Please kindly refer to the accepted version in the journal GMD (https://doi.org/10.5194/gmd-2024-38).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.

Deforestation alters the exchange of heat, moisture, and momentum between the Earth's surface and the atmosphere, which can significantly affect the surface energy balance and water budget. However, changes in surface heat fluxes in response to deforestation are diverse among multi-model simulations. Changes in surface heat fluxes may lead to further energy partitioning and different land-atmosphere interactions. This study explores factors that might cause different changes in surface fluxes under tropical deforestation. The mediating effect of the Bowen ratio on changes in turbulent surface fluxes in response to the removal of tropical rainforests is examined with the Community Earth System Model of the National Center for Atmospheric Research. Different flux partitioning in the mean state of the Bowen ratio is associated with various flux changes under deforestation. When the mean Bowen ratio is smaller, deforestation tends to increase sensible heat fluxes and reduce latent heat fluxes. Our research further indicates that the simulated mean-state Bowen ratios in the Land Use Model Intercomparison Project model archive might modulate changes in surface heat fluxes that provide some clues for the land surface model developments.

Low-clouds and fog moderate the diurnal temperature range (DTR) through radiative effects. Consequently, frequent foggy events make montane cloud forests (MCFs) stable and unique. However, observations in the understory of the forest are rare. To investigate the DTR variation in elevations, we surveyed the Central Cross-Island Highway in central Taiwan transects with MCFs. The results from paired weather stations revealed that the DTR increases significantly with altitude in open fields but not in the forest’s understory. Furthermore, the continuous observations in altitude across non-cloud forest and MCFs indicate that DTR decreases in both the open field and understory of MCFs. The DTR discontinuity highlights the indispensability of MCF for the mountain ecosystem. Further simulating the integrative effect of the climate and land-use change on fog is crucial for the ecoclimate in mountainous regions.

Accurate quantification of irrigation water is necessary in order to examine the realistic effect of agricultural water use on the hydrological cycle and the climate. However, due to a lack of survey-based statistics, the amount of irrigation water is often estimated by irrigation demand derived from hydrologic models without a proper ground validation. This study attempts to construct the first State-level time series of irrigation water volume over India based solely on survey statistics, with the aim of estimating historical irrigation conditions. By assuming that the ratio of irrigated area between States remained constant throughout the period, the annual statistics of the State-level irrigated area were extended from the period of 1990–2014 to the period of 1950–2014. The annual State-level irrigation water volumes were then estimated as a function of the above irrigated area data over 1950¬¬–2014 and calibrated using an independent subset of State-level irrigation water quantity statistics. The irrigation water volume data produced in the current study is compared with a widely used irrigation water demand data. The comparison suggests that the previous data might be significantly overestimated (up to 80 Billion Cubic Metre) over most States with a few States with underestimated values (up to 10 Billion Cubic Metre). The irrigation area and volume data of this study is the first State-level estimate that better represents the historical irrigation condition in India.