The economy of most rural locations in the semi-arid region of Llano Estacado in the southern United States is predominantly based on agriculture, primarily beef and wheat (Triticum aestivum L.) production. This regio...The economy of most rural locations in the semi-arid region of Llano Estacado in the southern United States is predominantly based on agriculture, primarily beef and wheat (Triticum aestivum L.) production. This region is prone to drought and is projected to experience a drier climate. Droughts that coincide with the critical phenological phases of a crop can be remarkably costly. Although drought cannot be prevented, its losses can be minimized through mitigation measures if it is predicted in advance. Predicting yield loss from an imminent drought is an important need of stakeholders. One way to fulfill this need is using an agricultural drought index, such as the Agricultural Reference Index for Drought (ARID). Being plant physiology-based, ARID can represent drought-yield relationships accurately. This study developed an ARID-based yield model for predicting the drought-induced yield loss for winter wheat in this region by accounting for its phenological phase-specific sensitivity to water stress. The reasonable values of the drought sensitivity coefficients of the yield model indicated that it could reflect the phenomenon of water stress decreasing the winter wheat yields in this region reasonably. The values of the various metrics used to evaluate the model, including Willmott Index (0.86), Nash-Sutcliffe Index (0.61), and percentage error (26), indicated that the yield model performed fairly well at predicting the drought-induced yield loss for winter wheat. The yield model may be useful for predicting the drought-induced yield loss for winter wheat in the study region and scheduling irrigation allocation based on phenological phase-specific drought sensitivity.展开更多
A well-established and pre-calibrated crop model can normally represent the overall characteristics of crop growth and yield.However,it can hardly include all relevant factors that affect the yield,and usually overest...A well-established and pre-calibrated crop model can normally represent the overall characteristics of crop growth and yield.However,it can hardly include all relevant factors that affect the yield,and usually overestimates the crop yield when extreme weather conditions occur.In this study,the authors first introduced a drought index(the Standardized Precipitation Evapotranspiration Index)into a process-based crop model(the Agro-C model).Then,the authors evaluated the model’s performance in simulating the historical crop yields in a double cropping system in the Huang-Huai-Hai Plain of China,by comparing the model simulations to the statistical records.The results showed that the adjusted Agro-C model significantly improved its performance in simulating the yields of both maize and wheat as affected by drought events,compared with its original version.It can be concluded that incorporating a drought index into a crop model is feasible and can facilitate closing the gap between simulated and statistical yields.展开更多
The impacts of climate change on the discharge regimes in New Brunswick (Canada) were analyzed, using artificial neural network models. Future climate data were extracted from the Canadian Coupled General Climate Mode...The impacts of climate change on the discharge regimes in New Brunswick (Canada) were analyzed, using artificial neural network models. Future climate data were extracted from the Canadian Coupled General Climate Model (CGCM3.1) under the greenhouse gas emission scenarios B1 and A2 defined by the Intergovernmental Panel on Climate Change (IPCC). The climate change fields (temperatures and precipitation) were downscaled using the delta change approach. Using the artificial neural network, future river discharge was predicted for selected hydrometric stations. Then, a frequency analysis was carried out using the Generalized Extreme Value (GEV) distribution function, where the parameters of the distribution were estimated using L-moments method. Depending on the scenario and the time slice used, the increase in low return floods was about 30% and about 15% for higher return floods. Low flows showed increases of about 10% for low return droughts and about 20% for higher return droughts. An important part of the design process using frequency analysis is the estimation of future change in floods or droughts under climate scenarios at a given site and for specific return periods. This was carried out through the development of Regional Climate Index (RCI), linking future floods and droughts to their frequencies under climate scenarios B1 and A2.展开更多
Droughts have serious and widespread impacts on crop production with substantial economic losses. The frequency and severity of drought events may increase in the future due to climate change. We have developed three ...Droughts have serious and widespread impacts on crop production with substantial economic losses. The frequency and severity of drought events may increase in the future due to climate change. We have developed three meteorological drought scenarios for Austria in the period 2008-2040. The scenarios are defined based on a dry day index which is combined with bootstrapping from an observed daily weather dataset of the period 1975-2007. The severity of long-term drought scenarios is characterized by lower annual and seasonal precipitation amounts as well as more significant temperature increases compared to the observations. The long-term impacts of the drought scenarios on Austrian crop production have been analyzed with the biophysical process model EPIC (Environmental Policy Integrated Climate). Our simulation outputs show that—for areas with historical mean annual precipitation sums below 850 mm— already slight increases in dryness result in significantly lower crop yields i.e. depending on the drought severity, between 0.6% and 0.9% decreases in mean annual dry matter crop yields per 1.0% decrease in mean annual precipitation sums. The EPIC results of more severe droughts show that spring and summer precipitation may become a limiting factor in crop production even in regions with historical abundant precipitation.展开更多
The impact of climate change on drought main characteristics was assessed over Southern South America. This was done through the precipitation outputs from a multi-model ensemble of 15 climate models of the Coupled Mo...The impact of climate change on drought main characteristics was assessed over Southern South America. This was done through the precipitation outputs from a multi-model ensemble of 15 climate models of the Coupled Model Intercomparison Project Phase 5 (CMIP5). The Standardized Precipitation Index was used as a drought indicator, given its temporal flexibility and simplicity. Changes in drought characteristics were identified by the difference for early (2011-2040) and late (2071-2100) 21st century values with respect to the 1979-2008 baseline. In order to evaluate the multi-model outputs, model biases were identified through a comparison with the drought characteristics from the Global Precipitation Climatology Centre database for the baseline period. Future climate projections under moderate and high-emission scenarios showed that the occurrence of short-term and long-term droughts will be more frequent in the 21st century, with shorter durations and greater severities over much of the study area. These changes in drought characteristics are independent on the scenario considered, since no significant differences were observed on drought changes. The future changes scenario might be even more dramatic, taking into account that in most of the region the multi-model ensemble tends to produce less number of droughts, with higher duration and lower severity. Therefore, drought contingency plans should take these results into account in order to alleviate future water shortages that can have significant economic losses in the agricultural and water resources sectors of Southern South America.展开更多
Wheat (Triticum aestivum L.) production is a major economic activity in most regional and rural areas in the Southern Plains, a semi-arid region of the United States. This region is vulnerable to drought and is projec...Wheat (Triticum aestivum L.) production is a major economic activity in most regional and rural areas in the Southern Plains, a semi-arid region of the United States. This region is vulnerable to drought and is projected to experience a drier climate in the future. Since the interannual variability in climate in this region is linked to an ocean-atmospheric phenomenon, called El Niño-Southern Oscillation (ENSO), droughts in this region may be associated with ENSO. Droughts that occur during the critical growth phases of wheat can be extremely costly. However, the losses due to an impending drought can be minimized through mitigation measures if it is predicted in advance. Predicting the yield loss from an imminent drought is crucial for stakeholders. One of the reliable ways for such prediction is using a plant physiology-based agricultural drought index, such as Agricultural Reference Index for Drought (ARID). This study developed ENSO phase-specific, ARID-based models for predicting the drought-induced yield loss for winter wheat in this region by accounting for its phenological phase-specific sensitivity to drought. The reasonable values of the drought sensitivity coefficients of the yield model for each ENSO phase (El Niño, La Niña, or Neutral) indicated that the yield models reflected reasonably well the phenomena of water stress decreasing the winter wheat yields in this region during different ENSO phases. The values of various goodness-of-fit measures used, including the Nash-Sutcliffe Index (0.54 to 0.67), the Willmott Index (0.82 to 0.89), and the percentage error (20 to 26), indicated that the yield models performed fairly well at predicting the ENSO phase-specific loss of wheat yields from drought. This yield model may be useful for predicting yield loss from drought and scheduling irrigation allocation based on the phenological phase-specific sensitivity to drought as impacted by ENSO.展开更多
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
The latest development in the climate change forecast, using regional climate models, made it possible to provide more detailed information on the future changes in the climatic variables in the face of global warming...The latest development in the climate change forecast, using regional climate models, made it possible to provide more detailed information on the future changes in the climatic variables in the face of global warming. The PRECIS, UK Met office Hadley Centre’s Regional Climate Model is being used in simulating the future climate corresponding to the IPCC-SRES A1B emission scenario for the period 2040-2070 with reference to the base line year 1970-2000 for coastal region of Thiruvallur, South India. The results indicated a significant increase in the mean maximum temperature, mean minimum temperature and a slight decrease in the precipitation over the study area. The outcomes of the IMD method of Percent Deviation analysis show that the Thiruvallur has witnessed moderate to mild droughts during the period 1970 to 2011. Moderate drought years were mainly 1974, 1980, 1982 and 1999 with -35.78%, -30.09%, -30.54%, -27.30% rainfall deviations respectively. SPI-12 is also employed to analyze the occurrence and severity of drought events in the past. The analysis revealed that the year 1974 with SPI value -2.05 was the extremely severe drought year on record during the period 1970-2011. The years 1982 (-1.7), 1980 (-1.67), 1999 (-1.48) were severe dry years. Pearson’s correlation analysis proved that both the outputs have significant positive correlation (0.05 level) with R2 value of 0.992. It is necessary to develop early warning systems and apt drought preparedness strategies to cope with this natural hazard.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The economy of most rural locations in the semi-arid region of Llano Estacado in the southern United States is predominantly based on agriculture, primarily beef and wheat (Triticum aestivum L.) production. This region is prone to drought and is projected to experience a drier climate. Droughts that coincide with the critical phenological phases of a crop can be remarkably costly. Although drought cannot be prevented, its losses can be minimized through mitigation measures if it is predicted in advance. Predicting yield loss from an imminent drought is an important need of stakeholders. One way to fulfill this need is using an agricultural drought index, such as the Agricultural Reference Index for Drought (ARID). Being plant physiology-based, ARID can represent drought-yield relationships accurately. This study developed an ARID-based yield model for predicting the drought-induced yield loss for winter wheat in this region by accounting for its phenological phase-specific sensitivity to water stress. The reasonable values of the drought sensitivity coefficients of the yield model indicated that it could reflect the phenomenon of water stress decreasing the winter wheat yields in this region reasonably. The values of the various metrics used to evaluate the model, including Willmott Index (0.86), Nash-Sutcliffe Index (0.61), and percentage error (26), indicated that the yield model performed fairly well at predicting the drought-induced yield loss for winter wheat. The yield model may be useful for predicting the drought-induced yield loss for winter wheat in the study region and scheduling irrigation allocation based on phenological phase-specific drought sensitivity.
基金supported by the National Natural Science Foundation of China(Grant Nos.41775156 and 41590875)
文摘A well-established and pre-calibrated crop model can normally represent the overall characteristics of crop growth and yield.However,it can hardly include all relevant factors that affect the yield,and usually overestimates the crop yield when extreme weather conditions occur.In this study,the authors first introduced a drought index(the Standardized Precipitation Evapotranspiration Index)into a process-based crop model(the Agro-C model).Then,the authors evaluated the model’s performance in simulating the historical crop yields in a double cropping system in the Huang-Huai-Hai Plain of China,by comparing the model simulations to the statistical records.The results showed that the adjusted Agro-C model significantly improved its performance in simulating the yields of both maize and wheat as affected by drought events,compared with its original version.It can be concluded that incorporating a drought index into a crop model is feasible and can facilitate closing the gap between simulated and statistical yields.
文摘The impacts of climate change on the discharge regimes in New Brunswick (Canada) were analyzed, using artificial neural network models. Future climate data were extracted from the Canadian Coupled General Climate Model (CGCM3.1) under the greenhouse gas emission scenarios B1 and A2 defined by the Intergovernmental Panel on Climate Change (IPCC). The climate change fields (temperatures and precipitation) were downscaled using the delta change approach. Using the artificial neural network, future river discharge was predicted for selected hydrometric stations. Then, a frequency analysis was carried out using the Generalized Extreme Value (GEV) distribution function, where the parameters of the distribution were estimated using L-moments method. Depending on the scenario and the time slice used, the increase in low return floods was about 30% and about 15% for higher return floods. Low flows showed increases of about 10% for low return droughts and about 20% for higher return droughts. An important part of the design process using frequency analysis is the estimation of future change in floods or droughts under climate scenarios at a given site and for specific return periods. This was carried out through the development of Regional Climate Index (RCI), linking future floods and droughts to their frequencies under climate scenarios B1 and A2.
文摘Droughts have serious and widespread impacts on crop production with substantial economic losses. The frequency and severity of drought events may increase in the future due to climate change. We have developed three meteorological drought scenarios for Austria in the period 2008-2040. The scenarios are defined based on a dry day index which is combined with bootstrapping from an observed daily weather dataset of the period 1975-2007. The severity of long-term drought scenarios is characterized by lower annual and seasonal precipitation amounts as well as more significant temperature increases compared to the observations. The long-term impacts of the drought scenarios on Austrian crop production have been analyzed with the biophysical process model EPIC (Environmental Policy Integrated Climate). Our simulation outputs show that—for areas with historical mean annual precipitation sums below 850 mm— already slight increases in dryness result in significantly lower crop yields i.e. depending on the drought severity, between 0.6% and 0.9% decreases in mean annual dry matter crop yields per 1.0% decrease in mean annual precipitation sums. The EPIC results of more severe droughts show that spring and summer precipitation may become a limiting factor in crop production even in regions with historical abundant precipitation.
文摘The impact of climate change on drought main characteristics was assessed over Southern South America. This was done through the precipitation outputs from a multi-model ensemble of 15 climate models of the Coupled Model Intercomparison Project Phase 5 (CMIP5). The Standardized Precipitation Index was used as a drought indicator, given its temporal flexibility and simplicity. Changes in drought characteristics were identified by the difference for early (2011-2040) and late (2071-2100) 21st century values with respect to the 1979-2008 baseline. In order to evaluate the multi-model outputs, model biases were identified through a comparison with the drought characteristics from the Global Precipitation Climatology Centre database for the baseline period. Future climate projections under moderate and high-emission scenarios showed that the occurrence of short-term and long-term droughts will be more frequent in the 21st century, with shorter durations and greater severities over much of the study area. These changes in drought characteristics are independent on the scenario considered, since no significant differences were observed on drought changes. The future changes scenario might be even more dramatic, taking into account that in most of the region the multi-model ensemble tends to produce less number of droughts, with higher duration and lower severity. Therefore, drought contingency plans should take these results into account in order to alleviate future water shortages that can have significant economic losses in the agricultural and water resources sectors of Southern South America.
文摘Wheat (Triticum aestivum L.) production is a major economic activity in most regional and rural areas in the Southern Plains, a semi-arid region of the United States. This region is vulnerable to drought and is projected to experience a drier climate in the future. Since the interannual variability in climate in this region is linked to an ocean-atmospheric phenomenon, called El Niño-Southern Oscillation (ENSO), droughts in this region may be associated with ENSO. Droughts that occur during the critical growth phases of wheat can be extremely costly. However, the losses due to an impending drought can be minimized through mitigation measures if it is predicted in advance. Predicting the yield loss from an imminent drought is crucial for stakeholders. One of the reliable ways for such prediction is using a plant physiology-based agricultural drought index, such as Agricultural Reference Index for Drought (ARID). This study developed ENSO phase-specific, ARID-based models for predicting the drought-induced yield loss for winter wheat in this region by accounting for its phenological phase-specific sensitivity to drought. The reasonable values of the drought sensitivity coefficients of the yield model for each ENSO phase (El Niño, La Niña, or Neutral) indicated that the yield models reflected reasonably well the phenomena of water stress decreasing the winter wheat yields in this region during different ENSO phases. The values of various goodness-of-fit measures used, including the Nash-Sutcliffe Index (0.54 to 0.67), the Willmott Index (0.82 to 0.89), and the percentage error (20 to 26), indicated that the yield models performed fairly well at predicting the ENSO phase-specific loss of wheat yields from drought. This yield model may be useful for predicting yield loss from drought and scheduling irrigation allocation based on the phenological phase-specific sensitivity to drought as impacted by ENSO.
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The latest development in the climate change forecast, using regional climate models, made it possible to provide more detailed information on the future changes in the climatic variables in the face of global warming. The PRECIS, UK Met office Hadley Centre’s Regional Climate Model is being used in simulating the future climate corresponding to the IPCC-SRES A1B emission scenario for the period 2040-2070 with reference to the base line year 1970-2000 for coastal region of Thiruvallur, South India. The results indicated a significant increase in the mean maximum temperature, mean minimum temperature and a slight decrease in the precipitation over the study area. The outcomes of the IMD method of Percent Deviation analysis show that the Thiruvallur has witnessed moderate to mild droughts during the period 1970 to 2011. Moderate drought years were mainly 1974, 1980, 1982 and 1999 with -35.78%, -30.09%, -30.54%, -27.30% rainfall deviations respectively. SPI-12 is also employed to analyze the occurrence and severity of drought events in the past. The analysis revealed that the year 1974 with SPI value -2.05 was the extremely severe drought year on record during the period 1970-2011. The years 1982 (-1.7), 1980 (-1.67), 1999 (-1.48) were severe dry years. Pearson’s correlation analysis proved that both the outputs have significant positive correlation (0.05 level) with R2 value of 0.992. It is necessary to develop early warning systems and apt drought preparedness strategies to cope with this natural hazard.
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。