Integrating Multiple Datasets in Google Earth Engine for Advanced Hydrological Modeling Using the Soil Conservation Service Curve Number Method

Mansoor Adil, Muhammad Azmat, Mudassir Sohail
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Mansoor Adil, Muhammad Azmat, Mudassir Sohail: Institute of Geographical Information System, School of Civil and Environmental Engineering, (NUST, Islamabad)

International Journal of Innovations in Science & Technology, 2024, vol. 6, issue 6, 186-205

Abstract: This research explores the feasibility of using cloud computing and open data sources for hydrological modeling, specifically leveraging Google Earth Engine (GEE) and the Soil Conservation Service Curve Number (SCS CN) method to estimate runoff. The SCS CN approach is commonly applied in simulating rainfall-runoff processes and is effective for estimating water inflow into rivers, lakes, and streams. Google Earth Engine provides a range of functionalities, including algorithms for rapid data manipulation and visualization, and access to extensive global remote sensing and geographic information system (GIS) datasets. The study introduces an algorithm developed in GEE to analyze precipitation data and generate antecedent moisture condition (AMC) maps. This algorithm integrates MODIS land use/land cover (LULC) data with USDA soil texture data to classify hydrological soil groups. Runoff estimation utilizes three datasets: CHIRPS, GPM, and TRMM. A thorough analysis of the rainfall-runoff relationship in the Mangla watershed from 2005 to 2015 is conducted. The study quantifies runoff estimates from each dataset and performs comparative analysis to validate the accuracy and reliability of the hydrological modeling. Over the ten-year period (2005-2015), significant fluctuations in average rainfall and runoff levels are observed, with notable seasonal patterns. The highest average precipitation of 1412.194 mm occurred in 2015, resulting in an average runoff of 215.021 mm. Conversely, 2009 recorded the lowest average precipitation of 672.808 mm and an average runoff of 78.476 mm. The accuracy of the modeled runoff observations is validated using meteorological data from the Pakistan Meteorological Department (PMD), Water and Power Development Authority (WAPDA), and Climate Forecast System Reanalysis (CFSR). In 2008, 2009, and 2010, CHIRPS consistently demonstrated better accuracy compared to GPM and TRMM, with accuracies of 90%, 79%, and 86% respectively. Additionally, a sensitivity analysis of the SCS CN model parameters reveals the effects of initial abstraction and Curve Number values on runoff estimation. In conclusion, this research enhances the understanding of hydrological processes in monsoon-affected regions and offers valuable recommendations for implementing sustainable water resource management practices.

Keywords: SCS CN Method; Hydrological Modeling; Runoff Estimation; CHIRPS; Gpm; Trmm; Google Earth Engine; Cloud Computing (search for similar items in EconPapers)
Date: 2024
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