Assessing Floods and Droughts in Ungauged Small Reservoirs with Long-Term Landsat Imagery

Abstract

Small reservoirs have developed across semi-arid areas as a low cost solution for millions of rural small holders to harvest scarce water resources. Studies have highlighted limited agricultural water use and low water availability on individual reservoirs, but no information exists on the drought patterns of multiple small reservoirs. Their small size and dispersion prevents individualised hydrological monitoring, while hydrological modelling suffers from rainfall variability and heterogeneity across data sparse catchments and reservoirs. A semi-automated original approach exploiting free, archive Landsat satellite images is developed here for long-term monitoring of multiple ungauged small water bodies. Adapted and tested against significant hydrometric time series on three lakes, the method confirms its potential to monitor water availability on the smallest water bodies (1-10 ha) with a mean RMSE of 20,600 m 3 (NRMSE = 26%). Uncertainties from the absence of site-specific and updated surface-volume rating curves were here contained through a power relationship adapted over time for silting based on data from 15 surrounding lakes. Applied to 51 small reservoirs and 546 images over 1999-2014, results highlight the ability of this transposable method to shed light on flood dynamics and allow inter annual and inter lake comparisons of water availability. In the Merguellil upper catchment, in Central Tunisia, results reveal the significant droughts affecting over 80% of reservoirs, confirming the need for small reservoirs to maintain a supplementary irrigation objective only.

Big Data and Multiple Methods for Mapping Small Reservoirs: Comparing Accuracies for Applications in Agricultural Landscapes

“…Using a server-based approach also avoids the significant computer storage and processing requirements that create challenges to analyses of multiple Landsat images [16,24,39]. GEE is particularly useful for the analysis of many Landsat images, since each Landsat image is~1 GB in size with a footprint of approximately 185 km 2 and collected on a 16-day time step, so temporal analyses over large areas require hundreds of gigabytes of imagery.…”

“…Many previous studies have characterized inland surface water resources using free satellite imagery, such as from Landsat [13][14][15][16][17] and the Moderate Resolution Imaging Spectroradiometer (MODIS) [18][19][20][21][22]. Where the spatial and temporal resolution are sufficiently high, remotely sensed imagery provides a practical approach to small water body mapping and monitoring [23,24]. The highest resolution, freely available imagery that is collected on an intra-annual timestep over long time scales comes from the Landsat satellite series [25].…”

“…Researchers have successfully mapped water body locations and extents from Landsat imagery for several decades through the use of spectral indices [23,24,29]. Spectral indices are created by calculating the difference, ratio, or normalised difference of two multispectral image bands and identifying the threshold that enhances the reflectance of wavelengths for objects of interest, such as water [28].…”

Surface water monitoring in small water bodies: potential and limits of multi-sensor Landsat time series

“…These can be explained by the morphology of this large deep lake, where the uncertainties of the remote sensing method are significant compared to the amplitude of the surface area variations (80 000 - The method therefore provides a valuable tool to assess and compare water resources between years and lakes. Results here highlight the significant variability in terms of inter-lake and inter-annual water availability that can be observed across all small reservoirs in the catchment (Ogilvie et al, 2016a). then variable and depends on the lag between successive correct observations.…”

A Special Issue of Geosciences: Mapping and Assessing Natural Disasters Using Geospatial Technologies

“…Specifically, in this issue, advanced applications and theoretical discussion of geospatial tools in natural disasters are related to mapping, monitoring, and assessing landslides before and after they occur [6][7][8], assessing and monitoring tropical storm-induced flooding and coastal erosion [9,10], mapping and measuring floods and droughts [11,12], and wildfire and anomaly detection, mapping, and management [13,14]. We believe that, with the superlative articles published in this special issue, geospatial technologies will be more extensively accepted and applied in mapping, monitoring, and assessing natural disasters.…”