Articles | Volume 26, issue 1
https://doi.org/10.5194/hess-26-71-2022
© Author(s) 2022. 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-26-71-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| Highlight paper
| 
06 Jan 2022
Research article | Highlight paper |
| 06 Jan 2022
| 06 Jan 2022
Improved representation of agricultural land use and crop management for large-scale hydrological impact simulation in Africa using SWAT+
Hydrology and Hydraulic Engineering Department, Vrije Universiteit
Brussel (VUB), 1050 Brussels, Belgium
Celray James Chawanda
Hydrology and Hydraulic Engineering Department, Vrije Universiteit
Brussel (VUB), 1050 Brussels, Belgium
Jonas Jägermeyr
NASA Goddard Institute for Space Studies, New York, NY 10025, USA
Center for Climate Systems Research, Columbia University, New York,
NY 10025, USA
Climate Resilience, Potsdam Institute for Climate Impact Research
(PIK), Member of the Leibniz Association, 14412, Potsdam, Germany
Ann van Griensven
Hydrology and Hydraulic Engineering Department, Vrije Universiteit
Brussel (VUB), 1050 Brussels, Belgium
Water Science and Engineering Department, IHE Delft Institute for
Water Education, 2611 AX Delft, The Netherlands
Related authors
Celray James Chawanda, Albert Nkwasa, Wim Thiery, and Ann van Griensven
Hydrol. Earth Syst. Sci., 28, 117–138, https://doi.org/10.5194/hess-28-117-2024, https://doi.org/10.5194/hess-28-117-2024, 2024
Short summary
Short summary
Africa's water resources are being negatively impacted by climate change and land-use change. The SWAT+ hydrological model was used to simulate the hydrological cycle in Africa, and results show likely decreases in river flows in the Zambezi and Congo rivers and highest flows in the Niger River basins due to climate change. Land cover change had the biggest impact in the Congo River basin, emphasizing the importance of including land-use change in studies.
Joel Z. Harms, Julien J. Malard-Adam, Jan F. Adamowski, Ashutosh Sharma, and Albert Nkwasa
Hydrol. Earth Syst. Sci., 27, 1683–1693, https://doi.org/10.5194/hess-27-1683-2023, https://doi.org/10.5194/hess-27-1683-2023, 2023
Short summary
Short summary
To facilitate the meaningful participation of stakeholders in water management, model choice is crucial. We show how system dynamics models (SDMs), which are very visual and stakeholder-friendly, can be automatically combined with physically based hydrological models that may be more appropriate for modelling the water processes of a human–water system. This allows building participatory SDMs with stakeholders and delegating hydrological components to an external hydrological model.
Anna Msigwa, Celray James Chawanda, Hans C. Komakech, Albert Nkwasa, and Ann van Griensven
Hydrol. Earth Syst. Sci., 26, 4447–4468, https://doi.org/10.5194/hess-26-4447-2022, https://doi.org/10.5194/hess-26-4447-2022, 2022
Short summary
Short summary
Studies using agro-hydrological models, like the Soil and Water Assessment Tool (SWAT), to map evapotranspiration (ET) do not account for cropping seasons. A comparison between the default SWAT+ set-up (with static land use representation) and a dynamic SWAT+ model set-up (with seasonal land use representation) is made by spatial mapping of the ET. The results show that ET with seasonal representation is closer to remote sensing estimates, giving better performance than ET with static land use.
Jevgenijs Steinbuks, Yongyang Cai, Jonas Jaegermeyr, and Thomas W. Hertel
Geosci. Model Dev., 17, 4791–4819, https://doi.org/10.5194/gmd-17-4791-2024, https://doi.org/10.5194/gmd-17-4791-2024, 2024
Short summary
Short summary
This paper applies a cutting-edge numerical method, SCEQ, to show how uncertain climate change and technological progress affect the future utilization of the world's scarce land resources. The paper's key insight is to illustrate how much global cropland will expand when future crop yields are unknown. The study finds the range of outcomes for land use change to be smaller when using this novel method compared to existing deterministic models.
Celray James Chawanda, Albert Nkwasa, Wim Thiery, and Ann van Griensven
Hydrol. Earth Syst. Sci., 28, 117–138, https://doi.org/10.5194/hess-28-117-2024, https://doi.org/10.5194/hess-28-117-2024, 2024
Short summary
Short summary
Africa's water resources are being negatively impacted by climate change and land-use change. The SWAT+ hydrological model was used to simulate the hydrological cycle in Africa, and results show likely decreases in river flows in the Zambezi and Congo rivers and highest flows in the Niger River basins due to climate change. Land cover change had the biggest impact in the Congo River basin, emphasizing the importance of including land-use change in studies.
Katja Frieler, Jan Volkholz, Stefan Lange, Jacob Schewe, Matthias Mengel, María del Rocío Rivas López, Christian Otto, Christopher P. O. Reyer, Dirk Nikolaus Karger, Johanna T. Malle, Simon Treu, Christoph Menz, Julia L. Blanchard, Cheryl S. Harrison, Colleen M. Petrik, Tyler D. Eddy, Kelly Ortega-Cisneros, Camilla Novaglio, Yannick Rousseau, Reg A. Watson, Charles Stock, Xiao Liu, Ryan Heneghan, Derek Tittensor, Olivier Maury, Matthias Büchner, Thomas Vogt, Tingting Wang, Fubao Sun, Inga J. Sauer, Johannes Koch, Inne Vanderkelen, Jonas Jägermeyr, Christoph Müller, Sam Rabin, Jochen Klar, Iliusi D. Vega del Valle, Gitta Lasslop, Sarah Chadburn, Eleanor Burke, Angela Gallego-Sala, Noah Smith, Jinfeng Chang, Stijn Hantson, Chantelle Burton, Anne Gädeke, Fang Li, Simon N. Gosling, Hannes Müller Schmied, Fred Hattermann, Jida Wang, Fangfang Yao, Thomas Hickler, Rafael Marcé, Don Pierson, Wim Thiery, Daniel Mercado-Bettín, Robert Ladwig, Ana Isabel Ayala-Zamora, Matthew Forrest, and Michel Bechtold
Geosci. Model Dev., 17, 1–51, https://doi.org/10.5194/gmd-17-1-2024, https://doi.org/10.5194/gmd-17-1-2024, 2024
Short summary
Short summary
Our paper provides an overview of all observational climate-related and socioeconomic forcing data used as input for the impact model evaluation and impact attribution experiments within the third round of the Inter-Sectoral Impact Model Intercomparison Project. The experiments are designed to test our understanding of observed changes in natural and human systems and to quantify to what degree these changes have already been induced by climate change.
Weihang Liu, Tao Ye, Christoph Müller, Jonas Jägermeyr, James A. Franke, Haynes Stephens, and Shuo Chen
Geosci. Model Dev., 16, 7203–7221, https://doi.org/10.5194/gmd-16-7203-2023, https://doi.org/10.5194/gmd-16-7203-2023, 2023
Short summary
Short summary
We develop a machine-learning-based crop model emulator with the inputs and outputs of multiple global gridded crop model ensemble simulations to capture the year-to-year variation of crop yield under future climate change. The emulator can reproduce the year-to-year variation of simulated yield given by the crop models under CO2, temperature, water, and nitrogen perturbations. Developing this emulator can provide a tool to project future climate change impact in a simple way.
Joel Z. Harms, Julien J. Malard-Adam, Jan F. Adamowski, Ashutosh Sharma, and Albert Nkwasa
Hydrol. Earth Syst. Sci., 27, 1683–1693, https://doi.org/10.5194/hess-27-1683-2023, https://doi.org/10.5194/hess-27-1683-2023, 2023
Short summary
Short summary
To facilitate the meaningful participation of stakeholders in water management, model choice is crucial. We show how system dynamics models (SDMs), which are very visual and stakeholder-friendly, can be automatically combined with physically based hydrological models that may be more appropriate for modelling the water processes of a human–water system. This allows building participatory SDMs with stakeholders and delegating hydrological components to an external hydrological model.
Anna Msigwa, Celray James Chawanda, Hans C. Komakech, Albert Nkwasa, and Ann van Griensven
Hydrol. Earth Syst. Sci., 26, 4447–4468, https://doi.org/10.5194/hess-26-4447-2022, https://doi.org/10.5194/hess-26-4447-2022, 2022
Short summary
Short summary
Studies using agro-hydrological models, like the Soil and Water Assessment Tool (SWAT), to map evapotranspiration (ET) do not account for cropping seasons. A comparison between the default SWAT+ set-up (with static land use representation) and a dynamic SWAT+ model set-up (with seasonal land use representation) is made by spatial mapping of the ET. The results show that ET with seasonal representation is closer to remote sensing estimates, giving better performance than ET with static land use.
Inne Vanderkelen, Shervan Gharari, Naoki Mizukami, Martyn P. Clark, David M. Lawrence, Sean Swenson, Yadu Pokhrel, Naota Hanasaki, Ann van Griensven, and Wim Thiery
Geosci. Model Dev., 15, 4163–4192, https://doi.org/10.5194/gmd-15-4163-2022, https://doi.org/10.5194/gmd-15-4163-2022, 2022
Short summary
Short summary
Human-controlled reservoirs have a large influence on the global water cycle. However, dam operations are rarely represented in Earth system models. We implement and evaluate a widely used reservoir parametrization in a global river-routing model. Using observations of individual reservoirs, the reservoir scheme outperforms the natural lake scheme. However, both schemes show a similar performance due to biases in runoff timing and magnitude when using simulated runoff.
Estifanos Addisu Yimer, Ryan T. Bailey, Lise Leda Piepers, Jiri Nossent, and Ann van Griensven
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2022-169, https://doi.org/10.5194/hess-2022-169, 2022
Manuscript not accepted for further review
Short summary
Short summary
A recently developed groundwater module (gwflow) coupled with the soil water assessment tool (SWAT+) is used to simulate the streamflow of the Dijle catchment, Belgium. The standalone model (SWAT+) resulted in unsatisfactory streamflow simulations while SWAT+gwflow produced streamflow that considerably mimics the measured river discharge. Furthermore, modifications to the gwflow module are made to account for the vital hydrological process (groundwater-soil profile interactions).
Alemu Yenehun, Mekete Dessie, Fenta Nigate, Ashebir Sewale Belay, Mulugeta Azeze, Marc Van Camp, Derbew Fenetie Taye, Desale Kidane, Enyew Adgo, Jan Nyssen, Ann van Griensven, and Kristine Walraevens
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2021-527, https://doi.org/10.5194/hess-2021-527, 2021
Manuscript not accepted for further review
Short summary
Short summary
Population growth, industrial expansion, and climate change are causing stress on the limited freshwater resources of the globe. Groundwater is one of the important freshwater resources. Hence, managing these limited resources is a key task for the sector experts. To do so, understanding recharge processes and its quantification is vital. In this study, three different methods using measured data are applied to estimate recharge and identify the controlling factors.
Imeshi Weerasinghe, Wim Bastiaanssen, Marloes Mul, Li Jia, and Ann van Griensven
Hydrol. Earth Syst. Sci., 24, 1565–1586, https://doi.org/10.5194/hess-24-1565-2020, https://doi.org/10.5194/hess-24-1565-2020, 2020
Short summary
Short summary
Water resource allocation to various sectors requires an understanding of the hydrological cycle, where evapotranspiration (ET) is a key component. Satellite-derived products estimate ET but are hard to evaluate at large scales. This work presents an alternate evaluation methodology to point-scale observations in Africa. The paper enables users to select an ET product based on their performance regarding selected criteria using a ranking system. The highest ranked products are WaPOR and CMRSET.
Sibyll Schaphoff, Werner von Bloh, Anja Rammig, Kirsten Thonicke, Hester Biemans, Matthias Forkel, Dieter Gerten, Jens Heinke, Jonas Jägermeyr, Jürgen Knauer, Fanny Langerwisch, Wolfgang Lucht, Christoph Müller, Susanne Rolinski, and Katharina Waha
Geosci. Model Dev., 11, 1343–1375, https://doi.org/10.5194/gmd-11-1343-2018, https://doi.org/10.5194/gmd-11-1343-2018, 2018
Short summary
Short summary
Here we provide a comprehensive model description of a global terrestrial biosphere model, named LPJmL4, incorporating the carbon and water cycle and the quantification of agricultural production. The model allows for the consistent and joint quantification of climate and land use change impacts on the biosphere. The model represents the key ecosystem functions, but also the influence of humans on the biosphere. It comes with an evaluation paper to demonstrate the credibility of LPJmL4.
Tadesse Alemayehu, Ann van Griensven, Befekadu Taddesse Woldegiorgis, and Willy Bauwens
Hydrol. Earth Syst. Sci., 21, 4449–4467, https://doi.org/10.5194/hess-21-4449-2017, https://doi.org/10.5194/hess-21-4449-2017, 2017
Short summary
Short summary
The goal of this paper is to improve the vegetation growth modelling in SWAT for tropical ecosystems. Therefore, we propose a straightforward but robust soil moisture index (SMI) – a quotient of rainfall (P) and reference evapotranspiration (ETr) – to dynamically initiate a new growth cycle within a predefined period. Our results for the Mara Basin (Kenya/Tanzania) show that the simulated LAI corresponds well with the MODIS LAI for for evergreen forest, savanna grassland and shrubland.
Lan T. Ha, Wim G. M. Bastiaanssen, Ann van Griensven, Albert I. J. M. van Dijk, and Gabriel B. Senay
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2017-251, https://doi.org/10.5194/hess-2017-251, 2017
Preprint withdrawn
Short summary
Short summary
The paper shows a new approach in calibrating hydrological model using remote sensing data from open access sources. The innovation is that the parameters of the soil-vegetation processes were optimized that will make SWAT a useful tool for optimizing water conservation, agricultural outputs, and ecosystem services such as reduced soil erosion, better water quality standards, carbon sequestration, micro-climate cooling and appraising scenarios of green growth.
J. Jägermeyr, D. Gerten, J. Heinke, S. Schaphoff, M. Kummu, and W. Lucht
Hydrol. Earth Syst. Sci., 19, 3073–3091, https://doi.org/10.5194/hess-19-3073-2015, https://doi.org/10.5194/hess-19-3073-2015, 2015
Short summary
Short summary
We present a process-based simulation of global irrigation systems for the world’s major crop types. This study advances the global quantification of irrigation systems while providing a framework for assessing potential future transitions in these systems, a prerequisite for refined simulation of crop yields under climate change. We reveal for many river basins the potential for sizeable water savings and related increases in water productivity through irrigation improvements.
M. Arias-Hidalgo, B. Bhattacharya, A. E. Mynett, and A. van Griensven
Hydrol. Earth Syst. Sci., 17, 2905–2915, https://doi.org/10.5194/hess-17-2905-2013, https://doi.org/10.5194/hess-17-2905-2013, 2013
Related subject area
Subject: Catchment hydrology | Techniques and Approaches: Modelling approaches
On the use of streamflow transformations for hydrological model calibration
Simulation-based inference for parameter estimation of complex watershed simulators
Multi-scale soil moisture data and process-based modeling reveal the importance of lateral groundwater flow in a subarctic catchment
Catchment response to climatic variability: implications for root zone storage and streamflow predictions
Hybrid hydrological modeling for large alpine basins: a semi-distributed approach
Karst aquifer discharge response to rainfall interpreted as anomalous transport
HESS Opinions: Never train a Long Short-Term Memory (LSTM) network on a single basin
Large-sample hydrology – a few camels or a whole caravan?
Comment on “Are soils overrated in hydrology?” by Gao et al. (2023)
Multi-decadal fluctuations in root zone storage capacity through vegetation adaptation to hydro-climatic variability have minor effects on the hydrological response in the Neckar River basin, Germany
Projected future changes in the cryosphere and hydrology of a mountainous catchment in the upper Heihe River, China
On the importance of plant phenology in the evaporative process of a semi-arid woodland: could it be why satellite-based evaporation estimates in the miombo differ?
Regionalization of GR4J model parameters for river flow prediction in Paraná, Brazil
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
Lack of robustness of hydrological models: A large-sample diagnosis and an attempt to identify the hydrological and climatic drivers
The Significance of the Leaf-Area-Index on the Evapotranspiration Estimation in SWAT-T for Characteristic Land Cover Types of Western Africa
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
A large-sample modelling approach towards integrating streamflow and evaporation data for the Spanish catchments
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
Guillaume Thirel, Léonard Santos, Olivier Delaigue, and Charles Perrin
Hydrol. Earth Syst. Sci., 28, 4837–4860, https://doi.org/10.5194/hess-28-4837-2024, https://doi.org/10.5194/hess-28-4837-2024, 2024
Short summary
Short summary
We discuss how mathematical transformations impact calibrated hydrological model simulations. We assess how 11 transformations behave over the complete range of streamflows. Extreme transformations lead to models that are specialized for extreme streamflows but show poor performance outside the range of targeted streamflows and are less robust. We show that no a priori assumption about transformations can be taken as warranted.
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., 28, 4685–4713, https://doi.org/10.5194/hess-28-4685-2024, https://doi.org/10.5194/hess-28-4685-2024, 2024
Short summary
Short summary
Large-scale hydrologic simulators are a needed tool to explore complex watershed processes and how they may evolve with 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 using neural networks with a set of experiments based on streamflow in the upper Colorado River basin.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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.
Léonard Santos, Vazken Andréassian, Torben O. Sonnenborg, Göran Lindström, Alban de Lavenne, Charles Perrin, Lila Collet, and Guillaume Thirel
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-80, https://doi.org/10.5194/hess-2024-80, 2024
Revised manuscript accepted for HESS
Short summary
Short summary
This work aims at investigating how hydrological models can be transferred to a period in which climatic conditions are different to the ones of the period in which it was set up. The RAT method, built to detect dependencies between model error and climatic drivers, was applied to 3 different hydrological models on 352 catchments in Denmark, France and Sweden. Potential issues are detected for a significant number of catchments for the 3 models even though these catchments differ for each model.
Fabian Merk, Timo Schaffhauser, Faizan Anwar, Ye Tuo, Jean-Martial Cohard, and Markus Disse
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-131, https://doi.org/10.5194/hess-2024-131, 2024
Revised manuscript accepted for HESS
Short summary
Short summary
ET is computed from vegetation (plant transpiration) and soil (soil evaporation). In Western Africa, plant transpiration correlates with vegetation growth. Vegetation is often represented with the leaf-area-index (LAI). In this study, we evaluate the importance of LAI for the ET calculation. We take a close look at the LAI-ET interaction and show the relevance to consider both, LAI and ET. Our work contributes to the understanding of the processes of the terrestrial water cycle.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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.
Patricio Yeste, Matilde García-Valdecasas Ojeda, Sonia R. Gámiz-Fortis, Yolanda Castro-Díez, Axel Bronstert, and María Jesús Esteban-Parra
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-57, https://doi.org/10.5194/hess-2024-57, 2024
Revised manuscript accepted for HESS
Short summary
Short summary
Integrating streamflow and evaporation data can help improve the physical realism of hydrologic models. In this work we investigate the capabilities of the Variable Infiltration Capacity (VIC) to reproduce both hydrologic variables for 189 headwater located in Spain. Results from sensitivity analysis indicate that adding two vegetation is enough to improve the representation of evaporation, and the performance of VIC exceeded that of the largest modelling effort currently available in Spain.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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.
Cited articles
Abaci, O. and Papanicolaou, A. T.: Long-term effects of management practices
on water-driven soil erosion in an intense agricultural sub-watershed:
Monitoring and modelling, Hydrol. Process. Int. J., 23, 2818–2837, 2009.
Alemayehu, T., van Griensven, A., and Bauwens, W.: Evaluating CFSR and WATCH
Data as Input to SWAT for the Estimation of the Potential Evapotranspiration
in a Data-Scarce Eastern-African Catchment, J. Hydrol. Eng., 21, 05015028,
https://doi.org/10.1061/(ASCE)HE.1943-5584.0001305, 2016.
Alemayehu, T., van Griensven, A., Woldegiorgis, B. T., and Bauwens, W.: An improved SWAT vegetation growth module and its evaluation for four tropical ecosystems, Hydrol. Earth Syst. Sci., 21, 4449–4467, https://doi.org/10.5194/hess-21-4449-2017, 2017.
Arnold, J., Bieger, K., White, M., Srinivasan, R., Dunbar, J., and Allen,
P.: Use of decision tables to simulate management in SWAT+, Water, 10,
713, https://doi.org/10.3390/w10060713, 2018.
Arnold, J. G., Srinivasan, R., Muttiah, R. S., and Williams, J. R.: Large
Area Hydrologic Modeling and Assessment Part I: Model Development1, JAWRA J.
Am. Water Resour. Assoc., 34, 73–89,
https://doi.org/10.1111/j.1752-1688.1998.tb05961.x, 1998.
Arnold, J. G., Moriasi, D. N., Gassman, P. W., Abbaspour, K. C., White, M.
J., Srinivasan, R., Santhi, C., Harmel, R. D., Van Griensven, A., and Van
Liew, M. W.: SWAT: Model use, calibration, and validation, Trans. ASABE, 55,
1491–1508, 2012.
Arnold, J. G., Kiniry, J. R., Srinivasan, R., Williams, J. R., Haney, E. B.,
and Neitsch, S. L.: SWAT 2012 input/output documentation, Texas Water
Resources Institute, 2013.
Asfaw, A., Simane, B., Hassen, A., and Bantider, A.: Variability and time
series trend analysis of rainfall and temperature in northcentral Ethiopia:
A case study in Woleka sub-basin, Weather Clim. Extrem., 19, 29–41,
https://doi.org/10.1016/j.wace.2017.12.002, 2018.
Bégué, A., Arvor, D., Bellon, B., Betbeder, J., De Abelleyra, D., P.
D. Ferraz, R., Lebourgeois, V., Lelong, C., Simões, M., and R.
Verón, S.: Remote Sensing and Cropping Practices: A Review, Remote
Sens., 10, 99, https://doi.org/10.3390/rs10010099, 2018.
Betrie, G. D., Mohamed, Y. A., van Griensven, A., and Srinivasan, R.: Sediment management modelling in the Blue Nile Basin using SWAT model, Hydrol. Earth Syst. Sci., 15, 807–818, https://doi.org/10.5194/hess-15-807-2011, 2011.
Bieger, K., Arnold, J. G., Rathjens, H., White, M. J., Bosch, D. D., Allen,
P. M., Volk, M., and Srinivasan, R.: Introduction to SWAT+, A Completely
Restructured Version of the Soil and Water Assessment Tool, JAWRA J. Am.
Water Resour. Assoc., 53, 115–130,
https://doi.org/10.1111/1752-1688.12482, 2017.
Burakowski, E., Tawfik, A., Ouimette, A., Lepine, L., Novick, K., Ollinger,
S., Zarzycki, C., and Bonan, G.: The role of surface roughness, albedo, and
Bowen ratio on ecosystem energy balance in the Eastern United States, Agric.
For. Meteorol., 249, 367–376,
https://doi.org/10.1016/j.agrformet.2017.11.030, 2018.
Camberlin, P.: Nile Basin Climates, in: The Nile: Origin, Environments,
Limnology and Human Use, edited by: Dumont, H. J., Springer Netherlands,
Dordrecht, 307–333,
https://doi.org/10.1007/978-1-4020-9726-3_16, 2009.
Chawanda, C. J., Arnold, J., Thiery, W., and Griensven, A. van: Mass balance
calibration and reservoir representations for large-scale hydrological
impact studies using SWAT+, Clim. Change, 163, 1307–1327,
https://doi.org/10.1007/s10584-020-02924-x, 2020.
Chen, F. and Xie, Z.: Effects of crop growth and development on regional
climate: a case study over East Asian monsoon area, Clim. Dyn., 38,
2291–2305, https://doi.org/10.1007/s00382-011-1125-y, 2012.
Eldardiry, H. and Hossain, F.: Understanding Reservoir Operating Rules in
the Transboundary Nile River Basin Using Macroscale Hydrologic Modeling with
Satellite Measurements, J. Hydrometeorol., 20, 2253–2269,
https://doi.org/10.1175/JHM-D-19-0058.1, 2019.
Eltahir, E. A. B.: A Soil Moisture–Rainfall Feedback Mechanism: 1. Theory
and observations, Water Resour. Res., 34, 765–776,
https://doi.org/10.1029/97WR03499, 1998.
Estel, S., Kuemmerle, T., Levers, C., Baumann, M., and Hostert, P.: Mapping
cropland-use intensity across Europe using MODIS NDVI time series, Environ.
Res. Lett., 11, 024015, https://doi.org/10.1088/1748-9326/11/2/024015, 2016.
FAO: Using remote sensing in support of solutions to reduce agricultural
water productivity gaps, Rome, Italy, available
at: https://www.fao.org/3/i7315en/i7315en.pdf (last access: 20 February 2021), 2018.
Farr, T. G., Rosen, P. A., Caro, E., Crippen, R., Duren, R., Hensley, S.,
Kobrick, M., Paller, M., Rodriguez, E., and Roth, L.: The shuttle radar
topography mission, Rev. Geophys., 45, https://doi.org/10.1029/2005RG000183 (data available at: https://cgiarcsi.community/data/srtm-90m-digital-elevation-database-v4-1/, last access: 8 January 2021), 2007.
Fisher, J. B., Melton, F., Middleton, E., Hain, C., Anderson, M., Allen, R.,
McCabe, M. F., Hook, S., Baldocchi, D., Townsend, P. A., Kilic, A., Tu, K.,
Miralles, D. D., Perret, J., Lagouarde, J.-P., Waliser, D., Purdy, A. J.,
French, A., Schimel, D., Famiglietti, J. S., Stephens, G., and Wood, E. F.:
The future of evapotranspiration: Global requirements for ecosystem
functioning, carbon and climate feedbacks, agricultural management, and
water resources, Water Resour. Res., 53, 2618–2626,
https://doi.org/10.1002/2016WR020175, 2017.
Gong, T. T., Lei, H. M., Yang, D. W., Jiao, Y., and Yang, H. B.: Effects of vegetation change on evapotranspiration in a semiarid shrubland of the Loess Plateau, China, Hydrol. Earth Syst. Sci. Discuss., 11, 13571–13605, https://doi.org/10.5194/hessd-11-13571-2014, 2014.
Ha, L. T., Bastiaanssen, W. G. M., Van Griensven, A., Van Dijk, A. I. J. M.,
and Senay, G. B.: Calibration of Spatially Distributed Hydrological
Processes and Model Parameters in SWAT Using Remote Sensing Data and an
Auto-Calibration Procedure: A Case Study in a Vietnamese River Basin, Water,
10, 212, https://doi.org/10.3390/w10020212, 2018.
Haregeweyn, N., Tsunekawa, A., Nyssen, J., Poesen, J., Tsubo, M., Tsegaye
Meshesha, D., Schütt, B., Adgo, E., and Tegegne, F.: Soil erosion and
conservation in Ethiopia: A review, Prog. Phys. Geogr. Earth Environ., 39,
750–774, https://doi.org/10.1177/0309133315598725, 2015.
Haregeweyn, N., Tsunekawa, A., Poesen, J., Tsubo, M., Meshesha, D. T.,
Fenta, A. A., Nyssen, J., and Adgo, E.: Comprehensive assessment of soil
erosion risk for better land use planning in river basins: Case study of the
Upper Blue Nile River, Sci. Total Environ., 574, 95–108,
https://doi.org/10.1016/j.scitotenv.2016.09.019, 2017.
Hengl, T., Heuvelink, G. B. M., Kempen, B., Leenaars, J. G. B., Walsh, M.
G., Shepherd, K. D., Sila, A., MacMillan, R. A., Jesus, J. M. de, Tamene,
L., and Tondoh, J. E.: Mapping Soil Properties of Africa at 250 m
Resolution: Random Forests Significantly Improve Current Predictions, PLOS
ONE, 10, e0125814, https://doi.org/10.1371/journal.pone.0125814 (data available at: https://www.isric.org/projects/africa-soil-information-service-afsis, last access: 25 January 2021), 2015.
Hilker, T., Lyapustin, A. I., Tucker, C. J., Hall, F. G., Myneni, R. B.,
Wang, Y., Bi, J., de Moura, Y. M., and Sellers, P. J.: Vegetation dynamics
and rainfall sensitivity of the Amazon, P. Natl. Acad. Sci. USA, 111,
16041–16046, 2014.
Hurni, H.: Erosion-productivity-conservation systems in Ethiopia, Proceedings 4th International Conference on Soil Conservation, Maracay, Venezuela, 654–674, 1985.
Hurtt, G. C., Chini, L., Sahajpal, R., Frolking, S., Bodirsky, B. L., Calvin, K., Doelman, J. C., Fisk, J., Fujimori, S., Klein Goldewijk, K., Hasegawa, T., Havlik, P., Heinimann, A., Humpenöder, F., Jungclaus, J., Kaplan, J. O., Kennedy, J., Krisztin, T., Lawrence, D., Lawrence, P., Ma, L., Mertz, O., Pongratz, J., Popp, A., Poulter, B., Riahi, K., Shevliakova, E., Stehfest, E., Thornton, P., Tubiello, F. N., van Vuuren, D. P., and Zhang, X.: Harmonization of global land use change and management for the period 850–2100 (LUH2) for CMIP6, Geosci. Model Dev., 13, 5425–5464, https://doi.org/10.5194/gmd-13-5425-2020 (data available at: https://luh.umd.edu/, last access: 31 January 2021), 2020.
Iizumi, T., Kim, W., and Nishimori, M.: Modeling the Global Sowing and
Harvesting Windows of Major Crops Around the Year 2000, J. Adv. Model. Earth
Syst., 11, 99–112, https://doi.org/10.1029/2018MS001477, 2019.
Jägermeyr, J., Müller, C., Ruane, A. C., Elliott, J., Balkovic, J.,
Castillo, O., Faye, B., Foster, I., Folberth, C., Franke, J. A., Fuchs, K.,
Guarin, J. R., Heinke, J., Hoogenboom, G., Iizumi, T., Jain, A. K., Kelly,
D., Khabarov, N., Lange, S., Lin, T.-S., Liu, W., Mialyk, O., Minoli, S.,
Moyer, E. J., Okada, M., Phillips, M., Porter, C., Rabin, S. S., Scheer, C.,
Schneider, J. M., Schyns, J. F., Skalsky, R., Smerald, A., Stella, T.,
Stephens, H., Webber, H., Zabel, F., and Rosenzweig, C.: Climate impacts on
global agriculture emerge earlier in new generation of climate and crop
models, Nat. Food, 2, 873–885, https://doi.org/10.1038/s43016-021-00400-y (data available at: https://doi.org/10.5281/zenodo.5062513),
2021.
Lange, S.: EartH2Observe, WFDEI and ERA-Interim data Merged and
Bias-corrected for ISIMIP (EWEMBI), GFZ Data Services [data], https://doi.org/10.5880/PIK.2016.004,
2016.
Leff, B., Ramankutty, N., and Foley, J. A.: Geographic distribution of major
crops across the world, Glob. Biogeochem. Cycles, 18, GB1009,
https://doi.org/10.1029/2003GB002108, 2004.
Li, L., Friedl, M. A., Xin, Q., Gray, J., Pan, Y., and Frolking, S.: Mapping
Crop Cycles in China Using MODIS-EVI Time Series, Remote Sens., 6,
2473–2493, https://doi.org/10.3390/rs6032473, 2014.
Lin, J., Zhang, J., Gu, Z., Chen, J., and Lyu, H.: A new approach of
assessing soil erosion using the remotely sensed leaf area index and its
application in the hilly area, Yegetos, Int. J. Plant Res., 27, 1–12, 2014.
Lokupitiya, E., Denning, S., Paustian, K., Baker, I., Schaefer, K., Verma, S., Meyers, T., Bernacchi, C. J., Suyker, A., and Fischer, M.: Incorporation of crop phenology in Simple Biosphere Model (SiBcrop) to improve land-atmosphere carbon exchanges from croplands, Biogeosciences, 6, 969–986, https://doi.org/10.5194/bg-6-969-2009, 2009.
Lotsch, A., Friedl, M. A., Anderson, B. T., and Tucker, C. J.: Coupled
vegetation-precipitation variability observed from satellite and climate
records, Geophys. Res. Lett., 30, 1774, https://doi.org/10.1029/2003GL017506,
2003.
Lu, C. and Tian, H.: Global nitrogen and phosphorus fertilizer use for agriculture production in the past half century: shifted hot spots and nutrient imbalance, Earth Syst. Sci. Data, 9, 181–192, https://doi.org/10.5194/essd-9-181-2017 (data available at: https://doi.org/10.1594/PANGAEA.863323), 2017.
M. El-Marsafawy, S., Swelam, A., and Ghanem, A.: Evolution of Crop Water
Productivity in the Nile Delta over Three Decades (1985–2015), Water, 10,
1168, https://doi.org/10.3390/w10091168, 2018.
Ma, T., Duan, Z., Li, R., and Song, X.: Enhancing SWAT with remotely sensed
LAI for improved modelling of ecohydrological process in subtropics, J.
Hydrol., 570, 802–815, https://doi.org/10.1016/j.jhydrol.2019.01.024, 2019.
Makowski, D., Nesme, T., Papy, F., and Doré, T.: Global agronomy, a new
field of research. A review, Agron. Sustain. Dev., 34, 293–307,
https://doi.org/10.1007/s13593-013-0179-0, 2014.
Metzner, J. R. and Barnes, B. H.: Decision table languages and systems, Academic Press, ISBN 978-1483205366, 2014.
Molnár, D. K. and Julien, P. Y.: Estimation of upland erosion using GIS,
Comput. Geosci., 24, 183–192,
https://doi.org/10.1016/S0098-3004(97)00100-3, 1998.
Monteith, J. L.: Radiation and crops, Exp. Agric., 1, 241–251, 1965.
Msigwa, A., Komakech, H. C., Verbeiren, B., Salvadore, E., Hessels, T.,
Weerasinghe, I., and van Griensven, A.: Accounting for Seasonal Land Use
Dynamics to Improve Estimation of Agricultural Irrigation Water Withdrawals,
Water, 11, 2471, https://doi.org/10.3390/w11122471, 2019.
Mueller, B., Seneviratne, S. I., Jimenez, C., Corti, T., Hirschi, M.,
Balsamo, G., Ciais, P., Dirmeyer, P., Fisher, J. B., Guo, Z., Jung, M.,
Maignan, F., McCabe, M. F., Reichle, R., Reichstein, M., Rodell, M.,
Sheffield, J., Teuling, A. J., Wang, K., Wood, E. F., and Zhang, Y.:
Evaluation of global observations-based evapotranspiration datasets and IPCC
AR4 simulations, Geophys. Res. Lett., 38, 3–10,
https://doi.org/10.1029/2010GL046230, 2011.
Muthoni, F. K., Odongo, V. O., Ochieng, J., Mugalavai, E. M., Mourice, S.
K., Hoesche-Zeledon, I., Mwila, M., and Bekunda, M.: Long-term
spatial-temporal trends and variability of rainfall over Eastern and
Southern Africa, Theor. Appl. Climatol., 137, 1869–1882,
https://doi.org/10.1007/s00704-018-2712-1, 2019.
Neitsch, S. L., Arnold, J. G., Kiniry, J. R., Williams, J. R., and King, K.
W.: SWAT theoretical documentation, Soil Water Res. Lab. Grassl., 494,
234–235, 2005.
Neitsch, S. L., Arnold, J. G., Kiniry, J. R., and Williams, J. R.: Soil and
water assessment tool theoretical documentation version 2009, Texas Water
Resources Institute, 2011.
Ngetich, K. F., Mucheru-Muna, M., Mugwe, J. N., Shisanya, C. A., Diels, J.,
and Mugendi, D. N.: Length of growing season, rainfall temporal
distribution, onset and cessation dates in the Kenyan highlands, Agric. For.
Meteorol., 188, 24–32, https://doi.org/10.1016/j.agrformet.2013.12.011,
2014.
Nile Basin Initiative: Land use/cover in the Nile Basin – Nile Basin Water
Resources Atlas, available at: https://atlas.nilebasin.org/treatise/land-usecover-in-the-nile-basin/ (last access: 8 January 2021), 2016.
Nkwasa, A., Chawanda, C. J., Msigwa, A., Komakech, H. C., Verbeiren, B., and
van Griensven, A.: How Can We Represent Seasonal Land Use Dynamics in SWAT
and SWAT+ Models for African Cultivated Catchments?, Water, 12, 1541,
https://doi.org/10.3390/w12061541, 2020.
Nkwasa, A.: Agricultural land use and crop management implementation in large-scale SWAT+ model applications, Zenodo [code], https://doi.org/10.5281/zenodo.5797553, 2021.
O'Neal, M. R., Nearing, M. A., Vining, R. C., Southworth, J., and Pfeifer,
R. A.: Climate change impacts on soil erosion in Midwest United States with
changes in crop management, Catena, 61, 165–184, 2005.
Onyutha, C. and Willems, P.: Spatial and temporal variability of rainfall in the Nile Basin, Hydrol. Earth Syst. Sci., 19, 2227–2246, https://doi.org/10.5194/hess-19-2227-2015, 2015.
Portmann, F. T., Siebert, S., and Döll, P.: MIRCA2000 – Global monthly
irrigated and rainfed crop areas around the year 2000: A new high-resolution
data set for agricultural and hydrological modeling, Glob. Biogeochem.
Cycles, 24, GB1011, https://doi.org/10.1029/2008GB003435, 2010.
Potter, P., Ramankutty, N., Bennett, E. M., and Donner, S. D.:
Characterizing the spatial patterns of global fertilizer application and
manure production, Earth Interact., 14, 1–22, 2010.
Qi, J., Zhang, X., Yang, Q., Srinivasan, R., Arnold, J. G., Li, J.,
Waldholf, S. T., and Cole, J.: SWAT ungauged: Water quality modeling in the
Upper Mississippi River Basin, J. Hydrol., 584, 124601,
https://doi.org/10.1016/j.jhydrol.2020.124601, 2020.
Rajib, A., Kim, I. L., Golden, H. E., Lane, C. R., Kumar, S. V., Yu, Z., and
Jeyalakshmi, S.: Watershed Modeling with Remotely Sensed Big Data: MODIS
Leaf Area Index Improves Hydrology and Water Quality Predictions, Remote
Sens., 12, 2148, https://doi.org/10.3390/rs12132148, 2020.
Raymond, P. A., Oh, N.-H., Turner, R. E., and Broussard, W.:
Anthropogenically enhanced fluxes of water and carbon from the Mississippi
River, Nature, 451, 449–452, https://doi.org/10.1038/nature06505, 2008.
Rounsevell, M. D. A., Annetts, J. E., Audsley, E., Mayr, T., and Reginster,
I.: Modelling the spatial distribution of agricultural land use at the
regional scale, Agric. Ecosyst. Environ., 95, 465–479,
https://doi.org/10.1016/S0167-8809(02)00217-7, 2003.
Schuol, J. and Abbaspour, K. C.: Calibration and uncertainty issues of a
hydrological model (SWAT) applied to West Africa, Adv. Geosci., 9, 137–143,
2006.
Schuol, J., Abbaspour, K. C., Yang, H., Srinivasan, R., and Zehnder, A. J.
B.: Modeling blue and green water availability in Africa, Water Resour.
Res., 44, 1–18, https://doi.org/10.1029/2007WR006609, 2008.
Siad, S. M., Iacobellis, V., Zdruli, P., Gioia, A., Stavi, I., and
Hoogenboom, G.: A review of coupled hydrologic and crop growth models,
Agric. Water Manag., 224, 105746,
https://doi.org/10.1016/j.agwat.2019.105746, 2019.
Siebert, S., Henrich, V., Frenken, K., and Burke, J.: Global map of
irrigation areas version 5, Rheinische Friedrich-Wilhelms-Univ. Bonn Ger.
Agric. Organ. U. N. Rome Italy, 2, 1299–1327, available at: https://www.fao.org/aquastat/en/geospatial-information/global-maps-irrigated-areas/latest-version/ (last access: 16 February 2021), 2013.
Sietz, D., Conradt, T., Krysanova, V., Hattermann, F. F., and Wechsung, F.:
The Crop Generator: Implementing crop rotations to effectively advance
eco-hydrological modelling, Agric. Syst., 193, 103183,
https://doi.org/10.1016/j.agsy.2021.103183, 2021.
Sood, A. and Smakhtin, V.: Global hydrological models: a review, Hydrol.
Sci. J., 60, 549–565, https://doi.org/10.1080/02626667.2014.950580, 2015.
Souza, V. F. C. de, Bertol, I., and Wolschick, N. H.: Effects of soil
management practices on water erosion under natural rainfall conditions on a
Humic Dystrudept, Rev. Bras. Ciênc. Solo, 41, 1–14, https://doi.org/10.1590/18069657rbcs20160443, 2017.
Srinivasan, R., Zhang, X., and Arnold, J.: SWAT ungauged: hydrological
budget and crop yield predictions in the Upper Mississippi River Basin,
Trans. ASABE, 53, 1533–1546, 2010.
Srivastava, A., Kumari, N., and Maza, M.: Hydrological Response to
Agricultural Land Use Heterogeneity Using Variable Infiltration Capacity
Model, Water Resour. Manag., 34, 3779–3794,
https://doi.org/10.1007/s11269-020-02630-4, 2020.
Sugita, M., Matsuno, A., El-Kilani, R. M. M., Abdel-Fattah, A., and Mahmoud,
M. A.: Crop evapotranspiration in the Nile Delta under different irrigation
methods, Hydrol. Sci. J., 62, 1618–1635,
https://doi.org/10.1080/02626667.2017.1341631, 2017.
Sundborg, A. and White, W. R.: Sedimentation problems in river basins,
France, ISBN 9789231020148, 1982.
Swain, A.: Challenges for water sharing in the Nile basin: changing
geo-politics and changing climate, Hydrol. Sci. J., 56, 687–702,
https://doi.org/10.1080/02626667.2011.577037, 2011.
Tamene, L. and Le, Q. B.: Estimating soil erosion in sub-Saharan Africa
based on landscape similarity mapping and using the revised universal soil
loss equation (RUSLE), Nutr. Cycl. Agroecosystems, 102, 17–31,
https://doi.org/10.1007/s10705-015-9674-9, 2015.
Twine, T. E., Kucharik, C. J., and Foley, J. A.: Effects of Land Cover
Change on the Energy and Water Balance of the Mississippi River Basin, J.
Hydrometeorol., 5, 640–655,
https://doi.org/10.1175/1525-7541(2004)005<0640:EOLCCO>2.0.CO;2, 2004.
van Griensven, A., Ndomba, P., Yalew, S., and Kilonzo, F.: Critical review of SWAT applications in the upper Nile basin countries, Hydrol. Earth Syst. Sci., 16, 3371–3381, https://doi.org/10.5194/hess-16-3371-2012, 2012.
Viña, A., Gitelson, A. A., Nguy-Robertson, A. L., and Peng, Y.:
Comparison of different vegetation indices for the remote assessment of
green leaf area index of crops, Remote Sens. Environ., 115, 3468–3478,
https://doi.org/10.1016/j.rse.2011.08.010, 2011.
Waha, K., Müller, C., Bondeau, A., Dietrich, J. P., Kurukulasuriya, P.,
Heinke, J., and Lotze-Campen, H.: Adaptation to climate change through the
choice of cropping system and sowing date in sub-Saharan Africa, Glob.
Environ. Change, 23, 130–143,
https://doi.org/10.1016/j.gloenvcha.2012.11.001, 2013.
Waha, K., Dietrich, J. P., Portmann, F. T., Siebert, S., Thornton, P. K.,
Bondeau, A., and Herrero, M.: Multiple cropping systems of the world and the
potential for increasing cropping intensity, Glob. Environ. Change, 64,
102131, https://doi.org/10.1016/j.gloenvcha.2020.102131, 2020.
Wang, L., Good, S. P., and Caylor, K. K.: Global synthesis of vegetation
control on evapotranspiration partitioning, Geophys. Res. Lett., 41,
6753–6757, https://doi.org/10.1002/2014GL061439, 2014.
Williams, J. R. and Berndt, H. D.: Sediment yield prediction based on
watershed hydrology, Trans. ASAE, 20, 1100–1104, 1977.
Williams, J. R. and Singh, V.: The EPIC Model, Computer models of watershed
hydrology, Water Resour. Publ. Highl. Ranch Colo., 909–1000, 1995.
Xiong, J., Thenkabail, P. S., Gumma, M. K., Teluguntla, P., Poehnelt, J.,
Congalton, R. G., Yadav, K., and Thau, D.: Automated cropland mapping of
continental Africa using Google Earth Engine cloud computing, ISPRS J.
Photogramm. Remote Sens., 126, 225–244,
https://doi.org/10.1016/j.isprsjprs.2017.01.019, 2017.
Yin, X. and Struik, P. C.: C3 and C4 photosynthesis models: An overview from
the perspective of crop modelling, NJAS – Wagening. J. Life Sci., 57,
27–38, https://doi.org/10.1016/j.njas.2009.07.001, 2009.
Zhang, X., Friedl, M. A., Schaaf, C. B., Strahler, A. H., and Liu, Z.:
Monitoring the response of vegetation phenology to precipitation in Africa
by coupling MODIS and TRMM instruments, J. Geophys. Res.-Atmos., 110, D12103,
https://doi.org/10.1029/2004JD005263, 2005.
Zhang, Y., Wu, Z., Singh, V. P., He, H., He, J., Yin, H., and Zhang, Y.:
Coupled hydrology-crop growth model incorporating an improved
evapotranspiration module, Agric. Water Manag., 246, 106691,
https://doi.org/10.1016/j.agwat.2020.106691, 2021.
Zhao, W., Fu, B., and Qiu, Y.: An Upscaling Method for Cover-Management
Factor and Its Application in the Loess Plateau of China, Int. J. Environ.
Res. Public. Health, 10, 4752–4766, https://doi.org/10.3390/ijerph10104752,
2013.
Short summary
We present an approach on how to incorporate crop phenology in a regional hydrological model using decision tables and global datasets of rainfed and irrigated cropland with the associated cropping calendar and management practices. Results indicate improved temporal patterns of leaf area index (LAI) and evapotranspiration (ET) simulations in comparison with remote sensing data. In addition, the improvement of the cropping season also helps to improve soil erosion estimates in cultivated areas.
We present an approach on how to incorporate crop phenology in a regional hydrological model...
