Rain gauge optimization for network expansion in a data-sparse region

Document Type

Conference Proceeding

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Accurate rainfall estimation is critical to modeling and predicting landslides. Often sparse rain gauge networks need to be expanded for credible precipitation inputs. One of the chief optimization problems is to minimize the estimation errors from spatial interpolation of rain gauge values. Rain gauge network expansion with a particular focus for landslide monitoring needs to consider the existing landslide patterns. In this study, a multi-criteria approach involving landslide density, slope susceptibility, and land-use land cover classification is used to develop an initial set of probable rain gauge locations. Daily satellite precipitation from GPM (Global Precipitation Mission) IMERG-L (Integrated Multi-satellite Retrievals for GPM - Late) product aggregated over August 2018, corresponding to a period of peak flood, was used to generate synthetic rain gauge locations. The GPM data was subjected to resolution improvement, and its spatial resolution was increased from .1 degree to .025 degree. Each grid of the improved resolution product was considered as a synthetic rain gauge location. Grids with landslide density and slope susceptibility greater than a select threshold were regarded as probable rain gauge locations. A land-use land cover map was used to mask out the sites falling within forested areas. Spatial interpolation of the satellite rainfall measures corresponding to actual rain gauge locations were carried out using IDW (Inverse Distance Weighted) technique. Estimation errors were quantified as the difference between observed and predicted values. These residual values were used to add one rain gauge at a time to the existing network. Ten such rain gauges were added, and the new network showed improved estimation accuracies. The sum of estimation errors dropped from 1215 mm to 158 mm when the network expanded from five rain gauges to fifteen rain gauges.

Publication Title

SEG Technical Program Expanded Abstracts