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Search Results (1,365)

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29 pages, 27723 KiB  
Article
A Geospatial Analysis Approach to Investigate Effects of Wildfires on Vegetation, Hydrological Response, and Recovery Trajectories in a Mediterranean Watershed
by Konstantinos Soulis, Stergia Palli Gravani, Rigas Giovos, Evangelos Dosiadis and Dionissios Kalivas
Hydrology 2025, 12(3), 47; https://doi.org/10.3390/hydrology12030047 - 4 Mar 2025
Abstract
Wildfires are frequently observed in watersheds with a Mediterranean climate and seriously affect vegetation, soil, hydrology, and ecosystems as they cause abrupt changes in land cover. Assessing wildfire effects, as well as the recovery process, is critical for mitigating their impacts. This paper [...] Read more.
Wildfires are frequently observed in watersheds with a Mediterranean climate and seriously affect vegetation, soil, hydrology, and ecosystems as they cause abrupt changes in land cover. Assessing wildfire effects, as well as the recovery process, is critical for mitigating their impacts. This paper presents a geospatial analysis approach that enables the investigation of wildfire effects on vegetation, soil, and hydrology. The prediction of regeneration potential and the period needed for the restoration of hydrological behavior to pre-fire conditions is also presented. To this end, the catastrophic wildfire that occurred in August 2021 in the wider area of Varybobi, north of Athens, Greece, is used as an example. First, an analysis of the extent and severity of the fire and its effect on the vegetation of the area is conducted using satellite imagery. The history of fires in the specific area is then analyzed using remote sensing data and a regrowth model is developed. The effect on the hydrological behavior of the affected area was then systematically analyzed. The analysis is conducted in a spatially distributed form in order to delineate the critical areas in which immediate interventions are required for the rapid restoration of the hydrological behavior of the basin. The period required for the restoration of the hydrological response is then estimated based on the developed vegetation regrowth models. Curve Numbers and post-fire runoff response estimations were found to be quite similar to those derived from measured data. This alignment shows that the SCS-CN method effectively reflects post-fire runoff conditions in this Mediterranean watershed, which supports its use in assessing hydrological changes in wildfire-affected areas. The results of the proposed approach can provide important data for the restoration and protection of wildfire-affected areas. Full article
(This article belongs to the Section Hydrological and Hydrodynamic Processes and Modelling)
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Figure 1

Figure 1
<p>Geographic location of the burn scars created by the 2021 Varybobi wildfire and the adjacent watersheds. The above layers’ creation is described in the methodology section of the present study.</p>
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<p>Flowchart outlining the proposed methodology for the analysis of wildfires hydrological impact and recovery process.</p>
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<p>The CN–Rainfall data of the studied watershed and the fitted lines describing this relationship according to the Two-CN method [<a href="#B14-hydrology-12-00047" class="html-bibr">14</a>] and the Asymptotic CN [<a href="#B85-hydrology-12-00047" class="html-bibr">85</a>]. The corresponding CN values and the areas they cover are also shown.</p>
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<p>Variables used for the implementation of the model: (<b>a</b>) slope, (<b>b</b>) precipitation, (<b>c</b>) NDVI-produced 1-year vegetation regeneration, (<b>d</b>) TPI index, (<b>e</b>) dNBR index, and (<b>f</b>) model’s final regeneration prediction for the year 2031.</p>
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<p>Diachronic evolution of the NDVI index at the sites of the 1986 (red) and 1987 (blue) wildfires.</p>
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<p>NDVI difference between the pre-fire values (2021) and the 10-year regeneration prediction (2031).</p>
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<p>Diachronic evolution of soil–land use complexes, based on CLC data.</p>
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<p>Pre-fire CN ranges in the watersheds studied in the CLC reference years.</p>
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<p>Post-fire CN ranges in the study area after the implementation of the two methods on the 2018 CLC reference year; (<b>a</b>) 5, 10, 15, and 20 unit increases in the runoff CN value according to the burn severity classes [<a href="#B80-hydrology-12-00047" class="html-bibr">80</a>], and (<b>b</b>) post-fire CN values according to [<a href="#B28-hydrology-12-00047" class="html-bibr">28</a>].</p>
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<p>Direct runoff (mm) for the three return periods (5, 50, and 1000 years) estimated for the 2018 CLC reference year pre-fire and post-fire using two methods.</p>
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<p>Graphical illustration of runoff volume (mm) of the three study sub-areas for return periods of 5, 50, and 1000 years, respectively (<b>a</b>–<b>c</b>).</p>
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<p>Direct runoff values estimated with the post-fire CNs obtained by the two examined methods (Est. Direct Runoff—1 and 2 are the direct runoff estimations with method 1 and 2, correspondingly) plotted in comparison with the observed direct runoff.</p>
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22 pages, 13517 KiB  
Article
The Influence of Climate and Hydrological Factors on the Phenological Characteristics of Populus euphratica in the Oasis of the Central Taklamakan Desert
by Yulong Liu, Zhi Wang, Dinghao Li, Yanbo Wan and Qingdong Shi
Forests 2025, 16(3), 447; https://doi.org/10.3390/f16030447 - 2 Mar 2025
Viewed by 211
Abstract
The phenological characteristics of plants can reflect both their responses to environmental changes as well as an ecosystem’s sensitivity to climate change. Although there have been several phenological studies of plant species worldwide, there is minimal research on the phenology of vegetation found [...] Read more.
The phenological characteristics of plants can reflect both their responses to environmental changes as well as an ecosystem’s sensitivity to climate change. Although there have been several phenological studies of plant species worldwide, there is minimal research on the phenology of vegetation found in extremely arid environments within the context of climate change. To address this research gap, this study investigated the effects of climate–hydrological factors, including temperature, precipitation, surface temperature, and surface humidity, on the phenological characteristics (start of the growing season [SOS] and end of the growing season [EOS]) of Populus euphratica in the Tarim Desert Oasis. Using Landsat 7/8 satellite imagery and field data, we analyzed the spatial and temporal variations in the SOS and EOS from 2004 to 2023. The availability of water, particularly changes in groundwater depth and surface water, directly played a key role in shaping the spatial distribution and temporal dynamics of P. euphratica phenology. The impact of increasing temperatures on P. euphratica phenology varied under different moisture conditions: in high-moisture environments, increased temperatures promoted earlier SOS and delayed EOS, with the opposite conditions occurring in low-moisture environments. This study highlights the profound influence of moisture conditions on P. euphratica phenology in the context of climate change, especially in extreme arid regions. To accurately predict the response of P. euphratica phenology to climate change, future ecological models should incorporate hydrological factors, particularly changes in soil moisture, in cold and dry regions. These findings provide important insights for developing effective ecological protection and management strategies. Full article
(This article belongs to the Section Forest Meteorology and Climate Change)
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Figure 1
<p>An overview map of the study area. The triangles indicate the locations of phenological cameras, red dots show the positions of groundwater monitoring wells, and the yellow areas represent long-term phenological observation plots used to validate the accuracy of satellite-based phenological inversion. The numerical labels represent the plot numbers for each sampling site. (<b>a</b>) Oasis image, Sentinel-2 image with bands 8/4/3. (<b>b</b>) Location of the study area. (<b>c</b>) Phenological observation Plot 1. (<b>d</b>) Phenological observation Plot 2. (<b>e</b>) Phenological observation Plot 3.</p>
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<p>Annual average groundwater depth in the Darya Yabui Oasis (<b>a</b>). Surface water frequency over 20 years (<b>b</b>). Average surface humidity over 20 years (<b>c</b>).</p>
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<p>Seasonal dynamics of vegetation indices from Sentinel-2 satellite for (<b>a</b>) Plot 1, (<b>b</b>) Plot 2, and (<b>c</b>) Plot 3 phenology sample plots in 2020. Seasonal dynamics of radar indices from Sentinel-1 satellite for (<b>d</b>) Plot 1, (<b>e</b>) Plot 2, and (<b>f</b>) Plot 3 phenology sample plots in 2020. (<b>g</b>) Time series of groundwater depth changes in the three phenology sample plots in 2020.</p>
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<p>Correlation coefficients between different remote sensing indices and phenology camera data. (<b>a</b>) Correlation matrix between spectral indices, radar indices, and the ExGR index for Plot 1, (<b>b</b>) Plot 2, (<b>c</b>) and Plot 3. (<b>d</b>) Overall performance of spectral indices and radar indices in detecting canopy changes during the growing season of <span class="html-italic">P. euphratica</span>. The blue dots represent the scatter values of the indices, and the blue line indicates the fitted trend line.</p>
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<p><span class="html-italic">P. euphratica</span> distribution mapping results. The red boxes represent the sampling demonstration locations. (<b>a</b>) The area of uniform <span class="html-italic">P. euphratica</span> distribution, (<b>b</b>) the area of dense <span class="html-italic">P. euphratica</span> distribution, and (<b>c</b>) the area of sparse P. euphratica distribution; (<b>d</b>–<b>h</b>) represent areas near the river channel.</p>
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<p>The comparison of the contribution rates of each feature band to the classification results in the five classification scenarios.</p>
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<p>Average SOS (<b>a</b>) and EOS (<b>b</b>) from 2004 to 2023 in the Darya Yabui Oasis; (<b>c</b>) spatial distribution of SOS temporal change trends, (<b>d</b>) spatial distribution of EOS temporal change trends, and fitting of temporal changes in (<b>e</b>) SOS, EOS, and LOS. In (<b>a</b>,<b>b</b>), the line charts at the top of the figures represent the distribution changes of the 20-year average SOS and EOS across groundwater depth patterns, and the line charts on the right show how this change’s slope varied with increasing groundwater depth. For (<b>c</b>,<b>d</b>), the histograms in these figures display the area proportion of different temporal change trends in SOS and EOS. In the legend, “+” and “--” indicate increases (delay) or decreases (advancement) in days, and “*” indicates significant (<span class="html-italic">p</span> &lt; 0.05) and non-significant (<span class="html-italic">p</span> &gt; 0.05) temporal change trends. In (<b>e</b>), the interannual trends of annual SOS, EOS, and LOS in the study area and their linear regression fittings are represented.</p>
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<p>Analysis of <span class="html-italic">P. euphratica</span> phenology and climate–hydrological factors. (<b>a</b>–<b>f</b>) One-way ANOVA results of <span class="html-italic">P. euphratica</span> phenology characteristics in the GWD1 (0–8 m) and GWD2 (8–10 m) regions. Regression fitting results of (<b>g</b>) annual mean temperature and (<b>h</b>) surface temperature in the study area from 2004 to 2023. Temporal change trends: in the GWD1 (blue line) and GWD2 (orange line) regions, interannual changes in average (<b>i</b>) SOS and (<b>j</b>) EOS of <span class="html-italic">P. euphratica</span>. Spatial distribution differences: in the GWD1 (blue line) and GWD2 (orange line) region, changes in (<b>l</b>) SOS and (<b>m</b>) EOS of <span class="html-italic">P. euphratica</span> at different surface temperatures. Correlation analysis: correlations between climate–hydrological factors and phenology characteristics in the (<b>k</b>) GWD1 and (<b>n</b>) GWD2 regions.</p>
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<p>Influence of groundwater depth (GWD) on the partial correlation coefficients between air temperature (Temp), precipitation (Pre), surface temperature (LST), surface humidity (MNDWI), and the Start of the Growing Season (SOS) and End of the Growing Season (EOS) of <span class="html-italic">P. euphratica</span>. Changes in the partial correlation coefficients between annual average air temperature and (<b>a</b>) SOS and (<b>b</b>) EOS at different groundwater depths. Changes in the partial correlation coefficients between total annual precipitation and (<b>c</b>) SOS and (<b>d</b>) EOS at different groundwater depths. Changes in the partial correlation coefficients between annual average surface temperature (LST) and (<b>e</b>) SOS and (<b>f</b>) EOS at different groundwater depths. Changes in the partial correlation coefficients between annual average surface humidity (MNDWI) and (<b>g</b>) SOS and (<b>g</b>) EOS at different groundwater depths. In (<b>g</b>), the bar chart represents the normalized index of spring flood frequency, with higher values indicating higher flood frequency in the area. In (<b>h</b>), the bar chart represents the normalized index of autumn flood frequency.</p>
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39 pages, 12565 KiB  
Article
Integrating Land Use/Land Cover and Climate Change Projections to Assess Future Hydrological Responses: A CMIP6-Based Multi-Scenario Approach in the Omo–Gibe River Basin, Ethiopia
by Paulos Lukas, Assefa M. Melesse and Tadesse Tujuba Kenea
Climate 2025, 13(3), 51; https://doi.org/10.3390/cli13030051 - 28 Feb 2025
Viewed by 272
Abstract
It is imperative to assess and comprehend the hydrological processes of the river basin in light of the potential effects of land use/land cover and climate changes. The study’s main objective was to evaluate hydrologic response of water balance components to the projected [...] Read more.
It is imperative to assess and comprehend the hydrological processes of the river basin in light of the potential effects of land use/land cover and climate changes. The study’s main objective was to evaluate hydrologic response of water balance components to the projected land use/land cover (LULC) and climate changes in the Omo–Gibe River Basin, Ethiopia. The study employed historical precipitation, maximum and minimum temperature data from meteorological stations, projected LULC change from module for land use simulation and evaluation (MOLUSCE) output, and climate change scenarios from coupled model intercomparison project phase 6 (CMIP6) global climate models (GCMs). Landsat thematic mapper (TM) (2007) enhanced thematic mapper plus (ETM+) (2016), and operational land imager (OLI) (2023) image data were utilized for LULC change analysis and used as input in MOLUSCE simulation to predict future LULC changes for 2047, 2073, and 2100. The predictive capacity of the model was evaluated using performance evaluation metrics such as Nash–Sutcliffe Efficiency (NSE), the coefficient of determination (R2), and percent bias (PBIAS). The bias correction and downscaling of CMIP6 GCMs was performed via CMhyd. According to the present study’s findings, rainfall will drop by up to 24% in the 2020s, 2050s, and 2080s while evapotranspiration will increase by 21%. The findings of this study indicate that in the 2020s, 2050s, and 2080s time periods, the average annual Tmax will increase by 5.1, 7.3, and 8.7%, respectively under the SSP126 scenario, by 5.2, 10.5, and 14.9%, respectively under the SSP245 scenario, by 4.7, 11.3, and 20.7%, respectively, under the SSP585 scenario while Tmin will increase by 8.7, 13.1, and 14.6%, respectively, under the SSP126 scenario, by 1.5, 18.2, and 27%, respectively, under the SSP245 scenario, and by 4.7, 30.7, and 48.2%, respectively, under the SSP585 scenario. Future changes in the annual average Tmax, Tmin, and precipitation could have a significant effect on surface and subsurface hydrology, reservoir sedimentation, hydroelectric power generation, and agricultural production in the OGRB. Considering the significant and long-term effects of climate and LULC changes on surface runoff, evapotranspiration, and groundwater recharge in the Omo–Gibe River Basin, the following recommendations are essential for efficient water resource management and ecological preservation. National, regional, and local governments, as well as non-governmental organizations, should develop and implement a robust water resources management plan, promote afforestation and reforestation programs, install high-quality hydrological and meteorological data collection mechanisms, and strengthen monitoring and early warning systems in the Omo–Gibe River Basin. Full article
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Figure 1
<p>The study area map comprises meteorological stations, streamflow gauging stations, and stream networks.</p>
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<p>The general flowchart of the study comprises data input, preprocessing and processing, and outputs.</p>
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<p>Historical and projected LULC patterns of 2007, 2016, 2023, 2047, 2073, and 2100 in the Omo–Gibe River Basin.</p>
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<p>CMIP6 GCM selection procedure.</p>
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<p>Mean annual maximum temperature for the baseline (1985–2014), SSP126, SSP245, and SSP585 scenarios (2023–2100) considering the 95% confidence level in the OGRB.</p>
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<p>Mean annual minimum temperature for the baseline (1985–2014), SSP126, SSP245, and SSP585 scenarios (2023–2100) considering the 95% level of confidence in the OGRB.</p>
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<p>Mean annual minimum temperature for the baseline (1985–2014), SSP126, SSP245, and SSP585 scenarios (2023–2100) considering the 95% level of confidence in the OGRB.</p>
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<p>Anomalies of mean annual Tmax and Tmin for five CMIP6 models (<b>a</b>,<b>b</b>), and model ensemble mean for Tmax (<b>c</b>) and for Tmin (<b>d</b>) for the base historical period (1985–2014).</p>
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<p>Mean annual precipitation for the observed (1985–2022), SSP126, SSP245, and SSP585 scenarios (2023–2100) from five CMIP6 ensemble GCMs considering the 95% confidence level.</p>
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<p>Mean annual precipitation for the observed (1985–2022), SSP126, SSP245, and SSP585 scenarios (2023–2100) from five CMIP6 ensemble GCMs considering the 95% confidence level.</p>
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<p>Mean annual precipitation for the observed (1985–2022), SSP126, SSP245, and SSP585 scenarios (2023–2100) from five CMIP6 ensemble GCMs considering the 95% confidence level.</p>
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<p>Mean annual precipitation anomalies of five CMIP6 models (<b>a</b>) and model ensemble mean (<b>b</b>) for the base historical period (1985–2014).</p>
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<p>Mean annual precipitation anomalies of five CMIP6 models (<b>a</b>) and model ensemble mean (<b>b</b>) for the base historical period (1985–2014).</p>
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<p>Streamflow changes (<b>a</b>–<b>h</b>) in the observed and simulated data for the calibration (1995–2012) and validation periods (2013–2019).</p>
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<p>Streamflow changes (<b>a</b>–<b>h</b>) in the observed and simulated data for the calibration (1995–2012) and validation periods (2013–2019).</p>
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<p>Streamflow changes (<b>a</b>–<b>h</b>) in the observed and simulated data for the calibration (1995–2012) and validation periods (2013–2019).</p>
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<p>Effects of LULC changes on surface runoff during the 2020s (<b>left</b>), 2050s (<b>middle</b>), and 2080s (<b>right</b>) in the OGRB.</p>
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<p>Effects of LULC changes on evapotranspiration during the 2020s (<b>left</b>), 2050s, and 2080s in the OGRB.</p>
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<p>Effects of LULC changes on groundwater recharge during the 2020s, 2050s, and 2080s in the OGRB.</p>
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<p>Effects of climate change on surface runoff (mm) in the 2020s, 2050s, and 2080s from five CMIP6 GCMs under SSP126, SSP245, and SSP585 in the OGRB.</p>
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<p>Effects of climate change on evapotranspiration (mm) in the 2020s, 2050s, and 2080s from five CMIP6 GCMs under SSP126, SSP245, and SSP585 in the OGRB.</p>
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<p>Effects of climate change on groundwater recharge (mm) in the 2020s, 2050s, and 2080s from five CMIP6 GCMs under SSP126, SSP245, and SSP585 in the OGRB.</p>
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<p>Effects of LULC and climate changes on surface runoff (mm) in the 2020s, 2050s, and 2080s from five CMIP6 GCMs under SSP126, SSP245, and SSP585 in the OGRB.</p>
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<p>Effects of LULC and climate changes on evapotranspiration (mm) in the 2020s, 2050s, and 2080s from five CMIP6 GCMs under SSP126, SSP245, and SSP585 in the OGRB.</p>
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<p>Effects of LULC and climate changes on groundwater recharge (mm) in the 2020s, 2050s, and 2080s from five CMIP6 GCMs under SSP126, SSP245, and SSP585 in the OGRB.</p>
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26 pages, 28844 KiB  
Article
Assessment of the Impact of Extreme Hydrological Conditions on Migratory Bird Habitats of the Largest Freshwater Lake Wetlands in China Based on Multi-Source Remote Sensing Fusion Approach
by Jingfeng Qiu, Yu Li and Xinggen Liu
Sustainability 2025, 17(5), 1900; https://doi.org/10.3390/su17051900 - 24 Feb 2025
Viewed by 284
Abstract
Poyang Lake, the largest freshwater lake of China, serves as a crucial wintering site for migratory birds in the East Asian–Australasian Flyway, where habitat quality is essential for maintaining diverse bird populations. Recently, the frequent alternation of extreme wet years, e.g., 2020, and [...] Read more.
Poyang Lake, the largest freshwater lake of China, serves as a crucial wintering site for migratory birds in the East Asian–Australasian Flyway, where habitat quality is essential for maintaining diverse bird populations. Recently, the frequent alternation of extreme wet years, e.g., 2020, and dry years, e.g., 2022, have inflicted considerable perturbation on the local wetland ecology, severely impacting avian habitats. This study employed the spatiotemporal fusion method (ESTARFM) to obtain continuous imagery of Poyang Lake National Nature Reserve during the wintering seasons from 2020 to 2022. Habitat areas were identified based on wetland classification and water depth constraints. The results indicate that both extreme wet and dry conditions have exacerbated the fragmentation of migratory bird habitats. The shallow water habitats showed minor short-term fluctuations in response to water levels but were more significantly affected by long-term hydrological trends. These habitats exhibited considerable interannual variability across different hydrological years, affecting both their proportion within the overall habitat and their distribution within the study area. This study demonstrates the ability of ESTARFM to reveal the dynamic changes in migratory bird habitats and their responses to extreme hydrological conditions, highlighting the critical role of water depth in habitat analysis. The outcomes of this study improve the understanding of the impact of extreme water levels on migratory bird habitats, which may help expand knowledge about the protection of other floodplain wetlands around the world. Full article
(This article belongs to the Section Environmental Sustainability and Applications)
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Graphical abstract
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<p>Technical route flow chart.</p>
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<p>The location of (<b>a</b>) Poyang Lake, (<b>b</b>) PLNNR, and (<b>c</b>) sub-lakes.</p>
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<p>Temporal distribution of Landsat 8 OLI and MOD09A1 data acquired in this study for Poyang Lake. Vertical axis denotes years from 2020 to 2023, and horizontal axis denotes day of year (DOY). LC8s presentLandsat-8 OLI data. Each cell of the matrix color coded represents different water levels at Xingzi station.</p>
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<p>Comparison of fusion results with original images in the study area; (<b>a</b>) LANDSAT for the prediction period; (<b>b</b>) MODIS for the prediction period; (<b>c</b>) ESTARFM predicted images.</p>
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<p>Water levels and inundated area change during 2000–2023.</p>
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<p>Land cover classification results in different years.</p>
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<p>The proportion of land cover in different years.</p>
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<p>Relationship between water area and water levels.</p>
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<p>Shallow water habitat extent.</p>
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<p>Temporal habitat changes during the different years’ wintering periods.</p>
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<p>Comparison of habitat areas considering water depth conditions.</p>
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<p>Relationship between habitat areas and water levels.</p>
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<p>Total habitat area over three hydrological years (with the <span class="html-italic">x</span>-axis representing the days from 1 November).</p>
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25 pages, 1382 KiB  
Article
Water Security Under Climate Change: Challenges and Solutions Across 43 Countries
by Maridelly Amparo-Salcedo, Ana Pérez-Gimeno and Jose Navarro-Pedreño
Water 2025, 17(5), 633; https://doi.org/10.3390/w17050633 - 21 Feb 2025
Viewed by 241
Abstract
Different countries face significant challenges in managing water-related natural hazards, such as floods and shortages, while ensuring adequate water quality and quantity to satisfy human needs and preserve ecosystems. Climate change projections exacerbate this situation by intensifying the hydrological cycle, resulting in substantial [...] Read more.
Different countries face significant challenges in managing water-related natural hazards, such as floods and shortages, while ensuring adequate water quality and quantity to satisfy human needs and preserve ecosystems. Climate change projections exacerbate this situation by intensifying the hydrological cycle, resulting in substantial changes in precipitation patterns, evapotranspiration, and groundwater storage. This study reviews water security challenges across 43 countries, drawing on 128 articles obtained from databases including EBSCOHOST, Scopus and ResearchGate, as well as specific journals. Key search terms included “water security”, “water security and climate change”, “water scarcity”, “water risk index”, “water balance”, “water assessment”, and “land use and land cover change”. The analysis reveals the main water security issues present in 43 countries (flash floods, drought and water quality), and the response measures identified these challenges to water security. All the countries studied face one or more critical water-related effects. Afghanistan, Bangladesh, India, and Mexico were identified as the most severely affected, dealing with a combination of water scarcity, flooding, and water pollution. The most suggested strategies for improving water security include sustainable urban planning, improving consumption efficiency, strategic land-use planning, applying technologies to predict availability of water resources and planning according to variations in resource availability over time. In addition, other general actions include enhancing water storage infrastructure, improving consumption efficiency and adopting sustainable urban planning. Full article
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<p>Geographic distribution of number of articles published in each continent.</p>
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<p>Water security challenges in 43 studied countries.</p>
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<p>Principle measures to guarantee water security.</p>
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24 pages, 8696 KiB  
Article
Groundwater Vulnerability in the Aftermath of Wildfires at the El Sutó Spring Area: Model-Based Insights and the Proposal of a Post-Fire Vulnerability Index for Dry Tropical Forests
by Mónica Guzmán-Rojo, Luiza Silva de Freitas, Enrrique Coritza Taquichiri and Marijke Huysmans
Fire 2025, 8(3), 86; https://doi.org/10.3390/fire8030086 - 21 Feb 2025
Viewed by 790
Abstract
In response to the escalating frequency and severity of wildfires, this study carried out a preliminary assessment of their impact on groundwater systems by simulating post-fire effects on groundwater recharge. The study focuses on the El Sutó spring area in Santa Cruz, Bolivia, [...] Read more.
In response to the escalating frequency and severity of wildfires, this study carried out a preliminary assessment of their impact on groundwater systems by simulating post-fire effects on groundwater recharge. The study focuses on the El Sutó spring area in Santa Cruz, Bolivia, a region that is susceptible to water scarcity and frequent wildfires. The United States Geological Survey (USGS) Soil-Water-Balance model version 2.0 was utilized, adjusting soil texture and infiltration capacity parameters to reflect the changes induced by wildfire events. The findings indicated a significant decrease in groundwater recharge following a hypothetical high-severity wildfire, with an average reduction of approximately 39.5% in the first year post-fire. A partial recovery was modeled thereafter, resulting in an estimated long-term average reduction of 10%. Based on these results, the El Sutó spring was provisionally classified as having high vulnerability shortly after a wildfire and moderate vulnerability in the extended period. Building on these model-based impacts, a preliminary Fire-Related Forest Recharge Impact Score (FRIS) was proposed. This index is grounded in soil properties and recharge dynamics and is designed to assess hydrological vulnerability after wildfires in dry tropical forests. Although these findings remain exploratory, they offer a predictive framework intended to guide future studies and inform strategies for managing wildfire impacts on groundwater resources. Full article
(This article belongs to the Special Issue Advances in the Assessment of Fire Impacts on Hydrology, 2nd Edition)
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<p>Location of the El Sutó spring area, illustrating the intersection with the Santa Cruz la Vieja (SCLV) protected zone. Hydrogeological features (e.g., water wells, piezometers, and runoff stations), meteorological stations, the urban center of San José de Chiquitos, neighboring communities, and water bodies are also shown.</p>
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<p>The geological formations of the San Jose mountain range and an approach to the study site. Map (<b>a</b>) is derived from the official geological map of Bolivia, while map (<b>b</b>) is adapted from the “Geochemical Prospecting for Base Metals in the San José de Chiquitos Area” project [<a href="#B47-fire-08-00086" class="html-bibr">47</a>]. The second map focuses particularly on the El Sutó area, highlighting critical geological formations. Both maps incorporate inferences drawn from their respective sources to provide a detailed representation of the region’s geological features.</p>
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<p>Conceptual representation of aquifer recharge in fractured sandstones generating the spring, where arrows indicate recharge and discharge processes (<b>a</b>) and the seasonality of the predominant vegetation as reflected in the components of the water balance (precipitation P, interception I, evaporation E, transpiration T, surface runoff S<sub>off</sub>, and recharge R) influencing infiltration, with arrows showing water distribution in both seasons (<b>b</b>).</p>
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<p>(<b>a</b>) Vegetation distribution around the El Sutó spring, derived from the Territorial Development Plan for Good Living for San José de Chiquitos (2016) and (<b>b</b>,<b>c</b>) comparative maps of the leaf area index (LAI) during the rainy season (February 2015) and the dry season (July 2015), respectively.</p>
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<p>Annual rainfall deviations from the 40-year average in San José de Chiquitos, highlighting years with above- and below-average precipitation. The figure showcases the cyclical patterns of wet and dry phases, with visible clusters of rainy and dry years, especially during the last five years.</p>
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<p>Schematic representation of the SWB-USGS V2.0 model, based on the Thornthwaite–Mather daily soil moisture accounting approach [<a href="#B60-fire-08-00086" class="html-bibr">60</a>]. Arrows depict each computational cell’s primary inflows, outflows, and storage components.</p>
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<p>Soil–water interactions through three pairs of graphs. The first pair (<b>a</b>) examines the Curve Number (CN)’s impact on infiltration and recharge. The central graphs (<b>b</b>) explore the link between maximum infiltration capacity and its effects on infiltration and recharge rates. In contrast, the rightmost graphs (<b>c</b>) reveal the influence of root zone depth (Rzn) on these same variables. These parameter combinations were varied as part of a sensitivity analysis, where the three parameters were systematically assigned random values to test how they interact and to identify their relative influence on infiltration processes.</p>
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<p>Relationship between daily precipitation and groundwater recharge under three distinct conditions. (<b>a</b>) Pre-fire (baseline) represents normal infiltration and recharge before any fire disturbance. (<b>b</b>) First year post-fire depicts reduced infiltration and hence lower recharge due to immediate fire effects (ash deposition, hydrophobic layers). (<b>c</b>) Beyond two years post-fire reflects a partial recovery of soil structure, with hydrophobicity diminishing over time and recharge increasing relative to the first year but not necessarily returning to pre-fire levels.</p>
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<p>Spatial distribution of annual recharge for three scenarios, namely (<b>a</b>) normal (pre-fire) conditions, (<b>b</b>) the first year post-fire, and (<b>c</b>) beyond two years post-fire. Each scenario includes dry, rainy, and annual season outputs.</p>
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<p>Reduction in recharge after a simulated wildfire from the pre-fire baseline scenario, shown for (<b>a</b>) the first year post-fire and (<b>b</b>) more than two years later. Each panel presents dry, rainy, and annual season variations in recharge losses.</p>
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<p>Post-fire recharge vulnerability during a dry year. Panels compare (<b>a</b>) conditions in the first year following a simulated wildfire event with (<b>b</b>) the subsequent years, illustrating spatial patterns of recharge deficits.</p>
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<p>Fire-Related Forest Recharge Impact Score (FRIS) for post-fire conditions, categorizing recharge vulnerability into five discrete levels according to soil texture, infiltration capacity, and root depth based on the GOD method.</p>
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<p>(<b>a</b>) Comparison of recharge estimates in the San José de Chiquitos area using TerraClimate and (<b>b</b>) the extent of the 2008 burned region in the same vicinity [<a href="#B76-fire-08-00086" class="html-bibr">76</a>]. Color variations reflect areas with differing recharge patterns and burn severity.</p>
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<p>(<b>a</b>) Twenty-year recharge trends in San José de Chiquitos and (<b>b</b>,<b>c</b>) annual recharge trajectories at five Chiquitania sites derived from TerraClimate and FLDAS, respectively [<a href="#B76-fire-08-00086" class="html-bibr">76</a>,<a href="#B77-fire-08-00086" class="html-bibr">77</a>]. Negative slopes suggest declining recharge linked to regional disturbances.</p>
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42 pages, 2991 KiB  
Review
Event-Based vs. Continuous Hydrological Modeling with HEC-HMS: A Review of Use Cases, Methodologies, and Performance Metrics
by Golden Odey and Younghyun Cho
Hydrology 2025, 12(2), 39; https://doi.org/10.3390/hydrology12020039 - 17 Feb 2025
Viewed by 381
Abstract
This study critically examines the applications of the Hydrologic Engineering Center-Hydrologic Modeling System (HEC-HMS) in hydrological research from 2000 to 2023, with a focus on its use in event-based and continuous simulations. A bibliometric analysis reveals a steady growth in research productivity and [...] Read more.
This study critically examines the applications of the Hydrologic Engineering Center-Hydrologic Modeling System (HEC-HMS) in hydrological research from 2000 to 2023, with a focus on its use in event-based and continuous simulations. A bibliometric analysis reveals a steady growth in research productivity and identifies key thematic areas, including hydrologic modeling, climate change impact assessment, and land use analysis. Event-based modeling, employing methods such as the SCS curve number (CN) and SCS unit hydrograph, demonstrates exceptional performance in simulating short-term hydrological responses, particularly in flood risk management and stormwater applications. In contrast, continuous modeling excels in capturing long-term processes, such as soil moisture dynamics and groundwater contributions, using methodologies like soil moisture accounting and linear reservoir baseflow approaches, which are critical for water resource planning and climate resilience studies. This review highlights the adaptability of HEC-HMS, showcasing its successful integration of event-based precision and continuous process modeling through hybrid approaches, enabling robust analyses across temporal scales. By synthesizing methodologies, performance metrics, and case studies, this study offers practical insights for selecting appropriate modeling techniques tailored to specific hydrological objectives. Moreover, it identifies critical research gaps, including the need for advanced calibration methods, enhanced parameter sensitivity analyses, and improved integration with hydraulic models. These findings highlight HEC-HMS’s critical role in improving hydrological research and give a thorough foundation for its use in addressing current water resource concerns. Full article
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<p>Flowchart of the research methods.</p>
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<p>The total and cumulative number of publications produced each year between 2000 and 2023.</p>
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<p>Overlay visualization of country collaboration network.</p>
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<p>Visualization of keyword co-occurrence analysis for (<b>a</b>) timeline overlay network; (<b>b</b>) item density.</p>
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<p>Graphical results for a typical event-based modeling (adapted from [<a href="#B79-hydrology-12-00039" class="html-bibr">79</a>]).</p>
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<p>Graphical results for a typical continuous modeling (adapted from [<a href="#B83-hydrology-12-00039" class="html-bibr">83</a>]).</p>
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21 pages, 12117 KiB  
Article
A Novel Sensitivity Analysis Framework for Quantifying Permafrost Impacts on Runoff Variability in the Yangtze River Source Region
by Jiaxuan Chang, Xuefeng Sang, Yun Zhang, Yangwen Jia, Junlin Qu, Yang Zheng and Haokai Ding
Sustainability 2025, 17(4), 1570; https://doi.org/10.3390/su17041570 - 14 Feb 2025
Viewed by 361
Abstract
In the context of global climate change, understanding cryosphere degradation and its impact on water resources in alpine regions is crucial for sustainable development. This study investigates the relationship between permafrost degradation and runoff variations in the Source Region of the Yangtze River [...] Read more.
In the context of global climate change, understanding cryosphere degradation and its impact on water resources in alpine regions is crucial for sustainable development. This study investigates the relationship between permafrost degradation and runoff variations in the Source Region of the Yangtze River (SRYR), a critical water tower for sustainable water supply in Asia. We propose a novel method for assessing permafrost sensitivity, which establishes the correlation between cryosphere changes and hydrological responses, contributing to sustainable water resource management. Our research quantifies key uncertainties in runoff change attribution, providing essential data for sustainable decision making. Results show that changes in watershed characteristics account for up to 20% of runoff variation, with permafrost degradation (−0.02 sensitivity) demonstrating a greater influence than NDVI variations. The findings offer critical insights for the development of sustainable adaptation strategies, particularly in maintaining ecosystem services and ensuring long-term water security under changing climate conditions. This study offers a scientific basis for climate-resilient water management policies in high-altitude regions. Full article
(This article belongs to the Special Issue Sustainable Water Management in Rapid Urbanization)
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<p>Relative location of the SRYR and distribution of ZMD hydrological stations.</p>
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<p>Methodological framework of the study.</p>
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<p>Long-term trends in watershed hydroclimatic variables. (<b>a</b>) Annual maximum temperature; (<b>b</b>) annual minimum temperature; (<b>c</b>) average annual precipitation; (<b>d</b>) average annual potential evapotranspiration.</p>
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<p>Trends in NDVI change. The black solid line with inverted triangle markers represents the annual maximum NDVI values, while the green solid line with square markers represents the 5-year moving average.</p>
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<p>Trends in the multi-year averages for the SRYR. The blue bar chart represents the 5-year average ALT (m) (e.g., 1965 corresponds to the multi-year average for 1961–1965), while the gray solid line represents the 5-year average permafrost area (10<sup>4</sup> km<sup>2</sup>).</p>
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<p>Results of the TFPW-MK test with forward (UF) and backward (UB) curves at the ZMD hydrological station. The horizontal dashed lines represent the 95% significance level (|UF| = 1.96).</p>
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<p>Sliding <span class="html-italic">t</span>-test t-values for the annual runoff series. The red dashed line represents the critical t-value at a significance level of α = 0.01.</p>
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<p>Results of the (<b>a</b>) TFPW-MK and (<b>b</b>) Sliding <span class="html-italic">t</span>-test for annual runoff series at the ZMD hydrological station.</p>
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<p>Simulated runoff based on different Budyko functions. The blue solid line represents the observed runoff at the ZMD hydrological station (mm). The green dashed line with diamond markers represents the runoff simulated using Fu’s Budyko function (mm), while the orange dashed line with circular markers represents the runoff simulated using Yang’s Budyko function (mm).</p>
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<p>(<b>a</b>) The contribution of parameter n (R<sub>Wp</sub> = R<sub>n</sub>, where Wp denotes parameter n in Yang’s function) to runoff variation versus NDVI; (<b>b</b>) Rn versus permafrost area (10<sup>4</sup> km<sup>2</sup>); (<b>c</b>) Rn versus ALT (m); (<b>d</b>) permafrost area (10<sup>4</sup> km<sup>2</sup>) versus ALT (m).</p>
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<p>SRYR annual runoff change attribution. Orange, blue, and green colors in figure represent the contributions of E0, P, and Wp to runoff change, respectively.</p>
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<p>Relationship between Rn and parameter n. Rn represents the contribution of watershed characteristic changes to runoff variations, and n is the watershed characteristic parameter in Yang’s Budyko function. The dashed line denotes the linear trend between Rn and n.</p>
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<p>SRYR land use data for 1980, 1990, 1995, 2000, 2005, 2010, 2015, and 2020. Cr: cropland; Fo: forest; Gr: grassland; Wa: water; Bu: built-up; Ba: barren land. Bars represent the total area (%) of the watershed for each type.</p>
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27 pages, 6767 KiB  
Article
Analysis of the Spatiotemporal Patterns of Water Conservation in the Yangtze River Ecological Barrier Zone Based on the InVEST Model and SWAT-BiLSTM Model Using Fractal Theory: A Case Study of the Minjiang River Basin
by Xianqi Zhang, Jiawen Liu, Jie Zhu, Wanhui Cheng and Yuehan Zhang
Fractal Fract. 2025, 9(2), 116; https://doi.org/10.3390/fractalfract9020116 - 13 Feb 2025
Viewed by 393
Abstract
The Yangtze River Basin serves as a vital ecological barrier in China, with its water conservation function playing a critical role in maintaining regional ecological balance and water resource security. This study takes the Minjiang River Basin (MRB) as a case study, employing [...] Read more.
The Yangtze River Basin serves as a vital ecological barrier in China, with its water conservation function playing a critical role in maintaining regional ecological balance and water resource security. This study takes the Minjiang River Basin (MRB) as a case study, employing fractal theory in combination with the InVEST model and the SWAT-BiLSTM model to conduct an in-depth analysis of the spatiotemporal patterns of regional water conservation. The research aims to uncover the relationship between the spatiotemporal dynamics of watershed water conservation capacity and its ecosystem service functions, providing a scientific basis for watershed ecological protection and management. Firstly, fractal theory is introduced to quantify the complexity and spatial heterogeneity of natural factors such as terrain, vegetation, and precipitation in the Minjiang River Basin. Using the InVEST model, the study evaluates the water conservation service functions of the research area, identifying key water conservation zones and their spatiotemporal variations. Additionally, the SWAT-BiLSTM model is employed to simulate the hydrological processes of the basin, particularly the impact of nonlinear meteorological variables on hydrological responses, aiming to enhance the accuracy and reliability of model predictions. At the annual scale, it achieved NSE and R2 values of 0.85 during calibration and 0.90 during validation. At the seasonal scale, these values increased to 0.91 and 0.93, and at the monthly scale, reached 0.94 and 0.93. The model showed low errors (RMSE, RSR, RB). The findings indicate significant spatial differences in the water conservation capacity of the Minjiang River Basin, with the upper and middle mountainous regions serving as the primary water conservation areas, whereas the downstream plains exhibit relatively lower capacity. Precipitation, terrain slope, and vegetation cover are identified as the main natural factors affecting water conservation functions, with changes in vegetation cover having a notable regulatory effect on water conservation capacity. Fractal dimension analysis reveals a distinct spatial complexity in the ecosystem structure of the study area, which partially explains the geographical distribution characteristics of water conservation functions. Furthermore, simulation results based on the SWAT-BiLSTM model show an increasingly significant impact of climate change and human activities on the water conservation functions of the Minjiang River Basin. The frequent occurrence of extreme climate events, in particular, disrupts the hydrological processes of the basin, posing greater challenges for water resource management. Model validation demonstrates that the SWAT model integrated with BiLSTM achieves high accuracy in capturing complex hydrological processes, thereby better supporting decision-makers in formulating scientific water resource management strategies. Full article
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<p>Location of the study area.</p>
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<p>LSTM model.</p>
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<p>Technical flow chart.</p>
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<p>Increases or decreases in land use by type.</p>
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<p>Land use transfer chord map.</p>
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<p>Space segmentation (Numbers are subbasin subdivision serial numbers).</p>
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<p>Module mechanism diagram.</p>
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<p>Fractal dimension calculation results.</p>
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<p>Results of the regional water yield and water conservation analysis.</p>
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<p>Comparison of runoff volume during the calibration and validation periods with the results from different model simulations: red line is the actual value, blue line is the SWAT-BiLSTM simulation, green line is the SWAT simulation, black line is the calibration period on the left, and black line is the validation period on the right.</p>
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21 pages, 6975 KiB  
Article
A Real-Time Water Level and Discharge Monitoring Station: A Case Study of the Sakarya River
by Fatma Demir and Osman Sonmez
Appl. Sci. 2025, 15(4), 1910; https://doi.org/10.3390/app15041910 - 12 Feb 2025
Viewed by 448
Abstract
This study details the design and implementation of a real-time river monitoring station established on the Sakarya River, capable of instantaneously tracking water levels and flow rates. The system comprises an ultrasonic distance sensor, a GSM module (Global System for Mobile Communications), which [...] Read more.
This study details the design and implementation of a real-time river monitoring station established on the Sakarya River, capable of instantaneously tracking water levels and flow rates. The system comprises an ultrasonic distance sensor, a GSM module (Global System for Mobile Communications), which enables real-time wireless data transmission to a server via cellular networks, a solar panel, a battery, and a microcontroller board. The river monitoring station operates by transmitting water level data collected by the ultrasonic distance sensor to a server via a communication module developed on a microcontroller board using an Arduino program, and then sharing these data through a web interface. The developed system performs regular and continuous water level readings without the need for human intervention. During the installation and calibration of the monitoring station, laboratory and field tests were conducted, and the obtained data were validated by comparison with data from the hydropower plant located upstream. This system, mounted on a bridge, measures water levels twice per minute and sends these data to the relevant server via the GSM module. During this process, precipitation data were utilized as a critical reference point for validating measurement data for the 2023 hydrological year, with changes in precipitation directly correlated with river water levels and calculated flow values, which were analyzed accordingly. The real-time river monitoring station allows for instantaneous monitoring of the river, achieving a measurement accuracy of within 0.1%. The discharge values recorded by the system showed a high correlation (r2 = 0.92) with data from the hydropower plant located upstream of the system, providing an accurate and comprehensive database for water resource management, natural disaster preparedness, and environmental sustainability. Additionally, the system incorporates early warning mechanisms that activate when critical water levels are reached, enabling rapid response to potential flood risks. By combining energy-independent operation with IoT (Internet Of Things)-based communication infrastructure, the developed system offers a sustainable solution for real-time environmental monitoring. The system demonstrates strong applicability in field conditions and contributes to advancing technologies in flood risk management and water resource monitoring. Full article
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<p>Örencik Bridge and Doğançay HPP I.</p>
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<p>Real-Time River Monitoring Station Components.</p>
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<p>Cabin Design.</p>
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<p>Monitoring Station Integrated Components.</p>
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<p>Arduino Program Interface and Protocols.</p>
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<p>Php Functions.</p>
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<p>Arduino–Sensor Connection Flowchart.</p>
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<p>Laboratory calibration.</p>
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<p>Field Calibration.</p>
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<p>Real-Time Flow Monitoring Station Cabin Installation.</p>
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<p>Topography of the River and RMS (River Monitoring Station) Location.</p>
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<p>Schematic Representation of Sensor Placement on Örencik Bridge.</p>
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<p>Comparison of Hourly Discharge Variations.</p>
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<p>Comparison of Daily Discharge Variations.</p>
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<p>Comparison of Monthly Discharge Variations.</p>
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<p>River Monitoring Station 2023 Hydrological Year Discharge Variation and Daily Total Precipitation.</p>
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<p>Seasonal Water Level Variation in River Monitoring Station and Daily Total Precipitation.</p>
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<p>Seasonal Discharge Variation In River Monitoring Station.</p>
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25 pages, 8603 KiB  
Article
Hydrological Response of the Irrawaddy River Under Climate Change Based on CV-LSTM Model
by Xiangyang Luo, Xu Yuan, Zipu Guo, Ying Lu, Cong Li and Li Peng
Water 2025, 17(4), 479; https://doi.org/10.3390/w17040479 - 8 Feb 2025
Viewed by 343
Abstract
Climate change is impacting hydrological conditions in the Dulongjiang-Irrawaddy River basin. This study employs a CV-LSTM model to evaluate the hydrological responses of precipitation, temperature, and runoff under various climate change scenarios. The findings indicate the following: (1) The CV-LSTM model performed excellently [...] Read more.
Climate change is impacting hydrological conditions in the Dulongjiang-Irrawaddy River basin. This study employs a CV-LSTM model to evaluate the hydrological responses of precipitation, temperature, and runoff under various climate change scenarios. The findings indicate the following: (1) The CV-LSTM model performed excellently in simulating hydrological processes at the Pyay station. (2) From 2025 to 2100, precipitation in the Dulongjiang-Irrawaddy River basin is projected to increase, becoming more concentrated during the rainy season, with a more uneven annual distribution compared to the baseline period (1996–2010). The average temperature is also expected to rise, with an increase of 1.57 °C under the SSP245 scenario and 2.26 °C under the SSP585 scenario compared to the baseline period (1996–2010). (3) Multi-year average flow projections from three GCM models indicate changes of −1.1% to 20.6% under SSP245 and 7.8% to 31.5% under SSP585, relative to the baseline period (1996–2010). (4) Runoff will become more concentrated during the flood season, with greater annual variability, increasing the risks of flooding and drought. Full article
(This article belongs to the Special Issue Impact of Climate Change on Water and Soil Erosion)
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<p>The Dulongjiang-Irrawaddy River basin with the Pyay hydrological station.</p>
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<p>CV-LSTM model framework and its data input. All input remote sensing data products were converted into 8-bit grayscale images. Texture and intensity features were extracted in the CV module using the spatial pyramid matching (SPM) strategy and local binary pattern (LBP). Finally, the feature vectors containing spatial information, along with runoff data, were input into the LSTM model for runoff simulation.</p>
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<p>Dividing a complete remote sensing image into sub-regions according to the pyramid partitioning strategy.</p>
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<p>A diagram illustrating the conversion of remote sensing data from an 8-bit image to a binary mode image.</p>
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<p>The flow curves of daily runoff simulations at the Pyay station during the training period (1996–2005) and testing period (2006–2010).</p>
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<p>Interannual variability characteristics of precipitation in the Dulongjiang-Irrawaddy River basin under the baseline period (1996–2010) and future climate change scenarios (2025–2100). The black line represents the annual precipitation during the baseline period (1996–2010), the red line represents the annual precipitation under the GCM model, and the shaded area indicates the uncertainty of the model predictions, with larger areas indicating higher uncertainty.</p>
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<p>Characteristics of monthly precipitation distribution in the Dulongjiang-Irrawaddy River basin under the baseline period (1996–2010) and future climate change scenarios (2025–2100). The black line represents the monthly precipitation during the baseline period (1996–2010), the red line represents the monthly precipitation under the GCM model, and the shaded area indicates the uncertainty of the model predictions, with larger areas indicating higher uncertainty.</p>
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<p>Interannual variation characteristics of temperature in the Dulongjiang-Irrawaddy River basin under the baseline period (1996–2010) and future climate change scenarios (2025–2100). The black line represents the annual average temperature during the baseline period (1996–2010), the red line represents the annual average temperature under the GCM model, and the shaded area indicates the uncertainty of the model predictions, with larger areas indicating higher uncertainty.</p>
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<p>Characteristics of intra-annual temperature variation in the Dulongjiang-Irrawaddy River basin under the baseline period (1996–2010) and future climate change scenarios (2025–2100). The black line represents the monthly average temperature during the baseline period (1996–2010), the red line represents the monthly average temperature under the GCM model, and the shaded area indicates the uncertainty of the model predictions, with larger areas indicating higher uncertainty.</p>
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<p>Interannual variation trends of runoff at the Pyay Hydrological Station in the Dulongjiang-Irrawaddy River basin under future climate change scenarios. The red line represents the streamflow under the SSP245 scenario, while the blue line represents the streamflow under the SSP585 scenario, the red and blue dashed lines represent the overall flow change trends under their respective climate change scenarios.</p>
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<p>Change rate of average flow at the Pyay Hydrological Station in the Dulongjiang-Irrawaddy River basin compared to the baseline period (1996–2010) under future climate change scenarios. Black represents the near-term (2025–2050), red represents the mid-term (2051–2075), and blue represents the long-term (2076–2100).</p>
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<p>Annual distribution characteristics of flow at the Pyay Hydrological Station in the Dulongjiang-Irrawaddy River basin under future climate change scenarios. The black line represents the monthly average temperature under the GCM model, the red line represents the baseline period (1996–2010) monthly average runoff, and the shaded area indicates the uncertainty in the model’s predictions. The larger the area, the higher the uncertainty.</p>
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<p>Percentage change rate of the Q95 flood flow extremes at the Pyay hydrological station in the Dulongjiang-Irrawaddy River basin compared to the baseline period (1996–2010). The degree of change is represented by the depth of the color, with darker colors indicating a larger magnitude of change.</p>
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<p>Percentage change rate of the Q5 drought flow extremes at the Pyay hydrological station in the Dulongjiang-Irrawaddy River basin compared to the baseline period (1996–2010). The degree of change is represented by the depth of the color, with darker colors indicating a larger magnitude of change.</p>
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22 pages, 12315 KiB  
Article
Soil Texture, Soil Moisture, and Sentinel-1 Backscattering: Towards the Retrieval of Field-Scale Soil Hydrological Properties
by Claire Stanyer, Irene Seco-Rizo, Clement Atzberger and Belen Marti-Cardona
Remote Sens. 2025, 17(3), 542; https://doi.org/10.3390/rs17030542 - 5 Feb 2025
Viewed by 618
Abstract
Monitoring soil moisture (SM) on individual crop fields is of great interest for agricultural applications. Synthetic aperture radar (SAR) systems such as Sentinel-1 provide sensitivity to surface SM at a spatial resolution compatible with crop-field monitoring. Different algorithms have been proposed to relate [...] Read more.
Monitoring soil moisture (SM) on individual crop fields is of great interest for agricultural applications. Synthetic aperture radar (SAR) systems such as Sentinel-1 provide sensitivity to surface SM at a spatial resolution compatible with crop-field monitoring. Different algorithms have been proposed to relate SAR backscattering to SM, yet most overlook soil texture as a modulating factor. This study investigated the influence of soil texture, closely related to soil hydrological properties, on the relationship between Sentinel-1 C-band backscattering and surface SM using extensive data from the agricultural sites of the COSMOS-UK monitoring network. Our results evidenced the semi-empirical first-order relationship between SM and field-averaged VV backscattering, and found that the gradient of their linear regression was indicative of soil texture. For instance, in sandy loam soil the S1 response showed high sensitivity to SM with a change of 1.69% SM per dB; this compared with the lower sensitivity of a clayey soil at a change of 4.81% SM per dB. These findings lay the ground for the retrieval of field-scale soil hydrological properties from backscatter temporal patterns, when used in synergy with rainfall data and process-based soil-moisture models. Full article
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<p>COSMOS-UK agricultural and horticultural Locations and names.</p>
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<p>COSMOS-UK SM data: availability periods and VWC variation for the A&amp;H Locations, including minimum, maximum, and average. SM records were used from 2017 onwards, when both S1 and S2 data were available.</p>
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<p>Soil texture at the UK-COSMOS Locations, as described by UKSO and COSMOS-UK.</p>
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<p>Study methodology overview.</p>
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<p>Schematic describing the delineation of Field-Sectors for asample Location. The COSMOS sensor (black star) is at the center of the COSMOS footprint (red circle), which intersects three fields (green polygons). Varying vegetation levels in the fields mean that theree Field-Sectors (blue polygons) needed to be considered independently.</p>
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<p>COSMOS-UK A&amp;H Location_Field-Sector polygons: The red polygons approximate the COSMOS footprints, with the white polygons showing the delineated Field-Sectors used for backscattering avergaing and vegetation assessment.</p>
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<p>Illustration of the low vegetation periods (L−periods) per Location_Field−Sector.</p>
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<p>SM vs. S1 backscatter for thee COCLP Location: (<b>a</b>) COSMOS footprint (red circle) and with COCLP Field−Sector (white polygons); (<b>b</b>) scatter plot of SM versus backscatter for all COCLP Sectors and L periods; (<b>c</b>–<b>h</b>) individual scatterplots for each Location_Field−Sector_L−period.</p>
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<p>Scatter plots of SM (VWC in %) against S1 VV backscattering (dB) for all L−periods by UK-COSMOS Location.</p>
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<p>Mean SM versus backscattering gradient for all Field-Sector L-periods for each Location. Note: Locations are organized by gradient, not by soil characteristics. <a href="#remotesensing-17-00542-f011" class="html-fig">Figure 11</a> presents the gradients determined for each location as color-coded boxes on the soil texture triangle. The color-code indicates the gradient magnitude, while their location on the triangle is determined the UKSO soil texture description for the site.</p>
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<p>Mean SM versus backscattering gradient per Location, displayed on the soil texture triangle according to the Location’s texture.</p>
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<p>Location and soil texture in increasing gradient order. Gray highlighting shows Locations with chalk potentially associated.</p>
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<p>(<b>a</b>) STGHT_P Location and (<b>b</b>) recorded S1 backscatter 01/07/2017 to 30/11/2022.</p>
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<p>STGHT Location S1 backscatter for all orbits (black) and incidence angle (blue) for 27/09/2019 to 09/02/2020.</p>
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<p>STGHT Location S1 backscatter separated into individual orbits.</p>
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<p>Orbit− and Location−based single value corrections applied to STGHT location.</p>
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<p>S1 backscatter for each orbit of the STGHT Location: (<b>a</b>) raw and (<b>b</b>) with the application of the calculated shift correction.</p>
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<p>S1 backscatter for each orbit of the STGHT Location: (<b>a</b>) raw and (<b>b</b>) with the application of the calculated shift correction.</p>
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<p>Each orbit of the STGHT location L−period 1, as recorded (<b>a</b>,<b>c</b>,<b>e</b>,<b>g</b>), and with the correction (<b>b</b>,<b>d</b>,<b>f</b>,<b>h</b>).</p>
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<p>All orbits for the STGHT location, with linear regressions: (<b>a</b>) raw and (<b>b</b>) with shift correction.</p>
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<p>Empirical shift correction values with angle.</p>
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22 pages, 10435 KiB  
Article
A Two-Decade Overview of the Environmental Carrying Capacity in Bahía Santa Maria–La Reforma Coastal Lagoon System
by Omar Calvario-Martínez, Julio Medina-Galvan, Virginia P. Domínguez-Jiménez, Rosalba Alonso-Rodríguez, Miguel A. Sánchez-Rodríguez, Paulina M. Reyes-Velarde, Miguel Betancourt-Lozano and David Serrano-Hernández
J. Mar. Sci. Eng. 2025, 13(2), 295; https://doi.org/10.3390/jmse13020295 - 5 Feb 2025
Viewed by 468
Abstract
Santa María Bay–La Reforma (SMBLR), with its 58,300 ha is one of Mexico’s most extensive estuarine lagoon systems. It is made up of islands, estuaries, and mangrove areas, which provide a vital part of the habitat and refuge of a significant number of [...] Read more.
Santa María Bay–La Reforma (SMBLR), with its 58,300 ha is one of Mexico’s most extensive estuarine lagoon systems. It is made up of islands, estuaries, and mangrove areas, which provide a vital part of the habitat and refuge of a significant number of birds, fish, amphibians, reptiles, and mammals. The fishing of blue and brown shrimp, marine and estuarine fish, as well as the exploitation of crab and bivalve mollusks, represent an important economic value for the communities that live there and for the state of Sinaloa, Mexico. This state ranked second in fisheries production and first in aquaculture production by 2023. However, the biological richness of this ecosystem has historically been threatened by economic activities such as agriculture, livestock, and aquaculture that, via watersheds, translate into continuous inputs of nutrients and other pollutants. This has led to modifications to the system such as changes in the structure of pelagic and benthic communities, mainly in response to eutrophication. To understand the dynamics of nutrient inputs to the ecosystem, this work presents a comparative analysis of the system’s carrying capacity and the magnitude of the main economic activities from 2007 to 2019. We found that during each season of the year and its transitions, the system functions as a nitrogen and phosphorus sink, which is associated with autotrophic net ecosystem metabolism and nitrogen fixation processes. We suggest that while water residence times in SMBLR are short, these are strongly influenced by the high volumes of water and nutrient loads determined by the spatio-temporal variations in hydrological drainage from the basins of influence of the system. The discharge of agriculture and aquaculture drains into SMBLR are areas of concern due to the high amount of nutrients. Although SMBLR is mostly an autotrophic system, there are signs that the carrying capacity during some seasons has been exceeded, and adverse ecological and socioeconomic effects in the basin are evident. Full article
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Graphical abstract

Graphical abstract
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<p>Geographical location and percentage contribution of municipal land cover of sub-basins draining to SMBLR (Angostura: An, Badiraguato: Ba, Mocorito: Mo, Navolato: Na, Guasave: Gu, Salvador Alvarado: Sa and Sinaloa: Si).</p>
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<p>The location of the 14 Points Control (PC) in the mouths is indicated with black squares, and the six IPs are indicated with stars. The blue line marks the 3 m isobath (<b>a</b>). Time series of TRIX concentration for IP-2 in September 2020. The arrow indicates the first relative maximum. The vertical line separates the ebb and flow of the tide. Blue side, ebb tide, when the TRIX concentration reaches the mouth. Green side, tidal flow, when “new water” enters the system and the TRIX concentration at the mouth decreases (<b>b</b>).</p>
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<p>Water and salt balances in the four seasons (rainy, rainy–dry, dry, and dry–rainy) in the SMBLR lagoon system. The units of water volumes are given in m<sup>3</sup> day<sup>−1</sup> and salinity in g kg<sup>−1</sup>. VR values indicate residual volume, VE evaporation, VP rainfall, VW wastewater, VX volume of exchange between the system and ocean, and SA absolute salinity.</p>
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<p>DIN and DIP flows were observed in the four study seasons in the SMBLR lagoon system. (+) indicates source, (–) indicates sink. The units of the flows are given in mol day<sup>−1</sup> and standardized in mmol m<sup>−2</sup> day<sup>−1</sup>. Y<sub>ANCMAW</sub> values indicate the contribution of N and P influenced by agricultural, urban, and shrimp wastewater, while Y<sub>NCM</sub> is the average amount of DIN and DIP between two boundaries. VX is the average quantity between the system and the ocean.</p>
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<p>Agricultural planted area and yield for (<b>a</b>) the northern zone (Corrientes Huyaqui Group, Mocorito 1 River and Mocorito 2 River sub-basins) and (<b>b</b>) the southern zone (Corrientes Reforma Group, Pericos 1 River and Pericos 2 River sub-basins). <span style="color:#538234">■</span> Corn crop; <span style="color:#A8D18D">■</span> Other crops; <span style="color:#58664B">▬</span> Yield.</p>
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<p>Livestock production and performance for (<b>a</b>) the northern zone (Corrientes Huyaqui Group, Mocorito 1 River and Mocorito 2 River sub-basins) and (<b>b</b>) the southern zone (sub-basins: Corrientes Reforma Group, Pericos 1 River and Perico 2 River sub-basins). <span style="color:#ED7D30">■</span> Livestock production; <span style="color:#843C0C">▬</span> Slaughtered animal.</p>
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<p>Aquaculture area and performance, for (<b>a</b>) the Northern zone (Guasave Sur board) and (<b>b</b>) the Southern zone (Angostura board). <span style="color:#00B3ED">■</span> Pond area; <span style="color:#314660">▬ </span>Yield.</p>
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<p>Number of inhabitants, for the northern zone (Corrientes Huyaqui Group, Mocorito 1 River and Mocorito 2 River sub-basins) and the southern zone (Corrientes Reforma Group, Pericos 1 River and Pericos 2 River sub-basins). ■ Northern zone and <span style="color:#7F7F7F">■</span> Southern zone.</p>
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<p>Trophic status in the drains that discharge into SMBLR. The percentage of sampling stations for each trophic level (from [<a href="#B37-jmse-13-00295" class="html-bibr">37</a>]) is shown by area and date. <span style="color:#9BE598">■</span> oligotrophic, <span style="color:#FEFC87">■</span> mesotrophic, and <span style="color:#FF4D4D">■</span> hypertrophic state.</p>
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<p>Trophic states within the SMBLR lagoon system. The percentage of stations for each trophic level is shown by zone and date. <span style="color:#9BE598">■</span> oligotrophic, <span style="color:#FEFC87">■</span> mesotrophic, <span style="color:#FF9933">■</span> eutrophic, and <span style="color:#FF4D4D">■</span> hypertrophic state.</p>
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<p>Velocity field in SMBLR at spring tide (ST). Flow (<b>a</b>) and ebb (<b>b</b>). TRIX concentration in reflux after emanation of the contaminant for 12 days in the six IP (<b>c</b>).</p>
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14 pages, 4522 KiB  
Article
A Community-Led Assessment to Identify Groundwater-Dependent Lakes in Parkland County (Alberta, Canada)
by Brian Smerdon, Jenna Bahija Tarrabain Maccagno, Bradley Peter, Walter Neilson, Dave Mussell and David Trew
Water 2025, 17(3), 440; https://doi.org/10.3390/w17030440 - 5 Feb 2025
Viewed by 593
Abstract
Responding to a growing concern about impacts from anthropogenic activity on several dozen lakes, a group of citizens initiated and led a water quality sampling program that included characterizing groundwater dependence. The small lakes are located on hummocky glacial terrain near Edmonton, Alberta, [...] Read more.
Responding to a growing concern about impacts from anthropogenic activity on several dozen lakes, a group of citizens initiated and led a water quality sampling program that included characterizing groundwater dependence. The small lakes are located on hummocky glacial terrain near Edmonton, Alberta, Canada. A team of volunteers collected lake samples for a variety of limnological and ecological analyses to document lake health and trophic state, and collaborated with a university research group to identify groundwater dependence using specific environmental tracers (δ2H, δ18O, and 222Rn). Water chemistry and isotopic measurements are largely explained by the position of a lake within the local groundwater flow system. A simple metric to express the likelihood of groundwater dependence was calculated using the total dissolved solids (TDS), δ18O, and 222Rn values. Across the relatively small study area, a greater likelihood of groundwater dependence was determined for lakes located downgradient from an elevated recharge area. In contrast, where the water table was relatively flat, a lower likelihood of groundwater dependence was found. These results were similar to the spatial pattern of a trophic state, indicating that groundwater dependence may be one of the factors responsible for lake ecological status. The data generated by citizens and the knowledge gained about the hydrology of this area will help discussions between landowners and decision makers on how to best manage land use in this diverse landscape. Full article
(This article belongs to the Section Hydrogeology)
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<p>(<b>a</b>) Location of the Parkland County Lakes study area within the Province of Alberta, Canada. (<b>b</b>) Lake sampling sites.</p>
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<p>(<b>a</b>) Lake sampling sites shown with the thickness of sediment above the bedrock surface [<a href="#B23-water-17-00440" class="html-bibr">23</a>] and cross-section locations. (<b>b</b>–<b>d</b>) Cross-sections through the study area showing the hummocky topography and the position and depths of lakes.</p>
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<p>Colour-shaded water table map modified from [<a href="#B28-water-17-00440" class="html-bibr">28</a>] showing lake sampling sites. Water table contour line spacing is 10 m. Groundwater flow directions are indicated by the arrows.</p>
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<p>The median total dissolved solids (TDS) concentration of groundwater from the sediments above bedrock for quarter township blocks [<a href="#B39-water-17-00440" class="html-bibr">39</a>] shown with the general grouping of the lake sites (NE and SW).</p>
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<p>Values of (<b>a</b>) total dissolved solids and (<b>b</b>) total phosphorus for lakes sampled in 2021 [<a href="#B36-water-17-00440" class="html-bibr">36</a>,<a href="#B37-water-17-00440" class="html-bibr">37</a>]. Results are shown with the water table contours and groundwater flow directions (arrows) from <a href="#water-17-00440-f003" class="html-fig">Figure 3</a>.</p>
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<p>Piper diagram summarizing water chemistry for lakes sampled in 2021 and 2022, along with median values for groundwater in sediments above bedrock for quarter township blocks in study area [<a href="#B39-water-17-00440" class="html-bibr">39</a>]. Results are differentiated by northeast (NE) or southwest (SW) position in study area.</p>
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<p>Stable isotope values (δ<sup>2</sup>H and δ<sup>18</sup>O) for lakes in Parkland County sampled in the current study, differentiated by northeast (NE) or southwest (SW) position within the study area. The local meteoric water line (LMWL), local evaporation line (LEL), and results of [<a href="#B16-water-17-00440" class="html-bibr">16</a>,<a href="#B31-water-17-00440" class="html-bibr">31</a>] are shown for comparison. Analytical error bars are less than the size of the marker.</p>
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<p>The values of (<b>a</b>) δ<sup>18</sup>O and (<b>b</b>) <sup>222</sup>Rn for lakes sampled in 2021. The results are shown with the water table contours and groundwater flow directions (arrows) from <a href="#water-17-00440-f003" class="html-fig">Figure 3</a>.</p>
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<p>Groundwater dependence score calculated from lake TDS, δ<sup>18</sup>O, and <sup>222</sup>Rn values. The results are shown with the water table contours and groundwater flow directions (arrows) from <a href="#water-17-00440-f003" class="html-fig">Figure 3</a>.</p>
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10 pages, 4801 KiB  
Article
Hydrological Response of Land Use and Climate Change Impact on Sediment Rate in Upper Citarum Watershed
by Evi Anggraheni, Abdul Halim Hamdany, Farouk Maricar, Neil Andika, Dian Sisinggih, Fransiskus Sean Tanlie and Fransiskus Adinda Rio Respati
Fluids 2025, 10(2), 36; https://doi.org/10.3390/fluids10020036 - 31 Jan 2025
Viewed by 430
Abstract
The Citarum Watershed is indeed a critical water resource in Indonesia, playing a significant role in providing water to Jakarta and other areas in West Java. However, it faces severe environmental stress due to land use changes and climate changes. The Upper Citarum [...] Read more.
The Citarum Watershed is indeed a critical water resource in Indonesia, playing a significant role in providing water to Jakarta and other areas in West Java. However, it faces severe environmental stress due to land use changes and climate changes. The Upper Citarum Watershed, considered to be a conservation area, has experienced rapid development due to human activities and economic growth. Climate change not only affects the rainfall value but also the rainfall patterns and sediment flow. The sedimentation process significantly impacts the soil characteristics around the river body. Several factors such as topography, flow velocity, and soil texture influence the sediment characteristics. Given the critical condition of climate and land use change, this study aims to analyse the impacts of the hydrological response of land use and climate change on the sediment rate in the Upper Citarum Watershed. The land use change analysis was conducted by comparing the land use data in 2000, 2010, and 2023 in the Upper Citarum Watershed. The deposition process of solid particles such as sand, silt, and gravel that are transported in the Upper Citarum River were examined in a soil investigation. The sediment rate and deposition by river flow were analysed using HEC-RAS quasi-unsteady flow. The impact of climate change in this study was assessed by simulating the discharge in three conditions, with the first simulation using the discharge from 2000 to 2010, the second simulation using the discharge from 2011 to 2023, and the last simulation using the discharge from 2000 to 2023. Due to the land use change, the developed area increased from 4% to 24% between 2000 until 2023. The magnitude of low flow during the simulation step for three discharge gauges (Majalaya, Dayeuhkolot, and Nanjung) decreased up to 48%, but, on other hand, the sediment rate increased by 20% in Dayeuhkolot. Full article
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<p>Upper Citarum Watershed.</p>
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<p>Soil investigation locations around the Citarum River.</p>
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<p>Land Use Change from 2000, 2010, and 2023 in the Upper Citarum Watershed.</p>
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<p>Geological map of the Upper Citarum Watershed (<b>a</b>), Cikeruh Sediment Trap, (<b>b</b>) Nanjung Diversion Tunnel, (<b>c</b>) Citarum Main River, and (<b>d</b>) Component C Check Dam.</p>
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<p>Geometry data of the Upper Citarum Watershed.</p>
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<p>Sediment deposition in the Upper Citarum Watershed.</p>
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