Water Extent Research Articles

Impacts of climatic variability on surface water area observed by remotely sensed imageries in the Red River Basin

Recent wetting in the Northern Great Plain (NGP) exerted strong influences on lakes and wetlands. However, the influence of recent increase in precipitation on spatiotemporal variation of surface water area is poorly understood in the Red River Basin (RRB, northern United States and southern Canada). Here, we used a high-resolution global surface water dataset to understand spatiotemporal dynamics of the annual, total, permanent, and seasonal water extent in RRB. Monthly surface water area is investigated to detect the change in seasonal surface water extent. We found four distinct phases of variation in surface water: Phase 1 (1990–2001, wetting); Phase 2 (2002- 2005, dry); Phase 3 (2006–2013, recent wetting); and Phase 4 (2014–2019, recent drying). A bare land to a permanent and seasonal water area switch is observed during Phase 1, while the other phases have experienced relatively little fluctuation. Findings have implications for nutrient concentration assessment in lakes and wetlands.

Time-series analysis of Sentinel-1/2 data for flood detection using a discrete global grid system and seasonal decomposition

Automated flood detection using earth observation data is a crucial task for efficient flood disaster management. Current solutions to identify flooded areas usually rely on calculating the difference between new observations and static, pre-calculated water extents derived by either single acquisitions or timely aggregated products. Such pre-calculated datasets, however, lack representation of real-world seasonality and short-term changes in trend. In this paper we present a complete workflow to automatically detect hydrological extreme events and their spatial extent, which automatically adapts to local seasonality and trend. For that we rely on a novel combination of well-established algorithms and tools to detect anomalies in time-series of water extent across large study areas. The data is binned into a discrete global grid system H3, which greatly simplifies aggregation across spatial and temporal resolutions. For each grid cell of an H3 resolution we perform a time-series decomposition using Seasonal and Trend decomposition using Loess (STL) of the cell’s proportion which is covered with surface water. All cells receive an anomaly score, calculated with extended isolation forest (EIF) on the residuals for each step in time. A burst of anomalies represents a hydrological extreme event like a flood or low water level. The presented methodology is applied on Sentinel-1/2 data for two study areas, one near Sukkur, Pakistan and the other one in Mozambique. The detected anomalies correlate with reported floods and seasonal variations of the study areas. The performance of the process and the possibility to use different H3 resolutions make the proposed methodology suitable for large scale monitoring.

Contribution of anthropogenic and hydroclimatic factors on the variation of surface water extent across the contiguous United States

Human pressure and climate variability are significantly threatening freshwater resources, with cascading effects on societies and ecosystems. In this context, it is crucial to understand the anthropogenic and climatic impacts on surface water dynamics. Here, we examine the interaction between the variation of surface water extent and the change in five potential concurrent drivers across river basins of the contiguous United States (CONUS) during the period 1984–2020. In particular, built-up area, population, and irrigated land are regarded as the anthropogenic drivers, while hydroclimatic drivers are represented by precipitation and potential evapotranspiration (PET). We perform statistical analyses in order to quantify the change in the considered variables and then identify significantly different spatial patterns and possible interrelations. Results show that almost 79% (169 out of 204 river basins) of the CONUS experienced an expansion of surface water extent mainly in the continental and temperate climatic regions (mean expansion 158.33 km2). Increasing precipitation is found to be the most widespread driver of the gain in surface water extent, affecting nearly 70% of river basins. The remaining 35 river basins of the CONUS, mostly located in the arid southwestern region of the country, faced a reduction in surface water extent (mean reduction −146.73 km2). The expansion of built-up areas and increasing PET resulted to contribute to the loss of surface water in all the river basins, followed by population growth (in ∼75% of the river basins), decreasing precipitation (in ∼60% of the river basins, all situated in southwestern US), and irrigated land expansion (in ∼55% of the river basins). Our findings shed light on the potential impacts of the variability of anthropogenic and hydroclimatic factors on hydrology and surface water resources, which could support predictive adaptation strategies that ensure water conservation.

Assessing surface-groundwater interactions for sustaining spring wetlands of the Great Artesian Basin, Australia

The Great Artesian Basin (GAB) is one of the world’s largest actively recharging aquifers. Groundwater discharges from the GAB sustain numerous spring wetlands, which have great ecological, scientific, and socio-economic significance. However, groundwater extraction and variation over time have had an impact on the quantity and area of spring wetlands with a 38% decline in extent since 1900. A major barrier to understanding variability in surface–groundwater interactions in spring wetlands of the GAB is the lack of observational data across critical spatial and temporal scales. Satellite observations have the ability to overcome this barrier and allow the evaluation of spring wetland responses to groundwater storage (GWS) variation. We investigated how GWS, and its associated drivers such as evapotranspiration (ET), soil moisture storage (SMS), and rainfall, in the GAB, influence the extent of surface water at five spring supergroups (Eulo, Barcaldine, Flinders, Springsure, and Springvale). We used satellite observations (2002–2017) to assess ET, SMS, rainfall, the normalized difference vegetation index (NDVI) and the modified normalized difference water index (mNDWI) for observing surface–groundwater interactions. NDVI responded positively to the GWS variation over the GAB and varies from sub-basin to sub- basin, with higher correlations in the Carpentaria sub-basin and some parts of Central and Western Eromanga. GWS variations was correlated with ET, SMS, rainfall, NDVI, mNDWI and surface water level (SWL). After a strong La Niña began in 2010, we uncovered relatively higher linear relationships between different components (ET, SMS, rainfall, NDVI, mNDWI, and SWL) and GWS variation (R2 > 0.50) than before the La Niña (R2 < 0.50), with the Flinders spring supergroup being the exception. NDVI and SMS are found to be the most significant predictor variables among ET, rainfall, SWL, and mNDWI components to influence GWS. This study provides improved understanding of surface-ground water interaction in spring wetlands and the influence of different hydrological components on variation in spring wetland extent in the GAB region.

Rainwater harvesting systems: An urban flood risk mitigation measure in arid areas

Rainwater harvesting (RWH) systems have been developed to compensate for shortage in the water supply worldwide. Such systems are not very common in arid areas, particularly in the Gulf Region, due to the scarcity of rainfall and their reduced efficiency in covering water demand and reducing water consumption rates. In spite of this, RWH systems have the potential to reduce urban flood risks, particularly in densely populated areas. This study aimed to assess the potential use of RWH systems as urban flood mitigation measures in arid areas. Their utility in the retention of stormwater runoff and the reduction of water depth and extent were evaluated. The study was conducted in a residential area in Bahrain that experienced waterlogging after heavy rainfall events. The water demand patterns of housing units were analyzed, and the daily water balance for RWH tanks was evaluated. The effect of the implementation of RWH systems on the flood volume was evaluated with a two-dimensional hydrodynamic model. Flood simulations were conducted in several rainfall scenarios with different probabilities of occurrence. The results showed significant reductions in the flood depth and flood extent, but these effects were highly dependent on the rainfall intensity of the event. RWH systems are effective flood mitigation measures, particularly in urban arid regions short of proper stormwater control infrastructure, and they enhance the resilience of the built environment to urban floods.

Holistic Reduction to Compare and Create New Indices for Global Inter-Seasonal Monitoring: Case Study for High Resolution Surface Water Mapping

A remote sensing method that integrates virtual sampling from formalized visual interpretations is proposed to facilitate land cover mapping and enhance its accuracy, with an emphasis on spatial and temporal scalability. Indices are widely used for mapping and monitoring surface water across space and time; however, they typically display some kind of limitation across different environments and seasons. A decision matrix framework based on observations derived from interpretation keys was designed to compare the performance of existing indices alongside a set of newly developed indices. This comparison helped to shortlist indices that warranted further evaluation and accuracy assessment to identify effective indices for global inter-seasonal surface water extent mapping. Additional visual inspections were conducted for criteria that remained unresolved by the decision matrix to examine index consistency across the seasons in a wide range of geographic settings around the world, and further reduce the shortlist. An accuracy assessment was performed for three new shortlisted indices. On a global scale, CAWI (Comprehensive Automatic Water Index) was the best-performing index. Its distinct binary data distribution provides the possibility of regional automatic Otsu thresholding. CAWI was determined to be compatible for Sentinel-2 and Landsat 8 sensors, providing the highest possible spatial resolution as well as the longest time series for retrospective analyses with freely available multispectral imagery. Two alternative indices were identified for sensors limited to the visible and NIR bands. The first index, CATWIC (Clear and Turbid Water Index Combination), split the classification of water into two components, with one index for generally clear water and another index for turbid water. The second, NDCHRWI (Normalized Difference Colourimetric High Resolution Water Index), applied the hue angle from a normalized difference RGB. Masking indices based on modified HSV Saturation equations were developed to reduce misclassification due to other high reflectance features. The indices’ overall accuracies, respectively, were: 94.97%, 94.51%, and 94.85%. This study concludes with recommendations for the application of different indices for sensors possessing shortwave infrared bands and for sensors limited to the visible and NIR bands, with a simple stratification of six zones for Global Surface Water monitoring.

High-temporal-resolution monitoring of reservoir water storage of the Lancang-Mekong River

The Lancang-Mekong River (LMR) is an important transboundary river in Southeast Asia shared by China, Myanmar, Laos, Thailand, Cambodia, and Vietnam from upstream to downstream. Construction and operation of dams in the LMR basin has profoundly affected its natural streamflow regime. It is therefore important to monitor changes in reservoir water storage and to quantify the impact of reservoir operation on the redistribution of surface water resources over this basin. Given the difficulty in obtaining in-situ measurements of reservoirs on the LMR, we integrated multisource remote sensing, including satellite altimetry and optical and synthetic aperture radar (SAR) images, to generate weekly water levels and water storages of nine largest reservoirs on the main stem of the LMR from 2017 to 2021. Specifically, partial surface water extent (SWE) of reservoirs was extracted from Sentinel-1 SAR images and digital elevation models (DEMs), using Random Forest algorithms trained by partial SWE derived from Sentinel-2 optical images, showing an overall accuracy higher than 95%. Based on the partial SWE and water level estimates from ICESat-2 and Global Ecosystem Dynamics Investigation (GEDI, International Space Station-based) data, the relationships between water levels and partial SWE were derived to convert partial SWE into water level time series. Furthermore, water storage time series of the nine reservoirs were obtained from water level time series and hypsometric functions derived from SRTM DEMs that were corrected by ICESat-2 data to remove systematic errors. For the Xiaowan Reservoir on the Lancang River, there is close agreement between remote sensing-derived water levels and in-situ water levels in terms of a normalized RMSE lower than 5%. Results indicate that multisource remote sensing has large potential for high-temporal-resolution monitoring of reservoir water levels and water storage. This could more precisely evaluate impacts of cascade reservoirs on the streamflow of the LMR and facilitate drought and flood mitigation for riverine countries.

Hippos alter their aggregations to mitigate density‐dependent drought effects

AbstractMegaherbivores play a critical role in the ecology of African savannas and grasslands. In addition, these systems are forecast to experience more frequent and severe droughts as a product of changes in the global climate. Thus, the continued conservation of megaherbivores and their associated ecosystems will require a better understanding of how megaherbivores respond to drought by shifting their movement, diet and social behaviour. We address this need by investigating the factors affecting changes in the abundance of common hippopotamus (Hippopotamus amphibius; hereafter: ‘hippos’) throughout the six major rivers of Kruger National Park, South Africa, during and following the severe drought of 2015/2016. Specifically, we aimed to understand the role of two environmental characteristics that have relevance to hippos and that changed in response to drought: vegetation condition and the extent of pooled surface water. In addition, we investigated the extent to which pre‐drought density affected changes in hippo abundance. Although vegetation and daytime refugia both appeared to influence pre‐drought hippo abundance, these factors were less important to the change in hippo abundance related to the drought. Instead, the response to drought was most strongly related to the pre‐drought abundance of hippos, where river segments supporting more than 50 individuals prior to the drought in 2015 decreased by more than half on average. Furthermore, we show that the degree of aggregation decreased from 2015 to 2016 because of the drought, but then began to increase again as the rains returned in 2017. Our results suggest that in addition to the large pools that support large aggregations of hippos in typical years, additional smaller pools are likely important for accommodating this drought‐induced dispersion. However, maintaining this distribution of pools will likely become more challenging as southern Africa's population and water demands increase.

Living environment shaped residents’ willingness to pay for ecosystem services in Yangtze River Middle Reaches Megalopolis, China

Profound understanding on the diversity of local residents’ willingness to pay (WTP) for ecosystem services (ESs) may aid evaluation of policy impacts by uncovering trade-offs and synergies for achieving sustainable environmental resource management. However, the reasons for the spatial preference heterogeneity of WTP are still unclear. In this study, an extensive survey with 4,580 residents in the Yangtze River Middle Reaches Megalopolis (YRMRM), central China, was carried out to investigate the public’s WTP to maintain and improve ESs. We identified key influencing factors and explored how they shaped the preference heterogeneity in WTP by random forest model and surface fitting method. Our results showed that 81.4% of residents reported WTP for ESs greater than zero. Water supply, waste treatment, food supply and raw material supply were the preferred ESs for residents. Living environment factors (i.e., extent of farmland, water and forest near the residence) were found as the key drivers of WTP. Proximity to farmland, water and forests resulted in high WTP values for a series of specific ESs. The reasons for the heterogeneity of WTP are (1) the difference in proximity to farmland, water and forest (i.e., the contributor to ESs), (2) the divergence of local residents’ preferences for ESs. This study disclosed the heterogeneity and causes of public preferences in YRMRM, which could help policy and decision makers to develop eco-compensation initiatives towards conservative and sustainable use of the environmental resources.

Using High‐Resolution Satellite Imagery and Deep Learning to Track Dynamic Seasonality in Small Water Bodies

AbstractSmall water bodies (i.e., ponds; &lt;0.01 km2) play an important role in Earth System processes, including carbon cycling and emissions of methane. Detection and monitoring of ponds using satellite imagery has been extremely difficult and many water maps are biased toward lakes (&gt;0.01 km2). We leverage high‐resolution (3 m) optical satellite imagery from Planet Labs and deep learning methods to map seasonal changes in pond and lake areal extent across four regions in Alaska. Our water maps indicate that changes in open water extent over the snow‐free season are especially pronounced in ponds. To investigate potential impacts of seasonal changes in pond area on carbon emissions, we provide a case study of open water methane emission budgets using the new water maps. Our approach has widespread applications for water resources, habitat and land cover change assessments, wildlife management, risk assessments, and other biogeochemical modeling efforts.

Synthetic Aperture Radar and Optical Sensor Techniques Using Google Earth Engine For Flood Monitoring and Damage Assessment – A Case Study of Mumbwa District, Zambia

Radar and Optical based satellite sensors were used in the study of the Mumbwa flood of December 2020. The Synthetic Aperture Radar (SAR) based Sentinel-1B was processed in Google Earth Engine (GEE) and utilized to generate an image mosaic from December 2020 to May 2021 to delineate flood extent. A local water histogram threshold change detection approach by image ratio was utilized to determine the flood extent with an intensity value of 1.26 dB as it fitted the study area uniquely as opposed to the global value of 1.25 dB. After extracting the initial flood water extent, it was necessary to filter out regions which inundated during the flood period. This was carried out using the following datasets and parameters: The HydroSHEDS Digital Elevation Model (DEM) was used to filter out regions with a slope value of greater than 7% and the Global Surface Water Layer was used to clip out regions with existing permanent surface water. Once the flooded areas were identified, the Optical based Sentinel-2 was used in the production of a Land Use Land Cover (LULC) Map for August 2020 in order to superimpose the flooded areas with existing land features over the study area. The map also under went pre and post processing in GEE using the Random Forest Classification Algorithm that achieved an Overall Accuracy and Kappa Coefficient of 0.957 and 0.91519 respectively. Thereafter the flood analysis and damage assessment were carried out. The quantitative damages to Landcover were found to be: Wetland 6,338.97 Ha (33.27%), Shrubland 5,117.75 Ha (26.89%), Biochar Soil 3,660.47 Ha (19.21%), Trees 3,466.37 Ha (18.19%), Bare soil 273.47 Ha (1.44%), Crop Fields 190.69 Ha (1%) and Built-Up 4.13 Ha (0.02%). Therefore the use of SAR by local histogram threshold approach with Optical datasets for LULC map production proved successful in the study of flood damage.

Satellites reveal hotspots of global river extent change

Rivers are among the most diverse, dynamic, and productive ecosystems on Earth. River flow regimes are constantly changing, but characterizing and understanding such changes have been challenging from a long-term and global perspective. By analyzing water extent variations observed from four-decade Landsat imagery, we here provide a global attribution of the recent changes in river regime to morphological dynamics (e.g., channel shifting and anabranching), expansion induced by new dams, and hydrological signals of widening and narrowing. Morphological dynamics prevailed in ~20% of the global river area. Booming reservoir constructions, mostly skewed in Asia and South America, contributed to ~32% of the river widening. The remaining hydrological signals were characterized by contrasting hotspots, including prominent river widening in alpine and pan-Arctic regions and narrowing in the arid/semi-arid continental interiors, driven by varying trends in climate forcing, cryospheric response to warming, and human water management. Our findings suggest that the recent river extent dynamics diverge based on hydroclimate and socio-economic conditions, and besides reflecting ongoing morphodynamical processes, river extent changes show close connections with external forcings, including climate change and anthropogenic interference.

Variation of surface water extent in the great Sebkha of Oran (NW of Algeria), using Landsat data 1987–2019: Interaction of natural factors and anthropogenic impacts

The rapid and precise extraction of water information through satellite remote sensing has become a crucial technology in the study of water resources due to its ability to provide substantial and repeatable coverage instantly. This technology is particularly useful in monitoring wetlands, among other applications. It in this perceptive, this research aims to study the surface water extent (SWE) variation of the Great Sebkha of Oran (GSO). A wetland area classified as Ramsar, bordering Oran city. Extended over nearly 40,000 ha, the water extent is influenced by climatic, geological, and hydrological factors, which are typical of endorheic regions in semi-arid and arid climates. However, over the last decade, these natural effects have been overtaxed by anthropogenic impacts caused by the strong urban expansion of the city of Oran. This is particularly evident with the commissioning of the large El Kerma sewage wastewater treatment plant (WWTPK) in 2009. Located at NE edge of the GSO. The function of this plant is to treat a large proportion of the wastewater volume generated by the city of Oran. Nevertheless, for various reasons, a lag in the implantation of the various execution phases of a development project extended to the entire GSO basin has led to nearly a decade; the WWTPK has discharged partially treated wastewater directly into GSO. This had a major impact on the mode of surface water extent change of the sebkha and led to major pollution of the latter. To better understand the (SWE) dynamics, post and pre-discharges from the WWTPK in the sebkha were monitored monthly over 33 years (1987–2019). A consistent database of Landsat sensor images (TM, ETM+, and OLI) was collected and processed in the Google Earth Engine (GEE) platform to extract the SWE in the GSO, which was achieved using spectral indices: Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI1 and NDWI2), Modified Normalized Difference Water Index (MNDWI) and Dryness/Wetness index (DWI). Subsequently, the SWE, correlated with precipitation over the entire GSO watershed, was analyzed and discussed at different time steps: monthly, annual, and seasonal. Before 2009, the precipitation regime study concerning the SWE shows the complexity of the functional dynamics of the endorheic system of the GSO watershed. After 2009, the commissioning of WWTPK had disrupted the SWE variation of the GSO. The maximum average of the SWE during the 2009–2019 sub-period was 169 km2, while it was 122 km2 during the undisturbed sub-period (1987–2008). The use of remote sensing data analysis contributes effectively to this work, representing proven results to protect this sensitive natural environment. They remain necessary to help decision-makers to ensure and preserve the sustainable ecological viability of the GSO and the natural environment of wetlands of the entire Oran sublittoral furrow of Gdyel-Arzew: Daiet Morcelly, Telamine Lake, Sebkha golf Arzew.

Protonation of Strained Epoxy Resin under Wet Conditions via First-Principles Calculations Using the H+-Shift Method.

A significant challenge in adhesive bonding is the accelerated breaking of stretched adhesives under wet conditions, which is known as cohesive failure. One group of commonly used adhesives consists of the amine-cured epoxy resins. Based on deprotonation free-energy calculations of the unstrained resin in water, it has recently been proposed that these adhesives can undergo failure through breakage originating at the protonated amine group under neutral or acidic conditions [J. Phys. Chem. B 2021, 125, 8989-8996]. In this study, we comprehensively investigated the degree of protonation of the amine group under both stretched and compressed conditions by devising a robust first-principles protonation calculation method applicable to strained materials. It was found that the amine group was partially protonated in neutral water at 298 K and that the amine group was protonated when the epoxy resin was stretched to a greater extent in water, and vice versa. These findings support the physicochemical cause of cohesive failure due to protonation of the amine group in the stretched amine-cured epoxy resins.

Applicability of SWOT data in calibrating WRF-Hydro hydrological model over the Tawa River basin

Freshwater is considered an essential natural resource that must be monitored effectively. The Surface Water and Ocean Topography (SWOT) satellite mission scheduled for launch in December 2022 will enable river discharge estimation from surface water extents, elevations, and slopes. However, the SWOT-based discharges will be inconsistent due to infrequent temporal sampling (TS) of SWOT orbit. This study explores the potential of unique TS of SWOT to determine whether it influences the calibration of hydrological model. The Weather Research and Forecast Hydrological system (WRF-Hydro) has been used to simulate river discharge. Further WRF-Hydro model has been calibrated using proxy SWOT data based on a dynamically dimensioned search algorithm. Results show that more than 90% of parameter iterations have KGE values with less than 20% absolute percent difference when only SWOT TS alone has been considered. The results suggest that model calibration using SWOT-based discharge performs similarly to daily in-situ discharge.

Automatic Segmentation of Water Bodies Using RGB Data: A Physically Based Approach

A novel method is proposed to automatically segment water extent using optical data. The key features of this approach are (i) the development of a simple physically based model that utilises only RGB data for water extent segmentation; (ii) the achievement of high accuracy in the results, particularly in the estimation of water surface area and perimeter; (iii) the avoidance of any data training process; (iv) the requirement of minimal computational resources; and (v) the release of an open-source software package that provides both command-line codes and a user-friendly graphical interface, making it accessible for various applications, research, and educational purposes. The physically based model integrates reflectance of the water surface with spectral and quantum interpretation of light. The algorithm was tested on 27 rivers and compared to manually-based delimitation, with a resulting robust segmentation procedure. Quantified errors were RMSE = 11.91 (m2) for surface area, RMSE = 12.25 (m) for perimeter, and RMSE in x: 52 (px), RMSE in y: 93 (px) for centroid location. Processing time was faster for automatic segmentation than manual delimitation, with a time reduction of 40% (case-by-case analysis) and 65% (using all case studies together in one run). Shadows, light spots, and natural and non-natural elements in the field of view may affect the accuracy of results.

Improving the accuracy of the Water Detect algorithm using Sentinel-2, Planetscope and sharpened imagery: a case study in an intermittent river

ABSTRACT Mapping surface water using remotely sensed optical imagery is a particular challenge in intermittent rivers because water contracts down to narrow linear features and isolated pools, which require accurate water detection methods and reliable image datasets. Of the many methods that use optical sensors to identify water, the Water Detect algorithm stands out as one of the best options due to its classification accuracy, open-source code, and because it does not require ancillary data. However, in the original study, the Water Detect algorithm was only tested with Sentinel-2 imagery. High-resolution and high-frequency imagery, such as Planetscope, combined with sharpening and band synthesizing techniques have the potential to improve the accuracy of surface water mapping, but their benefit to the Water Detect algorithm remains unknown. Uncertainty also exists about the extent to which different input parameters (i.e. maximum clustering and regularization) influence the accuracy of Water Detect. Practitioners seeking to map surface water in intermittent rivers need guidance on a best-practice approach to improve the accuracy of Water Detect. To meet this need, we automated an existing method for sharpening and synthesizing bands and applied it to a series of multispectral Sentinel-2 and Planetscope images. We then developed a sensitivity analysis algorithm that compared the accuracy for all possible combinations of input parameters in a given range for the water detection process – enabling optimal parameters to be identified. We applied this workflow to an 81 km stretch of the lower Fitzroy River (Western Australia) to periods when spatial water extent varied markedly, i.e. mid-wet (February), early-dry (June), and late-dry season (October), across three years with variable wet season flow. We found that the ability to accurately detect surface water using multispectral imagery was increased by using input parameters identified by the sensitivity analysis and using Visible + Near-infrared (VNIR) bands, with relatively little gained by image sharpening unless the area of interest was burnt or experienced considerable shading. Also, the regularization parameter exerted less influence on results than maximum clustering. Importantly, the accuracy of the Water Detect algorithm can vary drastically if input parameters are not calibrated to local conditions. Results also revealed that our approach was adept at detecting linear features in intermittent rivers. We recommend that practitioners using Water Detect to identify surface water undertake a workflow similar to that described here to improve the accuracy of the Water Detect algorithm. The automated routines provided by this study will significantly assist practitioners in doing so. Increasing the accuracy with which we detect and map water in intermittent rivers will improve our understanding and management of these important systems which are under increasing threat.

Grassland ecosystems resilience to drought: The role of surface water ponds

AbstractCurrently, grassland ecosystems' stability is facing severe challenges due to climate variability and the increased frequency and intensity of extreme droughts. In many grasslands worldwide, closed‐basin ponds supply various ecosystem services by collecting rainfall and runoff. These ponds may provide a significant opportunity to mitigate climate change pressures and improve the resilience of grasslands. Nevertheless, their conservation is hampered by problems auditing these small and dynamic features. Understanding the dynamics that characterise the interactions of ponds and grassland vegetation with climate changes is, therefore, a priority for better quantifying future impacts of climate change on managed‐land systems. This study investigated variations of grassland vegetation quality from 2001 to 2021 in Lessinia Regional Park, a pre‐alpine historical rural landscape in northern Italy. We combined multi‐source data, weather information, and satellite‐derived vegetation quality indices. The work focuses the analysis using a ‘pond landscape dominance’ index, calculated as a proxy of hydrologic connectivity, to illustrate the function of ponds in regulating vegetation. We provided an unprecedented mapping of 459 ponds across the whole area. The results show a progressively decreasing vegetation quality in Lessinia, following rainfall and temperature variability. Overall, however, the analysis highlighted the benefit of water extent and closeness to ponds in providing higher resistance of grasslands against drought, especially during the driest study years. These results demonstrate that this approach has great potential for monitoring the resistance of grasslands in response to drought over large regions. This study supports the importance of identifying multiple co‐benefits and ecosystem services related to ponds, which can help communities cope with climate change and environmental challenges.

A QUICK SEASONAL DETECTION AND ASSESSMENT OF INTERNATIONAL SHADEGAN WETLAND WATER BODY EXTENT USING GOOGLE EARTH ENGINE CLOUD PLATFORM

Abstract. Understanding the variation of Water Extent (WE) can provide insights into Wetland conservation and management. In this study, and-inter inner-annual variations of WE were analyzed during 2019–2021 to understand the spatiotemporal changes of the International Shadegan Wetland, Iran. We utilized a thresholding process on Modified Normalized Difference Water Index (MNDWI) to extract the WE quickly and accurately using the Google Earth Engine (GEE) platform. The water surface analysis showed that: (1) WE had a downward trend from 2019 to 2021, with the overall average WE being 1405.23 km2; (2) the water area reached its peak due to the water supply to International Shadegan Wetland through the Jarahi River and upstream reservoirs at the end of 2019 and the beginning of 2020, and the largest water body appeared in Winter 2019, reaching 1953.31 km2. In contrast, the smallest water body appeared in Autumn 2021, reaching 563.56 km2; (3) The WE of the wetland showed predictable seasonal characteristics. The water area in Winter was the largest, with an average value of 1829.1 km2, while it was the smallest in Summer, with an average value of 1100.3 km2; (4) The average water area in 2019 was 1490.5 km2 whereas in 2020 and 2021 decreased by 9% and 25%, respectively, and reached 968.6 km2 and 811.9 km2. Finally, to evaluate the proposed model, its results were compared with the Random Forest (RF) classification results. Accordingly, Histogram Analysis (HA) classification achieved 94.6% of the average overall accuracy and the average Kappa coefficient of 0.93, but the RF method obtained 95.38% of the average overall accuracy and an average Kappa coefficient of 0.94.

Recent advancements of covalent organic frameworks (COFs) as proton conductors under anhydrous conditions for fuel cell applications.

Recent electrochemical energy conversion devices require more advanced proton conductors for their broad applications, especially, proton exchange membrane fuel cell (PEMFC) construction. Covalent organic frameworks (COFs) are an emerging class of organic porous crystalline materials that are composed of organic linkers and connected by strong covalent bonds. The unique characteristics including well-ordered and tailorable pore channels, permanent porosity, high degree of crystallinity, excellent chemical and thermal stability, enable COFs to be the potential proton conductors in fuel cell devices. Generally, proton conduction of COFs is dependent on the amount of water (extent of humidity). So, the constructed fuel cells accompanied complex water management system which requires large radiators and airflow for their operation at around 80 °C to avoid overheating and efficiency roll-off. To overcome such limitations, heavy-duty fuel cells require robust proton exchange membranes with stable proton conduction at elevated temperatures. Thus, proton conducting COFs under anhydrous conditions are in high demand. This review summarizes the recent progress in emerging COFs that exhibit proton conduction under anhydrous conditions, which may be prospective candidates for solid electrolytes in fuel cells.