A document by Zixuan Zhou. Click on the document to view its contents.

Irrigated cultivation, as a prevalent anthropogenic activity, exerts a significant influence on land use and land cover, resulting in notable modifications to land-atmosphere interaction and the hydrological cycle. Given the extensive cropland, high productivity, compact rotation, semi-arid climate, intense irrigation, and groundwater depletion in the North China Plain (NCP), the development of a comprehensive crop-irrigation-groundwater model becomes imperative for understanding agricultural-induced climate response in this region. This study presents an integrated crop model explicitly tailored to the NCP, which incorporates double-cropping rotation, irrigation practice, and groundwater interactions into the regional climate model. The modifications are implemented to: (1) enable a seamless transition from field scale application to regional scale application, facilitating the incorporation of spatial variability, (2) capture the distinctive attributes of the NCP region, ensuring the model accurately reflects its unique characteristics, and (3) reinforce the direct interaction among crop-related variables, thereby enhancing the model’s capacity to simulate their dynamic behaviors. The integrated crop modeling system demonstrates a commendable performance in crop simulations using climatic conditions, which is substantiated by its identification of crop stages, estimation of field biomass, prediction of crop yield, and finally the projection of monthly leaf area index. In our next phase, this integrated crop modeling system will be employed in long-term simulations to enhance our understanding of the intricate relationship between agricultural development and climate change.

When organic peat soils are sufficiently dry, they become flammable. In Southeast Asian peatlands, widespread deforestation and associated drainage create dry conditions that, when coupled with El Niño-driven drought, result in catastrophic fire events that release large amounts of carbon and deadly smoke to the atmosphere. While the effects of anthropogenic degradation on peat moisture and fire risk have been extensively demonstrated, climate change impacts to peat flammability are poorly understood. These impacts are likely to be mediated primarily through changes in soil moisture. Here, we used neural networks (trained on data from the NASA SMAP satellite) to model soil moisture as a function of climate, degradation, and location. The neural networks were forced with regional climate model projections for 1985-2005 and 2040-2060 climate under RCP8.5 forcing to predict changes in soil moisture. We find that reduced precipitation and increased evaporative demand will lead to median soil moisture decreases about half as strong as those observed during recent El Niño droughts. Such reductions may be expected to accelerate peat emissions. Our results also suggest that soil moisture in degraded areas with less tree cover may be more sensitive to climate change than in other land use types, motivating urgent peatland restoration. Climate change may play an important role in future soil moisture regimes and by extension, future peat fire in Southeast Asian peatlands.