ReaLSAT, a global dataset of reservoir and lake surface area variations
Ontology highlight
ABSTRACT:
SUBMITTER: Khandelwal A
PROVIDER: S-EPMC9213522 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:An increasing population in conjunction with a changing climate necessitates a detailed understanding of water abundance at multiple spatial and temporal scales. Remote sensing has provided massive data volumes to track fluctuations in water quantity, yet contextualizing water abundance with other local, regional, and global trends remains challenging by often requiring large computational resources to combine multiple data sources into analytically-friendly formats. To bridge this gap and facilitate future freshwater research opportunities, we harmonized existing global datasets to create the Global Lake area, Climate, and Population (GLCP) dataset. The GLCP is a compilation of lake surface area for 1.42?+?million lakes and reservoirs of at least 10?ha in size from 1995 to 2015 with co-located basin-level temperature, precipitation, and population data. The GLCP was created with FAIR (findable, accessible, interoperable, reusable) data principles in mind and retains unique identifiers from parent datasets to expedite interoperability. The GLCP offers critical data for basic and applied investigations of lake surface area and water quantity at local, regional, and global scales.
| S-EPMC7289843 | biostudies-literature
Project description:The potential of using Landsat for assessing long-term water surface dynamics of individual reservoirs at a global scale has been significantly hindered by contaminations from clouds, cloud shadows, and terrain shadows. A novel algorithm was developed towards the automatic correction of these contaminated image classifications. By applying this algorithm to the dataset by Pekel et al. (2016), time series of area values for 6817 global reservoirs (with an integrated capacity of 6099 km3) were generated from 1984 to 2015. The number of effective images that can be used in each time series has been improved by 81% on average. The long-term average area for these global reservoirs was corrected from 1.73×105 km2 to 3.94×105 km2. The results were proven to be robust through validation using observations, synthetic data, and visual inspection. This continuous reservoir surface area dataset can provide benefit to various applications (both at continental and local scales).
| S-EPMC8793793 | biostudies-literature
Project description:Land surface temperature (LST) plays a critical role in land surface processes. However, as one of the effective means for obtaining global LST observations, remote sensing observations are inherently affected by cloud cover, resulting in varying degrees of missing data in satellite-derived LST products. Here, we propose a solution. First, the data interpolating empirical orthogonal functions (DINEOF) method is used to reconstruct invalid LSTs in cloud-contaminated areas into ideal, clear-sky LSTs. Then, a cumulative distribution function (CDF) matching-based method is developed to correct the ideal, clear-sky LSTs to the real LSTs. Experimental results prove that this method can effectively reconstruct missing LST data and guarantee acceptable accuracy in most regions of the world, with RMSEs of 1-2 K and R values of 0.820-0.996 under ideal, clear-sky conditions and RMSEs of 4-7 K and R values of 0.811-0.933 under all weather conditions. Finally, a spatiotemporally continuous MODIS LST dataset at 0.05° latitude/longitude grids is produced based on the above method.
| S-EPMC8976064 | biostudies-literature
Project description:Spatial dataset representing the nutrient distribution in Karayar reservoir during pre and post-monsoon season is presented. Random sampling method was used for data collection and the sample location were fixed using a handheld global positioning system (Garmin GPSMAP-76). The nutrients were estimated using the standard techniques as described in the American Public Health Association (APHA) manual. Physical parameters were estimated using a Hanna portable multi water quality probe (HI-9828, USA). The spatial distribution of physical and nutrient content in surface water is carried out using an inverse distance weighted technique.
| S-EPMC5496461 | biostudies-literature
Project description:We studied community-environment relationships of lake macrophytes at two metacommunity scales using data from 16 regions across the world. More specifically, we examined (a) whether the lake macrophyte communities respond similar to key local environmental factors, major climate variables and lake spatial locations in each of the regions (i.e., within-region approach) and (b) how well can explained variability in the community-environment relationships across multiple lake macrophyte metacommunities be accounted for by elevation range, spatial extent, latitude, longitude, and age of the oldest lake within each metacommunity (i.e., across-region approach). In the within-region approach, we employed partial redundancy analyses together with variation partitioning to investigate the relative importance of local variables, climate variables, and spatial location on lake macrophytes among the study regions. In the across-region approach, we used adjusted R2 values of the variation partitioning to model the community-environment relationships across multiple metacommunities using linear regression and commonality analysis. We found that niche filtering related to local lake-level environmental conditions was the dominant force structuring macrophytes within metacommunities. However, our results also revealed that elevation range associated with climate (increasing temperature amplitude affecting macrophytes) and spatial location (likely due to dispersal limitation) was important for macrophytes based on the findings of the across-metacommunities analysis. These findings suggest that different determinants influence macrophyte metacommunities within different regions, thus showing context dependency. Moreover, our study emphasized that the use of a single metacommunity scale gives incomplete information on the environmental features explaining variation in macrophyte communities.
| S-EPMC6244864 | biostudies-literature
Project description:The global reach-scale "ICESat-2 River Surface Slope" (IRIS) dataset comprises average and extreme water surface slopes (WSS) derived from ICESat-2 observations between October 2018 and August 2022 as a supplement to 121,583 reaches from the "SWOT Mission River Database" (SWORD). To gain full advantage of ICESat-2's unique measurement geometry with six parallel lidar beams, the WSS is determined across pairs of beams or along individual beams, depending on the intersection angle of spacecraft orbit and river centerline. Combining both approaches maximizes spatial and temporal coverage. IRIS can be used to research river dynamics, estimate river discharge, and correct water level time series from satellite altimetry for shifting ground tracks. Additionally, by referencing SWORD as a common database, IRIS may be used in combination with observations from the recently launched SWOT mission.
| S-EPMC10244387 | biostudies-literature
Project description:This dataset [1] quantifies the extent and rate of annual change in surface water area (SWA) across India's rivers and basins over a period of 30 years spanning 1991 to 2020. This data has been derived from the Global Surface Water Explorer, which maps historical terrestrial surface water occurrence globally using the Landsat satellite image archive since 1984, at a spatial resolution of 30 m/pixel and a temporal resolution of once a month. This monthly time-series was used to create annual composites of wet-season (October, November, December), dry-season (February, March, April), and permanent (October, November, December, February, March, April) surface water extent, which were then used to estimate annual rates of change. To estimate SWA trends for both river networks and their basins, we conducted our analysis at two spatial scales - (1) cross-sectional reaches (transects) across river networks, and (2) sub-basins within river catchments. For each reach and sub-basin (henceforth basin), temporal trends in wet-season, dry-season, and permanent SWA were estimated using the non-parametric Sen's slope estimator. For every valid reach and basin, the temporal timeseries of invalid or missing data was also computed as a fractional area to inform the level of certainty associated with reported SWA trends estimates. In addition to a Zenodo data repository, this data [1] is presented as an interactive web application (https://sites.google.com/view/surface-water-trends-india/; henceforth Website) to allow users to visualize the trends of permanent, wet-season, and dry-season water along with the extent of missing data for individual transects or basins across India. The Website provides a simple user interface to enable users to download seasonal time-series of SWA for any region of interest at the scale of the river network or basin. The Website also provides details about accessing the annual permanent, dry and wet season composites, which are stored as publicly accessible cloud assets on the Google Earth Engine platform. The spatial (basin and reach) and temporal (wet season, dry season, and permanent water scenarios) scales of information provided in this dataset yield a granular understanding of water systems in India. We envision this dataset to serve as a baseline information layer that can be used in combination with other data sources to support regional analysis of hydrologic trends, watershed-based analysis, and conservation planning. Specific applications include, but are not limited to, monitoring and identifying at-risk wetlands, visualizing and measuring changes to surface water extent before and after water infrastructure projects (such as dams and water abstraction projects), mapping drought prone regions, and mapping natural and anthropogenic changes to SWA along river networks. Intended users include, but are not limited to, students, academics, decision-makers, planners, policymakers, activists, and others interested in water-related issues.
| S-EPMC10792741 | biostudies-literature
Project description:The increase of urbanization level has led to the rapid increase of impervious surface area (ISA). The aim of this work is to clarify the relationship between the ISA and water quality and lay a foundation for the improvement and protection of the water quality in the basin. Taking the Fuxian Lake Basin in Yunnan Province as an example, based on the Landsat ETM+ remote sensing image and the Gram-Schmidt (GS) image fusion algorithm, the four-terminal model and the linear spectral mixture model (LSMM) were used to extract the impervious surface of the watershed from 2006 to 2015. And statistical methods were used to distinguish its relationship with water quality. The results show that the four-terminal model and the linear spectral mixture model can effectively extract the impervious surface information of the Fuxian Lake Basin. The average root mean square error (RMS) of the image decomposition results from 2006 to 2015 was less than 0.02. In the past 10 years, the ISA has changed significantly in the Fuxian Lake Basin. The ISA showed an overall upward trend from 2006 to 2015. It increased from 24.73?km2 in 2006 to 35.14?km2 in 2015, an increase of 10.81?km2. From the value anomaly, the ISA in 2006 and 2009 is lower than the multiyear average, and those in the other years are higher than the multiyear average. The percentage of ISA in the basin was significantly positively correlated with Chemical Oxygen Demand-Mn (CODMn) and total phosphorus (TP) (r is 0.772, 0.763), and the correlation in the flooding season was greater than that in the dry season. The ISA threshold for water quality deterioration is around 10% in the Fuxian Lake Basin. Reducing ISA coverage, controlling ISA to less than 10%, and preventing nonpoint source pollution during flooding season will be the best measures to effectively improve the water quality environment in the basin.
| S-EPMC7196990 | biostudies-literature
Project description:Salinization of freshwater resources is a growing water quality challenge, which may negatively impact both sectoral water-use and food security, as well as biodiversity and ecosystem services. Although monitoring of salinity is relatively common compared to many other water quality parameters, no compilation and harmonisation of available datasets for both surface and groundwater components have been made yet at the global scale. Here, we present a new global salinity database, compiled from electrical conductivity (EC) monitoring data of both surface water (rivers, lakes/reservoirs) and groundwater locations over the period 1980-2019. The data were assembled from a range of sources, including local to global salinity databases, governmental organizations, river basin management commissions and water development boards. Our resulting database comprises more than 16.3 million measurements from 45,103 surface water locations and 208,550 groundwater locations around the world. This database could provide new opportunities for meta-analyses of salinity levels of water resources, as well as for addressing data and model-driven questions related to historic and future salinization patterns and impacts.
| S-EPMC7359304 | biostudies-literature
Project description:Surface water, which is changing constantly, is a crucial component in the global water cycle, as it greatly affects the water flux between the land and the atmosphere through evaporation. However, the influences of changing surface water area on the global water budget have largely been neglected. Here we estimate an extra water flux of 30.38 ± 15.51 km3/year omitted in global evaporation calculation caused by a net increase of global surface water area between periods 1984-1999 and 2000-2015. Our estimate is at a similar magnitude to the recent average annual change in global evapotranspiration assuming a stationary surface water area. It is also comparable to the estimated trends in various components of the hydrological cycle such as precipitation, discharge, groundwater depletion, and glacier melting. Our findings suggest that the omission of surface water area changes may cause considerable biases in global evaporation estimation, so an improved understanding of water area dynamics and its atmospheric coupling is crucial to reduce the uncertainty in the estimation of future global water budgets.
| S-EPMC6487836 | biostudies-literature