Kabir et al. 2022; Mekong Precipitation Uncertainty -- Water Resources Research

Numerous studies have examined the reliability of various precipitation products over the Mekong River Basin (MRB) and modeled its basin hydrology. However, there is a lack of comprehensive studies on precipitation-induced uncertainties in hydrological simulations using process-based land surface models. This study examines the propagation of precipitation uncertainty into hydrological simulations over the entire MRB using the Community Land Model version 5 (CLM5) at a high spatial resolution of 0.05° (~5 km) and without any parameter calibration. Simulations conducted using different precipitation datasets are compared to investigate the discrepancies in streamflow, terrestrial water storage (TWS), soil moisture, and evapotranspiration (ET) caused by precipitation uncertainty. Results indicate that precipitation is a key determinant of simulated streamflow in the MRB; peak flow and soil moisture are particularly sensitive to precipitation input. Further, precipitation data with a higher spatial resolution did not improve the simulations, contrary to the common perception that using meteorological forcing with higher spatial resolution would improve hydrological simulations. In addition, since high flow indicators are particularly influenced by precipitation data, the choice of precipitation data could directly impact flood pulse simulations in the MRB. Notable differences are also found among TWS, soil moisture, and ET simulated using different precipitation products. Moreover, TWS, soil moisture, and ET exhibit a varying degree of sensitivity to precipitation uncertainty. This study provides crucial insights on precipitation-induced uncertainties in process-based hydrological modeling and uncovers these uncertainties in the MRB.

诸多研究已针对湄公河流域（Mekong River Basin，MRB）内各类降水产品（precipitation products）的可靠性展开评估，并对该流域的水文过程开展了模拟建模。然而，目前仍缺乏针对采用基于过程的陆面模型（process-based land surface models）开展水文模拟时，由降水引发的不确定性的系统性研究。本研究利用通用陆面模型第5版（Community Land Model version 5，CLM5），以0.05°（约5 km）的高空间分辨率，在不进行任何参数率定的前提下，探究了整个湄公河流域内降水不确定性向水文模拟的传递过程。本研究对使用不同降水数据集开展的模拟结果进行了对比，以探究由降水不确定性引发的径流、陆地水储量（terrestrial water storage，TWS）、土壤湿度以及蒸散发（evapotranspiration，ET）的差异。研究结果表明，降水是湄公河流域模拟径流的关键影响因子；洪峰流量与土壤湿度对降水输入尤为敏感。此外，空间分辨率更高的降水数据并未提升模拟效果，这与"采用更高空间分辨率的气象强迫场可改善水文模拟"的普遍认知相悖。进一步而言，由于高流量指标受降水数据的影响尤为显著，降水数据集的选择可直接影响湄公河流域的洪水脉冲过程模拟。采用不同降水产品模拟得到的陆地水储量、土壤湿度与蒸散发之间也存在显著差异。进一步而言，陆地水储量、土壤湿度与蒸散发对降水不确定性的敏感程度各不相同。本研究为基于过程的水文模拟中由降水引发的不确定性提供了关键认知，并揭示了湄公河流域内的此类不确定性。