Articles | Volume 20, issue 12
https://doi.org/10.5194/hess-20-4949-2016
© 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-20-4949-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
16 Dec 2016
Research article |
| 16 Dec 2016
Identification of hydrological model parameter variation using ensemble Kalman filter
Chao Deng
State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan, China
Hubei Provincial Collaborative Innovation Center for Water Resources Security, Wuhan, China
State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan, China
Hubei Provincial Collaborative Innovation Center for Water Resources Security, Wuhan, China
Shenglian Guo
State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan, China
Hubei Provincial Collaborative Innovation Center for Water Resources Security, Wuhan, China
Zejun Li
State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan, China
Hubei Provincial Collaborative Innovation Center for Water Resources Security, Wuhan, China
Dingbao Wang
Department of Civil, Environmental & Construction Engineering, University of Central Florida, Orlando, FL, USA
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Manuscript not accepted for further review
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Hydrological model parameters may not be constant in a changing environment, i.e., climate change and human activities. The technique of ensemble Kalman filter (EnKF) is proposed to identify the temporal variation of parameters for a two-parameter monthly water balance model. Through a synthesis experiment and two case studies, the EnKF is demonstrated to be useful for the identification of parameter variation. The temporal variation parameter can be explained by catchment characteristic.
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Understanding the projection performance of hydrological models under contrasting climatic conditions supports robust decision making, which highlights the need to adopt time-varying parameters in hydrological modeling to reduce performance degradation. This study improves our understanding of the spatial coherence of time-varying parameters, which will help improve the projection performance under differing climatic conditions.
Dingbao Wang
Hydrol. Earth Syst. Sci., 22, 6567–6578, https://doi.org/10.5194/hess-22-6567-2018, https://doi.org/10.5194/hess-22-6567-2018, 2018
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A novel distribution function is proposed for describing the spatial distribution of soil water storage capacity, and then the classical and empirical hydrologic model (the SCS curve number method) is derived as when the initial soil water storage is zero. This distribution function unifies the SCS curve number method and probability-distributed models such as the VIC and Xinanjiang models. The unified model provides a better way for modeling surface runoff.
Lei Ye, Lars S. Hanson, Pengqi Ding, Dingbao Wang, and Richard M. Vogel
Hydrol. Earth Syst. Sci., 22, 6519–6531, https://doi.org/10.5194/hess-22-6519-2018, https://doi.org/10.5194/hess-22-6519-2018, 2018
Yanlai Zhou, Fi-John Chang, Shenglian Guo, Huanhuan Ba, and Shaokun He
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2017-457, https://doi.org/10.5194/hess-2017-457, 2017
Revised manuscript not accepted
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Developing a robust recurrent ANFIS for modeling multi-step-ahead flood forecast. Fusing the LSE into GA for optimizing the parameters of recurrent ANFIS. Improving the robustness and generalization of recurrent ANFIS. An accurate and robust multi-step-ahead inflow forecast in the Three Gorges Reservoir will provide precious decision-making time for effectively managing contingencies and emergencies and greatly alleviating flood risk as well as loss of life and property.
Lingqi Li, Lihua Xiong, Chong-Yu Xu, Shenglian Guo, and Pan Liu
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2016-619, https://doi.org/10.5194/hess-2016-619, 2016
Revised manuscript not accepted
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The study offers insights into future design floods that are inferred with both AM and POT samplings under nonstationarity caused by changing climate. Future design floods in nonstationarity context are usually (lower than) but not necessarily more different from stationary estimates. AM-based projection is more sensitive to climate change than POT estimates. The over-dispersion in POT arrival rate leads to the invalidation of Poisson assumption that the misuse may induce overestimated floods.
Chao Deng, Pan Liu, Shenglian Guo, Zejun Li, and Dingbao Wang
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2015-407, https://doi.org/10.5194/hess-2015-407, 2016
Manuscript not accepted for further review
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Hydrological model parameters may not be constant in a changing environment, i.e., climate change and human activities. The technique of ensemble Kalman filter (EnKF) is proposed to identify the temporal variation of parameters for a two-parameter monthly water balance model. Through a synthesis experiment and two case studies, the EnKF is demonstrated to be useful for the identification of parameter variation. The temporal variation parameter can be explained by catchment characteristic.
U. Ehret, H. V. Gupta, M. Sivapalan, S. V. Weijs, S. J. Schymanski, G. Blöschl, A. N. Gelfan, C. Harman, A. Kleidon, T. A. Bogaard, D. Wang, T. Wagener, U. Scherer, E. Zehe, M. F. P. Bierkens, G. Di Baldassarre, J. Parajka, L. P. H. van Beek, A. van Griensven, M. C. Westhoff, and H. C. Winsemius
Hydrol. Earth Syst. Sci., 18, 649–671, https://doi.org/10.5194/hess-18-649-2014, https://doi.org/10.5194/hess-18-649-2014, 2014
X. Chen and D. Wang
Hydrol. Earth Syst. Sci., 17, 4283–4296, https://doi.org/10.5194/hess-17-4283-2013, https://doi.org/10.5194/hess-17-4283-2013, 2013
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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.
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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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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.
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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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Hydrologists strive to “Be right for the right reasons” when modeling the hydrologic cycle; however, the datasets available to validate hydrological models are sparse, and in many cases, they comprise streamflow observations at the outlets of large catchments. In this work, we show that matching streamflow observations at the outlet of a large basin is not a reliable indicator of a correct description of the small-scale runoff processes.
Lillian M. McGill, E. Ashley Steel, and Aimee H. Fullerton
Hydrol. Earth Syst. Sci., 28, 1351–1371, https://doi.org/10.5194/hess-28-1351-2024, https://doi.org/10.5194/hess-28-1351-2024, 2024
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This study examines the relationship between air and river temperatures in Washington's Snoqualmie and Wenatchee basins. We used classification and regression approaches to show that the sensitivity of river temperature to air temperature is variable across basins and controlled largely by geology and snowmelt. Findings can be used to inform strategies for river basin restoration and conservation, such as identifying climate-insensitive areas of the basin that should be preserved and protected.
Stephanie R. Clark, Julien Lerat, Jean-Michel Perraud, and Peter Fitch
Hydrol. Earth Syst. Sci., 28, 1191–1213, https://doi.org/10.5194/hess-28-1191-2024, https://doi.org/10.5194/hess-28-1191-2024, 2024
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To determine if deep learning models are in general a viable alternative to traditional hydrologic modelling techniques in Australian catchments, a comparison of river–runoff predictions is made between traditional conceptual models and deep learning models in almost 500 catchments spread over the continent. It is found that the deep learning models match or outperform the traditional models in over two-thirds of the river catchments, indicating feasibility in a wide variety of conditions.
Dipti Tiwari, Mélanie Trudel, and Robert Leconte
Hydrol. Earth Syst. Sci., 28, 1127–1146, https://doi.org/10.5194/hess-28-1127-2024, https://doi.org/10.5194/hess-28-1127-2024, 2024
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Calibrating hydrological models with multi-objective functions enhances model robustness. By using spatially distributed snow information in the calibration, the model performance can be enhanced without compromising the outputs. In this study the HYDROTEL model was calibrated in seven different experiments, incorporating the SPAEF (spatial efficiency) metric alongside Nash–Sutcliffe efficiency (NSE) and root-mean-square error (RMSE), with the aim of identifying the optimal calibration strategy.
Luis Andres De la Fuente, Mohammad Reza Ehsani, Hoshin Vijai Gupta, and Laura Elizabeth Condon
Hydrol. Earth Syst. Sci., 28, 945–971, https://doi.org/10.5194/hess-28-945-2024, https://doi.org/10.5194/hess-28-945-2024, 2024
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Long short-term memory (LSTM) is a widely used machine-learning model in hydrology, but it is difficult to extract knowledge from it. We propose HydroLSTM, which represents processes like a hydrological reservoir. Models based on HydroLSTM perform similarly to LSTM while requiring fewer cell states. The learned parameters are informative about the dominant hydrology of a catchment. Our results show how parsimony and hydrological knowledge extraction can be achieved by using the new structure.
Louise Mimeau, Annika Künne, Flora Branger, Sven Kralisch, Alexandre Devers, and Jean-Philippe Vidal
Hydrol. Earth Syst. Sci., 28, 851–871, https://doi.org/10.5194/hess-28-851-2024, https://doi.org/10.5194/hess-28-851-2024, 2024
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Modelling flow intermittence is essential for predicting the future evolution of drying in river networks and better understanding the ecological and socio-economic impacts. However, modelling flow intermittence is challenging, and observed data on temporary rivers are scarce. This study presents a new modelling approach for predicting flow intermittence in river networks and shows that combining different sources of observed data reduces the model uncertainty.
Elena Macdonald, Bruno Merz, Björn Guse, Viet Dung Nguyen, Xiaoxiang Guan, and Sergiy Vorogushyn
Hydrol. Earth Syst. Sci., 28, 833–850, https://doi.org/10.5194/hess-28-833-2024, https://doi.org/10.5194/hess-28-833-2024, 2024
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In some rivers, the occurrence of extreme flood events is more likely than in other rivers – they have heavy-tailed distributions. We find that threshold processes in the runoff generation lead to such a relatively high occurrence probability of extremes. Further, we find that beyond a certain return period, i.e. for rare events, rainfall is often the dominant control compared to runoff generation. Our results can help to improve the estimation of the occurrence probability of extreme floods.
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.
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.
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.
Qiutong Yu, Bryan A. Tolson, Hongren Shen, Ming Han, Juliane Mai, and Jimmy Lin
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2023-237, https://doi.org/10.5194/hess-2023-237, 2023
Revised manuscript accepted for HESS
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It is challenging to incorporate the spatial distribution information of input variables when implementing LSTM models for streamflow prediction. This paper presents a novel hybrid modeling 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 in predicting streamflow at large ungauged basin.
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.
Fengjing Liu, Martha H. Conklin, and Glenn D. Shaw
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2023-230, https://doi.org/10.5194/hess-2023-230, 2023
Revised manuscript accepted for HESS
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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 water falls in summer and negatively affecting tourism. Groundwater recharge in Yosemite Valley is primarily from the upper snow-rain transition (2,000–2,500m) and very vulnerable to shift in the snow-rain ratio.
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.
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.
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.
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.
Marcus Edmund Henry Buechel, Louise Slater, and Simon Dadson
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2023-138, https://doi.org/10.5194/hess-2023-138, 2023
Revised manuscript accepted for HESS
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Afforestation has been proposed internationally, but the hydrological implications of such large increases in 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.
Nutchanart Sriwongsitanon, Wasana Jandang, James Williams, Thienchart Suwawong, Ekkarin Maekan, and Hubert H. G. Savenije
Hydrol. Earth Syst. Sci., 27, 2149–2171, https://doi.org/10.5194/hess-27-2149-2023, https://doi.org/10.5194/hess-27-2149-2023, 2023
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We developed predictive semi-distributed rainfall–runoff models for nested sub-catchments in the upper Ping basin, which yielded better or similar performance compared to calibrated lumped models. The normalised difference infrared index proves to be an effective proxy for distributed root zone moisture capacity over sub-catchments and is well correlated with the percentage of evergreen forest. In validation, soil moisture simulations appeared to be highly correlated with the soil wetness index.
Yuchan Chen, Xiuzhi Chen, Meimei Xue, Chuanxun Yang, Wei Zheng, Jun Cao, Wenting Yan, and Wenping Yuan
Hydrol. Earth Syst. Sci., 27, 1929–1943, https://doi.org/10.5194/hess-27-1929-2023, https://doi.org/10.5194/hess-27-1929-2023, 2023
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This study addresses the quantification and estimation of the watershed-characteristic-related parameter (Pw) in the Budyko framework with the principle of hydrologically similar groups. The results show that Pw is closely related to soil moisture and fractional vegetation cover, and the relationship varies across specific hydrologic similarity groups. The overall satisfactory performance of the Pw estimation model improves the applicability of the Budyko framework for global runoff estimation.
Lena Katharina Schmidt, Till Francke, Peter Martin Grosse, Christoph Mayer, and Axel Bronstert
Hydrol. Earth Syst. Sci., 27, 1841–1863, https://doi.org/10.5194/hess-27-1841-2023, https://doi.org/10.5194/hess-27-1841-2023, 2023
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We present a suitable method to reconstruct sediment export from decadal records of hydroclimatic predictors (discharge, precipitation, temperature) and shorter suspended sediment measurements. This lets us fill the knowledge gap on how sediment export from glacierized high-alpine areas has responded to climate change. We find positive trends in sediment export from the two investigated nested catchments with step-like increases around 1981 which are linked to crucial changes in glacier melt.
Samantha Petch, Bo Dong, Tristan Quaife, Robert P. King, and Keith Haines
Hydrol. Earth Syst. Sci., 27, 1723–1744, https://doi.org/10.5194/hess-27-1723-2023, https://doi.org/10.5194/hess-27-1723-2023, 2023
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Gravitational measurements of water storage from GRACE (Gravity Recovery and Climate Experiment) can improve understanding of the water budget. We produce flux estimates over large river catchments based on observations that close the monthly water budget and ensure consistency with GRACE on short and long timescales. We use energy data to provide additional constraints and balance the long-term energy budget. These flux estimates are important for evaluating climate models.
Ting Su, Chiyuan Miao, Qingyun Duan, Jiaojiao Gou, Xiaoying Guo, and Xi Zhao
Hydrol. Earth Syst. Sci., 27, 1477–1492, https://doi.org/10.5194/hess-27-1477-2023, https://doi.org/10.5194/hess-27-1477-2023, 2023
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The Three-River Source Region (TRSR) plays an extremely important role in water resources security and ecological and environmental protection in China and even all of Southeast Asia. This study used the variable infiltration capacity (VIC) land surface hydrologic model linked with the degree-day factor algorithm to simulate the runoff change in the TRSR. These results will help to guide current and future regulation and management of water resources in the TRSR.
Andreas Hartmann, Jean-Lionel Payeur-Poirier, and Luisa Hopp
Hydrol. Earth Syst. Sci., 27, 1325–1341, https://doi.org/10.5194/hess-27-1325-2023, https://doi.org/10.5194/hess-27-1325-2023, 2023
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We advance our understanding of including information derived from environmental tracers into hydrological modeling. We present a simple approach that integrates streamflow observations and tracer-derived streamflow contributions for model parameter estimation. We consider multiple observed streamflow components and their variation over time to quantify the impact of their inclusion for streamflow prediction at the catchment scale.
Dharmaveer Singh, Manu Vardhan, Rakesh Sahu, Debrupa Chatterjee, Pankaj Chauhan, and Shiyin Liu
Hydrol. Earth Syst. Sci., 27, 1047–1075, https://doi.org/10.5194/hess-27-1047-2023, https://doi.org/10.5194/hess-27-1047-2023, 2023
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This study examines, for the first time, the potential of various machine learning models in streamflow prediction over the Sutlej River basin (rainfall-dominated zone) in western Himalaya during the period 2041–2070 (2050s) and 2071–2100 (2080s) and its relationship to climate variability. The mean ensemble of the model results shows that the mean annual streamflow of the Sutlej River is expected to rise between the 2050s and 2080s by 0.79 to 1.43 % for SSP585 and by 0.87 to 1.10 % for SSP245.
Monica Coppo Frias, Suxia Liu, Xingguo Mo, Karina Nielsen, Heidi Ranndal, Liguang Jiang, Jun Ma, and Peter Bauer-Gottwein
Hydrol. Earth Syst. Sci., 27, 1011–1032, https://doi.org/10.5194/hess-27-1011-2023, https://doi.org/10.5194/hess-27-1011-2023, 2023
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This paper uses remote sensing data from ICESat-2 to calibrate a 1D hydraulic model. With the model, we can make estimations of discharge and water surface elevation, which are important indicators in flooding risk assessment. ICESat-2 data give an added value, thanks to the 0.7 m resolution, which allows the measurement of narrow river streams. In addition, ICESat-2 provides measurements on the river dry portion geometry that can be included in the model.
Evgenia Koltsida, Nikos Mamassis, and Andreas Kallioras
Hydrol. Earth Syst. Sci., 27, 917–931, https://doi.org/10.5194/hess-27-917-2023, https://doi.org/10.5194/hess-27-917-2023, 2023
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Daily and hourly rainfall observations were inputted to a Soil and Water Assessment Tool (SWAT) hydrological model to investigate the impacts of rainfall temporal resolution on a discharge simulation. Results indicated that groundwater flow parameters were more sensitive to daily time intervals, and channel routing parameters were more influential for hourly time intervals. This study suggests that the SWAT model appears to be a reliable tool to predict discharge in a mixed-land-use basin.
Cited articles
Abaza, M., Anctil, F., Fortin, V., and Turcotte, R.: Sequential streamflow assimilation for short-term hydrological ensemble forecasting, J. Hydrol., 519, 2692–2706, https://doi.org/10.1016/j.jhydrol.2014.08.038, 2014.
Allen, R. G., Pereira, L. S., Raes, D., and Smith, M.: Crop Evapotranspiration-Guidelines for Computing Crop Water Requirements-FAO Irrigation and Drainage Paper 56, Food and Agriculture Organization of the United Nations, Rome, Italy, 1998.
Andréassian, V., Parent, E., and Michel, C.: A distribution-free test to detect gradual changes in watershed behavior, Water Resour. Res., 39, 1252, https://doi.org/10.1029/2003WR002081, 2003.
Brigode, P., Oudin, L., and Perrin, C.: Hydrological model parameter instability: A source of additional uncertainty in estimating the hydrological impacts of climate change?, J. Hydrol., 476, 410–425, https://doi.org/10.1016/j.jhydrol.2012.11.012, 2013.
Brown, A. E., Zhang, L., McMahon, T. A., Western, A. W., and Vertessy, R. A.: A review of paired catchment studies for determining changes in water yield resulting from alterations in vegetation, J. Hydrol., 310, 28–61, https://doi.org/10.1016/j.jhydrol.2004.12.010, 2005.
Burgers, G., van Leeuwen, P. J., and Evensen, G.: Analysis scheme in the ensemble Kalman filter, Mon. Weather Rev., 126, 1719–1724, https://doi.org/10.1175/1520-0493(1998)126<1719:ASITEK>2.0.CO;2, 1998.
Clark, M. P., Rupp, D. E., Woods, R. A., Zheng, X., Ibbitt, R. P., Slater, A. G., Schmidt, J., and Uddstrom, M. J.: Hydrological data assimilation with the ensemble Kalman filter: Use of streamflow observations to update states in a distributed hydrological model, Adv. Water Resour., 31, 1309–1324, https://doi.org/10.1016/j.advwatres.2008.06.005, 2008.
Coron, L., Andréassian, V., Perrin, C., Lerat, J., Vaze, J., Bourqui, M., and Hendrickx, F.: Crash testing hydrological models in contrasted climate conditions: An experiment on 216 Australian catchments, Water Resour. Res., 48, W05552, https://doi.org/10.1029/2011WR011721, 2012.
DeChant, C. M. and Moradkhani, H.: Toward a reliable prediction of seasonal forecast uncertainty: Addressing model and initial condition uncertainty with ensemble data assimilation and sequential Bayesian combination, J. Hydrol., 519, 2967–2977, https://doi.org/10.1016/j.jhydrol.2014.05.045, 2014.
Delijani, E. B., Pishvaie, M. R., and Boozarjomehry, R. B.: Subsurface characterization with localized ensemble Kalman filter employing adaptive thresholding, Adv. Water Resour., 69, 181–196, https://doi.org/10.1016/j.advwatres.2014.04.011, 2014.
Deng, C., Liu, P., Guo, S., Wang, H., and Wang, D.: Estimation of nonfluctuating reservoir inflow from water level observations using methods based on flow continuity, J. Hydrol., 529, 1198–1210, https://doi.org/10.1016/j.jhydrol.2015.09.037, 2015a.
Deng, C., Liu, P., Liu, Y., Wu, Z. H., and Wang, D.: Integrated hydrologic and reservoir routing model for real-time water level forecasts, J. Hydrol. Eng., 20, 05014032, https://doi.org/10.1061/(ASCE)HE.1943-5584.0001138, 2015b.
Evensen, G.: Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statistics, J. Geophys. Res., 99, 10143–10162, https://doi.org/10.1029/94JC00572, 1994.
Evensen, G.: The Ensemble Kalman filter: theoretical formulation and practical implementation, Ocean Dynam., 53, 343–367, https://doi.org/10.1007/s10236-003-0036-9, 2003.
Evensen, G. and van Leeuwen, P. J.: Assimilation of Geosat altimeter data for the Agulhas Current using the ensemble Kalman filter with a quasigeostrophic model, Mon. Weather Rev., 124, 85–96, https://doi.org/10.1175/1520-0493(1996)124<0085:AOGADF>2.0.CO;2, 1996.
Guo, S., Wang, J., Xiong, L., Ying, A., and Li, D.: A macro-scale and semi-distributed monthly water balance model to predict climate change impacts in China, J. Hydrol., 268, 1–15, https://doi.org/10.1016/S0022-1694(02)00075-6, 2002.
Guo, S., Chen, H., Zhang, H., Xiong, L., Liu, P., Pang, B., Wang, G., and Wang, Y.: A semi-distributed monthly water balance model and its application in a climate change impact study in the middle and lower Yellow River basin, Water Int., 30, 250–260, https://doi.org/10.1080/02508060508691864, 2005.
Han, E., Merwade, V., and Heathman, G. C.: Implementation of surface soil moisture data assimilation with watershed scale distributed hydrological model, J. Hydrol., 416–417, 98–117, https://doi.org/10.1016/j.jhydrol.2011.11.039, 2012.
Jeremiah, E., Marshall, L., Sisson, S. A., and Sharma, A.: Specifying a hierarchical mixture of experts for hydrologic modeling: Gating function variable selection, Water Resour. Res., 49, 2926–2939, https://doi.org/10.1002/wrcr.20150, 2013.
Kumar, S. V., Reichle, R. H., Harrison, K. W., Peters-Lidard, C. D., Yatheendradas, S., and Santanello, J. A.: A comparison of methods for a priori bias correction in soil moisture data assimilation, Water Resour. Res., 48, W03515, https://doi.org/10.1029/2010WR010261, 2012.
Kurtz, W., Hendricks Franssen, H.-J., and Vereecken, H.: Identification of time-variant river bed properties with the ensemble Kalman filter, Water Resour. Res., 48, W10534, https://doi.org/10.1029/2011WR011743, 2012.
Li, S., Xiong, L., Dong, L., and Zhang, J.: Effects of the Three Gorges Reservoir on the hydrological droughts at the downstream Yichang station during 2003–2011, Hydrol. Process., 27, 3981–3993, https://doi.org/10.1002/hyp.9541, 2013.
Li, X.-N., Xie, P., Li, B.-B., and Zhang, B.: A probability calculation method for different grade drought event under changing environment-Taking Wuding River basin as an example, Shuili Xuebao, J. Hydraul. Eng., 45, 585–594, https://doi.org/10.13243/j.cnki.slxb.2014.05.010, 2014 (in Chinese).
Li, Y., Ryu, D., Western, A. W., and Wang, Q. J.: Assimilation of stream discharge for flood forecasting: The benefits of accounting for routing time lags, Water Resour. Res., 49, 1887–1900, https://doi.org/10.1002/wrcr.20169, 2013.
Li, Z., Liu, P., Deng, C., Guo, S., He, P., and Wang, C.: Evaluation of the estimation of distribution algorithm to calibrate a computationally intensive hydrologic model, J. Hydrol. Eng., 21, 04016012, https://doi.org/10.1061/(ASCE)HE.1943-5584.0001350, 2015.
Liu, Y. and Gupta, H. V.: Uncertainty in hydrologic modeling: Toward an integrated data assimilation framework, Water Resour. Res., 43, 1–18, https://doi.org/10.1029/2006WR005756, 2007.
Liu, Y., Weerts, A. H., Clark, M., Hendricks Franssen, H.-J., Kumar, S., Moradkhani, H., Seo, D.-J., Schwanenberg, D., Smith, P., van Dijk, A. I. J. M., van Velzen, N., He, M., Lee, H., Noh, S. J., Rakovec, O., and Restrepo, P.: Advancing data assimilation in operational hydrologic forecasting: progresses, challenges, and emerging opportunities, Hydrol. Earth Syst. Sci., 16, 3863–3887, https://doi.org/10.5194/hess-16-3863-2012, 2012.
Lü, H. S., Hou, T., Horton, R., Zhu, Y. H., Chen, X., Jia, Y. W., Wang, W., and Fu, X. L.: The streamflow estimation using the Xinanjiang rainfall runoff model and dual state-parameter estimation method, J. Hydrol., 480, 102–114, https://doi.org/10.1016/j.jhydrol.2012.12.011, 2013.
Merz, R., Parajka, J., and Blöschl, G.: Time stability of catchment model parameters: Implications for climate impact analyses, Water Resour. Res., 47, W02531, https://doi.org/10.1029/2010WR009505, 2011.
Montzka, C., Grant, J. P., Moradkhani, H., Franssen, H.-J. H., Weihermüller, L., Drusch, M., and Vereecken, H.: Estimation of radiative transfer parameters from L-band passive microwave brightness temperatures using advanced data assimilation, Vadose Zone J., 12, 1–17, https://doi.org/10.2136/vzj2012.0040, 2013.
Moradkhani, H., Sorooshian, S., Gupta, H. V., and Houser, P. R.: Dual state–parameter estimation of hydrological models using ensemble Kalman filter, Adv. Water Resour., 28, 135–147, https://doi.org/10.1016/j.advwatres.2004.09.002, 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.
Nie, S., Zhu, J., and Luo, Y.: Simultaneous estimation of land surface scheme states and parameters using the ensemble Kalman filter: identical twin experiments, Hydrol. Earth Syst. Sci., 15, 2437–2457, https://doi.org/10.5194/hess-15-2437-2011, 2011.
Paik, K., Kim, J. H., Kim, H. S., and Lee, D. R.: A conceptual rainfall-runoff model considering seasonal variation, Hydrol. Process., 19, 3837–3850, https://doi.org/10.1002/hyp.5984, 2005.
Panzeri, M., Riva, M., Guadagnini, A., and Neuman, S. P.: Data assimilation and parameter estimation via ensemble Kalman filter coupled with stochastic moment equations of transient groundwater flow, Water Resour. Res., 49, 1334–1344, https://doi.org/10.1002/wrcr.20113, 2013.
Patil, S. D. and Stieglitz, M.: Comparing spatial and temporal transferability of hydrological model parameters, J. Hydrol., 525, 409–417, https://doi.org/10.1016/j.jhydrol.2015.04.003, 2015.
Pauwels, V. R. N. and Lannoy, G. J. M. D.: Improvement of Modeled Soil Wetness Conditions and Turbulent Fluxes through the Assimilation of Observed Discharge, J. Hydrometeorol., 7, 458–477, https://doi.org/10.1175/JHM490.1, 2006.
Peel, M. C. and Blöschl, G.: Hydrological modelling in a changing world, Prog. Phys. Geog., 35, 249–261, https://doi.org/10.1177/0309133311402550, 2011.
Samuel, J., Coulibaly, P., Dumedah, G., and Moradkhani, H.: Assessing model state and forecasts variation in hydrologic data assimilation, J. Hydrol., 513, 127–141, https://doi.org/10.1016/j.jhydrol.2014.03.048, 2014.
Seiller, G., Anctil, F., and Perrin, C.: Multimodel evaluation of twenty lumped hydrological models under contrasted climate conditions, Hydrol. Earth Syst. Sci., 16, 1171–1189, https://doi.org/10.5194/hess-16-1171-2012, 2012.
Shi, Y., Davis, K. J., Zhang, F., Duffy, C. J., and Yu, X.: Parameter estimation of a physically based land surface hydrologic model using the ensemble Kalman filter: A synthetic experiment, Water Resour. Res., 50, 706–724, https://doi.org/10.1002/2013WR014070, 2014.
Su, T., Feng, T., and Feng, G.: Evaporation variability under climate warming in five reanalyses and its association with pan evaporation over China, J. Geophys. Res.-Atmos., 120, 8080–8098, https://doi.org/10.1002/2014JD023040, 2015.
Tamura, H., Bacopoulos, P., Wang, D., Hagen, S. C., and Kubatko, E. J.: State estimation of tidal hydrodynamics using ensemble Kalman filter, Adv. Water Resour., 63, 45–56, https://doi.org/10.1016/j.advwatres.2013.11.002, 2014.
Thirel, G., Andréassian, V., Perrin, C., Audouy, J. N., Berthet, L., Edwards, P., Folton, N., Furusho, C., Kuentz, A., Lerat, J., Lindström, G., Martin, E., Mathevet, T., Merz, R., Parajka, J., Ruelland, D., and Vaze, J.: Hydrology under change: an evaluation protocol to investigate how hydrological models deal with changing catchments, Hydrolog. Sci. J., 60, 1184–1199, https://doi.org/10.1080/02626667.2014.967248, 2015.
Vrugt, J. A., ter Braak, C. J. F., Diks, C. G. H., and Schoups, G.: Hydrologic data assimilation using particle Markov chain Monte Carlo simulation: Theory, concepts and applications, Adv. Water Resour., 51, 457–478, https://doi.org/10.1016/j.advwatres.2012.04.002, 2013.
Wang, D., Chen, Y., and Cai, X.: State and parameter estimation of hydrologic models using the constrained ensemble Kalman filter, Water Resour. Res., 45, W11416, https://doi.org/10.1029/2008WR007401, 2009.
Wang, G. and Fan, Z.: A study of water and sediment changes in the Yellow River, Publishing House of Yellow River Water Conservancy, Zhengzhou, China, 2003 (in Chinese).
Weerts, A. H. and El Serafy, G. Y. H.: Particle filtering and ensemble Kalman filtering for state updating with hydrological conceptual rainfall-runoff models, Water Resour. Res., 42, 1–17, https://doi.org/10.1029/2005WR004093, 2006.
Westra, S., Thyer, M., Leonard, M., Kavetski, D., and Lambert, M.: A strategy for diagnosing and interpreting hydrological model nonstationarity, Water Resour. Res., 50, 5090–5113, https://doi.org/10.1002/2013WR014719, 2014.
Xie, X. and Zhang, D.: Data assimilation for distributed hydrological catchment modeling via ensemble Kalman filter, Adv. Water Resour., 33, 678–690, https://doi.org/10.1016/j.advwatres.2010.03.012, 2010.
Xie, X. and Zhang, D.: A partitioned update scheme for state-parameter estimation of distributed hydrologic models based on the ensemble Kalman filter, Water Resour. Res., 49, 7350–7365, https://doi.org/10.1002/2012WR012853, 2013.
Xie, X., Meng, S., Liang, S., and Yao, Y.: Improving streamflow predictions at ungauged locations with real-time updating: application of an EnKF-based state-parameter estimation strategy, Hydrol. Earth Syst. Sci., 18, 3923–3936, https://doi.org/10.5194/hess-18-3923-2014, 2014.
Xiong, L. and Guo, S.: A two-parameter monthly water balance model and its application, J. Hydrol., 216, 111–123, https://doi.org/10.1016/S0022-1694(98)00297-2, 1999.
Xiong, L. and Guo, S.: Appraisal of Budyko formula in calculating long-term water balance in humid watersheds of southern China, Hydrol. Process., 26, 1370–1378, https://doi.org/10.1002/hyp.8273, 2012.
Xiong, L., Yu, K.-X., and Gottschalk, L.: Estimation of the distribution of annual runoff from climatic variables using copulas, Water Resour. Res., 50, 7134–7152, https://doi.org/10.1002/2013WR015159, 2014.
Xu, J.: Variation in annual runoff of the Wudinghe River as influenced by climate change and human activity, Quatern. Int., 244, 230–237, https://doi.org/10.1016/j.quaint.2010.09.014, 2011.
Xue, L. and Zhang, D.: A multimodel data assimilation framework via the ensemble Kalman filter, Water Resour. Res., 50, 4197–4219, https://doi.org/10.1002/2013WR014525, 2014.
Yan, H., DeChant, C. M., and Moradkhani, H.: Improving soil moisture profile prediction with the particle filter-Markov chain Monte Marlo method, IEEE T. Geosci. Remote, 53, 6134–6147, https://doi.org/10.1109/tgrs.2015.2432067, 2015.
Ye, W., Bates, B. C., Viney, N. R., Sivapalan, M., and Jakeman, A. J.: Performance of conceptual rainfall-runoff models in low-yielding ephemeral catchments, Water Resour. Res., 33, 153–166, https://doi.org/10.1029/96WR02840, 1997.
Zhang, D., Liu, X. M., Liu, C. M., and Bai, P.: Responses of runoff to climatic variation and human activities in the Fenhe River, China, Stoch. Env. Res. Risk A., 27, 1293–1301, https://doi.org/10.1007/s00477-012-0665-y, 2013.
Zhang, H., Huang, G. H., Wang, D., and Zhang, X.: Multi-period calibration of a semi-distributed hydrological model based on hydroclimatic clustering, Adv. Water Resour., 34, 1292–1303, https://doi.org/10.1016/j.advwatres.2011.06.005, 2011.
Short summary
Hydrological model parameters may vary in time under nonstationary conditions, i.e., climate change and anthropogenic activities. The technique of the ensemble Kalman filter (EnKF) is proposed to identify the temporal variation of parameters for a two-parameter monthly water balance model. Through a synthesis experiment and two case studies, the EnKF is demonstrated to be useful for the identification of parameter variations.
Hydrological model parameters may vary in time under nonstationary conditions, i.e., climate...
