Estimating the Drought-Induced Yield Loss for Winter Wheat in a Semi-Arid Region of the Southern United States Using a Drought Index

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.

Projected Changes of Palmer Drought Severity Index under an RCP8.5 Scenario

The potential change of drought measured by the Palmer Drought Severity Index （PDSI） is projected by using a coupled climate system model under a Representative Pathway 8.5 （RCP8.5） scenario.The PDSI changes calculated by two potential evapotranspiration algorithms are compared.The algorithm of Thomthwaite equation overestimates the impact of surface temperature on evaporation and leads to an unrealistic increasing of drought frequency.The PM algorithm based on the Penman-Monteith equation is physically reasonably and necessary for climate change projections.The Flexible Global Ocean-Atmosphere-Land System model,Spectral Version 2 （FGOALS-s2） projects an increasing trend of drought during 2051-2100 in tropical and subtropical areas of North and South America,North Africa,South Europe,Southeast Asia,and the Australian continent.Both the moderate drought （PDSI ＜-2） and extreme drought （PDSI ＜-4） areas show statistically significant increasing trends under an RCP8.5 scenario.The uncertainty in the model projection is also discussed.

Combining RUSLE model and the vegetation health index to unravel the relationship between soil erosion and droughts in southeastern Tunisia

Droughts and soil erosion are among the most prominent climatic driven hazards in drylands,leading to detrimental environmental impacts,such as degraded lands,deteriorated ecosystem services and biodiversity,and increased greenhouse gas emissions.In response to the current lack of studies combining drought conditions and soil erosion processes,in this study,we developed a comprehensive Geographic Information System(GIS)-based approach to assess soil erosion and droughts,thereby revealing the relationship between soil erosion and droughts under an arid climate.The vegetation condition index(VCI)and temperature condition index(TCI)derived respectively from the enhanced vegetation index(EVI)MOD13A2 and land surface temperature(LST)MOD11A2 products were combined to generate the vegetation health index(VHI).The VHI has been conceived as an efficient tool to monitor droughts in the Negueb watershed,southeastern Tunisia.The revised universal soil loss equation(RUSLE)model was applied to quantitatively estimate soil erosion.The relationship between soil erosion and droughts was investigated through Pearson correlation.Results exhibited that the Negueb watershed experienced recurrent mild to extreme drought during 2000–2016.The average soil erosion rate was determined to be 1.8 t/(hm2•a).The mountainous western part of the watershed was the most vulnerable not only to soil erosion but also to droughts.The slope length and steepness factor was shown to be the most significant controlling parameter driving soil erosion.The relationship between droughts and soil erosion had a positive correlation(r=0.3);however,the correlation was highly varied spatially across the watershed.Drought was linked to soil erosion in the Negueb watershed.The current study provides insight for natural disaster risk assessment,land managers,and stake-holders to apply appropriate management measures to promote sustainable development goals in fragile environments.

Introducing a drought index to a crop model can help to reduce the gap between the simulated and statistical yield

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.

Regional Climate Index for Floods and Droughts Using Canadian Climate Model (CGCM3.1)

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.

Estimating the Yield Loss of Winter Wheat from Drought in the United States Southern Plains Region as Influenced by El Niño-Southern Oscillation (ENSO)

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.

Evaluation of the WRF Model with Different Land Surface Schemes: A Drought Event Simulation in Southwest China during 2009–10

The authors examined the performance of version 3.4.1 of the Weather Research and Forecasting Model(WRF) with various land surface schemes in simulating a severe drought event in Southwest China. Five numerical experiments were completed using the Noah land surface scheme, the Pleim-Xiu land surface scheme, the Noah-MP land surface schemes, the Noah- MP scheme with dynamic vegetation, and the Noah-MP scheme with dynamic vegetation and groundwater processes. In general, all the simulations reasonably reproduced the spatial and temporal variations in precipitation, but significant bias was also found, especially for the spatial pattern of simulated precipitation. The WRF simulations with the Noah-MP series land surface schemes performed slightly better than the WRF simulation with the Noah and Pleim-Xiu land surface schemes in reproducing the severe drought events in Southwest China. The leaf area index(LAI) simulated by the different land surface schemes showed significant deviations in Southwest China. The Pleim-Xiu scheme overestimated the value of LAI by a factor of two. The Noah-MP scheme with dynamical vegetation overestimated the magnitude of the annual cycle of the LAI, although the annual mean LAI was close to observations. The simulated LAI showed a long-term lower value from autumn 2009 to spring 2010 relative to normal years. This indicates that the LAI is a potential indictor to monitor drought events.

Future Changes in Drought Characteristics over Southern South America Projected by a CMIP5 Multi-Model Ensemble

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.

Spatially Explicit Modeling of Long-Term Drought Impacts on Crop Production in Austria

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.

Tree mortality and biomass loss in drought-aff ected forests of East Texas,USA

Changes in tree mortality due to severe drought can alter forest structure,composition,dynamics,ecosystem services,carbon fl uxes,and energy interactions between the atmosphere and land surfaces.We utilized long-term(2000‒2017,3 full inventory cycles)Forest Inventory and Analysis(FIA)data to examine tree mortality and biomass loss in drought-aff ected forests for East Texas,USA.Plots that experienced six or more years of droughts during those censuses were selected based on 12-month moderate drought severity[Standardized Precipitation Evaporation Index(SPEI)-1.0].Plots that experienced other disturbances and inconsistent records were excluded from the analysis.In total,222 plots were retained from nearly 4000 plots.Generalized nonlinear mixed models(GNMMs)were used to examine the changes in tree mortality and recruitment rates for selected plots.The results showed that tree mortality rates and biomass loss to mortality increased overall,and across tree sizes,dominant genera,height classes,and ecoregions.An average mortality rate of 5.89%year−1 during the study period could be incited by water stress created by the regional prolonged and episodic drought events.The overall plot and species-group level recruitment rates decreased during the study period.Forest mortality showed mixed results regarding basal area and forest density using all plots together and when analyzed the plots by stand origin and ecoregion.Higher mortality rates of smaller trees were detected and were likely compounded by densitydependent factors.Comparative analysis of drought-induced tree mortality using hydro-meteorological data along with drought severity and length gradient is suggested to better understand the eff ects of drought on tree mortality and biomass loss around and beyond East Texas in the southeastern United States.

Projected Warming and Occurrence of Meteorological Droughts—Insights from the Coasts of South India

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.

Recombinants for Enhanced Productivity and Drought Tolerance in Barley （Hordeum vulgare L.）

In an attempt to enhance productivity as well as drought tolerance of barley cultivar, a 5 × 5 diallel cross involving rainfed cultivars was made. Of the 10 crosses, cross K603 x K560 was most promising as it yielded highest number of recombinants （21 in irrigated and 36 in rainfed conditions）; the cultivar K506 was considered as drought resistant （drought susceptibility index 〈 1）. A total of 22 out of 64 and 18 out of 59 most promising F2 recombinants in irrigated and rainfed conditions, respectively from 6 crosses were evaluated for yield, harvest index as well as proline content. All the recombinants selected under rainfed condition （including a few from irrigated condition） showed enhanced level of proline content coupled with high grain yield and harvest index. Further, a total of twenty-nine segregants （12 rainfed and 17 irrigated derived cultures） showing significantly higher values of proline content and grain yield were grown during 2007-2008 under both the environments, rainfed and irrigated to determine the geometric mean （GM） and drought susceptibility index （S）. The segregants （Culture No. 8, 10 ＆ 13） derived from cross K 603 × K 560 and culture No. 5 from K 560 × RD 2508 gave maximum mean yield under rainfed and geometric mean coupled with lower drought susceptibility index （S）. Also, it has been observed that the transgressive segregants selected from limited water environment （rainfed） performed better than those selected from irrigated environment for higher grain yield and drought tolerance.

Future meteorological drought conditions in southwestern Iran based on the NEX-GDDP climate dataset

Investigation of the climate change effects on drought is required to develop management strategies for minimizing adverse social and economic impacts.Therefore,studying the future meteorological drought conditions at a local scale is vital.In this study,we assessed the efficiency of seven downscaled Global Climate Models(GCMs)provided by the NASA Earth Exchange Global Daily Downscaled Projections(NEX-GDDP),and investigated the impacts of climate change on future meteorological drought using Standard Precipitation Index(SPI)in the Karoun River Basin(KRB)of southwestern Iran under two Representative Concentration Pathway(RCP)emission scenarios,i.e.,RCP4.5 and RCP8.5.The results demonstrated that SPI estimated based on the Meteorological Research Institute Coupled Global Climate Model version 3(MRI-CGCM3)is consistent with the one estimated by synoptic stations during the historical period(1990-2005).The root mean square error(RMSE)value is less than 0.75 in 77%of the synoptic stations.GCMs have high uncertainty in most synoptic stations except those located in the plain.Using the average of a few GCMs to improve performance and reduce uncertainty is suggested by the results.The results revealed that with the areas affected by wetness decreasing in the KRB,drought frequency in the North KRB is likely to increase at the end of the 21st century under RCP4.5 and RCP8.5 scenarios.At the seasonal scale,the decreasing trend for SPI in spring,summer,and winter shows a drought tendency in this region.The climate-induced drought hazard can have vast consequences,especially in agriculture and rural livelihoods.Accordingly,an increasing trend in drought during the growing seasons under RCP scenarios is vital for water managers and farmers to adopt strategies to reduce the damages.The results of this study are of great value for formulating sustainable water resources management plans affected by climate change.

Resilience, reliability and risk analyses of maize, sorghum and sunflower in rain-fed systems using a soil moisture modeling approach

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.

PSO-SVR模型在吉林省干旱指数预测中的应用

随着气候变暖程度愈加严重,干旱问题成为我国农业生产的一大威胁,严重妨碍我国农业生产的发展,因此掌握科学预测干旱指数的技术,可以为未来旱情提供预防建议,防止旱情进一步扩大,进而保障粮食安全。基于吉林省40个地区的气压、气温、降水量和相对湿度等多个气象因子及SPEI指数,对其未来干旱指数进行预测,比较BP模型、RF模型、SVR模型及经过优化的PSO-SVR模型的4个误差指标,发现PSO-SVR模型表现最为优异,R2达到0.964,MSE达到0.021,优于其他3个模型,拟合效果更为显著。结果显示,PSO-SVR模型在吉林省SPEI指数的预测中表现出极高的可行性和准确性,其出色的性能为吉林省的防旱减灾研究提供强有力的理论支持。

基于EMD-GWO-LSTM模型的新疆标准化降水蒸散指数预测方法研究

干旱预测一直是干旱研究领域的重大挑战,提高干旱预测的准确性是解决干旱问题的关键。基于1961—2019年新疆34个气象站点月降水和月平均气温数据,计算得到标准化降水蒸散指数(Standardized Precipitation Evapotranspiration Index,SPEI),对新疆气象干湿变化进行分析,提出一种经验模态分解方法(empirical mode decomposition,EMD)-灰狼优化算法(grey wolf optimizer,GWO)-长短期神经网络(long short-term memory network,LSTM)的数据分解型干旱组合预测模型进行预测,并进行模型性能评价。结果表明:(1)干旱周期性变化整体呈现平稳且周期长的特点;(2)EMD能够有效优化数据的平稳性,GWO优化预测模型参数,组合模型的预测精度相较于单一预测模型有明显提高;(3)4个预测模型结果精度由高到低的排序为:EMD-GWO-LSTM、GWO-LSTM、GWO-支持向量回归(Support Vactor Regression,SVR)、LSTM,拟合优度分别为0.972、0.939、0.862、0.830,EMD-GWO-LSTM组合预测模型的预测精度优于其余3个预测模型。EMD-GWO-LSTM组合模型可有效提高气象干旱的预测精度,为新疆地区气象干旱预报及抗旱减灾工作提供了新的方法手段。

MODIS数据的干旱监测模型研究

对地观测卫星(ESO)中分辨率成像光谱仪(MODIS)传感器因其具有高时间分辨率、高光谱分辨率、适中的空间分辨率等特点,非常适合大范围、长时期、动态的干旱监测。借鉴以往的各种干旱监测方法,提出了一个基于MODIS的综合性干旱监测模型。该模型纳入与干旱有关的各种参数,并且首次将云指数作为一个重要参数引入模型。通过2003年湖北干旱的实例验证了此模型,结果显示此模型能很好的用于干旱预警与监测。

基于WOFOST模型的玉米干旱损失评估:以北京为例

以北京市山区春玉米和平原区夏玉米产量作为研究对象,利用气候资料和统计资料驱动,就WOFOST模型对北京地区玉米产量模拟的适用性进行评估,并基于模型开展北京地区玉米干旱灾害损失评估研究。结果表明:(1)山区春玉米和平原区夏玉米的产量模拟值与统计年鉴统计值的符合度指数均达0.9以上,模拟结果平均误差小于10%,说明WOFOST模型在北京地区春玉米和夏玉米的产量模拟方面具备较强的适用性;(2)在本文定义的干旱指数体系下,初夏旱对春玉米的产量影响较大,其中初夏轻旱导致春玉米减产率达15.91%,初夏重旱使春玉米减产率达22.99%;春旱和伏旱对春玉米、初夏旱和伏旱对夏玉米产量影响均较小,减产率均在10%以下。研究结果可为北京地区玉米生产和抗旱减灾提供科学依据。

基于PDSI指数的三江源干旱气候特征分析

根据三江源地区12个气象站点1971—2004年的气象资料,利用修正的Palmer旱度模式,计算分析了三江源地区PDSI指数的时空变化特征,并采用Morlet小波变换系数分析了PDSI指数的变化周期。结果表明:三江源地区有变干趋势,但变化趋势不显著,其中黄河源显著变干对三江源变干影响较大;三江源地区干旱发生年份与厄尔尼诺现象出现的年份基本相近;三江源地区西北较干旱、东南较湿润;经小波分析,三江源PDSI指数大约有20 a的第一主变化周期,与太阳双黑子活动周期相近,在20 a周期内还包括11～12 a和6 a的小周期;三江源干旱化与气候变暖、降水减少密切相关,尤其是夏、秋两季。

基于改进失水模式和增加建模站点的Palmer旱度模式

根据1965年Palmer旱度模式的思路,在1986年修正版及2003年修正版的基础上,为了使Palmer旱度模式更适用于我国北方干旱、半干旱地区,通过改进2003年修正的Palmer旱度模式在建模时表层失水模式的假设以及增加建模站点个数两个方面对Palmer旱度模式进行进一步修正。将计算的Palmer指数值与2003年计算的Palmer指数值及一些文献记载的实际旱涝灾情相对照进行验证,结果表明:新修正的Palmer旱度模式能更好地评估旱涝情况,扩大其在我国干旱地区的适用性。