Articles | Volume 26, issue 24
https://doi.org/10.5194/hess-26-6427-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-26-6427-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
21 Dec 2022
Research article |
| 21 Dec 2022
| 21 Dec 2022
Long-term reconstruction of satellite-based precipitation, soil moisture, and snow water equivalent in China
Wencong Yang
Department of Hydraulic Engineering, Tsinghua University, Beijing
100084, China
State Key Laboratory of Hydroscience and Engineering, Tsinghua
University, Beijing 100084, China
Department of Hydraulic Engineering, Tsinghua University, Beijing
100084, China
State Key Laboratory of Hydroscience and Engineering, Tsinghua
University, Beijing 100084, China
Changming Li
Department of Hydraulic Engineering, Tsinghua University, Beijing
100084, China
State Key Laboratory of Hydroscience and Engineering, Tsinghua
University, Beijing 100084, China
Taihua Wang
Department of Hydraulic Engineering, Tsinghua University, Beijing
100084, China
State Key Laboratory of Hydroscience and Engineering, Tsinghua
University, Beijing 100084, China
Ziwei Liu
Department of Hydraulic Engineering, Tsinghua University, Beijing
100084, China
State Key Laboratory of Hydroscience and Engineering, Tsinghua
University, Beijing 100084, China
Qingfang Hu
State Key Laboratory of Hydrology-Water Resources and Hydraulic
Engineering & Science, Nanjing Hydraulic Research Institute, Nanjing
210029, China
Dawen Yang
Department of Hydraulic Engineering, Tsinghua University, Beijing
100084, China
State Key Laboratory of Hydroscience and Engineering, Tsinghua
University, Beijing 100084, China
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Using a collocation-based approach, we developed a reliable global land evapotranspiration product (CAMELE) by merging multi-source datasets. The CAMELE product outperformed individual input datasets and showed satisfactory performance compared to reference data. It also demonstrated superiority for different plant functional types. Our study provides a promising solution for data fusion. The CAMELE dataset allows for detailed research and a better understanding of land–atmosphere interactions.
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This study quantified the causal effects of land cover changes and dams on the changes in annual maximum discharges (Q) in 757 catchments of China using panel regressions. We found that a 1 % point increase in urban areas causes a 3.9 % increase in Q, and a 1 unit increase in reservoir index causes a 21.4 % decrease in Q for catchments with no dam before. This study takes the first step to explain the human-caused flood changes on a national scale in China.
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The determination of the coefficient (α) in the PT equation is always on the empirical side. Here based on an atmospheric boundary layer model, we derived a physically clear expression to investigate the behavior of α. We pointed out that the temperature dominates changes in α and emphasized that the variation of α to temperature should be well considered for long-term hydrological predictions. Our works advance and promote the most classical models in the field.
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A long-term (1980–2020) global ET product is generated based on a collocation-based merging method. The produced Collocation-Analyzed Multi-source Ensembled Land Evapotranspiration Data (CAMELE) performed well over different vegetation coverage against in-situ data. For global comparison, the spatial distribution of multi-year average and annual variation were in consistent with inputs.The CAMELE products is freely available at https://doi.org/10.5281/zenodo.6283239 (Li et al., 2021).
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Hydrol. Earth Syst. Sci., 25, 3411–3427, https://doi.org/10.5194/hess-25-3411-2021, https://doi.org/10.5194/hess-25-3411-2021, 2021
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Wencong Yang, Hanbo Yang, Dawen Yang, and Aizhong Hou
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This study quantified the causal effects of land cover changes and dams on the changes in annual maximum discharges (Q) in 757 catchments of China using panel regressions. We found that a 1 % point increase in urban areas causes a 3.9 % increase in Q, and a 1 unit increase in reservoir index causes a 21.4 % decrease in Q for catchments with no dam before. This study takes the first step to explain the human-caused flood changes on a national scale in China.
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Hydrol. Earth Syst. Sci., 24, 2921–2930, https://doi.org/10.5194/hess-24-2921-2020, https://doi.org/10.5194/hess-24-2921-2020, 2020
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Many previous studies using offline drought indices report that future warming will increase worldwide drought. However, this contradicts observations/projections of vegetation greening and increased runoff. We resolved this paradox by re-calculating the same drought indices using direct climate model outputs and find no increase in future drought as the climate warms. We also find that accounting for the impact of CO2 on plant transpiration avoids the previous overestimation of drought.
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Research into climate change has been popular over the past few decades. Greenhouse gas emissions are found to be responsible for climate change. Among all the ecosystems, cropland is the main food source for mankind, therefore its carbon cycle and contribution to the global carbon balance interest us. Our evaluation of the typical wheat–maize rotation cropland over the North China Plain shows it is a net CO2 emission to the atmosphere and that emissions will continue to rise in the future.
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Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2019-283, https://doi.org/10.5194/hess-2019-283, 2019
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The Budyko hypothesis has been generally used to quantify how much precipitation transforms into evaporation in one catchment. To approach this hypothesis, previous studies proposed analytical formulas derived based on mathematic reasoning. Differently, this study drew a new derivation for this hypothesis based on fundamental physical principles. It clearly reveals the underlying assumptions in the previous mathematic reasoning and promotes hydrologic understanding on this hypothesis.
Xu Shan, Xindong Li, and Hanbo Yang
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2018-598, https://doi.org/10.5194/hess-2018-598, 2018
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The Budyko hypothesis has been generally used to quantify how much precipitation transforms into evaporation in one catchment. To approach this hypothesis, previous studies proposed analytical formulas derived based on mathematic reasoning. Differently, this study drew a new derivation for this hypothesis based on fundamental physical principles. It clearly reveals the underlying assumptions in the previous mathematic reasoning and promotes hydrologic understanding on this hypothesis.
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This study developed a distributed hydrological model coupled with cryospherical processes and applied it in order to simulate the long-term change of frozen ground and its effect on hydrology in the upper Heihe basin. Results showed that the permafrost area shrank by 8.8%, and the frozen depth of seasonally frozen ground decreased. Runoff in cold seasons and annual liquid soil moisture increased due to frozen soils change. Groundwater recharge was enhanced due to the degradation of permafrost.
Zhongwang Chen, Huimin Lei, Hanbo Yang, Dawen Yang, and Yongqiang Cao
Hydrol. Earth Syst. Sci., 21, 2233–2248, https://doi.org/10.5194/hess-21-2233-2017, https://doi.org/10.5194/hess-21-2233-2017, 2017
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The significant climate changes remind us to characterize the hydrological response to it. Based on the long-term observed hydrological and meteorological data in 291 catchments across China, we find a pattern of the response stating that
drier regions are more likely to become drier, whereas wetter regions are more likely to become wetter. We also reveal that the precipitation changes play the most significant role in this process.
Tingting Gong, Huimin Lei, Dawen Yang, Yang Jiao, and Hanbo Yang
Hydrol. Earth Syst. Sci., 21, 863–877, https://doi.org/10.5194/hess-21-863-2017, https://doi.org/10.5194/hess-21-863-2017, 2017
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Seasonal and inter-annual features of ET were analyzed over four periods. A normalization method was adopted to exclude the effects of potential evapotranspiration and soil water stress on ET. During the land degradation process, when natural vegetation (including leaves and branches), sand dunes, dry sand layers, and BSCs were all bulldozed, ET was observed to increase at a mild rate. In a vegetation rehabilitation process with sufficient groundwater, ET also increased at a faster rate.
Bing Gao, Dawen Yang, Yue Qin, Yuhan Wang, Hongyi Li, Yanlin Zhang, and Tingjun Zhang
The Cryosphere Discuss., https://doi.org/10.5194/tc-2016-289, https://doi.org/10.5194/tc-2016-289, 2017
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This study developed a distributed hydrological model coupled with cryospherical processes and used it to simulate the long-term change of frozen ground and hydrological impacts in the upper Heihe basin. Results showed that the permafrost area shrank by 9.5 %, and frozen depth of seasonally frozen ground decreased at a rate of 4.1 cm/10 yr. Runoff increased in cold season due to the increase in liquid soil moisture. Groundwater recharge was enhanced due to the degradation of permafrost.
Quan Zhang, Hui-Min Lei, Da-Wen Yang, Lihua Xiong, and Beijing Fang
Biogeosciences Discuss., https://doi.org/10.5194/bg-2016-484, https://doi.org/10.5194/bg-2016-484, 2016
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With the increasing concern about global warming, investigating carbon cycle becomes imperative to predict future climate trend. As cropland has great potentials in mitigating carbon emissions, therefore we designed a comprehensive carbon budget assessment in a typical cropland in North China Plain, the results indicate the high groundwater table contributes to carbon sink of this cropland. The conclusion confirms that field management has profound effect on cropland carbon cycle.
Zhongwei Huang, Hanbo Yang, and Dawen Yang
Hydrol. Earth Syst. Sci., 20, 2573–2587, https://doi.org/10.5194/hess-20-2573-2016, https://doi.org/10.5194/hess-20-2573-2016, 2016
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The hydrologic processes have been influenced by different climatic factors. However, the dominant climatic factor driving annual runoff change is still unknown in many catchments in China. By using the climate elasticity method proposed by Yang and Yang (2011), the elasticity of runoff to climatic factors was estimated, and the dominant climatic factors driving annual runoff change were detected at catchment scale over China.
D. Zhang, Z. Cong, G. Ni, D. Yang, and S. Hu
Hydrol. Earth Syst. Sci., 19, 1977–1992, https://doi.org/10.5194/hess-19-1977-2015, https://doi.org/10.5194/hess-19-1977-2015, 2015
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1. Catchments with higher snow ratio tend to have larger runoff index.
2. A modified Budyko method is proposed to illustrate the snow effect on runoff.
3. Snow ratio change has a significant contribution to runoff change, according to historical observations and projected future climate scenarios, especially in northwestern mountainous and northern high-latitude areas of China.
T. T. Gong, H. M. Lei, D. W. Yang, Y. Jiao, and H. B. Yang
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hessd-11-13571-2014, https://doi.org/10.5194/hessd-11-13571-2014, 2014
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Hydrol. Earth Syst. Sci., 28, 2505–2529, https://doi.org/10.5194/hess-28-2505-2024, https://doi.org/10.5194/hess-28-2505-2024, 2024
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Hydrol. Earth Syst. Sci., 28, 2483–2503, https://doi.org/10.5194/hess-28-2483-2024, https://doi.org/10.5194/hess-28-2483-2024, 2024
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Fengjing Liu, Martha H. Conklin, and Glenn D. Shaw
Hydrol. Earth Syst. Sci., 28, 2239–2258, https://doi.org/10.5194/hess-28-2239-2024, https://doi.org/10.5194/hess-28-2239-2024, 2024
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Hydrol. Earth Syst. Sci., 28, 2107–2122, https://doi.org/10.5194/hess-28-2107-2024, https://doi.org/10.5194/hess-28-2107-2024, 2024
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Marcus Buechel, Louise Slater, and Simon Dadson
Hydrol. Earth Syst. Sci., 28, 2081–2105, https://doi.org/10.5194/hess-28-2081-2024, https://doi.org/10.5194/hess-28-2081-2024, 2024
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Qian Zhu, Xiaodong Qin, Dongyang Zhou, Tiantian Yang, and Xinyi Song
Hydrol. Earth Syst. Sci., 28, 1665–1686, https://doi.org/10.5194/hess-28-1665-2024, https://doi.org/10.5194/hess-28-1665-2024, 2024
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Fabio Ciulla and Charuleka Varadharajan
Hydrol. Earth Syst. Sci., 28, 1617–1651, https://doi.org/10.5194/hess-28-1617-2024, https://doi.org/10.5194/hess-28-1617-2024, 2024
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We present a new method based on network science for unsupervised classification of large datasets and apply it to classify 9067 US catchments and 274 biophysical traits at multiple scales. We find that our trait-based approach produces catchment classes with distinct streamflow behavior and that spatial patterns emerge amongst pristine and human-impacted catchments. This method can be widely used beyond hydrology to identify patterns, reduce trait redundancy, and select representative sites.
Cyril Thébault, Charles Perrin, Vazken Andréassian, Guillaume Thirel, Sébastien Legrand, and Olivier Delaigue
Hydrol. Earth Syst. Sci., 28, 1539–1566, https://doi.org/10.5194/hess-28-1539-2024, https://doi.org/10.5194/hess-28-1539-2024, 2024
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Streamflow forecasting is useful for many applications, ranging from population safety (e.g. floods) to water resource management (e.g. agriculture or hydropower). To this end, hydrological models must be optimized. However, a model is inherently wrong. This study aims to analyse the contribution of a multi-model approach within a variable spatial framework to improve streamflow simulations. The underlying idea is to take advantage of the strength of each modelling framework tested.
Lele Shu, Xiaodong Li, Yan Chang, Xianhong Meng, Hao Chen, Yuan Qi, Hongwei Wang, Zhaoguo Li, and Shihua Lyu
Hydrol. Earth Syst. Sci., 28, 1477–1491, https://doi.org/10.5194/hess-28-1477-2024, https://doi.org/10.5194/hess-28-1477-2024, 2024
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We developed a new model to better understand how water moves in a lake basin. Our model improves upon previous methods by accurately capturing the complexity of water movement, both on the surface and subsurface. Our model, tested using data from China's Qinghai Lake, accurately replicates complex water movements and identifies contributing factors of the lake's water balance. The findings provide a robust tool for predicting hydrological processes, aiding water resource planning.
Ricardo Mantilla, Morgan Fonley, and Nicolás Velásquez
Hydrol. Earth Syst. Sci., 28, 1373–1382, https://doi.org/10.5194/hess-28-1373-2024, https://doi.org/10.5194/hess-28-1373-2024, 2024
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Hydrologists strive to “Be right for the right reasons” when modeling the hydrologic cycle; however, the datasets available to validate hydrological models are sparse, and in many cases, they comprise streamflow observations at the outlets of large catchments. In this work, we show that matching streamflow observations at the outlet of a large basin is not a reliable indicator of a correct description of the small-scale runoff processes.
Lillian M. McGill, E. Ashley Steel, and Aimee H. Fullerton
Hydrol. Earth Syst. Sci., 28, 1351–1371, https://doi.org/10.5194/hess-28-1351-2024, https://doi.org/10.5194/hess-28-1351-2024, 2024
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This study examines the relationship between air and river temperatures in Washington's Snoqualmie and Wenatchee basins. We used classification and regression approaches to show that the sensitivity of river temperature to air temperature is variable across basins and controlled largely by geology and snowmelt. Findings can be used to inform strategies for river basin restoration and conservation, such as identifying climate-insensitive areas of the basin that should be preserved and protected.
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.
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.
Yongshin Lee, Francesca Pianosi, Andres Peñuela, and Miguel Angel Rico-Ramirez
EGUsphere, https://doi.org/10.5194/egusphere-2023-2169, https://doi.org/10.5194/egusphere-2023-2169, 2023
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Following recent advancements in weather prediction technology, we explored how seasonal weather forecasts (one or more months ahead) could benefit practical water management in South Korea. Our findings highlight that using seasonal weather forecasts for predicting flow patterns 1 to 3 months ahead is effective, especially during dry years. This suggest that seasonal weather forecasts can be helpful in improving the management of water resources.
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.
Louise Akemi Kuana, Arlan Scortegagna Almeida, Emilio Graciliano Ferreira Mercuri, and Steffen Manfred Noe
EGUsphere, https://doi.org/10.5194/egusphere-2023-1755, https://doi.org/10.5194/egusphere-2023-1755, 2023
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The authors compared different regionalization methods for river flow prediction in watersheds with few data. We collected data on precipitation, evapotranspiration, river flow, and geographical and climatic factors for 126 catchments in the Paraná state, Brazil. The regionalization method based on physiographic-climatic similarity showed to be the most robust for predicting daily and Q95 reference flow. We also found patterns in data, grouping locations based on similarities.
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.
Cited articles
Abelen, S., Seitz, F., Abarca-del-Rio, R., and Guntner, A.: Droughts and
Floods in the La Plata Basin in Soil Moisture Data and GRACE, Remote
Sens.-Basel, 7, 7324–7349, https://doi.org/10.3390/rs70607324, 2015.
Beck, H. E., Wood, E. F., Pan, M., Fisher, C. K., Miralles, D. G., van Dijk,
A. I. J. M., McVicar, T. R., and Adler, R. F.: MSWEP V2 Global 3-Hourly 0.1
degrees Precipitation: Methodology and Quantitative Assessment, B. Am.
Meteorol. Soc., 100, 473–502, https://doi.org/10.1175/Bams-D-17-0138.1, 2019.
Beck, H. E., Pan, M., Lin, P., Seibert, J., van Dijk, A. I., and Wood, E.
F.: Global fully distributed parameter regionalization based on observed
streamflow from 4,229 headwater catchments, J. Geophys. Res.-Atmos., 125, e2019JD031485, https://doi.org/10.1029/2019jd031485, 2020.
Beck, H. E., Pan, M., Miralles, D. G., Reichle, R. H., Dorigo, W. A., Hahn, S., Sheffield, J., Karthikeyan, L., Balsamo, G., Parinussa, R. M., van Dijk, A. I. J. M., Du, J., Kimball, J. S., Vergopolan, N., and Wood, E. F.: Evaluation of 18 satellite- and model-based soil moisture products using in situ measurements from 826 sensors, Hydrol. Earth Syst. Sci., 25, 17–40, https://doi.org/10.5194/hess-25-17-2021, 2021.
Bergström, S.: The HBV model – its structure and applications, SMHI
Reports RH 4, Swedish Meteorological and Hydrological Institute (SMHI),
Norrköping, Sweden, https://www.smhi.se/polopoly_fs/1.83592!/Menu/general/extGroup/attachmentColHold/mainCol1/file/RH_4.pdf (last access: 20 October 2021), 1992.
Blöschl, G., Hall, J., Viglione, A., Perdigao, R. A. P., Parajka, J., Merz,
B., Lun, D., Arheimer, B., Aronica, G. T., Bilibashi, A., Bohac, M.,
Bonacci, O., Borga, M., Canjevac, I., Castellarin, A., Chirico, G. B.,
Claps, P., Frolova, N., Ganora, D., Gorbachova, L., Gul, A., Hannaford, J.,
Harrigan, S., Kireeva, M., Kiss, A., Kjeldsen, T. R., Kohnova, S., Koskela,
J. J., Ledvinka, O., Macdonald, N., Mavrova-Guirguinova, M., Mediero, L.,
Merz, R., Molnar, P., Montanari, A., Murphy, C., Osuch, M., Ovcharuk, V.,
Radevski, I., Salinas, J. L., Sauquet, E., Sraj, M., Szolgay, J., Volpi, E.,
Wilson, D., Zaimi, K., and Zivkovic, N.: Changing climate both increases and
decreases European river floods, Nature, 573, 108–111, https://doi.org/10.1038/s41586-019-1495-6, 2019.
Breiman, L.: Random forests, Mach. Learn., 45, 5–32, https://doi.org/10.1023/A:1010933404324, 2001.
Che, T. and Dai, L.: Long-term series of daily snow depth dataset in China
(1979–2020), National Tibetan Plateau Data Center [data set], https://doi.org/10.11888/Geogra.tpdc.270194, 2015.
Dorigo, W., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L.,
Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, P. D.,
Hirschi, M., Ikonen, J., de Jeu, R., Kidd, R., Lahoz, W., Liu, Y. Y.,
Miralles, D., Mistelbauer, T., Nicolai-Shaw, N., Parinussa, R., Pratola, C.,
Reimer, C., van der Schalie, R., Seneviratne, S. I., Smolander, T., and
Lecomte, P.: ESA CCI Soil Moisture for improved Earth system understanding:
State-of-the art and future directions, Remote Sens. Environ., 203, 185–215, https://doi.org/10.1016/j.rse.2017.07.001, 2017.
Foresee, F. D. and Hagan, M. T.: Gauss-Newton approximation to Bayesian
learning, in: Proceedings of international conference on neural networks,
IEEE, Houston, TX, USA, June 1997, 3, 1930–1935, https://doi.org/10.1109/icnn.1997.614194, 1997.
Gao, H. K., Dong, J. Z., Chen, X., Cai, H. Y., Liu, Z. Y., Jin, Z. H., Mao,
D. H., Yang, Z. J., and Duan, Z.: Stepwise modeling and the importance of
internal variables validation to test model realism in a data scarce glacier
basin, J. Hydrol., 591, 125457, https://doi.org/10.1016/j.jhydrol.2020.125457, 2020.
Hall, D. K. and Riggs, G. A.: MODIS/Aqua Snow Cover Daily L3 Global 0.05Deg
CMG, Version 61, NASA National Snow and Ice Data Center Distributed Active
Archive Center [data set], Boulder, Colorado USA, https://doi.org/10.5067/MODIS/MYD10C1.061, 2021a.
Hall, D. K. and Riggs, G. A.: MODIS/Terra Snow Cover Daily L3 Global 0.05Deg
CMG, Version 61, NASA National Snow and Ice Data Center Distributed Active
Archive Center [data set], Boulder, Colorado USA, https://doi.org/10.5067/MODIS/MOD10C1.061,
2021b.
Hall, D. K., Riggs, G. A., Salomonson, V. V., DiGirolamo, N. E., and Bayr,
K. J.: MODIS snow-cover products, Remote Sens. Environ., 83, 181–194, https://doi.org/10.1016/S0034-4257(02)00095-0, 2002.
He, J., Yang, K., Tang, W. J., Lu, H., Qin, J., Chen, Y. Y., and Li, X.: The
first high-resolution meteorological forcing dataset for land process
studies over China, Sci. Data, 7, 1–11, https://doi.org/10.1038/s41597-020-0369-y, 2020.
Huffman, G. J., Bolvin, D. T., Nelkin, E. J., Wolff, D. B., Adler, R. F., Gu, G., Hong, Y., Bowman, K. P., and Stocker, E. F.: The TRMM Multisatellite Precipitation Analysis (TMPA): quasiglobal, multiyear, combined-sensor precipitation estimates at fine scales, J. Hydrometeorol., 8, 38–55, https://doi.org/10.1175/jhm560.1, 2007.
Joyce, R. J., Janowiak, J. E., Arkin, P. A., and Xie, P. P.: CMORPH: A
method that produces global precipitation estimates from passive microwave
and infrared data at high spatial and temporal resolution, J. Hydrometeorol.,
5, 487–503, https://doi.org/10.1175/1525-7541(2004)005<0487:Camtpg>2.0.Co;2, 2004.
Li, D. Y., Lettenmaier, D. P., Margulis, S. A., and Andreadis, K.: The Role
of Rain-on-Snow in Flooding Over the Conterminous United States, Water
Resour. Res., 55, 8492–8513, https://doi.org/10.1029/2019wr024950, 2019.
Liang, S. L., Cheng, J., Jia, K., Jiang, B., Liu, Q., Xiao, Z. Q., Yao, Y.
J., Yuan, W. P., Zhang, X. T., Zhao, X., and Zhou, J.: The Global Land
Surface Satellite (GLASS) Product Suite, B. Am. Meteorol. Soc., 102, E323–E337, https://doi.org/10.1175/Bams-D-18-0341.1, 2021.
Luojus, K., Pulliainen, J., Takala, M., Lemmetyinen, J., Mortimer, C.,
Derksen, C., Mudryk, L., Moisander, M., Hiltunen, M., Smolander, T., Ikonen,
J., Cohen, J., Salminen, M., Norberg, J., Veijola, K., and Venalainen, P.:
GlobSnow v3.0 Northern Hemisphere snow water equivalent dataset, Sci. Data,
8, 163, https://doi.org/10.1038/s41597-021-00939-2, 2021.
Mao, G. and Liu, J.: WAYS v1: a hydrological model for root zone water storage simulation on a global scale, Geosci. Model Dev., 12, 5267–5289, https://doi.org/10.5194/gmd-12-5267-2019, 2019.
Miao, Y. and Wang, A. H.: A daily 0.25∘ × 0.25∘
hydrologically based land surface flux dataset for conterminous China,
1961–2017, J. Hydrol., 590, 125413, https://doi.org/10.1016/j.jhydrol.2020.125413, 2020.
Muñoz-Sabater, J., Dutra, E., Agustí-Panareda, A., Albergel, C., Arduini, G., Balsamo, G., Boussetta, S., Choulga, M., Harrigan, S., Hersbach, H., Martens, B., Miralles, D. G., Piles, M., Rodríguez-Fernández, N. J., Zsoter, E., Buontempo, C., and Thépaut, J.-N.: ERA5-Land: a state-of-the-art global reanalysis dataset for land applications, Earth Syst. Sci. Data, 13, 4349–4383, https://doi.org/10.5194/essd-13-4349-2021, 2021.
Myers, D. E.: Matrix Formulation of Co-Kriging, J. Int. Ass. Math. Geol., 14,
249–257, https://doi.org/10.1007/Bf01032887, 1982.
Parajka, J., Merz, R., and Blöschl, G.: Uncertainty and multiple objective
calibration in regional water balance modelling: case study in 320 Austrian
catchments, Hydrol. Process., 21, 435–446, https://doi.org/10.1002/hyp.6253, 2007.
Priestley, C. H. B. and Taylor, R. J.: Assessment of Surface Heat-Flux and
Evaporation Using Large-Scale Parameters, Mon. Weather Rev., 100, 81–92, https://doi.org/10.1175/1520-0493(1972)100<0081:Otaosh>2.3.Co;2, 1972.
Qi, W., Feng, L., Yang, H., and Liu, J. G.: Spring and summer potential
flood risk in Northeast China, J. Hydrol.-Reg. Stud., 38, 100951,
https://doi.org/10.1016/j.ejrh.2021.100951, 2021.
Reager, J. T., Thomas, B. F., and Famiglietti, J. S.: River basin flood
potential inferred using GRACE gravity observations at several months lead
time, Nat. Geosci., 7, 589–593, https://doi.org/10.1038/Ngeo2203, 2014.
Reichle, R. H., Liu, Q., Koster, R. D., Crow, W., De Lannoy, G. J. M.,
Kimball, J. S., Ardizzone, J. V., Bosch, D., Colliander, A., Cosh, M.,
Kolassa, J., Mahanama, S. P., Prueger, J., Starks, P., and Walker, J. P.:
Version 4 of the SMAP Level-4 Soil Moisture Algorithm and Data Product, J.
Adv. Model Earth Sy., 11, 3106–3130, https://doi.org/10.1029/2019ms001729, 2019.
Rodell, M., Houser, P. R., Jambor, U., Gottschalck, J., Mitchell, K., Meng,
C. J., Arsenault, K., Cosgrove, B., Radakovich, J., Bosilovich, M., Entin,
J. K., Walker, J. P., Lohmann, D., and Toll, D.: The global land data
assimilation system, B. Am. Meteorol. Soc., 85, 381–394, https://doi.org/10.1175/Bams-85-3-381,
2004.
Seibert, J. and Bergström, S.: A retrospective on hydrological catchment modelling based on half a century with the HBV model, Hydrol. Earth Syst. Sci., 26, 1371–1388, https://doi.org/10.5194/hess-26-1371-2022, 2022.
Sharma, A., Wasko, C., and Lettenmaier, D. P.: If Precipitation Extremes Are
Increasing, Why Aren't Floods?, Water Resour. Res., 54, 8545–8551, https://doi.org/10.1029/2018wr023749, 2018.
Shen, Y. and Xiong, A. Y.: Validation and comparison of a new gauge-based
precipitation analysis over mainland China, Int. J. Climatol., 36, 252–265, https://doi.org/10.1002/joc.4341, 2016.
Shen, Y., Zhao, P., Pan, Y., and Yu, J. J.: A high spatiotemporal
gauge-satellite merged precipitation analysis over China, J. Geophys. Res.-Atmos., 119, 3063–3075, https://doi.org/10.1002/2013jd020686, 2014.
Shen, Y., Hong, Z., Pan, Y., Yu, J. J., and Maguire, L.: China's 1 km Merged
Gauge, Radar and Satellite Experimental Precipitation Dataset, Remote
Sens.-Basel, 10, 264, https://doi.org/10.3390/rs10020264, 2018.
Stein, L., Clark, M. P., Knoben, W. J. M., Pianosi, F., and Woods, R. A.:
How Do Climate and Catchment Attributes Influence Flood Generating
Processes? A Large-Sample Study for 671 Catchments Across the Contiguous
USA, Water Resour. Res., 57, e2020WR028300, https://doi.org/10.1029/2020WR028300, 2021.
Tarasova, L., Basso, S., Wendi, D., Viglione, A., Kumar, R., and Merz, R.: A
Process-Based Framework to Characterize and Classify Runoff Events: The
Event Typology of Germany, Water Resour. Res., 56, e2019WR026951,
https://doi.org/10.1029/2019WR026951, 2020.
Van Steenbergen, N. and Willems, P.: Increasing river flood preparedness by
real-time warning based on wetness state conditions, J. Hydrol., 489, 227–237, https://doi.org/10.1016/j.jhydrol.2013.03.015, 2013.
Wolpert, D. H.: Stacked Generalization, Neural Networks, 5, 241–259, https://doi.org/10.1016/S0893-6080(05)80023-1, 1992.
Yamazaki, D., Ikeshima, D., Sosa, J., Bates, P. D., Allen, G. H., and
Pavelsky, T. M.: MERIT Hydro: A High-Resolution Global Hydrography Map Based
on Latest Topography Dataset, Water Resour. Res., 55, 5053–5073, https://doi.org/10.1029/2019wr024873, 2019.
Yang, J. W., Jiang, L. M., Wu, S. L., Wang, G. X., Wang, J., and Liu, X. J.:
Development of a Snow Depth Estimation Algorithm over China for the
FY-3D/MWRI, Remote Sens.-Basel, 11, 977, https://doi.org/10.3390/rs11080977, 2019.
Yang, J. W., Jiang, L. M., Lemmetyinen, J., Luojus, K., Takala, M., Wu, S.
L., and Pan, J. M.: Validation of remotely sensed estimates of snow water
equivalent using multiple reference datasets from the middle and high
latitudes of China, J. Hydrol., 590, 125499, https://doi.org/10.1016/j.jhydrol.2020.125499, 2020a.
Yang, W. C.: YANGOnion/Hydrological-Reconstruction-China (1.0.0), Zenodo [code], https://doi.org/10.5281/zenodo.7450278, 2022.
Yang, W. C., Yang, H. B., and Yang, D. W.: Classifying floods by quantifying
driver contributions in the Eastern Monsoon Region of China, J. Hydrol., 585,
124767, https://doi.org/10.1016/j.jhydrol.2020.124767, 2020b.
Yang, W. C., Yang, H. B., Li, C. M., Wang, T. H., Liu, Z. W., Hu, Q. F., and
Yang, D. W.: Long-term reconstruction of satellite-based precipitation, soil
moisture, and snow water equivalent in China (1.0), Zenodo [data set],
https://doi.org/10.5281/zenodo.5811099, 2021.
Zhang, X. J., Tang, Q. H., Pan, M., and Tang, Y.: A Long-Term Land Surface
Hydrologic Fluxes and States Dataset for China, J. Hydrometeorol., 15,
2067–2084, https://doi.org/10.1175/Jhm-D-13-0170.1, 2014.
Zhu, B. W., Xie, X. H., Lu, C. Y., Lei, T. J., Wang, Y. B., Jia, K., and
Yao, Y. J.: Extensive Evaluation of a Continental-Scale High-Resolution
Hydrological Model Using Remote Sensing and Ground-Based Observations,
Remote Sens.-Basel, 13, 1247, https://doi.org/10.3390/rs13071247, 2021.
Short summary
We produced a daily 0.1° dataset of precipitation, soil moisture, and snow water equivalent in 1981–2017 across China via reconstructions. The dataset used global background data and local on-site data as forcing input and satellite-based data as reconstruction benchmarks. This long-term high-resolution national hydrological dataset is valuable for national investigations of hydrological processes.
We produced a daily 0.1° dataset of precipitation, soil moisture, and snow water equivalent in...
