On-farm reservoir monitoring using Landsat inundation datasets

Image credit: Dennis Carman, White River Irrigation District, Hazen, AR

Abstract

On-farm reservoirs (OFRs)—artificial water impoundments that retain water from rainfall and run-off—enable farmers to store water during the wet season to be used for crop irrigation during the dry season. However, monitoring the inter- and intra-annual change of these water bodies remains a challenging task because they are typically small (< 10 ha) and occur in high numbers. Therefore, we used two existing Landsat inundation datasets—the U.S. Geological Survey Dynamic Surface Water Extent (DSWE) and the European Commission’s Joint Research Centre (JRC) Global Monthly Water History—to assess surface water area change of OFRs located in eastern Arkansas, the third most irrigated state in the U.S. that has seen a rapid increase of OFRs occurrence. We used an existent OFRs dataset as ground-truth. We aimed (i) to compare the performance of the DSWE and the JRC when characterizing OFRs of varied sizes and (ii) to assess the impact of climate variables (i.e., precipitation and temperature) on surface water area of OFRs. We found the highest mean percent errors (MPE) in size (~20%) for OFRs between 0 and 5 ha, the smallest size class in our study. The DSWE had a smaller MPE and higher agreement with our ground-truth dataset when compared to the JRC for OFRs smaller than 5 ha (p-value < 0.05). Both inundation datasets enabled us to estimate the seasonality in surface area change of OFRs, with the highest surface water extent between March–May, the months when the region receives most of the annual precipitation. Our results showed that both DSWE and JRC can be used to enhance hydrological assessments in poorly monitored basins that have a concentration of OFRs, and the methods can be applied to other study regions if the inundation datasets are available.

Publication
In Agriculture Water Management
Vinicius Perin
Vinicius Perin
PhD student in Geospatial Analytics

My research interests include surface water, agriculture, irrigation, remote sensing and hydrological modeling.