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

Deadline for manuscript submissions: closed (30 July 2023) | Viewed by 32392

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Prof. Dr. Haibo Yang

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Guest Editor

School of Water Conservancy and Transportation, Zhengzhou University, Zhengzhou, China
Interests: drought monitor; drought index; drought simulation; remote sensing; satellite precipitation; precipitation downscaling; flood
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Dr. Zheng Duan

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Guest Editor

Department of Physical Geography and Ecosystem Science, Lund University, S-22362 Lund, Sweden
Interests: environmental remote sensing; water quality sensing; machine learning; ecohydrological modelling
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Prof. Dr. Shengzhi Huang

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School of Water Resources and Hydropower, Xi’an University of Technology, Xi’an 710048, China
Interests: drought propagation; drought risk assessment; hydrological prediction
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Dr. Yuyan Zhou

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Department of Water Resources, China Institute of Water Resources and Hydropower Research, Beijing 100038, China
Interests: climate change; water resources; adaptation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Water resources are important for ecosystem, social and economic developments. Climate change has accelerated the heterogeneity of the spatiotemporal distribution of water resources, and increased the probability of extreme events leading to more water disasters and threats. According to plausible future scenarios for climate change from the IPCC report 2022, if global warming transiently exceeds 1.5°C in the coming decades, then humans and ecosystems will face additional severe risks, especially extreme precipitation and heat waves, bringing about more intensive water risks such as floods and droughts. Considerable efforts have been devoted to developing advanced remote sensing and other novel approaches in monitoring relevant variables such as precipitation, runoff, evaporation, soil moisture and groundwater. Various hydrological models have been developed to understand the hydrological processes and to quantify their responses to climate change and human activities. Machine learning and deep learning methods are also being increasingly used to facilitate water research. However, the question of how to accurately quantify and predict the impact of climate change on water resources and water risk still requires further study.

This Special Issue aims to gather contributions on the latest scientific research regarding the impact of climate change on water resources and water risks. This Special Issue hopes to encompass a broad spectrum of topics, including, but not limited to:

Climate change impact assessment;
Satellite hydrometeorological monitoring;
Hydrological modelling;
Drought monitoring;
Flood simulation and flood risk evaluation;
New technologies and approaches in water resource and water risk;

Prof. Dr. Haibo Yang
Dr. Zheng Duan
Prof. Dr. Shengzhi Huang
Dr. Yuyan Zhou
Guest Editors

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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

climate change
water resource management
drought
flood
remote sensing

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Related Special Issue

Impacts of Climate Change on Water Resources and Water Risks, the 2nd Edition in Water (5 articles)

Published Papers (11 papers)

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Research

Jump to: Review

21 pages, 35428 KiB

Open AccessArticle

Runoff Prediction in the Xijiang River Basin Based on Long Short-Term Memory with Variant Models and Its Interpretable Analysis

by Qingqing Tian, Hang Gao, Yu Tian, Yunzhong Jiang, Zexuan Li and Lei Guo

Water 2023, 15(18), 3184; https://doi.org/10.3390/w15183184 - 6 Sep 2023

Cited by 6 | Viewed by 1614

Abstract

The Long Short-Term Memory (LSTM) neural network model is an effective deep learning approach for predicting streamflow, and the investigation of the interpretability of deep learning models in streamflow prediction is of great significance for model transfer and improvement. In this study, four key hydrological stations in the Xijiang River Basin (XJB) in South China are taken as examples, and the performance of the LSTM model and its variant models in runoff prediction were evaluated under the same foresight period, and the impacts of different foresight periods on the prediction results were investigated based on the SHapley Additive exPlanations (SHAP) method to explore the interpretability of the LSTM model in runoff prediction. The results showed that (1) LSTM was the optimal model among the four models in the XJB; (2) the predicted results of the LSTM model decreased with the increase in foresight period, with the Nash–Sutcliffe efficiency coefficient (NSE) decreasing by 4.7% when the foresight period increased from one month to two months, and decreasing by 3.9% when the foresight period increased from two months to three months; (3) historical runoff had the greatest impact on streamflow prediction, followed by precipitation, evaporation, and the North Pacific Index (NPI); except evaporation, all the others were positively correlated. The results can provide a reference for monthly runoff prediction in the XJB. Full article

(This article belongs to the Special Issue Impacts of Climate Change on Water Resources and Water Risks)

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12 pages, 2464 KiB

Open AccessArticle

Simulating Changes in Hydrological Extremes—Future Scenarios for Morocco

by Laura Giustarini, Guy J. -P. Schumann, Albert J. Kettner, Andrew Smith and Raphael Nawrotzki

Water 2023, 15(15), 2722; https://doi.org/10.3390/w15152722 - 28 Jul 2023

Cited by 4 | Viewed by 2001

Abstract

This paper presents a comprehensive river discharge analysis to estimate past and future hydrological extremes across Morocco. Hydrological simulations with historical forcing and climate change scenario inputs have been performed to better understand the change in magnitude and frequency of extreme discharge events that cause flooding. Simulations are applied to all major rivers of Morocco, including a total of 16 basins that cover the majority of the country. An ensemble of temperature and precipitation input parameter sets was generated to analyze input uncertainty, an approach that can be extended to other regions of the world, including data-sparse regions. Parameter uncertainty was also included in the analyses. Historical simulations comprise the period 1979–2021, while future simulations (2015–2100) were performed under the Shared Socioeconomic Pathway (SSP) 2–4.5 and SSP5–8.5. Clear patterns of changing flood extremes are projected; these changes are significant when considered as a proportion of the land area of the country. Two types of basins have been identified, based on their different behavior in climate change scenarios. In the Northern/Mediterranean basins we observe a decrease in the frequency and intensity of events by 2050 under both SSPs, whereas for the remaining catchments higher and more frequent high-flow events in the form of flash floods are detected. Our analysis revealed that this is a consequence of the reduction in rainfall accumulation and intensity in both SSPs for the first type of basins, while the opposite applies to the other type. More generally, we propose a methodology that does not rely on observed time series of discharge, so especially for regions where those do not exist or are not available, and that can be applied to undertake future flood projections in the most data-scarce regions. This method allows future hydrological hazards to be estimated for essentially any region of the world. Full article

(This article belongs to the Special Issue Impacts of Climate Change on Water Resources and Water Risks)

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18 pages, 5958 KiB

Open AccessArticle

Spatiotemporal Evolution and Attribution Analysis of Water Yield in the Xiangjiang River Basin (XRB) Based on the InVEST Model

by Zongmin Wang, Qizhao Li, Lin Liu, Hongling Zhao, Hongen Ru, Jiapeng Wu and Yanli Deng

Water 2023, 15(3), 514; https://doi.org/10.3390/w15030514 - 28 Jan 2023

Cited by 11 | Viewed by 2495

Abstract

As a result of climate change and human activities, water resources in the Xiangjiang River Basin (XRB) are subject to seasonal and regional shortages. However, previous studies have lacked assessment of the spatiotemporal evolution of water yield in the XRB at seasonal and monthly scales and quantitative analysis of the driving forces of climate change and land use on water-yield change. Quantitative evaluation of water yield in the XRB is of great significance for optimizing water-resource planning and allocation and maintaining ecological balance in the basin. In this paper, the seasonal water-yield InVEST model and modified Morris sensitivity analysis were combined to study the characteristics of monthly water yield in the XRB. Seventeen attributes were identified using the Budyko framework. The results show that: (1) the water yield of the XRB showed an increase trend from northeast to southwest from 2006 to 2020; (2) the transfer-in of unused land, grassland, woodland and farmland as well as the transfer-out of water and construction land have positive effects on the increase in water yield, and the change to construction land has the greatest impact on water yield; (3) water yield is positively correlated with NDVI and precipitation and negatively correlated with potential evapotranspiration; (4) climate change and land-use change contributed to water-yield changes of 67.08% and 32.92%, respectively. Full article

(This article belongs to the Special Issue Impacts of Climate Change on Water Resources and Water Risks)

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17 pages, 5077 KiB

Open AccessArticle

Parameter Optimization of SWMM Model Using Integrated Morris and GLUE Methods

by Baoling Zhong, Zongmin Wang, Haibo Yang, Hongshi Xu, Meiyan Gao and Qiuhua Liang

Water 2023, 15(1), 149; https://doi.org/10.3390/w15010149 - 30 Dec 2022

Cited by 8 | Viewed by 3349

Abstract

The USEPA (United States Environmental Protection Agency) Storm Water Management Model (SWMM) is one of the most extensively implemented numerical models for simulating urban runoff. Parameter optimization is essential for reliable SWMM model simulation results, which are heterogeneously sensitive to a variety of parameters, especially when involving complicated simulation conditions. This study proposed a Genetic Algorithm-based parameter optimization method that combines the Morris screening method with the generalized likelihood uncertainty estimation (GLUE) method. In this integrated methodology framework, the Morris screening method is used to determine the parameters for calibration, the GLUE method is employed to narrow down the range of parameter values, and the Genetic Algorithm is applied to further optimize the model parameters by considering objective constraints. The results show that the set of calibrated parameters, obtained by the integrated Morris and GLUE methods, can reduce the peak error by 9% for a simulation, and then the multi-objective constrained Genetic Algorithm reduces the model parameters’ peak error in the optimization process by up to 6%. During the validation process, the parameter set determined from the combination of both is used to obtain the optimal values of the parameters by the Genetic Algorithm. The proposed integrated method shows superior applicability for different rainfall intensities and rain-type events. These findings imply that the automated calibration of the SWMM model utilizing a Genetic Algorithm based on the combined parameter set of both has enhanced model simulation performance. Full article

(This article belongs to the Special Issue Impacts of Climate Change on Water Resources and Water Risks)

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15 pages, 3841 KiB

Open AccessFeature PaperArticle

Impacts of Precipitation Type Variations on Runoff Changes in the Source Regions of the Yangtze and Yellow River Basins in the Past 40 Years

by Yingying Hu, Yuyan Zhou, Yicheng Wang, Fan Lu, Weihua Xiao, Baodeng Hou, Yuanhui Yu, Jianwei Liu and Wei Xue

Water 2022, 14(24), 4115; https://doi.org/10.3390/w14244115 - 16 Dec 2022

Cited by 8 | Viewed by 2273

Abstract

Variations of precipitation type can exert substantial impacts on hydrological processes, yet few studies have quantified the impacts of precipitation type variations on runoff changes in high−altitude regions. In this study, we attempted to examine the potential impacts of precipitation type variations induced by the warming climate on the runoff changes of the source regions of the Yangtze River and Yellow River basins from 1979 to 2018, where the mean elevation is over 4000 m. A modified precipitation type identification method using the wet-bulb temperature, and a runoff change attribution method based on a modified Budyko framework has been applied. Results showed that fluctuations of precipitation contributed to the majority of the runoff variations in the source regions of the Yangtze River basin, which accounted for 51.64%. However, the changes of characteristic parameter n, which indicates the impacts of the underlying surface, explained 56.22% of the runoff changes in the source regions of the Yellow River. It was shown that the trend of shifting from snowfall to rainfall due to a warming climate could result in runoff decreasing, which contributed to 24.06% and 11.29% of the runoff changes in the two source regions, comparatively. Full article

(This article belongs to the Special Issue Impacts of Climate Change on Water Resources and Water Risks)

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15 pages, 4944 KiB

Open AccessArticle

Nonstationary Annual Maximum Flood Frequency Analysis Using a Conceptual Hydrologic Model with Time-Varying Parameters

by Ling Zeng, Hongwei Bi, Yu Li, Xiulin Liu, Shuai Li and Jinfeng Chen

Water 2022, 14(23), 3959; https://doi.org/10.3390/w14233959 - 5 Dec 2022

Cited by 5 | Viewed by 2486

Abstract

Recent evidence of the impact of watershed underlying conditions on hydrological processes have made the assumption of stationarity widely questioned. In this study, the temporal variations of frequency distributions of the annual maximum flood were investigated by continuous hydrological simulation considering nonstationarity for Weihe River Basin (WRB) in northwestern China. To this end, two nonstationary versions of the GR4J model were introduced, where the production storage capacity parameter was regarded as a function of time and watershed conditions (e.g., reservoir storage and soil-water conservation land area), respectively. Then the models were used to generate long-term runoff series to derive flood frequency distributions, with synthetic rainfall series generated by a stochastic rainfall model as input. The results show a better performance of the nonstationary GR4J model in runoff simulation than the stationary version, especially for the annual maximum flow series, with the corresponding NSE metric increasing from 0.721 to 0.808. The application of the nonstationary flood frequency analysis indicates the presence of significant nonstationarity in the flood quantiles and magnitudes, where the flood quantiles for an annual exceedance probability of 0.01 range from 4187 m³/s to 8335 m³/s for the past decades. This study can serve as a reference for flood risk management in WRB and possibly for other basins undergoing drastic changes caused by intense human activities. Full article

(This article belongs to the Special Issue Impacts of Climate Change on Water Resources and Water Risks)

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19 pages, 4075 KiB

Open AccessArticle

Teaching Floods in the Context of Climate Change with the Use of Official Cartographic Viewers (Spain)

by Jorge Olcina, Álvaro-Francisco Morote and María Hernández

Water 2022, 14(21), 3376; https://doi.org/10.3390/w14213376 - 25 Oct 2022

Cited by 3 | Viewed by 2606

Abstract

Floods are the natural hazard that have the greatest economic impact and cause the most deaths in the Mediterranean region. The objective of this study is to present different proposals for teach the risk of flooding using the GIS viewers offered by the NFZMS (National Flood Zone Mapping System) and the PATRICOVA (Spain). The idea is that, based on the selection of the same area of study (the mouth of the Júcar River—Valencia—and the mouth of the Segura River—Alicante), students determine the similarities and differences, for educational purposes, of these two geographical viewers. These proposals are aimed at the 2nd year of the Baccalaureate (17–18 years; optional subject of Geography). The objective is to enhance the skills of the students for understand the territory, especially their immediate environment, in the learning process. Furthermore, it also seeks to expand the knowledge of students with regard to these extreme phenomena experienced by society. This proposal shows that these types of tools are important for students to understand the social and territorial part of flooding events (vulnerability and exposure), which is the most salient part in terms of finding solutions to minimise their effects. Full article

(This article belongs to the Special Issue Impacts of Climate Change on Water Resources and Water Risks)

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17 pages, 4431 KiB

Open AccessArticle

Effects of Climatic Variability on Soil Water Content in an Alpine Kobresia Meadow, Northern Qinghai–Tibetan Plateau, China

by Mengke Si, Xiaowei Guo, Yuting Lan, Bo Fan and Guangmin Cao

Water 2022, 14(17), 2754; https://doi.org/10.3390/w14172754 - 4 Sep 2022

Cited by 7 | Viewed by 2348

Abstract

Soil moisture dynamics play an active role in ecological and hydrological processes. Although the variation of the soil water moisture of multiple ecosystems have been well-documented, few studies have focused on soil hydrological properties by using a drying and weighing method in a long time series basis in the Qinghai-Tibet Plateau (QTP). In this study, 13 year (2008–2020) time-series observational soil moisture data and environmental factors were analyzed in a humid alpine Kobresia meadow on the Northern Qinghai–Tibetan Plateau. The results showed no significant upward trend in soil water content during the 2008–2020 period. In the growth season (May–October), the soil water content showed a trend of decreasing firstly, then increasing, and finally, decreasing. Correlation analysis revealed that five meteorology factors (temperature, humidity, net radiation, dew point temperature, and vapor pressure) and a biomass element (above-ground biomass) had a significant effect on the soil moisture, and air temperature impacted the soil water variation negatively in 0–50 cm, indicating that global warming would reduce soil moisture. Humidity and net radiation made a difference on shallow soil (0–10 cm), while dew point temperature and vapor pressure played a role on the deep soil (30–50 cm). Above-ground biomass only effected 30–50 cm soil moisture variation, and underground biomass had little effect on the soil moisture variation. This indirectly indicated that below-ground biomass is not limited by soil moisture. These results provide new insights for the rational allocation of water resources and management of vegetation in alpine meadows, in the context of climate change. Full article

(This article belongs to the Special Issue Impacts of Climate Change on Water Resources and Water Risks)

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14 pages, 6333 KiB

Open AccessArticle

Random Forest Model Has the Potential for Runoff Simulation and Attribution

by Xia Liu, Xiaolong Zhang, Xiaole Kong and Yan-Jun Shen

Water 2022, 14(13), 2053; https://doi.org/10.3390/w14132053 - 27 Jun 2022

Cited by 8 | Viewed by 2610

Abstract

Quantifying the impact of climate change and human activities on runoff changes is beneficial for developing sustainable water-management strategies within the local ecosystem. Machine-learning models were widely used in scientific research; yet, whether it is applicable for quantifying the contribution of climate change and human activities to runoff changes is not well understood. To provide a new pathway, we quantified the contribution of climate change and human activities to runoff changes using a machine-learning method (random forest model) in two semi-humid basins in this study. Results show that the random forest model provides good performances for runoff simulation; the contributions of climate change and human activities to runoff changes from 1982 to 2014 were found between 6–9% and 91–94% in the Zijinguan basin, and 31–44% and 56–69% in the Daomaguan basin, respectively. Furthermore, the model performances were also compared with those of well-known elasticity-based and double-mass curve methods, and the results of these models are approximate in the investigated basins, which implies that the random forest model has the potential for runoff simulation and for quantifying the impact of climate change and human activities on runoff changes. This study provides a new methodology for studying the impact of climate change and human activities on runoff changes, and the limited numbers of parameters make this methodology important for further applications to other basins elsewhere. Nevertheless, the physical interpretation should be made with caution and more comprehensive comparison work must be performed to assess the model’s applicability. Full article

(This article belongs to the Special Issue Impacts of Climate Change on Water Resources and Water Risks)

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16 pages, 3556 KiB

Open AccessArticle

Quantifying the Contributions of Climate Change and Human Activities to Maize Yield Dynamics at Multiple Timescales

by Pei Li, Shengzhi Huang, Qiang Huang, Jing Zhao, Xudong Zheng and Lan Ma

Water 2022, 14(12), 1927; https://doi.org/10.3390/w14121927 - 15 Jun 2022

Cited by 1 | Viewed by 2283

Abstract

Under a changing environment, the effect of climate change and human activities on maize yield is vital for ensuring food security and efficient socio-economic development. The time series of maize yield is generally non-stationary and contains different frequency components, such as long- and short-term oscillations. Nevertheless, there is no adequate understanding of the relative importance of climate change. In addition, human activities on maize yield at multiple timescales remain unclear, which help in further improving maize yield prediction. Based on the ensemble empirical mode decomposition method (EEMD), the method of dependent variable variance decomposition (DVVD) and the Sen-slope method, the effect of climate change including growing-season precipitation and temperature (i.e., GSP, GEP, CDD, GST, GSMAT, and GSMT) and human activities including effective irrigation area (EIA) and the consumption of chemical fertilizers (CCF) on maize yield were explored at multiple timescales during 1979–2015. The Heilongjiang Province, a highly important maize production area in China, was selected as a case study. The results of this work indicate the following: (1) The original maize yield series was divided into 3.1-, 7.4-, 18.5-, and 37-year timescale oscillations and a residual series with an increasing trend, where the 3.1-year timescale (IMF1), the 18.5-year timescale (IMF3), and the increasing trend (R) were dominant; (2) the original sequence was mainly affected by human activities; (3) climate change and human activities had different effects on maize yield at different timescales: The short-term oscillation (IMF1) of maize yield was primarily affected by climate change. However, human activities dominated the mid- and long-term oscillations (IMF3 and R) of maize yield. This study sheds new insight into multiple timescale analysis of the role of climate and human activities on maize yield dynamics. Full article

(This article belongs to the Special Issue Impacts of Climate Change on Water Resources and Water Risks)

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Review

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30 pages, 1726 KiB

Open AccessReview

Review of In-Situ and Remote Sensing-Based Indices and Their Applicability for Integrated Drought Monitoring in South Africa

by Mxolisi B. Mukhawana, Thokozani Kanyerere and David Kahler

Water 2023, 15(2), 240; https://doi.org/10.3390/w15020240 - 5 Jan 2023

Cited by 13 | Viewed by 6157

Abstract

The devastating socioeconomic impacts of recent droughts have intensified the need for improved drought monitoring in South Africa (SA). This study has shown that not all indices can be universally applicable at all regions worldwide, and there is no single index that can represent all aspects of droughts. The aim of this study was to review the performance and applicability of the Palmer drought severity index (PDSI), surface water supply index (SWSI), vegetation condition index (VCI), standardised precipitation index (SPI), standardised precipitation evapotranspiration index (SPEI), standardised streamflow index (SSI), standardised groundwater index (SGI), and GRACE (Gravity Recovery and Climate Experiment)-based drought indices in SA and provide guidelines for selecting feasible candidates for integrated drought monitoring. The review is based on the ‘2016 World Meteorological Organisation (WMO) Handbook of Drought Indicators and Indices’ guidelines. The PDSI and SWSI are not feasible in SA, mainly because they are relatively complex to compute and interpret and cannot use readily available and accessible data. Combining the SPI, SPEI, VCI, SSI, and SGI using multi-index or hybrid methods is recommended. Hence, with best fitting probability distribution functions (PDFs) used, and with an informed choice between parametric and non-parametric approaches, this combination has the potential for integrated drought monitoring. Due to the scarcity of groundwater data, investigations on the use of GRACE-based groundwater drought indices must be carried out. These findings may contribute to improved drought early warning and monitoring in SA. Full article

(This article belongs to the Special Issue Impacts of Climate Change on Water Resources and Water Risks)

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