Detection of rainwater harvesting ponds by matching terrain attributes with hydrologic response

Rainwater harvesting has proven to be a valuable strategy for mitigating the effects of global climate change. However, most of the techniques reported in the academic literature are based on capturing surface runoff generated by rainfall, and therefore, ponds are located close to surface currents, implying investments in infrastructure to conduct water to the place where it is utilized. The aims of this research are the following: i) develop a methodology for the detection of ponds for rainwater harvesting through hydrological and geospatial analyses based on terrain curvature, and ii) identify relevant variables to establish the potential storage volume in ponds. For this purpose, the study was performed in the Tolima region of Colombia with an area of 23,663 km2, where 32% corresponds to arid and semi-arid areas. The detection of ponds from the terrain curvature was carried out using geographic information systems and the estimation of daily storage in the rainwater harvesting pond (RWHP) system. Likewise, the principal component analysis identified key variables in the process of detecting and selecting ponds. The results show that the proposed methodology allows the detection of potential ponds for rainwater harvesting near cultivation and water demanding zones for various uses, which could introduce relevant variables in decision-making processes and, decrease water supply distribution costs in semi-arid areas. (c) 2021 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Superscript/Subscript Available

Automated summary

Rainwater harvesting is a valuable strategy for mitigating the effects of global climate change. This study aims to develop a methodology for detecting ponds for rainwater harvesting through hydrological and geospatial analyses, as well as identifying relevant variables for establishing potential storage volume. The results show that the proposed methodology allows for the detection of potential ponds near cultivation and water demanding zones, reducing water supply distribution costs in semi-arid areas.