A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
The water year based on minimizing the terrestrial water storage variation and its validity
Study region: 15 catchments in the Chinese Loess Plateau (CLP) Study focus: The concept of the water year has been widely used in analyzing the coupled water-energy balance at the annual scale. However, a comprehensive quantitative investigation about its determination and usability in catchment water balance analysis remains absent. Leveraging long-term hydrometeorological observations and terrestrial water storage (TWS) data, we ascertained the commencement month of the water year for 15 catchments in the CLP and explored the feasibility of the Budyko framework at the water year scale concerning evapotranspiration (ET) estimation and its change attribution. New hydrological insights for the region: (1) TWS recharge commenced predominately in July, with the minimal TWS variation (∆S) consistently observed for years starting from July. Consequently, July marked the start of the water year for CLP catchments. (2) Disregarding ∆S at the calendar year scale caused significant errors in estimating ET (MAE = 51.16 mm yr−1), while the MAE decreased by 35.40% at the water year scale. (3) The relative contributions of precipitation (P), potential evapotranspiration (ET0), and w to ET change deviated significantly from the actual values when ignoring ∆S at the calendar year scale. Conversely, these contributions aligned closely with actual values at the water year scale. Overall, the improved accuracy in ET estimation and enhanced consistency in ET change attribution results collectively justify the water year temporal framework.
The water year based on minimizing the terrestrial water storage variation and its validity
Study region: 15 catchments in the Chinese Loess Plateau (CLP) Study focus: The concept of the water year has been widely used in analyzing the coupled water-energy balance at the annual scale. However, a comprehensive quantitative investigation about its determination and usability in catchment water balance analysis remains absent. Leveraging long-term hydrometeorological observations and terrestrial water storage (TWS) data, we ascertained the commencement month of the water year for 15 catchments in the CLP and explored the feasibility of the Budyko framework at the water year scale concerning evapotranspiration (ET) estimation and its change attribution. New hydrological insights for the region: (1) TWS recharge commenced predominately in July, with the minimal TWS variation (∆S) consistently observed for years starting from July. Consequently, July marked the start of the water year for CLP catchments. (2) Disregarding ∆S at the calendar year scale caused significant errors in estimating ET (MAE = 51.16 mm yr−1), while the MAE decreased by 35.40% at the water year scale. (3) The relative contributions of precipitation (P), potential evapotranspiration (ET0), and w to ET change deviated significantly from the actual values when ignoring ∆S at the calendar year scale. Conversely, these contributions aligned closely with actual values at the water year scale. Overall, the improved accuracy in ET estimation and enhanced consistency in ET change attribution results collectively justify the water year temporal framework.
The water year based on minimizing the terrestrial water storage variation and its validity
Changwu Cheng (author) / Wenzhao Liu (author) / Qiang Li (author) / Tingting Ning (author) / Haixiang Zhou (author) / Zhaotao Mu (author) / Kang Du (author) / Kai Wang (author) / Xiaoyang Han (author)
2025
Article (Journal)
Electronic Resource
Unknown
Metadata by DOAJ is licensed under CC BY-SA 1.0
The water year based on minimizing the terrestrial water storage variation and its validity
| Elsevier | 2025
Terrestrial water storage change from temporal gravity variation
| TIBKAT | 2009
Minimizing risks with recycled water
| Tema Archive | 1996
Reconstructing terrestrial water storage anomalies using convolution-based support vector machine
| Elsevier | 2023