Articles | Volume 24, issue 5
https://doi.org/10.5194/hess-24-2343-2020
© Author(s) 2020. 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-24-2343-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
08 May 2020
Research article |
| 08 May 2020
| 08 May 2020
Multistep-ahead daily inflow forecasting using the ERA-Interim reanalysis data set based on gradient-boosting regression trees
Shengli Liao
Institute of Hydropower System and Hydroinformatics, Dalian University of Technology, Dalian 116024, China
Key Laboratory of Ocean Energy Utilization and Energy Conservation of Ministry of Education, Dalian University of Technology, Dalian 116024, China
Zhanwei Liu
CORRESPONDING AUTHOR
Institute of Hydropower System and Hydroinformatics, Dalian University of Technology, Dalian 116024, China
Key Laboratory of Ocean Energy Utilization and Energy Conservation of Ministry of Education, Dalian University of Technology, Dalian 116024, China
Benxi Liu
Institute of Hydropower System and Hydroinformatics, Dalian University of Technology, Dalian 116024, China
Key Laboratory of Ocean Energy Utilization and Energy Conservation of Ministry of Education, Dalian University of Technology, Dalian 116024, China
Chuntian Cheng
Institute of Hydropower System and Hydroinformatics, Dalian University of Technology, Dalian 116024, China
Key Laboratory of Ocean Energy Utilization and Energy Conservation of Ministry of Education, Dalian University of Technology, Dalian 116024, China
Xinfeng Jin
Institute of Hydropower System and Hydroinformatics, Dalian University of Technology, Dalian 116024, China
Key Laboratory of Ocean Energy Utilization and Energy Conservation of Ministry of Education, Dalian University of Technology, Dalian 116024, China
Zhipeng Zhao
Institute of Hydropower System and Hydroinformatics, Dalian University of Technology, Dalian 116024, China
Key Laboratory of Ocean Energy Utilization and Energy Conservation of Ministry of Education, Dalian University of Technology, Dalian 116024, China
Related subject area
Subject: Catchment hydrology | Techniques and Approaches: Modelling approaches
Technical note: How physically based is hydrograph separation by recursive digital filtering?
A comprehensive open-source course for teaching applied hydrological modelling in Central Asia
Impact of distributed meteorological forcing on simulated snow cover and hydrological fluxes over a mid-elevation alpine micro-scale catchment
Technical note: Extending the SWAT model to transport chemicals through tile and groundwater flow
Long-term reconstruction of satellite-based precipitation, soil moisture, and snow water equivalent in China
Disentangling scatter in long-term concentration–discharge relationships: the role of event types
Simulating the hydrological impacts of land use conversion from annual crop to perennial forage in the Canadian Prairies using the Cold Regions Hydrological Modelling platform
How can we benefit from regime information to make more effective use of long short-term memory (LSTM) runoff models?
On the value of satellite remote sensing to reduce uncertainties of regional simulations of the Colorado River
Assessing runoff sensitivity of North American Prairie Pothole Region basins to wetland drainage using a basin classification-based virtual modelling approach
A large-sample investigation into uncertain climate change impacts on high flows across Great Britain
Effects of passive-storage conceptualization on modeling hydrological function and isotope dynamics in the flow system of a cockpit karst landscape
Technical note: Data assimilation and autoregression for using near-real-time streamflow observations in long short-term memory networks
Attribution of climate change and human activities to streamflow variations with a posterior distribution of hydrological simulations
A time-varying distributed unit hydrograph method considering soil moisture
Flood patterns in a catchment with mixed bedrock geology and a hilly landscape: identification of flashy runoff contributions during storm events
A graph neural network (GNN) approach to basin-scale river network learning: the role of physics-based connectivity and data fusion
Improving hydrologic models for predictions and process understanding using neural ODEs
Response of active catchment water storage capacity to a prolonged meteorological drought and asymptotic climate variation
Machine learning based streamflow prediction in a hilly catchment for future scenarios using CMIP6 data
HESS Opinions: Participatory Digital eARth Twin Hydrology systems (DARTHs) for everyone – a blueprint for hydrologists
Development of a national 7-day ensemble streamflow forecasting service for Australia
Future snow changes and their impact on the upstream runoff in Salween
Technical note: Do different projections matter for the Budyko framework?
Representation of seasonal land use dynamics in SWAT+ for improved assessment of blue and green water consumption
Large-sample assessment of varying spatial resolution on the streamflow estimates of the wflow_sbm hydrological model
An algorithm for deriving the topology of belowground urban stormwater networks
Assessing the influence of water sampling strategy on the performance of tracer-aided hydrological modeling in a mountainous basin on the Tibetan Plateau
Flood forecasting with machine learning models in an operational framework
Precipitation fate and transport in a Mediterranean catchment through models calibrated on plant and stream water isotope data
High-resolution satellite products improve hydrological modeling in northern Italy
Analysis of high streamflow extremes in climate change studies: how do we calibrate hydrological models?
A conceptual-model-based sediment connectivity assessment for patchy agricultural catchments
The Great Lakes Runoff Intercomparison Project Phase 4: the Great Lakes (GRIP-GL)
Spatial extrapolation of stream thermal peaks using heterogeneous time series at a national scale
Revisiting parameter sensitivities in the variable infiltration capacity model across a hydroclimatic gradient
Deep learning rainfall–runoff predictions of extreme events
Diel streamflow cycles suggest more sensitive snowmelt-driven streamflow to climate change than land surface modeling does
Teaching hydrological modelling: illustrating model structure uncertainty with a ready-to-use computational exercise
River hydraulic modelling with ICEsat-2 land and water surface elevation
Effects of spatial and temporal variability in surface water inputs on streamflow generation and cessation in the rain–snow transition zone
Quantifying multi-year hydrological memory with Catchment Forgetting Curves
On constraining a lumped hydrological model with both piezometry and streamflow: results of a large sample evaluation
Influences of land use changes on the dynamics of water quantity and quality in the German lowland catchment of the Stör
Impact of spatial distribution information of rainfall in runoff simulation using deep learning method
Towards effective drought monitoring in the Middle East and North Africa (MENA) region: implications from assimilating leaf area index and soil moisture into the Noah-MP land surface model for Morocco
The effects of spatial and temporal resolution of gridded meteorological forcing on watershed hydrological responses
Hydrological response of a peri-urban catchment exploiting conventional and unconventional rainfall observations: the case study of Lambro Catchment
Assessing hydrological sensitivity of grassland basins in the Canadian Prairies to climate using a basin classification-based virtual modelling approach
The value of satellite soil moisture and snow cover data for the transfer of hydrological model parameters to ungauged sites
Klaus Eckhardt
Hydrol. Earth Syst. Sci., 27, 495–499, https://doi.org/10.5194/hess-27-495-2023, https://doi.org/10.5194/hess-27-495-2023, 2023
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An important hydrological issue is to identify components of streamflow that react to precipitation with different degrees of attenuation and delay. From the multitude of methods that have been developed for this so-called hydrograph separation, a specific, frequently used one is singled out here. It is shown to be derived from plausible physical principles. This increases confidence in its results.
Beatrice Sabine Marti, Aidar Zhumabaev, and Tobias Siegfried
Hydrol. Earth Syst. Sci., 27, 319–330, https://doi.org/10.5194/hess-27-319-2023, https://doi.org/10.5194/hess-27-319-2023, 2023
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Numerical modelling is often used for climate impact studies in water resources management. It is, however, not yet highly accessible to many students of hydrology in Central Asia. One big hurdle for new learners is the preparation of relevant data prior to the actual modelling. We present a robust, open-source workflow and comprehensive teaching material that can be used by teachers and by students for self study.
Aniket Gupta, Alix Reverdy, Jean-Martial Cohard, Basile Hector, Marc Descloitres, Jean-Pierre Vandervaere, Catherine Coulaud, Romain Biron, Lucie Liger, Reed Maxwell, Jean-Gabriel Valay, and Didier Voisin
Hydrol. Earth Syst. Sci., 27, 191–212, https://doi.org/10.5194/hess-27-191-2023, https://doi.org/10.5194/hess-27-191-2023, 2023
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Patchy snow cover during spring impacts mountainous ecosystems on a large range of spatio-temporal scales. A hydrological model simulated such snow patchiness at 10 m resolution. Slope and orientation controls precipitation, radiation, and wind generate differences in snowmelt, subsurface storage, streamflow, and evapotranspiration. The snow patchiness increases the duration of the snowmelt to stream and subsurface storage, which sustains the plants and streamflow later in the summer.
Hendrik Rathjens, Jens Kiesel, Michael Winchell, Jeffrey Arnold, and Robin Sur
Hydrol. Earth Syst. Sci., 27, 159–167, https://doi.org/10.5194/hess-27-159-2023, https://doi.org/10.5194/hess-27-159-2023, 2023
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The SWAT model can simulate the transport of water-soluble chemicals through the landscape but neglects the transport through groundwater or agricultural tile drains. These transport pathways are, however, important to assess the amount of chemicals in streams. We added this capability to the model, which significantly improved the simulation. The representation of all transport pathways in the model enables watershed managers to develop robust strategies for reducing chemicals in streams.
Wencong Yang, Hanbo Yang, Changming Li, Taihua Wang, Ziwei Liu, Qingfang Hu, and Dawen Yang
Hydrol. Earth Syst. Sci., 26, 6427–6441, https://doi.org/10.5194/hess-26-6427-2022, https://doi.org/10.5194/hess-26-6427-2022, 2022
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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.
Felipe A. Saavedra, Andreas Musolff, Jana von Freyberg, Ralf Merz, Stefano Basso, and Larisa Tarasova
Hydrol. Earth Syst. Sci., 26, 6227–6245, https://doi.org/10.5194/hess-26-6227-2022, https://doi.org/10.5194/hess-26-6227-2022, 2022
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Nitrate contamination of rivers from agricultural sources is a challenge for water quality management. During runoff events, different transport paths within the catchment might be activated, generating a variety of responses in nitrate concentration in stream water. Using nitrate samples from 184 German catchments and a runoff event classification, we show that hydrologic connectivity during runoff events is a key control of nitrate transport from catchments to streams in our study domain.
Marcos R. C. Cordeiro, Kang Liang, Henry F. Wilson, Jason Vanrobaeys, David A. Lobb, Xing Fang, and John W. Pomeroy
Hydrol. Earth Syst. Sci., 26, 5917–5931, https://doi.org/10.5194/hess-26-5917-2022, https://doi.org/10.5194/hess-26-5917-2022, 2022
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This study addresses the issue of increasing interest in the hydrological impacts of converting cropland to perennial forage cover in the Canadian Prairies. By developing customized models using the Cold Regions Hydrological Modelling (CRHM) platform, this long-term (1992–2013) modelling study is expected to provide stakeholders with science-based information regarding the hydrological impacts of land use conversion from annual crop to perennial forage cover in the Canadian Prairies.
Reyhaneh Hashemi, Pierre Brigode, Pierre-André Garambois, and Pierre Javelle
Hydrol. Earth Syst. Sci., 26, 5793–5816, https://doi.org/10.5194/hess-26-5793-2022, https://doi.org/10.5194/hess-26-5793-2022, 2022
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Hydrologists have long dreamed of a tool that could adequately predict runoff in catchments. Data-driven long short-term memory (LSTM) models appear very promising to the hydrology community in this respect. Here, we have sought to benefit from traditional practices in hydrology to improve the effectiveness of LSTM models. We discovered that one LSTM parameter has a hydrologic interpretation and that there is a need to increase the data and to tune two parameters, thereby improving predictions.
Mu Xiao, Giuseppe Mascaro, Zhaocheng Wang, Kristen M. Whitney, and Enrique R. Vivoni
Hydrol. Earth Syst. Sci., 26, 5627–5646, https://doi.org/10.5194/hess-26-5627-2022, https://doi.org/10.5194/hess-26-5627-2022, 2022
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As the major water resource in the southwestern United States, the Colorado River is experiencing decreases in naturalized streamflow and is predicted to face severe challenges under future climate scenarios. Here, we demonstrate the value of Earth observing satellites to improve and build confidence in the spatiotemporal simulations from regional hydrologic models for assessing the sensitivity of the Colorado River to climate change and supporting regional water managers.
Christopher Spence, Zhihua He, Kevin R. Shook, John W. Pomeroy, Colin J. Whitfield, and Jared D. Wolfe
Hydrol. Earth Syst. Sci., 26, 5555–5575, https://doi.org/10.5194/hess-26-5555-2022, https://doi.org/10.5194/hess-26-5555-2022, 2022
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We learnt how streamflow from small creeks could be altered by wetland removal in the Canadian Prairies, where this practice is pervasive. Every creek basin in the region was placed into one of seven groups. We selected one of these groups and used its traits to simulate streamflow. The model worked well enough so that we could trust the results even if we removed the wetlands. Wetland removal did not change low flow amounts very much, but it doubled high flow and tripled average flow.
Rosanna A. Lane, Gemma Coxon, Jim Freer, Jan Seibert, and Thorsten Wagener
Hydrol. Earth Syst. Sci., 26, 5535–5554, https://doi.org/10.5194/hess-26-5535-2022, https://doi.org/10.5194/hess-26-5535-2022, 2022
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This study modelled the impact of climate change on river high flows across Great Britain (GB). Generally, results indicated an increase in the magnitude and frequency of high flows along the west coast of GB by 2050–2075. In contrast, average flows decreased across GB. All flow projections contained large uncertainties; the climate projections were the largest source of uncertainty overall but hydrological modelling uncertainties were considerable in some regions.
Guangxuan Li, Xi Chen, Zhicai Zhang, Lichun Wang, and Chris Soulsby
Hydrol. Earth Syst. Sci., 26, 5515–5534, https://doi.org/10.5194/hess-26-5515-2022, https://doi.org/10.5194/hess-26-5515-2022, 2022
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We developed a coupled flow–tracer model to understand the effects of passive storage on modeling hydrological function and isotope dynamics in a karst flow system. Models with passive storages show improvement in matching isotope dynamics performance, and the improved performance also strongly depends on the number and location of passive storages. Our results also suggested that the solute transport is primarily controlled by advection and hydrodynamic dispersion in the steep hillslope unit.
Grey S. Nearing, Daniel Klotz, Jonathan M. Frame, Martin Gauch, Oren Gilon, Frederik Kratzert, Alden Keefe Sampson, Guy Shalev, and Sella Nevo
Hydrol. Earth Syst. Sci., 26, 5493–5513, https://doi.org/10.5194/hess-26-5493-2022, https://doi.org/10.5194/hess-26-5493-2022, 2022
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When designing flood forecasting models, it is necessary to use all available data to achieve the most accurate predictions possible. This manuscript explores two basic ways of ingesting near-real-time streamflow data into machine learning streamflow models. The point we want to make is that when working in the context of machine learning (instead of traditional hydrology models that are based on
bio-geophysics), it is not necessary to use complex statistical methods for injecting sparse data.
Xiongpeng Tang, Guobin Fu, Silong Zhang, Chao Gao, Guoqing Wang, Zhenxin Bao, Yanli Liu, Cuishan Liu, and Junliang Jin
Hydrol. Earth Syst. Sci., 26, 5315–5339, https://doi.org/10.5194/hess-26-5315-2022, https://doi.org/10.5194/hess-26-5315-2022, 2022
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In this study, we proposed a new framework that considered the uncertainties of model simulations in quantifying the contribution rate of climate change and human activities to streamflow changes. Then, the Lancang River basin was selected for the case study. The results of quantitative analysis using the new framework showed that the reason for the decrease in the streamflow at Yunjinghong station was mainly human activities.
Bin Yi, Lu Chen, Hansong Zhang, Vijay P. Singh, Ping Jiang, Yizhuo Liu, Hexiang Guo, and Hongya Qiu
Hydrol. Earth Syst. Sci., 26, 5269–5289, https://doi.org/10.5194/hess-26-5269-2022, https://doi.org/10.5194/hess-26-5269-2022, 2022
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An improved GIS-derived distributed unit hydrograph routing method considering time-varying soil moisture was proposed for flow routing. The method considered the changes of time-varying soil moisture and rainfall intensity. The response of underlying surface to the soil moisture content was considered an important factor in this study. The SUH, DUH, TDUH and proposed routing methods (TDUH-MC) were used for flood forecasts, and the simulated results were compared and discussed.
Audrey Douinot, Jean François Iffly, Cyrille Tailliez, Claude Meisch, and Laurent Pfister
Hydrol. Earth Syst. Sci., 26, 5185–5206, https://doi.org/10.5194/hess-26-5185-2022, https://doi.org/10.5194/hess-26-5185-2022, 2022
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The objective of the paper is to highlight the seasonal and singular shift of the transfer time distributions of two catchments (≅10 km2).
Based on 2 years of rainfall and discharge observations, we compare variations in the properties of TTDs with the physiographic characteristics of catchment areas and the eco-hydrological cycle. The paper eventually aims to deduce several factors conducive to particularly rapid and concentrated water transfers, which leads to flash floods.
Alexander Y. Sun, Peishi Jiang, Zong-Liang Yang, Yangxinyu Xie, and Xingyuan Chen
Hydrol. Earth Syst. Sci., 26, 5163–5184, https://doi.org/10.5194/hess-26-5163-2022, https://doi.org/10.5194/hess-26-5163-2022, 2022
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High-resolution river modeling is of great interest to local governments and stakeholders for flood-hazard mitigation. This work presents a physics-guided, machine learning (ML) framework for combining the strengths of high-resolution process-based river network models with a graph-based ML model capable of modeling spatiotemporal processes. Results show that the ML model can approximate the dynamics of the process model with high fidelity, and data fusion further improves the forecasting skill.
Marvin Höge, Andreas Scheidegger, Marco Baity-Jesi, Carlo Albert, and Fabrizio Fenicia
Hydrol. Earth Syst. Sci., 26, 5085–5102, https://doi.org/10.5194/hess-26-5085-2022, https://doi.org/10.5194/hess-26-5085-2022, 2022
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Neural ODEs fuse physics-based models with deep learning: neural networks substitute terms in differential equations that represent the mechanistic structure of the system. The approach combines the flexibility of machine learning with physical constraints for inter- and extrapolation. We demonstrate that neural ODE models achieve state-of-the-art predictive performance while keeping full interpretability of model states and processes in hydrologic modelling over multiple catchments.
Jing Tian, Zhengke Pan, Shenglian Guo, Jiabo Yin, Yanlai Zhou, and Jun Wang
Hydrol. Earth Syst. Sci., 26, 4853–4874, https://doi.org/10.5194/hess-26-4853-2022, https://doi.org/10.5194/hess-26-4853-2022, 2022
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Most of the literature has focused on the runoff response to climate change, while neglecting the impacts of the potential variation in the active catchment water storage capacity (ACWSC) that plays an essential role in the transfer of climate inputs to the catchment runoff. This study aims to systematically identify the response of the ACWSC to a long-term meteorological drought and asymptotic climate change.
Dharmaveer Singh, Manu Vardhan, Rakesh Sahu, Debrupa Chatterjee, Pankaj Chauhan, and Shiyin Liu
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2022-339, https://doi.org/10.5194/hess-2022-339, 2022
Revised manuscript accepted for HESS
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This study very first time examines the potential of various machine learning models in streamflow prediction over the Sutlej River Basin (rainfall dominated zone) in western Himalaya during the period 2041–2070 (2050s) and 2071–2100 (2080s) and its relationship to climate variability. The mean ensemble of model results show that the mean annual streamflow of the Sutlej River is expected to rise between 2050s and 2080s by 5.51 to 6.04 % for SSP585 and by 6.65 to 6.75 % for SSP245.
Riccardo Rigon, Giuseppe Formetta, Marialaura Bancheri, Niccolò Tubini, Concetta D'Amato, Olaf David, and Christian Massari
Hydrol. Earth Syst. Sci., 26, 4773–4800, https://doi.org/10.5194/hess-26-4773-2022, https://doi.org/10.5194/hess-26-4773-2022, 2022
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The
Digital Earth(DE) metaphor is very useful for both end users and hydrological modelers. We analyse different categories of models, with the view of making them part of a Digital eARth Twin Hydrology system (called DARTH). We also stress the idea that DARTHs are not models in and of themselves, rather they need to be built on an appropriate information technology infrastructure. It is remarked that DARTHs have to, by construction, support the open-science movement and its ideas.
Hapu Arachchige Prasantha Hapuarachchi, Mohammed Abdul Bari, Aynul Kabir, Mohammad Mahadi Hasan, Fitsum Markos Woldemeskel, Nilantha Gamage, Patrick Daniel Sunter, Xiaoyong Sophie Zhang, David Ewen Robertson, James Clement Bennett, and Paul Martinus Feikema
Hydrol. Earth Syst. Sci., 26, 4801–4821, https://doi.org/10.5194/hess-26-4801-2022, https://doi.org/10.5194/hess-26-4801-2022, 2022
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Methodology for developing an operational 7-day ensemble streamflow forecasting service for Australia is presented. The methodology is tested for 100 catchments to learn the characteristics of different NWP rainfall forecasts, the effect of post-processing, and the optimal ensemble size and bootstrapping parameters. Forecasts are generated using NWP rainfall products post-processed by the CHyPP model, the GR4H hydrologic model, and the ERRIS streamflow post-processor inbuilt in the SWIFT package
Chenhao Chai, Lei Wang, Deliang Chen, Jing Zhou, Hu Liu, Jingtian Zhang, Yuanwei Wang, Tao Chen, and Ruishun Liu
Hydrol. Earth Syst. Sci., 26, 4657–4683, https://doi.org/10.5194/hess-26-4657-2022, https://doi.org/10.5194/hess-26-4657-2022, 2022
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This work quantifies future snow changes and their impacts on hydrology in the upper Salween River (USR) under SSP126 and SSP585 using a cryosphere–hydrology model. Future warm–wet climate is not conducive to the development of snow. The rain–snow-dominated pattern of runoff will shift to a rain-dominated pattern after the 2040s under SSP585 but is unchanged under SSP126. The findings improve our understanding of cryosphere–hydrology processes and can assist water resource management in the USR.
Remko C. Nijzink and Stanislaus J. Schymanski
Hydrol. Earth Syst. Sci., 26, 4575–4585, https://doi.org/10.5194/hess-26-4575-2022, https://doi.org/10.5194/hess-26-4575-2022, 2022
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Most catchments plot close to the empirical Budyko curve, which allows for the estimation of the long-term mean annual evaporation and runoff. The Budyko curve can be defined as a function of a wetness index or a dryness index. We found that differences can occur and that there is an uncertainty due to the different formulations.
Anna Msigwa, Celray James Chawanda, Hans C. Komakech, Albert Nkwasa, and Ann van Griensven
Hydrol. Earth Syst. Sci., 26, 4447–4468, https://doi.org/10.5194/hess-26-4447-2022, https://doi.org/10.5194/hess-26-4447-2022, 2022
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Studies using agro-hydrological models, like the Soil and Water Assessment Tool (SWAT), to map evapotranspiration (ET) do not account for cropping seasons. A comparison between the default SWAT+ set-up (with static land use representation) and a dynamic SWAT+ model set-up (with seasonal land use representation) is made by spatial mapping of the ET. The results show that ET with seasonal representation is closer to remote sensing estimates, giving better performance than ET with static land use.
Jerom P. M. Aerts, Rolf W. Hut, Nick C. van de Giesen, Niels Drost, Willem J. van Verseveld, Albrecht H. Weerts, and Pieter Hazenberg
Hydrol. Earth Syst. Sci., 26, 4407–4430, https://doi.org/10.5194/hess-26-4407-2022, https://doi.org/10.5194/hess-26-4407-2022, 2022
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In recent years gridded hydrological modelling moved into the realm of hyper-resolution modelling (<10 km). In this study, we investigate the effect of varying grid-cell sizes for the wflow_sbm hydrological model. We used a large sample of basins from the CAMELS data set to test the effect that varying grid-cell sizes has on the simulation of streamflow at the basin outlet. Results show that there is no single best grid-cell size for modelling streamflow throughout the domain.
Taher Chegini and Hong-Yi Li
Hydrol. Earth Syst. Sci., 26, 4279–4300, https://doi.org/10.5194/hess-26-4279-2022, https://doi.org/10.5194/hess-26-4279-2022, 2022
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Belowground urban stormwater networks (BUSNs) play a critical and irreplaceable role in preventing or mitigating urban floods. However, they are often not available for urban flood modeling at regional or larger scales. We develop a novel algorithm to estimate existing BUSNs using ubiquitously available aboveground data at large scales based on graph theory. The algorithm has been validated in different urban areas; thus, it is well transferable.
Yi Nan, Zhihua He, Fuqiang Tian, Zhongwang Wei, and Lide Tian
Hydrol. Earth Syst. Sci., 26, 4147–4167, https://doi.org/10.5194/hess-26-4147-2022, https://doi.org/10.5194/hess-26-4147-2022, 2022
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Tracer-aided hydrological models are useful tool to reduce uncertainty of hydrological modeling in cold basins, but there is little guidance on the sampling strategy for isotope analysis, which is important for large mountainous basins. This study evaluated the reliance of the tracer-aided modeling performance on the availability of isotope data in the Yarlung Tsangpo river basin, and provides implications for collecting water isotope data for running tracer-aided hydrological models.
Sella Nevo, Efrat Morin, Adi Gerzi Rosenthal, Asher Metzger, Chen Barshai, Dana Weitzner, Dafi Voloshin, Frederik Kratzert, Gal Elidan, Gideon Dror, Gregory Begelman, Grey Nearing, Guy Shalev, Hila Noga, Ira Shavitt, Liora Yuklea, Moriah Royz, Niv Giladi, Nofar Peled Levi, Ofir Reich, Oren Gilon, Ronnie Maor, Shahar Timnat, Tal Shechter, Vladimir Anisimov, Yotam Gigi, Yuval Levin, Zach Moshe, Zvika Ben-Haim, Avinatan Hassidim, and Yossi Matias
Hydrol. Earth Syst. Sci., 26, 4013–4032, https://doi.org/10.5194/hess-26-4013-2022, https://doi.org/10.5194/hess-26-4013-2022, 2022
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Early flood warnings are one of the most effective tools to save lives and goods. Machine learning (ML) models can improve flood prediction accuracy but their use in operational frameworks is limited. The paper presents a flood warning system, operational in India and Bangladesh, that uses ML models for forecasting river stage and flood inundation maps and discusses the models' performances. In 2021, more than 100 million flood alerts were sent to people near rivers over an area of 470 000 km2.
Matthias Sprenger, Pilar Llorens, Francesc Gallart, Paolo Benettin, Scott T. Allen, and Jérôme Latron
Hydrol. Earth Syst. Sci., 26, 4093–4107, https://doi.org/10.5194/hess-26-4093-2022, https://doi.org/10.5194/hess-26-4093-2022, 2022
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Our catchment-scale transit time modeling study shows that including stable isotope data on evapotranspiration in addition to the commonly used stream water isotopes helps constrain the model parametrization and reveals that the water taken up by plants has resided longer in the catchment storage than the water leaving the catchment as stream discharge. This finding is important for our understanding of how water is stored and released, which impacts the water availability for plants and humans.
Lorenzo Alfieri, Francesco Avanzi, Fabio Delogu, Simone Gabellani, Giulia Bruno, Lorenzo Campo, Andrea Libertino, Christian Massari, Angelica Tarpanelli, Dominik Rains, Diego G. Miralles, Raphael Quast, Mariette Vreugdenhil, Huan Wu, and Luca Brocca
Hydrol. Earth Syst. Sci., 26, 3921–3939, https://doi.org/10.5194/hess-26-3921-2022, https://doi.org/10.5194/hess-26-3921-2022, 2022
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This work shows advances in high-resolution satellite data for hydrology. We performed hydrological simulations for the Po River basin using various satellite products, including precipitation, evaporation, soil moisture, and snow depth. Evaporation and snow depth improved a simulation based on high-quality ground observations. Interestingly, a model calibration relying on satellite data skillfully reproduces observed discharges, paving the way to satellite-driven hydrological applications.
Bruno Majone, Diego Avesani, Patrick Zulian, Aldo Fiori, and Alberto Bellin
Hydrol. Earth Syst. Sci., 26, 3863–3883, https://doi.org/10.5194/hess-26-3863-2022, https://doi.org/10.5194/hess-26-3863-2022, 2022
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In this work, we introduce a methodology for devising reliable future high streamflow scenarios from climate change simulations. The calibration of a hydrological model is carried out to maximize the probability that the modeled and observed high flow extremes belong to the same statistical population. Application to the Adige River catchment (southeastern Alps, Italy) showed that this procedure produces reliable quantiles of the annual maximum streamflow for use in assessment studies.
Pedro V. G. Batista, Peter Fiener, Simon Scheper, and Christine Alewell
Hydrol. Earth Syst. Sci., 26, 3753–3770, https://doi.org/10.5194/hess-26-3753-2022, https://doi.org/10.5194/hess-26-3753-2022, 2022
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Patchy agricultural landscapes have a large number of small fields, which are separated by linear features such as roads and field borders. When eroded sediments are transported out of the agricultural fields by surface runoff, these features can influence sediment connectivity. By use of measured data and a simulation model, we demonstrate how a dense road network (and its drainage system) facilitates sediment transport from fields to water courses in a patchy Swiss agricultural catchment.
Juliane Mai, Hongren Shen, Bryan A. Tolson, Étienne Gaborit, Richard Arsenault, James R. Craig, Vincent Fortin, Lauren M. Fry, Martin Gauch, Daniel Klotz, Frederik Kratzert, Nicole O'Brien, Daniel G. Princz, Sinan Rasiya Koya, Tirthankar Roy, Frank Seglenieks, Narayan K. Shrestha, André G. T. Temgoua, Vincent Vionnet, and Jonathan W. Waddell
Hydrol. Earth Syst. Sci., 26, 3537–3572, https://doi.org/10.5194/hess-26-3537-2022, https://doi.org/10.5194/hess-26-3537-2022, 2022
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Model intercomparison studies are carried out to test various models and compare the quality of their outputs over the same domain. In this study, 13 diverse model setups using the same input data are evaluated over the Great Lakes region. Various model outputs – such as streamflow, evaporation, soil moisture, and amount of snow on the ground – are compared using standardized methods and metrics. The basin-wise model outputs and observations are made available through an interactive website.
Aurélien Beaufort, Jacob S. Diamond, Eric Sauquet, and Florentina Moatar
Hydrol. Earth Syst. Sci., 26, 3477–3495, https://doi.org/10.5194/hess-26-3477-2022, https://doi.org/10.5194/hess-26-3477-2022, 2022
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We developed one of the largest stream temperature databases to calculate a simple, ecologically relevant metric – the thermal peak – that captures the magnitude of summer thermal extremes. Using statistical models, we extrapolated the thermal peak to nearly every stream in France, finding the hottest thermal peaks along large rivers without forested riparian zones and groundwater inputs. Air temperature was a poor proxy for the thermal peak, highlighting the need to grow monitoring networks.
Ulises M. Sepúlveda, Pablo A. Mendoza, Naoki Mizukami, and Andrew J. Newman
Hydrol. Earth Syst. Sci., 26, 3419–3445, https://doi.org/10.5194/hess-26-3419-2022, https://doi.org/10.5194/hess-26-3419-2022, 2022
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This paper characterizes parameter sensitivities across more than 5500 grid cells for a commonly used macroscale hydrological model, including a suite of eight performance metrics and 43 soil, vegetation and snow parameters. The results show that the model is highly overparameterized and, more importantly, help to provide guidance on the most relevant parameters for specific target processes across diverse climatic types.
Jonathan M. Frame, Frederik Kratzert, Daniel Klotz, Martin Gauch, Guy Shalev, Oren Gilon, Logan M. Qualls, Hoshin V. Gupta, and Grey S. Nearing
Hydrol. Earth Syst. Sci., 26, 3377–3392, https://doi.org/10.5194/hess-26-3377-2022, https://doi.org/10.5194/hess-26-3377-2022, 2022
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The most accurate rainfall–runoff predictions are currently based on deep learning. There is a concern among hydrologists that deep learning models may not be reliable in extrapolation or for predicting extreme events. This study tests that hypothesis. The deep learning models remained relatively accurate in predicting extreme events compared with traditional models, even when extreme events were not included in the training set.
Sebastian A. Krogh, Lucia Scaff, James W. Kirchner, Beatrice Gordon, Gary Sterle, and Adrian Harpold
Hydrol. Earth Syst. Sci., 26, 3393–3417, https://doi.org/10.5194/hess-26-3393-2022, https://doi.org/10.5194/hess-26-3393-2022, 2022
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We present a new way to detect snowmelt using daily cycles in streamflow driven by solar radiation. Results show that warmer sites have earlier and more intermittent snowmelt than colder sites, and the timing of early snowmelt events is strongly correlated with the timing of streamflow volume. A space-for-time substitution shows greater sensitivity of streamflow timing to climate change in colder rather than in warmer places, which is then contrasted with land surface simulations.
Wouter J. M. Knoben and Diana Spieler
Hydrol. Earth Syst. Sci., 26, 3299–3314, https://doi.org/10.5194/hess-26-3299-2022, https://doi.org/10.5194/hess-26-3299-2022, 2022
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This paper introduces educational materials that can be used to teach students about model structure uncertainty in hydrological modelling. There are many different hydrological models and differences between these models impact their usefulness in different places. Such models are often used to support decision making about water resources and to perform hydrological science, and it is thus important for students to understand that model choice matters.
Monica Coppo Frias, Suxia Liu, Xingguo Mo, Karina Nielsen, Heidi Randall, Liguang Jiang, Jun Ma, and Peter Bauer-Gottwein
EGUsphere, https://doi.org/10.5194/egusphere-2022-377, https://doi.org/10.5194/egusphere-2022-377, 2022
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This paper uses remote sensing data from ICESat-2 to calibrate a 1D hydraulic model. With the model, we can make estimations of discharge and water surface elevation, which are important indicators in flooding risk assessment. ICESat-2 data gives an added value thanks to its 0.7 meters resolution, which allows for measuring narrow river streams. In addition, ICESat-2 provides measurements on the river dry portion geometry that can be included in the model.
Leonie Kiewiet, Ernesto Trujillo, Andrew Hedrick, Scott Havens, Katherine Hale, Mark Seyfried, Stephanie Kampf, and Sarah E. Godsey
Hydrol. Earth Syst. Sci., 26, 2779–2796, https://doi.org/10.5194/hess-26-2779-2022, https://doi.org/10.5194/hess-26-2779-2022, 2022
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Climate change affects precipitation phase, which can propagate into changes in streamflow timing and magnitude. This study examines how variations in rainfall and snowmelt affect discharge. We found that annual discharge and stream cessation depended on the magnitude and timing of rainfall and snowmelt and on the snowpack melt-out date. This highlights the importance of precipitation timing and emphasizes the need for spatiotemporally distributed simulations of snowpack and rainfall dynamics.
Alban de Lavenne, Vazken Andréassian, Louise Crochemore, Göran Lindström, and Berit Arheimer
Hydrol. Earth Syst. Sci., 26, 2715–2732, https://doi.org/10.5194/hess-26-2715-2022, https://doi.org/10.5194/hess-26-2715-2022, 2022
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A watershed remembers the past to some extent, and this memory influences its behavior. This memory is defined by the ability to store past rainfall for several years. By releasing this water into the river or the atmosphere, it tends to forget. We describe how this memory fades over time in France and Sweden. A few watersheds show a multi-year memory. It increases with the influence of groundwater or dry conditions. After 3 or 4 years, they behave independently of the past.
Antoine Pelletier and Vazken Andréassian
Hydrol. Earth Syst. Sci., 26, 2733–2758, https://doi.org/10.5194/hess-26-2733-2022, https://doi.org/10.5194/hess-26-2733-2022, 2022
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A large part of the water cycle takes place underground. In many places, the soil stores water during the wet periods and can release it all year long, which is particularly visible when the river level is low. Modelling tools that are used to simulate and forecast the behaviour of the river struggle to represent this. We improved an existing model to take underground water into account using measurements of the soil water content. Results allow us make recommendations for model users.
Chaogui Lei, Paul D. Wagner, and Nicola Fohrer
Hydrol. Earth Syst. Sci., 26, 2561–2582, https://doi.org/10.5194/hess-26-2561-2022, https://doi.org/10.5194/hess-26-2561-2022, 2022
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We presented an integrated approach to hydrologic modeling and partial least squares regression quantifying land use change impacts on water and nutrient balance over 3 decades. Results highlight that most variations (70 %–80 %) in water quantity and quality variables are explained by changes in land use class-specific areas and landscape metrics. Arable land influences water quantity and quality the most. The study provides insights on water resources management in rural lowland catchments.
Yang Wang and Hassan A. Karimi
Hydrol. Earth Syst. Sci., 26, 2387–2403, https://doi.org/10.5194/hess-26-2387-2022, https://doi.org/10.5194/hess-26-2387-2022, 2022
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We found that rainfall data with spatial information can improve the model's performance, especially when simulating the future multi-day discharges. We did not observe that regional LSTM as a regional model achieved better results than LSTM as individual model. This conclusion applies to both one-day and multi-day simulations. However, we found that using spatially distributed rainfall data can reduce the difference between individual LSTM and regional LSTM.
Wanshu Nie, Sujay V. Kumar, Kristi R. Arsenault, Christa D. Peters-Lidard, Iliana E. Mladenova, Karim Bergaoui, Abheera Hazra, Benjamin F. Zaitchik, Sarith P. Mahanama, Rachael McDonnell, David M. Mocko, and Mahdi Navari
Hydrol. Earth Syst. Sci., 26, 2365–2386, https://doi.org/10.5194/hess-26-2365-2022, https://doi.org/10.5194/hess-26-2365-2022, 2022
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The MENA (Middle East and North Africa) region faces significant food and water insecurity and hydrological hazards. Here we investigate the value of assimilating remote sensing data sets into an Earth system model to help build an effective drought monitoring system and support risk mitigation and management by countries in the region. We highlight incorporating satellite-informed vegetation conditions into the model as being one of the key processes for a successful application for the region.
Pin Shuai, Xingyuan Chen, Utkarsh Mital, Ethan T. Coon, and Dipankar Dwivedi
Hydrol. Earth Syst. Sci., 26, 2245–2276, https://doi.org/10.5194/hess-26-2245-2022, https://doi.org/10.5194/hess-26-2245-2022, 2022
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Using an integrated watershed model, we compared simulated watershed hydrologic variables driven by three publicly available gridded meteorological forcings (GMFs) at various spatial and temporal resolutions. Our results demonstrated that spatially distributed variables are sensitive to the spatial resolution of the GMF. The temporal resolution of the GMF impacts the dynamics of watershed responses. The choice of GMF depends on the quantity of interest and its spatial and temporal scales.
Greta Cazzaniga, Carlo De Michele, Michele D'Amico, Cristina Deidda, Antonio Ghezzi, and Roberto Nebuloni
Hydrol. Earth Syst. Sci., 26, 2093–2111, https://doi.org/10.5194/hess-26-2093-2022, https://doi.org/10.5194/hess-26-2093-2022, 2022
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Rainfall estimates are usually obtained from rain gauges, weather radars, or satellites. An alternative is the measurement of the signal loss induced by rainfall on commercial microwave links (CMLs). In this work, we assess the hydrologic response of Lambro Basin when CML-retrieved rainfall is used as model input. CML estimates agree with rain gauge data. CML-driven discharge simulations show performance comparable to that from rain gauges if a CML-based calibration of the model is undertaken.
Christopher Spence, Zhihua He, Kevin R. Shook, Balew A. Mekonnen, John W. Pomeroy, Colin J. Whitfield, and Jared D. Wolfe
Hydrol. Earth Syst. Sci., 26, 1801–1819, https://doi.org/10.5194/hess-26-1801-2022, https://doi.org/10.5194/hess-26-1801-2022, 2022
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We determined how snow and flow in small creeks change with temperature and precipitation in the Canadian Prairie, a region where water resources are often under stress. We tried something new. Every watershed in the region was placed in one of seven groups based on their landscape traits. We selected one of these groups and used its traits to build a model of snow and streamflow. It worked well, and by the 2040s there may be 20 %–40 % less snow and 30 % less streamflow than the 1980s.
Rui Tong, Juraj Parajka, Borbála Széles, Isabella Greimeister-Pfeil, Mariette Vreugdenhil, Jürgen Komma, Peter Valent, and Günter Blöschl
Hydrol. Earth Syst. Sci., 26, 1779–1799, https://doi.org/10.5194/hess-26-1779-2022, https://doi.org/10.5194/hess-26-1779-2022, 2022
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The role and impact of using additional data (other than runoff) for the prediction of daily hydrographs in ungauged basins are not well understood. In this study, we assessed the model performance in terms of runoff, soil moisture, and snow cover predictions with the existing regionalization approaches. Results show that the best transfer methods are the similarity and the kriging approaches. The performance of the transfer methods differs between lowland and alpine catchments.
Cited articles
Amiri, E.: Forecasting daily river flows using nonlinear time series models, J. Hydrol., 527, 1054–1072, https://doi.org/10.1016/j.jhydrol.2015.05.048, 2015. a
Amorocho, J. and Espildora, B.: Entropy in the assessment of uncertainty in
hydrologic systems and models, Water Resour. Res., 9, 1511–1522,
https://doi.org/10.1029/WR009i006p01511, 1973. a
Badrzadeh, H., Sarukkalige, R., and Jayawardena, A.: Impact of multi-resolution analysis of artificial intelligence models inputs on multi-step ahead river flow forecasting, J. Hydrol., 507, 75–85,
https://doi.org/10.1016/j.jhydrol.2013.10.017, 2013. a, b, c, d
Bennett, J. C., Wang, Q. J., Li, M., Robertson, D. E., and Schepen, A.:
Reliable long-range ensemble streamflow forecasts: Combining calibrated climate forecasts with a conceptual runoff model and a staged error model,
Water Resour. Res., 52, 8238–8259, https://doi.org/10.1002/2016WR019193, 2016. a
Berrisford, P., Kållberg, P., Kobayashi, S., Dee, D., Uppala, S., Simmons, A. J., Poli, P., and Sato, H.: Atmospheric conservation properties in ERA-Interim, Q. J. Roy. Meteorol. Soc., 137, 1381–1399, https://doi.org/10.1002/qj.864, 2011. a
Bontempi, G., Taieb, S. B., and Le Borgne, Y.-A.: Machine learning strategies
for time series forecasting, in: European business intelligence summer
school, Springer, Switzerland, 62–77,
https://doi.org/10.1007/978-3-319-61164-8, 2012. a, b
Bowden, G. J. D.-G. M. H.: Input determination for neural network models in
water resources applications. Part 1 - background and methodology, J. Hydrol., 301, 75–92, https://doi.org/10.1016/j.jhydrol.2004.06.021, 2005. a
Breiman, L.: Arcing the edge, Report, Statistics Department, Technical Report 486, University of California, available at:
https://statistics.berkeley.edu/sites/default/files/tech-reports/486.pdf
(last access: 1 February 2020), 1997. a
Chapman, T. G.: Entropy as a Measure of Hydrologic Data Uncertainty and Model
Performance, J. Hydrol., 85, 111–126, https://doi.org/10.1016/0022-1694(86)90079-X, 1986. a
Chau, K., Wu, C., and Li, Y.: Comparison of several flood forecasting models in Yangtze River, J. Hydrol. Eng., 10, 485–491,
https://doi.org/10.1061/(ASCE)1084-0699(2005)10:6(485), 2005. a
Chau, K.-W.: Use of meta-heuristic techniques in rainfall-runoff modelling,
Water, 9, 1–6, https://doi.org/10.3390/w9030186, 2017. a
Cheng, C. T., Feng, Z.-K., Niu, W.-J., and Liao, S.-L.: Heuristic Methods for
Reservoir Monthly Inflow Forecasting: A Case Study of Xinfengjiang Reservoir
in Pearl River, China, Water, 7, 4477–4495, https://doi.org/10.3390/w7084477, 2015. a
Chicco, D.: Ten quick tips for machine learning in computational biology, Biodata Min., 10, 35, https://doi.org/10.1186/s13040-017-0155-3, 2017. a
Dee, D. P., Uppala, S., Simmons, A., Berrisford, P., Poli, P., Kobayashi, S.,
Andrae, U., Balmaseda, M., Balsamo, G., and Bauer, D. P.: The ERA‐Interim
reanalysis: Configuration and performance of the data assimilation system, Q.
J. Roy. Meteorol. Soc., 137, 553–597, https://doi.org/10.1002/qj.828, 2011. a
Dhanya, C. T. and Kumar, D. N.: Predictive uncertainty of chaotic daily
streamflow using ensemble wavelet networks approach, Water Resour. Res., 47,
W06507, https://doi.org/10.1029/2010WR010173, 2011. a
Dibike, Y. B., Velickov, S., Solomatine, D., and Abbott, M. B.: Model Induction with Support Vector Machines: Introduction and Applications, J. Comput. Civil Eng., 15, 208–216, https://doi.org/10.1061/(ASCE)0887-3801(2001)15:3(208), 2001. a
Duan, Q., Sorooshian, S., and Gupta, V.: Effective and efficient global
optimization for conceptual rainfall-runoff models, Water Resour. Res., 28,
1015–1031, https://doi.org/10.1029/91WR02985, 1992. a
El-Shafie, A. and Noureldin, A.: Generalized versus non-generalized neural network model for multi-lead inflow forecasting at Aswan High Dam, Hydrol. Earth Syst. Sci., 15, 841–858, https://doi.org/10.5194/hess-15-841-2011, 2011. a
El-Shafie, A., Taha, M. R., and Noureldin, A.: A neuro-fuzzy model for inflow
forecasting of the Nile river at Aswan high dam, Water Resour. Manage., 21,
533–556, https://doi.org/10.1007/s11269-006-9027-1, 2007. a
European Centre for Medium Range Weather Forecasts: ERA Interim, available at: https://apps.ecmwf.int/datasets/data/interim-full-daily/levtype=sfc/, last access: 1 July 2019. a
Fan, F. M., Schwanenberg, D., Collischonn, W., and Weerts, A.: Verification of inflow into hydropower reservoirs using ensemble forecasts of the TIGGE
database for large scale basins in Brazil, J. Hydrol., 4, 196–227,
https://doi.org/10.1016/j.ejrh.2015.05.012, 2015. a
Fienen, M. N., Nolan, B. T., Kauffman, L. J., and Feinstein, D. T.:
Metamodeling for groundwater age forecasting in the Lake Michigan Basin,
Water Resour. Res., 54, 4750–4766, https://doi.org/10.1029/2017WR022387, 2018. a, b
Fotovatikhah, F., Herrera, M., Shamshirband, S., Chau, K.-W.,
Faizollahzadeh Ardabili, S., and Piran, M. J.: Survey of computational
intelligence as basis to big flood management: Challenges, research
directions and future work, Eng. Appl. Comp. Fluid., 12, 411–437,
https://doi.org/10.1080/19942060.2018.1448896, 2018. a
Ge, R., Zhou, M., Luo, Y., Meng, Q., Mai, G., Ma, D., Wang, G., and Zhou, F.:
McTwo: a two-step feature selection algorithm based on maximal information
coefficient, Bmc Bioinform., 17, 142–155, https://doi.org/10.1186/s12859-016-0990-0, 2016. a
Ghimire, S., Deo, R. C., Downs, N. J., and Raj, N.: Global solar radiation
prediction by ANN integrated with European Centre for medium range weather
forecast fields in solar rich cites of queensland Australia, J. Clean. Prod.,
216, 288–310, https://doi.org/10.1016/j.jclepro.2019.01.158, 2019. a
Ghorbani, M. A., Kazempour, R., Chau, K.-W., Shamshirband, S., and Ghazvinei,
P. T.: Forecasting pan evaporation with an integrated artificial neural
network quantum-behaved particle swarm optimization model: a case study in
Talesh, Northern Iran, Eng. Appl. Comp. Fluid., 12, 724–737,
https://doi.org/10.1080/19942060.2018.1517052, 2018. a
Jiang, R.: Focus on the focus of the “Three abandoned” electricity nearly 110 billion kWh, the loss of 48.7 billion yuan! How much electricity will be discarded in 2017 by water, fire, wind, light, and nuclear?, available at: https://www.in-en.com/article/html/energy-2266458.shtml (last access: 9 February 2020), 2018. a
Kinney, J. B. and Atwal, G. S.: Equitability, mutual information, and the
maximal information coefficient, P. Natl. Acad. Sci. USA, 111, 3354–3359,
https://doi.org/10.1073/pnas.1309933111, 2014. a
Kishore, P., Ratnam, M. V., Namboothiri, S., Velicogna, I., Basha, G., Jiang,
J., Igarashi, K., Rao, S., and Sivakumar, V.: Global (50∘ S–50∘ N) distribution of water vapor observed by COSMIC GPS RO: Comparison with GPS radiosonde, NCEP, ERA-Interim, and JRA-25 reanalysis data sets, J. Atmos. Sol-Terr. Phy., 73, 1849–1860, https://doi.org/10.1016/j.jastp.2011.04.017, 2011. a
Knoben, W. J. M., Freer, J. E., and Woods, R. A.: Technical note: Inherent
benchmark or not? Comparing Nash–Sutcliffe and Kling–Gupta efficiency
scores, Hydrol. Earth Syst. Sci., 23, 4323–4331,
https://doi.org/10.5194/hess-23-4323-2019, 2019. a
Lima, A. R., Hsieh, W. W., and Cannon, A. J.: Variable complexity online
sequential extreme learning machine, with applications to streamflow
prediction, J. Hydrol., 555, 983–994, https://doi.org/10.1016/j.jhydrol.2017.10.037,
2017. a
Lin, J. Y., Cheng, C.-T., and Chau, K.-W.: Using support vector machines for
long-term discharge prediction, Hydrolog. Sci. J., 51, 599–612,
https://doi.org/10.1623/hysj.51.4.599, 2006. a, b
Linares-Rodríguez, A., Ruiz-Arias, J. A., Pozo-Vázquez, D., and
Tovar-Pescador, J.: Generation of synthetic daily global solar radiation data
based on ERA-Interim reanalysis and artificial neural networks, Energy, 36,
5356–5365, https://doi.org/10.1016/j.energy.2011.06.044, 2011. a
Liu, Z., Zhou, P., and Zhang, Y.: A Probabilistic Wavelet–Support Vector
Regression Model for Streamflow Forecasting with Rainfall and Climate
Information Input, J. Hydrometeorol., 16, 2209–2229, https://doi.org/10.1175/JHM-D-14-0210.1, 2015. a
Luo, X., Yuan, X., Zhu, S., Xu, Z., Meng, L., and Peng, J.: A hybrid support
vector regression framework for streamflow forecast, J. Hydrol., 568, 184–193, https://doi.org/10.1016/j.jhydrol.2018.10.064, 2019. a
Lyu, H., Wan, M., Han, J., Liu, R., and Cheng, W.: A filter feature selection
method based on the Maximal Information Coefficient and Gram-Schmidt
Orthogonalization for biomedical data mining, Comput. Biol. Med., 89, 264–274, https://doi.org/10.1016/j.compbiomed.2017.08.021, 2017. a
Mason, L., Baxter, J., Bartlett, P. L., and Frean, M. R.: Boosting algorithms as gradient descent, in: Advances in Neural Information Processing Systems 12, Neural Information Processing Systems, 29 November–4 December 1999, Colorado, USA, 512–518, 1999. a
May, R., Dandy, G., and Maier, H.: Review of Input Variable Selection Methods
for Artificial Neural Networks, in: Artificial Neural Networks, chap. 2, edited by: Suzuki, K., IntechOpen, Rijeka, https://doi.org/10.5772/16004, 2011. a
Mehr, A. D., Jabarnejad, M., and Nourani, V.: Pareto-optimal MPSA-MGGP: A new
gene-annealing model for monthly rainfall forecasting, J. Hydrol., 571, 406–415, https://doi.org/10.1016/j.jhydrol.2019.02.003, 2019. a
Mekong River Commission: Overview of the Hydrology of the Mekong Basin, Report, Mekong River Commission, Vientiane, available at:
http://www.mekonginfo.org/assets/midocs/0001968-inland-waters-overview-of-hydrology-of-the-mekong-basin.pdf
(last access: 1 February 2020), 2005. a
Moazenzadeh, R., Mohammadi, B., Shamshirband, S., and Chau, K.-W.: Coupling a
firefly algorithm with support vector regression to predict evaporation in
northern Iran, Eng. Appl. Comp. Fluid., 12, 584–597,
https://doi.org/10.1080/19942060.2018.1482476, 2018. a
Mosavi, A., Ozturk, P., and Chau, K.-W.: Flood prediction using machine
learning models: Literature review, Water, 10, 1536, https://doi.org/10.3390/w10111536,
2018. a
Pal, I., Lall, U., Robertson, A. W., Cane, M. A., and Bansal, R.: Predictability of Western Himalayan river flow: melt seasonal inflow into Bhakra Reservoir in northern India, Hydrol. Earth Syst. Sci., 17, 2131–2146, https://doi.org/10.5194/hess-17-2131-2013, 2013. a
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel,
O., Blondel, M., Prettenhofer, P., Weiss, R., and Dubourg, V.: Scikit-learn:
Machine learning in Python, J. Mach. Learn Res., 12, 2825–2830, 2011. a
Python Software Foundation: Python Language Reference, version 3.7, available at: http://www.python.org (last access: 1 July 2019), 2020. a
Rajaee, T., Ebrahimi, H., and Nourani, V.: A review of the artificial
intelligence methods in groundwater level modeling, J. Hydrol., 572,
336–351, https://doi.org/10.1016/j.jhydrol.2018.12.037, 2019. a
Rasouli, K., Hsieh, W. W., and Cannon, A. J.: Daily streamflow forecasting by
machine learning methods with weather and climate inputs, J. Hydrol., 414,
284–293, https://doi.org/10.1016/j.jhydrol.2011.10.039, 2012. a, b, c
Reshef, D. N., Reshef, Y. A., Finucane, H. K., Grossman, S. R., McVean, G.,
Turnbaugh, P. J., Lander, E. S., Mitzenmacher, M., and Sabeti, P. C.: Detecting novel associations in large data sets, Science, 334, 1518–1524,
https://doi.org/10.1126/science.1205438, 2011. a
Robertson, D., Pokhrel, P., and Wang, Q.: Improving statistical forecasts of
seasonal streamflows using hydrological model output, Q. J. Roy. Meteorol. Soc., 17, 579–593, https://doi.org/10.5194/hess-17-579-2013, 2013. a
Salas, J. D.: Analysis and modelling of hydrological time series, in:
Stochastic Water Resources Technology, Springer, Switzerland, 20–66, https://doi.org/10.1007/978-1-349-03467-3_2, 1993. a
Shoaib, M., Shamseldin, A. Y., Melville, B. W., and Khan, M. M.: Runoff
forecasting using hybrid Wavelet Gene Expression Programming (WGEP) approach,
J. Hydrol., 527, 326–344, https://doi.org/10.1016/j.jhydrol.2015.04.072, 2015. a
Siqueira, H., Boccato, L., Luna, I., Attux, R., and Lyra, C.: Performance
analysis of unorganized machines in streamflow forecasting of Brazilian
plants, Appl. Soft. Comput., 68, 494–506, https://doi.org/10.1016/j.asoc.2018.04.007,
2018. a
Snieder, E., Shakir, R., and Khan, U.: A comprehensive comparison of four input variable selection methods for artificial neural network flow forecasting models, J. Hydrol., 583, 124299, https://doi.org/10.1016/j.jhydrol.2019.124299, 2020. a, b
Sohu: The causes and Countermeasures of hydropower waste water in Sichuan and Yunnan, available at: https://www.sohu.com/a/209379703_357198 (last access: 9 February 2020), 2017. a
Stopa, J. E. and Cheung, K. F.: Intercomparison of wind and wave data from the ECMWF Reanalysis Interim and the NCEP Climate Forecast System Reanalysis,
Ocean Model., 75, 65–83, https://doi.org/10.1016/j.ocemod.2013.12.006, 2014. a
Sun, G., Li, J., Dai, J., Song, Z., and Lang, F.: Feature selection for IoT
based on maximal information coefficient, Future Gener. Comp. Sy., 89, 606–616, https://doi.org/10.1016/j.future.2018.05.060, 2018. a
Taieb, S. B., Bontempi, G., Atiya, A. F., and Sorjamaa, A.: A review and
comparison of strategies for multi-step ahead time series forecasting based
on the NN5 forecasting competition, Expert Syst. Appl., 39, 7067–7083,
https://doi.org/10.1016/j.eswa.2012.01.039, 2012. a
Tongal, H. and Booij, M. J.: Simulation and forecasting of streamflows using
machine learning models coupled with base flow separation, J. Hydrol., 564,
266–282, https://doi.org/10.1016/j.jhydrol.2018.07.004, 2018.
a
Valipour, M., Banihabib, M. E., and Behbahani, S. M. R.: Comparison of the
ARMA, ARIMA, and the autoregressive artificial neural network models in
forecasting the monthly inflow of Dez dam reservoir, J. Hydrol., 476, 433–441, https://doi.org/10.1016/j.jhydrol.2012.11.017, 2013. a
Verkade, J., Brown, J., Reggiani, P., and Weerts, A.: Post-processing ECMWF
precipitation and temperature ensemble reforecasts for operational hydrologic
forecasting at various spatial scales, J. Hydrol., 501, 73–91,
https://doi.org/10.1016/j.jhydrol.2013.07.039, 2013. a
Vogel, R. M. and Fennessey, N. M.: Flow-Duration Curves. I: New Interpretation and Confidence Intervals, J. Water Res. Pl., 120, 485–504,
https://doi.org/10.1061/(ASCE)0733-9496(1994)120:4(485), 1994. a
Wang, E., Zhang, Y., Luo, J., Chiew, F. H., and Wang, Q.: Monthly and seasonal streamflow forecasts using rainfall‐runoff modeling and historical weather data, Water Resour. Res., 47, W05516, https://doi.org/10.1029/2010WR009922, 2011. a
Wei, Z., Meng, Y., Zhang, W., Peng, J., and Meng, L.: Downscaling SMAP soil
moisture estimation with gradient boosting decision tree regression over the
Tibetan Plateau, Remote Sens. Environ., 225, 30–44, https://doi.org/10.1016/j.rse.2019.02.022, 2019. a
Willmott, C. J.: On the validation of models, Phys. Geogr., 2, 184–194,
https://doi.org/10.1080/02723646.1981.10642213, 1981. a
Yang, Q., Zhang, H., Wang, G., Luo, S., Chen, D., Peng, W., and Shao, J.:
Dynamic runoff simulation in a changing environment: A data stream approach,
Environ. Model. Softw., 112, 157–165, https://doi.org/10.1016/j.envsoft.2018.11.007,
2019. a
Yaseen, Z. M., Sulaiman, S. O., Deo, R. C., and Chau, K.-W.: An enhanced
extreme learning machine model for river flow forecasting: state-of-the-art,
practical applications in water resource engineering area and future research
direction, J. Hydrol., 569, 387–408, https://doi.org/10.1016/j.jhydrol.2018.11.069,
2019. a
Yilmaz, K. K., Gupta, H. V., and Wagener, T.: A process-based diagnostic
approach to model evaluation: Application to the NWS distributed hydrologic
model, Water Resour. Res., 44, W09417, https://doi.org/10.1029/2007WR006716, 2008. a
Zhan, X., Zhang, S., Szeto, W. Y., and Chen, X.: Multi-step-ahead traffic speed forecasting using multi-output gradient boosting regression tree, J. Intell Transport. S., 24, 1547–2442, https://doi.org/10.1080/15472450.2019.1582950, 2019. a
Zhang, D., Lin, J., Peng, Q., Wang, D., Yang, T., Sorooshian, S., Liu, X., and Zhuang, J.: Modeling and simulating of reservoir operation using the
artificial neural network, support vector regression, deep learning algorithm, J. Hydrol., 565, 720–736, https://doi.org/10.1016/j.jhydrol.2018.08.050,
2018. a
Zhao, X., Deng, W., and Shi, Y.: Feature selection with attributes clustering
by maximal information coefficient, Procedia Comput. Sci., 17, 70–79,
https://doi.org/10.1016/j.procs.2013.05.011, 2013. a, b
Short summary
Inflow forecasting plays an essential role in reservoir management and operation. To improve the accuracy of multistep-ahead daily inflow forecasting, the paper develops a new hybrid inflow forecast framework using ERA-Interim data. We find that the framework significantly enhances the accuracy of inflow forecasting at lead times of 4–10 d compared with widely used and mature methods. This research provides a reference for operational inflow forecasting in remote regions.
Inflow forecasting plays an essential role in reservoir management and operation. To improve the...
