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A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "Water Resources Management, Policy and Governance".

Deadline for manuscript submissions: 30 June 2024 | Viewed by 6913

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Special Issue Editors

Prof. Dr. Yaning Chen

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State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China
Interests: climate change; water resources; ecohydrology; water management; arid regions
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Zhi Li

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Guest Editor

State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China
Interests: drought; eco-hydrological process; climate change; land surface processes; water resource management
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Prof. Dr. Weili Duan

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Guest Editor

State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China
Interests: remote sensing for hydrological applications; hydrological big data; sustainable water resource management; extreme climate events
Special Issues, Collections and Topics in MDPI journals

Dr. Chenggang Zhu

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Guest Editor

State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China
Interests: ecology; ecohydrology in arid regions; hydraulic conductance; breeding of Populus euphratica
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Dr. Gonghuan Fang

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Guest Editor

State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China
Interests: water cycle; hydrological modeling; sensitivity and uncertainty analysis; climate change
Special Issues, Collections and Topics in MDPI journals

Dr. Yupeng Li

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Guest Editor

State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China
Interests: snowfall; water cycle; eco-hydrological process; climate change; High-Mountain Asia

Special Issue Information

Dear Colleagues,

Water is the most critical element constraining economic and social development and the ecological environment in arid zones. In the context of climate change, water cycle patterns have changed, resulting in phenomena such as glacier retreat, reduced snowfall fraction, intensified extreme precipitation and droughts, leading to increased hydrological fluctuations and enlarged water resource variability in arid zones. In addition, the growing population has put greater pressure on the management of oasis-agriculture-dominated systems in arid zones. Therefore, this Special Issue aims at the promotion of water resource management under climate change and anthropogenic pressures in arid and semi-arid regions. We welcome research/review papers dealing with the assessment of water resources and strategies and case studies focused on the engineering and technological measurement of water resource security, climate change adaptation and mitigation, water risk, the prediction of future water cycle and water resource trends, water–food–ecology synergistic development, drought and vulnerability. This Special Issue will serve as an invaluable reference for water resource management in arid and semi-arid regions, promoting the achievement of the UN Sustainable Development Goals.

Prof. Dr. Yaning Chen
Prof. Dr. Zhi Li
Prof. Dr. Weili Duan
Dr. Chenggang Zhu
Dr. Gonghuan Fang
Dr. Yupeng Li
Guest Editors

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.

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Keywords

water resource management
climate change
extreme climate
drought
flood
water security
sustainable development
arid regions

Published Papers (7 papers)

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Research

11 pages, 3352 KiB

Open AccessCommunication

Effects of Winter Warming on Alpine Permafrost Streamflow in Xinjiang China and Teleconnections with the Siberian High

by Jingshi Liu, Guligena Halimulati, Yuting Liu, Jianxin Mu and Namaiti Tuoheti

Water 2024, 16(7), 993; https://doi.org/10.3390/w16070993 - 29 Mar 2024

Viewed by 516

Abstract

The climatic warming-induced shrinking of permafrost currently encompasses 65% of alpine areas in North China, where a large population relies on its water and land resources. With increasing recognition of the economic and ecological impacts of permafrost basins, forecasts of environmental vulnerability have gained prominence. However, the links between permafrost and winter water resources remain inadequately explored, with most studies focusing on in-situ measurements related to snow cover and frozen layer thickness. Evaluating more complex phenomena, such as the magnitude and persistence of air temperature or low streamflow, depends on numerous climate-driven factors interacting through various subsurface flow mechanisms, basin drainage mechanics, and hydro-climatic correlations at a macroscale. The present study focuses on winter warming, flow increases, and their teleconnections in Xinjiang, China. The research analyzes their links to the atmospheric cycle of the Siberian High (SH) using long-term data spanning 55 years from two large alpine permafrost basins. Changes in variability and correlation persistence were explored for the past decades, and significant variability and connections were constructed using statistical correlation. The years 1980 and 1990 were a turning point when both winter temperatures and winter river flow began to exhibit a notable and consistent upward trend. Subsequently, the period from the mid-1990s to 2013 was characterized by high variability and persistence in these trends. The influence of the SH plays a dominant role in regard to both winter temperatures and river flow, and these variabilities and correlations can be utilized to estimate and predict winter flow in ungauged permafrost rivers in Xinjiang China. Full article

(This article belongs to the Special Issue Water Management in Arid and Semi-arid Regions)

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20 pages, 11119 KiB

Open AccessArticle

Characteristics of Dry and Wet Changes and Future Trends in the Tarim River Basin Based on the Standardized Precipitation Evapotranspiration Index

by Yansong Li, Yaning Chen, Yapeng Chen, Weili Duan, Jiayou Wang and Xu Wang

Water 2024, 16(6), 880; https://doi.org/10.3390/w16060880 - 19 Mar 2024

Viewed by 762

Abstract

Global changes in drought and wetness and their future trends in arid regions have recently become a major focus of research attention. The Tarim River Basin (TRB) in Xinjiang, China, is among the most climate-sensitive regions in the world. This study uses data from the past 60 years (1962–2021) to analyze the spatial and temporal features of drought and wetness conditions in the TRB, calculating the Standardized Precipitation Evapotranspiration Index (SPEI). Trend detection for SPEI is performed using the BEAST mutation test, identification of drought events using the theory of operations, and spatial and temporal analyses of dry and wet changes using Empirical Orthogonal Function (EOF) decomposition. Additionally, the CMIP6 dataset is used to estimate future changes. The study results indicate the following: (1) From 1962 to 1998, the TRB exhibited a “warm and wet” trend that suddenly shifted from “wet-to-dry” in 1998 and subsequently transitioned to a pronounced “warm and dry” trend. (2) After the “wet-to-dry” shift, the frequency of drought events noticeably increased. The northern section of the basin witnessed more frequent drought events, albeit with lower severity, while the southern part had fewer occurrences but with higher severity. The spatial distribution of drought event frequency and severity is inconsistent. (3) The EOF decomposition results for SPEI-variable fields at 1-, 3-, and 6-month time scales show that the cumulative variance contribution rate of the first three principal spatial modal feature vectors exceeds 70%. The spatial distribution of the modes includes a consistent pattern across the entire basin, a north–south opposite pattern, and an east–west opposite pattern. (4) The future trend of drought in the TRB is expected to intensify, manifesting a spatial pattern characterized by dryness in the middle of the basin and wetness around the periphery. These research findings can provide support for decisions addressing regional drought risks. Full article

(This article belongs to the Special Issue Water Management in Arid and Semi-arid Regions)

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31 pages, 5658 KiB

Open AccessArticle

Artificial Neural Network for Forecasting Reference Evapotranspiration in Semi-Arid Bioclimatic Regions

by Ahmed Skhiri, Ali Ferhi, Anis Bousselmi, Slaheddine Khlifi and Mohamed A. Mattar

Water 2024, 16(4), 602; https://doi.org/10.3390/w16040602 - 18 Feb 2024

Viewed by 636

Abstract

A correct determination of irrigation water requirements necessitates an adequate estimation of reference evapotranspiration (ETo). In this study, monthly ETo is estimated using artificial neural network (ANN) models. Eleven combinations of long-term average monthly climatic data of air temperature (min and max), wind speed (WS), relative humidity (RH), and solar radiation (SR) recorded at nine different weather stations in Tunisia are used as inputs to the ANN models to calculate ETo given by the FAO-56 PM (Penman–Monteith) equation. This research study proposes to: (i) compare the FAO-24 BC, Riou, and Turc equations with the universal PM equation for estimating ETo; (ii) compare the PM method with the ANN technique; (iii) determine the meteorological parameters with the greatest impact on ETo prediction; and (iv) determine how accurate the ANN technique is in estimating ETo using data from nearby weather stations and compare it to the PM method. Four statistical criteria were used to evaluate the model’s predictive quality: the determination coefficient (R²), the index of agreement (d), the root mean square error (RMSE), and the mean absolute error (MAE). It is quite evident that the Blaney–Criddle, Riou, and Turc equations underestimate or overestimate the ETo values when compared to the PM method. Values of ETo underestimation ranged from 1.9% to 66.1%, while values of overestimation varied from 0.9% to 25.0%. The comparisons revealed that the ANN technique could be adeptly utilized to model ETo using the available meteorological data. Generally, the ANN technique performs better on the estimates of ETo than the conventional equations studied. Among the meteorological parameters considered, maximum temperature was identified as the most significant climatic parameter in ETo modeling, reaching values of R and d of 0.936 and 0.983, respectively. The research showed that trained ANNs could be used to yield ETo estimates using new data from nearby stations not included in the training process, reaching high average values of R and d values of 0.992 and 0.997, respectively. Very low values of MAE (0.233 mm day⁻¹) and RMSE (0.326 mm day⁻¹) were also obtained. Full article

(This article belongs to the Special Issue Water Management in Arid and Semi-arid Regions)

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12 pages, 3810 KiB

Open AccessArticle

Water Consumption Structure and Root Water Absorption Source of an Oasis Cotton Field in an Arid Area of China

by Yang Zhao, Yaning Chen, Shunjun Hu, Yanjun Shen, Fan Liu and Yucui Zhang

Water 2023, 15(23), 4140; https://doi.org/10.3390/w15234140 - 29 Nov 2023

Viewed by 768

Abstract

This research, conducted at the National Field Science Observation and Research Station of the Aksu Farmland Ecosystem in Xinjiang, was performed to partition evapotranspiration components, identify the main water absorption depth, and quantify the contribution of soil water at different depths during different growing stages of cotton by combining hydrogen and oxygen stable isotopes and the MixSIAR model. The results indicated that evapotranspiration in the seeding stage, bud stage, flowering and boll stage, boll opening stage, and harvesting stage were 88 mm, 137 mm, 542 mm, 214 mm, and 118 mm, respectively, and the corresponding transpiration accounted for 51%, 82%, 88%, 85%, and 72% of evapotranspiration. With the development of cotton roots, the water absorption depth gradually increased, and the main absorption depths in the late bud stage, mid flowering and boll stage, late flowering and boll stage, boll opening stage, and harvesting stage were 0–20 cm, 40–60 cm, 60–80 cm, 80–100 cm, and 0–20 cm, respectively, with corresponding contributions of 35.4%, 40.9%, 27.7%, 29.9%, and 22.5%. Our results can provide a theoretical foundation for the accurate irrigation management of cotton fields. Full article

(This article belongs to the Special Issue Water Management in Arid and Semi-arid Regions)

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20 pages, 15137 KiB

Open AccessArticle

Groundwater Level Dynamic Impacted by Land-Cover Change in the Desert Regions of Tarim Basin, Central Asia

by Wanrui Wang, Yaning Chen, Weihua Wang, Yapeng Chen and Yifeng Hou

Water 2023, 15(20), 3601; https://doi.org/10.3390/w15203601 - 14 Oct 2023

Cited by 2 | Viewed by 1123

Abstract

Groundwater is essential to residents, ecology, agriculture, and industry. The depletion of groundwater impacted by climatic variability and intense human activities could threaten water, food, and socioeconomic security in arid regions. A thorough understanding of groundwater level dynamics and its response to land-cover change is necessary for groundwater management and ecosystem improvement, which are poorly understood in arid desert regions due to a scarcity of field monitoring data. In our study, spatiotemporal characteristics of groundwater level impacted by land-cover change and its relationship with vegetation were examined using 3-years in-situ monitoring data of 30 wells in the desert regions of Tarim Basin during 2019–2021. The results showed that the depth to groundwater level (DGL) exhibited obvious spatial and seasonal variations, and the fluctuation of DGL differed significantly among the wells. The cultivated land area increased by 1174.6, 638.0, and 732.2 km² during 2000–2020 in the plains of Yarkand, Weigan-Kuqa, and Dina Rivers, respectively, mainly transferring from bare land and grassland. Annual average Normalized Difference Vegetation Index (NDVI) values increased with time during the period in the plains. DGL generally exhibited a weakly increasing trend from 2019 to 2021, mainly due to human activities. Land-cover change significantly affected the groundwater level dynamic. Generally, the groundwater system was in negative equilibrium near the oasis due to agricultural irrigation, was basically in dynamic equilibrium in the desert region, and was in positive equilibrium near the Tarim River Mainstream due to irrigation return water and streamflow. NDVI of natural desert vegetation was negatively correlated with DGL in the desert regions (R² = 0.78, p < 0.05). Large-scale land reclamation and groundwater overexploitation associated with water-saving irrigation agriculture development have caused groundwater level decline in arid oasis-desert regions. Hence, controlling groundwater extraction intensity, strengthening groundwater monitoring, and promoting water-saving technology would be viable methods to sustainably manage groundwater and maintain the ecological environment in arid areas. Full article

(This article belongs to the Special Issue Water Management in Arid and Semi-arid Regions)

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22 pages, 4641 KiB

Open AccessArticle

Simulation and Reconstruction of Runoff in the High-Cold Mountains Area Based on Multiple Machine Learning Models

by Shuyang Wang, Meiping Sun, Guoyu Wang, Xiaojun Yao, Meng Wang, Jiawei Li, Hongyu Duan, Zhenyu Xie, Ruiyi Fan and Yang Yang

Water 2023, 15(18), 3222; https://doi.org/10.3390/w15183222 - 10 Sep 2023

Cited by 3 | Viewed by 1194

Abstract

Runoff from the high-cold mountains area (HCMA) is the most important water resource in the arid zone, and its accurate forecasting is key to the scientific management of water resources downstream of the basin. Constrained by the scarcity of meteorological and hydrological stations in the HCMA and the inconsistency of the observed time series, the simulation and reconstruction of mountain runoff have always been a focus of cold region hydrological research. Based on the runoff observations of the Yurungkash and Kalakash Rivers, the upstream tributaries of the Hotan River on the northern slope of the Kunlun Mountains at different time periods, and the meteorological and atmospheric circulation indices, we used feature analysis and machine learning methods to select the input elements, train, simulate, and select the preferences of the machine learning models of the runoffs of the two watersheds, and reconstruct the missing time series runoff of the Kalakash River. The results show the following. (1) Air temperature is the most important driver of runoff variability in mountainous areas upstream of the Hotan River, and had the strongest performance in terms of the Pearson correlation coefficient (ρ_XY) and random forest feature importance (FI) (ρ_XY = 0.63, FI = 0.723), followed by soil temperature (ρ_XY = 0.63, FI = 0.043), precipitation, hours of sunshine, wind speed, relative humidity, and atmospheric circulation were weakly correlated. A total of 12 elements were selected as the machine learning input data. (2) Comparing the results of the Yurungkash River runoff simulated by eight machine learning methods, we found that the gradient boosting and random forest methods performed best, followed by the AdaBoost and Bagging methods, with Nash–Sutcliffe efficiency coefficients (NSE) of 0.84, 0.82, 0.78, and 0.78, while the support vector regression (NSE = 0.68), ridge (NSE = 0.53), K-nearest neighbor (NSE = 0.56), and linear regression (NSE = 0.51) were simulated poorly. (3) The application of four machine learning methods, gradient boosting, random forest, AdaBoost, and bagging, to simulate the runoff of the Kalakash River for 1978–1998 was generally outstanding, with the NSE exceeding 0.75, and the results of reconstructing the runoff data for the missing period (1999–2019) could well reflect the characteristics of the intra-annual and inter-annual changes in runoff. Full article

(This article belongs to the Special Issue Water Management in Arid and Semi-arid Regions)

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19 pages, 6334 KiB

Open AccessArticle

Spatially Consistent Drought Hazard Modeling Approach Applied to West Africa

by Catherine Araujo Bonjean, Abdoulaye Sy and Marie-Eliette Dury

Water 2023, 15(16), 2935; https://doi.org/10.3390/w15162935 - 14 Aug 2023

Viewed by 871

Abstract

A critical stage in drought risk assessment is the measurement of drought hazard, the probability of occurrence of a potentially damaging event. The standard approach to assess drought hazard is based on the standardized precipitation index (SPI) and a drought intensity classification established according to a fixed set of SPI values. We show that this method does not allow for the assessment of region-specific hazards, and we propose an alternative method based on the extreme value theory. We model precipitation using an extreme value mixture model, with a normal distribution for the bulk, and a generalized Pareto distribution for the upper and lower tails. The model estimation allows us to identify the threshold value below which precipitation can be qualified as extreme. The quantile function is used to measure the intensity of each category of droughts and calculate the drought hazard index (DHI). By construction, the DHI value varies according to the specific characteristics of the left tail of the precipitation distribution. To test the relevance of our approach, we estimate the DHI over a gridded set of rainfall data covering West Africa, a large and climatically heterogeneous region. The results show that our mixture model fits the data better than the model used for SPI calculation. In particular, our model performs better to identify extreme precipitation in the left tail of the distribution. The DHI map highlights clusters of high drought hazard located in the central part of the region under study. Full article

(This article belongs to the Special Issue Water Management in Arid and Semi-arid Regions)

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