Application of meteorological and vegetation indices for evaluation of drought impact: a case study for Rajasthan, India

Drought is a serious climatic condition that affects nearly all climatic zones worldwide, with semi-arid regions being especially susceptible to drought conditions because of their low annual precipitation and sensitivity to climate changes. Drought indices such as the standardized precipitation index (SPI) using meteorological data and vegetation indices from satellite data were developed for quantifying drought conditions. Remote sensing of semi-arid vegetation can provide vegetation indices which can be used to link drought conditions when correlated with various meteorological data based drought indices. The present study was carried out for drought monitoring for three districts namely Bhilwara, Kota and Udaipur of Rajasthan state in India using SPI, normalized difference vegetation index (NDVI), water supply vegetation index (WSVI) and vegetation condition index (VCI) derived from the Advanced Very High resolution Radiometer (AVHRR). The SPI was computed at different time scales of 1, 2, 3, 6, 9 and 12 months using monthly rainfall data. The NDVI and WSVI were correlated to the SPI and it was observed that for the three stations, the correlation coefficient was high for different time scales. Bhilwara district having the best correlation for the 9-month time scale shows late response while Kota district having the best correlation for 1-month shows fast response. On the basis of the SPI analysis, it was found that the area was worst affected by drought in the year 2002. This was validated on the basis of NDVI, WSVI and VCI. The study clearly shows that integrated analysis of ground measured data and satellite data has a great potential in drought monitoring.     

A comparative frequency analysis of three standardized drought indices in the Poyang Lake basin,China

Regional drought frequency analysis was carried out in the Poyang Lake basin (PLB) from 1960–2014 based on three standardized drought indices: the standardized precipitation index (SPI), the standardized precipitation evapotranspiration index (SPEI) and the standardized Palmer drought index (SPDI). Drought events and characteristics were extracted. A Gumbel–Hougaard (GH) copula was selected to construct the bivariate probability distribution of drought duration and severity, and the joint return periods (T _a ) were calculated. Results showed that there were 50 (50 and 40) drought events in the past 55 years based on the SPI (SPEI and SPDI), and 9 (8 and 10) of them were severe with T _a more than 10 years, occurred in the 1960s, the 1970s and the 2000s. Overall, the three drought indices could detect the onset of droughts and performed similarly with regard to drought identification. However, for the SPDI, moisture scarcity was less frequent, but it showed more severe droughts with substantially higher severity and longer duration droughts. The conditional return period (Ts|d) was calculated for the spring drought in 2011, and it was 66a and 54a, respectively, based on the SPI and SPDI, which was consistent with the record. Overall, the SPI, only considering the precipitation, can as effectively as the SPEI and SPDI identify the drought process over the PLB under the present changing climate. However, drought is affected by climate and land-cover changes; thus, it is necessary to integrate the results of drought frequency analysis based on different drought indices to improve the drought risk management.     

Land subsidence (2004–2013) in Changzhou in Central Yangtze River delta revealed by MT-InSAR

Droughts are hindrances to economic and social developments in many parts of the world, especially in developing nations like Kenya. In North Eastern Kenya (NEK), drought is very prevalent. The communities in the region are mainly dependent on animal farming, and drought occurrence leads to great socioeconomic setback. Drought indices used in most studies consider rainfall as the only parameter for assessing drought occurrences. This study analyzes drought in NEK using the Standardized Precipitation Index (SPI) and the Combined Drought Index (CDI) using rainfall and temperature values and Normalized Difference Vegetation Index values for the period 1980–2010. The results of the two indices show significant correlation. However, CDI is preferred in the analysis of the drought compared to the SPI as it gives drought in more detail, showing extreme, severe, moderate and mild. The study recommends the use of the two methods independently since they give similar results and further recommends trial in other parts of the country affected by drought.

     

多尺度干旱指数在江淮流域的适应性研究

利用1960-2010年江淮流域34个地面气象观测站的逐日降水、日平均气温、相对湿度等实测资料, 分别计算了江淮流域的Z指数、降水距平百分率、相对湿润指数、标准化降水指数以及CI指数, 经与江淮流域干旱记录对比分析, 结果表明: 月尺度的Z指数在5种干旱指数中应用效果最好, 符合率达70%以上;在时间域上, 月尺度的Z指数仅在春季吻合率稍差, 其余月份均在70%以上;月尺度的SPI指数在冬季吻合率较差, 其余月份同Z指数总体相当;MI指数效果最差;日尺度的CI指数应用效果存在时空差异, 在河南最好, 在山东最差, 夏季效果最好, 春季、冬季最差.     

基于条件分布模型的气象水文干旱传递分析

气象干旱发展到一定程度可以传递为水文干旱。以潘家口水库流域1961—2010年逐月平均降水数据和潘家口水库的入库径流序列为基础数据,分别计算了1、3、6、12个月时间尺度的标准化降水指数（SPI）和标准化径流指数（SRI）,以表征研究区域的气象干旱和水文干旱。基于条件分布模型,分析了不同时间尺度的气象干旱传递到未来的不同等级和不同的预测期（或滞后期）的水文干旱的概率。结果表明,当SPI时间尺度较短或预测期（滞后期）较短时,其对应的SRI水文干旱等级越倾向于维持与SPI相同的干旱等级;随着SPI时间尺度的增长或预测期（滞后期）延长,其对应的SRI水文干旱等级略低于气象干旱或恢复到正常状态。     

IMERG卫星降水数据在嘉陵江流域的干旱监测效用评估

针对嘉陵江流域存在雨热同期,水旱灾害频发的现象,为快速且准确地把握流域内降水与干旱情况,利用覆盖范围广且分辨率高的网格化IMERG卫星降水数据对嘉陵江流域进行多时空尺度反演,并基于卫星降水数据采用标准化降水指数(SPI)对流域实行干旱监测。结果表明：1)根据分类指标与统计指标的计算结果,三种卫星降水数据中的IMERG-F能更准确地反映流域内的日降水量,与地面降水数据CC达0.737,整体高估地面降水2.6%,具有在干旱监测方面的应用潜力。2)三种卫星降水数据驱动的SPI指数在干旱监测方面存在一定的差异,IMERG-F驱动的SPI指数与地面降水数据驱动的SPI指数保持较高的一致性(CC>0.9),较近实时产品IMERG-F更能准确地呈现出流域的干湿特征。3)卫星识别降水与干旱监测的能力受地形地貌的影响,IMERG卫星降水数据在平原丘陵地带具有较好的适用性。     

Investigating remote sensing indices to monitor drought impacts on a local scale (case study: Fars province,Iran)

As drought occurs in different climates, assessment of drought impacts on parameters such as vegetation cover is of utmost importance. Satellite remote sensing images with various spectral and spatial resolutions represent information about different land covers such as vegetation cover. Hence, the purpose of this study was to investigate the performance of satellite vegetation indices to monitor the agricultural drought on a local scale. In this regard, satellite images including Moderate Resolution Imaging Spectroradiometer (MODIS) and Advanced Very High Resolution Radiometer (AVHRR) data were used to evaluate vegetation cover and their gradual changes effects on agricultural drought. Fars province in Iran with relatively low precipitation values was selected as the study area. Modified Perpendicular Drought Index (MPDI), MPDI1, Vegetation Condition Index (VCI), Normalized Difference Vegetation Index Anomalies (NDVIA), and Standardized Vegetation Index (SVI), were evaluated to select the remote sensing based index with the best performance in drought monitoring. The performance of such indices were investigated during 13 years (2000–2013) for MODIS and 29 years (1985–2013) for AVHRR. To assess the efficiency of the satellite indices in drought investigation, Standardized Precipitation Index (SPI) data of five selected stations were used for 3, 6, and 9 month periods on August. The results showed that NDVI-based vegetation indices had the highest correlation with SPI in cold climate and long-term timescale (6 and 9 month). The highest correlation values between remote sensing based indices and SPI were acquired, respectively, in 9-month and 6-month time-scales, with the values of 43.5% and 40%. Moreover, VCI showed the highest capability for agricultural drought investigating in different climate regions of the study area. Overall, the results proved that NDVI-based indices can be used for drought monitoring and assessment in a long-term timescale on a local time-scale.

     

Application of the self-calibrated palmer drought severity index and standardized precipitation index for estimation of drought impact on maize grain yield in Pannonian part of Croatia

Recent global warming and more frequent droughts are causing significant damage to maize production. A reliable estimate of drought intensity and duration is essential for testing maize hybrids to drought tolerance. For this purpose, the self-calibrating 10-day palmer drought severity index (scPDSI) and standardized precipitation index (SPI) for 1, 2, 3, 6, 9, 18, 27, and 36 10-day scales were used to estimate the effects of drought on grain yield of 32 maize hybrids evaluated in 2017 and 2018 at eight experimental locations in the Pannonian part of Croatia. Time series of observed 10-day mean air temperature, relative humidity, and precipitation totals for a set of “reference” weather stations of the croatian meteorological and hydrological service (DHMZ) for the period 1981–2018 were used to calculate the scPDSI and SPI indices. According to the 10-day scPDSI and SPI for different time scales, 2018 proved to be a “normal year,” while 2017 experienced a “mild to moderate drought,” which resulted in a 13% reduction in maize grain yield at eight experimental locations compared to 2018. The correlation between grain yield and drought indices for summer months was the highest for the 10-day scPDSI. To some extent, correlations between summer months’ SPI for the 3 10-day time scale and maize grain yield were comparable to the corresponding correlations for the 10-day scPDSI. However, for other SPI time scales considered, the corresponding correlations were weaker and less informative. The dependence of grain yield on scPDSI values was not the same for all hybrids, indicating their different tolerance to drought. The reduction in grain yield due to drought was primarily caused by insufficient grain filling (lower 1000-grain weight) and, to some extent, by a reduction in the number of grains. In this study, application of 10-day scPDSI data proved to be more relevant in detecting effects of drought on agronomic traits than application of SPI data for the most time scales.

     

Drought forecasting through statistical models using standardised precipitation index: a systematic review and meta-regression analysis

Quality and reliable drought prediction is essential for mitigation strategies and planning in disaster-stricken regions globally. Prediction models such as empirical or data-driven models play a fundamental role in forecasting drought. However, selecting a suitable prediction model remains a challenge because of the lack of succinct information available on model performance. Therefore, this review evaluated the best model for drought forecasting and determined which differences if any were present in model performance using standardised precipitation index (SPI). In addition, the most effective combination of the SPI with its respective timescale and lead time was investigated. The effectiveness of data-driven models was analysed using meta-regression analysis by applying a linear mixed model to the coefficient of determination and the root mean square error of the validated model results. Wavelet-transformed neural networks had superior performance with the highest correlation and minimum error. Preprocessing data to eliminate non-stationarity performed substantially better than did the regular artificial neural network (ANN) model. Additionally, the best timescale to calculate the SPI was 24 and 12 months and a lead time of 1–3 months provided the most accurate forecasts. Studies from China and Sicily had the most variation based on geographical location as a random effect; while studies from India rendered consistent results overall. Variation in the result can be attributed to geographical differences, seasonal influence, incorporation of climate indices and author bias. Conclusively, this review recommends use of the wavelet-based ANN (WANN) model to forecast drought indices.

     

植被状态指数VCI与几种气象干旱指数的对比——以河南省为例

使用1982-2006年GIMMS AVHRR NDVI数据集与同期的CI、K、Pa、SPI、Z、PDSI等干旱指数做了对比分析, 讨论了河南省植被状态指数VCI对气象干旱的滞后效应及干旱监测能力. 结果表明: VCI指数与气象干旱指数的相关性受不同下垫面的影响较大, 农地的VCI与气象干旱指数相关性要明显高于林地, 农地VCI与气象干旱指数呈现正相关关系. 在河南省不同的作物生长阶段, VCI对气象干旱有着不同的滞后效应, 其中, 3-5月份冬小麦生长期VCI对气象条件的反应滞后1~3个月, 7、9月份夏玉米生长期VCI对气象条件的反应滞后1月. 总体上看, 结合前期的气象数据, VCI对河南省气象干旱有一定的指示作用和监测能力.     

Evaluation of drought and wetness episodes in a cold region (Northeast China) since 1898 with different drought indices

Drought identification and drought severity characterization are crucial to understand water scarcity processes. Evolution of drought and wetness episodes in the upper Nen River (UNR) basin have been analyzed for the period of 1951–2012 using meteorological drought indices and for the period of 1898–2010 using hydrological drought indices. There were three meteorological indices: one based on precipitation [the Standardized Precipitation Index (SPI)] and the other two based on water balance with different formulations of potential evapotranspiration (PET) in the Standardized Precipitation Evapotranspiration Index (SPEI). Moreover, two hydrological indices, the Standardized Runoff Index and Standardized Streamflow Index, were also applied in the UNR basin. Based on the meteorological indices, the results showed that the main dry period of 1965–1980 and wet periods of 1951–1964 and 1981–2002 affected this cold region. It was also found that most areas of the UNR basin experienced near normal condition during the period of 1951–2012. As a whole, the UNR basin mainly had the drought episodes in the decades of 1910, 1920, 1970 and 2000 based on hydrological indices. Also, the severity of droughts decreased from the periods of 1898–1950 to 1951–2010, while the severity of floods increased oppositely during the same periods. A correlation analysis showed that hydrological system needs a time lag of one or more months to respond to meteorological conditions in this cold region. It was also found that although precipitation had a major role in explaining temporal variability of drought, the influence of PET was not negligible. However, the sole temperature driver of PET had an opposite effect in the UNR basin (i.e., misestimating the drought detection) and was inferior to the SPI, which suggests that the PET in the SPEI should be determined by using underlying physical principles. This finding is an important implication for the drought research in future.     

District-wide drought climatology of the southwest monsoon season over India based on standardized precipitation index (SPI)

District-wide drought climatology over India for the southwest monsoon season (June–September) has been examined using two simple drought indices; Percent of Normal Precipitation (PNP) and Standardized Precipitation Index (SPI). The season drought indices were computed using long times series (1901–2003) of southwest monsoon season rainfall data of 458 districts over the country. Identification of all India (nation-wide) drought incidences using both PNP and SPI yielded nearly similar results. However, the district-wide climatology based on PNP was biased by the aridity of the region. Whereas district-wide drought climatology based on SPI was not biased by aridity. This study shows that SPI is a better drought index than PNP for the district-wide drought monitoring over the country. SPI is also suitable for examining break and active events in the southwest monsoon rainfall over the country. The trend analysis of district-wide season (June–September) SPI series showed significant negative trends over several districts from Chattisgarh, Bihar, Kerala, Jharkhand, Assam and Meghalaya, Uttaranchal, east Madhya Pradesh, Vidarbha etc., Whereas significant positive trends in the SPI series were observed over several districts from west Uttar Pradesh, west Madhya Pradesh, South & north Interior Karnataka, Konkan and Goa, Madhya Maharashtra, Tamil Nadu, East Uttar Pradesh, Punjab, Gujarat etc.     

Drought assessment and uncertainty analysis for Dapoling basin

In the present study, the Standardized precipitation index (SPI) was employed to analyze the drought status of the Dapoling basin over a period of autumn from September to November, because drought events frequently occur during this period. Three time scales were used, 3-, 6- and 12-month time scale. Daily precipitation data from 13 weather stations covering a period of 31 years from 1980 to 2010 were collected, and the Tyson polygon method was used to calculate the monthly precipitation of the basin. Based on the SPI value, the classification of drought was provided. Besides, considering the fact that the length of sample used to calculate the SPI influences the accuracy on SPI estimation, in turn to lead to the uncertainty of drought classification, the bootstrap technique was employed to analyze the uncertainty of SPI estimation and drought assessment. Results showed that, for September, October or November, drought event mainly occurred in 1985, 1986, 1988, 1990, 1992, 1993, 1994, 1997, 1999, 2001, 2002 and 2007. Especially in 1999 and 2001, severe drought and extreme drought occurred. And the uncertainty analysis results indicated that in term of expected estimation, the two methods with consideration and no-consideration of impact of sample on SPI calculation has no considerable difference, while in term of confidence interval estimation of SPI, there are obviously different between the two methods. This means the impact of the sampling uncertainty on SPI calculation and drought assessment should be noted and not ignored.     

近30年雷州半岛季节性气象干旱时空特征

为进一步分析日尺度有效干旱指数(Effective Drought Index,EDI)的适应性,基于雷州半岛1984~2013年逐日降水资料进行验证,对比EDI和月尺度标准化降水指数(Standardized Precipitation Index,SPI)的干旱识别效果,进而结合线性趋势、M-K趋势检验和空间插值方法 ,分析雷州半岛季节性气象干旱时空特征。结果表明:(1)日尺度EDI和6个月时间尺度SPI(SPI-6)适用于雷州半岛的干旱监测,但EDI对严重干旱和突发干旱的识别比SPI-6更准确;(2)1984~2013年,雷州半岛秋冬季干旱频率和干旱站次比均呈减少趋势,但春夏季干旱频率和干旱站次比略有增加趋势;(3)春旱频率从南向北递增,重旱高频地区位于西北部,而夏季重旱高频区位于西部沿海;秋旱南部重于北部,高频中心在雷中西部沿海和曾家周边;冬季重旱以西部沿海、雷州市和徐闻县交界处频率最高。     

青海省玉树旱涝态势变化规律分析

选取青海省玉树1953~2013年月降水数据,基于标准化降水指数SPI,利用频率分析、小波周期分析等方法对玉树县近61年来旱涝强度、频率分布、周期性变化等旱涝态势演变特征进行了研究。研究结果表明:(1)SPI-3、SPI-6和SPI-12三种尺度标准化降水指数对旱涝指示程度存在差别,相比大时间尺度,小时间尺度的标准化降水指数值更为分散,波动幅度更大,对干旱和洪涝的识别更为敏感。近年来,随着玉树县水土流失和沙化,当地土壤持水力程下降趋势,对干旱和洪涝较为敏感,因此玉树县可采用三种尺度标准化降水指数。(2)近61年,玉树县重旱平均发生概率为2.1%,重涝平均发生概率为1.3%。(3)未来几年,预计玉树县仍呈现偏涝趋势。(4)玉树县SPI-12以18a为主周期进行变化。     

Application of factor analysis in defining drought prone areas in Lake Urmia Basin

Data reduction methods such as principal components analysis and factor analysis can be used to define drought prone areas of a basin. In this study, factor analysis method applied for the purpose of projecting the information space on the few dominant axes. The main aim of this study is regionalization of Lake Urmia Basin from the view of drought using factor analysis. For this purpose, monthly precipitation data of 30 weather stations in the period 1972–2009 were used. For each of the selected stations, 3- and 12-month Standardized Precipitation Index (SPI) values were calculated. Factor analysis conducted on SPI values to delineate the study area with respect to drought characteristics. Homogeneity of obtained regions tested using the S statistics proposed by Wiltshire. Results of factor analysis of 3- and 12-month SPI values showed that 5 (6) factors having eigenvalues >1 accounted for 68.08 (78.88) % of total variance. The Lake Urmia Basin was delineated into the five distinct homogeneous regions using the 3-month SPI time series. This was six in the case of the 12-month SPI time series. It can be concluded that there are different distinct regions in Lake Urmia Basin according to drought characteristics. The map of regions defined using the 3- and 6-month SPI time series presented in this paper for Lake Urmia Basin.     

吉林西部季节性气象干旱的时空演化特征

为减轻季节性干旱对吉林西部农业生产造成的影响,以吉林西部6个气象站1957-2010年的月降水量资料为基础,采用标准化降水指数(SPI)作为气象干旱指标。利用Daubechies小波分析法、重标极差分析法(R/S)和干旱频率法对吉林西部SPI时空演化特征进行了研究。结果表明:研究区各站点冬旱整体上有减轻趋势,而秋旱有加重趋势;各站点气象干旱状况呈现出持续性特征,乾安(夏季和秋季)、前郭(夏季)、通榆(春季、夏季和秋季)、长岭(夏季)干旱持续性更加强烈。研究区春旱高频区为扶余,夏旱高频区为扶余和白城,秋旱高频区为白城、通榆、乾安、前郭和长岭,冬旱高频区为扶余、长岭和白城。研究结果可为吉林西部防旱减灾提供参考。     

基于最大熵理论的降雨SPI研究

为了克服目前对标准化降水指数(SPI)计算必须首先假设服从某种分布的不足,依据最大熵理论分布对SPI进行计算,以东江流域为例,分别利用最大熵理论分布、Gamma分布、Weibull分布以及对数正态分布四种概率密度函数拟合多年不同时间尺度的降雨数据,并利用AIC、KS、AD法进行拟合度检验,最后将最大熵理论分布与Gamma分布计算的SPI结果进行对比分析。结果表明:相对于其他三种分布,最大熵理论分布的概率密度函数更适用于东江流域15个站点的3、6、12个月的降雨分布;在极端干旱(洪涝)的情况下,相对于Gamma分布,最大熵理论分布的SPI值更小(大),表明其对极端干旱(洪涝)的识别更为敏感。     

Determination and prediction of standardized precipitation index (SPI) using TRMM data in arid ecosystems

Drought over a period threatens the water resources, agriculture, and socioeconomic activities. Therefore, it is crucial for decision makers to have a realistic anticipation of drought events to mitigate its impacts. Hence, this research aims at using the standardized precipitation index (SPI) to predict drought through time series analysis techniques. These adopted techniques are autoregressive integrating moving average (ARIMA) and feed-forward backpropagation neural network (FBNN) with different activation functions (sigmoid, bipolar sigmoid, and hyperbolic tangent). After that, the adequacy of these two techniques in predicting the drought conditions has been examined under arid ecosystems. The monthly precipitation data used in calculating the SPI time series (SPI 3, 6, 12, and 24 timescales) have been obtained from the tropical rainfall measuring mission (TRMM). The prediction of SPI was carried out and compared over six lead times from 1 to 6 using the model performance statistics (coefficient of correlation (R), mean absolute error (MAE), and root mean square error (RMSE)). The overall results prove an excellent performance of both predicting models for anticipating the drought conditions concerning model accuracy measures. Despite this, the FBNN models remain somewhat better than ARIMA models with R?≥?0.7865, MAE?≤?1.0637, and RMSE?≤?1.2466. Additionally, the FBNN based on hyperbolic tangent activation function demonstrated the best similarity between actual and predicted for SPI 24 by 98.44%. Eventually, all the activation function of FBNN models has good results respecting the SPI prediction with a small degree of variation among timescales. Therefore, any of these activation functions can be used equally even if the sigmoid and bipolar sigmoid functions are manifesting less adjusted R² and higher errors (MAE and RMSE). In conclusion, the FBNN can be considered a promising technique for predicting the SPI as a drought monitoring index under arid ecosystems.     

近300a来塔里木河流域旱涝灾害特征分析

干旱与洪涝是极端水文事件中最具有代表性的水文事件,在气候变化的影响下旱涝灾害事件越来越引起人们的关注. 采用传统的气象干旱指标-标准化降水指数SPI和小波分析法、反距离加权法以及线性回归分析,研究了近300 a来塔里木河流域旱涝灾害分布特征及关键影响因素. 结果表明：近300 a来塔里木河流域旱涝灾害呈增加的趋势,且洪涝事件较干旱事件明显. 其中,喀什、阿克苏等地的发生频率最高,并表现为群发性;近60 a塔里木河流域自西向东旱涝灾害事件呈交替现象. 小波分析结果表明,塔里木河流域旱涝灾害呈现15 a的周期性,由此推断未来5~10 a研究区湿润化面积仍有扩大的可能. 大气环流指数与多尺度下的SPI指相关性检验表明,PNA对秋季和冬季的SPI值的影响较为显著;旱涝灾害对农牧业的影响较为严重,其中,洪涝灾害的影响大于干旱.