A geospatial study of the drought impact on surface water reservoirs: study cases from Texas,USA

Satellite images have been used historically to measure and monitor fluctuations in the surface water reservoirs. This study integrates remote sensing and Geographic Information System (GIS) technologies to investigate the impact of drought on 10 selected surface water reservoirs in San Angelo and Dallas, Texas. Oscillations in summer and winter months throughout the 2005–2016 period were assessed using multispectral images from Landsat-5, ?7, and ?8, and changes in the reservoirs were characterized and correlated against local climate data of each reservoir. For quantitative comparisons of the time-series measurements, a robust density slicing approach was employed to classify the range of values of the raster cells in the near-infrared band of Landsat images for each lake into three desired classes (deep water, shallow water, and dry area) based on the natural breaks inherent in the dataset. Statistical analysis shows that the overall accuracy of the classification is about 94%, which demonstrates the efficiency of the density slicer to accurately estimate surface water area changes from an individual Landsat band. Shrinkage in the surface water area over the study period reveals the concrete impact that the drought along with other factors have on the 10 selected lakes. The San Angelo lakes located in west central Texas experienced a nearly consistent pattern of change during most of the study period; whereas the Dallas lakes in northeast Texas followed the oscillating pattern of drought and correlated closely to the local conditions. Shockingly, the extreme drought caused complete vanishing of several lakes, and consequently Texas had to remove them from its recreational plans. Our new findings can certainly help with the water resource management in Texas and our study approach can be adapted for monitoring lake oscillations in other areas across the world. This geospatial study demonstrates the societal benefits from incorporating remote sensing and GIS in investigating geo-environmental problems associated with severe climate changes.     

顾及空间非平稳特征的遥感干旱监测

遥感技术具备实时快速、时空连续、广覆盖尺度等独特优势,在全球气候恶化大背景下,利用遥感干旱监测方法相比于传统地面监测手段,能够提供实时、准确、稳定的旱情信息,辅助科学决策。目前常用遥感旱情监测方法大多依赖全域性数学模型建模,假定了旱情模式的空间平稳特性,因而难以准确反映旱情模式的局部差异特征。本文提出利用地理加权回归模型GWR (Geographically Weighted Regression),考虑旱情模式的空间非平稳特性,综合多种遥感地面旱情监测指数,以实现传统全域旱情监测模型的局部优化。以美国大陆为研究区,监测2002年—2011年共10年的旱情状态。研究表明,GWR模型能够提供空间变化的局部最佳估计模型参数,监测结果更加吻合标准美国旱情监测USDM (U.S Drought Monitor)验证数据,且与地面实测值的最高相关系数R达到0.8552,均方根误差RMSE达到0.972,显著优于其他遥感旱情监测模型。GWR模型具备空间非平稳探测优势,实现了旱情模式的局部精细探测,能够显著提升遥感旱情监测精度,具备较好的应用前景。     

四川盆地干旱灾害统计特征

利用四川盆地1980—2009年17市103个县(市)实测逐日降水资料,按照四川省气象局制定的四川盆地的干旱地方标准DB51/T581—2007,对四川盆地近30年干旱灾害进行统计分析。结果表明:四川盆地夏旱出现的频率最高,夏旱的高发区集中在盆地西北部的成都、德阳和绵阳,伏旱的发生频率最低,强度最强。春、夏、伏旱的空间分布高发区依次从盆地西北部向东南转移。干旱发生的频率整体呈增长趋势,且严重干旱发生频次增长明显,与20世纪80年代相比90年代增幅达到110.3%,21世纪00年代在90年代的基础上又递增20.0%,21世纪00年代发生的严重干旱频次为80年代的2.5倍。     

2022年夏季中国高温干旱气候特征及成因探讨

2022年夏季,中国中东部发生了极端高温干旱气候异常,给经济、农业、人民生活造成了严重影响。本文回顾了此次高温干旱气候异常的时空特征,分析了其主要成因。2022年夏季,中国中东部区域平均的极端高温频次、日最高温度平均值、高温日数等指标均达到了1979年以来的最大值,区域平均降水则达到了1979年以来的最低值。此次气候异常主要是由于夏季中国中东部受强大的高压系统控制,与偏强的西太副高、中纬度的西风带扰动以及热带海温的影响有关。此外,本文探讨了全球增暖趋势对极端高温事件增多的影响,以及未来中国地区高温和干旱事件的可能变化。     

土壤墒情遥感反演与旱情诊断

土壤墒情与植被生长状况和地表温度之间存在密切联系?贑OST模型算法和单窗算法,开展了TM/ETM+多光谱数据的地表反射率、地表温度(LST)和土壤调整植被指数反演(MSAVI),分析了地表温度和植被指数的线性关系,提出了土壤墒情几何特征指数和旱情诊断函数,结合土壤含水量实测数据,建立了横山县土壤墒情遥感反演模型。实证结果表明,基于TM/ETM+数据反演的长度指数可进行旱情诊断;对土壤含水量的反演模型进行T检验,差异不显著,而基于地面温度的土壤墒情反演模型优于土壤调整植被指数反演模型。     

基于GM(1,1)模型的塔里木盆地南缘孤立绿洲降水灾变预测

我们选取了具有一定代表性的塔里木盆地南缘的孤立绿洲民丰县作为研究区,在对数据有效性的验证之后,利用研究区1980~1999年的降水资料,运用灰色系统理论的季节灾变预测方法,对该地区未来年份的降水量进行预测,并建立灰色季节灾变GM(1,1)预测模型,预测未来旱季到来的年份,为预测未来降水趋势提供了信息。结果表明:该研究区下一个旱灾灾变年大致指向2006年至2007年左右。这对合理利用本地气候资源具有实际意义,并且能够为研究干旱区气候变化提供一定的帮助。     

旱涝灾害灾后经济损失评估模型及对策

在分析呼伦贝尔市旱涝灾害的基础上,为了更好地减轻其损失,建立了“历史相似综合评估”和“气候背景统计评估”两个旱涝灾害经济损失评估模型。经5年的业务应用,效果很好。可以投入辅助决策服务使用。     

非平稳标准化降水蒸散指数构建及中国未来干旱时空格局

旱灾是一种致灾因子与成害机理均非常复杂的自然灾害,也是目前对其检测与风险防御最为困难的自然灾害种类之一。随着全球气候变化,干旱的变化逐渐趋于非平稳化,水文气象序列的非平稳性已有广泛研究,但在干旱检测指标中却鲜有考虑。基于标准化降水蒸散指数（SPEI）和非平稳性理论,构建非平稳性标准化降水蒸散指数（NSPEI）并进行适用性评价,利用NSPEI评估未来不同排放情景下中国气象干旱时空格局演变规律。结果表明：① 非平稳性站点集中在东北平原、黄淮海平原、长三角地区、青藏高原及周边区域,NSPEI拟合最优的站点占中国气象站点的88%（2177个站点）。② SPEI对温度较为敏感,在评估未来干旱变化时会高估干旱强度和持续时间性,而NSPEI能够克服这一弱点,较SPEI可更好的检测中国气象干旱,且能很好的刻画中国未来干旱变化。③ 低、高排放情景下中国北方干旱加剧,南方呈湿润化趋势;中排放情景下中国北方湿润化趋势明显,而中国南方则呈干旱化。基于NSPEI干旱检测结果,中高排放情景下中国未来极端干湿历时与发生频率均呈增加趋势。     

ARIMA-LSTM组合模型在基于SPI干旱预测中的应用——以青海省为例

开展干旱预测是有效应对干旱风险的前提基础。利用1958—2017年青海省38个气象站点逐日降水量数据计算多尺度标准化降水指数（SPI）,并建立了SPI序列自回归移动平均模型（ARIMA）、长短时记忆神经网络模型（LSTM）和基于二者优点提出的ARIMA-LSTM组合模型;对模型参数进行率定和验证后,利用所建立的模型,以西宁站点为例,对多尺度SPI值进行预测,借助均方根误差（RMSE）、平均绝对百分比误差（MAPE）和决定系数R²对所有预测模型的有效性进行判定。结果表明：ARIMA-LSTM组合模型在SPI1和SPI12的RMSE值分别为0.159 7和0.181 0,均低于ARIMA模型的1.265 4和0.293 3,说明ARIMA模型与ARIMA-LSTM组合模型对SPI的预测精度都与时间尺度有关,ARIMA模型的预测精度随着时间尺度的增加而逐渐提高;结合GIS并利用实测数据与模型的预测数据相比较说明ARIMA-LSTM组合模型相比于单一ARIMA模型的预测精度更高,且能够很好拟合不同时间尺度的SPI值。     

Effect of farmland expansion on drought over the past century in Songnen Plain,Northeast China

The effects of human activities on climate change are a significant area of research in the field of global environmental change. Land use and land cover change(LUCC) has a greater effect on climate than greenhouse gases, and the effect of farmland expansion on regional drought is particularly important. From the 1910 s to the 2010 s, cultivated land in Songnen Plain increased by 2.67 times, the area of cultivated land increased from 4.92×10~4 km~2 to 13.14×10~4 km~2, and its percentage of all land increased from 25% to 70%. This provides an opportunity to study the effects of the conversion of natural grassland to farmland on climate. In this study, the drought indices in Songnen Plain were evaluated from the 1910 s to the 2010 s, and the effect of farmland expansion on drought was investigated using statistical methods and the Weather Research and Forecasting Model based on UK's Climatic Research Unit data. The resulting dryness index, Palmer drought severity index, and standardized precipitation index values indicated a significant drying trend in the study area from 1981 to 2010. This trend can be attributed to increases in maximum temperature and diurnal temperature range, which increased the degree of drought. Based on statistical analysis and simulation, the maximum temperature, diurnal temperature range, and sensible heat flux increased during the growing season in Songnen Plain over the past 100 years, while the minimum temperature and latent heat flux decreased. The findings indicate that farmland expansion caused a drying trend in Songnen Plain during the study period.