Remote sensing-aided rainfall–runoff modeling in the tropics of Costa Rica

Saúl Arciniega-Esparza,José Agustín Breña-Naranjo,Berit Arheimer,Andrés Chavarría-Palma,Christian Birkel

doi:10.5194/hess-26-975-2022

Abstract

Abstract. Streamflow simulation across the tropics is limited by the lack of data to calibrate and validate large-scale hydrological models. Here, we applied the process-based, conceptual HYPE (Hydrological Predictions for the Environment) model to quantitatively assess Costa Rica's water resources at a national scale. Data scarcity was compensated for by using adjusted global topography and remotely sensed climate products to force, calibrate, and independently evaluate the model. We used a global temperature product and bias-corrected precipitation from Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) as model forcings. Daily streamflow from 13 gauges for the period 1990–2003 and monthly Moderate Resolution Imaging Spectroradiometer (MODIS) potential evapotranspiration (PET) and actual evapotranspiration (AET) for the period 2000–2014 were used to calibrate and evaluate the model applying four different model configurations (M1, M2, M3, M4). The calibration consisted of step-wise parameter constraints preserving the best parameter sets from previous simulations in an attempt to balance the variable data availability and time periods. The model configurations were independently evaluated using hydrological signatures such as the baseflow index, runoff coefficient, and aridity index, among others. Results suggested that a two-step calibration using monthly and daily streamflow (M2) was a better option than calibrating only with daily streamflow (M1), with similar mean Kling–Gupta efficiency (KGE ∼ 0.53) for daily streamflow time series, but with improvements to reproduce the flow duration curves, with a median root mean squared error (RMSE) of 0.42 for M2 and a median RMSE of 1.15 for M1. Additionally, including AET (M3 and M4) in the calibration statistically improved the simulated water balance and better matched hydrological signatures, with a mean KGE of 0.49 for KGE in M3–M4, in comparison to M1–M2 with mean KGE < 0.3. Furthermore, Kruskal–Wallis and Mann–Whitney statistical tests support a similar model performance for M3 and M4, suggesting that monthly PET-AET and daily streamflow (M3) represents an appropriate calibration sequence for regional modeling. Such a large-scale hydrological model has the potential to be used operationally across the humid tropics informing decision-making at relatively high spatial and temporal resolution.

Highlights

Tropical regions differ from temperate regions by their larger energy inputs, more intense atmospheric dynamics, higher precipitation rates, larger streamflow, and greater sediment yields (Dehaspe et al, 2018; Esquivel-Hernández et al, 2017; Wohl et al, 2012)
Our results suggested that low flows were improved using potential evapotranspiration (PET) and actual evapotranspiration (AET) for calibration (Fig. 9), where FDC exhibited an average RMSLE (Eq 16) value of ∼ 0.5 ± 0.22 compared to 1.1 ± 0.53 from model configuration 1 (M1), constraining vertical fluxes and regulating discharge from soil layers (Massari et al, 2015; Rakovec et al, 2016)
Uct CHIRPS and remotely sensed Moderate Resolution Imaging Spectroradiometer (MODIS) 16 PET and AET products to improve the performance of Hydrological Predictions for the Environment (HYPE) in a step-wise calibration procedure towards a well-balanced model useful for water resources management

Summary

Introduction

Tropical regions differ from temperate regions by their larger energy inputs, more intense atmospheric dynamics, higher precipitation rates, larger streamflow, and greater sediment yields (Dehaspe et al, 2018; Esquivel-Hernández et al, 2017; Wohl et al, 2012). Tropical regions are among the fastest-changing environments, with a hydrological cycle pressurized by population growth (Wohl et al, 2012; Ziegler et al, 2007), land use/cover modifications. S. Arciniega-Esparza et al.: Remote sensing-aided rainfall–runoff modeling in the tropics of Costa Rica (Gibbs et al, 2010), and altered precipitation and runoff patterns (Esquivel-Hernández et al, 2017) due to climate change. Hydrological models have been widely used to assess the spatiotemporal variability of water resources and to provide insights into potential future climate and management decisions (Andersson et al, 2015; Xiong and Zeng, 2019)

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Methods

Results

Discussion

Conclusion