This paper is aimed at examining the applicability of methods for resilience, reliability and risk analyses of rain-fed agricultural systems from modeled continuous soil moisture availability in rain-fed crop lands. T...This paper is aimed at examining the applicability of methods for resilience, reliability and risk analyses of rain-fed agricultural systems from modeled continuous soil moisture availability in rain-fed crop lands. The methodology involves integration of soil and climatic data in a simple soil moisture accounting model to assess soil moisture availability, and a risk used as indicator of sustainability of rain-fed agricultural systems. It is also attempted to demonstrate the role of soil moisture modeling in risk analysis and agricultural water management in a semi-arid region in Limpopo Basin where rain-fed agriculture is practiced. For this purpose, a daily-time step soil moisture accounting model is employed to simulate daily soil moisture, evaporation, surface runoff, and deep percolation using 40 years (1961-2000) of agroclimatic data, and cropping cycle data of maize, sorghum and sunflower. Using a sustainability criterion on crop water requirement and soil moisture availability, we determined resilience, risk and reliability as a quantitative measure of sustainability of rain-fed agriculture of these three crops. These soil moisture simulations and the sustainability criteria revealed further confirmation of the relative sensitivity to drought of these crops. Generally it is found that the risk of failure is relatively low for sorghum and relatively high for maize and sunflower in the two sites with some differences of severity of failure owing to the slightly different agroclimatic settings.展开更多
获取高精度的土壤相对湿度对开展土壤墒情和旱涝精细化监测评估和预报预警有重要意义。该研究基于2020–2023年4–11月中国气象局陆面数据同化系统(China Meteorological Administration Land Data Assimilation System,CLDAS)逐日土壤...获取高精度的土壤相对湿度对开展土壤墒情和旱涝精细化监测评估和预报预警有重要意义。该研究基于2020–2023年4–11月中国气象局陆面数据同化系统(China Meteorological Administration Land Data Assimilation System,CLDAS)逐日土壤相对湿度、全国土壤水分自动站逐小时土壤相对湿度以及土地利用类型、土壤属性、地理信息等数据,采用随机森林和支持向量机模型构建土壤水分自动站观测和CLDAS反演的土壤相对湿度动态融合订正模型,基于融合的土壤相对湿度构建土壤旱涝强度-面积-时间多维度评估指数,开展多维度旱涝监测评估。结果表明:1)采用随机森林模型融合后,0~10、0~20、0~50 cm土壤相对湿度与观测的土壤相对湿度的决定系数分别为0.79、0.81、0.80,相对均方根误差分别为13.81%、11.40%、9.50%,优于支持向量机模型。2)全国土壤缺墒日数百分率呈东南至西北增加趋势,内蒙古中西部、西北地区大部普遍在70%、甚至80%以上,内蒙古东南部、华北中北部、西南地区中西部为50%~70%,中东部大部在40%以下;土壤过湿日数百分率呈东南至西北减小趋势,华南东部和南部、西南地区南部、东北地区东北部多数在50%以上。3)基于融合土壤相对湿度数据构建的土壤缺墒、土壤过湿、墒情指数以及旱涝面积、持续时间指数,明显提升了2022年长江流域高温干旱、2023年台风“杜苏芮”和“卡努”等典型灾害性天气过程动态评估的定量化、精细化水平。土壤湿度融合数据及其旱涝评估指数可有效助力旱涝灾害多维度精细化定量评估,为防灾减灾提供重要支撑。展开更多
文摘This paper is aimed at examining the applicability of methods for resilience, reliability and risk analyses of rain-fed agricultural systems from modeled continuous soil moisture availability in rain-fed crop lands. The methodology involves integration of soil and climatic data in a simple soil moisture accounting model to assess soil moisture availability, and a risk used as indicator of sustainability of rain-fed agricultural systems. It is also attempted to demonstrate the role of soil moisture modeling in risk analysis and agricultural water management in a semi-arid region in Limpopo Basin where rain-fed agriculture is practiced. For this purpose, a daily-time step soil moisture accounting model is employed to simulate daily soil moisture, evaporation, surface runoff, and deep percolation using 40 years (1961-2000) of agroclimatic data, and cropping cycle data of maize, sorghum and sunflower. Using a sustainability criterion on crop water requirement and soil moisture availability, we determined resilience, risk and reliability as a quantitative measure of sustainability of rain-fed agriculture of these three crops. These soil moisture simulations and the sustainability criteria revealed further confirmation of the relative sensitivity to drought of these crops. Generally it is found that the risk of failure is relatively low for sorghum and relatively high for maize and sunflower in the two sites with some differences of severity of failure owing to the slightly different agroclimatic settings.
文摘获取高精度的土壤相对湿度对开展土壤墒情和旱涝精细化监测评估和预报预警有重要意义。该研究基于2020–2023年4–11月中国气象局陆面数据同化系统(China Meteorological Administration Land Data Assimilation System,CLDAS)逐日土壤相对湿度、全国土壤水分自动站逐小时土壤相对湿度以及土地利用类型、土壤属性、地理信息等数据,采用随机森林和支持向量机模型构建土壤水分自动站观测和CLDAS反演的土壤相对湿度动态融合订正模型,基于融合的土壤相对湿度构建土壤旱涝强度-面积-时间多维度评估指数,开展多维度旱涝监测评估。结果表明:1)采用随机森林模型融合后,0~10、0~20、0~50 cm土壤相对湿度与观测的土壤相对湿度的决定系数分别为0.79、0.81、0.80,相对均方根误差分别为13.81%、11.40%、9.50%,优于支持向量机模型。2)全国土壤缺墒日数百分率呈东南至西北增加趋势,内蒙古中西部、西北地区大部普遍在70%、甚至80%以上,内蒙古东南部、华北中北部、西南地区中西部为50%~70%,中东部大部在40%以下;土壤过湿日数百分率呈东南至西北减小趋势,华南东部和南部、西南地区南部、东北地区东北部多数在50%以上。3)基于融合土壤相对湿度数据构建的土壤缺墒、土壤过湿、墒情指数以及旱涝面积、持续时间指数,明显提升了2022年长江流域高温干旱、2023年台风“杜苏芮”和“卡努”等典型灾害性天气过程动态评估的定量化、精细化水平。土壤湿度融合数据及其旱涝评估指数可有效助力旱涝灾害多维度精细化定量评估,为防灾减灾提供重要支撑。
