Modeling Spatio-temporal Drought Events Based on Multi-temporal,Multi-source Remote Sensing Data Calibrated by Soil Humidity

Inspired by recent significant agricultural yield losses in the eastern China and a missing operational monitoring system,we developed a comprehensive drought monitoring model to better understand the impact of individual key factors contributing to this issue.The resulting model,the‘Humidity calibrated Drought Condition Index’(HcDCI)was applied for the years 2001 to 2019 in form of a case study to Weihai County,Shandong Province in East China.Design and development are based on a linear combination of the Vegetation Condition Index(VCI),the Temperature Condition Index(TCI),and the Rainfall Condition Index(RCI)using multi-source satellite data to create a basic Drought Condition Index(DCI).VCI and TCI were derived from MODIS(Moderate Resolution Imaging Spectroradiometer)data,while precipitation is taken from CHIRPS(Climate Hazards Group InfraRed Precipitation with Station data)data.For reasons of accuracy,the decisive coefficients were determined by the relative humidity of soils at depth of 10-20 cm of particular areas collected by an agrometeorological ground station.The correlation between DCI and soil humidity was optimized with the factors of 0.53,0.33,and 0.14 for VCI,TCI,and RCI,respectively.The model revealed,light agricultural droughts from 2003 to 2013 and in 2018,while more severe droughts occurred in 2001 and 2002,2014-2017,and 2019.The droughts were most severe in January,March,and December,and our findings coincide with historical records.The average temperature during 2012-2019 is 1℃ higher than that during the period 2001-2011 and the average precipitation during 2014-2019 is 192.77 mm less than that during 2008-2013.The spatio-temporal accuracy of the HcDCI model was positively validated by correlation with agricultural crop yield quantities.The model thus,demonstrates its capability to reveal drought periods in detail,its transferability to other regions and its usefulness to take future measures.

Risk assessment of maize drought disaster in southwest China using the Environmental Policy Integrated Climate model

The East Asian monsoon has a tremendous impact on agricultural production in China. An assessment of the risk of drought disaster in maize-producing regions is therefore important in ensuring a reduction in such disasters and an increase in food security. A risk assessment model, EPIC(Environmental Policy Integrated Climate) model, for maize drought disasters based on the Erosion Productivity Impact Calculator crop model is proposed for areas with the topographic characteristics of the mountainous karst region in southwest China. This region has one of the highest levels of environmental degradation in China. The results showed that the hazard risk level for the maize zone of southwest China is generally high. Most hazard index values were between 0.4 and 0.5,accounting for 47.32% of total study area. However,the risk level for drought loss was low. Most of the loss rate was <0.1, accounting for 96.24% of the total study area. The three high-risk areas were mainlydistributed in the parallel ridge–valley areas in the east of Sichuan Province, the West Mountain area of Guizhou Province, and the south of Yunnan Province.These results provide a scientific basis and support for the reduction of agricultural drought disasters and an increase in food security in the southwest China maize zone.

Assessing Maize Drought Hazard for Agricultural Areas Based on the Fuzzy Gamma Model

Drought is one of the severe meteorological disasters and causes of serious losses for agricultural productions, and early assessment of drought hazard degree is critical in management of maize farming. This study proposes a novel method for assessment of maize drought hazard in different growth stages. First, the study divided the maize growth period into four critical growth stages, including seeding, elongation, tasseling, and filling. Second, maize drought causal factors were selected and the fuzzy membership function was established. Finally, the study built a fuzzy gamma model to assess maize drought hazards, and the gamma 0.93 was finally established using Monte Carlo Analysis. Performing fuzzy gamma operation with 0.93 for gamma and classifying the area yielded a map of maize drought hazards with four zones of light, moderate, severe, and extreme droughts. Using actual field collected data, seven selected samples for drought hazard degree were examined, the model output proved to be a valid tool in the assessment maize drought hazard. This model will be very useful in analyzing the spatial change of maize drought hazard and influence on yield, which is significant for drought management in major agricultural areas.

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.

Impacts of Upper Tropospheric Cooling upon the Late Spring Drought in East Asia Simulated by a Regional Climate Model

Responses of late spring （21 April 20 May） rainfall to the upper tropospheric cooling over East Asia are investigated with a regional climate model based on Laboratoire de M6t6orologie Dynamique Zoom （LMDZ4-RCM）. A control experiment is performed with two runs driven by the mean ERA-40 data during 1958-1977 and 1981 2000, respectively. The model reproduces the major decadal-scale circulation changes in late spring over East Asia, including a cooling in the upper troposphere and an anomalous meridional cell. Accordingly, the precipitation decrease is also captured in the southeast of the upper-level cooling region. To quantify the role of the upper-level cooling in the drought mechanism, a sensitivity experiment is further conducted with the cooling imposed in the upper troposphere. It is demonstrated that the upper-level cooling can generate the anomalous meridional cell and consequently the drought to the southeast of the cooling center. Therefore, upper tropospheric cooling should have played a dominant role in the observed late spring drought over Southeast China in recent decades.

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.

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

农田作物生产模型是一个用于评估众多环境和人为因子影响下作物产量变化的重要工具。然而,将所有与作物生长相关的环境因子考虑进模型中是不现实的。因此,模型往往会在一些极端环境条件发生时高估作物的产量。在本文中,我们试图将一个干旱指数(SPEI)应用到一个农田生态系统过程模型(Agro-C)。同时,对比了模型是否考虑干旱指数的情况下与统计产量之间的差距;用于对比的统计产量是黄淮海平原各县近20年来的小麦和玉米统计数据。研究结果显示:在干旱发生的年份里,模型确实部分高估了作物产量;而在模型中加入干旱指数,则能够一定程度提高模型模拟干旱年份粮食作物的精度。

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.

Seasonal Prediction Experiments of the Summer Droughts and Floods during the Early 1990′s in East Asia with Numerical Models

It has been shown by the observed data that during the early 1990′s, the severe disastrous climate occurred in East Asia. In the summer of 1991, severe flood occurred in the Yangtze River and the Huaihe River basin of China and in South Korea, and it also appeared in South Korea in the summer of 1993. However, in the summer of 1994, a dry and hot summer was caused in the Huaihe River basin of China and in R. O. K.. In order to investigate the seasonal predictability of the summer droughts and floods during the early 1990′s in East Asia, the seasonal prediction experiments of the summer droughts and floods in the summers of 1991-1994 in East Asia have been made by using the Institute of Atmopsheric Physics-Two-Level General Circulation Model (IAP-L2 AGCM), the IAP-Atmosphere/Ocean Coupled Model (IAP-CGCM) and the IAP-L2 AGCM including a filtering scheme, respectively. Compared with the observational facts, it is shown that the IAP-L2 AGCM or IAP-CGCM has some predictability for the summer droughts and floods during the early 1990′s in East Asia, especially for the severe droughts and floods in China and R. O. K.. In this study, a filtering scheme is used to improve the seasonal prediction experiments of the summer droughts and floods during the early 1990′s in East Asia. The predicted results show that the filtering scheme to remain the planetary-scale disturbances is an effective method for the improvement of the seasonal prediction of the summer droughts and floods in East Asia.

Prediction of Meteorological Drought in the Lower Nu River by Statistical Model

Global climate change, temperature rise and some kinds of extreme meteorological disaster, such as the drought, threaten the development of the natural ecosystem and human society. Forecasting in drought is an important step toward developing a disaster mitigation system. In this study, we utilized the statistical, autoregressive integrated moving average (ARIMA) model to predict drought conditions based on the standardized precipitation index (SPI) and standardized precipitation evapotranspiration index (SPEI) in a major tributary in the lower reaches of Nu River. We employed data from 2001 to 2010 to fit the model and data from 2011 to 2013 for model validation. The results showed that the coefficients of determination (R2) was over 0.85 in each index series, and the root-mean-square error and mean absolute error were low, implying that the ARIMA model is effective and adequate for this region.

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.

淠史杭灌区干旱推演及抗旱预案调控研究

水资源干旱是限制灌区可持续发展的关键因素。为提高灌区的干旱防治能力,使其更好的发挥其在节水、减灾方面的作用,以淠史杭灌区为研究区,通过划分水资源配置子单元和设置调蓄节点,采用公平性最优和供水缺水率最小作为目标函数,总量控制、供水能力、分质供水等作为约束条件,采用基于精英策略的非支配遗传改进算法求解,构建区域General Water Allocation and Simulation Model(GWAS)模型;以2022年为现状基准年,与2023规划年组合,分为连续干旱与不连续干旱两大类,基于灌区水资源“应急干旱三次平衡”调控思想,分析灌区水资源在不同干旱情景下缺水的基础上,展开水资源抗旱配置研究,推演分析不同抗旱方案下水资源供需平衡情况。结果表明:连续干旱年中,灌区2023规划年在情景Ⅰ(P=90%)、情景Ⅱ(P=80%)来水频率下,各乡镇配置单元均存在不同程度的缺水情况,区域总缺水率分别为35.1%、20.8%;不连续干旱年中,2023规划年在情景Ⅲ(P=50%)来水频率下,模型基准配置水量基本可以满足区域各乡镇水量需求,区域总缺水率为5.9%。经不同抗旱方案尾部泵站补水、调整作物种植结构及外调水的优化配置后,三种情景下区域总缺水率最终都降为0%,优化后各配置单元供水改善效果显著。研究成果可为淠史杭灌区未来在应对不同干旱类型情景下水资源的合理调整提供技术支撑,并且可为实现该区域水资源统一管理和水量的统一调配提供理论依据。

大气环流因子对黄河流域长预见期农业干旱预测性能的影响

准确的干旱预测对于减轻或规避干旱对区域粮食生产和水资源配置的不利影响至关重要。大气环流因子可能会通过遥相关影响农业干旱的发生、发展和传递过程,在干旱预测模型中引入大气环流因子是否会改善农业干旱的预测性能尚不明晰。该研究以农业干旱、高温和大气环流因子为预测因子,在不同预见期(1、12、24、36、48个月)下采用Meta-Gaussian(MG)模型预测黄河流域典型年份的农业干旱事件,通过纳什效率系数(Nash-Sutcliffe efficiency coefficient,NSE)和均方根误差(root mean square error,RMSE)探究在MG模型中引入大气环流因子对农业干旱预测性能的影响。结果表明:大气环流因子中12个月时间尺度的标准化西太平洋副高强度指数(standardized western Pacific subtropical high intensity index,SWPSHI)与农业干旱相关性最为显著;以典型年2014年8月份为例发现MG模型预测值受预见期长度、预测因子影响较大;相比于单因子预测,引入大气环流因子的MG模型的评价指标NSE和RMSE改善网格占比最高达46%,空间上在内蒙古、宁夏、甘肃、陕西等省区1 a以上预见期明显改善,而考虑大气环流因子和高温的MG模型进一步提升了模型的预测性能,扩大了网格占比。因此在上述省区干旱预测时需考虑大气环流因子的影响。

中国北方地区四种熟型藜属杂草始花期的模拟

藜属(Chenopodium)草本植物是我国常见杂草,其花粉量大,持续时间长,致敏性强,对大气环境质量和人类健康都有一定危害,尤其在气候变化和退耕还林还草背景下,致敏风险增加。为精准预报藜属杂草始花期,本文基于中国北方地区1981—2018年52个站点的藜属杂草始花期实测数据,构建了5种基于逐日气温、降水、光周期序列驱动的物候过程模型,并进行了参数率定和优选。结果表明,温度是主导始花期的环境因子,光周期也起着重要的调控作用,而冷激对4种熟型藜属杂草始花期的影响并不显著。此外在干旱区考虑降水因子进行藜属杂草始花期预报是有必要的,干旱会延迟藜属杂草始花期,而降水则能起到一定的促进作用。本研究可作为我国北方地区藜属杂草花粉花期预报的辅助手段,为防治藜属杂草花粉过敏提供帮助。

南水北调中线工程水源区和受水区的降水时空变化特征及丰枯遭遇

[目的]基于1950—2022年时间序列ERA5再分析降水数据,探究南水北调中线工程水源区和受水区降水量时空变化特征,旨在保障跨流域调水的可行性和持续性,为南水北调中线工程水资源调度和运行管理提供科学参考。[方法]采用Mann-Kendall检验、小波分析、云模型以及Copula函数等方法,分析水源区和受水区降水变化趋势以及丰枯遭遇特征。[结果]①1950—2022年南水北调工程水源区和受水区分别按照年降水量34.75和39.35mm/(10a)幅度减少,水源区年降水量存在43a的主周期,受水区年降水量存在55a的主周期。②云模型隶属云图夏季降水量分布不均且离散程度较高,而冬季分布均匀且较集中。③调水不利的组合汛期和非汛期遭遇频率分别为23.15%和25.92%。[结论]水源区和受水区年降水量均呈现明显减少趋势,水源区丰枯交替变化显著;南北丰枯异步概率大于丰枯同步,同枯频率不高,有利于调水工程的安全性和可靠性。研究结果可为制定南水北调中线工程相关政策和技术指导提供理论依据,有助于工程的规划和管理,提高水资源利用效率和可持续性。

干旱胁迫下环境与生理因子对晋西黄土区刺槐人工林树干液流的影响

【目的】探究干旱胁迫对晋西黄土区刺槐人工林蒸腾耗水的影响,揭示干旱胁迫下环境和生理对刺槐蒸腾耗水的调控机制,为晋西黄土区刺槐人工林的经营和水分管理提供理论依据。【方法】以晋西黄土区刺槐人工林为研究对象,通过降雨拦截试验(整个生长季减雨50%),连续监测刺槐的树干液流变化,并同步监测环境因子和水分生理变化,分析环境和水分生理对树干液流的影响。【结果】(1)干旱处理显著降低了刺槐的液流速率(Js),干旱处理下平均Js(0.92 g/(cm^(2)·h))显著低于对照处理下的平均Js(1.87 g/(cm^(2)·h)),但其变化规律相似。(2)干旱处理影响了刺槐的枝、叶相对含水量,且随着干旱处理时间的延长,枝、叶相对含水量降低。不同处理条件下,刺槐枝、叶水势和水容均呈“V”字型变化,干旱处理下的枝、叶片水势(-1.22 MPa、-0.72 MPa)低于对照处理(-1.15 MPa、-0.60 MPa)。干旱处理下枝、叶水容降低,分别为0.52 g/(cm^(3)·MPa)和1.05×10^(-2)g/(cm^(2)·MPa)。(3)刺槐树干液流的变化受气象因子、土壤水分条件和水分生理因子的共同影响,不同处理条件下蒸散的主控因子均为太阳总辐射,干旱处理下枝、叶水容对刺槐树干液流速率的影响减弱,表明相较于干旱处理,对照条件下刺槐更依赖于组织水容储水来满足蒸腾耗水。【结论】研究揭示了干旱胁迫显著降低了刺槐液流速率,但未改变其液流活动规律。刺槐人工林树干液流变化受多种因子的共同影响,主导因子依次为气象因子、土壤水分和水分生理因子。研究结果有助于深入理解干旱条件下人工林的蒸腾耗水过程的变化特征,对评估气候变化条件下黄土区人工林生态系统的稳定性和生态水文过程具有重要意义。

气候变化情景下北江流域骤发干旱特征变化预测

以北江流域为研究对象,利用VIC(Variable Infiltration Capacity)模型与CMIP5全球气候模式耦合构建了流域水循环模型,基于模型输出的蒸发和潜在蒸发数据,构建基于SESR指数的骤发干旱(简称骤旱)识别方法,评估2021—2050年三种排放情景下(RCP2.6、RCP4.5、RCP8.5)骤旱特征(频次、历时、强度)相对于基准期(1971—2000)的变化及不确定性。结果表明,在RCP情景下:(1)流域呈持续增暖趋势,且排放情景越高升温幅度越大,降水、蒸发和潜在蒸发呈总体呈减小趋势,但潜在蒸发减小幅度小于蒸发;(2)骤旱频次在流域西部(东部)减少(增加),且增幅随排放情景上升而增大(<80%),流域东北部和南部预计会遭遇强度更高、历时更长的骤旱过程;(3)不同骤旱特征预测的不确定性差异较大,且均呈现出较强的空间异质性。

鄱阳湖浮游植物时空变化特征及其对极端洪枯事件的响应

鄱阳湖作为中国最大的淡水湖泊,其水生态健康状态始终是人们关注的热点。近些年,鄱阳湖极端洪旱灾害频发,浮游植物生长受极端洪旱的影响发生了明显变化。为分析浮游植物时空变化特征、探究环境因子对鄱阳湖浮游植物影响机制以及极端洪枯事件对浮游植物的影响,利用结构方程模型(SEM)构建浮游植物与环境因子的影响路径模型,定量分析环境因子对浮游植物的影响程度。结果表明,鄱阳湖浮游植物以蓝藻、绿藻为主且有明显的季节特征,在7月丰水期浮游植物密度达到最高;由结构方程模型(SEM)可知,影响浮游植物密度最关键的因子为物理因子(水温>pH>透明度>溶解氧),其次为营养物质(总氮>硝态氮>总磷>磷酸盐),浮游植物对高温、高营养和高pH较偏好。2020年极端洪水和2022年极端高温干旱,浮游植物密度主要受水温、溶解氧、透明度等物理因子的影响;在影响较小的营养物质中,主要的限制性因素分别为磷和氮。与正常年份相比,极端洪水年鄱阳湖受入湖来水及降雨的增多,湖区水量急剧增加,“稀释”作用超过水温和透明度对浮游植物生长的促进作用造成浮游植物密度和生物量有所下降;在营养物质中,磷成为主要限制性因素。而极端高温干旱年受入湖来水的减少及湖水的快速蒸发,“浓缩”作用超过水温和透明度对浮游植物生长的抑制作用造成浮游植物密度和生物量显著增加,同时,营养物质对浮游植物的作用更加凸显。研究结果表明极端洪枯事件导致鄱阳湖浮游植物变化明显,确定其对浮游植物的影响机制,可以为极端洪枯事件下浮游植物监测、管理提供一定的理论基础。

区域农业抗旱减灾能力影响因素及指标模型构建

为了对区域农业灾害进行防控,分析了山东省旱灾相关影响因素,通过旱灾影响因素构建旱灾预警模型,并对各因素耦合关系进行分析,最后将构建的农业旱灾综合评估模型应用于具体农业生产环境。结果表明,不同农业区域在抗旱减灾能力上存在明显差异,农业旱灾综合评估排名前3的城市为烟台市、日照市、青岛市,农业旱灾综合评估值分别为2.90、2.87、2.86。山东省经济发达区域集中在沿海地区,这些地区有充足的技术、物资、人才及财政支持,农业抗旱减灾能力强。而对于雨水贫瘠、经济较弱区域,农业抗旱减灾能力较差,集中在德州市、济宁市、东营市等地区。由此可见,要提升这些地区抗旱减灾能力,需要从财政投入、农业技术、应急管理等方面入手,确保地区农业的科学化发展。