scholarly journals A global lake and reservoir volume analysis using a surface water dataset and satellite altimetry

2018 ◽  
Author(s):  
Tim Busker ◽  
Ad de Roo ◽  
Emiliano Gelati ◽  
Christian Schwatke ◽  
Marko Adamovic ◽  
...  
Keyword(s):  
Surface Water ◽  
Satellite Altimetry ◽  
Google Earth ◽  
Water Levels ◽  
Water Volume ◽  
Lake Area ◽  
Reservoir Volume ◽  
Long Time ◽  
Lakes And Reservoirs ◽  
Computationally Intensive

Abstract. Lakes and reservoirs are crucial elements of the hydrological and biochemical cycle and are a valuable resource for hydropower, domestic and industrial water use and irrigation. Although their monitoring is crucial in times of increased pressure on water resources by both climate change and human interventions, publically available datasets of lakes and reservoir levels and volumes are scarce. Within this study, a time series of variation in lake and reservoir volume between 1984 and 2015 were analysed for 135 lakes over all continents by combining the JRC Global Surface Water (GSW) dataset and the satellite altimetry database DAHITI. The GSW dataset is a highly accurate surface water dataset at 30 m resolution compromising the whole L1T Landsat 5, 7 and 8 archive, which allowed for detailed lake area calculations globally over a very long time period using Google Earth Engine. Therefore, the estimates in water volume fluctuations using the GSW dataset are expected to improve compared to current techniques as they are not constrained by complex and computationally intensive classification procedures. Lake areas and water levels were combined in a regression to derive the hypsometry relationship (dh/dA) for all lakes. Nearly all lakes showed a linear regression, and 42 % of the lakes showed a strong linear relationship with an R2 > 0.8 and an average R2 of 0.91. For these lakes and for lakes with a nearly constant lake area (coefficient of variation 

scholarly journals A global lake and reservoir volume analysis using a surface water dataset and satellite altimetry

2019 ◽  
Vol 23 (2) ◽  
pp. 669-690 ◽  
Author(s):  
Tim Busker ◽  
Ad de Roo ◽  
Emiliano Gelati ◽  
Christian Schwatke ◽  
Marko Adamovic ◽  
...  
Keyword(s):  
Time Series ◽  
Standard Deviation ◽  
Surface Water ◽  
Linear Relationship ◽  
Satellite Altimetry ◽  
Google Earth ◽  
Lake Area ◽  
Lake Volume ◽  
Reservoir Volume ◽  
Lakes And Reservoirs

Abstract. Lakes and reservoirs are crucial elements of the hydrological and biochemical cycle and are a valuable resource for hydropower, domestic and industrial water use, and irrigation. Although their monitoring is crucial in times of increased pressure on water resources by both climate change and human interventions, publically available datasets of lake and reservoir levels and volumes are scarce. Within this study, a time series of variation in lake and reservoir volume between 1984 and 2015 were analysed for 137 lakes over all continents by combining the JRC Global Surface Water (GSW) dataset and the satellite altimetry database DAHITI. The GSW dataset is a highly accurate surface water dataset at 30 m resolution compromising the whole L1T Landsat 5, 7 and 8 archive, which allowed for detailed lake area calculations globally over a very long time period using Google Earth Engine. Therefore, the estimates in water volume fluctuations using the GSW dataset are expected to improve compared to current techniques as they are not constrained by complex and computationally intensive classification procedures. Lake areas and water levels were combined in a regression to derive the hypsometry relationship (dh ∕ dA) for all lakes. Nearly all lakes showed a linear regression, and 42 % of the lakes showed a strong linear relationship with a R2 > 0.8, an average R2 of 0.91 and a standard deviation of 0.05. For these lakes and for lakes with a nearly constant lake area (coefficient of variation < 0.008), volume variations were calculated. Lakes with a poor linear relationship were not considered. Reasons for low R2 values were found to be (1) a nearly constant lake area, (2) winter ice coverage and (3) a predominant lack of data within the GSW dataset for those lakes. Lake volume estimates were validated for 18 lakes in the US, Spain, Australia and Africa using in situ volume time series, and gave an excellent Pearson correlation coefficient of on average 0.97 with a standard deviation of 0.041, and a normalized RMSE of 7.42 %. These results show a high potential for measuring lake volume dynamics using a pre-classified GSW dataset, which easily allows the method to be scaled up to an extensive global volumetric dataset. This dataset will not only provide a historical lake and reservoir volume variation record, but will also help to improve our understanding of the behaviour of lakes and reservoirs and their representation in (large-scale) hydrological models.

scholarly journals Spatiotemporal change analysis of long time series inland water in Sri Lanka based on remote sensing cloud computing

2022 ◽  
Vol 12 (1) ◽  
Author(s):  
Jianfeng Li ◽  
Jiawei Wang ◽  
Liangyan Yang ◽  
Huping Ye
Keyword(s):  
Remote Sensing ◽  
Cloud Computing ◽  
Surface Water ◽  
Water Resources ◽  
Sri Lanka ◽  
Water Resource Management ◽  
Google Earth ◽  
Water Extraction ◽  
Inland Lakes ◽  
Lakes And Reservoirs

AbstractSri Lanka is an important hub connecting Asia-Africa-Europe maritime routes. It receives abundant but uneven spatiotemporal distribution of rainfall and has evident seasonal water shortages. Monitoring water area changes in inland lakes and reservoirs plays an important role in guiding the development and utilisation of water resources. In this study, a rapid surface water extraction model based on the Google Earth Engine remote sensing cloud computing platform was constructed. By evaluating the optimal spectral water index method, the spatiotemporal variations of reservoirs and inland lakes in Sri Lanka were analysed. The results showed that Automated Water Extraction Index (AWEIsh) could accurately identify the water boundary with an overall accuracy of 99.14%, which was suitable for surface water extraction in Sri Lanka. The area of the Maduru Oya Reservoir showed an overall increasing trend based on small fluctuations from 1988 to 2018, and the monthly area of the reservoir fluctuated significantly in 2017. Thus, water resource management in the dry zone should focus more on seasonal regulation and control. From 1995 to 2015, the number and area of lakes and reservoirs in Sri Lanka increased to different degrees, mainly concentrated in arid provinces including Northern, North Central, and Western Provinces. Overall, the amount of surface water resources have increased.

scholarly journals Assessment of Minimum Water Level in Lakes and Reservoirs Based on Their Morphological and Hydrological Features

Hydrology ◽  
2020 ◽  
Vol 7 (4) ◽  
pp. 83
Author(s):  
Charalampos Doulgeris ◽  
Panagiota Koukouli ◽  
Pantazis Georgiou ◽  
Paschalis Dalampakis ◽  
Dimitrios Karpouzos
Keyword(s):  
Water Level ◽  
Catchment Area ◽  
Abiotic Factors ◽  
Environmental Water ◽  
Water Volume ◽  
Lake Ecosystem ◽  
Economic Activities ◽  
Northern Greece ◽  
Long Time ◽  
Lakes And Reservoirs

The sustainable management of lakes and reservoirs requires the determination of their minimum environmental water level. Even though the assessment of minimum water level depends on a number of biotic and abiotic factors of the lake ecosystem, in many cases these factors are not entirely known and, furthermore, their evaluation is usually a challenging and laborious task. On the other hand, the lakes/reservoirs may comprise an important water resource to meet the requirements arising from economic activities. In this paper, the morphological and hydrological features of four lakes of northern Greece were analysed in order to assess their minimum environmental water level. The hydromorphological analysis was based on the relationship of the lake surface area and volume with water level as well as the water inflow from the lake’s hydrological catchment area, considering as the lake’s critical volume storage, the annual water volume flowing into a lake from its hydrological catchment area with a probability of exceedance 50% of a long time series of hydrological years. By combining morphological and hydrological features, the proposed methodology aimed to extend the analysis based solely on morphological features, and assess more comprehensively the minimum environmental water level in the four lakes, ensuring also the rising from the minimum level to the maximum (overflow) level for most of the hydrological years.

scholarly journals Interannual variations of the terrestrial water storage in the Lower Ob' Basin from a multisatellite approach

2010 ◽  
Vol 14 (12) ◽  
pp. 2443-2453 ◽  
Author(s):  
F. Frappart ◽  
F. Papa ◽  
A. Güntner ◽  
S. Werth ◽  
G. Ramillien ◽  
...  
Keyword(s):  
Surface Water ◽  
Root Zone ◽  
Water Storage ◽  
Temporal Variations ◽  
Water Levels ◽  
The Arctic ◽  
Water Volume ◽  
Hydrological Models ◽  
Radar Altimetry ◽  
Terrestrial Water

Abstract. Temporal variations of surface water volume over inundated areas of the Lower Ob' Basin in Siberia, one of the largest contributor of freshwater to the Arctic Ocean, are estimated using combined observations from a multisatellite inundation dataset and water levels over rivers and floodplains derived from the TOPEX/POSEIDON (T/P) radar altimetry. We computed time-series of monthly maps of surface water volume over the common period of available T/P and multisatellite data (1993–2004). The results exhibit interannual variabilities similar to precipitation estimates and river discharge observations. This study also presents monthly estimates of groundwater and permafrost mass anomalies during 2003–2004 based on a synergistic analysis of multisatellite observations and hydrological models. Water stored in the soil is isolated from the total water storage measured by GRACE when removing the contributions of both the surface reservoir, derived from satellite imagery and radar altimetry, and the snow estimated by inversion of GRACE measurements. The time variations of groundwater and permafrost are then obtained when removing the water content of the root zone reservoir simulated by hydrological models.

scholarly journals Volume Variations of Small Inland Water Bodies from a Combination of Satellite Altimetry and Optical Imagery

2020 ◽  
Vol 12 (10) ◽  
pp. 1606 ◽  
Author(s):  
Christian Schwatke ◽  
Denise Dettmering ◽  
Florian Seitz
Keyword(s):  
Time Series ◽  
Water Level ◽  
Surface Area ◽  
Satellite Altimetry ◽  
Water Levels ◽  
Inland Waters ◽  
Data Sets ◽  
Surface Areas ◽  
Lakes And Reservoirs ◽  
Optical Imagery

In this study, a new approach for estimating volume variations of lakes and reservoirs using water levels from satellite altimetry and surface areas from optical imagery is presented. Both input data sets, namely water level time series and surface area time series, are provided by the Database of Hydrological Time Series of Inland Waters (DAHITI), developed and maintained by the Deutsches Geodätisches Forschungsinsitut der Technischen Universität München (DGFI-TUM). The approach is divided into three parts. In the first part, a hypsometry model based on the new modified Strahler approach is computed by combining water levels and surface areas. The hypsometry model describes the dependency between water levels and surface areas of lakes and reservoirs. In the second part, a bathymetry between minimum and maximum surface area is computed. For this purpose, DAHITI land-water masks are stacked using water levels derived from the hypsometry model. Finally, water levels and surface areas are intersected with the bathymetry to estimate a time series of volume variations in relation to the minimum observed surface area. The results are validated with volume time series derived from in-situ water levels in combination with bathymetric surveys. In this study, 28 lakes and reservoirs located in Texas are investigated. The absolute volumes of the investigated lakes and reservoirs vary between 0.062 km 3 and 6.041 km 3 . The correlation coefficients of the resulting volume variation time series with validation data vary between 0.80 and 0.99. Overall, the relative errors with respect to volume variations vary between 2.8% and 14.9% with an average of 8.3% for all 28 investigated lakes and reservoirs. When comparing the resulting RMSE with absolute volumes, the absolute errors vary between 1.5% and 6.4% with an average of 3.1%. This study shows that volume variations can be calculated with a high accuracy which depends essentially on the quality of the used water levels and surface areas. In addition, this study provides a hypsometry model, high-resolution bathymetry and water level time series derived from surface areas based on the hypsometry model. All data sets are publicly available on the Database of Hydrological Time Series of Inland Waters.

scholarly journals Quantification of surface water volume changes in the Mackenzie Delta using satellite multi-mission data

2018 ◽  
Vol 22 (2) ◽  
pp. 1543-1561 ◽  
Author(s):  
Cassandra Normandin ◽  
Frédéric Frappart ◽  
Bertrand Lubac ◽  
Simon Bélanger ◽  
Vincent Marieu ◽  
...  
Keyword(s):  
Surface Water ◽  
Water Levels ◽  
The Arctic ◽  
Water Volume ◽  
Mackenzie Delta ◽  
Flood Peak ◽  
Upstream Part ◽  
Flooded Area ◽  
Water Height

Abstract. Quantification of surface water storage in extensive floodplains and their dynamics are crucial for a better understanding of global hydrological and biogeochemical cycles. In this study, we present estimates of both surface water extent and storage combining multi-mission remotely sensed observations and their temporal evolution over more than 15 years in the Mackenzie Delta. The Mackenzie Delta is located in the northwest of Canada and is the second largest delta in the Arctic Ocean. The delta is frozen from October to May and the recurrent ice break-up provokes an increase in the river's flows. Thus, this phenomenon causes intensive floods along the delta every year, with dramatic environmental impacts. In this study, the dynamics of surface water extent and volume are analysed from 2000 to 2015 by combining multi-satellite information from MODIS multispectral images at 500 m spatial resolution and river stages derived from ERS-2 (1995–2003), ENVISAT (2002–2010) and SARAL (since 2013) altimetry data. The surface water extent (permanent water and flooded area) peaked in June with an area of 9600 km2 (±200 km2) on average, representing approximately 70 % of the delta's total surface. Altimetry-based water levels exhibit annual amplitudes ranging from 4 m in the downstream part to more than 10 m in the upstream part of the Mackenzie Delta. A high overall correlation between the satellite-derived and in situ water heights (R > 0.84) is found for the three altimetry missions. Finally, using altimetry-based water levels and MODIS-derived surface water extents, maps of interpolated water heights over the surface water extents are produced. Results indicate a high variability of the water height magnitude that can reach 10 m compared to the lowest water height in the upstream part of the delta during the flood peak in June. Furthermore, the total surface water volume is estimated and shows an annual variation of approximately 8.5 km3 during the whole study period, with a maximum of 14.4 km3 observed in 2006. The good agreement between the total surface water volume retrievals and in situ river discharges (R= 0.66) allows for validation of this innovative multi-mission approach and highlights the high potential to study the surface water extent dynamics.

scholarly journals Spatiotemporal Change Analysis of Long Time Series Inland Water in Sri Lanka Based on Remote Sensing Cloud Computing

2021 ◽  
Author(s):  
Jianfeng Li ◽  
Jiawei Wang ◽  
Liangyan Yang ◽  
Huping Ye
Keyword(s):  
Remote Sensing ◽  
Cloud Computing ◽  
Surface Water ◽  
Water Resources ◽  
Sri Lanka ◽  
Google Earth ◽  
Change Analysis ◽  
Index Method ◽  
Inland Lakes ◽  
Lakes And Reservoirs

Abstract Sri Lanka is an important hub connecting Asia-Africa-Europe maritime routes, with abundant rainfall but uneven spatiotemporal distribution, and obvious seasonal water shortage. Monitoring water area change in its inland lakes and reservoirs plays an important role in guiding the development and utilization of water resources. In this study, a rapid extraction model of surface water based on Google Earth Engine remote sensing cloud computing platform was constructed. On the basis of evaluating the optimal spectral water index method, the spatiotemporal variation of typical reservoir and inland lakes and reservoirs in Sri Lanka were analyzed. The results showed that AWEIsh can accurately identify the water boundary, with an overall accuracy of 99.14%, which was suitable for the extraction of surface water in Sri Lanka. The area of Maduru Oya Reservoir showed an overall increasing trend on the basis of small fluctuations from 1988 to 2018, and the monthly area of reservoir fluctuated greatly in 2017, thus water resources management in dry zone should pay more attention to seasonal regulation and control. From 1995 to 2015, the number and area of lakes and reservoirs in Sri Lanka increased in different degrees, mainly concentrated in arid provinces such as Northern Province, North Central Province, Western Province, etc., and the amount of surface water resources increased as a whole.

scholarly journals Satellite-based remote sensing data set of global surface water storage change from 1992 to 2018

2020 ◽  
Author(s):  
Riccardo Tortini ◽  
Nina Noujdina ◽  
Samantha Yeo ◽  
Martina Ricko ◽  
Charon M Birkett ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Surface Water ◽  
Water Surface ◽  
Satellite Altimetry ◽  
Water Storage ◽  
Data Set ◽  
Lakes And Reservoirs ◽  
Global Surface ◽  
Surface Water Storage

Abstract. The recent availability of freely and openly available satellite remote sensing products has enabled the implementation of global surface water monitoring to a level not previously possible. Here we present a global set of satellite-derived time series of surface water storage variations for lakes and reservoirs for a period that covers the satellite altimetry era. Our goal is to promote the use of satellite-derived products for the study of large inland water bodies, and to set the stage for the expected availability of products from the Surface Water and Ocean Topography (SWOT) mission, which will vastly expand the spatial coverage of such products, expected from 2021 on. Our general strategy is to estimate global surface water storage changes (ΔV) in large lakes and reservoirs using a combination of paired water surface elevation (WSE) and water surface area (WSA) extent products. Specifically, we use data produced by multiple satellite altimetry missions (TOPEX-Poseidon, Jason-1, Jason-2, Jason-3, and ENVISAT) from 1992 on, with surface extent estimated from Terra/Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) from 2000 on. We leverage from relationships between elevation and surface area (i.e., hypsometry) to produce estimates of ΔV even during periods when either of the variables was not available. This approach is successful provided that there are strong relationships between the two variables during an overlapping period. Our target is to produce time series of ΔV as well as WSE and WSA for a set of 347 lakes and reservoirs globally for the 1992–2018 period. The data sets presented are publicly available and distributed via NASA’s Jet Propulsion Laboratory’s Physical Oceanography Distributed Active Archive Center (PO DAAC; https://podaac.jpl.nasa.gov/). Specifically, the WSE data set is available at https://doi.org/10.5067/UCLRS-GREV2 (Birkett et al., 2019), the WSA data set is available at https://doi.org/10.5067/UCLRS-AREV2 (Khandelwal and Kumar, 2019), and the ΔV data set is available at https://doi.org/10.5067/UCLRS-STOV2 (Tortini et al., 2019). The records we describe represent the most complete global surface water time series available from the launch of TOPEX-Poseidon in 1992 (beginning of the satellite altimetry era) to near-present. The production of long-term, consistent, and calibrated records of surface water cycle variables such as the data set presented here is of fundamental importance to baseline future SWOT products.

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