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Located within the Altiplano at 3,686 m above sea level, Lake Poopo is remarkably shallow and very sensitive to hydrologic recharge. Progressive drying has been observed in the entire Titicaca-Poopo-Desaguadero-Salar de Coipasa (TPDS) system during the last decade, causing dramatic changes to Lake Poopo's surface and its regional water supplies. Ou...
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... MODIS surfaces ( Figure 5) are linked to bathymetry through the Landsat/MODIS calibration curve ( Figure 6) and a correlation coefficient of R 2 = 0.97 was obtained. A top and inclined view of the final bathymetry is presented in Figure 7a,b. Table 2 shows relations between isolines vertical elevation, the elevation standard deviation (SD) and the number of profiles used for the calculation. ...
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Citations
... With the development of Earth observation information technology, e.g., satellite remote sensing instruments, quantitative estimation and dynamic monitoring of water quantity using remote sensing techniques has become an important area of hydrological research (Mashala et al., 2023). Since remote sensing technology has the advantages of being able to cover large areas at a high spatial and temporal resolution, at relatively low cost (compared to traditional field-based data collection methods), remote sensing technology has a unique advantage in calculation and monitoring the dynamic reservoir capacity (Arsen et al., 2014;Armon et al.,2020). The current remote sensing-based reservoir capacity calculation models belong to two main categories: statistical empirical models and physical measurement models (Yang et al., 2022). ...
Dynamic monitoring of reservoir water storage in arid areas is important for water resources assessment, hydroelectric power generation and agricultural irrigation. However, existing reservoir water calculation methods often rely on in-situ measurements, which limits their application in data scarce regionals and for regional scale analyses. Hence, we propose a novel method to estimate the water storage of channel-type reservoirs in arid areas with unknown underwater topography, with the Bosten Lake watershed serving as a case study site. The method first divides reservoirs into three types based on their upstream and downstream topography: V-shape, U-shape, and flat-shape reservoirs. For the V-shape and U-shape reservoirs, the underwater topography was produced by fitting a linear fit and a polynomial based on the observed elevation above the water surface, respectively. Meanwhile, extrapolation or splining techniques were used to derive the underwater topography for the flat-shape reservoir. The proposed methods are able to measure the underwater topography of the Bosten Lake watershed accurately, with the coefficient of determination (R 2) values of 0.83, 0.75 and 0.61 for the V-shape, U-shape, and flat-shape reservoirs, respectively. In addition, the fit of the in-situ water depths of un-manned ships was matched to the simulated water depths for the Xiaoshankou and Bayi reservoirs, yielding R 2 values of 0.91 and 0.83 as well as root mean square error (RMSE) of 1.27 m and 1.18 m, respectively. Our approach may be applied in other areas where river underwater topography data is lacking or sparse, and provide important basis for rational water resources management in these areas.
... Given the frequent lack of bathymetric data to characterize the bottom surface of reservoirs, remote sensing techniques and spatial interpolation rise as potentially suitable alternatives to unfeasible vessel-based surveys (Arsen et al. 2013;Brando et al. 2009;Misra et al. 2018;Tseng et al. 2016). These bathymetric estimation alternatives become increasingly viable when local conditions prevent fieldwork; assessments are advancing through early stages in which highly accurate data are not indispensable; or equipment, resources, and technical capabilities are limited. ...
Despite their importance in social, economic, and hydroecological terms, numerous dams and reservoirs spread worldwide lack bathymetric or even storage data of any kind. This scarcity of minimal storage and morphology data represents an obstacle to evaluating the current functions and impacts of these artificial reservoirs, hindering planning efforts for their future development or removal and limiting hydrologic, hydrodynamic, or water quality modeling efforts on dammed watersheds. Although some global databases provide records with basic information on the location and surface extent of large reservoirs—including attributes such as dam height, total storage capacity, or even storage curves—bathymetries remain rarely available. Surveying bathymetric data is challenging and expensive, especially in the least-developed countries, regions experiencing war conflicts, or remote areas exposed to natural risks. This article presents a method for deriving synthetic bathymetries using cubic spline spatial interpolation. The method uses a digital terrain model (DTM) surrounding the reservoir as the only required input, assuming geomorphological and hydrological continuity and imposing longitudinal depth controls to the interpolation process. In comparison with a sample of 12 vessel-based reservoir bathymetric surveys, available from the United States Bureau of Reclamation (USBR), the synthetic bathymetries resulting from this interpolation method show bias performances within a range of
±
25
%
and Kling–Gupta efficiencies (KGE) over 0.5. Except for vessel-based bathymetric surveys, any indirect depth estimation method that applies remote sensing or interpolation is susceptible to some level of uncertainty and varied reliability. However, cubic spline spatial interpolation can produce bathymetric models with a reasonable level of accuracy depending on the projected application. The performance levels suggest that the proposed method can produce synthetic bathymetries that offer valuable information for early-stage hydrological or environmental studies or assessments. Some potential applications include initial evaluations involving problems like dam failure and dam removal or restoration, where bathymetric data play a relevant role as an input of hydrological, hydraulic, environmental, and risk simulations. In prefeasibility or conceptual stages, these types of projects and assessments can tolerate higher levels of uncertainty in the input data before making decisions to proceed to advanced stages of assessment, design, construction, restoration, or demolition. The method offers a low-cost and time-efficient alternative solution if vessel-based bathymetric surveys or remote sensing techniques turn out to be unfeasible for future studies or assessments in data-scarce regions. Reliable synthetic bathymetric interpolation turns out to be especially valuable in remote or dangerous sites posing physical risks, social conflicts, or any other safety threats for field surveys.
... Relative water volume variation modeling based on the area-water level relationship is one of the most used methods for estimating relative water volume variations with multi-source remote sensing data (Gao et al., 2012;Duan and Bastiaanssen, 2013;Arsen et al., 2014;Tong et al., 2016). During modeling, lake water volume (V) was divided into constant water volume (V o ) and variable water volume (ΔV). ...
... Also in central Sahel, Fowe et al. (2015) studied the water balance of a small reservoir in southern Burkina Faso, highlighting the variations caused by anthropogenic water withdrawal. Other studies assessed lake topography through bathymetry (Arsen et al., 2013) or a digital elevation model (DEM; Avisse et al., 2017) to retrieve lake storage. The variability in the reservoirs at the global scale has been addressed by some recent works. ...
In the Sahelian semi-arid region, water resources, especially small waterbodies such as ponds, small lakes, and reservoirs in rural areas are of vital importance. However,
because of their high number and the scarce in situ monitoring networks, these resources and their spatiotemporal variability are not well known at the regional scale. This study investigates the hydrological regime of 37 small waterbodies, located in Mali, Niger, and Burkina Faso in central Sahel. We propose a method based on remote sensing data only, which consists of combining water height data from Sentinel3 Synthetic Aperture Radar Altimeter (SRAL) with water area data obtained with the Sentinel-2 MultiSpectral Instrument (MSI) to create a dense water height time series. Water height variations are then compared to the evaporation
estimated by the Penman–Monteith method, using ERA5 reanalysis by the European Centre for Medium-Range Weather Forecasts (ECMWF) to infer water regimes during the dry season. Three main regimes stand out, namely a net water loss, mainly resulting from anthropogenic withdrawals, a net water supply occurring after the end of the rainy season through river network or water table exchange, and a balanced behaviour, where water losses during the dry season closely correspond to evaporation rates. Spatial patterns have been identified; in central Burkina Faso, most of the reservoirs show a net dry season water loss, which is explained by frequent irrigation, while reservoirs in northern Burkina Faso, generally show little water loss, indicating that water withdrawal is not significant in this area. Lakes located in the Inner Niger Delta in Mali and connected to the Niger River network generally show an important water supply, particularly at the beginning of the dry season. Lakes in Niger tend to show a weak signal toward water inflow that could be explained by exchange processes with the groundwater. These results show that satellite data are effective at estimating hydrological regimes and the anthropogenic impact on water resources at the large scale, including resources found in small
waterbodies.
... Many methods have been developed for WA delineations from satellite images, of which using spectral indices such as the Normalized Difference Water Index, NDWI (McFeeters, 1996), the Normalized Difference Lake Index, NDLI (Morriss et al., 2003;Hereher, 2015) or the Automated Water Extraction Index, AWEI; (Feyisa et al., 2014) is often preferred because of its rapid, simple and accurate procedure. The vertical dimension of water volume (WL and WD) can also be measured using the laser (Chipman et al., 2007;Wang et al., 2011;Zhang et al., 2011;Arsen et al., 2013) or radar altimetry (Swenson & Wahr, 2009;Birkett, 2000;Crétaux & Birkelt, 2006;Pham-Duc et al., 2019;2022). In some cases, lake depths are also estimated from optical images such as Landsat-8 and Sentinel-2 (Pope et al., 2016;Duan et al., 2022). ...
Water stored by reservoirs is critical for irrigation, electricity generation, drinking water supply, recreation, fisheries, and flood control. Therefore, the reservoir's water storage volume (SW) must be measured and monitored frequently for better watershed management. Since SW data is often not publicly available, finding a method to quantify SW objectively and accurately but to facilitate local water management is necessary. This study proposes a method for monitoring water surface area and storage volume using multi-sensor satellite remote sensing data through the Tuyen Quang Reservoir case study in Northern Vietnam. Accordingly, the water surface area was first delineated from multi-temporal optical satellite images, such as Landsat series and Sentinel-2 images, using the Modified Normalized Difference Water Index and resampled into 30-m pixel resolution data. Using the Shuttle Radar Topography Mission Digital Elevation Model data, the water depth at each pixel was then calculated by the difference between its elevation and the reservoir shoreline's mean elevation. The results showed that the reservoir's water surface area increased rapidly during 2003-2007 (from 579 ha to 5,516 ha), fluctuated insignificantly in 2008-2020, and reached 7,196 ha in 2021. Consequently, SW was raised from 11.8 million m 3 in 2003 to 1.68 billion m 3 in 2021. Our estimations agree with the depth and SW of Tuyen Quang Reservoir published in 2019. Our proposed method could be an effective water resource management tool in developing countries where the number of impounding reservoirs increases dramatically yearly without the financial afford to build gauging stations.
... To overcome the disadvantages of existing bathymetry derivation techniques, several studies either integrate waterlines obtained from a series of images with a spatial interpolation of geocoded heights [150]- [152] or combine multisatellite observations for deriving bathymetry (e.g., optical imagery, radar, and laser altimetry) [153]- [155]. For example, Abileah and Vignudelli [156] proposed the combination of radar altimetry and multitemporal Landsat data to calculate bathymetry in Lake Nasser. ...
Surface water, which refers to water stored in rivers, streams, lakes, reservoirs, ponds, and wetlands, is a precious resource in terms of biodiversity, ecology, water management, and economics. As a significant hydrological parameter, surface water storage (SWS) influences the exchange of water and energy between the land/water surface and atmosphere. The quantification of SWS and its dynamics is crucial for a better understanding of global hydrological and biogeochemical processes. For more than 30 years, Earth observation (EO) technology has shown that SWS can be measured to some degree, and a variety of techniques have been proposed to facilitate this purpose.
... Satellite scenes from Synthetic Aperture Radars (SAR) are being used for wetland characterization and monitoring (Hess et al., 2003;Salvia et al., 2009;Arsen et al., 2013). These images provide information about the geometric and dielectric characteristics of the observed target. ...
With the launch of the Sentinel-1 mission, for the first time, multitemporal and dual-polarization C-band SAR data with a short revisit time is freely available. How can we use this data to generate accurate vegetation cover maps on a local scale? Our main objective was to assess the use of multitemporal C-Band Sentinel-1 data to generate wetland vegetation maps. We considered a portion of the Lower Delta of the Paraná River wetland (Argentina). Seventy-four images were acquired and 90 datasets were created with them, each one addressing a combination of seasons (spring, autumn, winter, summer, complete set), polarization (VV, HV, both), and texture measures (included or not). For each dataset, a Random Forest classifier was trained. Then, the kappa index values (κ) obtained by the 90 classifications made were compared. Considering the datasets formed by the intensity values, for the winter dates the achieved kappa index values (κ) were higher than 0.8, while all summer datasets achieved κ up to 0.76. Including feature textures based on the GLCM showed improvements in the classifications: for the summer datasets, the κ improvements were between 9% and 22% and for winter datasets improvements were up to 15%. Our results suggest that for the analyzed context, winter is the most informative season. Moreover, for dates associated with high biomass, the textures provide complementary information.
... Combined with Landsat series and Sentinel-2 satellite images, the functional relationship between the water level and water storage capacity is constructed, and Ngoring Lake has been continuously monitored for nearly 30 years, including the changes in water level, lake area, and water storage capacity. Compared with previous studies [16][17][18], the data and methods used in this paper are novel and have a longer observation time. It is of great benefit to the follow-up study of lakes. ...
Mastering the fluctuation of water levels and the water storage capacity of plateau lakes is greatly important for monitoring the water balance of the Tibetan Plateau and predicting regional and global climate change. The water level of plateau lakes is difficult to measure, and the ground measured data of long-time series are difficult to obtain. Ngoring Lake is considered in this study, using spaceborne single-photon lidar ICESat-2/ATL13 inland lake standard data products, the water level values provided by Hydroweb laboratory, and the image data of an optical remote sensing satellite. A new method is proposed in the absence of measured data. The method uses multisource remote sensing data to estimate the long-term changes in the water levels, surface area, and water storage capacity of Ngoring Lake in the past three decades. The results show that the water level values of ICESat-2 and Hydroweb on overlapping observation days are highly correlated, with R2 = 0.9776, MAE = 0.420 m, RMSE = 0.077 m, and the average absolute height difference is 0.049 m. The fusion of multiple altimetry data can obtain more continuous long-time series water-level observation results. From 1992 to 2021, the water body information of Ngoring Lake basin fluctuated greatly and showed different variation characteristics in different time periods. The lowest water level in January 1997 was approximately 4268.49 m, and it rose to its highest in October 2009, approximately 4272.44 m. The change in the water level in the basin was mainly affected by natural factors, such as precipitation, air temperature, and human activities. The analysis shows that ICESat-2 can be combined with other remote sensing data to realize the long-time series dynamic monitoring of plateau lakes, showing great advantages in the comprehensive observation of plateau lakes in no man’s land.
... The Normalized Difference Water Index (NDWI) and its alternations, e.g., modified NDWI, or other approaches such as support vector machines, are used to estimate from satellite data the water area of inland waters [5,[18][19][20][21][22]. Regarding the water level detection, altimetry satellite missions, including ICESat-1 and ICESat-2, SARAL, Sentinel-3, etc., are utilized either with ready-to-use products or with sophisticated procedures, regarding the correction of atmospheric, geoid, and instrument parameters of satellite measurements [2,17,18,[23][24][25][26]. Water level and water area can effectively be combined in order to estimate the water volume change (variation) of a lake or the water storage of a reservoir. Moreover, additional approaches have been developed to monitor the bathymetry of lakes and reservoirs but cannot yet be sufficiently applied in all types of lakes and reservoirs, especially deeper ones [27][28][29][30]. Regarding the abovementioned approaches of the remote sensing of inland water, the majority applied are specific case studies. ...
Inland water resources are facing increasing quantitative and qualitative pressures, deriving from anthropogenic causes and the ongoing climate change. The monitoring of reservoirs is essential for sustainable management and preparation against water scarcity and extreme events, such as droughts. This research, relying on the Sentinel-2 and 3 missions, attempts to demonstrate the efficiency of combining remotely sensed water level and water area estimations, in order to estimate the water storage variation of Yliki reservoir. The case study is conducted in one of the few sufficiently monitored reservoirs in Greece, enabling a direct comparison of the proposed methodology results
with in situ observations. Moreover, this research work proposes a weekly time interval for pairing level and area estimations, instead of shorter time intervals. The results strongly demonstrate the efficiency of remote sensing in the production of empirical level–area–storage (L–A–S) curves. Correlation to in situ monitored storage- and satellite-derived water level, area stand for 98.81% and 99.27% respectively. Water storage variation is estimated and compared to the observed time series, resulting in an RMSE of 1.28% of the reservoir capacity and a correlation of 96.14%. The empirical L–S relationship underestimates storage, while the A–S relationship overestimates storage when
compared to the existing L–A–S curve.
... Satellite images are able to detect water bodies and, using spectral indices and classification algorithms, calculate their extent. The extent of water can also be useful for characterizing the geometry of lakes in combination with satellite altimetry (Arsen et al., 2014;Baup et al., 2014) and in situ water levels (Collischon & Clarke, 2016). Other approaches have been tested in Northeast Brazil using a combination of satellite imagery and field surveys (Lopes & Araújo, 2019), digital elevation models derived from synthetic aperture radar (SAR) images (Zhang et al., 2016), and supervised classification of maximum likelihood (Toledo et al., 2014). ...
... On the other hand, the results encourage the application of the ISODATA method for the calculation of CAVs in other reservoirs in the Northeast semiarid region. Unsupervised methods such as ISODATA do not need to determine the threshold to classify water, as verified in applications using spectral indices (NDVI, NDWI, MNDWI, among others) (Arsen et al., 2014, Feyisa et al., 2014, Schwatke et al., 2019. The results obtained with the ISODATA in the Poço da Cruz reservoir, for example, were better than the application of NDWI carried out by Costa (2019) and had a reduced tendency to underestimate the surface area. ...
Reservoirs are the primary source of water supply in the semiarid region of Pernambuco state, Brazil, because of the constant water scarcity affecting this region. Knowledge of the amount of water available is essential for the effective management of water resources. The volume of water stored in the reservoirs is calculated using the depth-area-volume relationship. However, in most reservoirs in the semiarid region, this relationship is currently out of date. Therefore, the objective of this study was to explore the potential and limitations of the application of the ISODATA unsupervised classification method to calculate the depth-area-volume relationships of reservoirs in the semiarid region of Pernambuco, Brazil. The application of the ISODATA method was evaluated in three reservoirs in the state of Pernambuco, i.e., Poço da Cruz, Barra do Juá, and Jucazinho. The results were compared with the updated curves of reservoirs obtained from bathymetry and recent LiDAR surveys. The ISODATA method presented satisfactory results for the three reservoirs analyzed. The mean absolute error of the volume in Poço da Cruz and Barra do Juá was lower than 1% of the maximum capacity. The use of the ISODATA method meant that the surface area underestimation tendency in the Poço da Cruz reservoir was less than when spectral indices were used.
