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Water Cycle Processes under the Influence of Climate Change and Human Activities

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A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Sustainability in Geographic Science".

Deadline for manuscript submissions: closed (25 March 2023) | Viewed by 5944

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Special Issue Editors

Dr. Sidong Zeng

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Guest Editor

Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing 400714, China
Interests: climate change; land use and cover change; ecohydrology; water resources allocation and regulation; water cycle modeling

Prof. Dr. Liping Zhang

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Guest Editor

School of Water Resources and Hydropower Engineering, Wuhan University, Wuhan 430072, China
Interests: water resources; climate change

Prof. Dr. Dunxian She

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Guest Editor

School of Water Resources and Hydropower Engineering, Wuhan University, Wuhan 430072, China
Interests: climate change and extreme hydrological events; urban hydrology
Special Issues, Collections and Topics in MDPI journals

Dr. Jilong Chen

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Guest Editor

Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing, China
Interests: remote sensing; solar radiation; vegetation carbon sequestration; reservior riparian zone

Special Issue Information

Dear Colleagues,

It is widely recognized that climate change and human activities are the two driving factors affecting the water cycle, which is a key concern for global change research. Global climate change has greatly affected water cycle processes such as precipitation, runoff, evapotranspiration, and river flow at regional and global scales. At the same time, such processes are also affected by human activities such as irrigation, land use/cover change and water construction. With recent rapid environmental changes, the water cycle is experiencing high levels of spatio-temporal variability, resulting in many water-related issues that pose challenges to the management of sustainable water resources. Therefore, it is important to understand the mechanisms of water cycle processes under the influence of climate change and human activities.

This Special Issue invites research papers related to the influences of climate change and human activities on water cycle processes, including rainfall, evapotranspiration, runoff, soil water, river flows, floods and drought, and their impacts on the management of sustainable water resources. We invite studies highlighting the joint impacts of climate change and human activities on water cycle processes using different methods, including observation, physical modelling, statistical analysis, and remote sensing. Especially welcome are studies that use new approaches coupling natural and social water systems to study the water cycle in the changing environment.

Dr. Sidong Zeng
Prof. Dr. Liping Zhang
Dr. Dunxian She
Dr. Jilong Chen
Guest Editors

Manuscript Submission Information

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Keywords

water cycle processes
climate change
human activities
sustainable water resources management
modelling
statistical analysis
remote sensing

Published Papers (4 papers)

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Research

25 pages, 5730 KiB

Open AccessArticle

Estimating the Role of Climate Internal Variability and Sources of Uncertainties in Hydrological Climate-Impact Projections

by Wenjun Cai, Jia Liu, Xueping Zhu, Xuehua Zhao and Xiaoli Zhang

Sustainability 2022, 14(19), 12201; https://doi.org/10.3390/su141912201 - 26 Sep 2022

Viewed by 856

Abstract

Hydrological climate-impact projections in the future are limited by large uncertainties from various sources. Therefore, this study aimed to explore and estimate the sources of uncertainties involved in climate change-impacted assessment, in a representative watershed of Northeastern China. Moreover, recent studies have indicated that the climate internal variability (CIV) plays an important role in various hydrological climate-impact projections. Six downscaled global climate models (GCMs) under two emission scenarios, and a calibrated Soil and Water Assessment Tool (SWAT) model were used to obtain hydrological projections in future periods. The CIV and signal-to-noise ratio (SNR) are investigated to analyze the role of internal variability in hydrological projections. The results shows that the internal variability shows a considerable influence on hydrological projections, which need to be particularly partitioned and quantified. Moreover, it is worth noting the CIV can propagate from precipitation and ET to runoff projections through the hydrological simulation process. In order to partition the CIV and the sources of uncertainties, the uncertainty decomposed frameworks based on analysis of variance (ANOVA) are established. The results demonstrate that the CIV and GCMs are the dominant contributors of runoff in the rainy season. In contrast, the CIV and SWAT model parameter sets provided obvious uncertainty to the runoff in January to May, and October to December. The findings of this study advised that the uncertainty is complex in the hydrological simulation process; hence, it is meaningful and necessary to estimate the uncertainty in the climate simulation process. The uncertainty analysis results can effectively provide efforts for reducing uncertainty, and then give some positive suggestions to stakeholders for adaption countermeasures under climate change. Full article

(This article belongs to the Special Issue Water Cycle Processes under the Influence of Climate Change and Human Activities)

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23 pages, 4771 KiB

Open AccessArticle

Predicting Monthly Runoff of the Upper Yangtze River Based on Multiple Machine Learning Models

by Xiao Li, Liping Zhang, Sidong Zeng, Zhenyu Tang, Lina Liu, Qin Zhang, Zhengyang Tang and Xiaojun Hua

Sustainability 2022, 14(18), 11149; https://doi.org/10.3390/su141811149 - 06 Sep 2022

Cited by 8 | Viewed by 1746

Abstract

Accurate monthly runoff prediction is significant to extreme flood control and water resources management. However, traditional statistical models without multi-variable input may fail to capture runoff changes effectively due to the dual effect of climate change and human activities. Here, we used five multi-input machine learning (ML) models to predict monthly runoff, where multiple global circulation indexes and surface meteorological indexes were selected as explanatory variables by the stepwise regression or copula entropy methods. Moreover, four univariate models were adopted as benchmarks. The multi-input ML models were tested at two typical hydrological stations (i.e., Gaochang and Cuntan) in the Upper Yangtze River. The results indicate that the LSTM_Copula (long short-term memory model combined with copula entropy method) model outperformed other models in both hydrological stations, while the GRU_Step (gate recurrent unit model combined with stepwise regression method) model and the RF_Copula (random forest model combined with copula entropy method) model also showed satisfactory performances. In addition, the ML models with multi-variable input provided better predictability compared with four univariate statistical models, and the MAPE (mean absolute percentage error), RMSE (root mean square error), NSE (Nash–Sutcliffe efficiency coefficient), and R (Pearson’s correlation coefficient) values were improved by 5.10, 4.16, 5.34, and 0.43% for the Gaochang Station, and 10.84, 17.28, 13.68, and 3.55% for the Cuntan Station, suggesting the proposed ML approaches are practically applicable to monthly runoff forecasting in large rivers. Full article

(This article belongs to the Special Issue Water Cycle Processes under the Influence of Climate Change and Human Activities)

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17 pages, 2889 KiB

Open AccessArticle

Research on the ECC of Chengdu–Chongqing’s Urban Agglomeration in China Based on System Dynamics

by Xiaohu Ci, Liping Zhang, Tongxiang Wang, Yi Xiao and Jun Xia

Sustainability 2022, 14(17), 10896; https://doi.org/10.3390/su141710896 - 31 Aug 2022

Cited by 2 | Viewed by 1243

Abstract

The ecological carrying capacity (ECC) is a prerequisite for China’s regional and green developments. Since the Chengdu–Chongqing urban agglomeration (CCUA) is an important economic area, it is important to study the development of its ECC in order to establish its green development and to promote its regionally coordinated development in China. This paper first establishes the ECC evaluation index system based on the Pressure–State–Response (PSR) model and AHP-TOPSIS. Secondly, it estimates the ECC of the CCUA between 2000 and 2018. Thirdly, it constructs a system dynamics model of the ECC and, finally, it simulates and predicts the ECC from 2021 to 2050 based on shared socioeconomic pathways. The results show that the ECC indices of 16 cities in the CCUA have increased significantly in 18 years and the annual ECC indices from 2021 to 2050 all show significant growth trends. This paper will show that the CCUA should select the most suitable development mode to be adopted in the different periods. The development should follow SSP2 from 2021 to 2025, SSP1 from 2026 to 2035, and the development characteristics of SSP5 should be referred to at levels between 2036 and 2050, based on the CCUA’s overall development in accordance with SSP1. Full article

(This article belongs to the Special Issue Water Cycle Processes under the Influence of Climate Change and Human Activities)

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23 pages, 5094 KiB

Open AccessArticle

Spatiotemporal Variations of Extreme Precipitation in Wuling Mountain Area (China) and Their Connection to Potential Driving Factors

by Hong Du, Jun Xia, Yi Yan, Yumeng Lu and Jinhua Li

Sustainability 2022, 14(14), 8312; https://doi.org/10.3390/su14148312 - 07 Jul 2022

Cited by 3 | Viewed by 1413

Abstract

Changes in extreme precipitation have become a significant issue of regional disaster risk assessment and water resources management. Extreme precipitation variability is affected by multiple factors and shows disparities across different regions. Especially in mountain areas, geographic feature and local characteristics put more complexity and uncertainty on the changes of precipitation extremes. In this study, ten extreme precipitation indices of Wuling Mountain Area (WMA) during 1960–2019 have been used to analyzed the spatiotemporal variations of precipitation extremes. The relationships between extreme precipitation and potential driving factors, including geographic factors, global warming, local temperature, and climate indices, were investigated via correlation analysis. The results indicated that extreme precipitation tends to have a shorter duration and stronger intensity in WMA. Decreasing trends in R10mm, R20mm, R25mm, and the consecutive wet days (CWD) series account for 92%, 68%, 52%, and 96% of stations, while most stations in WMA have rising trends in Rx1day (68%), SDII (64%), R95p (72%), and R99p (72%). Significant abrupt changes in extreme precipitation indices mainly occurred in the 1980s–1990s. Geographic factors, local temperature, and climate indices exert different impacts on extreme precipitation. Longitude and elevation instead of latitude significantly affect extreme precipitation indices except for the maximum duration of wet spells. Global warming is likely to increase the intensity and decrease the duration of extreme precipitation, while the influence of local temperature is not exactly the same as that of global warming. The study reveals that summer monsoon indices are the dominant climate factor for variations of precipitation extremes in WMA. The correlation coefficient between extreme precipitation indices (such as Rx1day, R95p, R99p) and the East Asian summer monsoon index is around 0.5 and passed the significant test at the 0.01 level. The weakening of the summer monsoon indices tends to bring extreme precipitation with stronger intensity. The findings provide more understanding of the drivers and reasons of extreme precipitation changes in the mountain area. Full article

(This article belongs to the Special Issue Water Cycle Processes under the Influence of Climate Change and Human Activities)

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