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Zheng Duan

Universitetslektor

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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

Författare

  • Yan He
  • Xianfeng Song
  • Tatsuya Nemoto
  • Chen Wang
  • Jinghao Hu
  • Huihui Mao
  • Runkui Li
  • Junzhi Liu
  • Venkatesh Raghavan
  • Zheng Duan

Summary, in English

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.

Avdelning/ar

  • Institutionen för naturgeografi och ekosystemvetenskap
  • BECC: Biodiversity and Ecosystem services in a Changing Climate

Publiceringsår

2025-12

Språk

Engelska

Publikation/Tidskrift/Serie

Journal of Hydrology

Volym

662

Dokumenttyp

Artikel i tidskrift

Förlag

Elsevier

Ämne

  • Oceanography, Hydrology and Water Resources

Nyckelord

  • Hydrological modeling
  • local Moran's I
  • remotely sensed ET products
  • Spatial autocorrelation
  • Spatial pattern

Aktiv

Published

ISBN/ISSN/Övrigt

  • ISSN: 0022-1694