A method for balancing the terrestrial water budget and improving the estimation of individual budget components

Luo, Zengliang; Gao, Zichao; Wang, Lunche; Wang, Shaoqiang; Wang, Lizhe

doi:10.1016/j.agrformet.2023.109667

Cited by 3 publications

(3 citation statements)

References 54 publications

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“…According to previous studies, the ∆Res (due to errors in budget components) has been commonly used to assess the imbalance in the water budget [13,18]. It is calculated based on Equations ( 5) and (6). A small ∆Res represents high water budget closure accuracy, i.e., the high data consistency in the budget component products.…”

Section: Water Budget Closure Assessmentmentioning

confidence: 99%

“…According to previous studies, some water budget closure correction methods, which focus on enforcing terrestrial water budget closure, have been proposed, such as the Proportional Redistribution, the Constrained Kalman Filter, the Multiple Collocation and the Minimized Series Deviation methods [13,24,41,54]. In addition, Luo et al [6] proposed a method for balancing the terrestrial water budget and improving the estimation of individual budget components by combining existing water budget closure correction methods and observations of budget components. These methods can be further combined with P inversion/simulation algorithms for improving the water budget closure accuracy of P inversion in the future.…”

Section: Monthly and Seasonal Variations Of The ∆Resmentioning

confidence: 99%

“…Precipitation (P) is the main driver of the terrestrial hydrological cycle [1][2][3]. A great number of satellite and reanalysis precipitation (SRP) products, characterized by global coverage and high temporal and spatial resolution, have been produced and widely applied in large-scale meteorological and hydrological analyses [4][5][6][7]. This is because, despite the high accuracy of in situ rainfall observations, they are sparse, and the record length in many basins around the world is insufficient, limiting the accuracy of hydrological research in these basins greatly [8].…”

Section: Introductionmentioning

confidence: 99%

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Assessing the Water Budget Closure Accuracy of Satellite/Reanalysis-Based Hydrological Data Products over Mainland China

Luo,

Yu,

Liu

et al. 2023

Remote Sensing

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A good water budget involving four variables, including precipitation (P), evapotranspiration (ET), streamflow (R), and terrestrial water storage change (TWSC), is reflected in two aspects: a high accuracy against observations for each budget component and the low water budget closure residual error (ΔRes). Due to the lack of consideration of observations of budget components in existing water budget closure assessment methods (BCMs), when the ΔRes of budget components is low, their error against respective observations may still be high. In this study, we assess the water budget closure accuracy of satellite/reanalysis-based hydrological data products over mainland China based on six popular P products and multiple datasets of additional budget components (ET, R, and TWSC). The results indicated that the ΔRes changes between ±15 mm over mainland China. Satellite P products such as GPM IMERG showed better performance by comparing them with rain gauge-based observations. However, reanalysis P products such as GLDAS and FLDAS showed a better water budget closure since the selected datasets of additional budget components (ET and R) are also derived from reanalysis datasets. This indicates that these same data sources for budget components make it easier to close the water budget. The further development of satellite P products should consider the closure of the water budget with other water cycle variables.

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Section: Water Budget Closure Assessmentmentioning

confidence: 99%

Section: Monthly and Seasonal Variations Of The ∆Resmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Assessing the Water Budget Closure Accuracy of Satellite/Reanalysis-Based Hydrological Data Products over Mainland China

Luo,

Yu,

Liu

et al. 2023

Remote Sensing

Self Cite

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Spatiotemporal differentiation of water–carbon–light compound use efficiency and its sensitivity to the dominant factor

Chen,

Wang,

Chen

2024

Hydrological Sciences Journal

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Developing mathematical models to calculate annual evapotranspiration rates in Iraq using climate data.

Azeez,

Jowad

2024

J. Phys.: Conf. Ser.

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Evapotranspiration is considered the fuel that controls the water cycle in the natural world. It is necessary for the stability of ecosystems and is involved in many important aspects in calculating groundwater quantities, determining optimal locations for building dams and reservoirs and knowing the water needs of crops. The work is divided into two main parts. The first includes the use of The Penman-Monteith method, which is considered the most standard method in the world according to the Food and Agriculture Organization (FAO), for calculating the annual rate of evapotranspiration in Iraq for the past fifteen years (2008-2022). The second part is a modification of a simple method for calculating evapotranspiration, which is the Thornthwaite method, to give results close to the accuracy of the Penman-Monteith method, as the modification was made using different statistical methods. The study showed the possibility of using the modified Thornthwaite method as an alternative to the Penman-Monteith method to calculate evapotranspiration in all regions of Iraq.

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A method for balancing the terrestrial water budget and improving the estimation of individual budget components

Cited by 3 publications

References 54 publications

Assessing the Water Budget Closure Accuracy of Satellite/Reanalysis-Based Hydrological Data Products over Mainland China

Assessing the Water Budget Closure Accuracy of Satellite/Reanalysis-Based Hydrological Data Products over Mainland China

Spatiotemporal differentiation of water–carbon–light compound use efficiency and its sensitivity to the dominant factor

Developing mathematical models to calculate annual evapotranspiration rates in Iraq using climate data.

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