Water Extent Research Articles

Multiple subglacial water bodies below the south pole of Mars unveiled by new MARSIS data

The detection of liquid water by the Mars Advanced Radar for Subsurface and Ionosphere Sounding (MARSIS) at the base of the south polar layered deposits in Ultimi Scopuli has reinvigorated the debate about the origin and stability of liquid water under present-day Martian conditions. To establish the extent of subglacial water in this region, we acquired new data, achieving extended radar coverage over the study area. Here, we present and discuss the results obtained by a new method of analysis of the complete MARSIS dataset, based on signal processing procedures usually applied to terrestrial polar ice sheets. Our results strengthen the claim of the detection of a liquid water body at Ultimi Scopuli and indicate the presence of other wet areas nearby. We suggest that the waters are hypersaline perchlorate brines, known to form at Martian polar regions and thought to survive for an extended period of time on a geological scale at below-eutectic temperatures.

Integrating Lateral Inflows Into a SWOT Mission River Discharge Algorithm

AbstractEstimating river discharge from observed surface water extents and elevations is central to the Surface Water and Ocean Topography (SWOT) mission. Although near global in coverage, SWOT will only observe rivers wider than 50 to 100 m, overlooking smaller tributaries draining into observable river reaches. This is problematic for the Metropolis‐Manning (MetroMan) discharge algorithm, which assumes changes in discharge per location must be balanced by a change in cross‐sectional area, not accounting for potential flow contributions SWOT will not observe within the inversion region analyzed. Here, we quantify the effect of these lateral inflows on the performance of estimated discharges along the Muskingum River using MetroMan. Three scenarios are considered: (1) disregarding lateral inflows, (2) providing MetroMan with observed lateral inflows, and (3) providing MetroMan with uncertain model‐derived lateral inflows to assess the discharge algorithm's effectiveness. Scenarios are expanded to consider multiple lateral inflow magnitudes and distributions. Results indicate discharge retrievals were degraded once unaccounted lateral inflows exceeded 5% of average river discharge. When MetroMan is informed by observed lateral inflows, the derived discharges have a relative root‐mean‐square error (rRMSE) of 23% as compared to 360% when lateral inflows are neglected. More importantly, when MetroMan uses simulated lateral inflows, with peak flow condition percent errors as high as 93%, discharge retrieval performance is similar (rRMSE = 17%). These findings highlight the importance of accounting for lateral flows, even in the absence of perfect measurements.

Automated surface water detection from space: a Canada-wide, open-source, automated, near-real time solution

The goal of this research was to develop a fully automated method to map open water extent that is operationally practical on a national scale. Such a system needs to produce acceptable results in all regions of the country and particularly in the Prairie Potholes Region where understanding water surface dynamics is important for predicting flooding, agriculture/water availability and for evaporation calculations in weather models. A system was developed to automate, ingest, and process Radarsat-2 (RS2) imagery, from which mapping open water body extents in near-real time was carried out using a machine learning classification technique. A Random Forest classification algorithm was trained using the data extracted from the Global Surface Water (GSW) occurrence dataset. The GSW occurrence thresholds used to extract the training data were examined and there was little influence of uncertainty on the classification. The quality of classifications generated from RS2 Fine Wide mode imagery improved with increasing incidence angle. All Fine Quad incident angles produced acceptable results, but Standard and Wide mode imagery produced results below the accuracy thresholds deemed acceptable for this operational solution. Validation was carried out by comparing mapped water extents to temporally coincident high resolution multi-spectral imagery and to the USGS Global Land Cover Characteristics dataset, that is currently used as a land-water mask by ECCC in weather numerical weather modelling. The system that has been developed will allow new image datasets (e.g. Radarsat Constellation Mission) or training data that becomes available to be used to improve models. The open source code will be made available on Github.

A Machine Learning Method for Inland Water Detection Using CYGNSS Data

The inland water bodies are critical components of ecosystems and hydrologic cycles. Thus, the water extent data are crucially important for hydrological and ecological studies. Due to its high temporal resolution, the Cyclone Global Navigation Satellite System (CYGNSS) has the potential for real-time inland water monitoring. In this letter, a high-resolution machine learning (ML) method for detecting inland water content using the CYGNSS data is implemented via the random undersampling boosted (RUSBoost) algorithm. The CYGNSS data of the year 2018 over the Congo and Amazon basins are gridded into <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$0.01^{\circ }\, \times \, 0.01^{\circ }$ </tex-math></inline-formula> cells. The RUSBoost-based classifier is trained and tested with the CYGNSS data over the Congo basin. The data of the Amazon basin that is unknown to the classifier are then used for further evaluation. By only using the observables extracted from the CYGNSS data, the proposed technique is able to detect 95.4% and 93.3% of the water bodies over the Congo and Amazon basins, respectively. The performance of the RUSBoost-based classifier is also compared with an image processing-based inland water detection method. For the Congo and Amazon basins, the RUSBoost-based classifier has a 3.9% and 14.2% higher water detection accuracy, respectively.

Assimilation of future SWOT-based river elevations, surface extent observations and discharge estimations into uncertain global hydrological models

Global estimates of river dynamics are needed in order to manage water resources, mainly in developing countries where in-situ observation is limited. Remote sensors such as nadir altimeters can complement ground data. Current altimeters miss however a large number of continental surface water bodies. This issue will be largely resolved by the future Surface Water and Ocean Topography (SWOT) mission, thanks to its wide swath altimeter. SWOT will provide almost globally two-dimensional water elevation maps for rivers over 100 m wide and water bodies over 250 m × 250 m. During this research, we investigated the potential of SWOT to correct hydrological models on a global/continental scale, through data assimilation. For this purpose, an Observing System Simulation Experiment (OSSE), also known as “twin experiment”, has been implemented. Model forcings and parameters were perturbed to jointly achieve global hydrological models (GHMs) uncertainties, which is the expected scenario in which the SWOT community will mainly evaluate the future SWOT data. SWOT-like observations of water surface elevation (WSE), flooded water extent (FWE), and/or SWOT derived discharge (Q) were used to correct modelled Q, WSE and FWE from a large-scale hydrological and hydrodynamic model (MGB – portuguese acronym of “Modelo de Grandes Bacias”), using a Ensemble Kalman filter (EnKF). The results indicate that SWOT products could largely improve hydrological simulations on a global and continental scale. SWOT-like discharge can reduce ~40% of model errors in daily discharge. Furthermore, when anomalies of the WSE DA approach were implemented, the error reduction was even greater for all state variables compared to the absolute WSE DA, achieving average error reduction values of about ~30% compared to ~24%. Finally, the simultaneous DA of all the SWOT-like variables together reduces errors from ~14% to ~22% compared to the average of assimilating only one variable.

High-Resolution Surface Water Classifications of the Xingu River, Brazil, Pre and Post Operationalization of the Belo Monte Hydropower Complex

We describe a new high spatial resolution surface water classification dataset generated for the Xingu river, Brazil, from its confluence with the Iriri river to the Pimental dam prior to construction of the Belo Monte hydropower complex, and after its operationalization. This river is well-known for its exceptionally high diversity and endemism in ichthyofauna. Pre-existing datasets generated from moderate resolution satellite imagery (e.g., 30 m) do not adequately capture the extent of the river. Accurate measurements of water extent are important for a range of applications utilizing surface water data, including greenhouse gas emission estimation, land cover change mapping, and habitat loss/change estimates, among others. We generated the new classifications from RapidEye imagery (5 m pixel size) for 2011 and PlanteScope imagery (3 m pixel size) for 2019 using a Geographic Object Based Image Analysis (GEOBIA) approach.

Construction of the Long-Term Global Surface Water Extent Dataset Based on Water-NDVI Spatio-Temporal Parameter Set

Inland surface water is highly dynamic, seasonally and inter-annually, limiting the representativity of the water coverage information that is usually obtained at any single date. The long-term dynamic water extent products with high spatial and temporal resolution are particularly important to analyze the surface water change but unavailable up to now. In this paper, we construct a global water Normalized Difference Vegetation Index (NDVI) spatio-temporal parameter set based on the Moderate-resolution Imaging Spectroradiometer (MODIS) NDVI. Employing the Google Earth Engine, we construct a new Global Surface Water Extent Dataset (GSWED) with coverage from 2000 to 2018, having an eight-day temporal resolution and a spatial resolution of 250 m. The results show that: (1) the MODIS NDVI-based surface water mapping has better performance compared to other water extraction methods, such as the normalized difference water index, the modified normalized difference water index, and the OTSU (maximal between-cluster variance method). In addition, the water-NDVI spatio-temporal parameter set can be used to update surface water extent datasets after 2018 as soon as the MODIS data are updated. (2) We validated the GSWED using random water samples from the Global Surface Water (GSW) dataset and achieved an overall accuracy of 96% with a kappa coefficient of 0.9. The producer’s accuracy and user’s accuracy were 97% and 90%, respectively. The validated comparisons in four regions (Qinghai Lake, Selin Co Lake, Utah Lake, and Dead Sea) show a good consistency with a correlation value of above 0.9. (3) The maximum global water area reached 2.41 million km2 between 2000 and 2018, and the global water showed a decreasing trend with a significance of P = 0.0898. (4) Analysis of different types of water area change regions (Selin Co Lake, Urmia Lake, Aral Sea, Chiquita Lake, and Dongting Lake) showed that the GSWED can not only identify the seasonal changes of the surface water area and abrupt changes of hydrological events but also reflect the long-term trend of the water changes. In addition, GSWED has better performance in wetland areas and shallow areas. The GSWED can be used for regional studies and global studies of hydrology, biogeochemistry, and climate models.

Linkage of the physical environments in the northern Antarctic Peninsula region to the Southern Annular Mode and the implications for the phytoplankton production

The long-term (almost 20 years) hydrographic and primary production data collected by the U.S. Antarctic Marine Living Resources (AMLR) program during the austral summer near Elephant Island and the South Shetland Islands were combined with satellite observations to assess interannual variability in environmental conditions and production of the northern Antarctic Peninsula (NAP). Correlation analyses show that interannual variability of the region is related to the dominant mode of the Southern Hemisphere extratropical climate variability, the Southern Annular Mode (SAM). Post 2000, significant correlations (r > 0.5, p < 0.1) are detected between SAM and environmental properties that potentially affect NAP phytoplankton production, particularly mixed layer depth (MLD), the extent of the nutrient-rich Circumpolar Deep Water (CDW), and photosynthetically available radiation (PAR). The relationship of these properties to SAM exhibits spatial variability. Near Elephant Island, interannual variations of the summertime MLD and PAR are significantly and positively correlated to the variation of the summer SAM index (r = 0.89 and p = 0.0003 for MLD; r = 0.64 and p = 0.04 for PAR). Significant correlations also exist between chlorophyll concentration and the summer SAM index (r = 0.7, p = 0.02), which are attributed to the SAM-related change in PAR and vertical mixing. Near the South Shetland Islands, the correlation between MLD and summer SAM index is weakened (r = 0.59, p = 0.05). Significant correlations are found between CDW extent and the spring SAM index as well as the annual SAM index (r > 0.7, p ≤ 0.01). Significant correlations also exist between chlorophyll concentration and the spring and annual SAM indices (r > 0.6, p ≤ 0.06). The statistical relationship between chlorophyll concentration and SAM is used with the predicted variation of SAM based on CMIP5 models to make projections of biological production change over the next 50 years in the NAP and adjacent areas. With an estimated SAM trend of 0.03 yr−1, in the next 50 years the surface chlorophyll concentration over the NAP and WAP outer shelf and slope will increase by as much as 0.5 mg m−3, and the integrated chlorophyll concentration over the upper 100-m water column will increase in the NAP area by 10 mg m−2 to 50 mg m−2.

Monitoring and assessment of the seasonal and inter-annual pan inundation dynamics in the Kgalagadi Transfrontier Park, Southern Africa

In arid regions, pans form a critical water source that supports local communities, livestock, and wildlife, with readily available water resources. However, these waterbodies are unevenly distributed across the landscape and highly ephemeral and this influences their ability to provide water, especially in remote and water scarce areas. In addition, it remains difficult, impractical, and costly to monitor their variability using traditional hydrometric networks. In this regard, their contribution as water sources remains uncertain. The availability of spatially explicit data, with improved sensor design characteristics such as Landsat-8 enables the monitoring of their surface water extent over space and time. This study therefore, for the first time investigated the potential of using Landsat-8 in detecting and monitoring the spatial and temporal dynamics of pan inundation in the water scarce region of Kgalagadi in Southern Africa between 2016 and 2018. This was achieved by testing the performance of multiple indices, namely; the Normalised Difference Water Index (NDWI), Modified Normalised Difference Water Index (MNDWI), Automated Water Extraction Index for Shadow (AWEIsh), Water Ratio Index (WRI) and Land Surface Water Index (LSWI). Overall, the results have shown the potential of remote sensing data to monitor pan inundation. The MNDWI produced the highest overall classification accuracy of 84.91% comparatively. The MNDWI was then used for monitoring and assessing pan inundation dynamics over different seasons between 2016 and 2018. During the study period, pan inundation varied significantly (α = 0.05) for different seasons. Nevertheless, 2017 had the largest surface water extent covering 23 195.8 m2, during the wet season and 17 913.3 m2 in the dry season. On the other hand, 2018 showed the smallest spatial coverage of 13 076 m2 for the wet season and 6032.587 m2 during the dry season. The observed spatial variability in pan inundation was attributed to rainfall and temperature variability. The study thus revealed the utility of remotely sensed data sets in providing a more robust approach for monitoring seasonal and inter-annual pan inundation variations in semi-arid environments of Southern Africa. This information is important for water management decision making, specifically for water-limited areas, to conserve these water sources to ensure their sustainability in supporting local communities, livestock and wildlife population.

A nutrient control on marine anoxia during the end-Permian mass extinction

Oxygen deprivation and hydrogen sulfide toxicity are considered potent kill mechanisms during the mass extinction just before the Permian–Triassic boundary (~251.9 million years ago). However, the mechanism that drove vast stretches of the ocean to an anoxic state is unclear. Here, we present palaeoredox and phosphorus speciation data for a marine bathymetric transect from Svalbard. This shows that, before the extinction, enhanced weathering driven by Siberian Traps volcanism increased the influx of phosphorus, thus enhancing marine primary productivity and oxygen depletion in proximal shelf settings. However, this non-sulfidic state efficiently sequestered phosphorus in the sediment in association with iron minerals, thus restricting the intensity and spatial extent of oxygen-depleted waters. The collapse of vegetation on land immediately before the marine extinction changed the relative weathering influx of iron and sulfate. The resulting transition to euxinic (sulfidic) conditions led to enhanced remobilization of bioavailable phosphorus, initiating a feedback that caused the spread of anoxic waters across large portions of the shelf. This reconciles a lag of >0.3 million years between the onset of enhanced weathering and the development of widespread, but geographically variable, ocean anoxia, with major implications for extinction selectivity. Phosphorus remobilized from seafloor sediments due to a reduced influx of iron-oxide from land led to widespread anoxia during the end-Permian mass extinction, according to palaeoredox and phosphorus speciation proxy records from Svalbard.

Exploring the Effect of Rainfall Variability and Water Extent in Tanguar Haor, Sunamganj

Tanguar haor is located in the north-eastern region of Bangladesh and frequently experienced extreme events such as high rainfall that affect the haor basin area with flash floods on a regular basis. Tanguar haor is usually foreseen to severe disastrous conditions for the reason of changes in rainfall patterns and water extent. The study was conducted at Tanguar haor of Sunamganj district. This study mainly focuses on the year to year rainfall variability (2001-2018) and water extent (2002-2018) in the Tanguar haor area. The specific objective of the study was to explore the effect of rainfall variability and water extent in Tanguar haor. Information and data were collected mainly from secondary sources. Analysis of data and assess the ecological effect of changes in rainfall patterns and water extent were the main activities of the study along with others. The findings of the study were changeability in rainfall patterns and water extent frequently caused a flash flood in pre-monsoon and monsoon season in the Tanguar haor area. Flash flood causes a vast amount of financial loss for the living people of the Tanguar haor that makes them more vulnerable to live well. The result of this study may help to gather new knowledge on the consequences of rainfall variability and water extent in the haor basin area. It may add significance to the management of flash flood and severe stress in the haor basin area.

Greenland cockles (Serripes groenlandicus Mohr 1786) from Bjørnøya (Bear Island), Svalbard record environmental change: Local and regional drivers of growth

Long time series are necessary to separate natural variability from that due to climate change and make it possible to interpret the impact of environmental change on ecosystems. Sclerochronological analysis of environmental records in the shells of mollusks make it possible to reconstruct environmental-ecological linkages over sufficiently long time scales to detect ecosystem change. Annually resolved archives can be key indicators of how climate change manifests in the marine ecosystem and are especially important in areas with limited instrumental records. We developed a master growth chronology of the Greenland Cockle (Serripes groenlandicus) from Bear Island (Bjørnøya), Svalbard, Norway (74°41′N, 18°56′E) from analysis of annual shell growth increments. The chronology was developed from 13 individuals collected from 80 m deep and ranging in age from 40 to 48 years old (the oldest known individuals of this species to date). The master chronology, expressed as a growth index (GI), extended from 1968 to 2012 and exhibited a cyclical pattern with decadal periods of high growth alternating with slower growth intervals. We identified significant relationships between variations in large-scale climate regimes (e.g. Atlantic Multidecadal Oscillation), local environmental conditions (e.g., sea temperature, sea ice), and shell growth. A model using sea temperature and sea ice explained 34–49% of the annual variations in shell growth patterns. The growth chronology and environmental linkages for cockles at this site in southern Svalbard near the southern extent of Arctic water in the Barents Sea were substantially different than at other locations in Svalbard. We conclude that the Greenland Cockle is quite sensitive to environmental changes over annual to decadal scales and therefore can serve as a proxy of climate change effects on ecosystem processes in the Arctic, but that local environmental processes that regulate food availability to the seabed are key determinants of environmental regulation on shell growth.

Global lake responses to climate change

Climate change is one of the most severe threats to global lake ecosystems. Lake surface conditions, such as ice cover, surface temperature, evaporation and water level, respond dramatically to this threat, as observed in recent decades. In this Review, we discuss physical lake variables and their responses to climate change. Decreases in winter ice cover and increases in lake surface temperature modify lake mixing regimes and accelerate lake evaporation. Where not balanced by increased mean precipitation or inflow, higher evaporation rates will favour a decrease in lake level and surface water extent. Together with increases in extreme-precipitation events, these lake responses will impact lake ecosystems, changing water quantity and quality, food provisioning, recreational opportunities and transportation. Future research opportunities, including enhanced observation of lake variables from space (particularly for small water bodies), improved in situ lake monitoring and the development of advanced modelling techniques to predict lake processes, will improve our global understanding of lake responses to a changing climate.

Integrated Proteomics and Metabolomics Analysis Highlights Correlative Metabolite-Protein Networks in Soybean Seeds Subjected to Warm-Water Soaking.

Soaking of soybean seeds is a prerequisite for the production of soy foods, and it has been shown that the extent of water absorbed during different imbibition conditions directly affects the quality of the subsequent soybean seed products by yet unknown mechanisms. In order to elucidate the molecular changes in soybean seeds during different soaking temperatures, we performed an integrated proteomics and metabolomics analysis of seeds soaked at 4, 25, and 55 °C. Proteomics analysis revealed that various enzymes related to carbohydrate and protein hydrolysis were activated in soybean seeds during water soaking at 55 °C. Interestingly, results obtained from this integrated proteomics and metabolomics study showed changes in various metabolites, including isoflavones, amino acids, and sugars, that were positively correlated with proteome changes occurring upon soaking at 55 °C. Furthermore, soaking of soybean seeds at 55 °C resulted in degradation of indigestible anti-nutrients such as raffinose oligosaccharides. Taken together, our results suggest that the seed soaking at a high temperature (55 °C) increases the nutritional value of soybean seeds by decreasing the contents of some of the common anti-nutrients.

Mapping small and medium-sized water reservoirs using Sentinel-1A: a case study in Chiapas, Mexico

Using satellite data to study small water bodies (SWB) and medium-sized water bodies (MSWB) is extremely useful for understanding their status, how to conserve them as water reservoirs, and their vulnerability to climate variability. The images studied in our work correspond to different-sized lagoons located in areas with high and low topography in a tropical region of Chiapas, Mexico. Our research project delineates SWB and MSWB. For this analysis, we considered water bodies to be uniform regions in a synthetic aperture radar image. The robustness of the method was determined based on an analysis of the morphologies of 23 lagoons. Several methods, including Hermite transform, were analyzed and compared with other image denoising methods used to improve speckle reduction. To obtain additional spatial information for image classification, we analyzed texture using the gray-level co-occurrence matrix. The results indicate that the Hermite filter is the best method for identifying water bodies. The advantage of this filter is the identification of local patterns such as edges and lines. It also preserves and improves aspects related to the homogeneity of water bodies, using the Hermite coefficient selection criteria for local pattern feature selection/extraction. The lake water extent products demonstrate that Sentinel-1 is useful for identifying SWB in this study area. The results show very high detection of water bodies, with adequate detection for water bodies larger than 2 ha, and an area accuracy of 80%.

Spatiotemporal Analysis of Waterbodies in Ranchi District, Jharkhand using Satellite Data: An Input for Jal Shakti Abhiyan

Keeping in the view of water conservation the Government of India has initiated the Jal Shakti Abhiyan, which needs the inventory of waterbodies for further planning and monitoring. Lakes and Ponds in year 2005 and year 2018 were mapped in Ranchi district at 1:5000 scale using high resolution satellite data and reservoirs at 1:50,000 scale using moderate resolution satellite data. Seasonal fluctuation in water extent of reservoirs was estimated using Pre- monsoon and post-monsoon satellite data. Total number of lakes and ponds mapped in Ranchi district in year 2005 and year 2018 were 2170 and 2556, respectively. Total area under lakes and ponds was 1425 ha and 1618 ha, respectively in year 2005 and year 2018, which was 0.29% and 0.32% of total geographical area of Ranchi district. A total of 19 and 20 reservoirs mapped in Ranchi district in year 2006-07 and year 2018-19, respectively. Area recorded under reservoir category in Ranchi district was 2854 ha and 2829 ha in pre-monsoon season in the year 2007 and 2019, respectively. In Post-monsoon season the area occupied by reservoirs was 3428 ha and 3352 ha in Year 2007 and 2018, respectively. It has been suggested to undertake large scale mapping of lakes and ponds at 1:500/1000 scale using seasonal satellite data for preparation of inventory and estimation of pre and post-monsoon variation in number and area of lakes and ponds. Maps and statistics generated in present study are useful for planning, monitoring and management of waterbodies in present scenario of urbanization, environmental degradation and climate change. Keywords: High resolution data, urbanization, pre-monsoon, post-monsoon, lakes and ponds

Satellite-derived river width and its spatiotemporal patterns in China during 1990–2015

Rivers are essential to the Earth's ecosystem, but the current understanding of river width variability is limited, owing to the sparse distribution of gauging stations. Remote sensing data enable the surveying and analysis of river geomorphology by providing multi-temporal Earth observation data from satellites at fine spatial and temporal resolutions. We proposed an optimized RivWidth method to automatically calculate width for all channels in a water map and parallelized it to produce the Multi-temporal China River Width (MCRW) dataset, which is the first 30-m multi-temporal river width dataset for China during 1990–2015, including estimates under both seasonal fluctuations and dynamic inundation frequencies. The MCRW dataset is made up of 1.3 × 108 seasonal estimates of river width, and covers 1.4 × 105 km of rivers in China. We validated the MCRW dataset against in-situ measurements. The MCRW estimates at maximum water extent showed a satisfactory accuracy of 15.0% and 15.2% for the mean absolute percentage error (MAPE) and the relative root-mean-square error (RRMSE), respectively. The MCRW dataset was further compared with the current state-of-the-art global product, the Global River Widths from Landsat (GRWL) dataset, which demonstrated the superiority of the MCRW in describing the basins of China. Our analysis indicated that the mean river widths of China in both summer and winter have increased over the past 25 years, and river width of the Yangtze River mainstream in the lower drainage region has shown a downward trend while the its middle reaches and tributaries (upstream of the Three Gorges Dam) have shown an upward trend. We also developed a locally adaptive search method to quantify seasonal (summer and winter) river width variability. The results revealed that most of the rivers were wider in summer during the study period, and mainstream of Yangtze River in middle/lower region exhibited less seasonal variability than its tributaries. Larger widths were observed in the middle reaches of the Yellow River and the upper reaches of the Black River in winter due to ice-jam floods. Overall, the generated MCRW dataset has the potential to serve as a fundamental resource in Earth system science, and could provide valuable support to surface water resource and riverine management.

Analyzing the Lake Urmia restoration progress using ground-based and spaceborne observations

Lake Urmia, located in the North West of Iran, was once the most extensive permanent hypersaline lake in the world. Unsustainable water management in response to increasing demand together with climatic extremes have given rise to the lake's depletion during the last two decades. The Urmia Lake Restoration Program (ULRP) was established in 2013 and aims to restore the lake within a 10-year program. This study aims to monitor these restoration endeavours using spaceborne and ground-based observations. We analyzed the in-situ water level, the surface water extent, and the water volume of the lake. The water storage change of the Urmia Lake catchment is quantified using the Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On satellite observations, which gives us a holistic view of hydrological components. Our analysis shows a positive trend of 14.5 cm/yr, 204 km2/yr, and 0.42 km3/yr in the time series of lake water level, lake water area, and water volume from 2015 to 2019 which indicates a short-lived stabilization of Lake Urmia. This has been achieved mainly due to an increase of 0.35 km3/yr in inflow from rivers to the lake, predominantly driven by anomalous precipitation events in 2016 and early 2019. However, the long-term trend from 2003 to 2019 still shows negative values of −22 cm/yr, −200 km2/yr, and −0.72 km3/yr for the water level, the surface area, and the water volume of the lake, respectively. The stabilization seems to be fragile however, since most of the increase in the water volume of the lake has spread over the large shallow southern region with high evaporation potential during hot seasons. Furthermore, due to the high correlation between the lake water level and precipitation, the recovery observed in 2016 and the first half of 2019 might not continue in case of a long drought period.

Influence of global climate on freshwater changes in Africa's largest endorheic basin using multi-scaled indicators

The poor investments in gauge measurements for hydro-climatic research in Africa has necessitated the need to investigate how decision makers can leverage on sophisticated space-borne measurements to improve knowledge on surface water hydrology that can feed directly into water accounting processes, and risk assessment from extreme droughts and its impacts. To demonstrate such potential, a suite of satellite earth observations (Sentinel-2, altimetry, Landsat, GRACE, and TRMM) and model data are combined with the standardized precipitation evapotranspiration index to assess the impacts of global climate on freshwater dynamics over the LCB (Lake Chad basin), Africa's largest endorheic basin. As shown in the results of this study, the significant relationship of climate modes (AMO; r=0.68 and 0.59; and AMM; r=0.42 and 0.47) with drought patterns in the LCB highlights the evidence of global climate influence in the region. The significant declines in drought extents and their intensities (2004 − 2015) over LCB coincide with the rise in surface water extent of the Lake Chad during the same period. Change detection analysis of open water features in the southern pool of Lake Chad during the 2015 − 2019 period shows that on the average, only 28.4% of inundated areas within the vicinity of the Lake persisted during the period. While the association of terrestrial water storage (TWS) with model-derived surface water storage (SWS) is strongest (r=0.89) in the catchments that provide the most nourishment to the Lake Chad, the relationship of rainfall (2002 − 2017) with TWS (r=0.85), model TWS (r=0.87) and SWS (r=0.88) confirm that the LCB's hydrology is predominantly climate-driven. This notion is further reinforced as the predicted SWS over the LCB using a support vector machine regression scheme was found to be strongly correlated (r=0.95 at α=0.05) with observed SWS.

Monitoring Water-Related Ecosystems with Earth Observation Data in Support of Sustainable Development Goal (SDG) 6 Reporting

Lack of national data on water-related ecosystems is a major challenge to achieving the Sustainable Development Goal (SDG) 6 targets by 2030. Monitoring surface water extent, wetlands, and water quality from space can be an important asset for many countries in support of SDG 6 reporting. We demonstrate the potential for Earth observation (EO) data to support country reporting for SDG Indicator 6.6.1, ‘Change in the extent of water-related ecosystems over time’ and identify important considerations for countries using these data for SDG reporting. The spatial extent of water-related ecosystems, and the partial quality of water within these ecosystems is investigated for seven countries. Data from the Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat 5, 7, and 8 with Shuttle Radar Topography Mission (SRTM) are used to measure surface water extent at 250 m and 30 m spatial resolution, respectively, in Cambodia, Jamaica, Peru, the Philippines, Senegal, Uganda, and Zambia. The extent of mangroves is mapped at 30 m spatial resolution using Landsat 8 Operational Land Imager (OLI), Sentinel-1, and SRTM data for Jamaica, Peru, and Senegal. Using Landsat 8 and Sentinel 2A imagery, total suspended solids and chlorophyll-a are mapped over time for a select number of large surface water bodies in Peru, Senegal, and Zambia. All of the EO datasets used are of global coverage and publicly available at no cost. The temporal consistency and long time-series of many of the datasets enable replicability over time, making reporting of change from baseline values consistent and systematic. We find that statistical comparisons between different surface water data products can help provide some degree of confidence for countries during their validation process and highlight the need for accuracy assessments when using EO-based land change data for SDG reporting. We also raise concern that EO data in the context of SDG Indicator 6.6.1 reporting may be more challenging for some countries, such as small island nations, than others to use in assessing the extent of water-related ecosystems due to scale limitations and climate variability. Country-driven validation of the EO data products remains a priority to ensure successful data integration in support of SDG Indicator 6.6.1 reporting. Multi-country studies such as this one can be valuable tools for helping to guide the evolution of SDG monitoring methodologies and provide a useful resource for countries reporting on water-related ecosystems. The EO data analyses and statistical methods used in this study can be easily replicated for country-driven validation of EO data products in the future.