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Climate and Water: Impacts of Climate Change on Hydrological Processes...
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Climate and Water: Impacts of Climate Change on Hydrological Processes and Water Resources

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

Deadline for manuscript submissions: closed (20 September 2024) | Viewed by 22314

Special Issue Editors

Prof. Jianhua Xu

E-Mail Website
Guest Editor
School of Geographical Science, East China Normal University, Shanghai, China
Interests: climate change; climate-related hydrological processes; water resources; geographic modeling; GIS
Dr. Zhongsheng Chen

E-Mail Website
Guest Editor
School of Geographical Sciences, China West Normal University, Nanchong, China
Interests: hydroclimate; hydrology; water resources; geographic modeling; GIS

Special Issue Information

Dear Colleagues,

Climate change has become a serious problem around the world. With global and regional climate change, the water cycle and hydrological processes are gradually being changed, which not only affect the natural ecosystem, but also bring great challenges to the utilization of human water resources. Global warming and regional climate change have changed the original atmospheric circulation model, the thermodynamic process of the hydrological cycle, and the space–time process of precipitation and evapotranspiration, thus further affecting the water resources available to human beings and ecosystems. Over the past 20 years, scientists have conducted a lot of research on the impact of climate change on hydrological processes and water resources. However, there is still a lack of systematic understanding of issues such as the mechanism of climate-related hydrological process and the impact of future climate change on the security of water resources. We organize this Special Issue to call on colleagues to study the impact of climate change on water resources from various perspectives, using various methods and technologies, and make contributions to the systematic understanding of the response of hydrological processes to climate change.

Prof. Jianhua Xu
Dr. Zhongsheng Chen
Guest Editors

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Keywords

  • climate change
  • hydrological cycle
  • climate-related hydrological processes
  • distributed model
  • statistical analysis
  • water resource and its utilization
  • future scenario simulation
  • remote sensing application
  • GIS application

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Published Papers (8 papers)

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Research

20 pages, 9220 KiB  
Article
Research on the Characteristics of Raindrop Spectrum and Its Water Vapour Transport Sources in the Southwest Vortex: A Case Study of 15–16 July 2021
by Ting Wang, Maoshan Li, Ming Gong, Yuchen Liu, Yonghao Jiang, Pei Xu, Yaoming Ma and Fanglin Sun
Water 2024, 16(6), 837; https://doi.org/10.3390/w16060837 - 14 Mar 2024
Viewed by 1063
Abstract
This study investigated the convective weather features, precipitation microphysical characteristics, and water vapour transport characteristics associated with a southwest vortex precipitation event that occurred on the eastern edge of the Qinghai–Tibet Plateau, coinciding with a southwest vortex event, from 15 to 16 July [...] Read more.
This study investigated the convective weather features, precipitation microphysical characteristics, and water vapour transport characteristics associated with a southwest vortex precipitation event that occurred on the eastern edge of the Qinghai–Tibet Plateau, coinciding with a southwest vortex event, from 15 to 16 July 2021, using conventional observations of raindrop spectra, ERA5 reanalysis data, CMORPH precipitation data, and the HYSPLIT_v4 backward trajectory model. The findings aim to provide theoretical insights for improving the forecasting and numerical simulations of southwest vortex precipitation events. The findings revealed that the precipitation event induced by the southwestern vortex at Emeishan Station on 15–16 July 2021 was characterised by high rainfall intensity and significant precipitation accumulation. The raindrop spectrum exhibited a broad distribution with a notable bimodal structure. Both the Sichuan Basin and the Tibetan Plateau were dominated by the South Asian high pressure at higher altitudes, while a pronounced low-pressure system developed at mid and low altitudes within the basin, establishing a meteorological context marked by upper-level divergence and lower-level convergence. Throughout the event, notable vertical uplift velocities were recorded across the Sichuan Basin and Tibetan Plateau, along with distinct positive vorticity zones in the lower and middle strata of the Sichuan Basin, indicating that the atmosphere was in a state of thermal instability. The majority of moisture was in the mid and lower troposphere with evident convergence movements, which played a crucial role in the southwest vortex’s development. WRF numerical simulations of the Emeishan precipitation event more accurately modelled the weather conditions for this precipitation but tended to overestimate the level of precipitation. It was observed that the region around Emei Mountain primarily received moisture influx from the southern Bay of Bengal and the South China Sea, with moisture transport chiefly originating from the Sichuan Basin and in a south-westward trajectory. Full article
(This article belongs to the Special Issue Climate and Water: Impacts of Climate Change on Hydrological Processes and Water Resources)
►▼ Show Figures

Figure 1

Figure 1
<p>Topography of the study area and location of the observation point.</p> Full article ">Figure 2
<p>Model area map.</p> Full article ">Figure 3
<p>Raindrop spectra and gamma fits for 15–16 July 2021 at Emeishan Station. Raindrop spectra of different precipitation cloud types and particle diameter contribution to concentration NT and rainfall rate R for 15–16 July 2021 at Emeishan Station ((<b>a</b>) is the raindrop spectra and gamma fit, (<b>b</b>) is the raindrop spectra for different precipitation cloud types, and (<b>c</b>) is the contributions).</p> Full article ">Figure 4
<p>Map of the potential height field from 00:00 on 15 July 2021 to 23:00 on 16 July 2021 ((<b>a</b>) is the 200 hpa potential height field, (<b>b</b>) is the 500 hpa potential height field, and the black arrows are the wind field maps).</p> Full article ">Figure 5
<p>The 500 hpa and 700 hpa potential height field at 08:00 on 16 July 2021, and the 500 hpa and 700 hpa potential height field at 20:00 on 16 July 2021 ((<b>a</b>) is the 700 hpa height field at 08:00, (<b>b</b>) is the 700 hpa height field at 20:00, (<b>c</b>) is the 500 hpa height field at 08:00, (<b>d</b>) is the 500 hpa height field at 20:00; the yellow line and black line is the height field, and the red arrows are the wind field diagram).</p> Full article ">Figure 6
<p>Mean vertical velocity profile along 30° N on 15–16 July 2021. (<b>a</b>) Vertical velocity profile along 30° N on 15–16 July 2021; (<b>b</b>) vertical velocity profile along 30° N at 08:00 on 16 July 2021 (vector arrows: 10 m/s; topographic maps of the Tibetan Plateau and the Sichuan Basin on the black background).</p> Full article ">Figure 7
<p>Equivalent potential temperature profile and relative vorticity profile along 30° N at 08:00 on 15 July 2021 and 08:00 on 16 July 2021 ((<b>a</b>) the equivalent potential temperature profile at 08:00 on 15 July 2021 (unit: K); (<b>b</b>) the relative vorticity profile at 08:00 on 15 July 2021 (unit: 10<sup>−4</sup> s<sup>−1</sup>); (<b>c</b>) the equivalent potential temperature profile at 08:00 on 16 July 2021 (unit: K); and (<b>d</b>) the relative vorticity profile at 08:00 on 16 July 2021 (unit: 10<sup>−4</sup> s<sup>−1</sup>)).</p> Full article ">Figure 8
<p>Longitude pressure vertical profile of average specific humidity from 15 to 16 July 2021 (unit: g kg<sup>−1</sup>).</p> Full article ">Figure 9
<p>Radial and latitudinal mean water vapour flux maps ((<b>a</b>) the latitudinal mean water vapour flux map; (<b>b</b>) the radial mean water vapour flux map).</p> Full article ">Figure 10
<p>The 700 hpa and 500 hpa wind field and water vapour flux dispersion diagrams ((<b>a</b>) 700 hpa wind field and water vapour flux dispersion diagram; (<b>b</b>) 500 hpa wind field and water vapour flux dispersion diagram; vector arrows in m/s; colour-filled graph in 10 × 10<sup>−4</sup> kg/(m<sup>2</sup>∙s∙hPa)).</p> Full article ">Figure 11
<p>(<b>a</b>) is a map of the distribution of the low-level wind field (arrows; units:m/s) and the maximum rate of ascent (filled graph; units:m/s) for the simulation of 15–16 July. (<b>b</b>) is a map of the distribution of mean specific humidity (contours; units:g/kg) and water vapour flux dispersion (filled graph; units: 10 × 10<sup>−6</sup> kg/(m<sup>2</sup>∙s)).</p> Full article ">Figure 12
<p>The 48 h cumulative precipitation map from the 15th to the 16th ((<b>a</b>) the CMORPH precipitation map; (<b>b</b>) the WRF simulated precipitation map, unit: mm).</p> Full article ">Figure 13
<p>Hourly precipitation of CMORPH data and WRF numerical model at Emeishan Station from 15 to 16 July 2021.</p> Full article ">Figure 14
<p>Backward trajectories of 1000 m, 1500 m, and 3000 m at 240 h on 15 July 2021 for Linzhi Station; 500 m, 1000 m, and 1500 m at 240 h on 15 July 2021 for Emeishan Station; and 100 m, 500 m, and 1000 m at 240 h on 15 July 2021 for Yushu Station and Naqu Station (The black five-pointed star in the figure represents the station, and the different coloured lines represent the different water vapour transport channels. (<b>a</b>) Linzhi Station; (<b>b</b>) Emeishan Station; (<b>c</b>) Naqu Station; (<b>d</b>) Yushu Station).</p> Full article ">Figure 15
<p>Backward trajectory plot of 1500 m at 240 h on 15 July 2021 for Linzhi and Emeishan stations, as well as a cluster analysis plot of 1500 m at 72 h on 15 July 2021 at Emeishan Station (The black pentagram in figure a and the red triangular star in figure b both represent stations. (<b>a</b>) the backward trajectory plot, where the blue trajectory is for Linzhi Station and the red trajectory is for Emeishan Station; (<b>b</b>) the cluster analysis plot).</p> Full article ">
19 pages, 7514 KiB  
Article
Assessing Changes in Exceptional Rainfall in Portugal Using ERA5-Land Reanalysis Data (1981/1982–2022/2023)
by Luis Angel Espinosa, Maria Manuela Portela and Salem Gharbia
Water 2024, 16(5), 628; https://doi.org/10.3390/w16050628 - 20 Feb 2024
Cited by 4 | Viewed by 1947
Abstract
This research examines the intricate changes in the number of occurrences and cumulative rainfall of exceptional events in Portugal spanning 42 hydrological years (from 1981/1982 to 2022/2023). The study has two primary objectives: assessing the hydrological spatial dynamics of a region susceptible to [...] Read more.
This research examines the intricate changes in the number of occurrences and cumulative rainfall of exceptional events in Portugal spanning 42 hydrological years (from 1981/1982 to 2022/2023). The study has two primary objectives: assessing the hydrological spatial dynamics of a region susceptible to climate-induced variations in exceptional rainfall and evaluating the proficiency of a ERA5-Land reanalysis rainfall dataset in capturing exceptional rainfall. Confronting methodological and data-related challenges (e.g., incomplete record series), the investigation uses continuous daily ERA5-Land rainfall series. Validation against the Sistema Nacional de Informação de Recursos Hídricos (SNIRH) and the Portuguese Institute for Sea and Atmosphere (IPMA) ensures the reliability of ERA5-Land data. Empirical non-exceedance probability curves reveal a broad consensus between reanalysis data and observational records, establishing the dataset’s suitability for subsequent analysis. Spatial representations of occurrences, cumulative rainfall, and rainfall intensity of events above thresholds throughout the overall 42-year period and two subperiods (late: 1981/1982–2001/2002; and recent: 2002/2003–2022/2023) are presented, illustrating spatial and temporal variations. A noteworthy shift in the spatial distribution of intense events from south to north is observed, emphasising the dynamism of such hydrological processes. The study introduces a novel dimension with a severity heat map, combining some key findings from the occurrences and cumulative rainfall through subperiods. This study significantly contributes to the understanding of hydrological dynamics in Portugal, providing valuable insights for risk management and the development of sustainable strategies tailored to the evolving patterns of exceptional rainfall. Full article
(This article belongs to the Special Issue Climate and Water: Impacts of Climate Change on Hydrological Processes and Water Resources)
►▼ Show Figures

Figure 1

Figure 1
<p>Geographical features of Portugal. (<b>a</b>) Elevation map in metres above sea level (masl), displaying the spatial distribution of the 1012 ERA5-Land grid points represented by bullets, with stars indicating the nine selected validation grid points. The validation points are identified by their codes (P), followed by the names of the regions or urban areas they are located in. (<b>b</b>) Annual average rainfall for the overall period (1981/1982–2022/2023) based on the 1012 ERA5-Land rainfall series.</p> Full article ">Figure 2
<p>Empirical non-exceedance probability curves corresponding to daily rainfall values from the ERA5-Land reanalysis dataset (dashed lines) and the SNIRH and IPMA meteorological stations (solid lines) during the validation period from 1 January 1980 to 31 December 2021—accounting for complete reanalysis data series and variable record availability in the meteorological station series.</p> Full article ">Figure 3
<p>Mean annual number of daily rainfall occurrences above the threshold (based on ERA5-Land data), in days per year, for the overall period from 1 October 1981 to 30 September 2023.</p> Full article ">Figure 4
<p>Mean annual number of daily rainfall occurrences above the threshold (based on ERA5-Land data), in days per year, for the late subperiod from 1 October 1981 to 30 September 2002.</p> Full article ">Figure 5
<p>Mean annual number of daily rainfall occurrences above the threshold (based on ERA5-Land data), in days per year, for the recent subperiod from 1 October 2002 to 30 September 2023.</p> Full article ">Figure 6
<p>Mean annual cumulative rainfall above the threshold (based on ERA5-Land data), in millimetres per year, for the overall period from 1 October 1981 to 30 September 2023.</p> Full article ">Figure 7
<p>Mean annual cumulative rainfall above the threshold (based on ERA5-Land data), in millimetres per year, for the late subperiod from 1 October 1981 to 30 September 2002.</p> Full article ">Figure 8
<p>Mean annual cumulative rainfall above the threshold (based on ERA5-Land data), in millimetres per year, for the recent subperiod from 1 October 2002 to 30 September 2023.</p> Full article ">Figure 9
<p>Mean annual rainfall intensity of events above threshold (based on ERA5-Land data), in millimetres per day, for the overall period from 1 October 1981 to 30 September 2023.</p> Full article ">Figure 10
<p>Mean annual rainfall intensity of events above threshold (based on ERA5-Land data), in millimetres per day, for the late subperiod from 1 October 1981 to 30 September 2002.</p> Full article ">Figure 11
<p>Mean annual rainfall intensity of events above threshold (based on ERA5-Land data), in millimetres per day, for the recent subperiod from 1 October 2002 to 30 September 2023.</p> Full article ">Figure 12
<p>Severity heat map of exceptional rainfall at different thresholds. The severity <math display="inline"><semantics> <mrow> <mn>2</mn> <mo>×</mo> <mn>2</mn> </mrow> </semantics></math> matrix relates to the mean annual number of occurrences of exceptional rainfall, <math display="inline"><semantics> <msub> <mi>N</mi> <mrow> <mi>m</mi> <mi>e</mi> <mi>a</mi> <mi>n</mi> </mrow> </msub> </semantics></math>, and the mean annual cumulative rainfall above the threshold, <math display="inline"><semantics> <msub> <mi>P</mi> <mrow> <mi>m</mi> <mi>e</mi> <mi>a</mi> <mi>n</mi> </mrow> </msub> </semantics></math>. The matrix intends to quantify the relative changes in the potential harmfulness of the exceptional events from the late subperiod (1981/1982–2001/2002) and the recent subperiod (2002/2003–2022/2023).</p> Full article ">
16 pages, 3698 KiB  
Article
Impact of Soil Surface Temperature on Changes in the Groundwater Level
by Mukhamadkhan Khamidov, Javlonbek Ishchanov, Ahmad Hamidov, Ermat Shermatov and Zafar Gafurov
Water 2023, 15(21), 3865; https://doi.org/10.3390/w15213865 - 6 Nov 2023
Cited by 4 | Viewed by 4203
Abstract
The relationship between the soil surface temperature and groundwater level is complex and influenced by various factors. As the soil surface temperature increases, water evaporates quickly from the soil, which can lead to a decrease in the groundwater level. In this study, we [...] Read more.
The relationship between the soil surface temperature and groundwater level is complex and influenced by various factors. As the soil surface temperature increases, water evaporates quickly from the soil, which can lead to a decrease in the groundwater level. In this study, we analyzed the impact of soil surface temperature on changes in the groundwater level in the Bukhara region of Uzbekistan using data from 1991 to 2020. The Bukhara region experiences regular water shortages, increased soil salinization, and inefficient energy in lift-irrigated areas, which is a typical constellation of challenges to the water–energy–food–environment (WEFE) nexus. The soil surface temperature data were collected from the Hydrometeorological Service Agency, whereas groundwater level data were obtained from the database of the Amelioration Expedition under the Amu-Bukhara Basin Irrigation Systems Authority. We used linear regression analysis and Analysis of Variance (ANOVA) tests to establish the significance of the relationship between the soil surface temperature and groundwater level, as well as the impact of the location of the groundwater level measurements. The results indicate that the model was a good fit to the data, and both the intercept and the soil surface temperature were significant factors that affected groundwater level. The results further suggest that the strength of the relationship between solar radiation and soil surface temperature is very high, with a correlation coefficient of 0.840. This means that when solar radiation increases, soil surface temperature also tends to increase. The analysis also showed that 53.5% of the changes in groundwater level were observed by the regression model, indicating a moderately correlated relationship between the groundwater level and soil surface temperature. Finally, higher solar radiation leads to higher soil surface temperature and higher evapotranspiration rates, which can lead to a decrease in groundwater level. As a result, we observe that the soil surface temperature determines changes in the groundwater level in the study region. Full article
(This article belongs to the Special Issue Climate and Water: Impacts of Climate Change on Hydrological Processes and Water Resources)
►▼ Show Figures

Figure 1

Figure 1
<p>Location of the study area.</p> Full article ">Figure 2
<p>Change of annual average air temperature in Bukhara region from 1991 to 2020.</p> Full article ">Figure 3
<p>Changes in annual and growing season precipitation in Bukhara during 1991–2020.</p> Full article ">Figure 4
<p>Changes in groundwater level during the annual and growing season in Bukhara from 1991 to 2020.</p> Full article ">Figure 5
<p>The relationship between solar radiation and soil surface temperature in the Bukhara region of Uzbekistan.</p> Full article ">Figure 6
<p>The relationship between soil surface temperature and groundwater level in the Bukhara region of Uzbekistan.</p> Full article ">
25 pages, 5557 KiB  
Article
A General Method to Improve Runoff Prediction in Ungauged Basins Based on Remotely Sensed Actual Evapotranspiration Data
by Ziling Gui, Feng Zhang, Da Chang, Aili Xie, Kedong Yue and Hao Wang
Water 2023, 15(18), 3307; https://doi.org/10.3390/w15183307 - 19 Sep 2023
Cited by 2 | Viewed by 3132
Abstract
The availability of remotely sensed (RS) actual evapotranspiration (ET) provides a possibility for improving runoff prediction in ungauged basins. To develop a general practical method to improve runoff prediction by directly incorporating RS-ET into rainfall-runoff (RR) models, two modeling schemes are proposed: (i) [...] Read more.
The availability of remotely sensed (RS) actual evapotranspiration (ET) provides a possibility for improving runoff prediction in ungauged basins. To develop a general practical method to improve runoff prediction by directly incorporating RS-ET into rainfall-runoff (RR) models, two modeling schemes are proposed: (i) using RS-ET as direct input; and (ii) using RS-ET as partial direct input. The principle is to use RS-ET in cases where the runoff prediction can be improved. The two schemes are compared in over 200 basins using three RR models (Xinanjiang model, SIMHYD, and GR4J) and RS-ET inverted from AVHRR, and the modeling results in ungauged basins are assessed using the spatial proximity method. Results show that: (i) it is beneficial to incorporate RS-ET into the Xinanjiang model for over 85% of the basins, but this is not the case for SIMHYD and GR4J models; (ii) further model improvements can be obtained by using RS-ET as partial direct input, and are achieved in 91.1%, 59.0%, and 53.2% of the basins for Xinanjiang, SIMHYD, and GR4J, respectively; and (iii) incorporation of RS-ET is more applicable for Xinanjiang while less so for GR4J, and the efficacy is superior for basins that are relatively arid and were originally poorly simulated. Overall, using RS-ET as partial direct input is recommended. Full article
(This article belongs to the Special Issue Climate and Water: Impacts of Climate Change on Hydrological Processes and Water Resources)
►▼ Show Figures

Figure 1

Figure 1
<p>Spatial distribution of 208 MOPEX basins used in this study and their average annual precipitation (mm).</p> Full article ">Figure 2
<p>Schematic of the methodology of runoff prediction in ungauged basins based on RS-ET.</p> Full article ">Figure 3
<p>Schematic overview of the Xinanjiang [<a href="#B39-water-15-03307" class="html-bibr">39</a>], SIMHYD [<a href="#B40-water-15-03307" class="html-bibr">40</a>], and GR4J [<a href="#B41-water-15-03307" class="html-bibr">41</a>] models.</p> Full article ">Figure 3 Cont.
<p>Schematic overview of the Xinanjiang [<a href="#B39-water-15-03307" class="html-bibr">39</a>], SIMHYD [<a href="#B40-water-15-03307" class="html-bibr">40</a>], and GR4J [<a href="#B41-water-15-03307" class="html-bibr">41</a>] models.</p> Full article ">Figure 4
<p>Relationship of the relative performances (Δ<span class="html-italic">NSE<sub>Q</sub></span><sub>2−1</sub> = <span class="html-italic">NSE<sub>Q</sub></span><sub>2</sub> − <span class="html-italic">NSE<sub>Q</sub></span><sub>1</sub>) between Scheme 2 and Scheme 1 versus <span class="html-italic">NSE<sub>E</sub></span> of Scheme 1 in the calibration period. The percent below the vertical dashed line is the different <span class="html-italic">NSE<sub>thr</sub></span> value, and the number in the bracket is the number of basins with the corresponding <span class="html-italic">NSE<sub>thr</sub></span> exceeded. The percent above the vertical dashed line is the basin percent of Scheme 2 surpassing Scheme 1 when the corresponding <span class="html-italic">NSE<sub>thr</sub></span> is exceeded.</p> Full article ">Figure 4 Cont.
<p>Relationship of the relative performances (Δ<span class="html-italic">NSE<sub>Q</sub></span><sub>2−1</sub> = <span class="html-italic">NSE<sub>Q</sub></span><sub>2</sub> − <span class="html-italic">NSE<sub>Q</sub></span><sub>1</sub>) between Scheme 2 and Scheme 1 versus <span class="html-italic">NSE<sub>E</sub></span> of Scheme 1 in the calibration period. The percent below the vertical dashed line is the different <span class="html-italic">NSE<sub>thr</sub></span> value, and the number in the bracket is the number of basins with the corresponding <span class="html-italic">NSE<sub>thr</sub></span> exceeded. The percent above the vertical dashed line is the basin percent of Scheme 2 surpassing Scheme 1 when the corresponding <span class="html-italic">NSE<sub>thr</sub></span> is exceeded.</p> Full article ">Figure 5
<p>Comparison of the <span class="html-italic">NSE<sub>Q</sub></span> values among the three schemes for three RR models computed on 208 basins. The red line in the boxplots represents the median value, the ends of the boxes represent the 1st and 3rd quartiles, the whiskers represent the values at 1.5 standard deviations, and outliers (more than 1.5 standard deviations from the mean) are shown as red crosses.</p> Full article ">Figure 6
<p>Comparison of the relative performances (Δ<span class="html-italic">NSE<sub>Q</sub></span>) of Scheme 2 and Scheme 3 versus Scheme 1 for three RR models computed on 208 basins. The orange bar represents the average value of increased <span class="html-italic">NSE<sub>Q</sub></span> (positive Δ<span class="html-italic">NSE<sub>Q</sub></span>), the blue bar represents the average value of decreased <span class="html-italic">NSE<sub>Q</sub></span> (negative Δ<span class="html-italic">NSE<sub>Q</sub></span>), the bar filled with diagonals represents the average net Δ<span class="html-italic">NSE<sub>Q</sub></span> value, and the three RR models (Xinanjiang, SIMHYD, and GR4J) are referred to as ‘X’, ‘S’ and ‘G’.</p> Full article ">Figure 7
<p>Comparison of simulated streamflow hydrographs between Scheme 1 and Scheme 2 in regionalization for the representative basins with <span class="html-italic">NSE<sub>E</sub></span> &gt; <span class="html-italic">NSE<sub>thr</sub></span> and <span class="html-italic">NSE<sub>E</sub></span> &lt; <span class="html-italic">NSE<sub>thr</sub></span> for three RR models. Only a representative segment from the whole simulation period is shown here.</p> Full article ">Figure 7 Cont.
<p>Comparison of simulated streamflow hydrographs between Scheme 1 and Scheme 2 in regionalization for the representative basins with <span class="html-italic">NSE<sub>E</sub></span> &gt; <span class="html-italic">NSE<sub>thr</sub></span> and <span class="html-italic">NSE<sub>E</sub></span> &lt; <span class="html-italic">NSE<sub>thr</sub></span> for three RR models. Only a representative segment from the whole simulation period is shown here.</p> Full article ">Figure 8
<p>Comparison of <span class="html-italic">WBI</span> values among the three schemes for the three RR models computed on 208 basins. The red line in the boxplots represents the median value, the ends of the boxes represent the 1st and 3rd quartiles, the whiskers represent the values at 1.5 standard deviations, and outliers (more than 1.5 standard deviations from the mean) are shown as red crosses.</p> Full article ">Figure 9
<p>Relationship between the │<span class="html-italic">WBI</span>│ values of Scheme 2 versus (<b>a</b>) <span class="html-italic">NSE<sub>E</sub></span> of Scheme 1 and (<b>b</b>) <span class="html-italic">NSE<sub>Q</sub></span> of Scheme 2 in the validation period.</p> Full article ">Figure 9 Cont.
<p>Relationship between the │<span class="html-italic">WBI</span>│ values of Scheme 2 versus (<b>a</b>) <span class="html-italic">NSE<sub>E</sub></span> of Scheme 1 and (<b>b</b>) <span class="html-italic">NSE<sub>Q</sub></span> of Scheme 2 in the validation period.</p> Full article ">Figure 10
<p>Relationship between (<b>a</b>) the <span class="html-italic">NSE<sub>Q</sub></span> values of Scheme 1 and (<b>b</b>) the NSEQ values of Scheme 3 versus the average annual precipitation (mm) in validation and regionalization.</p> Full article ">Figure 11
<p>Relationship between Δ<span class="html-italic">NSE<sub>Q3</sub></span><sub>−1</sub> (<span class="html-italic">NSE<sub>Q</sub></span><sub>3</sub> − <span class="html-italic">NSE<sub>Q</sub></span><sub>1</sub>) versus the (<b>a</b>) average annual precipitation (mm) and (<b>b</b>) <span class="html-italic">NSE<sub>Q</sub></span> of Scheme 1 in validation and regionalization.</p> Full article ">
15 pages, 4857 KiB  
Article
Long-Term Variability of the Hydrological Regime and Its Response to Climate Warming in the Zhizdra River Basin of the Eastern European Plain
by Bing Bai, Qiwei Huang, Ping Wang, Shiqi Liu, Yichi Zhang, Tianye Wang, Sergey P. Pozdniakov, Natalia L. Frolova and Jingjie Yu
Water 2023, 15(15), 2678; https://doi.org/10.3390/w15152678 - 25 Jul 2023
Cited by 1 | Viewed by 1388
Abstract
Climate warming globally has a profound effect on the hydrological regime, amplifying evapotranspiration and precipitation and accelerating the processes of snow melt and permafrost thaw. However, in the context of small river basins—those encompassing less than 10,000 km2—the response of the [...] Read more.
Climate warming globally has a profound effect on the hydrological regime, amplifying evapotranspiration and precipitation and accelerating the processes of snow melt and permafrost thaw. However, in the context of small river basins—those encompassing less than 10,000 km2—the response of the hydrological regime to climate change is intricate and has not yet been thoroughly understood. In this study, the Zhizdra River Basin, a typical small river basin in the eastern European plain with a total drainage area of 6940 km2, was selected to investigate the long-term variability of the hydrological regime and its responses to climate warming. Our results show that during the period of 1958–2016, the average runoff in the Zhizdra River Basin was approximately 170 mm, with significant fluctuations but no trend. Sensitivity analysis by the Budyko framework revealed that the runoff was more sensitive to changes in precipitation (P) compared to potential evapotranspiration (E0), implying that the Zhizdra River Basin is limited by water availability and has a slightly dry trend. A comprehensive analysis based on the seasonality of hydrometeorological data revealed that temperature predominantly affects spring runoff, while P mainly controls autumn runoff. Both factors make significant contributions to winter runoff. In response to climate change, the nonuniformity coefficient (Cv) and concentration ratio (Cn) of runoff have noticeably declined, indicating a more stabilized and evenly distributed runoff within the basin. The insights gleaned from this research illuminate the complex hydrological responses of small river basins to climate change, underlining the intricate interrelation among evapotranspiration, precipitation, and runoff. This understanding is pivotal for efficient water resource management and sustainable development in the era of global warming. Full article
(This article belongs to the Special Issue Climate and Water: Impacts of Climate Change on Hydrological Processes and Water Resources)
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<p>Geographic location of the Zhizdra River Basin (EEP refers to the East European Plain).</p> Full article ">Figure 2
<p>Time series of (<b>a</b>) precipitation (<span class="html-italic">P</span>), (<b>b</b>) temperature (T), (<b>c</b>) potential evapotranspiration (<span class="html-italic">E</span><sub>0</sub>), and (<b>d</b>) evapotranspiration (<span class="html-italic">E</span>). The time series for (<b>a</b>–<b>d</b>) spans 1958 to 2016. The black solid line represents the annual average value of meteorological elements. (<b>e</b>) The monthly average temperature and precipitation in the Zhizdra River Basin from 1958 to 2016.</p> Full article ">Figure 3
<p>(<b>a</b>) Annual average runoff depth (mm) and 11-year moving average for the Zhizdra River Basin from 1958 to 2016 and (<b>b</b>) monthly average runoff depth (mm) for 1958–2016 in the Zhizdra River Basin.</p> Full article ">Figure 4
<p>Nonuniformity index (<b>a</b>) and concentration ratio (<b>b</b>) of the annual runoff distribution.</p> Full article ">Figure 5
<p>Sliding <span class="html-italic">t</span>-test for (<b>a</b>) runoff (<span class="html-italic">Q</span>), (<b>b</b>) temperature (<span class="html-italic">T</span>), (<b>c</b>) potential evapotranspiration (<span class="html-italic">E</span><sub>0</sub>), (<b>d</b>) evapotranspiration (<span class="html-italic">E</span>), and (<b>e</b>) precipitation (<span class="html-italic">P</span>). The time series spans 1958 to 2016. The red line represents 0.05 significance level. The blue line represents the <span class="html-italic">t</span>-value of 0.</p> Full article ">Figure 6
<p>Relationships among the aridity ratio, evapotranspiration index, and underlying surface parameter (n).</p> Full article ">Figure 7
<p>Contour plots of seasonal changes as a function of annual variations in precipitation (<span class="html-italic">P</span>) and potential evapotranspiration (<span class="html-italic">E</span><sub>0</sub>). Q<sub>spr</sub> is the average spring runoff from 1958 to 2016; Q<sub>sum</sub>, Q<sub>aut</sub>, and Q<sub>win</sub> are the average values of summer, autumn, and winter runoff, respectively. ΔE<sub>0</sub> and std(ΔE<sub>0</sub>) are <span class="html-italic">E</span><sub>0</sub> departure from the average annual <span class="html-italic">E</span><sub>0</sub> and its standard deviation; ΔP and std(ΔP) are the relative changes in annual <span class="html-italic">P</span> to the average annual <span class="html-italic">P</span> and its standard deviation; ΔQ is the relative changes in seasonal runoff to their average annual values.</p> Full article ">
18 pages, 12918 KiB  
Article
Spatiotemporal Patterns of Hydrological Variables in Water-Resource Regions of China
by Chao Zang, Huan Liu, Guotao Cui and Jing Liu
Water 2023, 15(9), 1643; https://doi.org/10.3390/w15091643 - 23 Apr 2023
Cited by 1 | Viewed by 2286
Abstract
The spatiotemporal patterns of key hydrological variables across China were illustrated based on the developed Water and Energy Transfer Processes model in China (WEP-CN model). Time series of four key hydrological variables, namely, precipitation (P), runoff (R), infiltration ( [...] Read more.
The spatiotemporal patterns of key hydrological variables across China were illustrated based on the developed Water and Energy Transfer Processes model in China (WEP-CN model). Time series of four key hydrological variables, namely, precipitation (P), runoff (R), infiltration (Inf), and actual evapotranspiration (ETa), were obtained over 60 years. Then, the temporal trends and spatial differences of these variables were analyzed using the Mann-Kendall and linear methods on a national scale and on the water resource regional scale. Moreover, we explored the drivers and constraints for changes in R, Inf, and ETa. The results showed: (1) Based on the coefficient of variations of P (5.24%), R (11.80%), Inf (2.57%), and ETa (3.77%), R was more fluctuating than the other variables. (2) These variables followed a similar trend of gradually decreasing from the southeast coast to the northwest inland. (3) Changes in R and Inf were caused mainly by P, having correlation coefficients with precipitation of 0.74 and 0.73, respectively. The ETa was constrained by a combination of P and energy. The results improved the refined and quantitative research on hydrological processes in China, identified the differences in hydrological variables between water-resource regions, and provided a useful supplement to the research of the large-scale hydrological process. Full article
(This article belongs to the Special Issue Climate and Water: Impacts of Climate Change on Hydrological Processes and Water Resources)
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<p>Distribution of main rivers and Class I WRRs of China.</p> Full article ">Figure 2
<p>Schematic diagram of main components in the WEP-CN model. <span class="html-italic">P</span> is precipitation (mm), <span class="html-italic">ET<sub>a</sub></span> is actual evapotranspiration (mm), <span class="html-italic">E<sub>w</sub></span> is evaporation (mm) of surface interception and waterbody, <span class="html-italic">E<sub>v</sub></span> is vegetation transpiration (mm), <span class="html-italic">E<sub>s</sub></span> includes soil evaporation (mm) and phreatic evaporation (mm); <span class="html-italic">R</span> is runoff depth (mm), including overland flow and base flow; <span class="html-italic">Inf</span> (mm) is infiltration.</p> Full article ">Figure 3
<p>Mean spatial pattern of hydrological variables in China (1956~2017). (<b>a</b>) <span class="html-italic">P</span>; (<b>b</b>) <span class="html-italic">R</span>; (<b>c</b>) <span class="html-italic">Inf</span>, (<b>d</b>) <span class="html-italic">ET<sub>a</sub></span>.</p> Full article ">Figure 4
<p>The linear trend of hydrological variables over China from 1956 to 2017.</p> Full article ">Figure 5
<p>Spatial pattern of results for M-K trend test. (<b>a</b>) <span class="html-italic">Z<sub>p</sub></span>, (<b>b</b>) <span class="html-italic">Z<sub>r</sub></span>, (<b>c</b>) <span class="html-italic">Z<sub>inf</sub></span>, (<b>d</b>) <span class="html-italic">Zet<sub>a</sub></span>.</p> Full article ">Figure 6
<p>Linear regression relationship and point density distribution map. (<b>a</b>): Linear regression relationship between <span class="html-italic">Z<sub>inf</sub></span> and <span class="html-italic">Z<sub>p</sub></span>; (<b>b</b>): Linear regression relationship between <span class="html-italic">Z<sub>r</sub></span> and <span class="html-italic">Z<sub>p</sub></span>.</p> Full article ">Figure 7
<p>Spatial distribution of the mean <span class="html-italic">PET</span>.</p> Full article ">Figure 8
<p>Linear regression relationship between <span class="html-italic">ET<sub>a</sub></span> and <span class="html-italic">P</span> in north and in south Class III WRRs. <span class="html-italic">R</span><sup>2</sup> is the goodness of fit.</p> Full article ">Figure 9
<p>Spatial distribution of M-K trend test for (<b>a</b>) <span class="html-italic">ET<sub>f</sub></span>, (<b>b</b>) <span class="html-italic">ET<sub>g</sub></span>, and (<b>c</b>) <span class="html-italic">E<sub>w</sub></span>; <span class="html-italic">ET</span> is evapotranspiration and <span class="html-italic">E</span> is the evaporation; Subscript <span class="html-italic">f</span>, <span class="html-italic">g</span>, <span class="html-italic">w</span> means forest, grassland, and surface waterbody respectively.</p> Full article ">Figure 10
<p>Spatial distribution of (<b>a</b>) <span class="html-italic">IRWR<sub>d</sub></span> and (<b>b</b>) <span class="html-italic">Z<sub>irwr</sub></span>.</p> Full article ">Figure A1
<p>Annual averages (1956–2017) of <span class="html-italic">IRWR<sub>d</sub></span> and 2019 water consumption.</p> Full article ">
20 pages, 25881 KiB  
Article
The Impacts of Climate Change on the Hydrological Process and Water Quality in the Three Gorges Reservoir Area, China
by Yidian Sun, Wanshun Zhang, Hong Peng, Feng Zhou, Anna Jiang, Xiaomin Chen and Hao Wang
Water 2023, 15(8), 1542; https://doi.org/10.3390/w15081542 - 14 Apr 2023
Cited by 5 | Viewed by 3904
Abstract
With the intensification of climate change, understanding the impacts of climate change on the water cycle is vital for integrated watershed management. Based on the precipitation and temperature data from 1980 to 2018, the climatic change characteristics of the Three Gorges Reservoir Area [...] Read more.
With the intensification of climate change, understanding the impacts of climate change on the water cycle is vital for integrated watershed management. Based on the precipitation and temperature data from 1980 to 2018, the climatic change characteristics of the Three Gorges Reservoir Area were analyzed. The Soil and Water Assessment Tool (SWAT) was used to simulate the spatial and temporal distribution of runoff and water quality. The result indicated that precipitation showed clear inter-annual fluctuation, and the maximum and minimum temperatures showed an increasing trend with rates of 0.38 °C/10a and 0.29 °C/10a, respectively. The moving averages revealed that the annual averages of runoff, total nitrogen (TN), and total phosphorus (TP) loads showed a decreasing trend followed by an increasing trend, which experienced strong inter-annual fluctuations. The hydrological processes changed significantly at different spatial scales, and the most affected area was the middle and head of reservoir area. The highest correlation was found between precipitation and runoff (0.91), followed by TP (0.81), and TN (0.60), while extreme precipitation could result in a high probability of water pollution events. These findings provide useful information to support the utilization of water resources, especially in the face of strong climate change impacts. Full article
(This article belongs to the Special Issue Climate and Water: Impacts of Climate Change on Hydrological Processes and Water Resources)
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<p>Location map of the Three Gorges Reservoir area (TGRA). The Digital Elevation Model (DEM) (<b>a</b>), soil type (<b>b</b>), land use (<b>c</b>), and SWAT sub-basins (<b>d</b>) of the TGRA.</p> Full article ">Figure 2
<p>The calibration and validation results of runoff (<b>a</b>), TN (<b>b</b>), and TP (<b>c</b>) loads.</p> Full article ">Figure 3
<p>The season variation trend (<b>a</b>–<b>c</b>), moving average value (<b>d</b>–<b>f</b>), anomaly change (<b>g</b>–<b>i</b>) and Mann-Kendall test (<b>j</b>–<b>l</b>) of annual precipitation, minimum and maximum temperature.</p> Full article ">Figure 4
<p>The real part (<b>a</b>–<b>c</b>), modular square (<b>d</b>–<b>f</b>) and wavelet variance (<b>g</b>–<b>i</b>) of Morlet wavelet transform coefficient of precipitation, minimum and maximum temperature.</p> Full article ">Figure 5
<p>Spatial distribution of precipitation (<b>a</b>), minimum (<b>b</b>), and maximum temperature (<b>c</b>) in the TGRA.</p> Full article ">Figure 6
<p>The annual distribution of extreme precipitation days (<b>a</b>–<b>d</b>) and extreme temperature days (<b>e</b>–<b>h</b>) from 1980 to 2018 in the TGRA.</p> Full article ">Figure 7
<p>The moving average value, annual, season, and month variation trend of runoff (<b>a</b>–<b>c</b>); TN (<b>d</b>–<b>f</b>); and TP (<b>g</b>–<b>i</b>) loads.</p> Full article ">Figure 8
<p>The spatial distribution differences of runoff (<b>a</b>,<b>d</b>,<b>g</b>,<b>j</b>), TN (<b>b</b>,<b>e</b>,<b>h</b>,<b>k</b>), and TP (<b>c</b>,<b>f</b>,<b>i</b>,<b>l</b>) loads in the TGRA.</p> Full article ">Figure 9
<p>Correlation analysis between precipitation, runoff (<b>a</b>), TN (<b>b</b>), and TP (<b>c</b>) loads in the TGRA.</p> Full article ">Figure 10
<p>Wavelet coherency (<b>a</b>–<b>c</b>) and cross-wavelet spectrum (<b>d</b>–<b>f</b>) analysis of the relations between precipitation, runoff, TN, and TP loads in the TGRA.</p> Full article ">
23 pages, 40810 KiB  
Article
Long-Term Change of Lake Water Storage and Its Response to Climate Change for Typical Lakes in Arid Xinjiang, China
by Zijin Huang, Jianhua Xu and Lilin Zheng
Water 2023, 15(8), 1444; https://doi.org/10.3390/w15081444 - 7 Apr 2023
Cited by 8 | Viewed by 3134
Abstract
Lakes play a role as the sentinel of climate change. Surrounded by vast expanses of barren land with limited infrastructure, there is also a lack of knowledge about the dynamics of dryland lakes. The change of lake area can be effectively monitored by [...] Read more.
Lakes play a role as the sentinel of climate change. Surrounded by vast expanses of barren land with limited infrastructure, there is also a lack of knowledge about the dynamics of dryland lakes. The change of lake area can be effectively monitored by remote sensing, and multi-source satellite altimetry datasets provide the possibility to obtain long-term lake water level data. Using the Global Surface Water Monthly Historical dataset and altimetry water level dataset (Hydroweb), we reconstructed a time series of lake water storage changes in Xinjiang, Northwestern China, by establishing the empirical models based on the statistical relationship between the surface area and water level of each lake. We further explored lake response to climate change. The results show that the storage of water at Ayakkum Lake, Aqqikkol Lake and Aksayquin Lake have been undergoing an obvious expanding trend from 2000 to 2020, at a rate of 3.59×108m3/a, 9.43×108m3/a and 0.44×108m3/a, respectively. In the plain and transition zone, Ulungur Lake showed an upward tendency (0.413×108m3/a) in water storage, while Manas Lake and Bosten Lake experienced shrinkage with descending rates of −0.1×108m3/a and −0.86×108m3/a. Temperature changes significantly affect the lake water storage on plateaus, especially those lakes supplied with a large proportion of glacial meltwater. Precipitation is a key factor for changes of lake storage in the plain and transition zones. Meanwhile, extreme weather and man-made factors also play crucial roles. To reduce the risk of flood and drought disasters, rational regulation of water resources is required, and a large-scale integrated catchment management plan can avoid inadvertent trade-offs. This research provides a new perspective for lake water storage inversion, as well as data support for water resources management in arid areas including Xinjiang. Full article
(This article belongs to the Special Issue Climate and Water: Impacts of Climate Change on Hydrological Processes and Water Resources)
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<p>Distribution of six typical lakes in Xinjiang NO.: GS(2019)3333.</p> Full article ">Figure 2
<p>The quadratic models of lake area and lake water level (<b>a</b>) Ulungur Lake; (<b>b</b>) Manas Lake; (<b>c</b>) Bosten Lake; (<b>d</b>) Ayakkum Lake; (<b>e</b>) Aqqikkol Lake; (<b>f</b>) Aksayquin Lake.</p> Full article ">Figure 3
<p>Annual water level changes (<b>a</b>) Ulungur Lake; (<b>b</b>) Manas Lake; (<b>c</b>) Bosten Lake; (<b>d)</b> Ayakkum Lake; (<b>e</b>) Aqqikkol Lake; (<b>f</b>) Aksayquin Lake.</p> Full article ">Figure 4
<p>Variation of lake area from 2000 to 2020 (<b>a</b>) Ulungur Lake; (<b>b</b>) Manas Lake; (<b>c</b>) Bosten Lake; (<b>d</b>) Ayakkum Lake; (<b>e</b>) Aqqikkol Lake; (<b>f</b>) Aksayquin Lake.</p> Full article ">Figure 5
<p>Annual variation of lake storages in 2000–2020 (<b>a</b>) Ulungur Lake; (<b>b</b>) Manas Lake; (<b>c</b>) Bosten Lake; (<b>d</b>) Ayakkum Lake; (<b>e</b>) Aqqikkol Lake; (<b>f</b>) Aksayquin Lake.</p> Full article ">Figure 6
<p>Statistical curve of the MK method test of accumulative lake storage changes (<b>a</b>) Ulungur Lake; (<b>b</b>) Bosten Lake.</p> Full article ">Figure 7
<p>Variations in equivalent water mass on Xinjiang from GRACE satellite gravimetry.</p> Full article ">Figure 8
<p>Precipitation of six lakes in Xinjiang from 2000 to 2020 (<b>a</b>) Ulungur Lake; (<b>b</b>) Manas Lake; (<b>c</b>) Bosten Lake; (<b>d</b>) Ayakkum Lake; (<b>e</b>) Aqqikkol Lake; (<b>f</b>) Aksayquin Lake.</p> Full article ">Figure 9
<p>Temperature of six lakes in Xinjiang from 2000 to 2020 (<b>a</b>) Ulungur Lake; (<b>b</b>) Manas Lake; (<b>c</b>) Bosten Lake; (<b>d</b>) Ayakkum Lake; (<b>e</b>) Aqqikkol Lake; (<b>f</b>) Aksayquin Lake.</p> Full article ">Figure 10
<p>Water consumption of prefecture-level cities where six lakes are located.</p> Full article ">Figure A1
<p>Precipitation in the upstream basins of lakes from 2000 to 2020 (<b>a</b>) Manas Lake; (<b>b</b>) Bosten Lake.</p> Full article ">Figure A2
<p>Temperature in the upstream basins of lakes from 2000 to 2020 (<b>a</b>) Manas Lake; (<b>b</b>) Bosten Lake.</p> Full article ">Figure A3
<p>Glacier distribution of Xinjiang.</p> Full article ">
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