Estimating water volume variations in Lake Victoria over the past 22 years using multi-mission altimetry and remotely sensed images

Tong, Xiaohua; Pan, Huitang; Xie, Huan; Xu, Xiong; Li, Fengting; Chen, Lei; Luo, Xin; Liu, Shijie; Chen, Peng; Jin, Yanmin

doi:10.1016/j.rse.2016.10.012

Cited by 68 publications

(48 citation statements)

References 46 publications

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“…Several recent studies show that water volume variations can be estimated indirectly using the combination of satellite altimetry and imagery data [24][25][26][27]. The innovation of this research is the application to Lake Chad and the investigation of the water budget components.…”

Section: Introductionmentioning

confidence: 99%

Monitoring Recent Fluctuations of the Southern Pool of Lake Chad Using Multiple Remote Sensing Data: Implications for Water Balance Analysis

2017

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Abstract:The drought episodes in the second half of the 20th century have profoundly modified the state of Lake Chad and investigation of its variations is necessary under the new circumstances. Multiple remote sensing observations were used in this paper to study its variation in the recent 25 years. Unlike previous studies, only the southern pool of Lake Chad (SPLC) was selected as our study area, because it is the only permanent open water area after the serious lake recession in [1973][1974][1975]. Four satellite altimetry products were used for water level retrieval and 904 Landsat TM/ETM+ images were used for lake surface area extraction. Based on the water level (L) and surface area (A) retrieved (with coinciding dates), linear regression method was used to retrieve the SPLC's L-A curve, which was then integrated to estimate water volume variations (∆V). The results show that the SPLC has been in a relatively stable phase, with a slight increasing trend from 1992 to 2016. On annual average scale, the increase rate of water level, surface area and water volume is 0.5 cm year −1 , 0.14 km 2 year −1 and 0.007 km 3 year −1 , respectively. As for the intra-annual variations of the SPLC, the seasonal variation amplitude of water level, lake area and water volume is 1.38 m, 38.08 km 2 and 2.00 km 3 , respectively. The scatterplots between precipitation and ∆V indicate that there is a time lag of about one to two months in the response of water volume variations to precipitation, which makes it possible for us to predict ∆V. The water balance of the SPLC is significantly different from that of the entire Lake Chad. While evaporation accounts for 96% of the lake's total water losses, only 16% of the SPLC's losses are consumed by evaporation, with the other 84% offset by outflow.

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Section: Introductionmentioning

confidence: 99%

Monitoring Recent Fluctuations of the Southern Pool of Lake Chad Using Multiple Remote Sensing Data: Implications for Water Balance Analysis

2017

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“…These include quantifying land use for each watershed in Panamanian drainage basins [55]; identifying types of sampling sites based on the hydrology and land-use characteristics to monitor contaminants in river sediments [46][47][48][49][50][51][52][53][54][55][56][57], discriminating fire types from MODIS active fire products, such as forest fire, grassland fire, agricultural burning and so on [58]; assessing flooded arable land of a major flood in Myanmar [46]; selecting eddy-covariance flux towers with relatively homogenous land cover in the light use efficiency models to simulate GPP [59]; analyzing habitat of bats in Lao PDR and Cambodia [60][61][62]; and providing validation sources to evaluate the classification performance of the water body extraction from MODIS eight-day products [62]. In particular, GlobeLand30 data has been used to derive useful information about the status and change of land cover, to examine their causes and consequence analysis, and to explore future development scenarios.…”

Section: Application Analysismentioning

confidence: 99%

Analysis and Applications of GlobeLand30: A Review

Chen

Cao

Peng

et al. 2017

IJGI

164

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GlobeLand30, donated to the United Nations by China in September 2014, is the first wall-to-wall 30 m global land cover (GLC) data product. GlobeLand30 is widely used by scientists and users around the world. This paper provides a review of the analysis and applications of GlobeLand30 based on its data-downloading statistics and published studies. An average accuracy of 80% for full classes or one single class is achieved by third-party researchers from more than 10 countries through sample-based validation or comparison with existing data. GlobeLand30 has users from more than 120 countries on five continents, and from all five Social Benefit Areas. The significance of GlobeLand30 is demonstrated by a number of published papers dealing with land-cover status and change analysis, cause-and-consequence analysis, and the environmental parameterization of Earth system models. Accordingly, scientific data sharing in the field of geosciences and Earth observation is promoted, and fine-resolution GLC mapping and applications worldwide are stimulated. The future development of GlobeLand30, including comprehensive validation, continuous updating, and monitoring of sustainable development goals, is also discussed.

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“…Where lakes areas have been calculated to any great extent, this has only been done for a couple of lakes or at a lower resolution (e.g. Smith and Pavelsky, 2009;Tong et al, 2016). Thus, measuring lake volume variations from space is commonly a trade-off between the number of lakes analysed, the resolution of the lake area calculation and the 10 number of historical lake areas that can be calculated.…”

Section: )mentioning

confidence: 99%

“…Numerous studies have already shown the potential of estimating water level time series over inland waters using different altimeter missions such as Topex/Poseidon (Birkett, 1995), Envisat (Frappart et al, 2006a), Saral (Schwatke et al, 2015b), Cryosat-2 (Villadsen et al, 25 2015) or ICESat (Zhang et al, 2011). Water levels from satellite altimetry have also been used for hydrological applications such as the estimation of river discharge (Kouraev et al, 2004;Tourian et al, 2017;Zakharova et al, 2006) and lake volumes (Duan and Bastiaanssen, 2013;Tong et al, 2016;Zhou et al, 2016).…”

Section: Satellite Altimetrymentioning

confidence: 99%

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

Busker¹,

Roo²,

Gelati³

et al. 2018

Preprint

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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 10 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 15 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 R 2 > 0.8 and an average R 2 of 0.91. For these lakes and for lakes with a nearly constant lake area 20 (coefficient of variation < 0.008), volume variations were calculated. Lakes with a poor linear relationship were not considered. Reasons for low R 2 values were found to be (1) a nearly constant lake area, (2) winter ice coverage, (3) small lake sizes and (4) a predominance of no data within the GSW dataset for those lakes. Lake volume estimates were validated for 18 lakes in the U.S., Spain, Australia and Africa using in situ volume time series, and gave an excellent Pearson correlation coefficient of on average 0.97, and a normalized RMSE of 7.42 %. These results show a high potential for 25 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 help to validate large scale hydrological models, to improve regional and global flood and drought forecasting systems and to update hydropower estimations.Hydrol. Earth Syst. Sci. Discuss., https://doi

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Estimating water volume variations in Lake Victoria over the past 22 years using multi-mission altimetry and remotely sensed images

Cited by 68 publications

References 46 publications

Monitoring Recent Fluctuations of the Southern Pool of Lake Chad Using Multiple Remote Sensing Data: Implications for Water Balance Analysis

Monitoring Recent Fluctuations of the Southern Pool of Lake Chad Using Multiple Remote Sensing Data: Implications for Water Balance Analysis

Analysis and Applications of GlobeLand30: A Review

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

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