Articles | Volume 27, issue 14
https://doi.org/10.5194/hess-27-2725-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-27-2725-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
24 Jul 2023
Research article |
| 24 Jul 2023
| 24 Jul 2023
Can the combining of wetlands with reservoir operation reduce the risk of future floods and droughts?
Yanfeng Wu
Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun, Jilin 130102, China
Jingxuan Sun
Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun, Jilin 130102, China
University of Chinese Academy of Sciences, Beijing 100049, China
Boting Hu
Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun, Jilin 130102, China
University of Chinese Academy of Sciences, Beijing 100049, China
Y. Jun Xu
School of Renewable Natural Resources, Louisiana State University
Agricultural Center, 227 Highland Road, Baton Rouge, LA 70803, USA
Alain N. Rousseau
INRS-ETE/Institut National de la Recherche Scientifique – Eau Terre Environnement, 490 rue de la Couronne, G1K 9A9 Quebec City, Quebec, Canada
Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun, Jilin 130102, China
Related authors
Boting Hu, Liwen Chen, Yanfeng Wu, Jingxuan Sun, Y. Jun Xu, Qingsong Zhang, and Guangxin Zhang
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-71, https://doi.org/10.5194/hess-2024-71, 2024
Preprint under review for HESS
Short summary
Short summary
This study presents a novel framework to accurately quantify wetland depression water storage capacity. The framework and its concept are transferable to other wetland areas in the world where field measurements and/or high-resolution terrain data are unavailable. Moreover, the framework provides accurate distribution and depth-area relations of wetland depressions which can be incorporated into wetland modules of hydrological models to improve the accuracy of flow and storage predictions.
Boting Hu, Liwen Chen, Yanfeng Wu, Jingxuan Sun, Y. Jun Xu, Qingsong Zhang, and Guangxin Zhang
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-71, https://doi.org/10.5194/hess-2024-71, 2024
Preprint under review for HESS
Short summary
Short summary
This study presents a novel framework to accurately quantify wetland depression water storage capacity. The framework and its concept are transferable to other wetland areas in the world where field measurements and/or high-resolution terrain data are unavailable. Moreover, the framework provides accurate distribution and depth-area relations of wetland depressions which can be incorporated into wetland modules of hydrological models to improve the accuracy of flow and storage predictions.
S. He and Y. J. Xu
Biogeosciences Discuss., https://doi.org/10.5194/bgd-12-18425-2015, https://doi.org/10.5194/bgd-12-18425-2015, 2015
Manuscript not accepted for further review
Short summary
Short summary
The study found that salinity strongly affects spatiotemporal distributions of Sr and Ca concentrations and their ratios, but has no effect on Ba concentration, which appears mainly geochemically controlled. These results indicate that concentrations of Sr and Ca in the world’s estuaries will very likely increase in the future as sea level rise continues, which could affect aquatic environments and biological communities.
P. Gagnon and A. N. Rousseau
Hydrol. Earth Syst. Sci., 18, 1695–1704, https://doi.org/10.5194/hess-18-1695-2014, https://doi.org/10.5194/hess-18-1695-2014, 2014
Related subject area
Subject: Catchment hydrology | Techniques and Approaches: Modelling approaches
Widespread flooding dynamics under climate change: characterising floods using grid-based hydrological modelling and regional climate projections
HESS Opinions: The sword of Damocles of the impossible flood
Metamorphic testing of machine learning and conceptual hydrologic models
The influence of human activities on streamflow reductions during the megadrought in central Chile
Elevational control of isotopic composition and application in understanding hydrologic processes in the mid Merced River catchment, Sierra Nevada, California, USA
Enhancing long short-term memory (LSTM)-based streamflow prediction with a spatially distributed approach
Broadleaf afforestation impacts on terrestrial hydrology insignificant compared to climate change in Great Britain
Impacts of spatiotemporal resolutions of precipitation on flood event simulation based on multimodel structures – a case study over the Xiang River basin in China
A network approach for multiscale catchment classification using traits
Multi-model approach in a variable spatial framework for streamflow simulation
Advancing understanding of lake–watershed hydrology: a fully coupled numerical model illustrated by Qinghai Lake
Technical note: Testing the connection between hillslope-scale runoff fluctuations and streamflow hydrographs at the outlet of large river basins
Empirical stream thermal sensitivity cluster on the landscape according to geology and climate
Deep learning for monthly rainfall–runoff modelling: a large-sample comparison with conceptual models across Australia
On optimization of calibrations of a distributed hydrological model with spatially distributed information on snow
Toward interpretable LSTM-based modeling of hydrological systems
Flow intermittence prediction using a hybrid hydrological modelling approach: influence of observed intermittence data on the training of a random forest model
What controls the tail behaviour of flood series: rainfall or runoff generation?
Seasonal prediction of end-of-dry-season watershed behavior in a highly interconnected alluvial watershed in northern California
Glaciers determine the sensitivity of hydrological processes to perturbed climate in a large mountainous basin on the Tibetan Plateau
Leveraging gauge networks and strategic discharge measurements to aid the development of continuous streamflow records
On the need for physical constraints in deep learning rainfall–runoff projections under climate change: a sensitivity analysis to warming and shifts in potential evapotranspiration
Evaluation of hydrological models on small mountainous catchments: impact of the meteorological forcings
Impacts of climate and land-surface change on catchment evapotranspiration and runoff from 1951–2020 in Saxony, Germany
Projecting sediment export from two highly glacierized alpine catchments under climate change: exploring non-parametric regression as an analysis tool
Evolution of river regime in the Mekong River basin over eight decades and role of dams in recent hydrologic extremes
A framework for parameter estimation, sensitivity analysis, and uncertainty analysis for holistic hydrologic modeling using SWAT+
On understanding mountainous carbonate basins of the Mediterranean using parsimonious modeling solutions
Comparing quantile regression forest and mixture density long short-term memory models for probabilistic post-processing of satellite precipitation-driven streamflow simulations
Recent ground thermo-hydrological changes in a southern Tibetan endorheic catchment and implications for lake level changes
Towards robust seasonal streamflow forecasts in mountainous catchments: impact of calibration metric selection in hydrological modeling
Modelling flood frequency and magnitude in a glacially conditioned, heterogeneous landscape: testing the importance of land cover and land use
When ancient numerical demons meet physics-informed machine learning: adjoint-based gradients for implicit differentiable modeling
Direct integration of reservoirs' operations in a hydrological model for streamflow estimation: coupling a CLSTM model with MOHID-Land
Skill of seasonal flow forecasts at catchment-scale: an assessment across South Korea
Modelling the regional sensitivity of snowmelt, soil moisture, and streamflow generation to climate over the Canadian Prairies using a basin classification approach
To what extent does river routing matter in hydrological modeling?
Calibrating macroscale hydrological models in poorly gauged and heavily regulated basins
An advanced tool integrating failure and sensitivity analysis into novel modeling of the stormwater flood volume
To Bucket or not to Bucket? Analyzing the performance and interpretability of hybrid hydrological models with dynamic parameterization
airGRteaching: an open-source tool for teaching hydrological modeling with R
Assessing the impact of climate change on high return levels of peak flows in Bavaria applying the CRCM5 Large Ensemble
To What Extent Do Extreme Storm Events Change Future Flood Hazards?
Stable water isotopes and tritium tracers tell the same tale: no evidence for underestimation of catchment transit times inferred by stable isotopes in StorAge Selection (SAS)-function models
Regionalization of GR4J model parameters for river flow prediction in Paraná, Brazil
Uncertainty in water transit time estimation with StorAge Selection functions and tracer data interpolation
Changes in Mediterranean flood processes and seasonality
Knowledge-informed deep learning for hydrological model calibration: an application to Coal Creek Watershed in Colorado
When best is the enemy of good – critical evaluation of performance criteria in hydrological models
The suitability of differentiable, physics-informed machine learning hydrologic models for ungauged regions and climate change impact assessment
Adam Griffin, Alison L. Kay, Paul Sayers, Victoria Bell, Elizabeth Stewart, and Sam Carr
Hydrol. Earth Syst. Sci., 28, 2635–2650, https://doi.org/10.5194/hess-28-2635-2024, https://doi.org/10.5194/hess-28-2635-2024, 2024
Short summary
Short summary
Widespread flooding is a major problem in the UK and is greatly affected by climate change and land-use change. To look at how widespread flooding changes in the future, climate model data (UKCP18) were used with a hydrological model (Grid-to-Grid) across the UK, and 14 400 events were identified between two time slices: 1980–2010 and 2050–2080. There was a strong increase in the number of winter events in the future time slice and in the peak return periods.
Alberto Montanari, Bruno Merz, and Günter Blöschl
Hydrol. Earth Syst. Sci., 28, 2603–2615, https://doi.org/10.5194/hess-28-2603-2024, https://doi.org/10.5194/hess-28-2603-2024, 2024
Short summary
Short summary
Floods often take communities by surprise, as they are often considered virtually
impossibleyet are an ever-present threat similar to the sword suspended over the head of Damocles in the classical Greek anecdote. We discuss four reasons why extremely large floods carry a risk that is often larger than expected. We provide suggestions for managing the risk of megafloods by calling for a creative exploration of hazard scenarios and communicating the unknown corners of the reality of floods.
Peter Reichert, Kai Ma, Marvin Höge, Fabrizio Fenicia, Marco Baity-Jesi, Dapeng Feng, and Chaopeng Shen
Hydrol. Earth Syst. Sci., 28, 2505–2529, https://doi.org/10.5194/hess-28-2505-2024, https://doi.org/10.5194/hess-28-2505-2024, 2024
Short summary
Short summary
We compared the predicted change in catchment outlet discharge to precipitation and temperature change for conceptual and machine learning hydrological models. We found that machine learning models, despite providing excellent fit and prediction capabilities, can be unreliable regarding the prediction of the effect of temperature change for low-elevation catchments. This indicates the need for caution when applying them for the prediction of the effect of climate change.
Nicolás Álamos, Camila Alvarez-Garreton, Ariel Muñoz, and Álvaro González-Reyes
Hydrol. Earth Syst. Sci., 28, 2483–2503, https://doi.org/10.5194/hess-28-2483-2024, https://doi.org/10.5194/hess-28-2483-2024, 2024
Short summary
Short summary
In this study, we assess the effects of climate and water use on streamflow reductions and drought intensification during the last 3 decades in central Chile. We address this by contrasting streamflow observations with near-natural streamflow simulations. We conclude that while the lack of precipitation dominates streamflow reductions in the megadrought, water uses have not diminished during this time, causing a worsening of the hydrological drought conditions and maladaptation conditions.
Fengjing Liu, Martha H. Conklin, and Glenn D. Shaw
Hydrol. Earth Syst. Sci., 28, 2239–2258, https://doi.org/10.5194/hess-28-2239-2024, https://doi.org/10.5194/hess-28-2239-2024, 2024
Short summary
Short summary
Mountain snowpack has been declining and more precipitation falls as rain than snow. Using stable isotopes, we found flows and flow duration in Yosemite Creek are most sensitive to climate warming due to strong evaporation of waterfalls, potentially lengthening the dry-up period of waterfalls in summer and negatively affecting tourism. Groundwater recharge in Yosemite Valley is primarily from the upper snow–rain transition (2000–2500 m) and very vulnerable to a reduction in the snow–rain ratio.
Qiutong Yu, Bryan A. Tolson, Hongren Shen, Ming Han, Juliane Mai, and Jimmy Lin
Hydrol. Earth Syst. Sci., 28, 2107–2122, https://doi.org/10.5194/hess-28-2107-2024, https://doi.org/10.5194/hess-28-2107-2024, 2024
Short summary
Short summary
It is challenging to incorporate input variables' spatial distribution information when implementing long short-term memory (LSTM) models for streamflow prediction. This work presents a novel hybrid modelling approach to predict streamflow while accounting for spatial variability. We evaluated the performance against lumped LSTM predictions in 224 basins across the Great Lakes region in North America. This approach shows promise for predicting streamflow in large, ungauged basin.
Marcus Buechel, Louise Slater, and Simon Dadson
Hydrol. Earth Syst. Sci., 28, 2081–2105, https://doi.org/10.5194/hess-28-2081-2024, https://doi.org/10.5194/hess-28-2081-2024, 2024
Short summary
Short summary
Afforestation has been proposed internationally, but the hydrological implications of such large increases in the spatial extent of woodland are not fully understood. In this study, we use a land surface model to simulate hydrology across Great Britain with realistic afforestation scenarios and potential climate changes. Countrywide afforestation minimally influences hydrology, when compared to climate change, and reduces low streamflow whilst not lowering the highest flows.
Qian Zhu, Xiaodong Qin, Dongyang Zhou, Tiantian Yang, and Xinyi Song
Hydrol. Earth Syst. Sci., 28, 1665–1686, https://doi.org/10.5194/hess-28-1665-2024, https://doi.org/10.5194/hess-28-1665-2024, 2024
Short summary
Short summary
Input data, model and calibration strategy can affect the accuracy of flood event simulation and prediction. Satellite-based precipitation with different spatiotemporal resolutions is an important input source. Data-driven models are sometimes proven to be more accurate than hydrological models. Event-based calibration and conventional strategy are two options adopted for flood simulation. This study targets the three concerns for accurate flood event simulation and prediction.
Fabio Ciulla and Charuleka Varadharajan
Hydrol. Earth Syst. Sci., 28, 1617–1651, https://doi.org/10.5194/hess-28-1617-2024, https://doi.org/10.5194/hess-28-1617-2024, 2024
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
The calibration of hydrological models over extensive spatial domains is often challenged by the lack of data on river discharge and the operations of hydraulic infrastructures. Here, we use satellite data to address the lack of data that could unintentionally bias the calibration process. Our study is underpinned by a computational framework that quantifies this bias and provides a safe approach to the calibration of models in poorly gauged and heavily regulated basins.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Åhlén, I., Hambäck, P., Thorslund, J., Frampton, A., Destouni,
G.Jarsjö, J.: Wetlandscape size thresholds for ecosystem service delivery: Evidence from the Norrström drainage basin, Sweden, Sci. Total
Environ., 704, 135452, https://doi.org/10.1016/j.scitotenv.2019.135452, 2020.
Åhlén, I., Thorslund, J., Hambäck, P., Destouni, G., and Jarsjö, J.: Wetland position in the landscape: Impact on water storage
and flood buffering, Ecohydrology, 15, e2458, https://doi.org/10.1002/eco.2458, 2022.
Ahmed, F.: Cumulative Hydrologic Impact of Wetland Loss: Numerical Modeling Study of the Rideau River Watershed, Canada, J. Hydrol. Eng., 19, 593–606, https://doi.org/10.1061/(ASCE)HE.1943-5584.0000817, 2014.
Alves, A., Gersonius, B., Kapelan, Z., Vojinovic, Z., and Sanchez, A.:
Assessing the Co-Benefits of green-blue-grey infrastructure for sustainable
urban flood risk management, J. Environ. Manage., 239, 244–254, https://doi.org/10.1016/j.jenvman.2019.03.036, 2019.
Anderson, C. C. and Renaud, F. G.: A review of public acceptance of
nature-based solutions: The `why', `when', and `how' of success for disaster
risk reduction measures, Ambio, 50, 1552–1573, https://doi.org/10.1007/s13280-021-01502-4, 2021.
Arsenault, R., Brissette, F., and Martel, J.: The hazards of split-sample
validation in hydrological model calibration, J. Hydrol., 566, 346–362, https://doi.org/10.1016/j.jhydrol.2018.09.027, 2018.
Beven, K.: Kinematic subsurface stormflow. Water Resour. Res., 17, 1419–1424, https://doi.org/10.1029/WR017i005p01419, 1981.
Blanchette, M., Rousseau, A. N., Foulon, É., Savary, S., and Poulin, M.: What would have been the impacts of wetlands on low flow support and high flow attenuation under steady state land cover conditions?, J. Environ. Manage., 234, 448–457, https://doi.org/10.1016/j.jenvman.2018.12.095, 2019.
Bosshard, T., Kotlarski, S., Ewen, T., and Schär, C.: Spectral
representation of the annual cycle in the climate change signal, Hydrol.
Earth Syst. Sci., 15, 2777–2788, https://doi.org/10.5194/hess-15-2777-2011, 2011.
Bouda, M., Rousseau, A. N., Konan, B., Gagnon, P., and Gumiere, S. J.: Bayesian Uncertainty Analysis of the Distributed Hydrological Model HYDROTEL, J. Hydrol. Eng., 17, 1021–1032, https://doi.org/10.1061/(ASCE)HE.1943-5584.0000550, 2012.
Bouda, M., Rousseau, A. N., Gumiere, S. J., Gagnon, P., Konan, B., and Moussa, R.: Implementation of an automatic calibration procedure for HYDROTEL based on prior OAT sensitivity and complementary identifiability analysis, Hydrol. Process., 28, 3947–3961, https://doi.org/10.1002/hyp.9882, 2013.
Boulange, J., Hanasaki, N., Yamazaki, D., and Pokhrel, Y.: Role of dams in
reducing global flood exposure under climate change, Nat. Commun., 12, 417, https://doi.org/10.1038/s41467-020-20704-0, 2021.
Brunner, M. I.: Reservoir regulation affects droughts and floods at local and
regional scales, Environ. Res. Lett., 16, 124016, https://doi.org/10.1088/1748-9326/ac36f6, 2021.
Brunner, M. I., Slater, L., Tallaksen, L. M., and Clark, M.: Challenges in
modeling and predicting floods and droughts: A review, WIREs Water, 8, e1520, https://doi.org/10.1002/wat2.1520, 2021.
Cammalleri, C., Vogt, J., and Salamon, P.: Development of an operational
low-flow index for hydrological drought monitoring over Europe, Hydrolog. Sci. J., 62, 346–358, https://doi.org/10.1080/02626667.2016.1240869, 2017.
Casal-Campos, A., Fu, G., Butler, D., and Moore, A.: An Integrated Environmental Assessment of Green and Gray Infrastructure Strategies for
Robust Decision Making, Environ. Sci. Technol., 49, 8307–8314, https://doi.org/10.1021/es506144f, 2015.
Chen, L., Wu, Y., Xu, Y. J., and Guangxin, Z.: Alteration of flood pulses by
damming the Nenjiang River, China – Implication for the need to identify a
hydrograph-based inundation threshold for protecting floodplain wetlands,
Ecol. Indic., 124, 107406, https://doi.org/10.1016/j.ecolind.2021.107406, 2021.
Cheng, C., Brabec, E., Yang, Y., and Ryan, R. Rethinking stormwater management in a changing world: Effects of detention for flooding hazard
mitigation under climate change scenarios in the Charles River Watershed, in:
Proceedings of 2013 CELA Conference, 27–30 March 2013, Austin, Texas, 2013.
Chiang, F., Mazdiyasni, O., and AghaKouchak, A.: Evidence of anthropogenic
impacts on global drought frequency, duration, and intensity, Nat. Commun., 12, 2754, https://doi.org/10.1038/s41467-021-22314-w, 2021.
CMA: China Meteorological Data Service Centre, http://data.cma.cn (last access: 18 July 2023), 2023.
Cook, B. I., Mankin, J. S., Marvel, K., Williams, A. P., Smerdon, J. E., and Anchukaitis, K. J.: Twenty-First Century Drought Projections in the CMIP6 Forcing Scenarios, Earth's Future, 8, e2019EF001461, https://doi.org/10.1029/2019EF001461, 2020.
Dang, T. D., Chowdhury, A. F. M. K., and Galelli, S.: On the representation of water reservoir storage and operations in large-scale hydrological models:
implications on model parameterization and climate change impact assessments, Hydrol. Earth Syst. Sci., 24, 397–416, https://doi.org/10.5194/hess-24-397-2020, 2020.
Diffenbaugh, N. S., Swain, D. L., and Touma, D.: Anthropogenic warming has
increased drought risk in California, P. Natl. Acad. Sci. USA, 112, 3931–3936, https://doi.org/10.1073/pnas.1422385112, 2015.
Ding, W., Zhang, C., Peng, Y., Zeng, R., Zhou, H., and Cai, X.: An analytical framework for flood water conservation considering forecast uncertainty and acceptable risk, Water Resour. Res., 51, 4702–4726, https://doi.org/10.1002/2015WR017127, 2015.
Dobson, B., Wagener, T., and Pianosi, F.: An argument-driven classification
and comparison of reservoir operation optimization methods, Adv. Water Resour., 128, 74–86, 2019.
Eriyagama, N., Smakhtin, V., and Udamulla, L.: How much artificial surface
storage is acceptable in a river basin and where should it be located: A
review, Earth-Sci. Rev., 208, 103294, https://doi.org/10.1016/j.earscirev.2020.103294, 2020.
ESGF: Welcome to the ISIMIP Repository, https://esg.pik-potsdam.de/search/isimip/ (last access: 18 July 2023), 2023.
Evenson, G. R., Golden, H. E., Lane, C. R., and D'Amico, E.: Geographically isolated wetlands and watershed hydrology: A modified model analysis, J. Hydrol., 529, 240–256, https://doi.org/10.1016/j.jhydrol.2015.07.039, 2015.
Evenson, G. R., Golden, H. E., Lane, C.R., and D'Amico, E.: An improved
representation of geographically isolated wetlands in a watershed-scale
hydrologic model, Hydrol. Process., 30, 4168–4184, https://doi.org/10.1002/hyp.10930, 2016.
Evenson, G. R., Jones, C. N., Mclaughlin, D. L., Golden, H. E., Lane, C. R.,
Devries, B., Alexander, L. C., Lang, M. W., Mccarty, G. W., and Sharifi, A.: A watershed-scale model for depressional wetland-rich landscapes, J. Hydrol. X, 1, 100002, https://doi.org/10.1016/j.hydroa.2018.10.002 ,2018.
Fleig, A. K., Tallaksen, L. M., Hisdal, H., and Demuth, S.: A global evaluation of streamflow drought characteristics, Hydrol. Earth Syst. Sci.,
10, 535–552, https://doi.org/10.5194/hess-10-535-2006, 2006.
Fortin, J. P., Turcotte, R., Massicotte, S., Moussa, R., Fitzback, J., and
Villeneuve, J. P.: Distributed watershed model compatible with remote sensing and GIS data. I: Description of model, J. Hydrol. Eng., 6, 91–99, 2001.
Fossey, M. and Rousseau, A. N.: Assessing the long-term hydrological services provided by wetlands under changing climate conditions: A case study approach of a Canadian watershed, J. Hydrol., 541, 1287–1302, https://doi.org/10.1016/j.jhydrol.2016.08.032, 2016a.
Fossey, M. and Rousseau, A. N.: Can isolated and riparian wetlands mitigate the impact of climate change on watershed hydrology? A case study approach, J. Environ. Manage., 184, 327–339, https://doi.org/10.1016/j.jenvman.2016.09.043, 2016b.
Fossey, M., Rousseau, A. N., Bensalma, F., Savary, S., and Royer, A.: Integrating isolated and riparian wetland modules in the PHYSITEL/HYDROTEL modelling platform: model performance and diagnosis, Hydrol. Process., 29,
4683–4702, https://doi.org/10.1002/hyp.10534, 2015.
Fossey, M., Rousseau, A. N., and Savary, S.: Assessment of the impact of spatio-temporal attributes of wetlands on stream flows using a hydrological modelling framework: a theoretical case study of a watershed under temperate climatic conditions, Hydrol. Process., 30, 1768–1781, https://doi.org/10.1002/hyp.10750, 2016.
Foulon, É., Rousseau, A. N., and Gagnon, P.: Development of a methodology to assess future trends in low flows at the watershed scale using solely
climate data, J. Hydrol., 557, 774–790, https://doi.org/10.1016/j.jhydrol.2017.12.064, 2018.
Fowler, K., Peel, M., Western, A., and Zhang, L.: Improved Rainfall-Runoff
Calibration for Drying Climate: Choice of Objective Function, Water Resour.
Res., 54, 3392–3408, https://doi.org/10.1029/2017WR022466, 2018.
Garcia, F., Folton, N., and Oudin, L.: Which objective function to calibrate
rainfall–runoff models for low-flow index simulations?, Hydrolog. Sci. J., 62, 1149–1166, https://doi.org/10.1080/02626667.2017.1308511, 2017.
Golden, H. E., Lane, C. R., Rajib, A., and Wu, Q.: Improving global flood and
drought predictions: integrating non-floodplain wetlands into watershed
hydrologic models, Environ. Res. Lett., 16, 091002, https://doi.org/10.1088/1748-9326/ac1fbc, 2021.
Gómez-Baggethun, E., Tudor, M., Doroftei, M., Covaliov, S., Năstase,
A., Onără, D., Mierlă, M., Marinov, M., Doroşencu, A., Lupu, G., Teodorof, L., Tudor, I., Köhler, B., Museth, J., Aronsen, E., Ivar
Johnsen, S., Ibram, O., Marin, E., Crăciun, A., and Cioacă, E.: Changes in ecosystem services from wetland loss and restoration: An ecosystem
assessment of the Danube Delta (1960–2010), Ecosyst. Serv., 39, 100965,
https://doi.org/10.1016/j.ecoser.2019.100965, 2019.
Gourevitch, J. D., Singh, N. K., Minot, J., Raub, K. B., Rizzo, D. M., Wemple, B. C., and Ricketts, T. H.: Spatial targeting of floodplain restoration to equitably mitigate flood risk, Global Environ. Change, 61, 102050, https://doi.org/10.1016/j.gloenvcha.2020.102050, 2020.
Gulbin, S., Kirilenko, A. P., Kharel, G., and Zhang, X.: Wetland loss impact on long term flood risks in a closed watershed, Environ. Sci. Policy, 94,
112–122, https://doi.org/10.1016/j.envsci.2018.12.032, 2019.
Güneralp, B., Güneralp, O., and Liu, Y.: Changing global patterns of
urban exposure to flood and drought hazards, Global Environ. Change, 31, 217–225, https://doi.org/10.1016/j.gloenvcha.2015.01.002, 2015.
Guo, H., Hu, Q., Zhang, Q., and Feng, S.: Effects of the Three Gorges Dam on
Yangtze River flow and river interaction with Poyang Lake, China: 2003–2008,
J. Hydrol., 416, 19–27, https://doi.org/10.1016/j.jhydrol.2011.11.027, 2012.
Gupta, H. V., Kling, H., Yilmaz, K. K., and Martinez, G. F.: Decomposition of the mean squared error and NSE performance criteria: Implications for improving hydrological modelling, J. Hydrol., 377, 80–91,
https://doi.org/10.1016/j.jhydrol.2009.08.003, 2009.
Hagemann, S. and Jacob, D.: Gradient in the climate change signal of European discharge predicted by a multi-model ensemble, Climatic Change, 81, 309–327, https://doi.org/10.1007/s10584-006-9225-0, 2007.
Hallegatte, S., Green, C., Nicholls, R. J., and Corfee-Morlot, J.: Future
flood losses in major coastal cities, Nat. Clim. Change, 3, 802–806,
https://doi.org/10.1038/nclimate1979, 2013.
Hirabayashi, Y., Mahendran, R., Koirala, S., Konoshima, L., Yamazaki, D., Watanabe, S., Kim, H., and Kanae, S.: Global flood risk under climate change, Nat. Clim. Change, 3, 816–821, https://doi.org/10.1038/nclimate1911, 2013.
Hisdal, H. and Tallaksen, L. M.: Estimation of regional meteorological and
hydrological drought characteristics: a case study for Denmark, J. Hydrol., 281, 230–247, https://doi.org/10.1016/S0022-1694(03)00233-6, 2003.
Hutchinson, M. F. and Xu, T.: Anusplin version 4.2 user guide, Centre for Resource and Environmental Studies, The Australian National University, Canberra, 7–9, 2004.
Javaheri, A. and Babbar-Sebens, M.: On comparison of peak flow reductions,
flood inundation maps, and velocity maps in evaluating effects of restored
wetlands on channel flooding, Ecol. Eng., 73, 132–145, https://doi.org/10.1016/j.ecoleng.2014.09.021, 2014.
Jongman, B.: Effective adaptation to rising flood risk, Nat. Commun., 9, 1986, https://doi.org/10.1038/s41467-018-04396-1, 2018.
Kharrufa, N. S.: Simplified equation for evapotranspiration in arid regions, Hydrologie Sonderheft, 5, 39–47, 1985.
Kriegler, E., Bauer, N., Popp, A., Humpenöder, F., Leimbach, M., Strefler, J., Baumstark, L., Bodirsky, B. L., Hilaire, J., and Klein, D.:
Fossil-fueled development (SSP5): An energy and resource intensive scenario
for the 21st century, Global Environ. Change, 42, 297–315,
https://doi.org/10.1016/j.gloenvcha.2016.05.015, 2017.
Kumar, P., Debele, S. E., Sahani, J., Rawat, N., Marti-Cardona, B., Alfieri,
S. M., Basu, B., Basu, A. S., Bowyer, P., Charizopoulos, N., Gallotti, G.,
Jaakko, J., Leo, L. S., Loupis, M., Menenti, M., Mickovski, S. B., Mun, S.,
Gonzalez-Ollauri, A., Pfeiffer, J., Pilla, F., Pröll, J., Rutzinger, M.,
Santo, M. A., Sannigrahi, S., Spyrou, C., Tuomenvirta, H., and Zieher, T.:
Nature-based solutions efficiency evaluation against natural hazards: Modelling methods, advantages and limitations, Sci. Total Environ., 784,
147058, https://doi.org/10.1016/j.scitotenv.2021.147058, 2021.
Lee, S., Yeo, I. Y., Lang, M. W., Sadeghi, A. M., Mccarty, G. W., Moglen, G. E., and Evenson, G. R.: Assessing the cumulative impacts of geographically
isolated wetlands on watershed hydrology using the SWAT model coupled with
improved wetland modules, J. Environ. Manage., 223, 37–48,
https://doi.org/10.1016/j.jenvman.2018.06.006, 2018.
Li, F., Zhang, G., and Xu, Y. J.: Spatiotemporal variability of climate and
streamflow in the Songhua River Basin, northeast China, J. Hydrol., 514, 53–64, https://doi.org/10.1016/j.jhydrol.2014.04.010, 2014.
Li, W., Jiang, Y., Duan, Y., Bai, J., Zhou, D., and Ke, Y.: Where and how to
restore wetland by utilizing storm water at the regional scale: A case study
of Fangshan, China, Ecol. Indic., 122, 107246, https://doi.org/10.1016/j.ecolind.2020.107246, 2021.
Liu, Y., Yang, W., and Wang, X.: Development of a SWAT extension module to simulate riparian wetland hydrologic processes at a watershed scale, Hydrol. Process., 22, 2901–2915, https://doi.org/10.1002/hyp.6874, 2008.
Lobligeois, F., Andréassian, V., Perrin, C., Tabary, P., and Loumagne, C.: When does higher spatial resolution rainfall information improve streamflow simulation? An evaluation using 3620 flood events, Hydrol. Earth
Syst. Sci., 18, 575–594, https://doi.org/10.5194/hess-18-575-2014, 2014.
Maes, J., Barbosa, A., Baranzelli, C., Zulian, G., Batista E Silva, F.,
Vandecasteele, I., Hiederer, R., Liquete, C., Paracchini, M. L., Mubareka,
S., Jacobs-Crisioni, C., Castillo, C. P., and Lavalle, C.: More green
infrastructure is required to maintain ecosystem services under current
trends in land-use change in Europe, Landsc. Ecol., 30, 517–534,
https://doi.org/10.1007/s10980-014-0083-2, 2015.
Manfreda, S., Miglino, D., and Albertini, C.: Impact of detention dams on the probability distribution of floods, Hydrol. Earth Syst. Sci., 25, 4231–4242, https://doi.org/10.5194/hess-25-4231-2021, 2021.
Maraun, D.: Bias Correcting Climate Change Simulations – a Critical Review,
Curr. Clim. Change Rep., 2, 211–220, https://doi.org/10.1007/s40641-016-0050-x, 2016.
Martel, J. L., Brissette, F., Troin, M., Arsenault, R., Chen, J., Su, T., and Lucas Picher, P.: CMIP5 and CMIP6 model projection comparison for hydrological impacts over North America, Geophys. Res. Lett., 49, e2022GL098364, https://doi.org/10.1029/2022GL098364, 2022.
Martinez-Martinez, E., Nejadhashemi, A. P., Woznicki, S. A., and Love, B. J.:
Modeling the hydrological significance of wetland restoration scenarios, J.
Environ. Manage., 133, 121–134, https://doi.org/10.1016/j.jenvman.2013.11.046, 2014.
Melsen, L. A., Teuling, A. J., Torfs, P. J. J. F., Zappa, M., Mizukami, N.,
Mendoza, P. A., Clark, M. P., and Uijlenhoet, R.: Subjective modeling decisions can significantly impact the simulation of flood and drought events, J. Hydrol., 568, 1093–1104, https://doi.org/10.1016/j.jhydrol.2018.11.046, 2019.
Meng, B., Liu, J., Bao, K., and Sun, B.: Water fluxes of Nenjiang River Basin with ecological network analysis: Conflict and coordination between agricultural development and wetland restoration, J. Clean. Prod., 213, 933–943, https://doi.org/10.1016/j.jclepro.2018.12.243, 2019.
Moore, K., Pierson, D., Pettersson, K., Schneiderman, E., and Samuelsson, P.:
Effects of warmer world scenarios on hydrologic inputs to Lake Mälaren,
Sweden and implications for nutrient loads, Hydrobiologia, 599, 191–199,
https://doi.org/10.1007/978-1-4020-8379-2_23, 2008.
Moriasi, D. N.: Model Evaluation Guidelines for Systematic Quantification of
Accuracy in Watershed Simulations, T. ASABE, 50, 885–900, https://doi.org/10.13031/2013.23153, 2007.
Moriasi, D. N., Gitau, M. W., Pai, N., and Daggupati, P.: Hydrologic and water quality models: Performance measures and evaluation criteria, T. ASABE, 58, 1763–1785, https://doi.org/10.13031/trans.58.10715, 2015.
Muller, M.: Hydropower dams can help mitigate the global warming impact of
wetlands, Nature, 566, 315–317, https://doi.org/10.1038/d41586-019-00616-w, 2019.
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.
Nelson, D. R., Bledsoe, B. P., Ferreira, S., and Nibbelink, N. P.: Challenges to realizing the potential of nature-based solutions, Curr. Opin. Environ. Sustain., 45, 49–55, https://doi.org/10.1016/j.cosust.2020.09.001, 2020.
NESSDC-NSTI – National Earth System Science Data Center, National Science & Technology Infrastructure of China: http://www.geodata.cn (last access: 18 May 2021), 2020.
Nika, C. E., Gusmaroli, L., Ghafourian, M., Atanasova, N., Buttiglieri, G., and Katsou, E.: Nature-based solutions as enablers of circularity in water
systems: A review on assessment methodologies, tools and indicators, Water
Res., 183, 115988, https://doi.org/10.1016/j.watres.2020.115988, 2020.
Noël, P., Rousseau, A. N., Paniconi, C., and Nadeau, D. F.: Algorithm for
delineating and extracting hillslopes and hillslope width functions from
gridded elevation data, J. Hydrol. Eng., 19, 366–374, 2014.
O'Neill, B. C., Tebaldi, C., van Vuuren, D. P., Eyring, V., Friedlingstein,
P., Hurtt, G., Knutti, R., Kriegler, E., Lamarque, J.-F., Lowe, J., Meehl, G. A., Moss, R., Riahi, K., and Sanderson, B. M.: The Scenario Model Intercomparison Project (ScenarioMIP) for CMIP6, Geosci. Model Dev., 9,
3461–3482, https://doi.org/10.5194/gmd-9-3461-2016, 2016.
Park, J., Botter, G., Jawitz, J. W., and Rao, P. S. C.: Stochastic modeling of hydrologic variability of geographically isolated wetlands: Effects of
hydro-climatic forcing and wetland bathymetry, Adv. Water Resour., 69, 38–48, https://doi.org/10.1016/j.advwatres.2014.03.007, 2014.
Pool, S., Vis, M., Seibert, J., and Sveriges, L.: Evaluating model performance: towards a non-parametric variant of the Kling-Gupta efficiency, Hydrolog. Sci. J., 63, 1941–1953, https://doi.org/10.1080/02626667.2018.1552002, 2018.
Qing, Y., Wang, S., Zhang, B., and Wang, Y.: Ultra-high resolution regional climate projections for assessing changes in hydrological extremes and underlying uncertainties, Clim. Dynam., 55, 2031–2051, https://doi.org/10.1007/s00382-020-05372-6, 2020.
Rains, M. C., Leibowitz, S. G., Cohen, M. J., Creed, I. F., Golden, H. E., Jawitz, J. W., Kalla, P., Lane, C. R., Lang, M. W., and Mclaughlin, D. L.: Geographically isolated wetlands are part of the hydrological landscape, Hydrol. Process., 30, 153–160, https://doi.org/10.1002/hyp.10610, 2016.
Rajib, A., Golden, H. E., Lane, C. R., and Wu, Q.: Surface Depression and Wetland Water Storage Improves Major River Basin Hydrologic Predictions, Water Resour. Res., 56, e2019WR026561, https://doi.org/10.1029/2019WR026561, 2020.
Rathjens, H., Bieger, K., Srinivasan, R., Chaubey, I., and Arnold, J. G.: CMhyd User Manual, http://swat.tamu.edu/software/cmhyd/ (last access: 2 January 2020), 2016.
Rousseau, A. N., Fortin, J., Turcotte, R., Royer, A., Savary, S., Quévy,
F., Noël, P., and Paniconi, C.: PHYSITEL, a specialized GIS for supporting the implementation of distributed hydrological models, Water News, 31, 18–20, 2011.
Saharia, M., Kirstetter, P. E., Vergara, H., Gourley, J. J., Hong, Y., and Giroud, M.: Mapping Flash Flood Severity in the United States, J. Hydrometeorol., 18, 397–411, https://doi.org/10.1175/JHM-D-16-0082.1, 2017.
Schneider, C., Flörke, M., De Stefano, L., and Petersen-Perlman, J. D.:
Hydrological threats to riparian wetlands of international importance – a
global quantitative and qualitative analysis, Hydrol. Earth Syst. Sci., 21,
2799–2815, https://doi.org/10.5194/hess-21-2799-2017, 2017.
Seibert, J., Vis, M. J. P., Lewis, E., and Meerveld, H. J.: Upper and lower
benchmarks in hydrological modelling, Hydrol. Process., 32, 1120–1125, https://doi.org/10.1002/hyp.11476, 2018.
Shafeeque, M. and Luo, Y.: A multi-perspective approach for selecting CMIP6
scenarios to project climate change impacts on glacio-hydrology with a case
study in Upper Indus river basin, J. Hydrol., 599, 126466, https://doi.org/10.1016/j.jhydrol.2021.126466, 2021.
Shook, K., Papalexiou, S., and Pomeroy, J. W.: Quantifying the effects of Prairie depressional storage complexes on drainage basin connectivity, J. Hydrol., 593, 125846, https://doi.org/10.1016/j.jhydrol.2020.125846, 2021.
Smakhtin, V. U.: Low flow hydrology: a review, J. Hydrol., 240, 147–186, https://doi.org/10.1016/S0022-1694(00)00340-1, 2001.
Staudinger, M., Stahl, K., Seibert, J., Clark, M. P., and Tallaksen, L. M.:
Comparison of hydrological model structures based on recession and low flow
simulations, Hydrol. Earth Syst. Sci., 15, 3447–3459,
https://doi.org/10.5194/hess-15-3447-2011, 2011.
Tallaksen, L. M. and van Lanen, H. A. J.: Hydrological drought; processes and
estimation methods for streamflow and groundwater, Dev. Water Sci., 48, 164–273, 2004.
Thorslund, J., Jarsjo, J., Jaramillo, F., Jawitz, J. W., Manzoni, S., Basu,
N. B., Chalov, S. R., Cohen, M. J., Creed, I. F., Goldenberg, R., Hylin, A.,
Kalantari, Z., Koussis, A. D., Lyon, S. W., Mazi, K., Mard, J., Persson, K.,
Pietro, J., Prieto, C., Quin, A., Van Meter, K., and Destouni, G.: Wetlands as large-scale nature-based solutions: Status and challenges for research,
engineering and management, Ecol. Eng., 108, 489–497, https://doi.org/10.1016/j.ecoleng.2017.07.012, 2017.
Tolson, B. A. and Shoemaker, C. A.: Dynamically dimensioned search algorithm
for computationally efficient watershed model calibration, Water Resour. Res., 43, 1–6, https://doi.org/10.1029/2005WR004723, 2007.
Turcotte, R., Fortin, L. G., Fortin, V., Fortin, J. P., and Villeneuve, J. P.: Operational analysis of the spatial distribution and the temporal evolution of the snowpack water equivalent in southern Québec, Canada, Hydrol. Res., 38, 211–234, https://doi.org/10.2166/nh.2007.009, 2007.
UNISDR: The human cost of natural disasters: A global perspective, https://doi.org/10.13140/RG.2.2.21032.78085, 2015.
Van Vuuren, D. P., Riahi, K., Calvin, K., Dellink, R., Emmerling, J., Fujimori, S., and O'Neill, B.: The Shared Socio-economic Pathways: Trajectories for human development and global environmental change, Global Environ. Change, 42, 148–152, https://doi.org/10.1016/j.gloenvcha.2016.10.009, 2017.
Walz, Y., Janzen, S., Narvaez, L., Ortiz-Vargas, A., Woelki, J., Doswald, N., and Sebesvari, Z.: Disaster-related losses of ecosystems and their services. Why and how do losses matter for disaster risk reduction?, Int. J. Disast. Risk Reduct., 63, 102425, https://doi.org/10.1016/j.ijdrr.2021.102425, 2021.
Wang, L., Chen, X., Shao, Q., and Li, Y.: Flood indicators and their
clustering features in Wujiang River, South China, Ecol. Eng., 76, 66–74, https://doi.org/10.1016/j.ecoleng.2014.03.018, 2015.
Wang, S., Zhang, L., She, D., Wang, G., and Zhang, Q.: Future projections of
flooding characteristics in the Lancang-Mekong River Basin under climate
change, J. Hydrol., 602, 126778, https://doi.org/10.1016/j.jhydrol.2021.126778, 2021.
Wang, X.: Using Hydrologic Equivalent Wetland Concept Within SWAT to Estimate Streamflow in Watersheds with Numerous Wetlands, T. ASABE, 51, 55–72, https://doi.org/10.13031/2013.24227, 2008.
Ward, P. J., de Ruiter, M. C., Mård, J., Schröter, K., Van Loon, A.,
Veldkamp, T., von Uexkull, N., Wanders, N., Aghakouchak, A., Arnbjerg-Nielsen, K., Capewell, L., Carmen Llasat, M., Day, R., Dewals, B.,
Di Baldassarre, G., Huning, L. S., Kreibich, H., Mazzoleni, M., Savelli, E.,
Teutschbein, C., van den Berg, H., van der Heijden, A., Vincken, J. M. R.,
Waterloo, M. J., and Wens, M.: The need to integrate flood and drought disaster risk reduction strategies, Water Secur., 11, 100070,
https://doi.org/10.1016/j.wasec.2020.100070, 2020.
Wu, Y., Zhang, G., Qi, P., Rousseau, A. N., Hu, B., Song, Z., and Yu, C.: Integration of wetland modules into the watershed hydrological model: assessment of simulation accuracy, Adv. Water Sci., 30, 326–336, https://doi.org/10.14042/j.cnki.32.1309.2019.03.003, 2019.
Wu, Y., Zhang, G., Rousseau, A. N., and Xu, Y. J.: Quantifying streamflow regulation services of wetlands with an emphasis on quickflow and baseflow responses in the Upper Nenjiang River Basin, Northeast China, J. Hydrol., 583, 124565, https://doi.org/10.1016/j.jhydrol.2020.124565, 2020a.
Wu, Y., Zhang, G., Rousseau, A. N., Xu, Y. J., and Foulon, É.: On how wetlands can provide flood resilience in a large river basin: A case study in Nenjiang river Basin, China, J. Hydrol., 587, 125012, https://doi.org/10.1016/j.jhydrol.2020.125012, 2020b.
Wu, Y., Zhang, G., Xu, Y. J., and Rousseau, A. N.: River Damming Reduces Wetland Function in Regulating Flow, J. Water Resour. Pl. Manage.-ASCE, 147, 05021014, https://doi.org/10.1061/(ASCE)WR.1943-5452.0001434, 2021.
Wu, Y., Sun, J., Hu, B., Zhang, G., and Rousseau, A. N.: Wetland-based solutions against extreme flood and severe drought: Efficiency evaluation of risk mitigation, Clim. Risk Manage., 40, 100505, https://doi.org/10.1016/j.crm.2023.100505, 2023.
Xu, X., Liu, J., Zhang, S., Li, R., Yan, C., and Wu, S.: China multi-period land use remote sensing monitoring dataset, RESDC [data set], https://doi.org/10.12078/2018070201, 2018.
Xu, X., Wang, Y. C., Kalcic, M., Muenich, R. L., Yang, Y. E., and Scavia, D.: Evaluating the impact of climate change on fluvial flood risk in a mixed-use watershed, Environ. Model. Softw., 122, 104031, https://doi.org/10.1016/j.envsoft.2017.07.013, 2019.
Yassin, F., Razavi, S., Elshamy, M., Davison, B., Sapriza-Azuri, G., and
Wheater, H.: Representation and improved parameterization of reservoir
operation in hydrological and land-surface models, Hydrol. Earth Syst. Sci.,
23, 3735–3764, https://doi.org/10.5194/hess-23-3735-2019, 2019.
Zedler, J. B. and Kercher, S.: Wetland Resources: Status, Trends, Ecosystem
Services, and Restorability, Annu. Rev. Environ. Resour., 30, 39–74,
https://doi.org/10.1146/annurev.energy.30.050504.144248, 2005.
Zelenhasić, E. and Salvai, A.: A method of streamflow drought analysis, Water Resour. Res., 23, 156–168, https://doi.org/10.1029/WR023i001p00156, 1987.
Zeng, L., Shao, J., and Chu, X.: Improved hydrologic modeling for
depression-dominated areas, J. Hydrol., 590, 125269,
https://doi.org/10.1016/j.jhydrol.2020.125269, 2020.
Zhang, X. and Song, Y.: Optimization of wetland restoration siting and zoning in flood retention areas of river basins in China: A case study in Mengwa,
Huaihe River Basin, J. Hydrol., 519, 80–93, https://doi.org/10.1016/j.jhydrol.2014.06.043, 2014.
Zhao, G., Gao, H., Naz, B. S., Kao, S., and Voisin, N.: Integrating a reservoir regulation scheme into a spatially distributed hydrological model, Adv. Water Resour., 98, 16–31, https://doi.org/10.1016/j.advwatres.2016.10.014, 2016.
Zhao, Y., Dong, N., Li, Z., Zhang, W., Yang, M., and Wang, H.: Future
precipitation, hydrology and hydropower generation in the Yalong River Basin: Projections and analysis, J. Hydrol., 602, 126738,
https://doi.org/10.1016/j.jhydrol.2021.126738, 2021.
Short summary
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.
Reservoirs and wetlands are important regulators of watershed hydrology, which should be...
