Influence of recent climatic events on the surface water storage of the Tonle Sap Lake

Profiling dynamics of the Southeast Asia's largest lake, Tonle Sap Lake

Lakes, as vital components of the Earth's ecosystem with crucial roles in global biogeochemical cycles, are experiencing pervasive and irreparable worldwide losses due to natural factors and intensive anthropogenic interferences. In this study, we investigated the long-term dynamic patterns of the Tonle Sap Lake, the largest freshwater lake in the Mekong River Basin, using a series of hydrological data and remote sensing images between 2000 and 2020. Our findings revealed a significant decline in the annual average water level of the lake by approximately 2.1 m over 20 years, accompanied by an annual average reduction in surface area of about 1400 km2. The Tonle Sap Lake exhibited episodic declines in water level and surface area, characterized by the absence of flooding during the flood season and increasing aridity during the dry season. Furthermore, the shoreline of the lake has significantly advanced towards the lake in the northwestern and southern regions during the dry season, primarily due to sedimentation-induced shallowing of the lake edge depth and decreased water levels. In contrast, lake shorelines in the eastern region remained relatively stable due to the constructed embankments for the protection of the cultivated farmland. While the seasonal fluctuations of the Tonle Sap Lake are regulated by regional precipitation in the Mekong River Basin, the prolonged shrinking of the lake can be mainly ascribed to intensive anthropogenic activities. The interception of dams along the upper Mekong River has resulted in a decrease in the inflow to Tonle Sap Lake, exacerbating its shrinkage. Moreover, there are minor impacts from agricultural land expansion and irrigation on the lake. This study highlights the driving forces behind the evolution of Tonle Sap Lake, providing valuable information for lake managers to develop strategies aimed at conserving and restoring the ecological integrity of the Tonle Sap Lake.

Characterization of the impacts of hydro-dams on wetland inundations in Southeast Asia

Inundations of wetlands play a significant role in wetland ecosystems, but they are vulnerable to hydrological alterations. In Southeast Asia, many hydro-dams, which significantly alter the hydrology, have been built, but little is known about the influences of dams on wetland inundations. In this study, we quantified the characteristics of inundations and related the alterations to the dams by distinguishing them from influences of climate variabilities and local human activities. A multi-sensor approach using Landsat 8, Sentinel-1, and MODIS was devised to delineate the weekly inundations of 362 Southeast Asian wetlands from 2014 to 2021. The four hydrological characteristics (cyclical patterns, trends, intra-annual variability, and amplitude of inundations) were quantified, and the alteration of the characteristics caused by dams was separated from climate variabilities and local human activities using correlation analysis and logistic regression models. The results found that cyclical patterns, trends, intra-annual variability, and amplitude of wetland inundations changed significantly over the period, but the magnitudes vary significantly depending on their geographic locations with respect to the dams. Findings showed that dams critically affect the wetlands even though dams are located distantly from the dams. This indicates that wetlands should be monitored and conserved for reducing the influences of dams. This study advances our understanding of the effects of dams on wetlands by using the multi-sensor approach and distinguishing them from climate variabilities and local human activities.

Spatiotemporal impacts of climate change and human activities on water resources and ecological sensitivity in the Mekong subregion in Cambodia

Water resources in the Mekong subregion in Cambodia (MSC) have experienced dramatic changes in past decades, threatening regional ecosystem quality and sustainable development. Thus, it is important to explore the spatiotemporal impacts of climate change and human activities on water resources and ecological sensitivity. This study proposed an effective framework including spatiotemporal analysis of land use/cover change (LUCC) and ecological sensitivity assessment by combining remote sensing (RS) and geographic information system/science (GIS). An optimized feature space and a machine learning classification algorithm were constructed to extract four typical land cover types in the MSC from 1990 to 2020. An ecological sensitivity evaluation system, including four sub-sensitivities calculated by twelve indicators, was then constructed. The results suggest that severe shrinkage of water resources occurred before 2006, decreasing by 21.68%. The correlation between water resources and climate conditions displays a high to low level as human activity becomes involved. A significant spatiotemporal evolutionary pattern of ecological sensitivity was observed under the impact of external interference. Generally, the largest proportion of MSC belongs to the lightly sensitive level, which is mainly concentrated in the lower reaches, with an average of 33.93%. The highly sensitive area with a significant value in ecological protection has a slightly downward trend from 23.72 in 1990 to 22.55% in 2020.

Hydrologic balance and inundation dynamics of Southeast Asia's largest inland lake altered by hydropower dams in the Mekong River basin

Inland lakes have been increasingly impacted by climate change and human activities, leading to unprecedented environmental consequences. Among many rapidly changing lakes is the Tonlé Sap Lake (TSL) in Cambodia-Southeast Asia's largest inland lake-which is under growing threats from altered flows and inundation dynamics due to compounding effects of climate change and dam construction in the Mekong River basin (MRB). While previous studies have examined the potential causes of recent changes in open water areas, a mechanistic quantification of the lake's shifting hydrologic balance and inundation dynamics due to natural climate variability and dam operations is lacking. Here, using a hydrological-hydrodynamic modeling system that includes the major dams in the MRB, we show that while climate variability has been a key driver of inter-decadal variabilities in the lake's water balance, the operation of Mekong dams has exerted a growing influence-especially after 2010-on the Mekong flood pulse, Tonlé Sap River's flow reversal, and the TSL's inundation dynamics. The dam-induced dampening of the Mekong's peak discharge increased from 1-2% during 1979-2009 to ~7% in the 2010s, causing comparable alterations in the peak of inflow from the Mekong into TSL. More crucially, during the 2010s, the dams caused a reduction in annual inflow volume into TSL by 10-25% and shortened the annual inundation duration by up to 15 days in the lake's periphery. Further, seasonally inundated areas decreased (increased) most substantially by ~245 km² or ~3% (~270 km² or ~6%) in August (April) during the 2010s. These results demonstrate that Mekong dams have already caused substantial alterations in the hydrologic balance and inundation dynamics of the TSL. Our findings offer critical insights relevant for improved transboundary water management and decision making in light of growing concerns about the adverse impacts of large dams in the MRB.

Modified Hydrological Drought Risk Assessment Based on Spatial and Temporal Approaches

This study was conducted using a modified drought risk assessment in which hydrological data were processed to assess the hazards and risk of hydrological drought in Peninsular Malaysia. Although drought vulnerability and risk assessment are critical components of the drought phenomenon, the lack of a comprehensive integrated drought risk assessment in Malaysia has led to increased socio-economic impacts. The Hydrological Drought Risk Index (HDRI) illustrates the relative importance of hazard and vulnerability assessments in drought risk management for Peninsular Malaysia using the Streamflow Drought Index (SDI). This study presents an integrated drought characteristics-based risk assessment framework to improve hydrological drought risk assessment. The hydrological drought risk assessment was conducted by combining physical hazard and socioeconomic variables across the region. The periods of 1998–1999 and 2017–2018 mark the most critical hydrological droughts. The results also show that the trend of hydrological drought in river basins is towards dry conditions with no change in the foreseeable future. This study proposes a theoretical framework for effective drought risk management that can be used by the government and its stakeholders for sustainable water resource management.

The effect of proximity to protected areas on community adaptation to environmental change

The consequences of protected areas for proximal human communities are diverse. Protected areas can alleviate poverty by providing a range of economic opportunities for people that live and work within them. Equally, however, they may also disempower and disposes local communities. For communities adapting to systemic environmental change, proximity to protected areas can act to limit potential adaptive pathways. Here, we employ social science methods to explore the impact of an internationally significant protected area on adjacent communities in the Tonle Sap Lake basin, Cambodia. Semi-structured interviews, informed by a scenario framework, reveal an awareness of declining fish yields and a perceived lack of economic alternatives. Vulnerability to hydroclimatic extremes, particularly storms, flood, drought and - increasingly - fire, are exacerbated as a result of proximity to the protected area. We conclude that the impact of protected areas on local communities is heterogenous, and that the development of adaptive and effective management policies requires sensitivity to local conditions and impacts.

Surface Water Storage in Rivers and Wetlands Derived from Satellite Observations: A Review of Current Advances and Future Opportunities for Hydrological Sciences

Surface water storage (SWS), the amount of freshwater stored in rivers/wetlands/floodplains/lakes, and its variations are key components of the water cycle and land surface hydrology, with strong feedback and linkages with climate variability. They are also very important for water resources management. However, it is still very challenging to measure and to obtain accurate estimates of SWS variations for large river basins at adequate time/space sampling. Satellite observations offer great opportunities to measure SWS changes, and several methods have been developed combining multisource observations for different environments worldwide. With the upcoming launch in 2022 of the Surface Water and Ocean Topography (SWOT) satellite mission, which will provide, for the first time, direct estimates of SWS variations with an unprecedented spatial resolution (~100 m), it is timely to summarize the recent advances in the estimates of SWS from satellite observations and how they contribute to a better understanding of large-scale hydrological processes. Here, we review the scientific literature and present major results regarding the dynamic of surface freshwater in large rivers, floodplains, and wetlands. We show how recent efforts have helped to characterize the variations in SWS change across large river basins, including during extreme climatic events, leading to an overall better understanding of the continental water cycle. In the context of SWOT and forthcoming SWS estimates at the global scale, we further discuss new opportunities for hydrological and multidisciplinary sciences. We recommend that, in the near future, SWS should be considered as an essential water variable to ensure its long-term monitoring.

Mapping regional surface water volume variation in reservoirs in northeastern Brazil during 2009-2017 using high-resolution satellite images

The multiple-year drought that started in 2011 and reached climax in 2015 was the most severe and prolonged one in the semiarid northeastern (NE) Brazil in recent decades. This study aimed to investigate the reservoir surface water volume (SWV) variation in NE Brazil from 2009 to 2017 in four representative regions covering a total area of approximately 10,000 km² there and encompassing 2,140 reservoirs (areas range from 0.003 to 21 km²). High-resolution (10 m) digital elevation models (DEMs) were generated from the TanDEM-X data acquired during October-December 2015 to represent the reservoirs' bathymetric maps. The water extents in the reservoirs were delineated from high-resolution (6.5 m) RapidEye images acquired during 2009-2017. The combination of the aforementioned two variables yielded reservoir SWV with an accuracy of 0.64 × 10⁶-1.06 × 10⁶ m³, corresponding to 3.1%-5.6% of the maximum SWV in the reservoirs. The results showed that: 1) 81%-99% of the reservoirs in the four regions were from the groups with maximum water extent <50 ha and contributed 2%-59% of the regional reservoir SWV. In contrast, 0.6%-20% of the reservoirs were from the group of >50 ha and contributed 40%-98% to the regional SWV; 2) From 2009 to 2017, reservoir SWV in the four regions decreased at the rates of 2.3 × 10⁶-17.8 × 10⁶ m³/year; and 3) The SWV in the reservoirs responded differently to the regional terrestrial water budget, i.e. the differences between precipitation and evapotranspiration (P-ET). This study filled the data gap of bathymetric maps for the 2140 reservoirs, regardless of their sizes and macrophyte coverage. The SWV variations derived in those reservoirs over a period covering the recent drought can support better preparedness for drought in NE Brazil and better understanding of the regional hydrology in semi-arid regions.

Shrinking Tonlé Sap and the recent intensification of sand mining in the Cambodian Mekong River

This paper investigates the impacts of extensive riverbed mining in the Lower Mekong on the water level, extent and volume of the Tonlé Sap Lake, the largest freshwater lake in Southeast Asia. Our results indicate that the lake's volume has decreased from 1980 to 2018 (p-value = 0.016), with water levels at Phnom Penh Port and Phnom Penh Bassac showing decreasing trends since 1980 (p-values <0.0001). However, discharge at Phnom Penh Bassac (1960-2002) presented an insignificant trend (p-value = 0.147), indicating that riverbed incision due to extensive sand mining in Phnom Penh has affected the Mekong's water levels more than basin-scale climatic factors. Similarly, the modulation of a limited portion of water by upstream dams is unlikely to have caused dramatic inundation variation along the Lower Mekong River around Tonlé Sap. A hysteretic relationship between water levels at Prek Kdam and Tonlé Sap indicates that Tonlé Sap's water level is largely controlled by Tonlé Sap River and the Mekong, and declining water levels at Prek Kdam due to extensive sand mining in Phnom Penh is directly related to the shrinking of the lake. Although there are three main inflows into Tonlé Sap (from the Mekong, local tributaries and direct precipitation), the Mekong's contribution is the largest; tributary discharge and rainfall did not display any significant trend over the investigated period as well. Additionally, the analysis of high-resolution images revealed a recent intensification of riverbed mining, with Phnom Penh being a mining hotspot in the Lower Mekong. Considering its economic and ecological importance, the shrinking of Tonlé Sap would have grave repercussions for the region. Since sand demand is unlikely to weaken in the foreseeable future, these new insights can potentially help inform regulatory frameworks in ensuring sustainable sand extraction rates.

Evaluation of the Performances of Radar and Lidar Altimetry Missions for Water Level Retrievals in Mountainous Environment: The Case of the Swiss Lakes

Radar altimetry is now commonly used to provide long-term monitoring of inland water levels in complement to or for replacing disappearing in situ networks of gauge stations. Recent improvements in tracking and acquisition modes improved the quality the water retrievals. The newly implemented Open Loop mode is likely to increase the number of monitored water bodies owing to the use of an a priori elevation, especially in hilly and mountainous areas. The novelty of this study is to provide a comprehensive evaluation of the performances of the past and current radar altimetry missions according to their acquisition (Low Resolution Mode or Synthetic Aperture Radar) and tracking (close or open loop) modes, and acquisition frequency (Ku or Ka) in a mountainous area where tracking losses of the signal are likely to occur, as well as of the recently launched ICESat-2 and GEDI lidar missions. To do so, we evaluate the quality of water level retrievals from most radar altimetry missions launched after 1995 over eight lakes in Switzerland, using the recently developed ALtimetry Time Series software, to compare the performances of the new tracking and acquisition modes and also the impact of the frequency used. The combination of the Open Loop tracking mode with the Synthetic Aperture Radar acquisition mode on SENTINEL-3A and B missions outperforms the classical Low Resolution Mode of the other missions with a lake observability greater than 95%, an almost constant bias of (−0.17 ± 0.04) m, a RMSE generally lower than 0.07 m and a R most of the times higher than 0.85 when compared to in situ gauge records. To increase the number of lakes that can be monitored and the temporal sampling of the water level retrievals, data acquired by lidar altimetry missions were also considered. Very accurate results were also obtained with ICESat-2 data with RMSE lower than 0.06 and R higher than 0.95 when compared to in situ water levels. An almost constant bias (0.42 ± 0.03) m was also observed. More contrasted results were obtained using GEDI. As these data were available on a shorter time period, more analyses are necessary to determine their potential for retrieving water levels.

Improving Stage–Discharge Relation in The Mekong River Estuary by Remotely Sensed Long-Period Ocean Tides

Ocean tidal backwater reshapes the stage–discharge relation in the fluvial-to-marine transition zone at estuaries, rendering the cautious use of these data for hydrological studies. While a qualitative explanation is traditionally provided by examining a scatter plot of water discharge against water level, a quantitative assessment of long-period ocean tidal effect on the stage–discharge relation has been rarely investigated. This study analyzes the relationship among water level, water discharge, and ocean tidal height via their standardized forms in the Mekong Delta. We found that semiannual and annual components of ocean tides contribute significantly to the discrepancy between standardized water level and standardized water discharge time series. This reveals that the long-period ocean tides are the significant factors influencing the stage–discharge relation in the river delta, implying a potential of improving the relation as long as proper long-period ocean tidal components are taken into consideration. By isolating the short-period signals (i.e., less than 15 days) from land surface hydrology and ocean tides, better consistent stage–discharge relations are obtained, in terms of improving the Pearson correlation coefficient (PCC) from ~0.4 to ~0.8 and from ~0.6 to ~0.9 for the stations closest to the estuary and at the Mekong Delta entrance, respectively. By incorporating the long-period ocean tidal height time series generated from a remotely sensed global ocean tide model into the stage–discharge relation, further refined stage–discharge relations are obtained with the PCC higher than 0.9 for all employed stations, suggesting the improvement of daily averaged water level and water discharge while ignoring the short-period intratidal variability. The remotely sensed global ocean tide model, OSU12, which contains annual and semiannual ocean tide components, is capable of generating accurate tidal height time series necessary for the partial recovery of the stage–discharge relation.

Groundwater Storage Changes in the Major North African Transboundary Aquifer Systems during the GRACE Era (2003–2016)

Groundwater is an essential component of the terrestrial water cycle and a key resource for supplying water to billions of people and for sustaining domestic and economic (agricultural and industrial) activities, especially in arid and semi-arid areas. The goal of this study is to analyze the recent groundwater changes which occurred in the major North African transboundary aquifers in the beginning of the 21st century. Groundwater storage anomalies were obtained by removing soil moisture in the root zone (and surface water in the case of the Nubian Sandstone Aquifer System) from the terrestrial water storage anomalies estimated using the Gravity Recovery and Climate Experiment (GRACE) over the 2003–2016 time period. Spatio-temporal changes in groundwater storage contrast significantly among the different transboundary aquifers. Low changes (lower than 10 km3) were observed in the Tindouf Aquifer System but they were found to be highly correlated (R = 0.74) to atmospheric fluxes (precipitation minus evapotranspiration, P − ET) at annual scale. The GRACE data revealed huge water loss in the North Western Sahara and the Nubian Sandstone Aquifer Systems, above 30 km3 and around 50 km3, respectively. In the former case, the aquifer depletion can be attributed to both climate (R = 0.67 against P − ET) and water abstraction, and only to water abstraction in the latter case. The increase in water abstraction results from an increase in irrigated areas and population growth. For these two aquifers, a deceleration in the water loss observed after 2013 is likely to be attributed either to an increase in rainfall favoring rain-fed agriculture or to measures taken to reduce the over-exploitation of the groundwater resources.

Lake water volume fluctuations in response to climate change in Xinjiang, China from 2002 to 2018

Climate change has a global impact on the water cycle and its spatial patterns, and these impacts are more pronounced in eco-fragile regions. Arid regions are significantly affected by human activities like farming, and climate change, which influences lake water volumes, especially in different latitudes. This study integrates radar altimetry data from 2002 to 2018 with optical remote sensing images to analyze changes in the lake areas, levels, and volumes at different altitudes in Xinjiang, China. We analyzed changes in lake volumes in March, June, and October and studied their causes. The results showed large changes in the surface areas, levels, and volumes of lakes at different altitudes. During 2002–2010, the lakes in low- and medium-altitude areas were shrinking but lakes in high altitude areas were expanding. Monthly analysis revealed more diversified results: the lake water levels and volumes tended to decrease in March (−0.10 m/year, 37.55×10⁸ m³) and increase in June (0.03 m/year, 3.48×10⁸ m³) and October (0.04 m/year, 26.90×10⁸ m³). The time series lake water volume data was reconstructed for 2011 to 2018 based on the empirical model and the total lake water volume showed a slightly increasing trend during this period (71.35×10⁸ m³). We hypothesized that changes in lake water at high altitudes were influenced by temperature-induced glacial snow melt and lake water in low- to medium-altitude areas was most influenced by human activities like agricultural irrigation practices.

Analysis of GEDI Elevation Data Accuracy for Inland Waterbodies Altimetry

The Global Ecosystem Dynamics Investigation (GEDI) Light Detection And Ranging (LiDAR) altimetry mission was recently launched to the International Space Station with a capability of providing billions of high-quality measurements of vertical structures globally. This study assesses the accuracy of the GEDI LiDAR altimetry estimation of lake water levels. The difference between GEDI’s elevation estimates to in-situ hydrological gauge water levels was determined for eight natural lakes in Switzerland. The elevation accuracy of GEDI was assessed as a function of each lake, acquisition date, and the laser used for acquisition (beam). The GEDI elevation estimates exhibit an overall good agreement with in-situ water levels with a mean elevation bias of 0.61 cm and a standard deviation (std) of 22.3 cm and could be lowered to 8.5 cm when accounting for instrumental and environmental factors. Over the eight studied lakes, the bias between GEDI elevations and in-situ data ranged from −13.8 cm to +9.8 cm with a standard deviation of the mean difference ranging from 14.5 to 31.6 cm. Results also show that the acquisition date affects the precision of the GEDI elevation estimates. GEDI data acquired in the mornings or late at night had lower bias in comparison to acquisitions during daytime or over weekends. Even though GEDI is equipped with three identical laser units, a systematic bias was found based on the laser units used in the acquisitions. Considering the eight studied lakes, the beams with the highest elevation differences compared to in-situ data were beams 1 and 6 (standard deviations of −10.2 and +18.1 cm, respectively). In contrast, the beams with the smallest mean elevation difference to in-situ data were beams 5 and 7 (−1.7 and −2.5 cm, respectively). The remaining beams (2, 3, 4, and 8) showed a mean difference between −7.4 and +4.4 cm. The standard deviation of the mean difference, however, was similar across all beams and ranged from 17.2 and 22.9 cm. This study highlights the importance of GEDI data for estimating water levels in lakes with good accuracy and has potentials in advancing our understanding of the hydrological significance of lakes especially in data scarce regions of the world.

Improving the Estimation of Water Level over Freshwater Ice Cover using Altimetry Satellite Active and Passive Observations

Owing to its temporal resolution of 10-day and its polar orbit allowing several crossings over large lakes, the US National Aeronautics and Space Administration (NASA) and the French Centre National d’Etudes Spatiales (CNES) missions including Topex/Poseidon, Jason-1/2/3 demonstrated strong capabilities for the continuous and long-term monitoring (starting in 1992) of large and medium-sized water bodies. However, the presence of heterogeneous targets in the altimeter footprint, such as ice cover in boreal areas, remains a major issue to obtain estimates of water level over subarctic lakes of similar accuracy as over other inland water bodies using satellite altimetry (i.e., R ≥ 0.9 and RMSE ≤ 10 to 20 cm when compared to in-situ water stages). In this study, we aim to automatically identify the Jason-2 altimetry measurements corresponding to open water, ice and transition (water-ice) to improve the estimations of water level during freeze and thaw periods using only the point measurements of open water. Four Canadian lakes were selected to analyze active (waveform parameters) and passive (brightness temperature) microwave data acquired by the Jason-2 radar altimetry mission: Great Slave Lake, Lake Athabasca, Lake Winnipeg, and Lake of the Woods. To determine lake surface states, backscattering coefficient and peakiness at Ku-band derived from the radar altimeter waveform and brightness temperature at 18.7 and 37 GHz measured by the microwave radiometer contained in the geophysical data records (GDR) of Jason-2 were used in two different unsupervised classification techniques to define the thresholds of discrimination between open water and ice measurements. K-means technique provided better results than hierarchical clustering based upon silhouette criteria and the Calinski-Harabz index. Thresholds of discrimination between ice and water were validated with the Normalized Difference Snow Index (NDSI) snow cover products of the MODIS satellite. The use of open water threshold resulted in improved water level estimation compared to in situ water stages, especially in the presence of ice. For the four lakes, the Pearson coefficient (r) increased on average from about 0.8 without the use of the thresholds to more than 0.90. The unbiased RMSE were generally lower than 20 cm when the threshold of open water was used and more than 22 cm over smaller lakes, without using the thresholds.

Upstream GPS Vertical Displacement and its Standardization for Mekong River Basin Surface Runoff Reconstruction and Estimation

Surface runoff (R), which is another expression for river water discharge of a river basin, is a critical measurement for regional water cycles. Over the past two decades, river water discharge has been widely investigated, which is based on remotely sensed hydraulic and hydrological variables as well as indices. This study aims to demonstrate the potential of upstream global positioning system (GPS) vertical displacement (VD) and its standardization to statistically derive R time series, which has not been reported in recent literature. The correlation between the in situ R at estuaries and averaged GPS-VD and its standardization in the river basin upstream on a monthly temporal scale of the Mekong River Basin (MRB) is examined. It was found that the reconstructed R time series from the latter agrees with and yields a similar performance to that from the terrestrial water storage based on gravimetric satellite (i.e., Gravity Recovery and Climate Experiment (GRACE)) and traditional remote sensing data. The reconstructed R time series from the standardized GPS-VD was found to have a 2–7% accuracy increase against those without standardization. On the other hand, it is comparable to data that are obtained by the Palmer drought severity index (PDSI). Similar accuracies are exhibited by the estimated R when externally validated through another station location with in situ time series. The comparison of the estimated R at the entrance of river delta against that at the estuaries indicates a 1–3% relative error induced by the residual ocean tidal effect at the estuary. The reconstructed R from the standardized GPS-VD yields the lowest total relative error of less than 9% when accounting for the main upstream area of the MRB. The remaining errors may be the result of the combined effect of the proposed methodology, remaining environmental signals in the data time series, and potential time lag (less than a month) between the upstream MRB and estuary.

Long-Term Water Surface Area Monitoring and Derived Water Level Using Synthetic Aperture Radar (SAR) at Altevatn, a Medium-Sized Arctic Lake

Monitoring water storage in lakes and reservoirs is critical to water resource management, especially in a changing climate. Satellite microwave remote sensing offers a weather and light-independent solution for mapping water cover over large scales. We have used 13 years of synthetic aperture radar (SAR) data from three different sensors (Sentinel-1, RADARSAT-2, and Envisat advanced synthetic aperture radar (ASAR)) to develop a method for mapping surface water cover and thereby estimating the lake water extent (LWE). The method uses the unsupervised K-means clustering algorithm together with specific post-processing techniques to create binary maps of the water area. We have specifically tested and validated the method at Altevatn, a medium-sized arctic lake in Northern Norway, by using in-situ measurements of the water level. The multi-sensor SAR LWE time series were used in conjunction with the water level measurements to derive the lake hypsometry while at the same time quantifying the accuracy of our method. For Altevatn lake we estimated LWE with a root mean squared error (RMSE) of 0.89 km2 or 1.4% of the mean LWE, while the inferred lake water level (LWL) was associated with an RMSE of 0.40 m, or 2.5% of the maximum annual variation. We foresee that there is potential to further develop the algorithm by generalizing its use to other lakes worldwide and automating the process such that near real-time monitoring of LWE may be possible.

Upstream Remotely-Sensed Hydrological Variables and Their Standardization for Surface Runoff Reconstruction and Estimation of the Entire Mekong River Basin

River water discharge (WD) is an essential component when monitoring a regional hydrological cycle. It is expressed in terms of surface runoff (R) when a unit of river basin surface area is considered. To compensate for the decreasing number of hydrological stations, remotely-sensed WD estimation has been widely promoted over the past two decades, due to its global coverage. Previously, remotely-sensed WD was reconstructed either by correlating nearby remotely-sensed surface responses (e.g., indices and hydraulic variables) with ground-based WD observations or by applying water balance formulations, in terms of R, over an entire river basin, assisted by hydrological modeling data. In contrast, the feasibility of using remotely-sensed hydrological variables (RSHVs) and their standardized forms together with water balance representations (WBR) obtained from the river upstream to reconstruct estuarine R for an entire basin, has been rarely investigated. Therefore, our study aimed to construct a correlative relationship between the estuarine observed R and the upstream, spatially averaged RSHVs, together with their standardized forms and WBR, for the Mekong River basin, using estuarine R reconstructions, at a monthly temporal scale. We found that the reconstructed R derived from the upstream, spatially averaged RSHVs agreed well with the observed R, which was also comparable to that calculated using traditional remote sensing data (RSD). Better performance was achieved using spatially averaged, standardized RSHVs, which should be potentially attributable to spatially integrated information and the ability to partly bypass systematic biases by both human (e.g., dam operation) and environmental effects in a standardized form. Comparison of the R reconstructed using the upstream, spatially averaged, standardized RSHVs with that reconstructed from the traditional RSD, against the observed R, revealed a Pearson correlation coefficient (PCC) above 0.91 and below 0.81, a root-mean-squares error (RMSE) below 6.1 mm and above 8.5 mm, and a Nash–Sutcliffe model efficiency coefficient (NSE) above 0.823 and below 0.657, respectively. In terms of the standardized water balance representation (SWBR), the reconstructed R yielded the best performance, with a PCC above 0.92, an RMSE below 5.9 mm, and an NSE above 0.838. External assessment demonstrated similar results. This finding indicated that the standardized RSHVs, in particular its water balance representations, could lead to further improvement in estuarine R reconstructions for river basins affected by various systematic influences. Comparison between hydrological stations at the Mekong River Delta entrance and near the estuary mouth revealed tidally-induced backwater effects on the estimated R, with an RMSE difference of 4–5 mm (equivalent to 9–11% relative error).

Evaluation and Validation of CryoSat-2-Derived Water Levels Using In Situ Lake Data from China

CryoSat-2 altimetry has become a valuable tool for monitoring the water level of lakes. In this study, a concentrated probability density function (PDF) method was proposed for preprocessing CryoSat-2 Geophysical Data Record (GDR) data. CryoSat-2 altimetry water levels were preprocessed and evaluated by in situ gauge data from 12 lakes in China. Results showed that the accuracy of the raw GDR data was limited due to outliers in most of the along-track segments. The outliers were generally significantly lower than the in situ values by several meters, and some by more than 30 m. Outlier detection, therefore, improves upon the accuracy of CryoSat-2 measurements. The concentrated PDF method was able to greatly improve the accuracy of CryoSat-2 measurements. The preprocessed CryoSat-2 measurements were able to observe lake levels with a high accuracy at nine of the twelve lakes, with an absolute mean difference of 0.09 m, an absolute standard deviation difference of 0.04 m, a mean root mean square error of 0.27 m, and a mean correlation coefficient of 0.84. Overall, the accuracy of CryoSat-2-derived lake levels was validated in China. In addition, the accuracy of Database for Hydrological Time Series of Inland Waters (DAHITI) and HYDROWEB water level products was also validated by in situ gauge data.

Variations of Surface and Subsurface Water Storage in the Lower Mekong Basin (Vietnam and Cambodia) from Multisatellite Observations

In this study, we estimate monthly variations of surface-water storage (SWS) and subsurface water storage (SSWS, including groundwater and soil moisture) within the Lower Mekong Basin located in Vietnam and Cambodia during the 2003–2009 period. The approach is based on the combination of multisatellite observations using surface-water extent from MODIS atmospherically corrected land-surface imagery, and water-level variations from 45 virtual stations (VS) derived from ENVISAT altimetry measurements. Surface-water extent ranges from ∼6500 to ∼40,000 km 2 during low and high water stages, respectively. Across the study area, seasonal variations of water stages range from 8 m in the upstream parts to 1 m in the downstream regions. Annual variation of SWS is ∼40 km 3 for the 2003–2009 period that contributes to 40–45% of total water-storage (TWS) variations derived from Gravity Recovery And Climate Experiment (GRACE) data. By removing the variations of SWS from GRACE-derived TWS, we can isolate the monthly variations of SSWS, and estimate its mean annual variations of ∼50 km 3 (55–60% of the TWS). This study highlights the ability to combine multisatellite observations to monitor land-water storage and the variations of its different components at regional scale. The results of this study represent important information to improve the overall quality of regional hydrological models and to assess the impacts of human activities on the hydrological cycles.

Identifying Emerging Reservoirs along Regulated Rivers Using Multi-Source Remote Sensing Observations

The number of reservoirs is rapidly increasing owing to the growth of the world’s economy and related energy and water needs. Yet, for the vast majority of reservoirs around the world, their locations and related information, especially for newly dammed reservoirs, are not readily available due to financial, political, or legal considerations. This study proposes an automated method of identifying newly dammed reservoirs from time series of MODIS-derived NDWI (normalized difference water index) images. Its main idea lies in the detection of abrupt changes in the NDWI time series that are associated with land-to-water conversion due to the reservoir impoundment. The proposed method is tested in the upper reach of the Yellow River that is severely regulated by constructed reservoirs. Our results show that five newly dammed reservoirs were identified in the test area during 2000–2018. Validated against high-resolution Google Earth imagery, our method is effective to determine both locations of the emerging medium-size reservoirs and the timing of their initial water impoundments. Such information then allows for a refined calculation of the reservoir inundation extents and storage capacities through the combination of higher-resolution Landsat imagery and SRTM DEM. The comparison of our estimated reservoir areas and capacities against documented information further indicates that the integration of multi-mission remote sensing data may provide useful information for understanding reservoir operations and impacts on river discharges. Our method also demonstrates a potential for regional or global inventory of emerging reservoirs, which is crucial to assessing human impacts on river systems and the global water cycle.