Articles | Volume 29, issue 6
https://doi.org/10.5194/hess-29-1483-2025
© Author(s) 2025. 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-29-1483-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
19 Mar 2025
Research article |
| 19 Mar 2025
| 19 Mar 2025
Identifying irrigated areas using land surface temperature and hydrological modelling: application to the Rhine basin
Devi Purnamasari
CORRESPONDING AUTHOR
Hydrology and Environmental Hydraulics Group, Wageningen University, Wageningen, the Netherlands
Deltares, Delft, the Netherlands
Adriaan J. Teuling
Hydrology and Environmental Hydraulics Group, Wageningen University, Wageningen, the Netherlands
Albrecht H. Weerts
Hydrology and Environmental Hydraulics Group, Wageningen University, Wageningen, the Netherlands
Deltares, Delft, the Netherlands
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Janneke O. E. Remmers, Rozemarijn ter Horst, Ehsan Nabavi, Ulrike Proske, Adriaan J. Teuling, Jeroen Vos, and Lieke A. Melsen
EGUsphere, https://doi.org/10.5194/egusphere-2025-673, https://doi.org/10.5194/egusphere-2025-673, 2025
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In hydrological modelling, a notion exists that a model is a neutral tool. However, this notion has several, possibly harmful, consequences. In critical social sciences, this non-neutrality in methods and results is an established topic of debate. We propose that in order to deal with it in hydrological modelling, the hydrological modelling network can learn from, and with, critical social sciences. The main lesson, from our perspective, is that responsible modelling is a shared responsibility.
Junfu Gong, Xingwen Liu, Cheng Yao, Zhijia Li, Albrecht H. Weerts, Qiaoling Li, Satish Bastola, Yingchun Huang, and Junzeng Xu
Hydrol. Earth Syst. Sci., 29, 335–360, https://doi.org/10.5194/hess-29-335-2025, https://doi.org/10.5194/hess-29-335-2025, 2025
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Our study introduces a new method to improve flood forecasting by combining soil moisture and streamflow data using an advanced data assimilation technique. By integrating field and reanalysis soil moisture data and assimilating this with streamflow measurements, we aim to enhance the accuracy of flood predictions. This approach reduces the accumulation of past errors in the initial conditions at the start of the forecast, helping to better prepare for and respond to floods.
Steven Reinaldo Rusli, Victor F. Bense, Syed M. T. Mustafa, and Albrecht H. Weerts
Hydrol. Earth Syst. Sci., 28, 5107–5131, https://doi.org/10.5194/hess-28-5107-2024, https://doi.org/10.5194/hess-28-5107-2024, 2024
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In this paper, we investigate the impact of climatic and anthropogenic factors on future groundwater availability. The changes are simulated using hydrological and groundwater flow models. We find that future groundwater status is influenced more by anthropogenic factors than climatic factors. The results are beneficial for informing responsible parties in operational water management about achieving future (ground)water governance.
Adriaan J. Teuling, Belle Holthuis, and Jasper F. D. Lammers
Hydrol. Earth Syst. Sci., 28, 3799–3806, https://doi.org/10.5194/hess-28-3799-2024, https://doi.org/10.5194/hess-28-3799-2024, 2024
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The understanding of spatio-temporal variability of evapotranspiration (ET) is currently limited by a lack of measurement techniques that are low cost and that can be applied anywhere at any time. Here we show that evapotranspiration can be estimated accurately using observations made by smartphone sensors, suggesting that smartphone-based ET monitoring could provide a realistic and low-cost alternative for real-time ET estimation in the field.
Charles Nduhiu Wamucii, Pieter R. van Oel, Adriaan J. Teuling, Arend Ligtenberg, John Mwangi Gathenya, Gert Jan Hofstede, Meine van Noordwijk, and Erika N. Speelman
Hydrol. Earth Syst. Sci., 28, 3495–3518, https://doi.org/10.5194/hess-28-3495-2024, https://doi.org/10.5194/hess-28-3495-2024, 2024
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The study explored the role of serious gaming in strengthening stakeholder engagement in addressing human–water challenges. The gaming approach guided community discussions toward implementable decisions. The results showed increased active participation, knowledge gain, and use of plural pronouns. We observed decreased individual interests and conflicts among game participants. The study presents important implications for creating a collective basis for water resources management.
Jasper M. C. Denissen, Adriaan J. Teuling, Sujan Koirala, Markus Reichstein, Gianpaolo Balsamo, Martha M. Vogel, Xin Yu, and René Orth
Earth Syst. Dynam., 15, 717–734, https://doi.org/10.5194/esd-15-717-2024, https://doi.org/10.5194/esd-15-717-2024, 2024
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Heat extremes have severe implications for human health and ecosystems. Heat extremes are mostly introduced by large-scale atmospheric circulation but can be modulated by vegetation. Vegetation with access to water uses solar energy to evaporate water into the atmosphere. Under dry conditions, water may not be available, suppressing evaporation and heating the atmosphere. Using climate projections, we show that regionally less water is available for vegetation, intensifying future heat extremes.
Willem J. van Verseveld, Albrecht H. Weerts, Martijn Visser, Joost Buitink, Ruben O. Imhoff, Hélène Boisgontier, Laurène Bouaziz, Dirk Eilander, Mark Hegnauer, Corine ten Velden, and Bobby Russell
Geosci. Model Dev., 17, 3199–3234, https://doi.org/10.5194/gmd-17-3199-2024, https://doi.org/10.5194/gmd-17-3199-2024, 2024
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We present the wflow_sbm distributed hydrological model, recently released by Deltares, as part of the Wflow.jl open-source modelling framework in the programming language Julia. Wflow_sbm has a fast runtime, making it suitable for large-scale modelling. Wflow_sbm models can be set a priori for any catchment with the Python tool HydroMT-Wflow based on globally available datasets, which results in satisfactory to good performance (without much tuning). We show this for a number of specific cases.
Marjanne J. Zander, Pety J. Viguurs, Frederiek C. Sperna Weiland, and Albrecht H. Weerts
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2023-274, https://doi.org/10.5194/hess-2023-274, 2023
Manuscript not accepted for further review
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Flash floods are damaging natural hazard which often occur in the European Alps. High resolution climate model output is combined with high resolution distributed hydrological models to model changes in flash flood frequency and intensity. Results show a similar flash flood frequency for autumn in the future, but a decrease in summer. However, the future discharge simulations indicate an increase in the flash flood severity in both summer and autumn leading to more severe flash flood impacts.
Awad M. Ali, Lieke A. Melsen, and Adriaan J. Teuling
Hydrol. Earth Syst. Sci., 27, 4057–4086, https://doi.org/10.5194/hess-27-4057-2023, https://doi.org/10.5194/hess-27-4057-2023, 2023
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Using a new approach based on a combination of modeling and Earth observation, useful information about the filling of the Grand Ethiopian Renaissance Dam can be obtained with limited data and proper rainfall selection. While the monthly streamflow into Sudan has decreased significantly (1.2 × 109–5 × 109 m3) with respect to the non-dam scenario, the negative impact has been masked due to higher-than-average rainfall. We reveal that the dam will need 3–5 more years to complete filling.
Bas J. M. Wullems, Claudia C. Brauer, Fedor Baart, and Albrecht H. Weerts
Hydrol. Earth Syst. Sci., 27, 3823–3850, https://doi.org/10.5194/hess-27-3823-2023, https://doi.org/10.5194/hess-27-3823-2023, 2023
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In deltas, saltwater sometimes intrudes far inland and causes problems with freshwater availability. We created a model to forecast salt concentrations at a critical location in the Rhine–Meuse delta in the Netherlands. It requires a rather small number of data to make a prediction and runs fast. It predicts the occurrence of salt concentration peaks well but underestimates the highest peaks. Its speed gives water managers more time to reduce the problems caused by salt intrusion.
Marleen R. Lam, Alessia Matanó, Anne F. Van Loon, Rhoda A. Odongo, Aklilu D. Teklesadik, Charles N. Wamucii, Marc J. C. van den Homberg, Shamton Waruru, and Adriaan J. Teuling
Nat. Hazards Earth Syst. Sci., 23, 2915–2936, https://doi.org/10.5194/nhess-23-2915-2023, https://doi.org/10.5194/nhess-23-2915-2023, 2023
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There is still no full understanding of the relation between drought impacts and drought indices in the Horn of Africa where water scarcity and arid regions are also present. This study assesses their relation in Kenya. A random forest model reveals that each region, aggregated by aridity, has its own set of predictors for every impact category. Water scarcity was not found to be related to aridity. Understanding these relations contributes to the development of drought early warning systems.
Adrià Fontrodona-Bach, Bettina Schaefli, Ross Woods, Adriaan J. Teuling, and Joshua R. Larsen
Earth Syst. Sci. Data, 15, 2577–2599, https://doi.org/10.5194/essd-15-2577-2023, https://doi.org/10.5194/essd-15-2577-2023, 2023
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We provide a dataset of snow water equivalent, the depth of liquid water that results from melting a given depth of snow. The dataset contains 11 071 sites over the Northern Hemisphere, spans the period 1950–2022, and is based on daily observations of snow depth on the ground and a model. The dataset fills a lack of accessible historical ground snow data, and it can be used for a variety of applications such as the impact of climate change on global and regional snow and water resources.
Luuk D. van der Valk, Adriaan J. Teuling, Luc Girod, Norbert Pirk, Robin Stoffer, and Chiel C. van Heerwaarden
The Cryosphere, 16, 4319–4341, https://doi.org/10.5194/tc-16-4319-2022, https://doi.org/10.5194/tc-16-4319-2022, 2022
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Most large-scale hydrological and climate models struggle to capture the spatially highly variable wind-driven melt of patchy snow cover. In the field, we find that 60 %–80 % of the total melt is wind driven at the upwind edge of a snow patch, while it does not contribute at the downwind edge. Our idealized simulations show that the variation is due to a patch-size-independent air-temperature reduction over snow patches and also allow us to study the role of wind-driven snowmelt on larger scales.
Jerom P. M. Aerts, Rolf W. Hut, Nick C. van de Giesen, Niels Drost, Willem J. van Verseveld, Albrecht H. Weerts, and Pieter Hazenberg
Hydrol. Earth Syst. Sci., 26, 4407–4430, https://doi.org/10.5194/hess-26-4407-2022, https://doi.org/10.5194/hess-26-4407-2022, 2022
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In recent years gridded hydrological modelling moved into the realm of hyper-resolution modelling (<10 km). In this study, we investigate the effect of varying grid-cell sizes for the wflow_sbm hydrological model. We used a large sample of basins from the CAMELS data set to test the effect that varying grid-cell sizes has on the simulation of streamflow at the basin outlet. Results show that there is no single best grid-cell size for modelling streamflow throughout the domain.
Mar J. Zander, Pety J. Viguurs, Frederiek C. Sperna Weiland, and Albrecht H. Weerts
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2022-207, https://doi.org/10.5194/hess-2022-207, 2022
Manuscript not accepted for further review
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We perform a modelling study to research potential future changes in flash flood occurrence in the European Alps. We use new high-resolution numerical climate simulations, which can simulate the type of local, intense rainstorms which trigger flash floods, combined with high-resolution hydrological modelling. We find that flash floods would become less frequent in summers in our future climate scenario, with little change in autumns. However, the maximal severity would increase in both seasons.
Alessandro Montemagno, Christophe Hissler, Victor Bense, Adriaan J. Teuling, Johanna Ziebel, and Laurent Pfister
Biogeosciences, 19, 3111–3129, https://doi.org/10.5194/bg-19-3111-2022, https://doi.org/10.5194/bg-19-3111-2022, 2022
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We investigated the biogeochemical processes that dominate the release and retention of elements (nutrients and potentially toxic elements) during litter degradation. Our results show that toxic elements are retained in the litter, while nutrients are released in solution during the first stages of degradation. This seems linked to the capability of trees to distribute the elements between degradation-resistant and non-degradation-resistant compounds of leaves according to their chemical nature.
Linqi Zhang, Yi Liu, Liliang Ren, Adriaan J. Teuling, Ye Zhu, Linyong Wei, Linyan Zhang, Shanhu Jiang, Xiaoli Yang, Xiuqin Fang, and Hang Yin
Hydrol. Earth Syst. Sci., 26, 3241–3261, https://doi.org/10.5194/hess-26-3241-2022, https://doi.org/10.5194/hess-26-3241-2022, 2022
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In this study, three machine learning methods displayed a good detection capacity of flash droughts. The RF model was recommended to estimate the depletion rate of soil moisture and simulate flash drought by considering the multiple meteorological variable anomalies in the adjacent time to drought onset. The anomalies of precipitation and potential evapotranspiration exhibited a stronger synergistic but asymmetrical effect on flash droughts compared to slowly developing droughts.
Femke A. Jansen, Remko Uijlenhoet, Cor M. J. Jacobs, and Adriaan J. Teuling
Hydrol. Earth Syst. Sci., 26, 2875–2898, https://doi.org/10.5194/hess-26-2875-2022, https://doi.org/10.5194/hess-26-2875-2022, 2022
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We studied the controls on open water evaporation with a focus on Lake IJssel, the Netherlands, by analysing eddy covariance observations over two summer periods at two locations at the borders of the lake. Wind speed and the vertical vapour pressure gradient can explain most of the variation in observed evaporation, which is in agreement with Dalton's model. We argue that the distinct characteristics of inland waterbodies need to be taken into account when parameterizing their evaporation.
Laurène J. E. Bouaziz, Emma E. Aalbers, Albrecht H. Weerts, Mark Hegnauer, Hendrik Buiteveld, Rita Lammersen, Jasper Stam, Eric Sprokkereef, Hubert H. G. Savenije, and Markus Hrachowitz
Hydrol. Earth Syst. Sci., 26, 1295–1318, https://doi.org/10.5194/hess-26-1295-2022, https://doi.org/10.5194/hess-26-1295-2022, 2022
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Assuming stationarity of hydrological systems is no longer appropriate when considering land use and climate change. We tested the sensitivity of hydrological predictions to changes in model parameters that reflect ecosystem adaptation to climate and potential land use change. We estimated a 34 % increase in the root zone storage parameter under +2 K global warming, resulting in up to 15 % less streamflow in autumn, due to 14 % higher summer evaporation, compared to a stationary system.
Charles Nduhiu Wamucii, Pieter R. van Oel, Arend Ligtenberg, John Mwangi Gathenya, and Adriaan J. Teuling
Hydrol. Earth Syst. Sci., 25, 5641–5665, https://doi.org/10.5194/hess-25-5641-2021, https://doi.org/10.5194/hess-25-5641-2021, 2021
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East African water towers (WTs) are under pressure from human influences within and without, but the water yield (WY) is more sensitive to climate changes from within. Land use changes have greater impacts on WY in the surrounding lowlands. The WTs have seen a strong shift towards wetter conditions while, at the same time, the potential evapotranspiration is gradually increasing. The WTs were identified as non-resilient, and future WY may experience more extreme variations.
Peter T. La Follette, Adriaan J. Teuling, Nans Addor, Martyn Clark, Koen Jansen, and Lieke A. Melsen
Hydrol. Earth Syst. Sci., 25, 5425–5446, https://doi.org/10.5194/hess-25-5425-2021, https://doi.org/10.5194/hess-25-5425-2021, 2021
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Hydrological models are useful tools that allow us to predict distributions and movement of water. A variety of numerical methods are used by these models. We demonstrate which numerical methods yield large errors when subject to extreme precipitation. As the climate is changing such that extreme precipitation is more common, we find that some numerical methods are better suited for use in hydrological models. Also, we find that many current hydrological models use relatively inaccurate methods.
Dirk Eilander, Willem van Verseveld, Dai Yamazaki, Albrecht Weerts, Hessel C. Winsemius, and Philip J. Ward
Hydrol. Earth Syst. Sci., 25, 5287–5313, https://doi.org/10.5194/hess-25-5287-2021, https://doi.org/10.5194/hess-25-5287-2021, 2021
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Digital elevation models and derived flow directions are crucial to distributed hydrological modeling. As the spatial resolution of models is typically coarser than these data, we need methods to upscale flow direction data while preserving the river structure. We propose the Iterative Hydrography Upscaling (IHU) method and show it outperforms other often-applied methods. We publish the multi-resolution MERIT Hydro IHU hydrography dataset and the algorithm as part of the pyflwdir Python package.
Ruben Imhoff, Claudia Brauer, Klaas-Jan van Heeringen, Hidde Leijnse, Aart Overeem, Albrecht Weerts, and Remko Uijlenhoet
Hydrol. Earth Syst. Sci., 25, 4061–4080, https://doi.org/10.5194/hess-25-4061-2021, https://doi.org/10.5194/hess-25-4061-2021, 2021
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Significant biases in real-time radar rainfall products limit the use for hydrometeorological forecasting. We introduce CARROTS (Climatology-based Adjustments for Radar Rainfall in an OperaTional Setting), a set of fixed bias reduction factors to correct radar rainfall products and to benchmark other correction algorithms. When tested for 12 Dutch basins, estimated rainfall and simulated discharges with CARROTS generally outperform those using the operational mean field bias adjustments.
Joost Buitink, Lieke A. Melsen, and Adriaan J. Teuling
Earth Syst. Dynam., 12, 387–400, https://doi.org/10.5194/esd-12-387-2021, https://doi.org/10.5194/esd-12-387-2021, 2021
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Higher temperatures influence both evaporation and snow processes. These two processes have a large effect on discharge but have distinct roles during different seasons. In this study, we study how higher temperatures affect the discharge via changed evaporation and snow dynamics. Higher temperatures lead to enhanced evaporation but increased melt from glaciers, overall lowering the discharge. During the snowmelt season, discharge was reduced further due to the earlier depletion of snow.
Laurène J. E. Bouaziz, Fabrizio Fenicia, Guillaume Thirel, Tanja de Boer-Euser, Joost Buitink, Claudia C. Brauer, Jan De Niel, Benjamin J. Dewals, Gilles Drogue, Benjamin Grelier, Lieke A. Melsen, Sotirios Moustakas, Jiri Nossent, Fernando Pereira, Eric Sprokkereef, Jasper Stam, Albrecht H. Weerts, Patrick Willems, Hubert H. G. Savenije, and Markus Hrachowitz
Hydrol. Earth Syst. Sci., 25, 1069–1095, https://doi.org/10.5194/hess-25-1069-2021, https://doi.org/10.5194/hess-25-1069-2021, 2021
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We quantify the differences in internal states and fluxes of 12 process-based models with similar streamflow performance and assess their plausibility using remotely sensed estimates of evaporation, snow cover, soil moisture and total storage anomalies. The dissimilarities in internal process representation imply that these models cannot all simultaneously be close to reality. Therefore, we invite modelers to evaluate their models using multiple variables and to rely on multi-model studies.
Jolijn van Engelenburg, Erik van Slobbe, Adriaan J. Teuling, Remko Uijlenhoet, and Petra Hellegers
Drink. Water Eng. Sci., 14, 1–43, https://doi.org/10.5194/dwes-14-1-2021, https://doi.org/10.5194/dwes-14-1-2021, 2021
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This study analysed the impact of extreme weather events, water quality deterioration, and a growing drinking water demand on the sustainability of drinking water supply in the Netherlands. The results of the case studies were compared to sustainability issues for drinking water supply that are experienced worldwide. This resulted in a set of sustainability characteristics describing drinking water supply on a local scale in terms of hydrological, technical, and socio-economic characteristics.
Theresa C. van Hateren, Marco Chini, Patrick Matgen, and Adriaan J. Teuling
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2020-583, https://doi.org/10.5194/hess-2020-583, 2020
Manuscript not accepted for further review
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Agricultural droughts occur when the water content of the soil diminishes to such a level that vegetation is negatively impacted. Here we show that, although they are classified as the same type of drought, substantial differences between soil moisture and vegetation droughts exist. This duality is not included in the term agricultural drought, and thus is a potential issue in drought research. We argue that a distinction should be made between soil moisture and vegetation drought events.
Joost Buitink, Anne M. Swank, Martine van der Ploeg, Naomi E. Smith, Harm-Jan F. Benninga, Frank van der Bolt, Coleen D. U. Carranza, Gerbrand Koren, Rogier van der Velde, and Adriaan J. Teuling
Hydrol. Earth Syst. Sci., 24, 6021–6031, https://doi.org/10.5194/hess-24-6021-2020, https://doi.org/10.5194/hess-24-6021-2020, 2020
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The amount of water stored in the soil is critical for the productivity of plants. Plant productivity is either limited by the available water or by the available energy. In this study, we infer this transition point by comparing local observations of water stored in the soil with satellite observations of vegetation productivity. We show that the transition point is not constant with soil depth, indicating that plants use water from deeper layers when the soil gets drier.
Joost Buitink, Lieke A. Melsen, James W. Kirchner, and Adriaan J. Teuling
Geosci. Model Dev., 13, 6093–6110, https://doi.org/10.5194/gmd-13-6093-2020, https://doi.org/10.5194/gmd-13-6093-2020, 2020
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This paper presents a new distributed hydrological model: the distributed simple dynamical systems (dS2) model. The model is built with a focus on computational efficiency and is therefore able to simulate basins at high spatial and temporal resolution at a low computational cost. Despite the simplicity of the model concept, it is able to correctly simulate discharge in both small and mesoscale basins.
Jasper Foets, Carlos E. Wetzel, Núria Martínez-Carreras, Adriaan J. Teuling, Jean-François Iffly, and Laurent Pfister
Hydrol. Earth Syst. Sci., 24, 4709–4725, https://doi.org/10.5194/hess-24-4709-2020, https://doi.org/10.5194/hess-24-4709-2020, 2020
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Diatoms (microscopic algae) are regarded as useful tracers in catchment hydrology. However, diatom analysis is labour-intensive; therefore, only a limited number of samples can be analysed. To reduce this number, we explored the potential for a time-integrated mass-flux sampler to provide a representative sample of the diatom assemblage for a whole storm run-off event. Our results indicate that the Phillips sampler did indeed sample representative communities during two of the three events.
Caspar T. J. Roebroek, Lieke A. Melsen, Anne J. Hoek van Dijke, Ying Fan, and Adriaan J. Teuling
Hydrol. Earth Syst. Sci., 24, 4625–4639, https://doi.org/10.5194/hess-24-4625-2020, https://doi.org/10.5194/hess-24-4625-2020, 2020
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Vegetation is a principal component in the Earth system models that are used for weather, climate and other environmental predictions. Water is one of the main drivers of vegetation; however, the global distribution of how water influences vegetation is not well understood. This study looks at spatial patterns of photosynthesis and water sources (rain and groundwater) to obtain a first understanding of water access and limitations for the growth of global forests (proxy for natural vegetation).
Anne J. Hoek van Dijke, Kaniska Mallick, Martin Schlerf, Miriam Machwitz, Martin Herold, and Adriaan J. Teuling
Biogeosciences, 17, 4443–4457, https://doi.org/10.5194/bg-17-4443-2020, https://doi.org/10.5194/bg-17-4443-2020, 2020
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We investigated the link between the vegetation leaf area index (LAI) and the land–atmosphere exchange of water, energy, and carbon fluxes. We show that the correlation between the LAI and water and energy fluxes depends on the vegetation type and aridity. For carbon fluxes, however, the correlation with the LAI was strong and independent of vegetation and aridity. This study provides insight into when the vegetation LAI can be used to model or extrapolate land–atmosphere fluxes.
Cited articles
Abera, A., Verhoest, N. E., Tilahun, S., Inyang, H., and Nyssen, J.: Assessment of irrigation expansion and implications for water resources by using RS and GIS techniques in the Lake Tana Basin of Ethiopia, Environ. Monit. Assess., 193, 13, https://doi.org/10.1007/s10661-020-08778-1, 2021. a
Badgley, G., Fisher, J. B., Jiménez, C., Tu, K. P., and Vinukollu, R.: On uncertainty in global terrestrial evapotranspiration estimates from choice of input forcing datasets, J. Hydrometeorol., 16, 1449–1455, 2015. a
Berbel, J., Borrego-Marin, M. M., Exposito, A., Giannoccaro, G., Montilla-Lopez, N. M., and Roseta-Palma, C.: Analysis of irrigation water tariffs and taxes in Europe, Water Policy, 21, 806–825, 2019. a
BfG: Das Niedrigwasser 2018, https://doi.bafg.de/BfG/2019/Niedrigwasser_2018.pdf (last access: 10 June 2024), 2019. a
Boretti, A. and Rosa, L.: Reassessing the projections of the world water development report, NPJ Clean Water, 2, 15, https://doi.org/10.1038/s41545-019-0039-9, 2019. a
Breiman, L.: Random forests, Machine Learning, 45, 5–32, 2001. a
Bretreger, D., Yeo, I.-Y., Hancock, G., and Willgoose, G.: Monitoring irrigation using landsat observations and climate data over regional scales in the Murray-Darling Basin, J. Hydrol., 590, 125356, https://doi.org/10.1016/j.jhydrol.2020.125356, 2020. a
Buitink, J., Melsen, L. A., and Teuling, A. J.: Seasonal discharge response to temperature-driven changes in evaporation and snow processes in the Rhine Basin, Earth Syst. Dynam., 12, 387–400, https://doi.org/10.5194/esd-12-387-2021, 2021. a, b
Buitink, J., Tsiokanos, A., Geertsma, J., ten Velden, C., Bouaziz, L., and Weiland, F.: Implications of the KNMI’23 climate scenarios for the discharge of the Rhine and Meuse, https://publications.deltares.nl/11209265_002_0003.pdf (last access: 15 May 2024), 2023. a
Colombo, R., Meroni, M., Marchesi, A., Busetto, L., Rossini, M., Giardino, C., and Panigada, C.: Estimation of leaf and canopy water content in poplar plantations by means of hyperspectral indices and inverse modeling, Remote Sens. Environ., 112, 1820–1834, 2008. a
Dari, J., Quintana-Seguí, P., Escorihuela, M. J., Stefan, V., Brocca, L., and Morbidelli, R.: Detecting and mapping irrigated areas in a Mediterranean environment by using remote sensing soil moisture and a land surface model, J. Hydrol., 596, 126129, https://doi.org/10.1016/j.jhydrol.2021.126129, 2021. a, b
Dari, J., Brocca, L., Modanesi, S., Massari, C., Tarpanelli, A., Barbetta, S., Quast, R., Vreugdenhil, M., Freeman, V., Barella-Ortiz, A., Quintana-Seguí, P., Bretreger, D., and Volden, E.: Regional data sets of high-resolution (1 and 6 km) irrigation estimates from space, Earth Syst. Sci. Data, 15, 1555–1575, https://doi.org/10.5194/essd-15-1555-2023, 2023. a
Deines, J. M., Kendall, A. D., Crowley, M. A., Rapp, J., Cardille, J. A., and Hyndman, D. W.: Mapping three decades of annual irrigation across the US High Plains Aquifer using Landsat and Google Earth Engine, Remote Sens. Environ., 233, 111400, https://doi.org/10.1016/j.rse.2019.111400, 2019. a, b, c
Döll, P. and Siebert, S.: Global modeling of irrigation water requirements, Water Resour. Res., 38, 8-1–8-10, https://doi.org/10.1029/2001WR000355, 2002. a
Droogers, P., Immerzeel, W., and Lorite, I.: Estimating actual irrigation application by remotely sensed evapotranspiration observations, Agr. Water Manage., 97, 1351–1359, 2010. a
Eilander, D., van Verseveld, W., Yamazaki, D., Weerts, A., Winsemius, H. C., and Ward, P. J.: A hydrography upscaling method for scale-invariant parametrization of distributed hydrological models, Hydrol. Earth Syst. Sci., 25, 5287–5313, https://doi.org/10.5194/hess-25-5287-2021, 2021. a
European Environment Agency: Corine Land Cover (CLC) 2018, Version 20, European Environment Agency [data set], https://doi.org/10.2909/960998c1-1870-4e82-8051-6485205ebbac, 2018. a, b
Eurostat: National Reference Metadata in ESS Standard for Quality Reports Structure (ESQRS), https://ec.europa.eu/eurostat/cache/metadata/EN/ef_simsif_de.htm (last access: 15 May 2024), 2016. a
Eurostat: Irrigation: number of farms, areas and equipment by size of irrigated area and NUTS 2 region, Eurostat [data set], https://doi.org/10.2908/ef_poirrig, 2018. a
Fader, M., Shi, S., von Bloh, W., Bondeau, A., and Cramer, W.: Mediterranean irrigation under climate change: more efficient irrigation needed to compensate for increases in irrigation water requirements, Hydrol. Earth Syst. Sci., 20, 953–973, https://doi.org/10.5194/hess-20-953-2016, 2016. a
Farr, T. G., Rosen, P. A., Caro, E., Crippen, R., Duren, R., Hensley, S., Kobrick, M., Paller, M., Rodriguez, E., Roth, L., Seal, D., Shaffer, S., Shimada, J., Umland, J., Werner, M., Oskin, M., Burbank, D., and Alsdorf, D. E.: The Shuttle Radar Topography Mission, Rev. Geophys., 45, RG2004, https://doi.org/10.1029/2005RG000183, 2007. a
Feddes, R. A., Kowalik, P., Kolinska-Malinka, K., and Zaradny, H.: Simulation of field water uptake by plants using a soil water dependent root extraction function, J. Hydrol., 31, 13–26, https://doi.org/10.1016/0022-1694(76)90017-2, 1976. a
Fischer, G., Tubiello, F. N., Van Velthuizen, H., and Wiberg, D. A.: Climate change impacts on irrigation water requirements: Effects of mitigation, 1990–2080, Technol. Forecast. Soc., 74, 1083–1107, 2007. a
Foster, T., Brozović, N., and Butler, A. P.: Modeling irrigation behavior in groundwater systems, Water Resour. Res., 50, 6370–6389, 2014. a
Haddeland, I., Skaugen, T., and Lettenmaier, D. P.: Anthropogenic impacts on continental surface water fluxes, Geophys. Res. Lett., 33, L08406, https://doi.org/10.1029/2006GL026047, 2006. a
Hanel, M., Rakovec, O., Markonis, Y., Máca, P., Samaniego, L., Kyselỳ, J., and Kumar, R.: Revisiting the recent European droughts from a long-term perspective, Scientific Reports, 8, 9499, https://doi.org/10.1038/s41598-018-27464-4, 2018. a
Iglesias, A., Garrote, L., Quiroga, S., and Moneo, M.: A regional comparison of the effects of climate change on agricultural crops in Europe, Climatic Change, 112, 29–46, 2012. a
Imhoff, R., Van Verseveld, W., Van Osnabrugge, B., and Weerts, A.: Scaling point-scale (Pedo)transfer functions to seamless large-domain parameter estimates for high-resolution distributed hydrologic modeling: An example for the Rhine River, Water Resour. Res., 56, e2019WR026807, https://doi.org/10.1029/2019WR026807, 2020. a, b, c
Jalilvand, E., Tajrishy, M., Hashemi, S. A. G. Z., and Brocca, L.: Quantification of irrigation water using remote sensing of soil moisture in a semi-arid region, Remote Sens. Environ., 231, 111226, https://doi.org/10.1016/j.rse.2019.111226, 2019. a
Konapala, G., Mishra, A. K., Wada, Y., and Mann, M. E.: Climate change will affect global water availability through compounding changes in seasonal precipitation and evaporation, Nat. Commun., 11, 3044, https://doi.org/10.1038/s41467-020-16757-w, 2020. a
Kragh, S. J., Dari, J., Modanesi, S., Massari, C., Brocca, L., Fensholt, R., Stisen, S., and Koch, J.: An inter-comparison of approaches and frameworks to quantify irrigation from satellite data, Hydrol. Earth Syst. Sci., 28, 441–457, https://doi.org/10.5194/hess-28-441-2024, 2024. a
Lehmann, F., Vishwakarma, B. D., and Bamber, J.: How well are we able to close the water budget at the global scale?, Hydrol. Earth Syst. Sci., 26, 35–54, https://doi.org/10.5194/hess-26-35-2022, 2022. a, b
Lehner, B., Verdin, K., and Jarvis, A.: New global hydrography derived from spaceborne elevation data, Eos, Transactions American Geophysical Union, 89, 93–94, 2008. a
LSA-SAF: MSG Daily Downward Surface Shortwave Flux, LSA-SAF [data set], https://datalsasaf.lsasvcs.ipma.pt/PRODUCTS/MSG/MDIDSSF/NETCDF/, last access: 5 June 2024a. a
LSA-SAF: MSG 10-day Surface Albedo, LSA-SAF [data set], https://datalsasaf.lsasvcs.ipma.pt/PRODUCTS/MSG/MDAL/NETCDF/, last access: 5 June 2024b. a
Monfreda, C., Ramankutty, N., and Foley, J. A.: Farming the planet: 2. Geographic distribution of crop areas, yields, physiological types, and net primary production in the year 2000, Global Biogeochem. Cy., 22, GB1022, https://doi.org/10.1029/2007GB002947, 2008. a
Olivera-Guerra, L., Merlin, O., and Er-Raki, S.: Irrigation retrieval from Landsat optical/thermal data integrated into a crop water balance model: A case study over winter wheat fields in a semi-arid region, Remote Sens. Environ., 239, 111627, https://doi.org/10.1016/j.rse.2019.111627, 2020. a
Purnamasari, D., Teuling, A. J., and Weerts, A.: Maps of irrigated area of the Rhine basin developed in the research “Identifying irrigated areas using land surface temperature and hydrological modelling: Application to Rhine basin”, Version 2, 4TU.ResearchData [data set], https://doi.org/10.4121/66647538-ed17-4dd2-9af8-962ed0c61177.v2, 2025. a
Rauthe, M., Steiner, H., Riediger, U., Mazurkiewicz, A., and Gratzki, A.: A Central European precipitation climatology – Part I: Generation and validation of a high-resolution gridded daily data set (HYRAS), Meteorol. Z., 22, 235–256, https://doi.org/10.1127/0941-2948/2013/0436, 2013. a
Romaguera, M., Krol, M. S., Salama, M., Hoekstra, A. Y., and Su, Z.: Determining irrigated areas and quantifying blue water use in Europe using remote sensing Meteosat Second Generation (MSG) products and Global Land Data Assimilation System (GLDAS) data, Photogramm. Eng. Rem. S., 78, 861–873, 2012. a
Salmon, J., Friedl, M. A., Frolking, S., Wisser, D., and Douglas, E. M.: Global rain-fed, irrigated, and paddy croplands: A new high resolution map derived from remote sensing, crop inventories and climate data, Int. J. Appl. Earth Obs., 38, 321–334, https://doi.org/10.1016/j.jag.2015.01.014, 2015. a, b
Shahriar Pervez, M., Budde, M., and Rowland, J.: Mapping irrigated areas in Afghanistan over the past decade using MODIS NDVI, Remote Sens. Environ., 149, 155–165, https://doi.org/10.1016/j.rse.2014.04.008, 2014. a, b, c, d
Shamal, S. and Weatherhead, K.: Assessing spectral similarities between rainfed and irrigated croplands in a humid environment for irrigated land mapping, Outlook Agr., 43, 109–114, 2014. a
Siebert, S., Döll, P., Hoogeveen, J., Faures, J.-M., Frenken, K., and Feick, S.: Development and validation of the global map of irrigation areas, Hydrol. Earth Syst. Sci., 9, 535–547, https://doi.org/10.5194/hess-9-535-2005, 2005. a
Siebert, S., Henrich, V., Frenken, K., and Burke, J.: Update Of The Digital Global Map Of Irrigation Areas to Version 5, Tech. Rep., Rheinische Friedrich-Wilhelms-University, Bonn, Germany/Food and Agriculture Organization of the United Nations, Rome, Italy, https://openknowledge.fao.org/handle/20.500.14283/i9261en (last access: 11 March 2025), 2013. a, b
Spinoni, J., Vogt, J. V., Naumann, G., Barbosa, P., and Dosio, A.: Will drought events become more frequent and severe in Europe?, Int. J. Climatol., 38, 1718–1736, 2018. a
Talsma, C. J., Good, S. P., Jimenez, C., Martens, B., Fisher, J. B., Miralles, D. G., McCabe, M. F., and Purdy, A. J.: Partitioning of evapotranspiration in remote sensing-based models, Agr. Forest Meteorol., 260–261, 131–143, https://doi.org/10.1016/j.agrformet.2018.05.010, 2018. a
Thenkabail, P. S., Biradar, C. M., Noojipady, P., Dheeravath, V., Li, Y., Velpuri, M., Gumma, M., Gangalakunta, O. R. P., Turral, H., Cai, X., Vithanage, K., Schull, M. A., and Dutta, R.: Global irrigated area map (GIAM), derived from remote sensing, for the end of the last millennium, Int. J. Remote Sens., 30, 3679–3733, https://doi.org/10.1080/01431160802698919, 2009. a, b, c
Thiese, M. S., Ronna, B., and Ott, U.: P value interpretations and considerations, J. Thoracic Dis., 8, E928–E931, https://doi.org/10.21037/jtd.2016.08.16, 2016. a
Thom, A. S.: Momentum, mass and heat exchange of plant communities, Vegetation and the Atmosphere, 4, 57–109, 1975. a
Toreti, A., Belward, A., Perez-Dominguez, I., Naumann, G., Luterbacher, J., Cronie, O., Seguini, L., Manfron, G., Lopez-Lozano, R., Baruth, B., van den Berg, M., Dentener, F., Ceglar, A., Chatzopoulos, T., and Zampieri, M.: The Exceptional 2018 European Water Seesaw Calls for Action on Adaptation, Earth's Future, 7, 652–663, https://doi.org/10.1029/2019EF001170, 2019. a
Trigo, I. F., Dacamara, C. C., Viterbo, P., Roujean, J.-L., Olesen, F., Barroso, C., de Coca, F. C., Carrer, D., Freitas, S. C., García-Haro, J., Geiger, B., Gellens-Meulenberghs, F., Ghilain, N., Meliá, J., Pessanha, L., Siljamo, N., and Arboleda, A.: The Satellite Application Facility for Land Surface Analysis, Int. J. Remote Sens., 32, 2725–2744, https://doi.org/10.1080/01431161003743199, 2011. a
van Dijk, A. I. J. M., Warren, G., Van Niel, T. G., Byrne, G. T., Pollock, D., and Doody, T.: Derivation of data layers from medium resolution remote sensing to support mapping of groundwater dependent ecosystems, Technical Report, https://doi.org/10.13140/RG.2.1.1260.6804, 2015. a
van Dijk, A. I. J. M., Schellekens, J., Yebra, M., Beck, H. E., Renzullo, L. J., Weerts, A., and Donchyts, G.: Global 5 km resolution estimates of secondary evaporation including irrigation through satellite data assimilation, Hydrol. Earth Syst. Sci., 22, 4959–4980, https://doi.org/10.5194/hess-22-4959-2018, 2018. a
van Osnabrugge, B., Uijlenhoet, R., and Weerts, A.: Contribution of potential evaporation forecasts to 10-day streamflow forecast skill for the Rhine River, Hydrol. Earth Syst. Sci., 23, 1453–1467, https://doi.org/10.5194/hess-23-1453-2019, 2019. a, b
van Verseveld, W. J., Weerts, A. H., Visser, M., Buitink, J., Imhoff, R. O., Boisgontier, H., Bouaziz, L., Eilander, D., Hegnauer, M., ten Velden, C., and Russell, B.: Wflow_sbm v0.7.3, a spatially distributed hydrological model: from global data to local applications, Geosci. Model Dev., 17, 3199–3234, https://doi.org/10.5194/gmd-17-3199-2024, 2024. a, b, c
Velpuri, N., Thenkabail, P. S., Gumma, M. K., Biradar, C., Dheeravath, V., Noojipady, P., and Yuanjie, L.: Influence of resolution in irrigated area mapping and area estimation, Photogramm. Eng. Rem. S., 75, 1383–1395, 2009. a
Velpuri, N. M. and Senay, G. B.: Partitioning evapotranspiration into green and blue water sources in the conterminous United States, Scientific Reports, 7, 6191, https://doi.org/10.1038/s41598-017-06359-w, 2017. a
Vertessy, R. A. and Elsenbeer, H.: Distributed modeling of storm flow generation in an Amazonian rain forest catchment: Effects of model parameterization, Water Resour. Res., 35, 2173–2187, 1999. a
Vinukollu, R. K., Meynadier, R., Sheffield, J., and Wood, E. F.: Multi-model, multi-sensor estimates of global evapotranspiration: Climatology, uncertainties and trends, Hydrol. Process., 25, 3993–4010, 2011. a
Wan, Z., Hook, S., and Hulley, G.: MODIS/Aqua Land Surface Temperature/Emissivity Daily L3 Global 1km SIN Grid V061, NASA EOSDIS Land Processes Distributed Active Archive Center [data set], https://doi.org/10.5067/MODIS/MYD11A1.061, 2021a. a
Wan, Z., Hook, S., and Hulley, G.: MODIS/Terra Land Surface Temperature/Emissivity Daily L3 Global 1km SIN Grid V061, NASA EOSDIS Land Processes Distributed Active Archive Center [data set], https://doi.org/10.5067/MODIS/MOD11A1.061, 2021b. a, b
Wang, K. and Dickinson, R. E.: A review of global terrestrial evapotranspiration: Observation, modeling, climatology, and climatic variability, Rev. Geophys., 50, RG2005, https://doi.org/10.1029/2011RG000373, 2012. a
Xie, Y., Gibbs, H. K., and Lark, T. J.: Landsat-based Irrigation Dataset (LANID): 30 m resolution maps of irrigation distribution, frequency, and change for the US, 1997–2017, Earth Syst. Sci. Data, 13, 5689–5710, https://doi.org/10.5194/essd-13-5689-2021, 2021. a
Zappa, L., Dari, J., Modanesi, S., Quast, R., Brocca, L., De Lannoy, G., Massari, C., Quintana-Seguí, P., Barella-Ortiz, A., and Dorigo, W.: Benefits and pitfalls of irrigation timing and water amounts derived from satellite soil moisture, Agr. Water Manage., 295, 108773, https://doi.org/10.1016/j.agwat.2024.108773, 2024. a
Zhang, L., Marshall, M., Vrieling, A., and Nelson, A.: The divergence of energy-and water-balance evapotranspiration estimates in humid regions, J. Hydrol., 624, 129971, https://doi.org/10.1016/j.jhydrol.2023.129971, 2023. a
Zhang, Z., Lin, A., Zhao, L., and Zhao, B.: Attribution of local land surface temperature variations response to irrigation over the North China Plain, Sci. Total Environ., 826, 154104, https://doi.org/10.1016/j.scitotenv.2022.154104, 2022. a, b
Zhu, P. and Burney, J.: Untangling irrigation effects on maize water and heat stress alleviation using satellite data, Hydrol. Earth Syst. Sci., 26, 827–840, https://doi.org/10.5194/hess-26-827-2022, 2022. a
Zink, M., Mai, J., Cuntz, M., and Samaniego, L.: Conditioning a Hydrologic Model Using Patterns of Remotely Sensed Land Surface Temperature, Water Resour. Res., 54, 2976–2998, https://doi.org/10.1002/2017WR021346, 2018. a
Short summary
This paper introduces a method to identify irrigated areas by combining hydrology models with satellite temperature data. Our method was tested in the Rhine basin and aligns well with official statistics. It performs best in regions with large farms and less well in areas with small farms. Observed differences to existing data are influenced by data resolution and methods.
This paper introduces a method to identify irrigated areas by combining hydrology models with...
