Improving Hourly Precipitation Estimates for Flash Flood Modeling in Data-Scarce Andean-Amazon Basins: An Integrative Framework Based on Machine Learning and Multiple Remotely Sensed Data

Juseth E Chancay,Edgar Fabian Espitia-Sarmiento

doi:10.3390/rs13214446

Juseth E Chancay, Edgar Fabian Espitia-Sarmiento

Open Access

https://doi.org/10.3390/rs13214446

Copy DOI

Journal: Remote Sensing	Publication Date: Nov 5, 2021
Citations: 6	License type: CC BY 4.0

Affiliation: Universidad Regional Amazónica IKIAM

Abstract

Accurate estimation of spatiotemporal precipitation dynamics is crucial for flash flood forecasting; however, it is still a challenge in Andean-Amazon sub-basins due to the lack of suitable rain gauge networks. This study proposes a framework to improve hourly precipitation estimates by integrating multiple satellite-based precipitation and soil-moisture products using random forest modeling and bias correction techniques. The proposed framework is also used to force the GR4H model in three Andean-Amazon sub-basins that suffer frequent flash flood events: upper Napo River Basin (NRB), Jatunyacu River Basin (JRB), and Tena River Basin (TRB). Overall, precipitation estimates derived from the framework (BC-RFP) showed a high ability to reproduce the intensity, distribution, and occurrence of hourly events. In fact, the BC-RFP model improved the detection ability between 43% and 88%, reducing the estimation error between 72% and 93%, compared to the original satellite-based precipitation products (i.e., IMERG-E/L, GSMAP, and PERSIANN). Likewise, simulations of flash flood events by coupling the GR4H model with BC-RFP presented satisfactory performances (KGE* between 0.56 and 0.94). The BC-RFP model not only contributes to the implementation of future flood forecast systems but also provides relevant insights to several water-related research fields and hence to integrated water resources management of the Andean-Amazon region.

Highlights

Accurate estimation of spatiotemporal precipitation dynamics is crucial for several hydrological purposes, especially for operational flash flood forecasting [1,2]
This study aims to propose an integrative framework for improving the estimation of spatiotemporal precipitation dynamics at an hourly scale in the upper Napo River Basin
This study focuses on the upper part of the Ecuadorian Napo River Basin, located between the Eastern Andes and the Amazonia foothills

Summary

Introduction

Accurate estimation of spatiotemporal precipitation dynamics is crucial for several hydrological purposes, especially for operational flash flood forecasting [1,2]. Conventional approaches to estimate the precipitation patterns require rain gauge information. The spatial distribution of rain gauges strongly influences the uncertainty of precipitation estimates [3,4]. This implies important limitations over areas with complex topography, as in the case of the Andean-Amazon sub-basins, where implementing a suitable rain gauge density is often difficult and cost-prohibitive. Satellite-based precipitation products (hereafter SPPs) have been constituted as an alternative to overcome this limitation [5,6]. SPPs present multiple sources of random and systematic errors associated with retrieval algorithms, sampling time steps, detection ability, among others [7,8]

Objectives

Methods

Results