Water Budget Closure in the Upper Chao Phraya River Basin, Thailand Using Multisource Data

Abstract

Accurate quantification of the terrestrial water cycle relies on combinations of multisource datasets. This analysis uses data from remotely sensed, in-situ, and reanalysis records to quantify the terrestrial water budget/balance and component uncertainties in the upper Chao Phraya River Basin from May 2002 to April 2020. Three closure techniques are applied to merge independent records of water budget components, creating up to 72 probabilistic realizations of the monthly water budget for the upper Chao Phraya River Basin. An artificial neural network (ANN) model is used to gap-fill data in and between GRACE and GRACE-FO-based terrestrial water storage anomalies. The ANN model performed well with r ≥ 0.95, NRMSE = 0.24 − 0.37, and NSE ≥ 0.89 during the calibration and validation phases. The cumulative residual error in the water budget ensemble mean accounts for ~15% of the ensemble mean for both the precipitation and evapotranspiration. An increasing trend of 0.03 mm month−1 in the residual errors may be partially attributable to increases in human activity and the relative redistribution of biases among other water budget variables. All three closure techniques show similar directions of constraints (i.e., wet or dry bias) in water budget variables with slightly different magnitudes. Our quantification of water budget residual errors may help benchmark regional hydroclimate models for understanding the past, present, and future status of water budget components and effectively manage regional water resources, especially during hydroclimate extremes.

A Novel Two‐Step Method for Enforcing Water Budget Closure and an Intercomparison of Budget Closure Correction Methods Based on Satellite Hydrological Products

“…According to previous studies, water budgets based on data sets of budget components derived from various sources are rarely closed (Abhishek et al., 2022; Sahoo et al., 2011; Sheffield et al., 2009). The core of almost all BCCs is to reasonably redistribute ΔRes to the budget‐component products to obtain high‐precision budget‐corrected data sets that is, to enforce $${\Delta }\mathrm{R}\mathrm{e}\mathrm{s}=0$$ (or as close to 0 as possible) while reducing data uncertainties.…”

“…Three BCCs were employed to verify the performance of the proposed two‐step method, including the PR, CKF, and MCL methods (Abhishek et al., 2022; Abolafia‐Rosenzweig et al., 2021; Munier et al., 2014; Pan & Wood, 2006). The first step in Section 2.3.1 was also incorporated in the PR, CKF, and MCL methods to provide a fair comparison with the proposed MSD method.…”

“…Several budget closure correction methods (BCCs) have been developed to enforce the terrestrial water budget closure of budget components, including Proportional Redistribution (PR), Constrained Kalman Filter (CKF), and Multiple Collocation (MCL) (Abhishek et al., 2022; Abolafia‐Rosenzweig et al., 2021; Munier et al., 2014; Pan & Wood, 2006). The shared concept of these methods is to redistribute the ΔRes to data sets of budget components according to different weight redistribution principles.…”

“…and Multiple Collocation (MCL) (Abhishek et al, 2022;Abolafia-Rosenzweig et al, 2021;Munier et al, 2014;Pan & Wood, 2006). The shared concept of these methods is to redistribute the ΔRes to data sets of budget components according to different weight redistribution principles.…”

Annual runoff coefficient variation in a changing environment: a global perspective

“…The terrestrial water balance equation has been widely applied and proven to be reliable to estimate different hydrological fluxes (e.g. groundwater storage and evaporation) in many river basins globally by combining it with remote sensing, reanalysis, and in situ data (Feng et al 2013, Long et al 2014, Abhishek et al 2022. For a certain river basin, runoff depth (R, mm) is a response variable to the integration of precipitation (P, mm), evaporation (ET, mm), and changes in TWS (∆S, mm), as follows (Famiglietti and Rodell 2013):…”