Articles | Volume 27, issue 19
https://doi.org/10.5194/hess-27-3485-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-27-3485-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
06 Oct 2023
Research article |
| 06 Oct 2023
| 06 Oct 2023
Calibrating macroscale hydrological models in poorly gauged and heavily regulated basins
Dung Trung Vu
CORRESPONDING AUTHOR
Pillar of Engineering Systems and Design, Singapore University of Technology and Design, Singapore, Singapore
Thanh Duc Dang
Department of Civil and Environmental Engineering, University of South Florida, Tampa, FL, USA
Francesca Pianosi
School of Civil, Aerospace and Design Engineering, University of Bristol, Bristol, UK
Stefano Galelli
Pillar of Engineering Systems and Design, Singapore University of Technology and Design, Singapore, Singapore
School of Civil and Environmental Engineering, Cornell University, Ithaca, NY, USA
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The lack of data on how big dams are operated in the Upper Mekong, or Lancang, largely contributes to the ongoing controversy between China and the other Mekong countries. Here, we rely on satellite observations to reconstruct monthly storage time series for the 10 largest reservoirs in the Lancang. Our analysis shows how quickly reservoirs were filled in, what decisions were made during recent droughts, and how these decisions impacted downstream discharge.
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The MSEA-Res database offers an open-access dataset tracking absolute water storage for 185 large reservoirs across Mainland Southeast Asia from 1985–2023. It provides valuable insights into how reservoir storage has grown by 130 % between 2008 and 2017, driven by dams in key river basins. Our data also reveal how droughts, like the 2019–2020 event, significantly impacted water reservoirs. This resource can aid water management, drought planning, and research globally.
Saskia Salwey, Gemma Coxon, Francesca Pianosi, Rosanna Lane, Chris Hutton, Michael Bliss Singer, Hilary McMillan, and Jim Freer
Hydrol. Earth Syst. Sci., 28, 4203–4218, https://doi.org/10.5194/hess-28-4203-2024, https://doi.org/10.5194/hess-28-4203-2024, 2024
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Reservoirs are essential for water resource management and can significantly impact downstream flow. However, representing reservoirs in hydrological models can be challenging, particularly across large scales. We design a new and simple method for simulating river flow downstream of water supply reservoirs using only open-access data. We demonstrate the approach in 264 reservoir catchments across Great Britain, where we can significantly improve the simulation of reservoir-impacted flow.
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Hydrol. Earth Syst. Sci., 28, 3261–3279, https://doi.org/10.5194/hess-28-3261-2024, https://doi.org/10.5194/hess-28-3261-2024, 2024
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Following recent advancements in weather prediction technology, we explored how seasonal weather forecasts (1 or more months ahead) could benefit practical water management in South Korea. Our findings highlight that using seasonal weather forecasts for predicting flow patterns 1 to 3 months ahead is effective, especially during dry years. This suggest that seasonal weather forecasts can be helpful in improving the management of water resources.
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Donghoon Lee, Jia Yi Ng, Stefano Galelli, and Paul Block
Hydrol. Earth Syst. Sci., 26, 2431–2448, https://doi.org/10.5194/hess-26-2431-2022, https://doi.org/10.5194/hess-26-2431-2022, 2022
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To fully realize the potential of seasonal streamflow forecasts in the hydropower industry, we need to understand the relationship between reservoir design specifications, forecast skill, and value. Here, we rely on realistic forecasts and simulated hydropower operations for 753 dams worldwide to unfold such relationship. Our analysis shows how forecast skill affects hydropower production, what type of dams are most likely to benefit from seasonal forecasts, and where these dams are located.
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Hydrol. Earth Syst. Sci., 26, 2345–2364, https://doi.org/10.5194/hess-26-2345-2022, https://doi.org/10.5194/hess-26-2345-2022, 2022
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Andres Peñuela, Christopher Hutton, and Francesca Pianosi
Hydrol. Earth Syst. Sci., 24, 6059–6073, https://doi.org/10.5194/hess-24-6059-2020, https://doi.org/10.5194/hess-24-6059-2020, 2020
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Elisa Bozzolan, Elizabeth Holcombe, Francesca Pianosi, and Thorsten Wagener
Nat. Hazards Earth Syst. Sci., 20, 3161–3177, https://doi.org/10.5194/nhess-20-3161-2020, https://doi.org/10.5194/nhess-20-3161-2020, 2020
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We include informal housing in slope stability analysis, considering different slope properties and precipitation events (including climate change). The dominant failure processes are identified, and their relative role in slope failure is quantified. A new rainfall threshold is assessed for urbanised slopes. Instability
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Thanh Duc Dang, A. F. M. Kamal Chowdhury, and Stefano Galelli
Hydrol. Earth Syst. Sci., 24, 397–416, https://doi.org/10.5194/hess-24-397-2020, https://doi.org/10.5194/hess-24-397-2020, 2020
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A common problem in catchment hydrology lies in the representation of dams in numerical models. Here, we contribute to the existing literature by showing that the representation of water reservoirs can largely impact the model parameters, a result attained by comparing the parameters of a model for the upper Mekong basin built with or without reservoirs. We show that a flawed parameter estimation affects the representation of key physical processes and the downstream applications of the model.
Fanny Sarrazin, Andreas Hartmann, Francesca Pianosi, Rafael Rosolem, and Thorsten Wagener
Geosci. Model Dev., 11, 4933–4964, https://doi.org/10.5194/gmd-11-4933-2018, https://doi.org/10.5194/gmd-11-4933-2018, 2018
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We propose the first large-scale vegetation–recharge model for karst regions (V2Karst), which enables the analysis of the impact of changes in climate and land cover on karst groundwater recharge. We demonstrate the plausibility of V2Karst simulations against observations at FLUXNET sites and of controlling modelled processes using sensitivity analysis. We perform virtual experiments to further test the model and gain insight into its sensitivity to precipitation pattern and vegetation cover.
Sean W. D. Turner, James C. Bennett, David E. Robertson, and Stefano Galelli
Hydrol. Earth Syst. Sci., 21, 4841–4859, https://doi.org/10.5194/hess-21-4841-2017, https://doi.org/10.5194/hess-21-4841-2017, 2017
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This study investigates the relationship between skill and value of ensemble seasonal streamflow forecasts. Using data from a modern forecasting system, we show that skilled forecasts are more likely to provide benefits for reservoirs operated to maintain a target water level rather than reservoirs operated to satisfy a target demand. We identify the primary causes for this behaviour and provide specific recommendations for assessing the value of forecasts for reservoirs with supply objectives.
Susana Almeida, Elizabeth Ann Holcombe, Francesca Pianosi, and Thorsten Wagener
Nat. Hazards Earth Syst. Sci., 17, 225–241, https://doi.org/10.5194/nhess-17-225-2017, https://doi.org/10.5194/nhess-17-225-2017, 2017
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Landslides threaten communities globally, yet predicting their occurrence is challenged by uncertainty about slope properties and climate change. We present an approach to identify the dominant drivers of slope instability and the critical thresholds at which slope failure may occur. This information helps decision makers to target data acquisition to improve landslide predictability, and supports policy development to reduce landslide occurrence and impacts in highly uncertain environments.
A. Hartmann, T. Gleeson, R. Rosolem, F. Pianosi, Y. Wada, and T. Wagener
Geosci. Model Dev., 8, 1729–1746, https://doi.org/10.5194/gmd-8-1729-2015, https://doi.org/10.5194/gmd-8-1729-2015, 2015
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We present a new approach to assess karstic groundwater recharge over Europe and the Mediterranean. Cluster analysis is used to subdivide all karst regions into four typical karst landscapes and to simulate karst recharge with a process-based karst model. We estimate its parameters by a combination of a priori information and observations of soil moisture and evapotranspiration. Independent observations of recharge that present large-scale models significantly under-estimate karstic recharge.
S. Galelli and A. Castelletti
Hydrol. Earth Syst. Sci., 17, 2669–2684, https://doi.org/10.5194/hess-17-2669-2013, https://doi.org/10.5194/hess-17-2669-2013, 2013
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Subject: Catchment hydrology | Techniques and Approaches: Modelling approaches
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Zehua Chang, Hongkai Gao, Leilei Yong, Kang Wang, Rensheng Chen, Chuntan Han, Otgonbayar Demberel, Batsuren Dorjsuren, Shugui Hou, and Zheng Duan
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Hydrol. Earth Syst. Sci., 28, 3367–3390, https://doi.org/10.5194/hess-28-3367-2024, https://doi.org/10.5194/hess-28-3367-2024, 2024
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Huy Dang and Yadu Pokhrel
Hydrol. Earth Syst. Sci., 28, 3347–3365, https://doi.org/10.5194/hess-28-3347-2024, https://doi.org/10.5194/hess-28-3347-2024, 2024
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Yongshin Lee, Francesca Pianosi, Andres Peñuela, and Miguel Angel Rico-Ramirez
Hydrol. Earth Syst. Sci., 28, 3261–3279, https://doi.org/10.5194/hess-28-3261-2024, https://doi.org/10.5194/hess-28-3261-2024, 2024
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Following recent advancements in weather prediction technology, we explored how seasonal weather forecasts (1 or more months ahead) could benefit practical water management in South Korea. Our findings highlight that using seasonal weather forecasts for predicting flow patterns 1 to 3 months ahead is effective, especially during dry years. This suggest that seasonal weather forecasts can be helpful in improving the management of water resources.
Mariam Khanam, Giulia Sofia, and Emmanouil N. Anagnostou
Hydrol. Earth Syst. Sci., 28, 3161–3190, https://doi.org/10.5194/hess-28-3161-2024, https://doi.org/10.5194/hess-28-3161-2024, 2024
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Flooding worsens due to climate change, with river dynamics being a key in local flood control. Predicting post-storm geomorphic changes is challenging. Using self-organizing maps and machine learning, this study forecasts post-storm alterations in stage–discharge relationships across 3101 US stream gages. The provided framework can aid in updating hazard assessments by identifying rivers prone to change, integrating channel adjustments into flood hazard assessment.
Yalan Song, Wouter J. M. Knoben, Martyn P. Clark, Dapeng Feng, Kathryn Lawson, Kamlesh Sawadekar, and Chaopeng Shen
Hydrol. Earth Syst. Sci., 28, 3051–3077, https://doi.org/10.5194/hess-28-3051-2024, https://doi.org/10.5194/hess-28-3051-2024, 2024
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Differentiable models (DMs) integrate neural networks and physical equations for accuracy, interpretability, and knowledge discovery. We developed an adjoint-based DM for ordinary differential equations (ODEs) for hydrological modeling, reducing distorted fluxes and physical parameters from errors in models that use explicit and operation-splitting schemes. With a better numerical scheme and improved structure, the adjoint-based DM matches or surpasses long short-term memory (LSTM) performance.
Florian Willkofer, Raul R. Wood, and Ralf Ludwig
Hydrol. Earth Syst. Sci., 28, 2969–2989, https://doi.org/10.5194/hess-28-2969-2024, https://doi.org/10.5194/hess-28-2969-2024, 2024
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Severe flood events pose a threat to riverine areas, yet robust estimates of the dynamics of these events in the future due to climate change are rarely available. Hence, this study uses data from a regional climate model, SMILE, to drive a high-resolution hydrological model for 98 catchments of hydrological Bavaria and exploits the large database to derive robust values for the 100-year flood events. Results indicate an increase in frequency and intensity for most catchments in the future.
Maik Renner and Corina Hauffe
Hydrol. Earth Syst. Sci., 28, 2849–2869, https://doi.org/10.5194/hess-28-2849-2024, https://doi.org/10.5194/hess-28-2849-2024, 2024
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Climate and land surface changes influence the partitioning of water balance components decisively. Their impact is quantified for 71 catchments in Saxony. Germany. Distinct signatures in the joint water and energy budgets are found: (i) past forest dieback caused a decrease in and subsequent recovery of evapotranspiration in the affected regions, and (ii) the recent shift towards higher aridity imposed a large decline in runoff that has not been seen in the observation records before.
Zhen Cui, Shenglian Guo, Hua Chen, Dedi Liu, Yanlai Zhou, and Chong-Yu Xu
Hydrol. Earth Syst. Sci., 28, 2809–2829, https://doi.org/10.5194/hess-28-2809-2024, https://doi.org/10.5194/hess-28-2809-2024, 2024
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Ensemble forecasting facilitates reliable flood forecasting and warning. This study couples the copula-based hydrologic uncertainty processor (CHUP) with Bayesian model averaging (BMA) and proposes the novel CHUP-BMA method of reducing inflow forecasting uncertainty of the Three Gorges Reservoir. The CHUP-BMA avoids the normal distribution assumption in the HUP-BMA and considers the constraint of initial conditions, which can improve the deterministic and probabilistic forecast performance.
Mazda Kompanizare, Diogo Costa, Merrin L. Macrae, John W. Pomeroy, and Richard M. Petrone
Hydrol. Earth Syst. Sci., 28, 2785–2807, https://doi.org/10.5194/hess-28-2785-2024, https://doi.org/10.5194/hess-28-2785-2024, 2024
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A new agricultural tile drainage module was developed in the Cold Region Hydrological Model platform. Tile flow and water levels are simulated by considering the effect of capillary fringe thickness, drainable water and seasonal regional groundwater dynamics. The model was applied to a small well-instrumented farm in southern Ontario, Canada, where there are concerns about the impacts of agricultural drainage into Lake Erie.
Eduardo Acuña Espinoza, Ralf Loritz, Manuel Álvarez Chaves, Nicole Bäuerle, and Uwe Ehret
Hydrol. Earth Syst. Sci., 28, 2705–2719, https://doi.org/10.5194/hess-28-2705-2024, https://doi.org/10.5194/hess-28-2705-2024, 2024
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Hydrological hybrid models promise to merge the performance of deep learning methods with the interpretability of process-based models. One hybrid approach is the dynamic parameterization of conceptual models using long short-term memory (LSTM) networks. We explored this method to evaluate the effect of the flexibility given by LSTMs on the process-based part.
Adam Griffin, Alison L. Kay, Paul Sayers, Victoria Bell, Elizabeth Stewart, and Sam Carr
Hydrol. Earth Syst. Sci., 28, 2635–2650, https://doi.org/10.5194/hess-28-2635-2024, https://doi.org/10.5194/hess-28-2635-2024, 2024
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Widespread flooding is a major problem in the UK and is greatly affected by climate change and land-use change. To look at how widespread flooding changes in the future, climate model data (UKCP18) were used with a hydrological model (Grid-to-Grid) across the UK, and 14 400 events were identified between two time slices: 1980–2010 and 2050–2080. There was a strong increase in the number of winter events in the future time slice and in the peak return periods.
Alberto Montanari, Bruno Merz, and Günter Blöschl
Hydrol. Earth Syst. Sci., 28, 2603–2615, https://doi.org/10.5194/hess-28-2603-2024, https://doi.org/10.5194/hess-28-2603-2024, 2024
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Floods often take communities by surprise, as they are often considered virtually
impossibleyet are an ever-present threat similar to the sword suspended over the head of Damocles in the classical Greek anecdote. We discuss four reasons why extremely large floods carry a risk that is often larger than expected. We provide suggestions for managing the risk of megafloods by calling for a creative exploration of hazard scenarios and communicating the unknown corners of the reality of floods.
Peter Reichert, Kai Ma, Marvin Höge, Fabrizio Fenicia, Marco Baity-Jesi, Dapeng Feng, and Chaopeng Shen
Hydrol. Earth Syst. Sci., 28, 2505–2529, https://doi.org/10.5194/hess-28-2505-2024, https://doi.org/10.5194/hess-28-2505-2024, 2024
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We compared the predicted change in catchment outlet discharge to precipitation and temperature change for conceptual and machine learning hydrological models. We found that machine learning models, despite providing excellent fit and prediction capabilities, can be unreliable regarding the prediction of the effect of temperature change for low-elevation catchments. This indicates the need for caution when applying them for the prediction of the effect of climate change.
Nicolás Álamos, Camila Alvarez-Garreton, Ariel Muñoz, and Álvaro González-Reyes
Hydrol. Earth Syst. Sci., 28, 2483–2503, https://doi.org/10.5194/hess-28-2483-2024, https://doi.org/10.5194/hess-28-2483-2024, 2024
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In this study, we assess the effects of climate and water use on streamflow reductions and drought intensification during the last 3 decades in central Chile. We address this by contrasting streamflow observations with near-natural streamflow simulations. We conclude that while the lack of precipitation dominates streamflow reductions in the megadrought, water uses have not diminished during this time, causing a worsening of the hydrological drought conditions and maladaptation conditions.
Fengjing Liu, Martha H. Conklin, and Glenn D. Shaw
Hydrol. Earth Syst. Sci., 28, 2239–2258, https://doi.org/10.5194/hess-28-2239-2024, https://doi.org/10.5194/hess-28-2239-2024, 2024
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Mountain snowpack has been declining and more precipitation falls as rain than snow. Using stable isotopes, we found flows and flow duration in Yosemite Creek are most sensitive to climate warming due to strong evaporation of waterfalls, potentially lengthening the dry-up period of waterfalls in summer and negatively affecting tourism. Groundwater recharge in Yosemite Valley is primarily from the upper snow–rain transition (2000–2500 m) and very vulnerable to a reduction in the snow–rain ratio.
Qiutong Yu, Bryan A. Tolson, Hongren Shen, Ming Han, Juliane Mai, and Jimmy Lin
Hydrol. Earth Syst. Sci., 28, 2107–2122, https://doi.org/10.5194/hess-28-2107-2024, https://doi.org/10.5194/hess-28-2107-2024, 2024
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It is challenging to incorporate input variables' spatial distribution information when implementing long short-term memory (LSTM) models for streamflow prediction. This work presents a novel hybrid modelling approach to predict streamflow while accounting for spatial variability. We evaluated the performance against lumped LSTM predictions in 224 basins across the Great Lakes region in North America. This approach shows promise for predicting streamflow in large, ungauged basin.
Marcus Buechel, Louise Slater, and Simon Dadson
Hydrol. Earth Syst. Sci., 28, 2081–2105, https://doi.org/10.5194/hess-28-2081-2024, https://doi.org/10.5194/hess-28-2081-2024, 2024
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Afforestation has been proposed internationally, but the hydrological implications of such large increases in the spatial extent of woodland are not fully understood. In this study, we use a land surface model to simulate hydrology across Great Britain with realistic afforestation scenarios and potential climate changes. Countrywide afforestation minimally influences hydrology, when compared to climate change, and reduces low streamflow whilst not lowering the highest flows.
Qian Zhu, Xiaodong Qin, Dongyang Zhou, Tiantian Yang, and Xinyi Song
Hydrol. Earth Syst. Sci., 28, 1665–1686, https://doi.org/10.5194/hess-28-1665-2024, https://doi.org/10.5194/hess-28-1665-2024, 2024
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Input data, model and calibration strategy can affect the accuracy of flood event simulation and prediction. Satellite-based precipitation with different spatiotemporal resolutions is an important input source. Data-driven models are sometimes proven to be more accurate than hydrological models. Event-based calibration and conventional strategy are two options adopted for flood simulation. This study targets the three concerns for accurate flood event simulation and prediction.
Fabio Ciulla and Charuleka Varadharajan
Hydrol. Earth Syst. Sci., 28, 1617–1651, https://doi.org/10.5194/hess-28-1617-2024, https://doi.org/10.5194/hess-28-1617-2024, 2024
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We present a new method based on network science for unsupervised classification of large datasets and apply it to classify 9067 US catchments and 274 biophysical traits at multiple scales. We find that our trait-based approach produces catchment classes with distinct streamflow behavior and that spatial patterns emerge amongst pristine and human-impacted catchments. This method can be widely used beyond hydrology to identify patterns, reduce trait redundancy, and select representative sites.
Cyril Thébault, Charles Perrin, Vazken Andréassian, Guillaume Thirel, Sébastien Legrand, and Olivier Delaigue
Hydrol. Earth Syst. Sci., 28, 1539–1566, https://doi.org/10.5194/hess-28-1539-2024, https://doi.org/10.5194/hess-28-1539-2024, 2024
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Streamflow forecasting is useful for many applications, ranging from population safety (e.g. floods) to water resource management (e.g. agriculture or hydropower). To this end, hydrological models must be optimized. However, a model is inherently wrong. This study aims to analyse the contribution of a multi-model approach within a variable spatial framework to improve streamflow simulations. The underlying idea is to take advantage of the strength of each modelling framework tested.
Lele Shu, Xiaodong Li, Yan Chang, Xianhong Meng, Hao Chen, Yuan Qi, Hongwei Wang, Zhaoguo Li, and Shihua Lyu
Hydrol. Earth Syst. Sci., 28, 1477–1491, https://doi.org/10.5194/hess-28-1477-2024, https://doi.org/10.5194/hess-28-1477-2024, 2024
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We developed a new model to better understand how water moves in a lake basin. Our model improves upon previous methods by accurately capturing the complexity of water movement, both on the surface and subsurface. Our model, tested using data from China's Qinghai Lake, accurately replicates complex water movements and identifies contributing factors of the lake's water balance. The findings provide a robust tool for predicting hydrological processes, aiding water resource planning.
Ricardo Mantilla, Morgan Fonley, and Nicolás Velásquez
Hydrol. Earth Syst. Sci., 28, 1373–1382, https://doi.org/10.5194/hess-28-1373-2024, https://doi.org/10.5194/hess-28-1373-2024, 2024
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Hydrologists strive to “Be right for the right reasons” when modeling the hydrologic cycle; however, the datasets available to validate hydrological models are sparse, and in many cases, they comprise streamflow observations at the outlets of large catchments. In this work, we show that matching streamflow observations at the outlet of a large basin is not a reliable indicator of a correct description of the small-scale runoff processes.
Lillian M. McGill, E. Ashley Steel, and Aimee H. Fullerton
Hydrol. Earth Syst. Sci., 28, 1351–1371, https://doi.org/10.5194/hess-28-1351-2024, https://doi.org/10.5194/hess-28-1351-2024, 2024
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This study examines the relationship between air and river temperatures in Washington's Snoqualmie and Wenatchee basins. We used classification and regression approaches to show that the sensitivity of river temperature to air temperature is variable across basins and controlled largely by geology and snowmelt. Findings can be used to inform strategies for river basin restoration and conservation, such as identifying climate-insensitive areas of the basin that should be preserved and protected.
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. Discuss., https://doi.org/10.5194/hess-2024-81, https://doi.org/10.5194/hess-2024-81, 2024
Revised manuscript accepted for HESS
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We used hydrological models, field measurements and satellite-based data to study the soil moisture dynamics in a subarctic catchment. The role of groundwater was studied with different ways to model the groundwater dynamics, and via comparisons to the observational data. The choice of groundwater model was shown to have a strong impact, and representation of lateral flow was important to capture wet soil conditions. Our results provide insights for ecohydrological studies in boreal regions.
Stephanie R. Clark, Julien Lerat, Jean-Michel Perraud, and Peter Fitch
Hydrol. Earth Syst. Sci., 28, 1191–1213, https://doi.org/10.5194/hess-28-1191-2024, https://doi.org/10.5194/hess-28-1191-2024, 2024
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To determine if deep learning models are in general a viable alternative to traditional hydrologic modelling techniques in Australian catchments, a comparison of river–runoff predictions is made between traditional conceptual models and deep learning models in almost 500 catchments spread over the continent. It is found that the deep learning models match or outperform the traditional models in over two-thirds of the river catchments, indicating feasibility in a wide variety of conditions.
Dipti Tiwari, Mélanie Trudel, and Robert Leconte
Hydrol. Earth Syst. Sci., 28, 1127–1146, https://doi.org/10.5194/hess-28-1127-2024, https://doi.org/10.5194/hess-28-1127-2024, 2024
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Calibrating hydrological models with multi-objective functions enhances model robustness. By using spatially distributed snow information in the calibration, the model performance can be enhanced without compromising the outputs. In this study the HYDROTEL model was calibrated in seven different experiments, incorporating the SPAEF (spatial efficiency) metric alongside Nash–Sutcliffe efficiency (NSE) and root-mean-square error (RMSE), with the aim of identifying the optimal calibration strategy.
Luis Andres De la Fuente, Mohammad Reza Ehsani, Hoshin Vijai Gupta, and Laura Elizabeth Condon
Hydrol. Earth Syst. Sci., 28, 945–971, https://doi.org/10.5194/hess-28-945-2024, https://doi.org/10.5194/hess-28-945-2024, 2024
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Long short-term memory (LSTM) is a widely used machine-learning model in hydrology, but it is difficult to extract knowledge from it. We propose HydroLSTM, which represents processes like a hydrological reservoir. Models based on HydroLSTM perform similarly to LSTM while requiring fewer cell states. The learned parameters are informative about the dominant hydrology of a catchment. Our results show how parsimony and hydrological knowledge extraction can be achieved by using the new structure.
Nienke Tessa Tempel, Laurene Bouaziz, Riccardo Taormina, Ellis van Noppen, Jasper Stam, Eric Sprokkereef, and Markus Hrachowitz
EGUsphere, https://doi.org/10.5194/egusphere-2024-115, https://doi.org/10.5194/egusphere-2024-115, 2024
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This study explores the impact of climatic variability on root zone water storage capacities 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.
Louise Mimeau, Annika Künne, Flora Branger, Sven Kralisch, Alexandre Devers, and Jean-Philippe Vidal
Hydrol. Earth Syst. Sci., 28, 851–871, https://doi.org/10.5194/hess-28-851-2024, https://doi.org/10.5194/hess-28-851-2024, 2024
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Modelling flow intermittence is essential for predicting the future evolution of drying in river networks and better understanding the ecological and socio-economic impacts. However, modelling flow intermittence is challenging, and observed data on temporary rivers are scarce. This study presents a new modelling approach for predicting flow intermittence in river networks and shows that combining different sources of observed data reduces the model uncertainty.
Elena Macdonald, Bruno Merz, Björn Guse, Viet Dung Nguyen, Xiaoxiang Guan, and Sergiy Vorogushyn
Hydrol. Earth Syst. Sci., 28, 833–850, https://doi.org/10.5194/hess-28-833-2024, https://doi.org/10.5194/hess-28-833-2024, 2024
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In some rivers, the occurrence of extreme flood events is more likely than in other rivers – they have heavy-tailed distributions. We find that threshold processes in the runoff generation lead to such a relatively high occurrence probability of extremes. Further, we find that beyond a certain return period, i.e. for rare events, rainfall is often the dominant control compared to runoff generation. Our results can help to improve the estimation of the occurrence probability of extreme floods.
Alberto Bassi, Marvin Höge, Antonietta Mira, Fabrizio Fenicia, and Carlo Albert
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-47, https://doi.org/10.5194/hess-2024-47, 2024
Revised manuscript accepted for HESS
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The goal is to remove the impact of meteorological drivers in order to uncover the unique landscape fingerprints of a catchment from streamflow data. Our results reveal an optimal two-feature summary for most catchments, with a third feature needed for challenging cases, associated with aridity and intermittent flow. Baseflow index, aridity, and soil/vegetation attributes strongly correlate with learned features, indicating their importance for streamflow prediction.
Claire Kouba and Thomas Harter
Hydrol. Earth Syst. Sci., 28, 691–718, https://doi.org/10.5194/hess-28-691-2024, https://doi.org/10.5194/hess-28-691-2024, 2024
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In some watersheds, the severity of the dry season has a large impact on aquatic ecosystems. In this study, we design a way to predict, 5–6 months in advance, how severe the dry season will be in a rural watershed in northern California. This early warning can support seasonal adaptive management. To predict these two values, we assess data about snow, rain, groundwater, and river flows. We find that maximum snowpack and total wet season rainfall best predict dry season severity.
Yi Nan and Fuqiang Tian
Hydrol. Earth Syst. Sci., 28, 669–689, https://doi.org/10.5194/hess-28-669-2024, https://doi.org/10.5194/hess-28-669-2024, 2024
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This paper utilized a tracer-aided model validated by multiple datasets in a large mountainous basin on the Tibetan Plateau to analyze hydrological sensitivity to climate change. The spatial pattern of the local hydrological sensitivities and the influence factors were analyzed in particular. The main finding of this paper is that the local hydrological sensitivity in mountainous basins is determined by the relationship between the glacier area ratio and the mean annual precipitation.
Michael J. Vlah, Matthew R. V. Ross, Spencer Rhea, and Emily S. Bernhardt
Hydrol. Earth Syst. Sci., 28, 545–573, https://doi.org/10.5194/hess-28-545-2024, https://doi.org/10.5194/hess-28-545-2024, 2024
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Virtual stream gauging enables continuous streamflow estimation where a gauge might be difficult or impractical to install. We reconstructed flow at 27 gauges of the National Ecological Observatory Network (NEON), informing ~199 site-months of missing data in the official record and improving that accuracy of official estimates at 11 sites. This study shows that machine learning, but also routine regression methods, can be used to supplement existing gauge networks and reduce monitoring costs.
Sungwook Wi and Scott Steinschneider
Hydrol. Earth Syst. Sci., 28, 479–503, https://doi.org/10.5194/hess-28-479-2024, https://doi.org/10.5194/hess-28-479-2024, 2024
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We investigate whether deep learning (DL) models can produce physically plausible streamflow projections under climate change. We address this question by focusing on modeled responses to increases in temperature and potential evapotranspiration and by employing three DL and three process-based hydrological models. The results suggest that physical constraints regarding model architecture and input are necessary to promote the physical realism of DL hydrological projections under climate change.
Guillaume Evin, Matthieu Le Lay, Catherine Fouchier, David Penot, Francois Colleoni, Alexandre Mas, Pierre-André Garambois, and Olivier Laurantin
Hydrol. Earth Syst. Sci., 28, 261–281, https://doi.org/10.5194/hess-28-261-2024, https://doi.org/10.5194/hess-28-261-2024, 2024
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Hydrological modelling of mountainous catchments is challenging for many reasons, the main one being the temporal and spatial representation of precipitation forcings. This study presents an evaluation of the hydrological modelling of 55 small mountainous catchments of the northern French Alps, focusing on the influence of the type of precipitation reanalyses used as inputs. These evaluations emphasize the added value of radar measurements, in particular for the reproduction of flood events.
Lena Katharina Schmidt, Till Francke, Peter Martin Grosse, and Axel Bronstert
Hydrol. Earth Syst. Sci., 28, 139–161, https://doi.org/10.5194/hess-28-139-2024, https://doi.org/10.5194/hess-28-139-2024, 2024
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How suspended sediment export from glacierized high-alpine areas responds to future climate change is hardly assessable as many interacting processes are involved, and appropriate physical models are lacking. We present the first study, to our knowledge, exploring machine learning to project sediment export until 2100 in two high-alpine catchments. We find that uncertainties due to methodological limitations are small until 2070. Negative trends imply that peak sediment may have already passed.
Robert Hull, Elena Leonarduzzi, Luis De La Fuente, Hoang Viet Tran, Andrew Bennett, Peter Melchior, Reed M. Maxwell, and Laura E. Condon
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2023-264, https://doi.org/10.5194/hess-2023-264, 2024
Revised manuscript accepted for HESS
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Large-scale hydrologic a needed tool to explore complex watershed processes and how they may evolve under a changing climate. However, calibrating them can be difficult because they are costly to run and have many unknown parameters. We implement a state-of-the-art approach to model calibration with a set of experiments in the Upper Colorado River Basin.
Salam A. Abbas, Ryan T. Bailey, Jeremy T. White, Jeffrey G. Arnold, Michael J. White, Natalja Čerkasova, and Jungang Gao
Hydrol. Earth Syst. Sci., 28, 21–48, https://doi.org/10.5194/hess-28-21-2024, https://doi.org/10.5194/hess-28-21-2024, 2024
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Research highlights.
1. Implemented groundwater module (gwflow) into SWAT+ for four watersheds with different unique hydrologic features across the United States.
2. Presented methods for sensitivity analysis, uncertainty analysis and parameter estimation for coupled models.
3. Sensitivity analysis for streamflow and groundwater head conducted using Morris method.
4. Uncertainty analysis and parameter estimation performed using an iterative ensemble smoother within the PEST framework.
Shima Azimi, Christian Massari, Giuseppe Formetta, Silvia Barbetta, Alberto Tazioli, Davide Fronzi, Sara Modanesi, Angelica Tarpanelli, and Riccardo Rigon
Hydrol. Earth Syst. Sci., 27, 4485–4503, https://doi.org/10.5194/hess-27-4485-2023, https://doi.org/10.5194/hess-27-4485-2023, 2023
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We analyzed the water budget of nested karst catchments using simple methods and modeling. By utilizing the available data on precipitation and discharge, we were able to determine the response lag-time by adopting new techniques. Additionally, we modeled snow cover dynamics and evapotranspiration with the use of Earth observations, providing a concise overview of the water budget for the basin and its subbasins. We have made the data, models, and workflows accessible for further study.
Yuhang Zhang, Aizhong Ye, Bita Analui, Phu Nguyen, Soroosh Sorooshian, Kuolin Hsu, and Yuxuan Wang
Hydrol. Earth Syst. Sci., 27, 4529–4550, https://doi.org/10.5194/hess-27-4529-2023, https://doi.org/10.5194/hess-27-4529-2023, 2023
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Our study shows that while the quantile regression forest (QRF) and countable mixtures of asymmetric Laplacians long short-term memory (CMAL-LSTM) models demonstrate similar proficiency in multipoint probabilistic predictions, QRF excels in smaller watersheds and CMAL-LSTM in larger ones. CMAL-LSTM performs better in single-point deterministic predictions, whereas QRF model is more efficient overall.
Léo C. P. Martin, Sebastian Westermann, Michele Magni, Fanny Brun, Joel Fiddes, Yanbin Lei, Philip Kraaijenbrink, Tamara Mathys, Moritz Langer, Simon Allen, and Walter W. Immerzeel
Hydrol. Earth Syst. Sci., 27, 4409–4436, https://doi.org/10.5194/hess-27-4409-2023, https://doi.org/10.5194/hess-27-4409-2023, 2023
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Across the Tibetan Plateau, many large lakes have been changing level during the last decades as a response to climate change. In high-mountain environments, water fluxes from the land to the lakes are linked to the ground temperature of the land and to the energy fluxes between the ground and the atmosphere, which are modified by climate change. With a numerical model, we test how these water and energy fluxes have changed over the last decades and how they influence the lake level variations.
Cited articles
Biancamaria, S., Lettenmaier, D. P., and Pavelsky, T. M.: The SWOT mission
and its capabilities for land hydrology, Springer International Publishing, 117–147, https://doi.org/10.1007/978-3-319-32449-4_6, 2016. a
Bierkens, M. F. P.: Global hydrology 2015: State, trends, and directions,
Water Resour. Res., 51, 4923–4947, https://doi.org/10.1002/2015wr017173, 2015. a, b
Birkinshaw, S. J., O'Donnell, G. M., Moore, P., Kilsby, C. G., Fowler, H. J.,
and Berry, P. A. M.: Using satellite altimetry data to augment flow
estimation techniques on the Mekong River, Hydrol. Process., 24,
3811–3825, https://doi.org/10.1002/hyp.7811, 2010. a
Biswas, N. K., Hossain, F., Bonnema, M., Lee, H., and Chishtie, F.: Towards a
global Reservoir Assessment Tool for predicting hydrologic impacts and
operating patterns of existing and planned reservoirs, Environ. Model. Softw., 140, 105043, https://doi.org/10.1016/j.envsoft.2021.105043, 2021. a
Bonnema, M. and Hossain, F.: Inferring reservoir operating patterns across the Mekong Basin using only space observations, Water Resour. Res., 53,
3791–3810, https://doi.org/10.1002/2016wr019978, 2017. a, b
Bose, I., Jayasinghe, S., Meechaiya, C., Ahmad, S. K., Biswas, N., and Hossain, F.: Developing a baseline characterization of river bathymetry and
time-Varying height for Chindwin River in Myanmar using SRTM and
Landsat data, J. Hydrol. Eng., 26, 05021030,
https://doi.org/10.1061/(asce)he.1943-5584.0002126, 2021. a
Chow, V. T.: Open-channel hydraulic, McGraw-Hill Book Company, New York, ISBN 007085906X, ISBN 9780070859067, 1959. a
Chowdhury, A. K., Dang, T. D., Nguyen, H. T. T., Koh, R., and Galelli, S.: The Greater Mekong's climate-water-energy nexus: How ENSO-triggered
regional droughts affect power supply and CO2 emissions, Earth's Future,
9, e2020ef001814, https://doi.org/10.1029/2020ef001814, 2021. a
Chuphal, D. S. and Mishra, V.: Increased hydropower but with an elevated risk
of reservoir operations in India under the warming climate, iScience, 26, 105986, https://doi.org/10.1016/j.isci.2023.105986, 2023. a
Costa-Cabral, M. C., Richey, J. E., Goteti, G., Lettenmaier, D. P.,
Feldkötter, C., and Snidvongs, A.: Landscape structure and use, climate, and water movement in the Mekong River Basin, Hydrol. Process., 22,
1731–1746, https://doi.org/10.1002/hyp.6740, 2007. a
Critical-Infrastructure-Systems-Lab: VICRes, GitHub [code], https://github.com/Critical-Infrastructure-Systems-Lab/VICRes (last access: 29 September 2023), 2023. a
Dahiti: Database for Hydrological Time Series of Inland Waters (DAHITI), https://dahiti.dgfi.tum.de/ (last access: 29 September 2023), 2023. a
Dan, L., Ji, J., Xie, Z., Chen, F., Wen, G., and Richey, J. E.: Hydrological
projections of climate change scenarios over the 3H region of China: A
VIC model assessment, J. Geogr. Res., 117, D11102,
https://doi.org/10.1029/2011jd017131, 2012. a
Dang, T. D., Chowdhury, A. K., and Galelli, S.: On the representation of water reservoir storage and operations in large-scale hydrological models:
implications on model parameterization and climate change impact assessments, Hydrol. Earth Syst. Sci., 24, 397–416, https://doi.org/10.5194/hess-24-397-2020, 2020a. a, b, c, d, e
Dang, T. D., Vu, D. T., Chowdhury, A. K., and Galelli, S.: A software package
for the representation and optimization of water reservoir operations in the
VIC hydrologic model, Environ. Model. Softw., 126, 104673,
https://doi.org/10.1016/j.envsoft.2020.104673, 2020b. a, b
Dawson, C. W., Abrahart, R. J., and See, L. M.: HydroTest: Further
development of a web resource for the standardised assessment of hydrological
models, Environ. Model. Softw., 25, 1481–1482, https://doi.org/10.1016/j.envsoft.2009.01.001, 2010. a
Döll, P., Berkhoff, K., Bormann, H., Fohrer, N., Gerten, D., Hagemann, S., and Krol, M.: Advances and visions in large-scale hydrological modelling:
findings from the 11th Workshop on large-scale hydrological modelling,
Adv. Geosci., 18, 51–56, https://doi.org/10.5194/adgeo-18-51-2008, 2008. a
dtvu2205: 210520, GitHub [code and data st], https://github.com/dtvu2205/210520 (last access: 29 September 2023), 2023. a
Du, T. L. T., Lee, H., Bui, D. D., Arheimer, B., Li, H.-Y., Olsson, J., Darby, S. E., Sheffield, J., Kim, D., and Hwang, E.: Streamflow prediction in
“geopolitically ungauged” basins using satellite observations and
regionalization at subcontinental scale, J. Hydrol., 588, 125016,
https://doi.org/10.1016/j.jhydrol.2020.125016, 2020. a
Durand, M., Gleason, C. J., Garambois, P. A., Bjerklie, D., Smith, L. C., Roux, H., Rodriguez, E., Bates, P. D., Pavelsky, T. M., Monnier, J., Chen, X., Baldassarre, G. D., Fiset, J.-M., Flipo, N., d. M. Frasson, R. P., Fulton, J., Goutal, N., Hossain, F., Humphries, E., Minear, J. T., Mukolwe, M. M., Neal, J. C., Ricci, S., Sanders, B. F., Schumann, G., Schubert, J. E., and Vilmin, L.: An intercomparison of remote sensing river discharge estimation algorithms from measurements of river height, width, and slope, Water Resour. Res., 52, 4527–4549, https://doi.org/10.1002/2015wr018434, 2016. a
Efstratiadis, A. and Koutsoyiannis, D.: One decade of multi-objective
calibration approaches in hydrological modelling: A review, Hydrolog. Sci. J., 55, 58–78, https://doi.org/10.1080/02626660903526292, 2010. a
Egüen, M., Aguilar, C., Herrero, J., Millares, A., and Polo, M. J.: On the influence of cell size in physically-based distributed hydrological modelling to assess extreme values in water resource planning, Nat. Hazards Earth Syst. Sci., 12, 1573–1582, https://doi.org/10.5194/nhess-12-1573-2012, 2012. a
Engineering ToolBox: Manning's roughness coefficients,
https://www.engineeringtoolbox.com/mannings-roughness-d_799.html (last access: 22 December 2022), 2004. a
Franchini, M. and Pacciani, M.: Comparative analysis of several conceptual
rainfall-runoff models, J. Hydrol., 122, 161–219, https://doi.org/10.1016/0022-1694(91)90178-k, 1991. a
Funk, C., Peterson, P., Landsfeld, M., Pedreros, D., Verdin, J., Shukla, S.,
Rowland, G. H. J., Harrison, L., and Michaelsen, A. H. J.: The climate
hazards infrared precipitation with stations – a new environmental record for monitoring extremes, Sci. Data, 2, 150066, https://doi.org/10.1038/sdata.2015.66, 2015. a
Galelli, S., Dang, T. D., Ng, J. Y., Chowdhury, A. K., and Arias, M. E.:
Opportunities to curb hydrological alterations via dam re-operation in the
Mekong, Nat. Sustainabil., 5, 1058–1069, https://doi.org/10.1038/s41893-022-00971-z, 2022. a
Gao, H., Birkett, C., and Lettenmaier, D. P.: Global monitoring of large
reservoir storage from satellite remote sensing, Water Resour. Res., 48, w09504, https://doi.org/10.1029/2012wr012063, 2012. a
Gleason, C. J. and Smith, L. C.: Toward global mapping of river discharge using satellite images and at-many-stations hydraulic geometry, P. Natl. Acad. Sci. USA, 111, 4788–4791, https://doi.org/10.1073/pnas.1317606111, 2014. a
Grill, G., Lehner, B., Thieme, M., Geenen, B., Tickner, D., Antonelli, F.,
Babu, S., Borrelli, P., Cheng, L., Crochetiere, H., Macedo, H. E.,
Filgueiras, R., Goichot, M., Higgins, J., Hogan, Z., Lip, B., McClain, M. E.,
Meng, J., Mulligan, M., Nilsson, C., Olden, J. D., Opperman, J. J., Petry,
P., Liermann, C. R., Sáenz, L., Salinas-Rodríguez, S., Schelle, P., Schmitt, R. J. P., Snider, J., Tan, F., Tockner, K., Valdujo, P. H., van Soesbergen, A., and Zarfl, C.: Mapping the world's free-flowing rivers, Nature, 59, 215–221, https://doi.org/10.1038/s41586-019-1111-9, 2019. 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
Haddeland, I., Heinke, J., Biemans, H., Eisner, S., Florke, M., Hanasaki, N.,
Konzmann, M., and Ludwig, F.: Global water resources affected by human
interventions and climate change, P. Natl. Acad. Sci. USA, 111, 3251–3256,
https://doi.org/10.1073/pnas.1222475110, 2014. a
Hagemann, M. W., Gleason, C. J., and Durand, M. T.: BAM: Bayesian
AMHG-Manning inference of discharge using remotely sensed stream width,
slope, and height, Water Resour. Res., 53, 9692–9707, https://doi.org/10.1002/2017wr021626, 2017. a
Hecht, J. S., Lacombe, G., Arias, M. E., Dang, T. D., and Pimanh, T.:
Hydropower dams of the Mekong River Basin: A review of their
hydrological impactss, J. Hydrol., 568, 285–300, https://doi.org/10.1016/j.jhydrol.2018.10.045, 2019. a
Hrachowitz, M., Savenije, H. H. G., Blöschl, G., McDonnell, J. J., Sivapalan,
M., Pomeroy, J. W., Arheimer, B., Blume, T., Clark, M. P., Ehret, U.,
Fenicia, F., Freer, J. E., Gelfan, A., Gupta, H. V., Hughes, D. A., Hut,
R. W., Montanari, A., Pande, S., Tetzlaff, D., Troch, P. A., Uhlenbrook, S.,
Wagener, T., Winsemius, H. C., Woods, R. A., Zehe, E., and Cudennec, C.: A
decade of Predictions in Ungauged Basins (PUB) – a review, Hydrolog. Sci. J., 58, 1198–1255, https://doi.org/10.1080/02626667.2013.803183, 2013. a
Huang, Q., Long, D., Du, M., Han, Z., and Han, P.: Daily continuous river
discharge estimation for ungauged basins using a hydrologic model calibrated
by satellite altimetry: Implications for the SWOT mission, Water Resour. Res., 56, e2020wr027309, https://doi.org/10.1029/2020wr027309, 2020. a
International Rivers: The environmental and social impacts of Lancang dams,
https://archive.internationalrivers.org/sites/default/files/attached-files/ir_lancang_dams_researchbrief_final.pdf
(last access: 22 April 2023), 2014. a
Johnson, K.: China commits to share year-round water data with Mekong River Commission, Reuters,
https://www.reuters.com/article/us-mekong-river/china-commits-to-share-year-round-water-data-with-mekong
(last access: 22 December 2022), 2020. a
JPL: SWOT: Surface Water and Ocean Topography,
https://swot.jpl.nasa.gov/ (last access: 22 December 2022), 2022. a
Kabir, T., Pokhrel, Y., and Felfelani, F.: On the precipitation-induced
uncertainties in process-based hydrological modeling in the Mekong River
Basin, Water Resour. Res., 58, e2021wr030828, https://doi.org/10.1029/2021wr030828, 2022. a, b, c
Khan, S. I., Hong, Y., Vergara, H. J., Gourley, J. J., Brakenridge, G. R.,
Groeve, T. D., Flamig, Z. L., Policelli, F., and Yong, B.: Microwave satellite data for hydrologic modeling in ungauged basins, IEEE Geosci. Remote Sens. Lett., 9, 663–667, https://doi.org/10.1109/lgrs.2011.2177807, 2012. a
Liang, X., Lettenmaier, D. P., Wood, E. F., and Burges, S. J.: A simple
hydrologically based model of land surface water and energy fluxes for
general circulation models, J. Geophys. Res.-Atmos., 99, 14415–14428, https://doi.org/10.1029/94jd00483, 2014. a
Lima, F. N., Fernandes, W., and Nascimento, N.: Joint calibration of a
hydrological model and rating curve parameters for simulation of flash flood
in urban areas, Brazil. J. Water Resour., 24, https://doi.org/10.1590/2318-0331.241920180066, 2019. a, b
Liu, G., Schwartz, F. W., Tseng, K.-H., and Shum, C. K.: Discharge and
water-depth estimates for ungauged rivers: Combining hydrologic, hydraulic,
and inverse modeling with stage and water-area measurements from satellites,
Water Resour. Res., 51, 6017–6035, https://doi.org/10.1002/2015wr016971, 2015. a
Lohmann, D., Nolte-Holube, R., and Raschke, E.: A large-scale horizontal
routing model to be coupled to land surface parametrization schemes, Tellus A, 48, 708–721, https://doi.org/10.3402/tellusa.v48i5.12200, 1996. a
Lohmann, D., Raschke, E., Nijssen, B., and Lettenmaier, D. P.: Regional scale
hydrology: I. Formulation of the VIC-2L model coupled to a routing
model, Hydrolog. Sci. J., 43, 131–141, https://doi.org/10.1080/02626669809492107, 1998. a
MRC: The flow of the Mekong, Mekong River Commission Secretariat, Vientiane, https://www.mrcmekong.org/assets/Publications/report-management-develop/MRC-IM-No2-the-flow-of-the-mekong.pdf (last access: 22 December 2022), 2009. a
MRC – Mekong River Commission: Discharge Time-series, https://portal.mrcmekong.org/time-series/discharge/ (last access: 22 December 2022), 2022. a
Nazemi, A. and Wheater, H. S.: On inclusion of water resource management in
Earth system models – Part 1: Problem definition and representation of
water demand, Hydrol. Earth Syst. Sci., 19, 33–61, https://doi.org/10.5194/hess-19-33-2015, 2015a. a
Nazemi, A. and Wheater, H. S.: On inclusion of water resource management in
Earth system models – Part 2: Representation of water supply and allocation and opportunities for improved modeling, Hydrol. Earth Syst. Sci., 19, 63–90, https://doi.org/10.5194/hess-19-63-2015, 2015b. a
Papa, F., Bala, S. K., Pandey, R. K., Durand, F., Gopalakrishna, V. V., Rahman, A., and Rossow, W. B.: Ganga-Brahmaputra river discharge from Jason-2 radar altimetry: An update to the long-term satellite-derived estimates of continental freshwater forcing flux into the Bay of Bengal, J. Geophys. Res., 117, c11021, https://doi.org/10.1029/2012jc008158, 2012. a
Park, D. and Markus, M.: Analysis of a changing hydrologic flood regime using
the Variable Infiltration Capacity model, J. Hydrol., 515, 627–280, https://doi.org/10.1016/j.jhydrol.2014.05.004, 2014. a
Pianosi, F., Beven, K., Freer, J., Hall, J. W., Rougier, J., Stephenson, D. B., and Wagener, T.: Sensitivity analysis of environmental models: A systematic review with practical workflow, Environ. Model. Softw., 79,
214–232, https://doi.org/10.1016/j.envsoft.2016.02.008, 2016. a
Reed, P. M., Hadka, D., Herman, J. D., Kasprzyk, J. R., and Kollat, J. B.:
Evolutionary multiobjective optimization in water resources: The past,
present, and future, Adv. Water Resour., 51, 438–456, https://doi.org/10.1016/j.advwatres.2012.01.005, 2013. a, b
Ren-Jun, Z.: The Xinanjiang model applied in China, J. Hydrol., 135, 371–381, https://doi.org/10.1016/0022-1694(92)90096-e, 1992. a
Shin, S., Pokhrel, Y., Yamazaki, D., Huang, X., Torbick, N., Qi, J.,
Pattanakiat, S., Ngo-Duc, T., and Nguyen, T. D.: High resolution modeling of
river-floodplain-reservoir inundation dynamics in the Mekong River Basin, Water Resour. Res., 56, e2019wr026449, https://doi.org/10.1029/2019wr026449, 2020. a
Steyaert, J. C., Condon, L. E., Turner, S. W. D., and Voisin, N.: ResOpsUS, a dataset of historical reservoir operations in the contiguous United
States, Sci. Data, 9, 1–8, https://doi.org/10.1038/s41597-022-01134-7, 2022. a
Sun, W., Fan, J., Wang, G., Ishidaira, H., Bastola, S., Yu, J., Fu, Y. H.,
Kiem, A. S., Zuo, D., and Xu, Z.: Calibrating a hydrological model in a
regional river of the Qinghai–Tibet plateau using river water width
determined from high spatial resolution satellite images, Remote Sens. Environ., 214, 100–114, https://doi.org/10.1016/j.rse.2018.05.020, 2018. a
Tarpanelli, A., Paris, A., Sichangi, A. W., OLoughlin, F., and Papa, F.: Water resources in Africa: The role of earth observation data and hydrodynamic modeling to derive river discharge, Surv. Geophys., 44, 97–122,
https://doi.org/10.1007/s10712-022-09744-x, 2022. a
Tatsumi, K. and Yamashiki, Y.: Effect of irrigation water withdrawals on water and energy balance in the Mekong River Basin using an improved VIC land surface model with fewer calibration parameters, Agr. Water Manage., 159, 92–106, https://doi.org/10.1016/j.agwat.2015.05.011, 2015. a
Todini, E.: The ARNO rainfall–runoff model, J. Hydrol., 175, 339–382, https://doi.org/10.1016/S0022-1694(96)80016-3, 1996. a
USGS – United States Geological Survey: Space Shuttle Radar Topography Mission (SRTM) DEM, https://earthexplorer.usgs.gov/ (last access: 22 December 2022), 2022. a
van Vliet, M. T. H., Wiberg, D., Leduc, S., and Riahi, K.: Power-generation
system vulnerability and adaptation to changes in climate and water resources, Nat. Clim. Change, 6, 375–380, https://doi.org/10.1038/nclimate2903, 2016. a
Vegad, U. and Mishra, V.: Ensemble streamflow prediction considering the
influence of reservoirs in India, Hydrol. Earth Syst. Sci., 26, 6361–6378, https://doi.org/10.5194/hess-26-6361-2022, 2022. a
Vu, D. T.: Codes and data of satellite observations reveal 13 years of
reservoir filling strategies, operating rules, and hydrological alterations
in the Upper Mekong River Basin, Zenodo [code and data set], https://doi.org/10.5281/zenodo.6299041, 2022. a
Vu, D. T., Dang, T. D., Galelli, S., and Hossain, F.: Satellite observations
reveal 13 years of reservoir filling strategies, operating rules, and
hydrological alterations in the Upper Mekong River basin, Hydrol. Earth Syst. Sci., 26, 2345–2364, https://doi.org/10.5194/hess-26-2345-2022, 2022. a, b, c, d, e, f
Wagener, T. and Pianosi, F.: What has Global Sensitivity Analysis ever
done for us? A systematic review to support scientific advancement and to
inform policy-making in earth system modelling, Earth-Sci. Rev., 194, 1–18, https://doi.org/10.1016/j.earscirev.2019.04.006, 2019. a
Wi, S., Ray, P., M.C.Demaria, E., Steinschneider, S., and Brown, C.: A
user-friendly software package for VIC hydrologic model development,
Environ. Model. Softw., 98, 35–53, https://doi.org/10.1016/j.envsoft.2017.09.006, 2017. a
WLE Mekong: Greater Mekong dam observatory,
https://wle-mekong.cgiar.org/changes/our-research/greater-mekong-dams-observatory/
(last access: 22 December 2022), 2022. a
Xiong, J., Guo, S., and Yin, J.: Discharge estimation using integrated
satellite data and hybrid model in the Midstream Yangtze River, Remote
Sens., 13, 2272, https://doi.org/10.3390/rs13122272, 2021. a
Xue, X., Zhang, K., Hong, Y., and Gourley, J. J.: New multisite cascading
calibration approach for hydrological models: Case study in the Red
River Basin using the VIC model, J. Hydrol. Eng., 21, 05015019, https://doi.org/10.5194/hess-23-3735-2019, 2015.
a
Yeste, P., Ojeda, M. G.-V., Gámiz-Fortis, S. R., Castro-Díez, Y., and
Esteban-Parra, M. J.: Integrated sensitivity analysis of a macroscale
hydrologic model in the north of the Iberian Peninsula, J. Hydrol., 590, 125230, https://doi.org/10.1016/j.jhydrol.2020.125230, 2020. a, b
Yun, X., Tang, Q., Wang, J., Liu, X., Zhang, Y., Lu, H., Wang, Y., Zhang, L.,
and Chen, D.: Impacts of climate change and reservoir operation on streamflow
and flood characteristics in the Lancang-Mekong River Basin, J. Hydrol., 590, 125472, https://doi.org/10.1016/j.jhydrol.2020.125472, 2020. a
Zhai, K., Wu, X., Qin, Y., and Du, P.: Comparison of surface water extraction
performances of different classic water indices using OLI and TM
imageries in different situations, Geospat. Inform. Sci., 18, 34–42, https://doi.org/10.1080/10095020.2015.1017911, 2015. a
Zhang, S., Gao, H., and Naz, B. S.: Monitoring reservoir storage in South
Asia from multisatellite remote sensing, Water Resour. Res., 50, 8927–8943, https://doi.org/10.1002/2014wr015829, 2014. a
Short summary
The calibration of hydrological models over extensive spatial domains is often challenged by the lack of data on river discharge and the operations of hydraulic infrastructures. Here, we use satellite data to address the lack of data that could unintentionally bias the calibration process. Our study is underpinned by a computational framework that quantifies this bias and provides a safe approach to the calibration of models in poorly gauged and heavily regulated basins.
The calibration of hydrological models over extensive spatial domains is often challenged by the...
