Toward a framework for the multimodel ensemble prediction of soil nitrogen losses

Kaihua Liao, Ligang Lv, Xiaoming Lai and Qing Zhu

Ecological Modelling, 2021, vol. 456, issue C

Abstract: Soil nitrogen (N) loss is a part of N biogeochemical processes, which plays an important role in the agricultural, ecological and environmental management. Because it is difficult to assess the temporal and spatial changes of different N forms in leachates by field measurement methods, conceptual and physical models are usually used to describe soil N loss. However, soil N models are often associated with multiple sources of uncertainty (e.g., model parameter and structure), which may largely influence the reliability and accuracy of the models. The multimodel ensemble prediction (MEP) is specifically designed to reduce the parameter and structural uncertainty in N biogeochemical modelling by representing a set of candidate models. However, the existing MEP methods still need to be improved by integrating various kinds of prior knowledge and quantifying each part of predictive uncertainty. In addition, published studies mainly focused on the regional scale MEP of the land carbon balance. However, the regional scale MEP of soil N losses is lacking. This paper firstly proposed the MEP methods of soil N losses at different spatial scales: 1) using the Monte-Carlo sampling to randomly alter the soil and crop parameters governing the N cycle and driving multiple soil N models at plot scale; and 2) generating an ensemble of TIGGE (THORPEX Interactive Grand Global Ensemble) weather forecasts and an ensemble of random soil and crop parameters and driving multiple soil N models at regional scale. This study also discussed different methods used for realizing MEP. It is found that the ensemble mean produced a large bias when simulating soil N losses. By using the bias correction technique, the RMSEs of the ensemble mean decreased by 57.5%~86.0%. Overall, the MEP can enhance our understanding of soil N cycle. In addition, this study is also helpful to accurately estimate the response of soil N loss to global change and provide support for agricultural production and environmental protection.

Keywords: Nitrogen cycle; Biogeochemical processes; Model uncertainty; Vadose zone; Global change; Regional scale (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ecomod:v:456:y:2021:i:c:s0304380021002337

DOI: 10.1016/j.ecolmodel.2021.109675

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