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

Authors

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

Keywords:

SCS CN Method, Hydrological Modeling, Runoff Estimation, CHIRPS, Gpm, Trmm, Google Earth Engine, Cloud Computing

Abstract

This research investigates the feasibility of using cloud computing and open-data sources for hydrological modelling. It uses Google Earth Engine (GEE) and the Soil Conservation Service Curve Number (SCS CN) approach to estimate runoff. The SCS CN approach is frequently used in the simulation of rainfall-runoff processes, and it is especially useful in estimating water intake into rivers, lakes, and streams. Google Earth Engine offers a variety of functionalities, algorithms for rapid data manipulation and visualization, and access to large global remote sensing and geographic information system (GIS) datasets. This study describes the development of an algorithm that uses Google Earth Engine (GEE) to observe precipitation data and produce antecedent moisture condition (AMC) maps. The algorithm uses the Soil Conservation Service Curve Number (SCS CN) method, which combines MODIS land use/land cover (LULC) data with USDA soil texture data to classify hydrological soil groups. The runoff is estimated using three datasets: CHIRPS, GPM, and TRMM. A detailed analysis of the relationship between rainfall and runoff in the Mangla watershed from 2005 to 2015 is performed. The study not only quantifies the runoff estimated by each rainfall dataset, but it also performs a comparison analysis of the datasets to ensure the accuracy and reliability of hydrological modelling. The rainfall-runoff analysis over a time period ten years (2005-2015) reveals large fluctuations in average rainfall and runoff levels, as well as evident seasonal tendencies. The highest average precipitation (1412.194 mm) was recorded in 2015 resulting in an average runoff of 215.021 mm. In contrast, the minimum average precipitation of 672.808 mm was recorded in 2009, resulting in an average runoff of 78.476 mm. The accuracy and validity of the modeled runoff observations are demonstrated through validation using observed meteorological data collected from Pakistan Meteorological Department (PMD), Water and Power Development Authority (WAPDA), and Climate Forecast System Reanalysis (CFSR). In the years 2008, 2009, and 2010, CHIRPS consistently proves better accuracies in comparison to GPM and TRMM, with accuracies of 90%, 79%, and 86% respectively. Furthermore, the sensitivity analysis conducted on the parameters of the SCS CN model reveals the impact of initial abstraction and Curve Number values on the estimation of runoff. In conclusion, this research work offers significant contributions to the understanding of hydrological processes in regions primarily influenced by monsoons and presents useful suggestions for the implementation of sustainable practices in water resource management.

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