A New Model for Evaluating Ecosystem Services by Integrating Ecosystem Processes and Remote Sensing Data----Key Laboratory of Ecosystem Network Observation and Modeling,Institute of Geographic Sciences and Natural Resources Research,Chinese Academy of Sciences

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A New Model for Evaluating Ecosystem Services by Integrating Ecosystem Processes and Remote Sensing Data

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2021-09-09

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Ecosystem services (ESs) are the ecological characteristics, functions, or processes that contribute to human wellbeing. According to Millennium Ecosystem Assessment (2005), approximately 60% of the global ESs are either degraded or used in an unsustainable way. These modifications highlighted the need and importance of monitoring ESs. Compared to the most ecosystem services models (e.g., InVEST and ARIES), which ignored the relationships among ESs, process-based models can overcome this limitation, and the integration of ecological models with remote sensing data could greatly facilitate the investigation of the complex ecological processes.

Prof. HE Honglin's team at Institute of Geographic Sciences and Natural Resources Research of Chinese Academy of Sciences and National Ecosystem Science Data Center developed a process-based ecosystem services model (CEVSA-ES) using remotely-sensed LAI (leaf area index) data to evaluate four important ESs, including productivity provision, carbon sequestration, water retention, and soil retention. This article was published in Journal of Advances in Modeling Earth Systems in 2021.

The CEVSA-ES model is a new tool to understand the complex relationships among ESs through incorporating explicit representations of the biogeochemical and biophysical processes. Compared to the traditional terrestrial biosphere models, the main innovation of CEVSA-ES was the consideration of soil erosion processes and its impact on carbon cycling. The algorithms related to carbon and water cycles were also improved through integrating remote sensing data in the new version. In addition, a model-data fusion method was applied to optimize sensitive parameters and thus improved model performance based on multi-source observational data.

Simulation results showed good fits with ecosystem processes, explaining 95%, 92%, 76%, and 65% inter-annual variability of gross primary productivity, ecosystem respiration, net ecosystem productivity, and evapotranspiration, respectively. Meanwhile, the CEVSA-ES model with optimized parameters explained 47%–96% of the spatial and temporal variations of four ecosystem services in China.

“The process-based ecosystem services models could support exploring the mechanisms underpinning synergies and trade-offs between ES, and further guiding decision-making in ecosystem management and environmental policies,” said Prof. HE.

The study is a joint effort in collaboration with Peking University, Chinese Academy of Agricultural Sciences, Lanzhou University, Northwest Agriculture and Forest University, Tianjin University, Zhejiang A & F University, and Chongqing University of Posts and Telecommunications.

The study was supported by the National Key Research and Development Program of China, Key Program of National Natural Science Foundation of China, and Basic Science Center Program of National Natural Science Foundation of China.

Reference:

Niu Z, He H^*, Peng S, Ren X, Zhang L^*, et al. A Process-Based Model Integrating Remote Sensing Data for Evaluating Ecosystem Services[J]. Journal of Advances in Modeling Earth Systems, e2020MS002451.