This article describes the field application of small, low-cost robots for remote surfacedata collection and an automated workflow to support water balance computations and hydrologicunderstanding where water availability data is sparse. Current elevation measurement approaches,such as manual surveying and LiDAR, are costly and infrequent, leading to potential inefficiencies forquantifying the dynamic hydrologic storage capacity of the land surface over large areas. Experimentsto evaluate a team of two different robots, including an unmanned aerial vehicle (UAV) and anunmanned surface vehicle (USV), to collect hydrologic surface data utilizing sonar and visual sensorswere conducted at three different field sites within the Arkavathy Basin river network located nearBangalore in Karnataka, South India. Visual sensors were used on the UAV to capture high resolutionimagery for topographic characterization, and sonar sensors were deployed on the USV to capturebathymetric readings; the data streams were fused in an automated workflow to determine the storagecapacity of agricultural reservoirs (also known as “tanks”) at the three field sites. This study suggests:(i) this robot-assisted methodology is low-cost and suitable for novice users, and (ii) storage capacitydata collected at previously unmapped locations revealed strong power-type relationships betweensurface area, stage, and storage volume, which can be incorporated into modeling of landscape-scalehydrology. This methodology is of importance to water researchers and practitioners because itproduces local, high-resolution representations of bathymetry and topography and enables waterbalance computations at small-watershed scales, which offer insight into the present-day dynamicsof a strongly human impacted watershed.