Effect of Air Temperature on Historical Trend of Long-Term Droughts in Different Climates of Iran

Effective monitoring of drought plays an important role in water resources planning and management, especially under global warming effect. The aim of this paper is to study the effect of air temperature on historical long-term droughts in regions with diverse climates in Iran. To this end, monthly air temperature (T) and precipitation (P) data were gathered from 15 longest record meteorological stations in Iran covering the period 1951–2014. Long-term meteorological droughts behavior was quantified using two different drought indices, i.e. the Standardized Precipitation Index (SPI) and the Standardized Precipitation Evapotranspiration Index (SPEI). Linear and non-linear trends in T, P, SPI and SPEI were evaluated using non-parametric and parametric statistical approaches such as non-modified and modified Mann-Kendall Test, Theil-Sen approach, and simple regression. The results indicated that the significant trends for temperature are approximately all increasing (0.2 °C to 0.5 °C per decade), and for precipitation are mostly decreasing (?7.2 mm to ?14.8 mm per decade). It was also indicated that long-term drought intensities monitored by the SPI and SPEI have had significant downward trend (drought intensification with time) at most stations of interest. The observed trends in the SPI series can be worsen if air temperature (in addition to precipitation) participates in drought monitoring as SPEI. In arid and extra arid climates, it was observed that temperature has strong effects on historical drought characteristics when comparing the SPI and SPEI series. Due to the determinative role of temperature in mostly dry regions like Iran, the study suggests using the SPEI rather than SPI for more effective monitoring of droughts.     

Hydrological Drought Assessment in Northwestern Iran Based on Streamflow Drought Index (SDI)

Drought is a natural and worldwide phenomenon that occurs when water availability is significantly below normal levels during a significant period of time and cannot meet demand. This work focused on the hydrologic drought defined by the streamflow drought index (SDI) for overlapping periods of 3, 6, 9 and 12 months at 14 hydrometric stations in the northwest of Iran over the period 1975–2009. It was found that some of the streamflow volume series did not follow the normal distribution. The ability of the log-normal, exponential and uniform probability distributions was examined in order to choose the most suitable distribution, and the log-normal distribution was used to fit the long-term streamflow data. The results of the hydrological drought analysis based on the SDI showed that almost all the stations suffered from extreme droughts during the study period. Additionally, extreme droughts occurred most frequently in the last 12 years from 1997–1998 to 2008–2009.     

Evaluation and Assessment of Meteorological Drought by Different Methods in Trarza Region,Mauritania

Drought Indexes (DIs) are commonly used for assessing the effect of drought such as the duration and severity. In this study, long term precipitation records (monthly recorded for 44 years) in three stations (Boutilimit (station 1), Nouakchott (station 2), and Rosso (station 3)) are employed to investigate the drought characteristics in Trarza region in Mauritania. Six DI methods, namely normal Standardized Precipitation Index (normal-SPI), log normal Standardized Precipitation Index (log-SPI), Standardized Precipitation Index using Gamma distribution (Gamma-SPI), Percent of Normal (PN), the China-Z index (CZI), and Deciles are used for this purpose. The DI methods are based on 1-, 3-, 6-, and 12 month time periods. The results showed that DIs produce almost the same results for the Trarza region. The droughts are detected in the seventies and eighties more than the 1990s. Twelve drought years might be experienced in station 2 and six in stations 1 and 3 in every 44 years, according to reoccurrence probability of the gamma-SPI and log-SPI results. Stations 1 and 3 might experience fewer drought years than station 2, which is located right on the coast. In station 1, which is located inland, when the annual rainfall is less than 123 mm, it is likely that severe drought would occur. This is 63 mm/year for station 2 and 205 mm/year for station 3 which is located in the south west on the Senegal River. DI results indicate that the CZI and the gamma-SPI methods make similar predictions and the log-SPI makes extreme drought predictions for the monthly period for all the stations. For longer periods (3-, 6-, and 12 month period), for all the stations, the log-SPI and the gamma-SPI produce similar results, making severe drought predictions while the normal-SPI and the CZI methods predict more wet and fewer drought cases. The log-SPI, the gamma-SPI, PN and Deciles were able to capture the historical extreme and severe droughts observed in early 1970s and early 1980s.     

基于SPI指数的内蒙古地区干湿气候特征

探究气候的干湿格局及其演化趋势对旱涝灾害监测防御具有重要意义,围绕北方内蒙古生态功能区重要定位,依据1970-2020年45个气象监测站点月尺度降水资料,采用标准化降水指数(SPI),运用线性趋势、突变检验及Morlet小波分析等方法,探究内蒙古地区多时间尺度下气候的干湿特征及周期演化规律.结果 表明:1970-202...     

Spatiotemporal Analysis of Droughts Over Different Climate Regions Using Hybrid Clustering Method

Assessment of spatiotemporal variations of drought is an efficient method for implementing drought mitigation strategies and reducing its negative impacts. This study aimed to assess the spatiotemporal pattern of short- to long-term droughts for an area with different climates. Therefore, 31 stations located in Iran were selected and the Standardized Precipitation Index (SPI) series with timescales of 3, 6, and 12 months were computed during the 1951-2016 period. A hybrid methodology including Maximal Overlap Discrete Wavelet Transform (MODWT) and K-means methods was used for obtaining the SPIs time-frequency properties and multiscale zoning of the area. The energy amounts of the decomposed subseries via the MODWT were applied as inputs for K-means. Also, the statistics in drought features (i.e. drought duration, severity, and peak) were assessed and the results showed that shorter term droughts (i.e. SPI-3 and -6) were more frequent and severe in the northern parts where the lowest values were obtained for drought duration. It was observed that the regions with more droughts frequency had the highest energy values. For shorter term droughts a direct relationship was obtained between the energy values and the mean SPIs, drought severity, and drought peak, whereas an inverse relationship was obtained for longer term drought. It was found that increasing the degree of SPI led to more similarity between the stations of each cluster. Also, the homogeneity of stations for the SPI-12 was slightly higher than the SPI-3 and -6.

     

Wavelet transform‐based analysis of periodicities and trends of Sakarya basin (Turkey) streamflow data

In this study, the wavelet transform and the Mann–Kendall test are used to determine possible trends in annual streamflow series. The wavelet analysis provides detailed information about the time‐frequency contents of the data. Using wavelet components of the original data, it was aimed to find which periodicities are mainly responsible for a trend in the original data. Also, the global wavelet spectra and the continuous wavelet transform (CWT) were used for the analysis of the streamflow data, in order to explain its time‐frequency characteristics. Annual streamflow series across Turkey were used for the detection of trends for the original data and the periodic wavelet components (obtained by discrete wavelet transform). It was found that some periodic events clearly affect the trend in the streamflow series. The DW4 component (16‐yearly periodic component) at the stations of the Sakarya basin is the effective periodic component and is responsible for producing a real trend in the data. The effects of regional differences on the wavelet‐trend analysis are studied using records of the stations located in different climate areas. DW2 (4‐yearly component) and DW3 (8‐yearly component) are the dominant periodic components of this data. This study aims to explain the trend structure in the data. Copyright © 2009 John Wiley & Sons, Ltd.     

南盘江流域水文干旱对气象干旱的响应特征研究

选取南盘江流域3个站点40年(1970年-2009年)的逐月径流资料和20个站点同期逐月降水资料,计算标准化径流指数(SSFI)和不同时间尺度的标准化降水指数(SPI),从中选取水文干旱样本和同期的SPI进行统计分析,研究水文干旱对气象干旱的响应特征。结果表明:南盘江流域SSFI对SPI具有良好的响应关系;SSFI对SPI的响应关系随时间尺度的不同相关性不同;总体来看,流域内水文干旱对气象干旱的响应时间约为6个月。     

A Non-Stationary Reconnaissance Drought Index (NRDI) for Drought Monitoring in a Changing Climate

Traditionally, drought indices are calculated under stationary condition, the assumption that is not true in a changing environment. Under non-stationary conditions, it is assumed the probability distribution parameters vary linearly/non-linearly with time or other covariates. In this study, using the GAMLSS algorithm, a time-varying location parameter of lognormal distribution fitted to the initial values (α₀) of the traditional Reconnaissance Drought Index (RDI) was developed to establish a new index called the Non-Stationary RDI (NRDI), simplifying drought monitoring under non-stationarity. The fifteen meteorological stations having the longest records (1951–2014) in Iran were chose to evaluate the NRDI performances for drought monitoring. Trend analysis of the α₀ series at multiple time windows was tested by using the Mann-Kendall statistics. Although all stations detected decreasing trend in the α₀ series, eight of them were significant at the 5% probability level. The results showed that the time-dependent relationship is adequate to model the location parameter at the stations with the significant temporal trend. There were remarkable differences between the NRDI and the RDI, especially for the time windows larger than 6 months, implying monitoring droughts using the NRDI under non-stationarity. The study suggests using the NRDI where the significant time trend appears in the initial values of RDI due to changing climate.     

Seasonality Characteristics and Spatio-temporal Trends of 7-day Low Flows in a Large, Semi-arid Watershed

Seasonality characteristics and spatio-temporal trends of 7-day low flows were investigated for the 41,470 km² semi-arid Karkheh watershed (western Iran), representing 12 stations with record length of 51 years (1958–2008). Hierarchical Cluster Analysis (HCA) was used to identify low-flow seasonality (clusters of low flow seasons). Monotonic trends were detected by the Mann-Kendall test. Breaks in low flow trends were detected by the Lombard’s change point test. Breaks in short-term trends were detected by the Lepage test. According to results seasonal clusters were different in most of the stations, indicating uniqueness in station low flow behaviour. Among the 12 stations studied, four stations showed seasonal monotonic trend and change points, some with abrupt change (change point in consecutive years). Two stations near watershed outlet with no monotonic trend showed abrupt change. Most seasonal change points (dates) compared well with documented droughts, highlighting drought impacts on low flow trends at affected stations. The Lepage test detected change point in seasonal low flow trends of most stations during 5- and 10-year periods. This research has shown that in semi-arid environments low flow seasonality characteristics may vary among stations, an indication that low flow trends should be evaluated individually for each station.     

Drought Monitoring Using the Multivariate Standardized Precipitation Index (MSPI)

One major characteristic of the Standardized Precipitation Index (SPI) is its flexibility to be calculated in a variety of time scales and hence being aware of different types of droughts. However, various time scales may result in confusion of the water resources’ researchers, decision makers and users in identifying and specifying drought periods in a certain region. To solve this problem in this article, a multivariate approach has been utilized having the ability to aggregate a variety of the SPI time series into a new time series called the Multivariate Standardized Precipitation Index (MSPI). The MSPI is based on the principal components analysis (PCA) of the SPI time series in a certain location. Having specified the first principal component’s (PC₁’s) scores, the MSPI would be simply attained by dividing PC₁’s values by the monthly standard deviation. In this article, MSPI’s capability in depicting the variability of the SPI time series (in five ranges of time scales, including 3–6, 6–12, 3–12, 12–24, and 24–48 months) was studied at four weather stations representing the four different climates in Iran from the driest to the wettest climates. The results showed that the PC₁ is able to justify more than 74 % of the variability for the selected sets of the SPI time scales in the studied climates. Also, it became clear that the drought and wet severity classes monitored by MSPIs matched very satisfyingly to those of the five sets of the SPI time scales. Therefore, in cases where the aggregation time scales for calculating the SPI are not previously known, this study recommends the researchers use the MSPI.     

西南地区干旱时空变化特征研究

干旱是一种严重的自然灾害,西南地区是我国受干旱影响比较严重的地区。分析干旱的时空变化特征,可以为 应对气候变化和抗旱减灾提供科学的决策依据。本文利用西南地区 31 个气象站 1951—2010 年的月降水量数据,计算尺度分别为 1 个月、3 个月和 1 年的标准化降水指数（SPI）,分析了 60 年西南地区月、季和年干旱的时空变化特征。西南地 区干旱发生频率具有空间差异性,四川大部、云南南部发生干旱的频率高,而四川中部和广西东南部发生重旱的频率高。 60 年中西南地区降水量总体没有发生显著性变化,但是阶段性变化显著。年干旱、秋季的站次比具有明显增加的趋势,给西南地区抗旱带来更大的挑战。     

基于SPEI干旱指数的东北地区干旱时空分布特征

基于东北地区86个国家基本气象站1960年-2014年逐月降水量和平均气温数据,计算不同时间尺度下的标准化降水蒸散指数(SPEI),分析不同时间尺度下东北地区季节、年际以及年代际干旱时空分布特征,并与实际干旱成灾面积进行对比分析,验证SPEI在东北地区干旱评估中的适用性。结果表明:春季、夏季和冬季重旱发生频率随时间尺度增大而升高;中旱发生频率随时间尺度增大而降低。近55年东北地区旱涝情势交替出现,干旱频率随年代际变化逐渐升高,干旱程度逐渐加重。整体来看,随时间尺度增加,极旱发生范围逐渐扩大。东北三省在春季、夏季和秋季均有不同频率的干旱事件发生;冬季吉林和辽宁无极旱发生;黑龙江中部无重旱发生。     

Trend Detection of Drought in Arid and Semi-Arid Regions of Iran Based on Implementation of Reconnaissance Drought Index (RDI) and Application of Non-Parametrical Statistical Method

Drought is known as one of the main natural hazards especially in arid and semi-arid regions where there are considerable issues in regard to water resources management. Also, climate changes has been introduced as a global concern and therefore, under conditions of climate change and global warming, the investigation of drought severity trend in regions such as Iran which is mainly covered by arid and semi-arid climate conditions is in the primary of importance. Therefore, in this study, based on the application of Reconnaissance Drought Index (RDI) for assessment drought severities, and also the implementation of non-parametric Mann- Kendall statistics and Sen’s slope estimator, the trends in different time series of RDI (3, 6, 9, 12, 18 and 24 monthly time series) were investigated. Results indicated the frequent decreasing trends in RDI time series particularly for long term time series (12, 18 and 24 monthly time series) than short term ones. Decreasing trend in RDI time series means the increasing trend in drought severities. Since the water resources especially ground water in most cases are affected by long term droughts, therefore, increasing trend in drought intensities in long term ones can be a threat for water resources management in surveyed areas.     

Development of a Non-stationary Standardized Precipitation Evapotranspiration Index (NSPEI) for Drought Monitoring in a Changing Climate

In a changing climate, drought indices as well as drought definitions need to be revisited because some statistical properties, such as the long-term mean, of climate series may change over time. This study aims to develop a Non-stationary Standardized Precipitation Evapotranspiration Index (NSPEI) for reliable and robust quantification of drought characteristics in a changing climate. The proposed indicator is based on a non-stationary log-logistic probability distribution, assuming the location parameter of the distribution is a multivariable function of time and climate indices, as covariates. The optimal non-stationary model was obtained using a forward selection method in the framework of the Generalized Additive Models in Location, Scale, and Shape (GAMLSS) algorithm. The Non-stationary and Stationary forms of SPEI (i.e., NSPEI and SSPEI) were calculated using the monthly precipitation and temperature data of 32 weather stations in Iran for the common period of 1964–2014. The results showed that almost at all the stations studied, the non-stationary log-logistic distributions outperformed the stationary ones. The AICs of the non-stationary models for 97% of the stations were lower than those of the stationary models. The non-stationary models at 90% of the stations were statistically significant at the 5% significance level. While SSPEI identified the long-term and continuous drought and wet events, NSPEI revealed the short-term and frequent drought/wet periods at almost all the stations of interest. Finally, it was revealed that NSPEI, compared to SSPEI, was a more reliable and robust indicator of drought duration and drought termination in vegetation cover during the severest drought period (the 2008 drought). Therefore, it was suggested as a suitable drought index to quantify drought impacts on vegetation cover in Iran.

     

Multivariate Frequency Analysis of Meteorological Drought Using Copula

The multivariate frequency analysis of droughts for Agartala (India) was carried out in the present study. The meteorological drought was modelled using Standardised Precipitation Index(SPI) at the time scale of 1, 3, 6 and 12 months. Three droughts variables i.e., duration, severity, interval were determined for SPI at the time scale of 1, 3, 6 and 12 months. For the construction of bivariate and trivariate joint distributions Archimedean and metaelliptical copulas were used. Upper tail dependence test was also carried out. The best copula was selected based on minimum value Akaike’s information criteria (AIC)) and Schwarz information criterion(SIC). The drought risk was estimated using joint probabilities and return period for the study area.     

东江流域汛期降雨序列的小波分析

为了揭示流域汛期降雨变化的多时间尺度的复杂结构,采用Morlet小波函数,对东江流域1956~2005年汛期降雨时间序列进行了小波分析,找出了不同时间尺度下降雨序列变化的周期和突变点,并根据降雨主周期对未来汛期降雨变化进行了预测。研究结果表明,汛期降雨序列呈现微弱的减少趋势,倾向率为每10 a12.08 mm,近50 a汛期降雨量减少了约60.4 mm;汛期降雨量变化的特征时间尺度为2、4、7、12 a与19 a,其中4、7 a左右的周期振荡最强,为汛期降雨量变化主周期;依据主周期的变化趋势,预测2006年前后东江流域汛期降雨将偏少,在2008~2010年汛期降雨将偏多。     

Clustering Quantile Regression-Based Drought Trends in Taiwan

Drought is a normal, recurring climatic feature and occurs in all climatic zones. Imbalanced water availability induced by droughts has far-reaching and adverse impacts both on human lives and natural environments. This study aims to summarize temporal and spatial drought variations in Taiwan by combining quantile regression and cluster analysis. Three-monthly rainfall series covering the 1947–2012 period for 12 rainfall stations are used in this study. Quantile regression is applied to 3-month SPI, drought duration, drought severity, and drought frequency series for exploring temporal drought trends at different quantiles. Various quantile slopes for these 12 stations are then analyzed by hierarchical agglomerative clustering algorithm to detect regional variation patterns. The results show considerable spatial diversity over Taiwan. Stations along east coast are prone to more severity due to declined SPI trends associated with increasing drought duration and severity. Positive SPI slope associated with decreasing drought duration and severity are noted at stations located in the west and lead to lessened droughts. However, temporal variations in drought-duration and drought-severity series are insignificant at most quantiles and stations. In addition, a distinct behavior is found in drought frequency since severe droughts may not accompany frequent droughts.     

Streamflow variability in the Southern Appalachians and atmospheric teleconnections

We have examined streamflow variability in the Southern Appalachian region of the United States for the period 1950–2009. In particular, we have analysed the monthly discharge time series at two stations along the following rivers in North Carolina: (1) Little Tennessee River (LTR) near Prentiss and (2) French Broad River in Asheville. These two gauging stations are part of the Hydro‐Climatic Data Network (HCDN) system. The HCDN system was developed by the United States Geological Survey (USGS) to provide a long‐term database for tracking changes in flow and water quality of streams and rivers, with minimal human interventions. Using continuous wavelet transform (CWT), we have identified the dominant oscillatory modes in the monthly discharge data at these two rivers, and delineated the time intervals over which these modes may persist. It is found that in addition to the annual hydrologic cycle, the monthly discharge fluctuates at interannual timescales. These interannual variations may be linked to the Pacific North American (PNA) teleconnection pattern. Knowledge of the interannual periodicities may be useful for understanding long‐term streamflow variability in the Southern Appalachian region. Understanding patterns of streamflow variability may be important for water resources management operations in the surrounding area. Copyright © 2010 John Wiley & Sons, Ltd.     

基于SPEI的贵州省气象干旱时空演变特征及双变量区域频率分析

为深入探究贵州省干旱事件发展规律及区域重现期特征,采用贵州省1960—2017年逐月气象资料计算标准化降水蒸散指数（SPEI）,采用三阈值法游程理论、小波理论、Copula函数等方法对干旱时空演变特征、周期特征以及区域重现期特征进行分析。结果表明：1960—2017年贵州省干旱整体上呈严重化趋势,各分区存在差异,其中黔西南地区的干旱化趋势最为明显,SPEI线性倾向率为-0.003/10a;干旱强度由高到底的分区排序依次为黔东、黔北、黔西南、黔中、黔西北、黔南,且黔中地区的干旱发生频率最高;贵州省四季干旱的主周期分别为18、15、30和8 a,空间上南北分布差异显著,周期较长的区域范围呈现先增加后减少的趋势;贵州省黔北地区的干旱重现期最小,表明此区域发生极端干旱事件的概率较大,遭受干旱灾害的可能性较大。研究结果可为贵州省水资源管理及干旱风险防治提供科学依据。     

贵州省干旱变化特征及其与大气环流关系

根据贵州省1960-2018年17个气象站点的月降水量和平均气温数据,采用标准化降水蒸散指数(SPEI)定量表征干旱,利用M-K趋势检验及R/S法分析了贵州省近59年干旱变化趋势及未来变化趋势的持续性,采用连续小波变换和交叉小波变换分析贵州省SPEI和4种环流因子(MEI、NAO、AO、PDO)的振荡周期特征及其之间在时频域中的共同特征和相关关系。结果表明:贵州省月、春、秋和年SPEI序列呈显著下降趋势(通过显著性检验),即贵州省春旱和秋旱逐步加剧,其中秋季下降速率最大为-0.108/(10a),而夏旱和冬旱的增加趋势未通过显著性检验;未来春旱和秋旱将保持上升趋势,干旱程度可能愈发严重,而未来夏旱和冬旱强度存在减弱的可能性;干旱事件具有显著的16～48个月的年际振荡周期特征,干旱和四种环流因子的年际共振周期为24～110个月,年代际共振周期为128～250个月,SPEI与NAO、AO之间呈滞后的正相关关系;整体上,MEI主要影响贵州省干旱较短的年际周期变化,PDO和AO主要影响干旱较长的年代际周期变化,而NAO的影响较小。