Volumetric Analysis of Reservoirs in Drought-Prone Areas Using Remote Sensing Products

Abstract

Globally, the number of dams increased dramatically during the 20th century. As a result, monitoring water levels and storage volume of dam-reservoirs has become essential in order to understand water resource availability amid changing climate and drought patterns. Recent advancements in remote sensing data show great potential for studies pertaining to long-term monitoring of reservoir water volume variations. In this study, we used freely available remote sensing products to assess volume variations for Lake Mead, Lake Powell and reservoirs in California between 1984 and 2015. Additionally, we provided insights on reservoir water volume fluctuations and hydrological drought patterns in the region. We based our volumetric estimations on the area–elevation hypsometry relationship, by combining water areas from the Global Surface Water (GSW) monthly water history (MWH) product with corresponding water surface median elevation values from three different digital elevation models (DEM) into a regression analysis. Using Lake Mead and Lake Powell as our validation reservoirs, we calculated a volumetric time series for the GSWMWH–DEMmedian elevation combinations that showed a strong linear ‘area (WA) – elevation (WH)’ (R2 > 0.75) hypsometry. Based on ‘WA-WH’ linearity and correlation analysis between the estimated and in situ volumetric time series, the methodology was expanded to reservoirs in California. Our volumetric results detected four distinct periods of water volume declines: 1987–1992, 2000–2004, 2007–2009 and 2012–2015 for Lake Mead, Lake Powell and in 40 reservoirs in California. We also used multiscalar Standardized Precipitation Evapotranspiration Index (SPEI) for San Joaquin drainage in California to assess regional links between the drought indicators and reservoir volume fluctuations. We found highest correlations between reservoir volume variations and the SPEI at medium time scales (12–18–24–36 months). Our work demonstrates the potential of processed, open source remote sensing products for reservoir water volume variations and provides insights on usability of these variations in hydrological drought monitoring. Furthermore, the spatial coverage and long-term temporal availability of our data presents an opportunity to transfer these methods for volumetric analyses on a global scale.

Long-Term Interannual and Seasonal Links between the Nutrient Regime, Sestonic Chlorophyll and Dominant Bluegreen Algae under the Varying Intensity of Monsoon Precipitation in a Drinking Water Reservoir

“…The studies based on flood and drought dynamics have become even more significant due to the abruptly changing climatic conditions [ 54 ], and increasing anthropogenic impacts and South Korea is at a higher risk of climate change. Therefore, it is incredibly essential to study the potential effects of high and flow years on vital water chemistry factors.…”

Tracking lake surface elevations with proportional hypsometric relationships, Landsat imagery, and multiple DEMs

“…While useful in a wide range of applications, water surface area remains a two‐dimensional measurement whereas, at minimum, water volume change estimates are needed to improve our understanding of surface water dynamics and its effects upon climate and other fields. To extend the usefulness of water surface area measurements and fill in the gap of missing water surface elevations due to the limitations of in‐situ and altimeter‐based monitoring, several studies have used elevation data products such as digital elevation models and/or bathymetry to estimate water dynamics using hypsometric relationships relating surface area to volume and/or surface elevation (Avisse et al., 2017; Bhagwat et al., 2019; Pan et al., 2013; Zhang et al., 2016) or elevations extracted from water surface boundaries (Tseng et al., 2016; Weekley & Li, 2019). Unfortunately, each of these methods have limitations in terms of expanding and scaling their applications.…”

“…Bhagwat et al. (2019) used multiple digital elevation models and preprocessed JRC Global Surface Water Explorer data to track lake volume changes for 186 lakes. Despite the strong results of these and other previous studies, concerns about accuracy uncertainty at increasingly lower water levels, relationship fit, and application to varying water bodies remain for these techniques.…”

“…For example, Pan et al (2013) used Shuttle Radar Topography Mission (SRTM) digital elevation model data with Landsat imagery to produce 16 water surface elevations at 0.12 root-mean-square-error (RMSE) accuracy for Lake Champlain in Vermont, USA. Bhagwat et al (2019) used multiple digital elevation models and preprocessed JRC Global Surface Water Explorer data to track lake volume changes for 186 lakes. Despite the strong results of these and other previous studies, concerns about accuracy uncertainty at increasingly lower water levels, relationship fit, and application to varying water bodies remain for these techniques.…”

“…Despite the strong results of these and other previous studies, concerns about accuracy uncertainty at increasingly lower water levels, relationship fit, and application to varying water bodies remain for these techniques. Furthermore, most hypsometry implementations rely upon having full, clear observations, or preprocessed products like the JRC Global Surface Water Explorer (Pekel et al, 2016) used by Bhagwat et al (2019) as any image contamination (clouds, cloud shadow, snow, ice, sensor error) reduces the estimated water surface area resulting in erroneous elevation, volume, and volume change estimates. This issue is substantial as many areas of the planet have significant cloud cover and/or other image contamination for long periods each year and restricting water surface dynamic estimates to clear images only results in poor, uneven temporal resolution (Huang et al, 2018) and methods of filling data gaps and cloud contamination using data-filling or temporal windows are best suited only for larger water bodies (Ogilvie et al, 2018).…”