A novel ecohydrological model by capturing variations in climate change and vegetation coverage in a semi-arid region of China

Variations in vegetation are influenced by regional climate regimes and, in turn, control the water balance behavior in water-limited regions. Owing to its role in ecohydrological processes, vegetation is an essential link in modeling the relationships among climate conditions, vegetation patterns, and dynamic water balance behavior. However, previous ecohydrological models have been empirical and complex, without physically significant parameters. Here, we propose a novel ecohydrological model (a Budyko model-coupled vegetation model) that combines the impacts of climate change and vegetation variations, featuring simple and deterministic parameters. In addition to accounting for the fundamental water balance model and its factors, mean precipitation, potential evapotranspiration, runoff, and variations in water storage (delta S), the model showed better performance when incorporating delta S (RMSE = 2.72 mm yr(-1)) and its parameter epsilon-, which is mechanically and quantitively subject to the vegetation coverage (R-2 = 0.95, p < 0.01). This was estimated as a function of vegetation potential canopy conductance, mean rainstorm depth, mean time between storms, and potential rate of evapotranspiration in a semi-arid watershed with impulsive precipitation in China (R2 = 0.80, p < 0.01). The model also found that vegetation growth was mainly controlled by soil water content and decoupled the impact of the total amount of precipitation on vegetation in the northeastern area of the watershed. Hence, our method presents a new tool for building an ecohydrological model that includes deterministic parameters of mechanical significance.

Automated summary

Variations in vegetation play a crucial role in the water balance behavior of water-limited regions. This study introduces a novel ecohydrological model that combines the impacts of climate change and vegetation variations using simple and deterministic parameters, improving upon previous empirical and complex models.