Impacts of climate change on water footprint components of rainfed and irrigated wheat in a semi-arid environment.

Climate change is one of the biggest environmental challenges that significantly impact water resources and the quantity and quality of agricultural products. Assessment of these impacts during the historical period and under future climate is essential for achieving a sustainable agricultural system in the face of climate change threats and water scarcity. In this research, we evaluated the yield and water footprint of rainfed and irrigated wheat during the historical period (1986-2015) and two future periods (2016 to 2055) in a semi-arid environment in Fars province, Iran. The future climate data was selected from the CanESM2 model outputs (bias-corrected and downscaled using the SDSM model) under the RCP4.5 scenario, and the yield projection was made using the AquaCrop model. Our result showed that for both irrigated and rainfed wheat, the yield significantly increases in southern parts of the study area in future climates, primarily because of an increase in effective precipitation. Other regions will experience a marginal yield decrease or no yield changes (in the case of irrigated wheat). Our assessments of the water footprint of wheat production showed a significant reduction in green and blue water footprints in the southern regions. In other regions, various patterns emerged for irrigated and rainfed wheat, but an overall increase was observed. The southern regions of the study area will be more suitable for wheat production owing to the higher yield and lower water footprint.

Spatial modeling of land subsidence using machine learning models and statistical methods.

Land subsidence causes many problems every year and damages residential areas and agricultural lands. The purpose of this study is to prepare a susceptibility map to the phenomenon of land subsidence in the central and eastern plains of Fars province in Iran using statistical and machine learning models. Initially, with a wide inspection, the locations of land subsidence in the study region were recorded using the global positioning system (GPS), and a spatial distribution of subsidence was provided then for building and evaluating learning models; the data was partitioned into two sections of calibration (70%) and testing (30%) dataset. In the following stage, the maps of the factors affecting the land subsidence were prepared using basic information (geological and topographic maps and satellite images) in raster format, and the relationship between land subsidence locations and the effective factors including slope percentage, slope aspect, distance from the road, distance from the river, land use, plan curvature, topographic wetness index, geology (lithological units), distance from the faults, and groundwater level changes was considered in the study area. To investigate the multicollinearity between independent variables, tolerance and variance inflation factor (VIF) measures were used, and to prioritize the effective factors, the random forest (RF) algorithm was applied. The results indicated that the most important factors affecting land subsidence were groundwater level changes, land use, height, distance from the fault, distance from the river, and topographic wetness index, respectively. For further analysis, a land subsidence susceptibility zoning map was prepared using logistic regression (LR), random forest (RF), boosting regression tree (BRT), and support vector machine (SVM) models, and the results were evaluated. The evaluation results indicated that the models mentioned have high accuracy in modeling land subsidence such that the boosting regression tree and the logistic regression have high (0.873 and 0.853, respectively) and the random forest and support vector machine models have very high accuracy (0.953 and 0.926, respectively). The findings of this study indicated that the machine learning techniques and prepared maps can be applied for land use planning, groundwater management, and management of the study area for future agriculture tasks.

Impact of climate change on net primary production (NPP) in south Iran.

Climate change is a natural hazard which threatens the sustainable development of human health, food security, economic well-being, and natural resources. It also affects photosynthesis, plant respiration, and decomposition of organic matter that contribute to atmospheric carbon flow. The net primary production (NPP) is one of the main components of carbon balance. This study investigated the impact of climatic change on the net production in the Hormozgan county in south Iran. To obtain NPP, MODIS NPP product (MOD17A3) was used and future temperature and precipitation values were obtained using the HadGEM2-ES model under the RCP4.5 scenario. These values were downscaled using the LARSWG 6 statistical model, and precipitation and temperature were simulated for the RCP4.5 scenario. For further analysis, NPP was simulated based on the BIOME-BGC model and compared with the NPP data obtained from the MODIS images. Comparison of the climatic parameters of the basic (2001-2015) and future (2016-2030) periods indicated an increase in precipitation, minimum temperature, and maximum temperature of the study area and subsequently an increase in the NPP value in all biomes (averagely 17.73%) in the future. The highest NPP values were observed in the central and western parts of the region in biomes 4 (mangrove forest cover), 10 (broadleaf forest vegetation), and 6, 5, and 1 (rangeland vegetation), respectively, and the lowest values were observed in the eastern parts. Results showed that the increase in future NPP could be due to the increase in precipitation.