Hydrological modeling on Costa Rica using HYPE

This study aims to explore the use of the large-scale process-based HYPE (Hydrological Predictions for the Environment) model (Lindström et al., 2010) for Costa Rica. Due to the lack of ground meteorological data, precipitation and temperature from global products were used as model forcings. Moreover, PET and ET from MODIS additionally to streamflow timeseries were used to calibrate and validate the model. To deal with the lack of a common period between streamflow and PET-ET, different step-wise calibration procedures were tested to evaluate the most effective strategy to constraint the parameters space and reduce the model uncertainty. Our specific objectives were to: 1. Adjust the open-source conceptual rainfall-runoff model HYPE to simulate catchments at the national scale of Costa Rica. 2. Use remotely-sensed data and global products to drive and evaluate the model using four different step-wise calibration strategies. 3. Analyze the effect of remotely-sensed PET and ET data on model calibration and its capabilities to improve the water balance and the hydrological signatures.

本研究旨在探索基于大规模过程的HYPE（Hydrological Predictions for the Environment，环境水文预报）模型（Lindström等人，2010）在哥斯达黎加的应用。鉴于当地地面气象观测数据匮乏，研究采用全球数据集提供的降水与气温作为模型强迫驱动数据源。此外，除径流时间序列外，还额外使用MODIS反演的潜在蒸散量（PET）与实际蒸散量（ET）数据对模型进行率定与验证。针对径流与PET-ET序列缺乏共同观测时段的问题，本研究测试了多种分步式率定流程，以评估能够约束参数空间、降低模型不确定性的最优策略。本研究的具体目标如下：1. 适配该开源概念性降雨径流模型HYPE，以模拟哥斯达黎加全国尺度的流域；2. 借助遥感数据与全球数据集，通过四种不同的分步式率定策略驱动并评估该模型；3. 分析遥感反演的PET与ET数据对模型率定的影响，及其在改善水量平衡与水文特征指标方面的能力。