CEASA: Dominant spatial autocorrelation in dual-constraint calibration as the game-changer for hydrological modeling with high-uncertainty remotely sensed evaporation: Application to the Meichuan basin

Abstract

Accurate evapotranspiration (ET) estimation is vital for hydrological modeling, yet remotely sensed ET (RS-ET) products are often limited by algorithmic uncertainties and sensor biases. To mitigate error propagation and better capture spatial patterns, this study introduces the Composite Efficiency of Absolute ET and Spatial Autocorrelation (CEASA) —a dual-constraint framework that integrates absolute ET magnitude and spatial autocorrelation to enhance simulation accuracy, which marks a pivotal shift by moving beyond traditional individual-value-based calibration to incorporate spatially explicit pattern constraints.

Using four RS-ET products in China’s Meichuan Basin (three high-bias: MOD16, GLASS, SSEBop; one low-bias: PMLV2), CEASA demonstrated: (1) Dual-constraint superiority: CEASA outperformed single-constraint methods. Compared to the absolute-value-only scheme (M1), it reduced PBIAS by 18–33 % and improved KGE from 0.47 to 0.51 to 0.76–0.77 under high-bias datasets, meanwhile improving KGE to 0.84 and reducing PBIAS to 9.4 % under low-bias PMLV2. It also surpassed spatial-pattern-only approaches by 11 % in KGE under low-bias data. Notably, CEASA achieved comparable streamflow accuracy to streamflow-based calibration (M0) while improving ET simulation. (2) ​​Quality adaptivity​​: CEASA’s weighted dual-criteria architecture dynamically adapted to RS-ET quality—achieving peak performance for PMLV2 and maintaining stable accuracy for high-bias datasets by emphasizing spatial neighborhood information. (3) Spatial dominance: Entropy analysis showed spatial autocorrelation contributed >70 % of the optimization signal, with higher information content than absolute ET magnitude (2.85–3.42 vs. 0.39–1.22).

CEASA redefines RS-ET application by emphasizing spatial patterns, offering a bias-resilient solution for ungauged basins. Future work should explore scale-sensitive metrics and intelligent weighting schemes for broader applicability.