical model yielded robust fits between modeled and measured discharge values, especially for the distinctive long tails found during recession times. The findings shed light on the interplay of slow and fast flow in the karst system and establish the application of the model for simulating flow and transport in such systems.
Frederik Kratzert, Martin Gauch, Daniel Klotz, and Grey Nearing
Hydrol. Earth Syst. Sci., 28, 4187–4201, https://doi.org/10.5194/hess-28-4187-2024, https://doi.org/10.5194/hess-28-4187-2024, 2024
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Recently, a special type of neural-network architecture became increasingly popular in hydrology literature. However, in most applications, this model was applied as a one-to-one replacement for hydrology models without adapting or rethinking the experimental setup. In this opinion paper, we show how this is almost always a bad decision and how using these kinds of models requires the use of large-sample hydrology data sets.
Franziska Clerc-Schwarzenbach, Giovanni Selleri, Mattia Neri, Elena Toth, Ilja van Meerveld, and Jan Seibert
Hydrol. Earth Syst. Sci., 28, 4219–4237, https://doi.org/10.5194/hess-28-4219-2024, https://doi.org/10.5194/hess-28-4219-2024, 2024
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We show that the differences between the forcing data included in three CAMELS datasets (US, BR, GB) and the forcing data included for the same catchments in the Caravan dataset affect model calibration considerably. The model performance dropped when the data from the Caravan dataset were used instead of the original data. Most of the model performance drop could be attributed to the differences in precipitation data. However, differences were largest for the potential evapotranspiration data.
Ying Zhao, Mehdi Rahmati, Harry Vereecken, and Dani Or
Hydrol. Earth Syst. Sci., 28, 4059–4063, https://doi.org/10.5194/hess-28-4059-2024, https://doi.org/10.5194/hess-28-4059-2024, 2024
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Gao et al. (2023) question the importance of soil in hydrology, sparking debate. We acknowledge some valid points but critique their broad, unsubstantiated views on soil's role. Our response highlights three key areas: (1) the false divide between ecosystem-centric and soil-centric approaches, (2) the vital yet varied impact of soil properties, and (3) the call for a scale-aware framework. We aim to unify these perspectives, enhancing hydrology's comprehensive understanding.
Siyuan Wang, Markus Hrachowitz, and Gerrit Schoups
Hydrol. Earth Syst. Sci., 28, 4011–4033, https://doi.org/10.5194/hess-28-4011-2024, https://doi.org/10.5194/hess-28-4011-2024, 2024
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Root zone storage capacity (Sumax) changes significantly over multiple decades, reflecting vegetation adaptation to climatic variability. However, this temporal evolution of Sumax cannot explain long-term fluctuations in the partitioning of water fluxes as expressed by deviations ΔIE from the parametric Budyko curve over time with different climatic conditions, and it does not have any significant effects on shorter-term hydrological response characteristics of the upper Neckar catchment.
Zehua Chang, Hongkai Gao, Leilei Yong, Kang Wang, Rensheng Chen, Chuntan Han, Otgonbayar Demberel, Batsuren Dorjsuren, Shugui Hou, and Zheng Duan
Hydrol. Earth Syst. Sci., 28, 3897–3917, https://doi.org/10.5194/hess-28-3897-2024, https://doi.org/10.5194/hess-28-3897-2024, 2024
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An integrated cryospheric–hydrologic model, FLEX-Cryo, was developed that considers glaciers, snow cover, and frozen soil and their dynamic impacts on hydrology. We utilized it to simulate future changes in cryosphere and hydrology in the Hulu catchment. Our projections showed the two glaciers will melt completely around 2050, snow cover will reduce, and permafrost will degrade. For hydrology, runoff will decrease after the glacier has melted, and permafrost degradation will increase baseflow.
Henry M. Zimba, Miriam Coenders-Gerrits, Kawawa E. Banda, Petra Hulsman, Nick van de Giesen, Imasiku A. Nyambe, and Hubert H. G. Savenije
Hydrol. Earth Syst. Sci., 28, 3633–3663, https://doi.org/10.5194/hess-28-3633-2024, https://doi.org/10.5194/hess-28-3633-2024, 2024
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The fall and flushing of new leaves in the miombo woodlands co-occur in the dry season before the commencement of seasonal rainfall. The miombo species are also said to have access to soil moisture in deep soils, including groundwater in the dry season. Satellite-based evaporation estimates, temporal trends, and magnitudes differ the most in the dry season, most likely due to inadequate understanding and representation of the highlighted miombo species attributes in simulations.
Louise Akemi Kuana, Arlan Scortegagna Almeida, Emílio Graciliano Ferreira Mercuri, and Steffen Manfred Noe
Hydrol. Earth Syst. Sci., 28, 3367–3390, https://doi.org/10.5194/hess-28-3367-2024, https://doi.org/10.5194/hess-28-3367-2024, 2024
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The authors compared regionalization methods for river flow prediction in 126 catchments from the south of Brazil, a region with humid subtropical and hot temperate climate. The regionalization method based on physiographic–climatic similarity had the best performance for predicting daily and Q95 reference flow. We showed that basins without flow monitoring can have a good approximation of streamflow using machine learning and physiographic–climatic information as inputs.
Huy Dang and Yadu Pokhrel
Hydrol. Earth Syst. Sci., 28, 3347–3365, https://doi.org/10.5194/hess-28-3347-2024, https://doi.org/10.5194/hess-28-3347-2024, 2024
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By examining basin-wide simulations of a river regime over 83 years with and without dams, we present evidence that climate variation was a key driver of hydrologic variabilities in the Mekong River basin (MRB) over the long term; however, dams have largely altered the seasonality of the Mekong’s flow regime and annual flooding patterns in major downstream areas in recent years. These findings could help us rethink the planning of future dams and water resource management in the MRB.
Yongshin Lee, Francesca Pianosi, Andres Peñuela, and Miguel Angel Rico-Ramirez
Hydrol. Earth Syst. Sci., 28, 3261–3279, https://doi.org/10.5194/hess-28-3261-2024, https://doi.org/10.5194/hess-28-3261-2024, 2024
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Following recent advancements in weather prediction technology, we explored how seasonal weather forecasts (1 or more months ahead) could benefit practical water management in South Korea. Our findings highlight that using seasonal weather forecasts for predicting flow patterns 1 to 3 months ahead is effective, especially during dry years. This suggest that seasonal weather forecasts can be helpful in improving the management of water resources.
Mariam Khanam, Giulia Sofia, and Emmanouil N. Anagnostou
Hydrol. Earth Syst. Sci., 28, 3161–3190, https://doi.org/10.5194/hess-28-3161-2024, https://doi.org/10.5194/hess-28-3161-2024, 2024
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Flooding worsens due to climate change, with river dynamics being a key in local flood control. Predicting post-storm geomorphic changes is challenging. Using self-organizing maps and machine learning, this study forecasts post-storm alterations in stage–discharge relationships across 3101 US stream gages. The provided framework can aid in updating hazard assessments by identifying rivers prone to change, integrating channel adjustments into flood hazard assessment.
Yalan Song, Wouter J. M. Knoben, Martyn P. Clark, Dapeng Feng, Kathryn Lawson, Kamlesh Sawadekar, and Chaopeng Shen
Hydrol. Earth Syst. Sci., 28, 3051–3077, https://doi.org/10.5194/hess-28-3051-2024, https://doi.org/10.5194/hess-28-3051-2024, 2024
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Differentiable models (DMs) integrate neural networks and physical equations for accuracy, interpretability, and knowledge discovery. We developed an adjoint-based DM for ordinary differential equations (ODEs) for hydrological modeling, reducing distorted fluxes and physical parameters from errors in models that use explicit and operation-splitting schemes. With a better numerical scheme and improved structure, the adjoint-based DM matches or surpasses long short-term memory (LSTM) performance.
Florian Willkofer, Raul R. Wood, and Ralf Ludwig
Hydrol. Earth Syst. Sci., 28, 2969–2989, https://doi.org/10.5194/hess-28-2969-2024, https://doi.org/10.5194/hess-28-2969-2024, 2024
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Severe flood events pose a threat to riverine areas, yet robust estimates of the dynamics of these events in the future due to climate change are rarely available. Hence, this study uses data from a regional climate model, SMILE, to drive a high-resolution hydrological model for 98 catchments of hydrological Bavaria and exploits the large database to derive robust values for the 100-year flood events. Results indicate an increase in frequency and intensity for most catchments in the future.
Maik Renner and Corina Hauffe
Hydrol. Earth Syst. Sci., 28, 2849–2869, https://doi.org/10.5194/hess-28-2849-2024, https://doi.org/10.5194/hess-28-2849-2024, 2024
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Climate and land surface changes influence the partitioning of water balance components decisively. Their impact is quantified for 71 catchments in Saxony. Germany. Distinct signatures in the joint water and energy budgets are found: (i) past forest dieback caused a decrease in and subsequent recovery of evapotranspiration in the affected regions, and (ii) the recent shift towards higher aridity imposed a large decline in runoff that has not been seen in the observation records before.
Zhen Cui, Shenglian Guo, Hua Chen, Dedi Liu, Yanlai Zhou, and Chong-Yu Xu
Hydrol. Earth Syst. Sci., 28, 2809–2829, https://doi.org/10.5194/hess-28-2809-2024, https://doi.org/10.5194/hess-28-2809-2024, 2024
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Ensemble forecasting facilitates reliable flood forecasting and warning. This study couples the copula-based hydrologic uncertainty processor (CHUP) with Bayesian model averaging (BMA) and proposes the novel CHUP-BMA method of reducing inflow forecasting uncertainty of the Three Gorges Reservoir. The CHUP-BMA avoids the normal distribution assumption in the HUP-BMA and considers the constraint of initial conditions, which can improve the deterministic and probabilistic forecast performance.
Mazda Kompanizare, Diogo Costa, Merrin L. Macrae, John W. Pomeroy, and Richard M. Petrone
Hydrol. Earth Syst. Sci., 28, 2785–2807, https://doi.org/10.5194/hess-28-2785-2024, https://doi.org/10.5194/hess-28-2785-2024, 2024
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A new agricultural tile drainage module was developed in the Cold Region Hydrological Model platform. Tile flow and water levels are simulated by considering the effect of capillary fringe thickness, drainable water and seasonal regional groundwater dynamics. The model was applied to a small well-instrumented farm in southern Ontario, Canada, where there are concerns about the impacts of agricultural drainage into Lake Erie.
Eduardo Acuña Espinoza, Ralf Loritz, Manuel Álvarez Chaves, Nicole Bäuerle, and Uwe Ehret
Hydrol. Earth Syst. Sci., 28, 2705–2719, https://doi.org/10.5194/hess-28-2705-2024, https://doi.org/10.5194/hess-28-2705-2024, 2024
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Hydrological hybrid models promise to merge the performance of deep learning methods with the interpretability of process-based models. One hybrid approach is the dynamic parameterization of conceptual models using long short-term memory (LSTM) networks. We explored this method to evaluate the effect of the flexibility given by LSTMs on the process-based part.
Adam Griffin, Alison L. Kay, Paul Sayers, Victoria Bell, Elizabeth Stewart, and Sam Carr
Hydrol. Earth Syst. Sci., 28, 2635–2650, https://doi.org/10.5194/hess-28-2635-2024, https://doi.org/10.5194/hess-28-2635-2024, 2024
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Widespread flooding is a major problem in the UK and is greatly affected by climate change and land-use change. To look at how widespread flooding changes in the future, climate model data (UKCP18) were used with a hydrological model (Grid-to-Grid) across the UK, and 14 400 events were identified between two time slices: 1980–2010 and 2050–2080. There was a strong increase in the number of winter events in the future time slice and in the peak return periods.
Alberto Montanari, Bruno Merz, and Günter Blöschl
Hydrol. Earth Syst. Sci., 28, 2603–2615, https://doi.org/10.5194/hess-28-2603-2024, https://doi.org/10.5194/hess-28-2603-2024, 2024
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Floods often take communities by surprise, as they are often considered virtually
impossibleyet are an ever-present threat similar to the sword suspended over the head of Damocles in the classical Greek anecdote. We discuss four reasons why extremely large floods carry a risk that is often larger than expected. We provide suggestions for managing the risk of megafloods by calling for a creative exploration of hazard scenarios and communicating the unknown corners of the reality of floods.
Peter Reichert, Kai Ma, Marvin Höge, Fabrizio Fenicia, Marco Baity-Jesi, Dapeng Feng, and Chaopeng Shen
Hydrol. Earth Syst. Sci., 28, 2505–2529, https://doi.org/10.5194/hess-28-2505-2024, https://doi.org/10.5194/hess-28-2505-2024, 2024
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We compared the predicted change in catchment outlet discharge to precipitation and temperature change for conceptual and machine learning hydrological models. We found that machine learning models, despite providing excellent fit and prediction capabilities, can be unreliable regarding the prediction of the effect of temperature change for low-elevation catchments. This indicates the need for caution when applying them for the prediction of the effect of climate change.
Nicolás Álamos, Camila Alvarez-Garreton, Ariel Muñoz, and Álvaro González-Reyes
Hydrol. Earth Syst. Sci., 28, 2483–2503, https://doi.org/10.5194/hess-28-2483-2024, https://doi.org/10.5194/hess-28-2483-2024, 2024
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In this study, we assess the effects of climate and water use on streamflow reductions and drought intensification during the last 3 decades in central Chile. We address this by contrasting streamflow observations with near-natural streamflow simulations. We conclude that while the lack of precipitation dominates streamflow reductions in the megadrought, water uses have not diminished during this time, causing a worsening of the hydrological drought conditions and maladaptation conditions.
Fengjing Liu, Martha H. Conklin, and Glenn D. Shaw
Hydrol. Earth Syst. Sci., 28, 2239–2258, https://doi.org/10.5194/hess-28-2239-2024, https://doi.org/10.5194/hess-28-2239-2024, 2024
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Mountain snowpack has been declining and more precipitation falls as rain than snow. Using stable isotopes, we found flows and flow duration in Yosemite Creek are most sensitive to climate warming due to strong evaporation of waterfalls, potentially lengthening the dry-up period of waterfalls in summer and negatively affecting tourism. Groundwater recharge in Yosemite Valley is primarily from the upper snow–rain transition (2000–2500 m) and very vulnerable to a reduction in the snow–rain ratio.
Qiutong Yu, Bryan A. Tolson, Hongren Shen, Ming Han, Juliane Mai, and Jimmy Lin
Hydrol. Earth Syst. Sci., 28, 2107–2122, https://doi.org/10.5194/hess-28-2107-2024, https://doi.org/10.5194/hess-28-2107-2024, 2024
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It is challenging to incorporate input variables' spatial distribution information when implementing long short-term memory (LSTM) models for streamflow prediction. This work presents a novel hybrid modelling approach to predict streamflow while accounting for spatial variability. We evaluated the performance against lumped LSTM predictions in 224 basins across the Great Lakes region in North America. This approach shows promise for predicting streamflow in large, ungauged basin.
Marcus Buechel, Louise Slater, and Simon Dadson
Hydrol. Earth Syst. Sci., 28, 2081–2105, https://doi.org/10.5194/hess-28-2081-2024, https://doi.org/10.5194/hess-28-2081-2024, 2024
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Afforestation has been proposed internationally, but the hydrological implications of such large increases in the spatial extent of woodland are not fully understood. In this study, we use a land surface model to simulate hydrology across Great Britain with realistic afforestation scenarios and potential climate changes. Countrywide afforestation minimally influences hydrology, when compared to climate change, and reduces low streamflow whilst not lowering the highest flows.
Qian Zhu, Xiaodong Qin, Dongyang Zhou, Tiantian Yang, and Xinyi Song
Hydrol. Earth Syst. Sci., 28, 1665–1686, https://doi.org/10.5194/hess-28-1665-2024, https://doi.org/10.5194/hess-28-1665-2024, 2024
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Input data, model and calibration strategy can affect the accuracy of flood event simulation and prediction. Satellite-based precipitation with different spatiotemporal resolutions is an important input source. Data-driven models are sometimes proven to be more accurate than hydrological models. Event-based calibration and conventional strategy are two options adopted for flood simulation. This study targets the three concerns for accurate flood event simulation and prediction.
Fabio Ciulla and Charuleka Varadharajan
Hydrol. Earth Syst. Sci., 28, 1617–1651, https://doi.org/10.5194/hess-28-1617-2024, https://doi.org/10.5194/hess-28-1617-2024, 2024
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We present a new method based on network science for unsupervised classification of large datasets and apply it to classify 9067 US catchments and 274 biophysical traits at multiple scales. We find that our trait-based approach produces catchment classes with distinct streamflow behavior and that spatial patterns emerge amongst pristine and human-impacted catchments. This method can be widely used beyond hydrology to identify patterns, reduce trait redundancy, and select representative sites.
Cyril Thébault, Charles Perrin, Vazken Andréassian, Guillaume Thirel, Sébastien Legrand, and Olivier Delaigue
Hydrol. Earth Syst. Sci., 28, 1539–1566, https://doi.org/10.5194/hess-28-1539-2024, https://doi.org/10.5194/hess-28-1539-2024, 2024
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Streamflow forecasting is useful for many applications, ranging from population safety (e.g. floods) to water resource management (e.g. agriculture or hydropower). To this end, hydrological models must be optimized. However, a model is inherently wrong. This study aims to analyse the contribution of a multi-model approach within a variable spatial framework to improve streamflow simulations. The underlying idea is to take advantage of the strength of each modelling framework tested.
Lele Shu, Xiaodong Li, Yan Chang, Xianhong Meng, Hao Chen, Yuan Qi, Hongwei Wang, Zhaoguo Li, and Shihua Lyu
Hydrol. Earth Syst. Sci., 28, 1477–1491, https://doi.org/10.5194/hess-28-1477-2024, https://doi.org/10.5194/hess-28-1477-2024, 2024
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We developed a new model to better understand how water moves in a lake basin. Our model improves upon previous methods by accurately capturing the complexity of water movement, both on the surface and subsurface. Our model, tested using data from China's Qinghai Lake, accurately replicates complex water movements and identifies contributing factors of the lake's water balance. The findings provide a robust tool for predicting hydrological processes, aiding water resource planning.
Ricardo Mantilla, Morgan Fonley, and Nicolás Velásquez
Hydrol. Earth Syst. Sci., 28, 1373–1382, https://doi.org/10.5194/hess-28-1373-2024, https://doi.org/10.5194/hess-28-1373-2024, 2024
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Hydrologists strive to “Be right for the right reasons” when modeling the hydrologic cycle; however, the datasets available to validate hydrological models are sparse, and in many cases, they comprise streamflow observations at the outlets of large catchments. In this work, we show that matching streamflow observations at the outlet of a large basin is not a reliable indicator of a correct description of the small-scale runoff processes.
Lillian M. McGill, E. Ashley Steel, and Aimee H. Fullerton
Hydrol. Earth Syst. Sci., 28, 1351–1371, https://doi.org/10.5194/hess-28-1351-2024, https://doi.org/10.5194/hess-28-1351-2024, 2024
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This study examines the relationship between air and river temperatures in Washington's Snoqualmie and Wenatchee basins. We used classification and regression approaches to show that the sensitivity of river temperature to air temperature is variable across basins and controlled largely by geology and snowmelt. Findings can be used to inform strategies for river basin restoration and conservation, such as identifying climate-insensitive areas of the basin that should be preserved and protected.
Stephanie R. Clark, Julien Lerat, Jean-Michel Perraud, and Peter Fitch
Hydrol. Earth Syst. Sci., 28, 1191–1213, https://doi.org/10.5194/hess-28-1191-2024, https://doi.org/10.5194/hess-28-1191-2024, 2024
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To determine if deep learning models are in general a viable alternative to traditional hydrologic modelling techniques in Australian catchments, a comparison of river–runoff predictions is made between traditional conceptual models and deep learning models in almost 500 catchments spread over the continent. It is found that the deep learning models match or outperform the traditional models in over two-thirds of the river catchments, indicating feasibility in a wide variety of conditions.
Dipti Tiwari, Mélanie Trudel, and Robert Leconte
Hydrol. Earth Syst. Sci., 28, 1127–1146, https://doi.org/10.5194/hess-28-1127-2024, https://doi.org/10.5194/hess-28-1127-2024, 2024
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Calibrating hydrological models with multi-objective functions enhances model robustness. By using spatially distributed snow information in the calibration, the model performance can be enhanced without compromising the outputs. In this study the HYDROTEL model was calibrated in seven different experiments, incorporating the SPAEF (spatial efficiency) metric alongside Nash–Sutcliffe efficiency (NSE) and root-mean-square error (RMSE), with the aim of identifying the optimal calibration strategy.
Luis Andres De la Fuente, Mohammad Reza Ehsani, Hoshin Vijai Gupta, and Laura Elizabeth Condon
Hydrol. Earth Syst. Sci., 28, 945–971, https://doi.org/10.5194/hess-28-945-2024, https://doi.org/10.5194/hess-28-945-2024, 2024
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Long short-term memory (LSTM) is a widely used machine-learning model in hydrology, but it is difficult to extract knowledge from it. We propose HydroLSTM, which represents processes like a hydrological reservoir. Models based on HydroLSTM perform similarly to LSTM while requiring fewer cell states. The learned parameters are informative about the dominant hydrology of a catchment. Our results show how parsimony and hydrological knowledge extraction can be achieved by using the new structure.
Louise Mimeau, Annika Künne, Flora Branger, Sven Kralisch, Alexandre Devers, and Jean-Philippe Vidal
Hydrol. Earth Syst. Sci., 28, 851–871, https://doi.org/10.5194/hess-28-851-2024, https://doi.org/10.5194/hess-28-851-2024, 2024
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Modelling flow intermittence is essential for predicting the future evolution of drying in river networks and better understanding the ecological and socio-economic impacts. However, modelling flow intermittence is challenging, and observed data on temporary rivers are scarce. This study presents a new modelling approach for predicting flow intermittence in river networks and shows that combining different sources of observed data reduces the model uncertainty.
Elena Macdonald, Bruno Merz, Björn Guse, Viet Dung Nguyen, Xiaoxiang Guan, and Sergiy Vorogushyn
Hydrol. Earth Syst. Sci., 28, 833–850, https://doi.org/10.5194/hess-28-833-2024, https://doi.org/10.5194/hess-28-833-2024, 2024
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In some rivers, the occurrence of extreme flood events is more likely than in other rivers – they have heavy-tailed distributions. We find that threshold processes in the runoff generation lead to such a relatively high occurrence probability of extremes. Further, we find that beyond a certain return period, i.e. for rare events, rainfall is often the dominant control compared to runoff generation. Our results can help to improve the estimation of the occurrence probability of extreme floods.
Alberto Bassi, Marvin Höge, Antonietta Mira, Fabrizio Fenicia, and Carlo Albert
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-47, https://doi.org/10.5194/hess-2024-47, 2024
Revised manuscript accepted for HESS
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The goal is to remove the impact of meteorological drivers in order to uncover the unique landscape fingerprints of a catchment from streamflow data. Our results reveal an optimal two-feature summary for most catchments, with a third feature needed for challenging cases, associated with aridity and intermittent flow. Baseflow index, aridity, and soil/vegetation attributes strongly correlate with learned features, indicating their importance for streamflow prediction.
Claire Kouba and Thomas Harter
Hydrol. Earth Syst. Sci., 28, 691–718, https://doi.org/10.5194/hess-28-691-2024, https://doi.org/10.5194/hess-28-691-2024, 2024
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In some watersheds, the severity of the dry season has a large impact on aquatic ecosystems. In this study, we design a way to predict, 5–6 months in advance, how severe the dry season will be in a rural watershed in northern California. This early warning can support seasonal adaptive management. To predict these two values, we assess data about snow, rain, groundwater, and river flows. We find that maximum snowpack and total wet season rainfall best predict dry season severity.
Yi Nan and Fuqiang Tian
Hydrol. Earth Syst. Sci., 28, 669–689, https://doi.org/10.5194/hess-28-669-2024, https://doi.org/10.5194/hess-28-669-2024, 2024
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This paper utilized a tracer-aided model validated by multiple datasets in a large mountainous basin on the Tibetan Plateau to analyze hydrological sensitivity to climate change. The spatial pattern of the local hydrological sensitivities and the influence factors were analyzed in particular. The main finding of this paper is that the local hydrological sensitivity in mountainous basins is determined by the relationship between the glacier area ratio and the mean annual precipitation.
Michael J. Vlah, Matthew R. V. Ross, Spencer Rhea, and Emily S. Bernhardt
Hydrol. Earth Syst. Sci., 28, 545–573, https://doi.org/10.5194/hess-28-545-2024, https://doi.org/10.5194/hess-28-545-2024, 2024
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Virtual stream gauging enables continuous streamflow estimation where a gauge might be difficult or impractical to install. We reconstructed flow at 27 gauges of the National Ecological Observatory Network (NEON), informing ~199 site-months of missing data in the official record and improving that accuracy of official estimates at 11 sites. This study shows that machine learning, but also routine regression methods, can be used to supplement existing gauge networks and reduce monitoring costs.
Sungwook Wi and Scott Steinschneider
Hydrol. Earth Syst. Sci., 28, 479–503, https://doi.org/10.5194/hess-28-479-2024, https://doi.org/10.5194/hess-28-479-2024, 2024
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We investigate whether deep learning (DL) models can produce physically plausible streamflow projections under climate change. We address this question by focusing on modeled responses to increases in temperature and potential evapotranspiration and by employing three DL and three process-based hydrological models. The results suggest that physical constraints regarding model architecture and input are necessary to promote the physical realism of DL hydrological projections under climate change.
Guillaume Evin, Matthieu Le Lay, Catherine Fouchier, David Penot, Francois Colleoni, Alexandre Mas, Pierre-André Garambois, and Olivier Laurantin
Hydrol. Earth Syst. Sci., 28, 261–281, https://doi.org/10.5194/hess-28-261-2024, https://doi.org/10.5194/hess-28-261-2024, 2024
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Hydrological modelling of mountainous catchments is challenging for many reasons, the main one being the temporal and spatial representation of precipitation forcings. This study presents an evaluation of the hydrological modelling of 55 small mountainous catchments of the northern French Alps, focusing on the influence of the type of precipitation reanalyses used as inputs. These evaluations emphasize the added value of radar measurements, in particular for the reproduction of flood events.
Lena Katharina Schmidt, Till Francke, Peter Martin Grosse, and Axel Bronstert
Hydrol. Earth Syst. Sci., 28, 139–161, https://doi.org/10.5194/hess-28-139-2024, https://doi.org/10.5194/hess-28-139-2024, 2024
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How suspended sediment export from glacierized high-alpine areas responds to future climate change is hardly assessable as many interacting processes are involved, and appropriate physical models are lacking. We present the first study, to our knowledge, exploring machine learning to project sediment export until 2100 in two high-alpine catchments. We find that uncertainties due to methodological limitations are small until 2070. Negative trends imply that peak sediment may have already passed.
Robert Hull, Elena Leonarduzzi, Luis De La Fuente, Hoang Viet Tran, Andrew Bennett, Peter Melchior, Reed M. Maxwell, and Laura E. Condon
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2023-264, https://doi.org/10.5194/hess-2023-264, 2024
Revised manuscript accepted for HESS
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Large-scale hydrologic a needed tool to explore complex watershed processes and how they may evolve under a changing climate. However, calibrating them can be difficult because they are costly to run and have many unknown parameters. We implement a state-of-the-art approach to model calibration with a set of experiments in the Upper Colorado River Basin.
Laia Estrada, Xavier Garcia, Joan Saló, Rafael Marcé, Antoni Munné, and Vicenç Acuña
EGUsphere, https://doi.org/10.5194/egusphere-2023-3007, https://doi.org/10.5194/egusphere-2023-3007, 2024
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Hydrological modelling is a powerful tool to support decision-making. We assessed spatio-temporal patterns and trends of streamflow for 2001–2022 with a hydrological modelling integrating stakeholder expert knowledge on management operations. The results provide insight into how climate change and anthropogenic pressures affect water resources availability in regions vulnerable to water scarcity, thus raising the need for sustainable management practices and integrated hydrological modelling.
Salam A. Abbas, Ryan T. Bailey, Jeremy T. White, Jeffrey G. Arnold, Michael J. White, Natalja Čerkasova, and Jungang Gao
Hydrol. Earth Syst. Sci., 28, 21–48, https://doi.org/10.5194/hess-28-21-2024, https://doi.org/10.5194/hess-28-21-2024, 2024
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Research highlights.
1. Implemented groundwater module (gwflow) into SWAT+ for four watersheds with different unique hydrologic features across the United States.
2. Presented methods for sensitivity analysis, uncertainty analysis and parameter estimation for coupled models.
3. Sensitivity analysis for streamflow and groundwater head conducted using Morris method.
4. Uncertainty analysis and parameter estimation performed using an iterative ensemble smoother within the PEST framework.
Shima Azimi, Christian Massari, Giuseppe Formetta, Silvia Barbetta, Alberto Tazioli, Davide Fronzi, Sara Modanesi, Angelica Tarpanelli, and Riccardo Rigon
Hydrol. Earth Syst. Sci., 27, 4485–4503, https://doi.org/10.5194/hess-27-4485-2023, https://doi.org/10.5194/hess-27-4485-2023, 2023
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We analyzed the water budget of nested karst catchments using simple methods and modeling. By utilizing the available data on precipitation and discharge, we were able to determine the response lag-time by adopting new techniques. Additionally, we modeled snow cover dynamics and evapotranspiration with the use of Earth observations, providing a concise overview of the water budget for the basin and its subbasins. We have made the data, models, and workflows accessible for further study.
Yuhang Zhang, Aizhong Ye, Bita Analui, Phu Nguyen, Soroosh Sorooshian, Kuolin Hsu, and Yuxuan Wang
Hydrol. Earth Syst. Sci., 27, 4529–4550, https://doi.org/10.5194/hess-27-4529-2023, https://doi.org/10.5194/hess-27-4529-2023, 2023
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Our study shows that while the quantile regression forest (QRF) and countable mixtures of asymmetric Laplacians long short-term memory (CMAL-LSTM) models demonstrate similar proficiency in multipoint probabilistic predictions, QRF excels in smaller watersheds and CMAL-LSTM in larger ones. CMAL-LSTM performs better in single-point deterministic predictions, whereas QRF model is more efficient overall.
Cited articles
Abbaspour, M. and Nazaridoust, A.: Determination of environmental water
requirements of Lake Urmia, Iran: an ecological approach, Int. J. Environ. Stud., 64, 161–169, https://doi.org/10.1080/00207230701238416, 2007.
AghaKouchak, A., Norouzi, H., Madani, K., Mirchi, A., Azarderakhsh, M., Nazemi, A., Nasrollahi, N., Farahmand, A., Mehran, A., and Hasanzadeh, E.:
Aral Sea syndrome desiccates Lake Urmia: Call for action, J. Great Lakes Res., 41, 307–311, https://doi.org/10.1016/j.jglr.2014.12.007, 2015.
Ahmadzadeh, H., Morid, S., Delavar, M., and Srinivasan, R.: Using the SWAT
model to assess the impacts of changing irrigation from surface to pressurized systems on water productivity and water saving in the Zarrineh
Rud catchment, Agr. Water Manage., 175, 15–28, https://doi.org/10.1016/j.agwat.2015.10.026, 2016.
Alborzi, A., Mirchi, A., Moftakhari, H., Mallakpour, I., Alian, S., Nazemi, A., Hassanzadeh, E., Mazdiyasni, O., Ashraf, S., Madani, K., Norouzi, H.,
Azarderakhsh, M., Mehran, A., Sadegh, M., Castelletti, A., and AghaKouchak, A.: Climate-informed environmental inflows to revive a drying lake facing
meteorological and anthropogenic droughts, Environ. Res. Lett., 13, 084010, https://doi.org/10.1088/1748-9326/aad246, 2018.
Alizade Govarchin Ghale, Y., Altunkaynak, A., and Unal, A.: Investigation
Anthropogenic Impacts and Climate Factors on Drying up of Urmia Lake using
Water Budget and Drought Analysis, Water Resour. Manage., 32, 325–337,
https://doi.org/10.1007/s11269-017-1812-5, 2018.
Alizade Govarchin Ghale, Y., Baykara, M., and Unal, A.: Investigating the
interaction between agricultural lands and Urmia Lake ecosystem using remote
sensing techniques and hydro-climatic data analysis, Agr. Water Manage., 221, 566–579, https://doi.org/10.1016/j.agwat.2019.05.028, 2019.
Alizadeh-Choobari, O., Ahmadi-Givi, F., Mirzaei, N., and Owlad, E.: Climate
change and anthropogenic impacts on the rapid shrinkage of Lake Urmia, Int. J. Climatol., 36, 4276–4286, https://doi.org/10.1002/joc.4630, 2016.
Amiri, V., Nakhaei, M., Lak, R., and Kholghi, M.: Geophysical, isotopic, and
hydrogeochemical tools to identify potential impacts on coastal groundwater
resources from Urmia hypersaline Lake, NW Iran, Environ. Sci. Poll. Res., 23, 16738–16760, https://doi.org/10.1007/s11356-016-6859-y, 2016.
Ashraf, B., AghaKouchak, A., Alizadeh, A., Baygi, M. M., Moftakhari, H. R.,
Mirchi, A., and Madani, K.: Quantifying anthropogenic stress on groundwater
resources, Scient. Rep., 7, 12910, https://doi.org/10.1038/s41598-017-12877-4, 2017.
Azarnivand, A. and Banihabib, M. E.: A Multi-level Strategic Group Decision
Making for Understanding and Analysis of Sustainable Watershed Planning in
Response to Environmental Perplexities, Group Decis. Negotiat., 26, 629–648, https://doi.org/10.1007/s10726-016-9484-8, 2017.
Azarnivand, A., Camporese, M., Alaghmand, S., and Daly, E.: Simulated response of an intermittent stream to rainfall frequency patterns, Hydrol. Process., 34, 615–632, https://doi.org/10.1002/hyp.13610, 2020.
Banihabib, M. E., Azarnivand, A., and Peralta, R. C.: A new framework for
strategic planning to stabilize a shrinking lake, Lake Reserv. Manage., 31, 31–43, https://doi.org/10.1080/10402381.2014.987409, 2015.
Bavil, S. S., Zeinalzadeh, K., and Hessari, B.: The changes in the frequency
of daily precipitation in Urmia Lake basin, Iran, Theor. Appl. Climatol., 133, 205–214, https://doi.org/10.1007/s00704-017-2177-7, 2018.
Beven, K. and Freer, J.: Equifinality, data assimilation, and uncertainty
estimation in mechanistic modelling of complex environmental systems using the GLUE methodology, J. Hydrol., 249, 11–29, https://doi.org/10.1016/S0022-1694(01)00421-8, 2001.
Boroughani, M., Hashemi, H., Hosseini, S. H., Pourhashemi, S., and Berndtsson, R.: Desiccating Lake Urmia: A New Dust Source of Regional
Importance, IEEE Geosci. Remote Sens. Lett., https://doi.org/10.1109/LGRS.2019.2949132, in press, 2019.
Chaudhari, S., Felfelani, F., Shin, S., and Pokhrel, Y.: Climate and anthropogenic contributions to the desiccation of the second largest saline
lake in the twentieth century, J. Hydrol., 560, 342–353,
https://doi.org/10.1016/j.jhydrol.2018.03.034, 2018.
Danesh-Yazdi, M. and Ataie-Ashtiani, B.: Lake Urmia Crisis and Restoration Plan: Planning without Appropriate Data and Model Is Gambling, J. Hydrol., 576, 639–651, https://doi.org/10.1016/j.jhydrol.2019.06.068, 2019.
Deb, K., Pratap, A., Agarwal, S., and Meyarivan, T. A. M. T.: A fast and
elitist multiobjective genetic algorithm: NSGA-II, IEEE T. Evol. Comput., 6, 182–197, https://doi.org/10.1109/4235.996017, 2002.
Delju, A. H., Ceylan, A., Piguet, E., and Rebetez, M.: Observed climate
variability and change in Urmia Lake Basin, Iran, Theor. Appl. Climatol., 111, 285–296, https://doi.org/10.1007/s00704-012-0651-9, 2012.
Döll, P., Kaspar, F., and Lehner, B.: A global hydrological model for
deriving water availability indicators: model tuning and validation, J. Hydrol., 270, 105–134, https://doi.org/10.1016/S0022-1694(02)00283-4, 2003.
Döll, P., Hoffmann-Dobrev, H., Portmann, F. T., Siebert, S., Eicker, A.,
Rodell, M., Strassberg, G., and Scanlon, B. R.: Impact of water withdrawals
from groundwater and surface water on continental water storage variations,
J. Geodynam., 59, 143–156, https://doi.org/10.1016/j.jog.2011.05.001, 2012.
Döll, P., Douville, H., Güntner, A., Müller Schmied, H., and Wada, Y.: Modelling freshwater resources at the global scale: Challenges and
prospects, Surv. Geophys., 37, 195–221, https://doi.org/10.1007/s10712-015-9343-1, 2016.
Eimanifar, A. and Mohebbi, F.: Urmia Lake (northwest Iran): a brief review,
Saline Syst., 3, 5, https://doi.org/10.1186/1746-1448-3-5, 2007.
Farajzadeh, J., Fakheri Fard, A., and Lotfi, S.: Modeling of monthly rainfall and runoff of Urmia lake basin using “feed-forward neural network” and “time series analysis” model, Water Resour. Indust., 7–8, 38–48, https://doi.org/10.1016/j.wri.2014.10.003, 2014.
Farokhnia, A., Morid, S., Abbaspour, K., and Delavar, M.: Development of
SWAT-LU model for simulation of Urmia lake water level decrease and assessment of the proposed actions for its restoration; (role of anthropogenic and climatic factors on hydrological change of the basin and
lake), Iran. J. Irrig. Drain., 12, 1041–1058, 2018.
Fathian, F., Morid, S., and Kahya, E.: Identification of trends in hydrological and climatic variables in Urmia Lake basin, Iran, Theor. Appl. Climatol., 119, 443–464, https://doi.org/10.1007/s00704-014-1120-4, 2014.
Fazel, N., Torabi Haghighi, A., and Kløve, B.: Analysis of land use and
climate change impacts by comparing river flow records for headwaters and
lowland reaches, Global Planet. Change, 158, 47–56,
https://doi.org/10.1016/j.gloplacha.2017.09.014, 2017.
Forootan, E., Rietbroek, R., Kusche, J., Sharifi, M. A., Awange, J. L., Schmidt, M., Omondi, P., and Famiglietti, J.: Separation of large scale water storage patterns over Iran using GRACE, altimetry and hydrological data, Remote Sens. Environ., 140, 580–595, https://doi.org/10.1016/j.rse.2013.09.025, 2014.
Hasemi, M.: A socio-technical assessment framework for integrated water
resources management (IWRM) in Lake Urmia Basin, Iran, PhD thesis, School of Civil Engineering and Geosciences, Newcastle University, Newcastle, UK, 342 pp., 2011.
Hassanzadeh, E., Zarghami, M., and Hassanzadeh, Y.: Determining the main
factors in declining the urmia lake level by using system dynamics modeling,
Water Resour. Manage., 26, 129–145, https://doi.org/10.1007/s11269-011-9909-8, 2012.
Hesami, A. and Amini, A.: Changes in irrigated land and agricultural water use in the Lake Urmia basin, Lake Reserv. Manage., 32, 288–296,
https://doi.org/10.1080/10402381.2016.1211202, 2016.
Hosseini-Moghari, S. M., Araghinejad, S., and Ebrahimi, K.: Monthly
Precipitation Assessment: a misleading tool for understanding the effects of
climate change, in: 8th Global FRIEND-Water Conference, 6–9 November 2018, Beijing, China, available at: http://m.iahr.org.cn/Resource/abstractinfo/3955 (last access: 12 February 2020), 2018.
Jalili, S., Kirchner, I., Livingstone, D. M., and Morid, S.: The influence of large-scale atmospheric circulation weather types on variations in the water level of Lake Urmia, Iran, Int. J. Climatol., 32, 1990–1996, https://doi.org/10.1002/joc.2422, 2012.
Kamali, M. and Youneszadeh Jalili, S.: Investigation of landuse changes in
Lake Urmia Basin using remotely sensed images, Report of Urmia Lake Restoration Program (ULRP), ULRP, Tehran, Iran, 2015.
Karimi, N., Bagheri, M. H., Hooshyaripor, F., Farokhnia, A., and Sheshangosht, S.: Deriving and evaluating bathymetry maps and stage curves for shallow lakes using remote sensing data, Water Resour. Manage., 30, 5003–5020, https://doi.org/10.1007/s11269-016-1465-9, 2016.
Kelleher, C., McGlynn, B., and Wagener, T.: Characterizing and reducing
equifinality by constraining a distributed catchment model with regional
signatures, local observations, and process understanding, Hydrol. Earth Syst. Sci., 21, 3325–3352, https://doi.org/10.5194/hess-21-3325-2017, 2017.
Khatami, S., Peel, M. C., Peterson, T. J., and Western, A. W.: Equifinality
and Flux Mapping: A new approach to model evaluation and process representation under uncertainty, Water Resour. Res., 55, 8922–8941, https://doi.org/10.1029/2018WR023750, 2019.
Khazaei, B., Khatami, S., Alemohammad, S. H., Rashidi, L., Wu, C., Madani, K., and Aghakouchak, A.: Climatic or regionally induced by humans? Tracing
hydro-climatic and land-use changes to better understand the Lake Urmia tragedy, J. Hydrol., 569, 203–217, https://doi.org/10.1016/j.jhydrol.2018.12.004, 2019.
Landerer, F. W. and Swenson, S. C.: Accuracy of scaled GRACE terrestrial
water storage estimates, Water Resour. Res., 48, W04531, https://doi.org/10.1029/2011WR011453, 2012.
Madani, K., AghaKouchak, A., and Mirchi, A.: Iran's socio-economic drought:
challenges of a water-bankrupt nation, Iran. Stud., 49, 997–1016,
https://doi.org/10.1080/00210862.2016.1259286, 2016.
Mirjalili, S.: Genetic algorithm, in: Evolutionary Algorithms and Neural
Networks, Springer, Berlin, Heidelberg, Germany, 43–55, https://doi.org/10.1007/978-3-319-93025-1, 2019.
Müller Schmied, H., Eisner, S., Franz, D., Wattenbach, M., Portmann, F.
T., Flörke, M., and Döll, P.: Sensitivity of simulated global-scale
freshwater fluxes and storages to input data, hydrological model structure,
human water use and calibration, Hydrol. Earth Syst. Sci., 18, 3511–3538, https://doi.org/10.5194/hess-18-3511-2014, 2014.
Pengra, B.: The drying of Iran's Lake Urmia and its environmental consequences, UNEP-GRID, Sioux Falls, UNEP Global Environmental Alert
Service (GEAS), available at:
https://na.unep.net/geas/getUNEPPageWithArticleIDScript.php?article_id=79 (last access: 25 October 2017), 2012.
Saemian, P., Faridzad, M., Youneszadeh, S., Emadzadeh, M., Jalilvand, E.,
Farahmand, H., and Taheri, M.: Estimation of Evapotranspiration, water saving and agricultural water consumption at Urmia Lake Basin, 2010, Report of Urmia Lake Restoration Program (ULRP), ULRP, Tehran, Iran, 2015.
Save, H., Bettadpur, S., and Tapley, B. D.: High‐resolution CSR GRACE RL05 mascons, J. Geophys. Res.-Solid, 121, 7547–7569, https://doi.org/10.1002/2016JB013007, 2016.
Scrucca, L.: GA: a package for genetic algorithms in R, J. Stat. Softw., 53, 1–37, https://doi.org/10.18637/jss.v053.i04, 2013.
Shadkam, S., Ludwig, F., van Oel, P., Kirmit, Ç., and Kabat, P.: Impacts
of climate change and water resources development on the declining inflow into Iran's Urmia Lake, J. Great Lakes Res., 42, 942–952,
https://doi.org/10.1016/j.jglr.2016.07.033, 2016.
Siebert, S., Burke, J., Faures, J.-M., Frenken, K., Hoogeveen, J., Döll,
P., and Portmann, F. T.: Groundwater use for irrigation – a global inventory, Hydrol. Earth Syst. Sci., 14, 1863–1880,
https://doi.org/10.5194/hess-14-1863-2010, 2010.
Sima, S., and Tajrishy, M.: Using satellite data to extract volume–area–elevation relationships for Urmia Lake, Iran, J. Great Lakes Res., 39, 90–99, https://doi.org/10.1016/j.jglr.2012.12.013, 2013.
Tabari, H. and Talaee, P. H.: Analysis of trends in temperature data in arid and semi-arid regions of Iran, Global Planet. Change, 79, 1–10, https://doi.org/10.1016/j.gloplacha.2011.07.008, 2011a.
Tabari, H. and Talaee, P. H.: Temporal variability of precipitation over
Iran: 1966–2005, J. Hydrol., 396, 313–320, https://doi.org/10.1016/j.jhydrol.2010.11.034, 2011b.
Tourian, M. J., Elmi, O., Chen, Q., Devaraju, B., Roohi, S., and Sneeuw, N.:
A spaceborne multisensor approach to monitor the desiccation of Lake Urmia in Iran, Remote Sens. Environ., 156, 349–360, https://doi.org/10.1016/j.rse.2014.10.006, 2015.
Tsou, C. S.: nsga2R: Elitist Non-dominated Sorting Genetic Algorithm based
on R, R package version 1.0, available at: https://CRAN.R-project.org/package=nsga2R (last access: 19 August 2019), 2013.
ULRP: The trend of Lake Urmia changes, available at:
http://ulrp.sharif.ir/fa (last access: 17 July 2018), 2015a.
ULRP: Urmia Lake – Causes of shrinkage and potential threats, available at:
http://ulrp.sharif.ir/fa (last access: 17 July 2018), 2015b.
ULRP: Evaluation and deduction of Urmia Lake Restoration Program need at
least 3 years, available at: http://ulrp.sharif.ir/en/news/news-1435-evaluation-and-deduction-urmia-lake-restoration (last access: 25 October 2017), 2015c.
Vaheddoost, B. and Aksoy, H.: Interaction of groundwater with Lake Urmia in
Iran, Hydrol. Process., 32, 3283–3295, https://doi.org/10.1002/hyp.13263, 2018.
Werth, S. and Güntner, A.: Calibration analysis for water storage variability of the global hydrological model WGHM, Hydrol. Earth Syst. Sci., 14, 59–78, https://doi.org/10.5194/hess-14-59-2010, 2010.
Zeinoddini, M., Tofighi, M. A., and Vafaee, F.: Evaluation of dike-type causeway impacts on the flow and salinity regimes in Urmia Lake, Iran, J. Great Lakes Res., 35, 13–22, https://doi.org/10.1016/j.jglr.2008.08.001, 2009.
Short summary
This paper uses a multi-objective approach for calibrating the WGHM model to determine the role of human water use and climate variations in the recent loss of water storage in Lake Urmia basin, Iran. We found that even without human water use Lake Urmia would not have recovered from the significant loss of lake water volume caused by the drought year 2008.
This paper uses a multi-objective approach for calibrating the WGHM model to determine the role...