Spatial and temporal pattern of precipitation and drought in Gansu Province, Northwest China

Drought is one of the most harmful natural hazards in Gansu Province in Northwest China. The changes of precipitation affect the severity of drought. In order to recognize the trend of precipitation and understand the effect of rainfall change on water resources management and drought severity, Mann–Kendall test was used. Standardized Precipitation Index (SPI) was calculated to reconstruct the drought at different time scales and analyze the frequency of drought occurrence in the recent 50 years. The results show that the SPI is applicable in Gansu Province. The number of severe droughts differs among regions: it is more obvious as a 3-month drought in the Yellow River Basin and the Yangtze River Basin than in the Inland River Basin, and other droughts at 6-, 9-, and 12-month time scales have the same effect in the three regions. Mann–Kendall test results show that there is an upward trend in the summer periods and a downward trend in the autumn-winter-spring intervals ranging from 10.5 mm/10 years to −37.4 mm/10 years, which affect the local water resources management, droughts mitigation, and agriculture decision making. This situation poses challenges for future study.     

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.     

Assessment of precipitation and drought variability in the Weihe River Basin,China

The amount and distribution of precipitation play crucial roles in the occurrence of drought in the Weihe River Basin (WRB), China. Using the precipitation data (1960–2010) of 21 meteorological stations, the spatial and temporal characteristics of short-, medium-, and long-term droughts on 3-, 6-, and 12-month time scales, respectively, were examined using the theory of runs and the Standardized Precipitation Index (SPI). The trends of the drought characteristics were analyzed by a modified Mann-Kendall (MMK) test method. Furthermore, comparative analysis of the SPI at different time scales was conducted. The results showed that (1) the main drought type was moderate drought, which occurred frequently in July and October; (2) the drought intensity and frequency were highest in the 1990s, and the drought severity and drought duration in the northwest was more serious than that in the east; (3) an increasing trend of short droughts appeared mainly in the spring and fall; an increasing trend of medium droughts mainly occurred in the 1990s and that of long-term droughts were mainly presented in the northwest region of the WRB; (4) SPI-3 can better reflect precipitation in the current month, SPI-6 has an advantage in characterizing drought persistence, and SPI-12 performs well in capturing extraordinary droughts; and (5) it was also observed that there is a strong relation between the precipitation distribution and drought zones in the basin, and the drought conditions changed continuously with the seasons depending upon the amount and spatial distribution of precipitation .     

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.     

鄱阳湖流域年降水时间序列的小波分析

对鄱阳湖流域内的赣州、宜春、南昌和庐山4个气象站近60年的年降水量序列进行小波分析,研究了鄱阳湖流域降水时间序列的周期性变化规律。结果表明,鄱阳湖流域年降水量存在两个明显的周期变化,分别为30～35a和12～15a,2011年正处在30～35a周期转化的节点上,未来30a鄱阳湖流域可能将进入降水偏少的周期;而12～15a周期尺度在2000年以前比较明显,2000年以后12～15a周期特征趋于消失。通过对ENSO指数的变化周期尺度的研究,发现厄尔尼诺现象的出现周期与鄱阳湖流域年降水量周期十分相似,两者具有很高的相关性。     

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.

     

Prediction of drought in dry lands through feedforward artificial neural network abilities

Drought is one of the most important natural hazards in Iran. It is especially more prevalent in arid and hyper arid regions where there are serious limitations in regard to providing sufficient water resources. On the other hand, drought modeling and particularly its prediction can play important role in water resources management under conditions of lack of sufficient water resources. Therefore, in this study, drought prediction in a hyper arid location of Iran (Ardakan region) has been surveyed based on the abilities of artificial neural. Standardized Precipitation Index (SPI) in different time scales (3, 6, 9, 12, and 24 monthly time series) computed based on the data gathered from four rain gauge stations. After evaluation and testing of different artificial neural networks (ANN) structures, gradient descent back propagation (traingd) network showed higher abilities than others. Then, the predictions of SPI time series with different monthly lag times (1:12 months) were tested. Generally, drought prediction by ANNs in the Ardakan region has shown considerable results with the correlation coefficient (R) more than 0.79 and in the most cases and it rises more than 0.90, which indicates the ANN’s ability of drought prediction.     

The performance of SPI and PNPI in analyzing the spatial and temporal trend of dry and wet periods over Iran

Drought monitoring is carried out using various drought indices, including SPI, to generate time series of dry and wet periods. Furthermore, the dispersion of dry and wet periods was embossed with different intensities (high, medium, and low) over the data record years. Although these results were very necessary for planning and predicting future droughts, it appeared that the application of any trend over dry and wet periods could provide more accurate and unbiased or safer predictions in terms of analysis process. Generally, most of the researchers believed that the results of a drought trend analysis have been influenced by short-term persistence or significant autocorrelation with different lags on drought event time series and the mentioned impact should be preferably removed. Accordingly, drought monitoring was accomplished using SPI and PNPI drought indices to extract time series of dry and wet periods in terms of 50-year (1965–2014) annual rainfall data of 40 synoptic stations over Iran. Having used the basic and modified Mann–Kendall nonparametric tests, it was attempted to analyze the trend of dry and wet periods extracted from mentioned indices. The results represent the relative advantage of using the modified Mann–Kendall test in drought trend analysis. Furthermore, it was shown that the trend of dry and wet periods was negative in the majority of selected stations and that this trend was significant at 95% confidence level in northwest of Iran. Also, the results indicated the similar performance of SPI and PNPI indices in trend analysis of dry and wet periods.

     

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.

     

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。     

Characteristics of meteorological drought in Bangladesh

Meteorological drought events occur in Bangladesh are diagnosed using monthly rainfall and mean air temperature from the surface observations and Regional Climate Model (RegCM) by calculating Standardized Precipitation Index (SPI) and Palmer Drought Severity Index (PDSI) for the period 1961?C1990. The historical records of drought event obtained from the Bangladesh Bureau of Statistics and International Disaster Database are used to verify the SPI and PDSI detected events. The SPI and monthly PDSI are obtained for 27 station data across Bangladesh as well as for two subregions over the country. Result based on the observed data shows that regional information is better in drought diagnosis compared to the point information. The regional analysis is able to detect about 80?% of the drought events occurred during the study period. Frequency of moderate drought is higher for all over the country. The SPI calculated from RegCM rainfall shows that the detection of moderate drought events is 10, 7, and 21?% overestimated for 1-, 3-, and 6-month length, respectively, compared to using of observed data. For extreme drought cases, detection is overestimated (underestimated) by 25?% (79?%) for 1-month (6-month) length. The PDSI results for model and observed data are nearly same to SPI calculations. Model monthly PDSI result is overestimated (underestimated) by 29?% (50?%) for moderate (severe) drought events with reference to the observed PDSI. Hence, RegCM output may be useful to detect 3?C6-month (monthly to seasonal) length moderate drought events over a heavy rainfall region likely Bangladesh.     

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。     

Understanding the impact of droughts in the Yarmouk Basin,Jordan: monitoring droughts through meteorological and hydrological drought indices

This article assesses drought status in the Yarmouk Basin (YB), in northern Jordan, using the Standardized Precipitation Index (SPI), the Standardized Water-Level Index (SWI), and the Percent Departure from Normal rainfall (PDNimd) during the years 1993–2014. The results showed that the YB suffers from frequent and irregular periods of drought as variations in drought intensity and frequency have been observed. The SPI results revealed that the highest drought magnitude of ??2.34 appeared at Nuaimeh rainfall station in 1991. This station has also experienced severe drought particularly in years 1995, 1999, 2005, and 2012 with SPI values ranging from ??1.51 to ??1.59. Some other rainfall stations such as Baqura, Ibbin, Khanasiri, Kharja, Mafraq police, Ramtha, Turra, and Umm Qais have also suffered several periods of drought mostly in 1993. The SWI results show the highest extreme drought events in 2001 in Souf well while other extreme drought periods were observed at Wadi Elyabis well in 1994 and at Mafraq well in 1995. As compared to SPI maps, our SWI maps reflect severe and extreme drought events in most years, negatively impacting the groundwater levels in the study area.     

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.     

Vulnerability of water resources in northern Cameroon in the context of climate change

Water resources in northern Cameroon have continuously been reducing over the past years. Many studies have suggested two principal causes: (1) human activities such as poor farming practices, unsustainable use of water resources, increased demand of water, deforestation, land-use change, etc., (2) human-induced climate change. Northern Cameroon in this study includes: the Adamawa, North and Far North regions located closer to the Sahel regions of Africa. These regions are already water stressed because of their location and any further change in climate with rising temperature would impact water resource either positively or negatively. Time series analysis and a 12-month standardized precipitation index (SPI12) with digital data between 1957 and 2006 were used to investigate the variation of water resources in northern Cameroon. Results obtained varies between the different regions with an increased annual trend in temperature and precipitation for Ngaoundere (Adamawa region) and Garoua (north region), whereas Maroua (far north region) had a decreased annual trend in both precipitation and temperature. Further variability results obtained from a SPI12 show that wetter period out number drought period in all three regions. The study concluded that water resources vary with the changing climatic condition and the severity of the impact varies from region to region. Furthermore, water deficiency in northern Cameroon might not be due to climate change. The reasons might be a combination of poor water management and other factors such population growth, the environmental condition, etc.     

The applicability of Standardized Precipitation Index: drought characterization for early warning system and weather index insurance in West Africa

The Niger River basin is drought-prone, and farmers are often exposed to the vagaries of severe weather and extreme climate events of the region. Spatiotemporal characteristics of drought are important for its mitigation. With 52 years of gauged-based monthly rainfall, the study investigates the potentials of Standardized Precipitation Index (SPI) as standard measure for meteorological drought, its characterization, early warning systems and use in weather index-based insurance. Gamma probability distribution type 2, which best fits the rainfall frequency distribution of the region, was used for the transformation of the skewed rainfall data to derive the SPI. Results showed 9, 5, 5 and 6 drought events of severe to extreme intensities occurred in the headwaters of the basin, inner delta, middle Niger, and lower Niger sub-watersheds, respectively. Their magnitudes were in the range 1–5, 2–6, 2–8 and 2–7, respectively. Spatially, results further showed that the 1970s and 1980s drought events were dominantly of moderate (SPI values ?1 to ?1.49) and severe (SPI values ?1.5 to ?1.99) intensities, respectively, with sporadic cases of severe to extreme drought intensities occurring in 1970s and extreme to exceptional intensities in the 1980s. Further investigations show that 3-month SPI indicated 85% of variance in the standardized cereal crop yield, which suites well as weather index insurance variable. The study therefore proposes SPI weather index-based insurance as a pathway forward to ameliorate the negative impacts on insured farmers in this region in terms of indemnity payouts whenever drought disaster occurs.     

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.     

A probabilistic model for the prediction of meteorological droughts in Venezuela

Droughts occur when rainfalls diminish or cease for several days, months or years. In the last five years several meteorological droughts have occurred in Venezuela, impacting negatively water supply, hydro-power and agriculture sectors. In order to provide institutions with tools to manage the water resources, a probabilistic model has been developed and validated to predict in advance the occurrence of meteorological droughts in the country using monthly series of 632 rainfall stations. The standardized precipitation index (SPI) was used to identify dry events of each rainfall series. A principal component analysis associated to a geographic information system was used to define geographically continuous homogeneous sub-regions (HS) for the values of SPI. For each HS a representative station was selected (reference station, RS). A lagged correlation analysis was applied to the SPI series of the RS and the corresponding series of anomaly indices of 10 macroclimatic variables (MV). The four MV with higher correlation in each RS were organized into three levels (-1, 0 and +1), using the quartiles Q₂ and Q₄ as values of truncation. The SPI series are expressed in four ranges: non-dry, moderately dry, severely dry and extremely dry. The conditional probability of occurrence of the four ranges of SPI was determined in every combination that can occur in the four VM best correlated. The resulting model in each RS was validated using the SPI series from 20 meteorological stations operated by the Servicio de Meteorología de la Fuerza Aérea Venezolana (Meteorological Service of the Venezuelan Air Force) which were not used in the development of the models. Results indicate that models detected the occurrence of ES with an accuracy ranging from 85.19 to 100%; the success is directly proportional to the length of records used in the development of the model. This methodology could be applied in any country that has long, continuous and homogeneous rainfall series.     

The evolution analysis of flood and drought in Huai River Basin of China based on monthly precipitation characteristics

Based on the daily precipitation data of 38 weather stations in the Huai River Basin from 1961 to 2010, this study used SPI index, P-III curve to determine the flood/drought years, under what situations for droughts and floods easily happen, and to analyze the evolution law of flood and drought during inter-annual and intra-annual based on the characteristic of monthly precipitation. The results showed that: (1) annual rainfall of the Huai River Basin presented decreasing trend, maximum rainfall appeared from June to August, and multi-year average precipitation increased gradually from north to south; (2) the variation of monthly precipitation during flood years was more severe than other typical years, and precipitation in drought years showed nearly 50 % decline compared with normal years; (3) high rainfall of flood years was mainly caused by the increase in rainfall in flood season, and the strategy of flood control and drought relief was “short-term flood prevention and long-term drought relief”; (4) while precipitation of most months in drought year was reduced, the relevant strategies “annual basin-wide of long-term drought prevention” should be carried out; (5) combination events of floods and droughts occurred frequently. Persistent drought dominated in spring and summer while droughts and floods that happened alternately were mainly in summer and autumn.     

青海省春小麦干旱灾害风险评估与区划

基于青海省1961-2010年47个气象站和8个农气代表站气象资料、 干旱灾情资料的收集和整理, 分析了春小麦生育期土壤相对湿度与降水距平百分率的关系, 确定了青海省春小麦不同生育期干旱风险评估的实际阈值, 并对青海省春小麦不同生育期干旱进行风险区划.结果表明: 在青海省春小麦营养期, 轻旱易发生在祁连山地区、 青海湖地区西南部及东部农业区的少数地区, 轻旱的频率大都在15%以上; 中旱易发生在柴达木地区大部及东部农业区大部, 频率大都在10%以上; 重旱和特旱出现频率均在柴达木盆地西部较高, 频率分别为10.7%、 34%以上.生殖期的轻旱易发生在祁连山地区, 频率在15.3%~23.3%之间; 中旱易发生在青海湖地区南部及东部农业区少数地区, 频率大都在4.0%~5.7%之间; 重旱频率从东南部到西北呈现带状递减趋势, 频率最高的主要发生在东部农业区, 出现频率为8.3%~9.6%; 特旱易发生在柴达木盆地地区, 出现频率为27%~44.3%之间.产量形成期的轻旱、 中旱、 重旱均易发生在东部农业区, 频率分别为2.3%~3.3%、 8.7%~13.3%、 6.0%~9.0%之间, 特旱易发生在柴达木地区, 出现频率为43.3%~51.3%之间.