Articles | Volume 20, issue 9
https://doi.org/10.5194/hess-20-3561-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/hess-20-3561-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
06 Sep 2016
Research article |
| 06 Sep 2016
Error reduction and representation in stages (ERRIS) in hydrological modelling for ensemble streamflow forecasting
Ming Li
CORRESPONDING AUTHOR
CSIRO Data61, Floreat, WA, Australia
Q. J. Wang
CSIRO Land and Water, Clayton, VIC, Australia
James C. Bennett
CSIRO Land and Water, Clayton, VIC, Australia
David E. Robertson
CSIRO Land and Water, Clayton, VIC, Australia
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We assess a new streamflow forecasting system in Australia. The system is designed to meet the need of water agencies for 12-month forecasts. The forecasts perform well in a wide range of rivers. Forecasts for shorter periods (up to 6 months) are generally informative. Forecasts sometimes did not perform well in a few very dry rivers. We test several techniques for improving streamflow forecasts in drylands, with mixed success.
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Revised manuscript not accepted
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Lele Shu, Xiaodong Li, Yan Chang, Xianhong Meng, Hao Chen, Yuan Qi, Hongwei Wang, Zhaoguo Li, and Shihua Lyu
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Stephanie R. Clark, Julien Lerat, Jean-Michel Perraud, and Peter Fitch
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Luis Andres De la Fuente, Mohammad Reza Ehsani, Hoshin Vijai Gupta, and Laura Elizabeth Condon
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Long short-term memory (LSTM) is a widely used machine-learning model in hydrology, but it is difficult to extract knowledge from it. We propose HydroLSTM, which represents processes like a hydrological reservoir. Models based on HydroLSTM perform similarly to LSTM while requiring fewer cell states. The learned parameters are informative about the dominant hydrology of a catchment. Our results show how parsimony and hydrological knowledge extraction can be achieved by using the new structure.
Louise Mimeau, Annika Künne, Flora Branger, Sven Kralisch, Alexandre Devers, and Jean-Philippe Vidal
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Elena Macdonald, Bruno Merz, Björn Guse, Viet Dung Nguyen, Xiaoxiang Guan, and Sergiy Vorogushyn
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Claire Kouba and Thomas Harter
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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.
Maik Renner and Corina Hauffe
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-6, https://doi.org/10.5194/hess-2024-6, 2024
Revised manuscript accepted for HESS
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Climate and land-surface conditions influence the availability of fresh water resources. Their impact is quantified with data of 71 catchments in Saxony/Germany, for which distinct signatures in the joint water and energy budgets are found: (i) past forest dieback caused a decrease 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.
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.
Huy Dang and Yadu Pokhrel
EGUsphere, https://doi.org/10.5194/egusphere-2023-3158, https://doi.org/10.5194/egusphere-2023-3158, 2024
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By examining basin-wide simulations of the river regime over 83 years with and without dams, we present evidence that climate variation was a key driver of hydrologic variabilities in the Mekong River Basin (MRB) over the long-term; however, dams have largely altered the seasonality of Mekong’s flow regime and annual flooding patterns at major downstream areas in recent years. These findings could help rethink the planning of future dams and water resource management in the MRB.
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.
Diego Araya, Pablo A. Mendoza, Eduardo Muñoz-Castro, and James McPhee
Hydrol. Earth Syst. Sci., 27, 4385–4408, https://doi.org/10.5194/hess-27-4385-2023, https://doi.org/10.5194/hess-27-4385-2023, 2023
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Dynamical systems are used by many agencies worldwide to produce seasonal streamflow forecasts, which are critical for decision-making. Such systems rely on hydrology models, which contain parameters that are typically estimated using a target performance metric (i.e., objective function). This study explores the effects of this decision across mountainous basins in Chile, illustrating tradeoffs between seasonal forecast quality and the models' capability to simulate streamflow characteristics.
Alberto Montanari, Bruno Merz, and Günter Blöschl
EGUsphere, https://doi.org/10.5194/egusphere-2023-2420, https://doi.org/10.5194/egusphere-2023-2420, 2023
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Floods often take communities by surprise, as they are often considered virtually “impossible”, yet 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.
Pamela E. Tetford and Joseph R. Desloges
Hydrol. Earth Syst. Sci., 27, 3977–3998, https://doi.org/10.5194/hess-27-3977-2023, https://doi.org/10.5194/hess-27-3977-2023, 2023
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An efficient regional flood frequency model relates drainage area to discharge, with a major assumption of similar basin conditions. In a landscape with variable glacial deposits and land use, we characterize varying hydrological function using 28 explanatory variables. We demonstrate that (1) a heterogeneous landscape requires objective model selection criteria to optimize the fit of flow data, and (2) incorporating land use as a predictor variable improves the drainage area to discharge model.
Yalan Song, Wouter J. M. Knoben, Martyn P. Clark, Dapeng Feng, Kathryn E. Lawson, and Chaopeng Shen
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2023-258, https://doi.org/10.5194/hess-2023-258, 2023
Revised manuscript accepted for HESS
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Wouldn't it be nice to have both the accuracy of neural networks (NNs) and the interpretability of process-based models (PBMs)? Differentiable modeling gives you the best of both worlds by connecting NNs with PBMs. However, there was previously a major issue that iterative solution schemes would run into memory use trouble. This paper presents an operator called adjoint, which liberates all the iterative solvers. This is the first time adjoint is applied to large-scale hydrologic modeling.
Ana Ramos Oliveira, Tiago Brito Ramos, Lígia Pinto, and Ramiro Neves
Hydrol. Earth Syst. Sci., 27, 3875–3893, https://doi.org/10.5194/hess-27-3875-2023, https://doi.org/10.5194/hess-27-3875-2023, 2023
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This paper intends to demonstrate the adequacy of a hybrid solution to overcome the difficulties related to the incorporation of human behavior when modeling hydrological processes. Two models were implemented, one to estimate the outflow of a reservoir and the other to simulate the hydrological processes of the watershed. With both models feeding each other, results show that the proposed approach significantly improved the streamflow estimation downstream of the reservoir.
Nicolás Alamos, Camila Alvarez-Garreton, Ariel Muñoz, and Alvaro González-Reyes
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2023-246, https://doi.org/10.5194/hess-2023-246, 2023
Revised manuscript accepted for HESS
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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 three 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.
Zhihua He, Kevin Shook, Christopher Spence, John W. Pomeroy, and Colin Whitfield
Hydrol. Earth Syst. Sci., 27, 3525–3546, https://doi.org/10.5194/hess-27-3525-2023, https://doi.org/10.5194/hess-27-3525-2023, 2023
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This study evaluated the impacts of climate change on snowmelt, soil moisture, and streamflow over the Canadian Prairies. The entire prairie region was divided into seven basin types. We found strong variations of hydrological sensitivity to precipitation and temperature changes in different land covers and basins, which suggests that different water management and adaptation methods are needed to address enhanced water stress due to expected climate change in different regions of the prairies.
Nicolás Cortés-Salazar, Nicolás Vásquez, Naoki Mizukami, Pablo A. Mendoza, and Ximena Vargas
Hydrol. Earth Syst. Sci., 27, 3505–3524, https://doi.org/10.5194/hess-27-3505-2023, https://doi.org/10.5194/hess-27-3505-2023, 2023
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This paper shows how important river models can be for water resource applications that involve hydrological models and, in particular, parameter calibration. To this end, we conduct numerical experiments in a pilot basin using a combination of hydrologic model simulations obtained from a large sample of parameter sets and different routing methods. We find that routing can affect streamflow simulations, even at monthly time steps; the choice of parameters; and relevant streamflow metrics.
Dung Trung Vu, Thanh Duc Dang, Francesca Pianosi, and Stefano Galelli
Hydrol. Earth Syst. Sci., 27, 3485–3504, https://doi.org/10.5194/hess-27-3485-2023, https://doi.org/10.5194/hess-27-3485-2023, 2023
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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.
Francesco Fatone, Bartosz Szeląg, Przemysław Kowal, Arthur McGarity, Adam Kiczko, Grzegorz Wałek, Ewa Wojciechowska, Michał Stachura, and Nicolas Caradot
Hydrol. Earth Syst. Sci., 27, 3329–3349, https://doi.org/10.5194/hess-27-3329-2023, https://doi.org/10.5194/hess-27-3329-2023, 2023
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A novel methodology for the development of a stormwater network performance simulator including advanced risk assessment was proposed. The applied tool enables the analysis of the influence of spatial variability in catchment and stormwater network characteristics on the relation between (SWMM) model parameters and specific flood volume, as an alternative approach to mechanistic models. The proposed method can be used at the stage of catchment model development and spatial planning management.
Eduardo Acuña Espinoza, Ralf Loritz, Manuel Álvarez Chaves, Nicole Bäuerle, and Uwe Ehret
EGUsphere, https://doi.org/10.5194/egusphere-2023-1980, https://doi.org/10.5194/egusphere-2023-1980, 2023
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Hydrological hybrid models 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 LSTM networks. We explored this method to evaluate if the flexibility given by LSTM overwrites the interpretability of the process-based part. We showed that if a well-tested model architecture is combined with an LSTM, the latter can learn to operate the process-based model consistently.
Olivier Delaigue, Pierre Brigode, Guillaume Thirel, and Laurent Coron
Hydrol. Earth Syst. Sci., 27, 3293–3327, https://doi.org/10.5194/hess-27-3293-2023, https://doi.org/10.5194/hess-27-3293-2023, 2023
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Teaching hydrological modeling is an important, but difficult, matter. It requires appropriate tools and teaching material. In this article, we present the airGRteaching package, which is an open-source software tool relying on widely used hydrological models. This tool proposes an interface and numerous hydrological modeling exercises representing a wide range of hydrological applications. We show how this tool can be applied to simple but real-life cases.
Florian Willkofer, Raul Roger Wood, and Ralf Ludwig
EGUsphere, https://doi.org/10.5194/egusphere-2023-2019, https://doi.org/10.5194/egusphere-2023-2019, 2023
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Severe flood events pose threat to riverine areas, yet robust estimates about the dynamics of these events in the future due to climate change are rarely available. Hence, this study uses and benefits from data from a RCM SMILE to drive a high-resolution hydrological model for 98 catchments of the Hydrological Bavaria to exploit 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.
Mariam Khanam, Giulia Sofia, and Emmanouil N. Anagnostou
EGUsphere, https://doi.org/10.5194/egusphere-2023-1969, https://doi.org/10.5194/egusphere-2023-1969, 2023
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Due to climate change, flooding is expected to become more frequent globally in the coming decades. Locally, storm-induced channel geometry changes can drastically affect flood hazards, yet rivers are mostly treated as static elements in flood studies. This study tried to gain an understanding of the effects of major storm events on future flood hazards, promoting a framework for incorporating channel conveyance adjustments into flood hazard assessment.
Siyuan Wang, Markus Hrachowitz, Gerrit Schoups, and Christine Stumpp
Hydrol. Earth Syst. Sci., 27, 3083–3114, https://doi.org/10.5194/hess-27-3083-2023, https://doi.org/10.5194/hess-27-3083-2023, 2023
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This study shows that previously reported underestimations of water ages are most likely not due to the use of seasonally variable tracers. Rather, these underestimations can be largely attributed to the choices of model approaches which rely on assumptions not frequently met in catchment hydrology. We therefore strongly advocate avoiding the use of this model type in combination with seasonally variable tracers and instead adopting StorAge Selection (SAS)-based or comparable model formulations.
Arianna Borriero, Rohini Kumar, Tam V. Nguyen, Jan H. Fleckenstein, and Stefanie R. Lutz
Hydrol. Earth Syst. Sci., 27, 2989–3004, https://doi.org/10.5194/hess-27-2989-2023, https://doi.org/10.5194/hess-27-2989-2023, 2023
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We analyzed the uncertainty of the water transit time distribution (TTD) arising from model input (interpolated tracer data) and structure (StorAge Selection, SAS, functions). We found that uncertainty was mainly associated with temporal interpolation, choice of SAS function, nonspatial interpolation, and low-flow conditions. It is important to characterize the specific uncertainty sources and their combined effects on TTD, as this has relevant implications for both water quantity and quality.
Yves Tramblay, Patrick Arnaud, Guillaume Artigue, Michel Lang, Emmanuel Paquet, Luc Neppel, and Eric Sauquet
Hydrol. Earth Syst. Sci., 27, 2973–2987, https://doi.org/10.5194/hess-27-2973-2023, https://doi.org/10.5194/hess-27-2973-2023, 2023
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Mediterranean floods are causing major damage, and recent studies have shown that, despite the increase in intense rainfall, there has been no increase in river floods. This study reveals that the seasonality of floods changed in the Mediterranean Basin during 1959–2021. There was also an increased frequency of floods linked to short episodes of intense rain, associated with a decrease in soil moisture. These changes need to be taken into consideration to adapt flood warning systems.
Peter Reichert, Kai Ma, Marvin Höge, Fabrizio Fenicia, Marco Baity-Jesi, Dapeng Feng, and Chaopeng Shen
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2023-168, https://doi.org/10.5194/hess-2023-168, 2023
Revised manuscript accepted for HESS
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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.
Yanfeng Wu, Jingxuan Sun, Boting Hu, Y. Jun Xu, Alain N. Rousseau, and Guangxin Zhang
Hydrol. Earth Syst. Sci., 27, 2725–2745, https://doi.org/10.5194/hess-27-2725-2023, https://doi.org/10.5194/hess-27-2725-2023, 2023
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Reservoirs and wetlands are important regulators of watershed hydrology, which should be considered when projecting floods and droughts. We first coupled wetlands and reservoir operations into a semi-spatially-explicit hydrological model and then applied it in a case study involving a large river basin in northeast China. We found that, overall, the risk of future floods and droughts will increase further even under the combined influence of reservoirs and wetlands.
Peishi Jiang, Pin Shuai, Alexander Sun, Maruti K. Mudunuru, and Xingyuan Chen
Hydrol. Earth Syst. Sci., 27, 2621–2644, https://doi.org/10.5194/hess-27-2621-2023, https://doi.org/10.5194/hess-27-2621-2023, 2023
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We developed a novel deep learning approach to estimate the parameters of a computationally expensive hydrological model on only a few hundred realizations. Our approach leverages the knowledge obtained by data-driven analysis to guide the design of the deep learning model used for parameter estimation. We demonstrate this approach by calibrating a state-of-the-art hydrological model against streamflow and evapotranspiration observations at a snow-dominated watershed in Colorado.
Guillaume Cinkus, Naomi Mazzilli, Hervé Jourde, Andreas Wunsch, Tanja Liesch, Nataša Ravbar, Zhao Chen, and Nico Goldscheider
Hydrol. Earth Syst. Sci., 27, 2397–2411, https://doi.org/10.5194/hess-27-2397-2023, https://doi.org/10.5194/hess-27-2397-2023, 2023
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The Kling–Gupta Efficiency (KGE) is a performance criterion extensively used to evaluate hydrological models. We conduct a critical study on the KGE and its variant to examine counterbalancing errors. Results show that, when assessing a simulation, concurrent over- and underestimation of discharge can lead to an overall higher criterion score without an associated increase in model relevance. We suggest that one carefully choose performance criteria and use scaling factors.
Dapeng Feng, Hylke Beck, Kathryn Lawson, and Chaopeng Shen
Hydrol. Earth Syst. Sci., 27, 2357–2373, https://doi.org/10.5194/hess-27-2357-2023, https://doi.org/10.5194/hess-27-2357-2023, 2023
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Powerful hybrid models (called δ or delta models) embrace the fundamental learning capability of AI and can also explain the physical processes. Here we test their performance when applied to regions not in the training data. δ models rivaled the accuracy of state-of-the-art AI models under the data-dense scenario and even surpassed them for the data-sparse one. They generalize well due to the physical structure included. δ models could be ideal candidates for global hydrologic assessment.
Simon Ricard, Philippe Lucas-Picher, Antoine Thiboult, and François Anctil
Hydrol. Earth Syst. Sci., 27, 2375–2395, https://doi.org/10.5194/hess-27-2375-2023, https://doi.org/10.5194/hess-27-2375-2023, 2023
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A simplified hydroclimatic modelling workflow is proposed to quantify the impact of climate change on water discharge without resorting to meteorological observations. Results confirm that the proposed workflow produces equivalent projections of the seasonal mean flows in comparison to a conventional hydroclimatic modelling approach. The proposed approach supports the participation of end-users in interpreting the impact of climate change on water resources.
Zhen Cui, Shenglian Guo, Hua Chen, Dedi Liu, Yanlai Zhou, and Chong-Yu Xu
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2023-106, https://doi.org/10.5194/hess-2023-106, 2023
Revised manuscript accepted for HESS
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Ensemble forecasting facilitates reliable flood forecasting and warning. This study couples the copula-based hydrologic uncertainty processor (HUP) with the Bayesian model averaging (BMA) and proposes the novel CHUP-BMA method to reduce 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.
Cited articles
Ajami, N. K., Duan, Q. Y., and Sorooshian, S.: An integrated hydrologic Bayesian multimodel combination framework: Confronting input, parameter, and model structural uncertainty in hydrologic prediction, Water Resour. Res., 43, W01403, https://doi.org/10.1029/2005wr004745, 2007.
Alfieri, L., Burek, P., Dutra, E., Krzeminski, B., Muraro, D., Thielen, J., and Pappenberger, F.: GloFAS – global ensemble streamflow forecasting and flood early warning, Hydrol. Earth Syst. Sci., 17, 1161–1175, https://doi.org/10.5194/hess-17-1161-2013, 2013.
Bates, B. C. and Campbell, E. P.: A Markov chain Monte Carlo scheme for parameter estimation and inference in conceptual rainfall-runoff modeling, Water Resour. Res., 37, 937–947, https://doi.org/10.1029/2000wr900363, 2001.
Bennett, J. C., Robertson, D. E., Shrestha, D. L., Wang, Q. J., Enever, D., Hapuarachchi, P., and Tuteja, N. K.: A System for Continuous Hydrological Ensemble to lead times of 9 days Forecasting (SCHEF), J. Hydrol., 519, 2832–2846, https://doi.org/10.1016/j.jhydrol.2014.08.010, 2014a.
Bennett, J. C., Wang, Q. J., Pokhrel, P., and Robertson, D. E.: The challenge of forecasting high streamflows 1–3 months in advance with lagged climate indices in southeast Australia, Nat. Hazards Earth Syst. Sci., 14, 219–233, https://doi.org/10.5194/nhess-14-219-2014, 2014b.
Candille, G. and Talagrand, O.: Evaluation of probabilistic prediction systems for a scalar variable, Q. J. Roy. Meteor. Soc., 131, 2131–2150, https://doi.org/10.1256/qj.04.71, 2005.
Del Giudice, D., Honti, M., Scheidegger, A., Albert, C., Reichert, P., and Rieckermann, J.: Improving uncertainty estimation in urban hydrological modeling by statistically describing bias, Hydrol. Earth Syst. Sci., 17, 4209–4225, https://doi.org/10.5194/hess-17-4209-2013, 2013.
Demargne, J., Wu, L., Regonda, S. K., Brown, J. D., Lee, H., He, M., Seo, D.-J., Hartman, R., Herr, H. D., Fresch, M., Schaake, J., and Zhu, Y.: The Science of NOAA's Operational Hydrologic Ensemble Forecast Service, B. Am. Meteorol. Soc., 95, 79–98, https://doi.org/10.1175/bams-d-12-00081.1, 2014.
Diskin, M. H. and Simon, E.: A procedure for the selection of objective functions for hydrologic simulation models, J. Hydrol., 34, 129–149, https://doi.org/10.1016/0022-1694(77)90066-X, 1977.
Duan, Q. Y., Sorooshian, S., and Gupta, V. K.: Optimal Use of the Sce-Ua Global Optimization Method for Calibrating Watershed Models, J. Hydrol., 158, 265–284, https://doi.org/10.1016/0022-1694(94)90057-4, 1994.
Duan, Q. Y., Schaake, J., Andréassian, V., Franks, S., Goteti, G., Gupta, H. V., Gusev, Y. M., Habets, F., Hall, A., Hay, L., Hogue, T., Huang, M., Leavesley, G., Liang, X., Nasonova, O. N., Noilhan, J., Oudin, L., Sorooshian, S., Wagener, T., and Wood, E. F.: Model Parameter Estimation Experiment (MOPEX): An overview of science strategy and major results from the second and third workshops, J. Hydrol., 320, 3–17, https://doi.org/10.1016/j.jhydrol.2005.07.031, 2006.
Engeland, K., Renard, B., Steinsland, I., and Kolberg, S.: Evaluation of statistical models for forecast errors from the HBV model, J. Hydrol., 384, 142–155, https://doi.org/10.1016/j.jhydrol.2010.01.018, 2010.
Evin, G., Kavetski, D., Thyer, M., and Kuczera, G.: Pitfalls and improvements in the joint inference of heteroscedasticity and autocorrelation in hydrological model calibration, Water Resour. Res., 49, 4518–4524, https://doi.org/10.1002/wrcr.20284, 2013.
Evin, G., Thyer, M., Kavetski, D., McInerney, D., and Kuczera, G.: Comparison of joint versus postprocessor approaches for hydrological uncertainty estimation accounting for error autocorrelation and heteroscedasticity, Water Resour. Res., 50, 2350–2375, https://doi.org/10.1002/2013WR014185, 2014.
Gneiting, T. and Katzfuss, M.: Probabilistic Forecasting, Annu Rev. Stat. Appl., 1, 125–151, https://doi.org/10.1146/annurev-statistics-062713-085831, 2014.
Gneiting, T., Raftery, A. E., Westveld, A. H., and Goldman, T.: Calibrated probabilistic forecasting using ensemble model output statistics and minimum CRPS estimation, Mon. Weather Rev., 133, 1098–1118, https://doi.org/10.1175/Mwr2904.1, 2005.
Gneiting, T., Balabdaoui, F., and Raftery, A. E.: Probabilistic forecasts, calibration and sharpness, J. Roy. Stat. Soc. B, 69, 243–268, https://doi.org/10.1111/j.1467-9868.2007.00587.x, 2007.
Gragne, A. S., Sharma, A., Mehrotra, R., and Alfredsen, K.: Improving real-time inflow forecasting into hydropower reservoirs through a complementary modelling framework, Hydrol. Earth Syst. Sci., 19, 3695–3714, https://doi.org/10.5194/hess-19-3695-2015, 2015.
Grimit, E. P., Gneiting, T., Berrocal, V. J., and Johnson, N. A.: The continuous ranked probability score for circular variables and its application to mesoscale forecast ensemble verification, Q. J. Roy. Meteor. Soc., 132, 2925–2942, https://doi.org/10.1256/qj.05.235, 2006.
Hamill, T. M.: Interpretation of rank histograms for verifying ensemble forecasts, Mon. Weather Rev., 129, 550–560, https://doi.org/10.1175/1520-0493(2001)129<0550:IORHFV>2.0.CO;2, 2001.
Hersbach, H.: Decomposition of the continuous ranked probability score for ensemble prediction systems, Weather Forecast., 15, 559–570, https://doi.org/10.1175/1520-0434(2000)015<0559:DOTCRP>2.0.CO;2, 2000.
Jones, D. A., Wang, W., and Fawcett, R.: High-quality spatial climate data-sets for Australia, Australian Meteorological and Oceanographic Journal, 58, 233–248, 2009.
Kavetski, D., Kuczera, G., and Franks, S. W.: Bayesian analysis of input uncertainty in hydrological modeling: 1. Theory, Water Resour. Res., 42, W03407, https://doi.org/10.1029/2005wr004368, 2006a.
Kavetski, D., Kuczera, G., and Franks, S. W.: Bayesian analysis of input uncertainty in hydrological modeling: 2. Application, Water Resour. Res., 42, W03408, https://doi.org/10.1029/2005wr004376, 2006b.
Kelly, K. S. and Krzysztofowicz, R.: A bivariate meta-Gaussian density for use in hydrology, Stoch. Hydrol. Hydraul., 11, 17–31, https://doi.org/10.1007/Bf02428423, 1997.
Krzysztofowicz, R.: Transformation and normalization of variates with specified distributions, J. Hydrol., 197, 286–292, https://doi.org/10.1016/S0022-1694(96)03276-3, 1997.
Krzysztofowicz, R.: Bayesian theory of probabilistic forecasting via deterministic hydrologic model, Water Resour. Res., 35, 2739–2750, https://doi.org/10.1029/1999wr900099, 1999.
Krzysztofowicz, R.: The case for probabilistic forecasting in hydrology, J. Hydrol., 249, 2–9, https://doi.org/10.1016/S0022-1694(01)00420-6, 2001.
Krzysztofowicz, R. and Kelly, K. S.: Hydrologic uncertainty processor for probabilistic river stage forecasting, Water Resour. Res., 36, 3265–3277, https://doi.org/10.1029/2000WR900108, 2000.
Kuczera, G.: Improved Parameter Inference in Catchment Models. 1. Evaluating Parameter Uncertainty, Water Resour. Res., 19, 1151–1162, https://doi.org/10.1029/WR019i005p01151, 1983.
Kuczera, G., Kavetski, D., Franks, S., and Thyer, M.: Towards a Bayesian total error analysis of conceptual rainfall-runoff models: Characterising model error using storm-dependent parameters, J. Hydrol., 331, 161–177, https://doi.org/10.1016/j.jhydrol.2006.05.010, 2006.
Laio, F. and Tamea, S.: Verification tools for probabilistic forecasts of continuous hydrological variables, Hydrol. Earth Syst. Sci., 11, 1267–1277, https://doi.org/10.5194/hess-11-1267-2007, 2007.
Li, M., Wang, Q. J., and Bennett, J. C.: Accounting for seasonal dependence in hydrological model errors and prediction uncertainty, Water Resour. Res., 49, 5913–5929, https://doi.org/10.1002/wrcr.20445, 2013.
Li, M., Wang, Q. J., Bennett, J. C., and Robertson, D. E.: A strategy to overcome adverse effects of autoregressive updating of streamflow forecasts, Hydrol. Earth Syst. Sci., 19, 1–15, https://doi.org/10.5194/hess-19-1-2015, 2015.
Marshall, L., Sharma, A., and Nott, D.: Modeling the catchment via mixtures: Issues of model specification and validation, Water Resour. Res., 42, W11409, https://doi.org/10.1029/2005WR004613, 2006.
Montanari, A. and Brath, A.: A stochastic approach for assessing the uncertainty of rainfall-runoff simulations, Water Resour. Res., 40, W01106, https://doi.org/10.1029/2003wr002540, 2004.
Moradkhani, H., Hsu, K. L., Gupta, H., and Sorooshian, S.: Uncertainty assessment of hydrologic model states and parameters: Sequential data assimilation using the particle filter, Water Resour. Res., 41, W05012, https://doi.org/10.1029/2004wr003604, 2005.
Nash, J. E. and Sutcliffe, J. V.: River flow forecasting through conceptual models part I – A discussion of principles, J. Hydrol., 10, 282–290, https://doi.org/10.1016/0022-1694(70)90255-6, 1970.
Nelder, J. A. and Mead, R.: A Simplex Method for Function Minimization, The Computer Journal, 7, 308–313, https://doi.org/10.1093/comjnl/7.4.308, 1965.
NOAA National Weather Service: MOPEX (Model Parameter Estimation Experiment), available at: http://www.nws.noaa.gov/ohd/mopex/mo_datasets.htm, last access: August 2016.
Peng, Z. L., Wang, Q. J., Bennett, J. C., Schepen, A., Pappenberger, F., Pokhrel, P., and Wang, Z. R.: Statistical calibration and bridging of ECMWF System4 outputs for forecasting seasonal precipitation over China, J. Geophys Res.-Atmos., 119, 7116–7135, https://doi.org/10.1002/2013JD021162, 2014.
Perrin, C., Michel, C., and Andreassian, V.: Improvement of a parsimonious model for streamflow simulation, J. Hydrol., 279, 275–289, https://doi.org/10.1016/S0022-1694(03)00225-7, 2003.
Pianosi, F. and Raso, L.: Dynamic modeling of predictive uncertainty by regression on absolute errors, Water Resour. Res., 48, W03516, https://doi.org/10.1029/2011wr010603, 2012.
Reggiani, P., Renner, M., Weerts, A. H., and van Gelder, P. H. A. J. M.: Uncertainty assessment via Bayesian revision of ensemble streamflow predictions in the operational river Rhine forecasting system, Water Resour. Res., 45, W02428, https://doi.org/10.1029/2007wr006758, 2009.
Renard, B., Kavetski, D., Kuczera, G., Thyer, M., and Franks, S. W.: Understanding predictive uncertainty in hydrologic modeling: The challenge of identifying input and structural errors, Water Resour. Res., 46, W05521, https://doi.org/10.1029/2009wr008328, 2010.
Robertson, D. E., Shrestha, D. L., and Wang, Q. J.: Post-processing rainfall forecasts from numerical weather prediction models for short-term streamflow forecasting, Hydrol. Earth Syst. Sci., 17, 3587–3603, https://doi.org/10.5194/hess-17-3587-2013, 2013.
Schaefli, B., Talamba, D. B., and Musy, A.: Quantifying hydrological modeling errors through a mixture of normal distributions, J. Hydrol., 332, 303–315, https://doi.org/10.1016/j.jhydrol.2006.07.005, 2007.
Schoups, G. and Vrugt, J. A.: A formal likelihood function for parameter and predictive inference of hydrologic models with correlated, heteroscedastic, and non-Gaussian errors, Water Resour. Res., 46, W10531, https://doi.org/10.1029/2009wr008933, 2010.
Shrestha, D. L., Robertson, D. E., Bennett, J. C., and Wang, Q. J.: Improving Precipitation Forecasts by Generating Ensembles through Postprocessing, Mon. Weather Rev., 143, 3642–3663, https://doi.org/10.1175/MWR-D-14-00329.1, 2015.
Shrestha, D. L., Robertson, D. E., Wang, Q. J., Pagano, T. C., and Hapuarachchi, H. A. P.: Evaluation of numerical weather prediction model precipitation forecasts for short-term streamflow forecasting purpose, Hydrol. Earth Syst. Sci., 17, 1913–1931, https://doi.org/10.5194/hess-17-1913-2013, 2013.
Smith, T., Sharma, A., Marshall, L., Mehrotra, R., and Sisson, S.: Development of a formal likelihood function for improved Bayesian inference of ephemeral catchments, Water Resour. Res., 46, W12551, https://doi.org/10.1029/2010wr009514, 2010.
Solomatine, D. P. and Shrestha, D. L.: A novel method to estimate model uncertainty using machine learning techniques, Water Resour. Res., 45, W00B11, https://doi.org/10.1029/2008wr006839, 2009.
Sorooshian, S. and Dracup, J. A.: Stochastic parameter-estimation procedures for hydrologic rainfall-runoff models: Correlated and heteroscedastic error cases, Water Resour. Res., 16, 430–442, https://doi.org/10.1029/WR016i002p00430, 1980.
Thielen, J., Bartholmes, J., Ramos, M.-H., and de Roo, A.: The European Flood Alert System – Part 1: Concept and development, Hydrol. Earth Syst. Sci., 13, 125–140, https://doi.org/10.5194/hess-13-125-2009, 2009.
Thyer, M., Kuczera, G., and Wnag, Q. J.: Quantifying parameter uncertainty in stochastic models using the Box-Cox transformation, J. Hydrol., 265, 246–257, https://doi.org/10.1016/S0022-1694(02)00113-0, 2002.
Todini, E.: A model conditional processor to assess predictive uncertainty in flood forecasting, International Journal of River Basin Management, 6, 123–137, https://doi.org/10.1080/15715124.2008.9635342, 2008.
Vaze, J., Perraud, J. M., Teng, J., Chiew, F. H. S., Wang, B., and Yang, Z.: Catchment Water Yield Estimation Tools (CWYET), in: Proceedings of the 34th World Congress of the International Association for Hydro-Environment Research and Engineering: 33rd Hydrology and Water Resources Symposium and 10th Conference on Hydraulics in Water Engineering, edited by: Valentine, E., Apelt, C., Ball, J., Chanson, H., and Sargison, J., Engineers Australia, Brisbane, 1554–1561, 2011.
Vrugt, J. A., Diks, C. G. H., Gupta, H. V., Bouten, W., and Verstraten, J. M.: Improved treatment of uncertainty in hydrologic modeling: Combining the strengths of global optimization and data assimilation, Water Resour. Res., 41, W01017, https://doi.org/10.1029/2004wr003059, 2005.
Wang, Q. J. and Robertson, D. E.: Multisite probabilistic forecasting of seasonal flows for streams with zero value occurrences, Water Resour. Res., 47, W02546, https://doi.org/10.1029/2010WR009333, 2011.
Wang, Q. J., Robertson, D. E., and Chiew, F. H. S.: A Bayesian joint probability modeling approach for seasonal forecasting of streamflows at multiple sites, Water Resour. Res., 45, W05407, https://doi.org/10.1029/2008WR007355, 2009.
Wang, Q. J., Shrestha, D. L., Robertson, D. E., and Pokhrel, P.: A log-sinh transformation for data normalization and variance stabilization, Water Resour. Res., 48, W05514, https://doi.org/10.1029/2011WR010973, 2012.
Wang, Q. J., Bennett, J. C., Schepen, A., Robertson, D. E., Song, Y., and Li, M.: FoGSS – A model for generating forecast guided stochastic scenarios of monthly streamflows out to 12 months, CSIRO Water for a Healthy Country Flagship, Highett, Australia, Report, 34 pp., 2014.
Xiong, L. H. and O'Connor, K. M.: Comparison of four updating models for real-time river flow forecasting, Hydrolog. Sci. J., 47, 621–639, https://doi.org/10.1080/02626660209492964, 2002.
Yang, J., Reichert, P., Abbaspour, K. C., and Yang, H.: Hydrological modelling of the chaohe basin in china: Statistical model formulation and Bayesian inference, J. Hydrol., 340, 167–182, https://doi.org/10.1016/j.jhydrol.2007.04.006, 2007.
Zhao, T., Wang, Q. J., Bennett, J. C., Robertson, D. E., Shao, Q., and Zhao, J.: Quantifying predictive uncertainty of streamflow forecasts based on a Bayesian joint probability model, J. Hydrol., 528, 329–340, https://doi.org/10.1016/j.jhydrol.2015.06.043, 2015.
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