Surface Water Extent Research Articles

A surface water mapping framework combining optical and radar remote sensing and its application in China

Satellite remote sensing provides an efficient pathway to map inland surface water extent across different spatial and temporal scales. However, how to monitor the surface water distribution and its spatiotemporal variability via combining optical and radar remote sensing datasets still faces substantial challenges. In this study, we propose a Seamless Surface Water Mapping Framework (SSWMF) which synergizes both optical (MODIS, Landsat 8, Sentinel-2) and SAR (Sentinel-1) imageries. The validity of SSWMF was first proved over the middle and lower reaches of the Yangtze River of China with abundant lake resources, showing an overall accuracy of 90.72%, and the results indicate that SSWMF can provide surface water map with higher spatial and temporal continuity compared to the Joint Research Centre Global Surface Water dataset. Multi-source validation showed that the SSWMF-derived surface water maps can well capture the temporal fluctuation and spatial heterogeneity of water resources over China, with an overall accuracy of 92.39%. Overall, our results suggest that the proposed water mapping framework is promising and is readily applicable to large-scale water resource management and drought/flood monitoring.

Using Landsat and MODIS satellite collections to examine extent, timing, and potential impacts of surface water inundation in California croplands

The state of California, United States of America produces many crop products that are both utilized domestically and exported throughout the world. With nearly 39,000 km2 of croplands, monitoring unintentional and intentional surface water inundation is important for water resource management and flood hazard readiness. We examine inundation dynamics in California croplands from 2003 to 2020 by intersecting monthly surface water maps (n = 216 months) derived using two satellite remote sensing platforms (Landsat and Moderate Resolution Imaging Spectroradiometer [MODIS]) with a high-quality cropland map generated by the California Department of Water Resources. Surface water maps were produced using the Dynamic Surface Water Extent model, in which satellite image pixels are classified into different levels of detection confidence. Our analysis focused on calculating monthly and annual occurrence of “high confidence” water for each satellite collection across eight cropland types and 58 counties. Results indicate that 49.9% (MODIS) to 56.4% (Landsat) of croplands were inundated at least once during the 18-year timespan. Rice crops, due to their unique need of consistent surface water and dominance as a crop type in CA, had the highest proportion of and mean annual inundation area, while citrus crops had the lowest. Mean monthly inundation patterns in most croplands followed California's precipitation patterns with high inundation during the winter and spring rainy season. At the county level, croplands in the southern Central Valley typically had high occurrences of inundation in conjunction with large crop areas. Exposure and sensitivity of inundation for three crop types (citrus, deciduous, and vineyards) that are typically less associated with intentional inundation were geographically variable, but overall were generally highest in counties in the southern Central Valley, California's primary agricultural region. Flood and precipitation related crop insurance claims indicated that rice had the highest mean indemnity payout for any month with damages typically occurring in March and April. Insurance claims were also high in deciduous fruit and nut crops, which had peak damages in February. A comparison between inundation results and insurance claims suggests that the inundation mapped by our process coincides with claim activity. These data elucidate water inundation patterns across the state that can serve to inform farmers, insurers, decision makers, resource managers, and flood mitigation professionals.

Remote sensing of broad-scale controls on large river anabranching

River patterns reflect complex geomorphological processes and affect ecosystems and human development along floodplains. Physical controls on anabranching development have been studied primarily at local scales on relatively small rivers (i.e., discharge <1000 m3 s−1). However, there has been little systematic quantification of river anabranching (development of stable multiple channels) for large rivers globally. Here, we use remote sensing, cloud computing, and geospatial analysis to explore the importance of water surface slope, floodplain topography, sediment supply, substrate lithology, and permafrost to anabranching throughout 20 of the world's largest river basins. We use Landsat-derived surface water extent to compute an anabranching index (Ai) for ∼1 M km of river reaches, and compare it with global datasets of water surface slope, available floodplain extent, substrate lithology, and permafrost. We find that ∼49 ± 19% of large-river mainstems are anabranching, ranging from at least ∼17% (Mekong) to as much as ∼84% (Ob’). At the basin scale, anabranching channels comprise at least ∼17% (Yenisey) to as much as ∼55% (Kolyma) of all Landsat-observable river reaches, with a mean global value of 35 ± 11%. Anabranching channel patterns are most commonly associated with low water surface slope (normalized slope < 0.2; absolute slope < 0.2 m km−1) accompanying wide floodplains (normalized floodplain width > 0.6; absolute width > 42 km). Cross-sectionally averaged channel width increases in the most intensely anabranching reaches, suggesting net sediment storage due to reduced stream power. Unconsolidated sedimentary substrates promote both the prevalence and intensity of anabranching, whereas the presence of permafrost enhances intensity only. Overall, our results identify pervasive channel anabranching and some important control mechanisms throughout the world's large river basins, with strong implications for satellite-based discharge retrievals. We conclude that global analyses afforded by remote sensing and cloud computing offer additional, complementary approaches to traditional field-based approaches for understanding large river geomorphic processes and channel forms.

Spatio-temporal assessment of changing land surface temperature and depleting water in the Lake Chad area

Lake Chad is located at the south of the Sahara Desert in an arid region. The lake’s water resources are under severe pressure due to the basic needs of the growing population around the lake, global warming, and increasing irrigation demands. Numerous land cover change studies have measured the rate of depletion of the lake’s surface water. However, the contribution of the increasing high temperatures in the region which is also a compounding factor has received little attention. In this study, an assessment of the changes in surface water extent of Lake Chad from 1973-2017 was carried out through a land cover analysis. The potential influence of the rising land surface temperatures on the water losses was also studied. The extraction of the land cover was done using maximum likelihood classification. The results show that between 1973 and 1987, the lake lost 12,796.81km2 of its surface water area. This period coincided with a season of drought and dry seasons reported to have occurred in the lake’s area during the 1970s. Between 1987 and 2003, average temperature rise and change in surface water area was +1.54˚C and +962.71km2 respectively. Between 2003 and 2017, average temperature rise and change in surface water area was +3.69˚C and -25.17km2 respectively. These results provide further evidence of the alarming rate of water loss in the lake’s environment, and suggest a link between rising land surface temperatures and diminution of the lake’s water. The findings inform efforts directed at addressing the ecological problem facing the lake.

Identifying priority aquatic refuges to sustain freshwater biodiversity in intermittent streams in eastern Australia

AbstractThe hydrological variability of intermittent streams means that the spatial distribution of dry‐season aquatic refuges within river networks and the temporal dynamics of hydrological connectivity between them are critical for the persistence of aquatic biodiversity. Here, a new approach is demonstrated to identify surface water bodies as priority refuges for efficient conservation management of freshwater biodiversity in intermittent stream networks.Recently developed models of surface water extent and daily streamflow were used to represent spatio‐temporal variations in hydrological connectivity and surface water persistence within river networks of eastern Australia over a 107‐yr period. Using this information, systematic conservation planning was applied to prioritize aquatic areas for conservation of 25 fish species under two scenarios. One scenario identified priority refuges to complement those already occurring in protected areas, whereas the other did not consider protected area status.The priority networks identified concentrated on the main stems of river catchments where surface water was more likely to be persistent and aquatic refuges were more likely to be connected, but also included headwaters for rare fish species. All three set conservation targets for the 25 fish species can be met in the best solution of priority networks. Although the second scenario achieved the targets with a smaller size of priority network overall, it required more new aquatic refuges and was thus less efficient than the first scenario.The newly developed datasets are useful for freshwater conservation prioritization because they account for hydrological variability of intermittent streams. The systematic prioritization approach applied is transferable to other regions and freshwater taxa to identify aquatic refuges for biodiversity conservation within intermittent stream systems.

Monitoring Lake Volume Variation from Space Using Satellite Observations—A Case Study in Thac Mo Reservoir (Vietnam)

This study estimates monthly variation of surface water volume of Thac Mo hydroelectric reservoir (located in South Vietnam), during the 2016–2021 period. Variation of surface water volume is estimated based on variation of surface water extent, derived from Sentinel-1 observations, and variation of surface water level, derived from Jason-3 altimetry data. Except for drought years in 2019 and 2020, surface water extent of Thac Mo reservoir varies in the range 50–100 km2, while its water level varies in the range 202–217 m. Correlation between these two components is high (R = 0.948), as well as correlation between surface water maps derived from Sentinel-1 and free-cloud Sentinel-2 observations (R = 0.98), and correlation between surface water level derived from Jason-3 altimetry data and from in situ measurement (R = 0.99; RMSE = 0.86 m). We showed that water volume of Thac Mo reservoir varies between −0.3 and 0.4 km3 month−1, and it is in a very good agreement with in situ measurement (R = 0.95; RMSE = 0.0682 km3 month−1). This study highlights the advantages in using different types of satellite observations and data for monitoring variation of lakes’ water storage, which is very important for regional hydrological models. Similar research can be applied to monitor lakes in remote areas where in situ measurements are not available, or cannot be accessed freely.

Flood mapping from proxy surface water and ocean topography (SWOT) satellite mission data over India

The Surface Water and Ocean Topographic (SWOT) mission is aimed to improve flood mapping by providing high-resolution data of water surface elevation, slope and water extent for rivers wider than 100 m and water bodies as small as 62500 m2. Although, SWOT’s data latency and huge data size of rawest point cloud data make it impracticable for real or near-real-time flood forecasting but the SWOT data will provide new insight in the first-hand information of flood mapping. In this study, we explore the potential of SWOT mission for flood mapping over India. It is observed that the spatio-temporal resolution of SWOT allows the observation of 0.67%, 15.79%, 29.24%, 45.54%, and 8.06% of Indian districts with one, two, three, four and more than four observations per cycle of SWOT respectively. It is observed that SWOT would have observed 49.6% of flood events at least once if the mission were launched earlier. The SWOT could also observe 30.6% of small flood events with flood duration <4 at least once. Using the information, this study proposes a flood severity map by combining geomorphological and physical flood parameters for a reach of Brahmaputra river. From the map different flood severity zones can be observed.

How Have Global River Widths Changed Over Time?

Changes in a river's width reflect natural and anthropogenic impacts on local and upstream/downstream hydraulic and hydrologic processes. Temporal variation of river width also impacts biogeochemical exchange and reflects geomorphologic evolution. However, while global maps of mean river width and dynamic water surface extent exist, there is currently no standardized global assessment of river widths that documents changes over time. Therefore, we made repeated width measurements from Landsat images for all rivers wider than 90 m collected from 1984 to 2020 (named Global LOng‐term river Width, GLOW), which consists of ∼1.2 billion cross‐sectional river width measurements, with an average of 3,000 width measurements per 10‐km reach. With GLOW, we investigated the temporal variations of global river width, quantified by the interquartile range (IQR) and temporal trend. We found that 85% of global rivers have a width IQR <150 m. We also found that 37% of global river segments show significant temporal trends in width over the past 37 years, and this number is higher (46%) for human‐regulated rivers. Further, we leveraged machine learning to identify the most important factors explaining river width variations and revealed that these driving factors are significantly different between free‐flowing and non‐free‐flowing rivers. Specifically, the most important factor driving temporal variations in river width is the climate for free‐flowing rivers, and is soil condition for human‐impacted rivers. Finally, we anticipate that this study and the public release of GLOW will improve the understanding of river dynamics and catalyze additional interdisciplinary studies.

Spatio-temporal characterization of surface water dynamics with Landsat in endorheic Cuvelai-Etosha Basin (1990–2021)

Socio-economic damages caused by extreme floods have been increasing rapidly in recent years, mainly driven by changes in the climate and modulated by increasing human population in deltic areas and floodplains. The Cuvelai-Etosha Basin (CEB) in southern Africa, covering southern Angola and northern Namibia, experiences socially and economically devastating extreme floods. Yet, accurate information on past and current surface water changes and dynamics is lacking. Here, we estimate and map the surface water extents in the CEB and its surroundings (CEB + S) for 32 years (1990–2021) from Landsat data using random forest models to provide long-term baseline information on surface water changes and dynamics. Based on the reference data, a total of 15,677 ± 1080 km2 have been inundated by surface water in the CEB + S during 1990–2021. This extent was accurately mapped by our local water extent product (mapped area = 16,273 km2, user’s accuracy = 91.5 ± 2.5%, producer’s accuracy = 91.1 ± 6%). With user’s and producer’s accuracy of 91%, our overall water extent provides the first most accurate long-term baseline information on surface water inundation in CEB + S necessary for local spatial planning processes to minimise future negative impacts of floods in the basin. Interannual variability of surface water extent is, however, high, with water extent ranging from 520.8 ± 375.7 km2 to 12372.3 ± 1154.7 km2 during the 1990–2021 period. The largest annual water extents (>10,000 km2) were recorded in 2006, 2008, 2009, 2011, and 2017, whereas the smallest extents (<1000 km2) were recorded in 1992 and 2019. We found that over 40% of the area inundated in the CEB + S during 1990–2021 was inundated less than 9 times. With human population increasing rapidly in the CEB + S, rarely inundated areas with short water residence could become a prime target for human settlements, which may lead to huge socio-economic damages during extreme floods if no preventive measures are put in place. Globally available surface water maps from the Global Land Analysis and Discovery (GLAD) and European Commission’s Joint Research Centre (JRC) did not provide realistic surface water extent for CEB + S, especially during years with extreme floods. Therefore, locally adopted product for operational monitoring of surface water in the CEB + S is needed to provide accurate information for informing spatial planning processes and surface water resource management strategies in this endorheic basin and help minimise future negative impacts of floods.

Relic Groundwater and Prolonged Drought Confound Interpretations of Water Sustainability and Lithium Extraction in Arid Lands

AbstractDemand for lithium for batteries is growing rapidly with the global push to decarbonize energy systems. The Salar de Atacama, Chile holds ∼42% of the planet's reserves in the form of brine hosted in massive evaporite aquifers. The mining of these brines and associated freshwater use has raised concerns over the environmental responsibility of lithium extraction, yet large uncertainties remain regarding fundamental aspects of governing hydrological processes in these environments. This incomplete understanding has led to the perpetuation of misconceptions about what constitutes sustainable or renewable water use and therefore what justifies responsible allocation. We present an integrated hydrological assessment using tritium and stable oxygen, and hydrogen isotopes paired with remotely sensed and terrestrial hydroclimate data to define unique sources of water distinguished by residence time, physical characteristics, and connectivity to modern climate. Our results describe the impacts of prolonged drought on surface and groundwaters and demonstrate that nearly all inflow to the basin is composed of water recharged &gt;65 years ago. Still, modern precipitation is critical to sustaining important wetlands around the salar. Recent large rain events have increased surface water and vegetation extents and terrestrial water storage while mining‐related water withdrawals have continued. As we show, poor conceptualizations of these complex hydrological systems have perpetuated the misallocation of water and the misattribution of impacts. These fundamental issues apply to arid regions globally. Our new framework for hydrological assessment in these basins moves beyond calculating gross inputs‐outputs at a steady state to include all compartmentalized stores that constitute “modern” budgets.

Assessment of Landsat and Sentinel images integration for surface water extent mapping

A presença de nuvens e sombras e a baixa resolução temporal dos sensores podem ser fatores limitantes para as análises usando imagens de satélites. Considerando isso, o presente estudo almejou verificar se a integração das imagens dos sensores MSI (Sentinel 2) e OLI (Landsat 8), visando a ampliação do conjunto de dados, pode aprimorar o mapeamento dos espelhos d’água dos reservatórios artificiais. Para isso, foram calculados índices espectrais a partir de imagens OLI e MSI e comparados com dados de volume útil e cota coletados in situ no reservatório de Ceraíma (BA). A correlação (r) entre os valores de espelho d’água obtidos por imagens MSI e as variáveis in situ volume útil e cota são de 0,80 e 0,78. Enquanto, entre OLI e as variáveis volume útil e cota, os valores de correlação são 0,59 e 0,58. E entre MSI e OLI, r é de 0,89 e o índice de concordância é de 0,88. Este estudo concluiu que as diferenças de resoluções espaciais e temporais têm uma relevante influência na capacidade de integração entre as imagens de diferentes satélites para obtenção rápida e simples de resultados. A baixa resolução espacial dificulta a extração precisa dos contornos do reservatório, enquanto a temporal limita a quantidade de imagens de forma considerável para extração de nuvens e sombras.

Surface water area in a changing climate: Differential responses of Alaska’s subarctic lakes

Lake surface area in arctic and sub-arctic Alaska is changing in response to permafrost deterioration, changes in precipitation, and shifts in landscape hydrology. In interior Alaska, the National Park Service’s Central Alaska Network Shallow Lakes program studies lakes and ponds in a wide range of geomorphological settings ranging from alpine lakes to low lying lakes on fluvial plains. The purpose of this study was to determine if and how lake area was changing across this diverse environment. Using the USGS Dynamic Surface Water Extent product, we tested landscape-scale trends in surface water area from 2000–2019 in 32 distinct ecological areas, or ecological subsections, within the three parks. Surface water area declined in 9 subsections, largely in glaciated landscapes with coarse substrates and areas underlain by ice-rich permafrost. Surface water increase was seen in one subsection in the floodplain of the Copper River in Wrangell-St. Elias National Park. No net change was observed in many subsections, but individual lake analysis showed that within several ecological subsections some lakes were increasing in area while others decreased in area, masking changes in lake surface area within the subsection. Over the course of the study period, surface water area in all parks experienced similar fluctuations, likely due to oscillations in regional climate. Periods of high surface water area coincided with relatively warm, wet periods. Climate change models project increases in both temperature and precipitation in Alaska; our results suggest periods of regional wetting may mask longer-term declines in surface water area in some geomorphological settings. Overall, lake surface area declined over the study period; declines were greatest in the Glaciated Lowlands in Denali National Park and Preserve.

Analysis of Surface Water Trends for the Conterminous United States Using MODIS Satellite Data, 2003–2019

AbstractSatellite imagery is commonly used to map surface water extents over time, but many approaches yield discontinuous records resulting from cloud obstruction or image archive gaps. We applied the Dynamic Surface Water Extent (DSWE) model to downscaled (250‐m) daily Moderate Resolution Imaging Spectroradiometer (MODIS) data in Google Earth Engine to generate monthly surface water maps for the conterminous United States (US) from 2003 through 2019. The aggregation of daily observations to monthly maps of maximum water extent produced records with diminished cloud and cloud shadow effects across most of the country. We used the continuous monthly record to analyze spatiotemporal surface water trends stratified within Environmental Protection Agency Ecoregions. Although not all ecoregion trends were significant (p &lt; 0.05), results indicate that much of the western and eastern US underwent a decline in surface water over the 17‐year period, while many ecoregions in the Great Plains had positive trends. Trends were also generated from monthly streamgage discharge records and compared to surface water trends from the same ecoregion. These approaches agreed on the directionality of trend detected for 54 of 85 ecoregions, particularly across the Great Plains and portions of the western US, whereas trends were not congruent in select western deserts, the Great Lakes region, and the southeastern US. By describing the geographic distribution of surface water over time and comparing these records to instrumented discharge data across the conterminous US, our findings demonstrate the efficacy of using satellite imagery to monitor surface water dynamics and supplement traditional instrumented monitoring.

HydroSat: geometric quantities of the global water cycle from geodetic satellites

Abstract. Against the backdrop of global change, in terms of both climate and demography, there is a pressing need for monitoring of the global water cycle. The publicly available global database is very limited in its spatial and temporal coverage worldwide. Moreover, the acquisition of in situ data and their delivery to the database have been in decline since the late 1970s, be it for economical or political reasons. Given the insufficient monitoring from in situ gauge networks, and with no outlook for improvement, spaceborne approaches have been under investigation for some years now. Satellite-based Earth observation with its global coverage and homogeneous accuracy has been demonstrated to be a potential alternative to in situ measurements. This paper presents HydroSat as a database containing geometric quantities of the global water cycle from geodetic satellites. HydroSat provides time series and their uncertainty in water level from satellite altimetry, surface water extent from satellite imagery, terrestrial water storage anomaly represented in equivalent water height from satellite gravimetry, lake and reservoir water volume anomaly from a combination of satellite altimetry and imagery, and river discharge from either satellite altimetry or imagery. The spatial and temporal coverage of these datasets varies and depends on the availability of geodetic satellites. These products, which are complementary to existing products, can contribute to our understanding of the global water cycle within the Earth system in several ways. They can be incorporated for hydrological modeling, they can be complementary to current and future spaceborne observations, and they can define indicators of the past and future state of the global freshwater system. HydroSat is publicly available through http://hydrosat.gis.uni-stuttgart.de (last access: 18 May 2022​​​​​​​). Moreover, a snapshot of all the data (taken in April 2021) is available in GFZ Data Services at https://doi.org/10.5880/fidgeo.2021.017 (Tourian et al., 2021).

Stage estimation of Himalayan rivers using passive microwave radiometer and altimeter

ABSTRACT Hydrological variables such as surface water extent and soil moisture are continuously being monitored from satellites with good accuracy. However, orbital measurement of river stage and discharge remains a major challenge even today. Although altimeters provide valuable information on water level variations with a precision of few centimetres, they suffer from poor revisit time and limited gauging sites. Microwave radiometers that provide brightness temperature (BT) can be used to fill the gaps between altimeter passes with a revisit time of 1–2 days, although with limited accuracy. This paper presents an approach for estimation of river stage based on passive microwave radiometer using BT of river/measurement target (M) and upstream calibration target (C). We have used altimeter data from Jason-2/3 and BT data from Advanced Microwave Scanning Radiometer (AMSR2) at 36.5 GHz (descending orbit and horizontal polarization) for the period January 2013 to August 2020 to estimate river stage at 12 virtual gauging sites along Indus, Ganga, and Brahmaputra rivers. A new approach for selection of calibration targets is presented in this study which results in higher correlation between altimeter observed river stage and AMSR2 derived C/M ratio when compared to existing methods. Coefficient of determination (R 2) for C/M ratio with water level for the period 2013–2017 ranged from 0.45 to 0.89. Model functions were generated between C/M ratio and altimeter observed water level for all the gauging sites. Estimated water levels were validated against altimeter and ground observed values for the period January 2018–August 2020 with RMSE ranging from 0.35 to 1.45 m, which is 7–15% of total stage variability.

A combined use of in situ and satellite-derived observations to characterize surface hydrology and its variability in the Congo River basin

Abstract. The Congo River basin (CRB) is the second largest river system in the world, but its hydroclimatic characteristics remain relatively poorly known. Here, we jointly analyse a large record of in situ and satellite-derived observations, including a long-term time series of surface water height (SWH) from radar altimetry (a total of 2311 virtual stations) and surface water extent (SWE) from a multi-satellite technique, to characterize the CRB surface hydrology and its variability. First, we show that SWH from altimetry multi-missions agrees well with in situ water stage at various locations, with the root mean square deviation varying from 10 cm (with Sentinel-3A) to 75 cm (with European Remote Sensing satellite-2). SWE variability from multi-satellite observations also shows a plausible behaviour over a ∼25-year period when evaluated against in situ observations from the subbasin to basin scale. Both datasets help to better characterize the large spatial and temporal variability in hydrological patterns across the basin, with SWH exhibiting an annual amplitude of more than 5 m in the northern subbasins, while the Congo River main stream and Cuvette Centrale tributaries vary in smaller proportions (1.5 to 4.5 m). Furthermore, SWH and SWE help illustrate the spatial distribution and different timings of the CRB annual flood dynamic and how each subbasin and tributary contribute to the hydrological regime at the outlet of the basin (the Brazzaville/Kinshasa station), including its peculiar bimodal pattern. Across the basin, we estimate the time lag and water travel time to reach the Brazzaville/Kinshasa station to range from 0–1 month in its vicinity in downstream parts of the basin and up to 3 months in remote areas and small tributaries. Northern subbasins and the central Congo region contribute highly to the large peak in December–January, while the southern part of the basin supplies water to both hydrological peaks, in particular to the moderate one in April–May. The results are supported using in situ observations at several locations in the basin. Our results contribute to a better characterization of the hydrological variability in the CRB and represent an unprecedented source of information for hydrological modelling and to study hydrological processes over the region.

Where the wild beavers are: Climate and landscape controls on beaver pond area in snow‐dominated rangeland headwaters

AbstractBeavers are of increasing interest as a mechanism for ecosystem restoration. However, the physical watershed characteristics that allow for successful beaver re‐establishment are not consistent across habitat suitability models. We delineated the extent of beaver impounded surface water in the Pole Mountain Area (PMA) of south‐eastern Wyoming with six National Agricultural Imagery Program acquisitions over an 18‐year time period via a random forest classification algorithm. We computed beaver pond area for first‐ and second‐order watersheds within the PMA and related the impounded pond area to climatic metrics such as maximum annual snow water equivalent and snowmelt and rain input into the ground. There was no apparent relationship between annual climate metrics and beaver pond area. Further, we developed a random forest regression model to correlate beaver pond area with topographically derived watershed characteristics (e.g. hillslope size and riparian area size) to explore physical controls on impounded area at the watershed scale. Controls differed for first‐ and second‐order watersheds, with pond area in smaller watersheds largely being controlled by lateral water contributions from the hillslopes and in the larger watersheds by water from upstream and available space in the riparian areas. Interestingly, vegetation height did not emerge as an important predictor for pond area, likely because of generally good availability of forage material in all watersheds. This study improves our understanding of watershed characteristics that contribute to beaver habitat suitability and can be used to guide beaver reintroduction efforts through analysis targeted at identifying watersheds most appropriate to beaver pond construction.

Satellite Detection of Surface Water Extent: A Review of Methodology

Water is an imperative part of the Earth and an essential resource in human life and production. Under the effects of climate change and human activities, the spatial and temporal distribution of water bodies has been changing, and the shortage of water resources is becoming increasingly serious worldwide. Therefore, the monitoring of water bodies is indispensable. Remote sensing has the advantages of real time, wide coverage, and rich information and has become a brand-new technical means to quickly obtain water information. This study summarizes the current common methods of water extraction based on optical and radar images, including the threshold method, support vector machine, decision tree, object-oriented extraction, and deep learning, as well as the advantages and disadvantages of each method. These methods were applied to the Huai River Basin in China and Nam Co on the Qinghai-Tibet Plateau. The extraction results show that all the aforementioned approaches can obtain reliable results. Among them, the threshold segmentation method based on normalized difference water index is more robust than others. In the water extraction process, there are still many problems that restrict the accuracy of the results. In the future, researchers will continue to search for more automatic, extensive, and high-precision water extraction methods.

Merging Landsat and airborne LiDAR observations for continuous monitoring of floodplain water extent, depth and volume

The Darling River system in Australia is under pressure from water extraction and climate change. Management interventions such as environmental flow releases require understanding of water storage dynamics and the connectivity of floodplains and wetlands. Such knowledge can be gleaned from the long observational record of the Landsat series of satellite sensors and high (<5 m) resolution digital elevation models derived from airborne light detection and ranging (LiDAR). Here, for the first time, we develop and demonstrate an approach to reconstruct 16-day floodplain water dynamics, including extent, depth, and volume for a long Landsat time series (1987 to present). Time series mapping of surface water extent at 5-m resolution was achieved by topographic downscaling of Landsat-derived surface water data. We propose a simple and effective algorithm to restore missing data in the images caused by, e.g., cloud and shadows, swath edges and the Landsat 7 Scan Line Corrector (SLC) failure, thereby increasing the number of useable images five-fold. The 5-m surface water extent maps clearly delineate the narrow river channel and the boundary of floodplain wetlands. They can capture the development, peak and retreat of flood events. By combining Landsat and airborne LiDAR observations, we produced time series of surface water depth mapping at 5-m resolution, accounting for the degree of hydraulic surface water connectivity. Based on these maps, we derived 16-day floodplain volume dynamics for 1987 to present. The correlation coefficient between upstream river flow records and floodplain volume time series was 0.88, indicating that the estimates were robust. The algorithms developed can be used for ongoing very high-resolution mapping to assist in managing human water use and environmental health in the Murray-Darling Basin.

Effects of Climate and Anthropogenic Drivers on Surface Water Area in the Southeastern United States

AbstractSurface water is the most readily accessible water resource and provides an array of ecosystem services, but its availability and access are stressed by changes in climate, land cover, and population size. Understanding drivers of surface water dynamics in space and time is key to better managing our water resources. However, few studies estimating changes in surface water account for climate and anthropogenic drivers both independently and together. We used 19 years (2000–2018) of the newly developed Dynamic Surface Water Extent Landsat Science Product in concert with time series of precipitation, temperature, land cover, and population size to statistically model maximum seasonal percent surface water area as a function of climate and anthropogenic drivers in the southeastern United States. We fitted three statistical models (linear mixed effects, random forests, and mixed effects random forests) and three groups of explanatory variables (climate, anthropogenic, and their combination) to assess the accuracy of estimating percent surface water area at the watershed scale with different drivers. We found that anthropogenic drivers accounted for approximately 37% more of the variance in the percent surface water area than the climate variables. The combination of variables in the mixed effects random forest model produced the smallest mean percent errors (mean −0.17%) and the highest explained variance (R2 0.99). Our results indicate that anthropogenic drivers have greater influence when estimating percent surface water area than climate drivers, suggesting that water management practices and land‐use policies can be highly effective tools in controlling surface water variations in the Southeast.