Reply on RC2

Saul Arciniega

doi:10.5194/hess-2021-428-ac2

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 quantitively assess Costa Rica’s water resources at a national scale. Data scarcity was compensated 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 CHIRPS (Climate Hazards Group InfraRed Precipitation with Station data) as model forcings. Daily streamflow from 13 gauges for the period 1990–2003 and monthly MODIS (Moderate Resolution Imaging Spectroradiometer) 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. The calibration consisted in 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 was a better option instead of calibrating only with daily streamflow. Additionally, including PET and AET in the calibration improved the simulated water balance and better matched hydrological signatures. Thus, the constrained parameter uncertainty increased the confidence in the simulation results. 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 larger energy inputs, more intense atmospheric dynamics, higher precipitation rates, larger streamflow, and sediment yields (Dehaspe et al, 2018; Esquivel-Hernández et al, 2017; Wohl et al, 2012)
Underestimation of CHIRPS across the Caribbean slope was mainly observed in the Terron Colorado and Cariblanco catchments, with a bias factor (BF) between 1.2 and 1.4
The results suggested that at large scales, the precipitation bias was compensated since the mean bias factor (BF) was ~1, but underestimation of precipitation was observed in mountainous regions as wells as large overestimations in the drier northwest (Fig. 4)

Summary

Introduction

Tropical regions differ from temperate regions by larger energy inputs, more intense atmospheric dynamics, higher precipitation rates, larger streamflow, and sediment yields (Dehaspe et al, 2018; Esquivel-Hernández et al, 2017; Wohl et al, 2012). Opportunities exist in form of including additional variables to streamflow for model calibration and validation, providing 55 more realistic internal hydrological partitioning (Dal Molin et al, 2020; Rakovec et al, 2016; Xiong and Zeng, 2019). The latter comes at the expense of increased computational cost (Arheimer et al, 2020). Distributed landscape characteristics at large scales such as soil, topography, and land cover can result in complex hydrological models with many calibrated model parameters (Gurtz et al, 1999) and 70 resulting in greater uncertainty. The question as to how complex or simple a hydrological model should remain an open science debate considering that simpler models can lead to similar results in comparison with more complex and more highly parameterized models (Archfield et al, 2015; Rojas-Serna et al, 2016)

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Conclusion