IAHS2022-210
https://doi.org/10.5194/iahs2022-210
IAHS-AISH Scientific Assembly 2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

Long-term water imbalances of global watersheds resulting from biases in hydroclimatic datasets for water budget analyses

Xuezhi Tan
Xuezhi Tan
  • Center of water resources and environment, Sun Yat-sen University, Guangzhou, PR. China (tanxuezhi@mail.sysu.edu.cn)

Assessing the water budget closures and source of water budget imbalances is fundamental to improve our understanding on changes in the hydrological system and their associated impacts. We analyzed the long-term (1982-2016) water budget for 1543 global watersheds by using various datasets for precipitation (P), evapotranspiration (ET), observed streamflow (Q), and total water storage (TWS). The results show that 93%, 80%, 43% and 20% of the watersheds shows water imbalances less than 30%, 20%, 10%, and 5% of their individual precipitation. The global average of water budget imbalance is -41.6 mm year-1 (1.8% of P). Watersheds showing large water imbalance ratio values are mostly located in the biomes of Tropical and Subtropical Moist Broadleaf Forests, Boreal Forests/Taiga, and Tundra. Different P, ET, and Q dataset combinations resulting in different degrees of water imbalance. The water budget imbalance shows a significant negative relationship with humidity index and vegetation coverage while a positive relationship with the proportions of irrigation area and watershed area. Showing small water imbalances for most watersheds, ERA5 precipitation dataset, MTE evapotranspiration dataset, and JPL TWS dataset performed better than other datasets in water budget in most biomes. The uncertainties of P, ET, Q and TWSGRACE contribute to 43.4%, 20.1%, 15.9% and 20.6% of the water budget imbalance on average, respectively. Improving the accuracy of P and ET estimates, and streamflow measurements are critical to a better understanding of water budget and improves the modelling of hydrological process.

How to cite: Tan, X.: Long-term water imbalances of global watersheds resulting from biases in hydroclimatic datasets for water budget analyses, IAHS-AISH Scientific Assembly 2022, Montpellier, France, 29 May–3 Jun 2022, IAHS2022-210, https://doi.org/10.5194/iahs2022-210, 2022.