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Recent Research Developments in Hydrological Modelling, Climate Change, and Water...
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Recent Research Developments in Hydrological Modelling, Climate Change, and Water Resource Management

A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "Water and Climate Change".

Deadline for manuscript submissions: closed (20 June 2024) | Viewed by 6885

Special Issue Editors

Prof. Dr. Zhijia Li

E-Mail Website
Guest Editor
Department of Hydrology and Water Resources Engineering, Hohai University, Nanjing, China
Interests: hydrological modeling; water resources management; hydrologic and water resource modeling
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Zhenxiang Xing

E-Mail Website
Guest Editor Assistant
School of Water Conservancy and Civil Engineering, Northeast Agricultural University, Harbin, China
Interests: hydrological uncertainty analysis; probabilistic hydrological forecasting; regional drought evaluation; river health assessment; water resource assessment
Special Issues, Collections and Topics in MDPI journals
Dr. Pengnian Huang

E-Mail
Guest Editor Assistant
School of Hydrology and Water Resources, Nanjing University of Information Science & Technology, Nanjing, China
Interests: watershed hydrological modeling; rainfall-runoff forecasting; physical hydrology

Special Issue Information

Dear Colleagues,

Runoff undergoes a significant number of changes under the background of impact of climate change and human activities, leading to it serious effects on water resources in the river basin. Recently, water-related issues have become frequently debated topics between scholars around the world. Runoff is a vital medium in each stage of the water cycle, and it is the key factor in the process of hydrological modelling. Qualitatively and quantitatively analyzing the runoff is helpful in terms of understanding water resources’ evolution with the aim of achieving benefits and avoiding harmful effects under the background of global climate change. In addition, the hydrological model is regarded as a powerful tool for simulating runoff, and it is crucial to investigate the hydrological model and gain a full understanding water resources for water resource management. In modern runoff forecasting, hydrological response to climate change and human activities, eco-hydrological process simulation, water resources planning and management, and even in the mechanism analysis of drought disaster-forming, the hydrological model is widely used. This Special Issue is focused on exploring research developments as regards the theory, methodology and discovery involved in hydrological modeling, climate change, water resource management, drought evaluation, hydrological uncertainty, river health assessment, water environment protection. This Special Issue’s aim is to emphasize the effects observed in the hydrological cycle and social economy under the background of global warming, water shortage and human influence on land surface.

Prof. Dr. Zhijia Li
Guest Editor

Prof. Dr. Zhenxiang Xing
Dr. Pengnian Huang
Guest Editor Assistants

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Water is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • hydrologic modelling
  • climate change
  • hydrologic uncertainty
  • water resources allocation and management
  • regional drought evaluation
  • river health assessment
  • water environment and ecology
  • water and soil risks management, control and utilization
  • cooperative analysis water, food and energy

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Published Papers (5 papers)

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Research

14 pages, 2334 KiB  
Article
Enhanced Spatio-Temporal Modeling for Rainfall Forecasting: A High-Resolution Grid Analysis
by Nurnabi Meherul Alam, Sabyasachi Mitra, Surendra Kumar Pandey, Chayna Jana, Mrinmoy Ray, Sourav Ghosh, Sonali Paul Mazumdar, S. Vishnu Shankar, Ritesh Saha and Gouranga Kar
Water 2024, 16(13), 1891; https://doi.org/10.3390/w16131891 - 1 Jul 2024
Viewed by 1021
Abstract
Rainfall serves as a lifeline for crop cultivation in many agriculture-dependent countries including India. Being spatio-temporal data, the forecasting of rainfall becomes a more complex and tedious process. Application of conventional time series models and machine learning techniques will not be a suitable [...] Read more.
Rainfall serves as a lifeline for crop cultivation in many agriculture-dependent countries including India. Being spatio-temporal data, the forecasting of rainfall becomes a more complex and tedious process. Application of conventional time series models and machine learning techniques will not be a suitable choice as they may not adequately account for the complex spatial and temporal dependencies integrated within the data. This demands some data-driven techniques that can handle the intrinsic patterns such as non-linearity, non-stationarity, and non-normality. Space–Time Autoregressive Moving Average (STARMA) models were highly known for its ability to capture both spatial and temporal dependencies, offering a comprehensive framework for analyzing complex datasets. Spatial Weight Matrix (SWM) developed by the STARMA model helps in integrating the spatial effects of the neighboring sites. The study employed a novel dataset consisting of annual rainfall measurements spanning over 50 (1970–2019) years from 119 different locations (grid of 0.25 × 0.25 degree resolution) of West Bengal, a state of India. These extensive datasets were split into testing and training groups that enable the better understanding of the rainfall patterns at a granular level. The study findings demonstrated a notable improvement in forecasting accuracy by the STARMA model that can exhibit promising implications for agricultural management and planning, particularly in regions vulnerable to climate variability. Full article
(This article belongs to the Special Issue Recent Research Developments in Hydrological Modelling, Climate Change, and Water Resource Management)
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22 pages, 5351 KiB  
Article
The WRF-Driven Grid-Xin’anjiang Model and Its Application in Small and Medium Catchments of China
by Junchao Gong, Youbing Hu, Cheng Yao, Yanan Ma, Mingkun Sun, Junfu Gong, Zhuo Shi and Jingbing Li
Water 2024, 16(1), 103; https://doi.org/10.3390/w16010103 - 27 Dec 2023
Viewed by 1314
Abstract
The distributed Grid-Xin’anjiang (Grid-XAJ) model is very sensitive to the spatial and temporal distribution of data when used in humid and semi-humid small and medium catchments. We used the successive correction method to merge the gauged rainfall with rainfall forecasted by the Weather [...] Read more.
The distributed Grid-Xin’anjiang (Grid-XAJ) model is very sensitive to the spatial and temporal distribution of data when used in humid and semi-humid small and medium catchments. We used the successive correction method to merge the gauged rainfall with rainfall forecasted by the Weather Research and Forecasting (WRF) model to enhance the spatiotemporal accuracy of rainfall distribution. And we used the Penman–Monteith equation to calculate the potential evapotranspiration (PEPM). Then, we designed two forcing scenarios (WRF-driven rainfall (Wr) + PEPM, WRF-merged rainfall (Wm) + PEPM) to drive the Grid-XAJ model for flood forecasting. We found the WRF-driven Grid-XAJ model held significant potential in flood forecasting. The Grid-XAJ model provided only an approximation of flood hygrographs when driven by scenario Wr + PEPM. The results in scenario Wm + PEPM showed a high degree-of-fit with observed floods with mean Nash–Sutcliffe efficiency coefficient (NSE) values of 0.94 and 0.68 in two catchments. Additionally, scenario Wm + PEPM performed better flood hygrographs than scenario Wr + PEPM. The flood volumes and flow peaks in scenario Wm + PEPM had an obvious improvement compare to scenario Wr + PEPM. Finally, we observed that the model exhibited superior performance in forecasting flood hydrographs, flow peaks, and flood volumes in humid catchments compared with semi-humid catchments. Full article
(This article belongs to the Special Issue Recent Research Developments in Hydrological Modelling, Climate Change, and Water Resource Management)
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18 pages, 5860 KiB  
Article
Bias Correction of Hourly Satellite Precipitation Products and Their Application in Hydrological Modeling in a Hilly Watershed, China
by Jinyin Ye, Yang Lu, Xiaoying Yang, Zhixin He, Pengnian Huang and Xinxin Zheng
Water 2024, 16(1), 49; https://doi.org/10.3390/w16010049 - 22 Dec 2023
Cited by 1 | Viewed by 1556
Abstract
Correcting the bias of satellite precipitation products (SPPs) based on ground rainfall observations is one effective approach to improve their performance. To date, there have been limited efforts in correcting the bias of hourly SPPs with mixed results. In this study, ratio bias [...] Read more.
Correcting the bias of satellite precipitation products (SPPs) based on ground rainfall observations is one effective approach to improve their performance. To date, there have been limited efforts in correcting the bias of hourly SPPs with mixed results. In this study, ratio bias correction (RBC) and probability density matching (PDF) are used to correct the bias of four hourly SPPs (GSMaP_NRT, IMERG_E, IMERG_L, and IMERG) based on ground rainfall observations in a hilly watershed, China. Furthermore, SWAT (Soil and Water Assessment Tool) models are developed using ground rainfall observations, original SPPs, and bias-corrected SPPs to simulate the daily streamflow at the Yuetan Hydrological Station so as to comprehensively compare the performance of the two bias correction methods and evaluate the potentials of the four hourly SPPs in hydrological modeling applications. Our study results show that both RBC and PDF could improve the accuracy of hourly SPPs to various degrees, with PDF outperforming RBC considerably. After being corrected by PDF, the CC values of the four SPPs all reached 0.65. In addition, the SWAT models utilizing the PDF-corrected SPPs simulated the daily streamflow at the Yuetan Station better than those utilizing the RBC-corrected SPPs. Specifically, PDF-corrected IMERG_F performed the best among the four hourly SPPs, with a R2 of 0.89, NSE of 0.89, and RB of −8.14%. After bias correction, hourly satellite precipitation products can be well applied to hydrological modeling in the region. Full article
(This article belongs to the Special Issue Recent Research Developments in Hydrological Modelling, Climate Change, and Water Resource Management)
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15 pages, 7072 KiB  
Article
Attribution Analysis on Areal Change of Main Wetland and Its Influence on Runoff in the Naolihe River Basin
by Hong Ding, Qinghui Zeng, Qin Yang, Huan Liu, Peng Hu, Haifeng Zhu and Yinan Wang
Water 2023, 15(24), 4316; https://doi.org/10.3390/w15244316 - 18 Dec 2023
Viewed by 1153
Abstract
Wetlands have powerful runoff regulation functions, which can effectively store and retain surface runoff. The runoff regulation function of wetlands is affected by wetland areas, which affect the capacity of flood control. To explore the law of the area change of the main [...] Read more.
Wetlands have powerful runoff regulation functions, which can effectively store and retain surface runoff. The runoff regulation function of wetlands is affected by wetland areas, which affect the capacity of flood control. To explore the law of the area change of the main wetlands of the Naolihe River Basin (MWNRB), the visual interpretation method was used to extract wetlands. To identify the reasons for area changes in the MWNRB, the maximum likelihood method, minimum distance method, and neural network method were used to classify land use types from remote sensing images; the M-K variation point test and Theil-Sen trend analysis were used to test the variation point and calculate the trend of precipitation and temperature series. To clarify the influence of wetland areas on runoff, the Gini coefficient and SRI of runoff were used to calculate runoff temporal inhomogeneity. The results showed that the area of the MWNRB obviously decreased, with 74.5 × 106 m2/year from 1993 to 2008, and increased slowly from 2008 to 2015, with 27.7 × 106 m2/year. From 1993 to 2008, 50.74% and 38.87% of wetlands were transformed into paddy fields and dry fields, respectively. From 2008 to 2015, 61.69% and 7.76% of wetlands were transformed from paddy fields and dry fields, respectively. The temperature of the MWNRB increased slowly by 0.04 °C/year from 1993 to 2008 and increased obviously by 0.16 °C/year from 2008 to 2015. The precipitation decreased by 5.6–8.1 mm/year and increased by 16.6–41.2 mm/year in 1993–2008 and 2008–2015, respectively. Compared with precipitation and temperature, land use change caused by human activities is the main cause of wetland area change. The area change of the MWNRB has a certain influence on the runoff regulation and storage capacity. The Gini coefficient and SRI index increased from 0.002/year (0.008) to 0.023/year from 1993 to 2008 and decreased from 0.046/year (0.045) to 0.161/year from 2008 to 2015, respectively. Full article
(This article belongs to the Special Issue Recent Research Developments in Hydrological Modelling, Climate Change, and Water Resource Management)
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17 pages, 15014 KiB  
Article
Utilizing Entropy-Based Method for Rainfall Network Design in Huaihe River Basin, China
by Jian Liu, Yanyan Li, Yuankun Wang and Pengcheng Xu
Water 2023, 15(17), 3115; https://doi.org/10.3390/w15173115 - 30 Aug 2023
Viewed by 1094
Abstract
The nonstationary characteristics caused by significant variation in hydrometeorological series in the context of climate change inevitably have a certain impact on the selection of an optimal gauging network. This study proposes an entropy-based, multi-objective, rain gauge network optimization method to facilitate the [...] Read more.
The nonstationary characteristics caused by significant variation in hydrometeorological series in the context of climate change inevitably have a certain impact on the selection of an optimal gauging network. This study proposes an entropy-based, multi-objective, rain gauge network optimization method to facilitate the design of a 43 stations-based network in Huaihe River Basin (HRB), China. The first goal of this study is to improve the accuracy of gauge-related information estimation through the selection and comparison of discretization methods. The second goal of this study is to quantify the impact of trend-caused nonstationarity on optimal network design using the sliding window method. This study compares the divergence of three kinds of discretization methods, including the floor function-based approach, Scott’s equal bin width histogram (EWH-Sc) approach, and Sturges’s equal bin width histogram (EWH-St) approach. The matching degree of the variance and marginal entropy of the observed series is computed to select the most suitable of the above three discretization methods. The trend-caused nonstationarity in 75% of all stations in the HRB could definitely influence the final results of the optimal rain-gauge network design using the sliding window method. Therefore, in future studies of rain-gauge network optimization, it is necessary to carry out uncertainty research according to local conditions in view of climate change and human activities. Full article
(This article belongs to the Special Issue Recent Research Developments in Hydrological Modelling, Climate Change, and Water Resource Management)
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