Preprints
https://doi.org/10.5194/hess-2022-155
https://doi.org/10.5194/hess-2022-155
 
22 Apr 2022
22 Apr 2022
Status: a revised version of this preprint was accepted for the journal HESS and is expected to appear here in due course.

Revisiting large-scale interception patterns constrained by a synthesis of global experimental data

Feng Zhong1,2, Shanhu Jiang2,3, Albert I. J. M. van Dijk4, Liliang Ren2,3, Jaap Schellekens5, and Diego G. Miralles1 Feng Zhong et al.
  • 1Hydro-Climate Extremes Lab (H-CEL), Ghent University, Ghent, 9000, Belgium
  • 2State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing 210098, China
  • 3College of Hydrology and Water Resources, Hohai University, Nanjing, China
  • 4Fenner School of Environment & Society, Australian National University, ACT, Australia
  • 5Planet Labs, PBC, Haarlem, the Netherlands

Abstract. Rainfall interception loss remains one of the most uncertain fluxes in the global water balance, hindering water management in forested regions and precluding an accurate formulation in climate models. Here, a synthesis of interception loss data from past field experiments conducted worldwide is performed, resulting in a meta-analysis comprising 166 forest sites and 17 agricultural plots. This meta-analysis is used to constrain a global process-based model driven by satellite-observed vegetation dynamics, potential evaporation and precipitation. The model considers subgrid heterogeneity and vegetation dynamics, and formulates rainfall interception for tall and short vegetation separately. A global, 40-year (1980–2019), 0.1º spatial resolution, daily temporal resolution dataset is created, analysed and validated against in situ data. The validation shows a good consistency between the modelled interception and field observations over tall vegetation, both in terms of correlations and bias. While an underestimation is found in short vegetation, the degree to which it responds to in situ representativeness errors and difficulties inherent to the measurement of interception in short vegetated ecosystems is unclear. Global estimates are compared to existing datasets, showing overall comparable patterns. According to our findings, global interception averages to 73.81 mm yr–1 or 10.96 × 103 km3 yr–1, accounting for 10.53 % of continental rainfall, and approximately 14.06 % of terrestrial evaporation. The seasonal variability of interception follows the annual cycle of canopy cover, precipitation, and atmospheric demand for water. Tropical rainforests show low intra-annual vegetation variability, and seasonal patterns are dictated by rainfall. Interception shows a strong variance among vegetation types and biomes, supported by both the modelling and the meta-analysis of field data. The global synthesis of field observations and the new global interception dataset will serve as a benchmark for future investigations, and facilitate large-scale hydrological and climate research.

Feng Zhong et al.

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on hess-2022-155', Anonymous Referee #1, 19 May 2022
    • AC1: 'Reply on RC1', feng zhong, 03 Sep 2022
  • RC2: 'Comment on hess-2022-155', Yongqiang Zhang, 27 Jun 2022
    • AC2: 'Reply on RC2', feng zhong, 03 Sep 2022

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on hess-2022-155', Anonymous Referee #1, 19 May 2022
    • AC1: 'Reply on RC1', feng zhong, 03 Sep 2022
  • RC2: 'Comment on hess-2022-155', Yongqiang Zhang, 27 Jun 2022
    • AC2: 'Reply on RC2', feng zhong, 03 Sep 2022

Feng Zhong et al.

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Short summary
A synthesis of rainfall interception data from past field campaigns is performed, including 166 forests and 17 agricultural plots distributed worldwide. These site data are used to constrain and validate an interception model that considers sub-grid heterogeneity and vegetation dynamics. A global, 40-year (1980–2019) interception dataset is generated at a daily temporal and 0.1º spatial resolution. This dataset will serve as a benchmark for future investigations of the global hydrological cycle.