Stoch Environ Res Risk Assess DOI 10.1007/s00477-012-0589-6

ORIGINAL PAPER

Analysis of dry/wet conditions using the standardized precipitation index and its potential usefulness for drought/flood monitoring in Province,

Juan Du • Jian Fang • Wei Xu • Peijun Shi

Ó Springer-Verlag 2012

Abstract Local dry/wet conditions and extreme rainfall time scales may vary in its usefulness in drought/flood events are of great concern in regional water resource and monitoring, and this highlights the need for a comprehen- disaster risk management. Extensive studies have been sive consideration of various time scales when SPI is carried out to investigate the change of dry/wet conditions employed to monitor droughts and floods. and the adaptive responses to extreme rainfall events within the context of climate change. However, applicable Keywords Standardized precipitation index Drought/ tools and their usefulness are still not sufficiently studied, flood Dry/wet conditions Hunan Province China and in Hunan Province, a major grain-producing area in China that has been frequently hit by flood and drought, relevant research is even more limited. This paper inves- 1 Introduction tigates the spatiotemporal variation of dry/wet conditions and their annual/seasonal trends in Hunan with the stan- Global climate change, including both gradual and abrupt dardized precipitation index (SPI) at various time scales. changes, has a profound impact on land surface-atmo- Furthermore, to verify the potential usefulness of SPI for sphere interactions and regional social development. On drought/flood monitoring, the correlation between river the one hand, global warming, or gradual climate change, discharge and SPI at multiple time scales was examined, has a significant effect on atmospheric circulation and the and the relation between extreme SPI and the occurrence of hydrological cycle, and it alters the intensity and spatial historical drought/flood events is explored. The results distribution of precipitation (IPCC 2007; Arnell 1999), indicate that the upper reaches of the major rivers in Hunan which in turn changes local dry/wet conditions and affects Province have experienced more dry years than the middle the regional agriculture sector. On the other hand, within and lower reaches over the past 57 years, and the region the context of climate change, change can also occur in the shows a trend of becoming drier in the spring and autumn frequency of extreme weather events (Rosenzweig et al. seasons and wetter in the summer and winter seasons. We 2001), which can induce various meteorological hazards, also found a strong correlation between river discharge and such as floods, droughts and rainstorms. Studies have SPI series, with the maximum correlation coefficient shown that these extreme events appear to have become occurred at the time scale of 2 months. SPI at different more frequent in the middle to lower reaches of the River in China (Piao et al. 2010). Despite signif- icant achievements in science and technology and & J. Du W. Xu ( ) P. Shi improved environmental management in the 20th century, State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing 100875, China people still continue to suffer from the consequences of e-mail: [email protected] these meteorological hazards worldwide (Krysanova et al. 2008). Climate change is but one symptom of our past J. Du J. Fang W. Xu P. Shi failure to achieve sustainable development amongst many Academy of Disaster Reduction and Emergency Management, Ministry of Civil Affairs & Ministry of Education, the People’s other symptoms (Green 2010). To better understand the Republic of China, Beijing 100875, China recent climatic fluctuations, their manifestation in local 123 Stoch Environ Res Risk Assess places, and to further monitor drought and flood occur- the potential of using SPI as a tool for monitoring flood risk rence, it is worthwhile to investigate the spatiotemporal in the Southern Cordoba Province in Argentina. They variability of local dry/wet conditions. This knowledge will found that SPI satisfactorily explains the development of undoubtedly be useful for improving integrated water conditions leading up to the three main flood events in the resource management. region. However, very few studies have examined the The standardized precipitation index (SPI) is a probabil- capability of SPI to monitor hydrological floods, especially ity-based indicator that depicts the degree to which the the relationship between SPI values at multiple time scales accumulative precipitation of a specific period departs from and monthly river discharges. the average state. The SPI is space-independent and has a Therefore, the main objectives of this study were as sound performance when representing precipitation anom- follows: (1) to apply the SPI to evaluate the spatiotemporal aly (Mckee et al. 1993). Compared with other indices and variability of dry/wet conditions in Hunan Province, China; methods based on physical processes, the palmer drought (2) to examine the relationship between SPI values at severity index (PDSI) for example, SPI is easy to calculate multiple time scales and river discharge; and (3) to thor- and convenient to apply. It requires only precipitation as oughly investigate the potential capability of SPI for input data and escapes the problem of parameter calibration, drought/flood monitoring and management. thus is particularly suitable for drought/flood monitoring in areas where hydrological data is scarce (Yuan and Zhou 2004). Due to its robustness and convenience to use, SPI has 2 Study area and data sources already been widely used to characterize dry and wet con- ditions in many countries and regions, such as the United Hunan Province is an inland province located to the south States (Wu et al. 2007); Canada (Quiring and Papakryiakou of the Yangtze River and is composed of 14 municipalities 2003); Italy (Piccarreta et al. 2004; Vergni and Todisco with a total area of 211, 800 km2, which is 2.21 % of the 2010); Iran (Moradi et al. 2011; Nafarzadegana et al. 2012); total land area of China. Hunan Province is a mountainous Korea (Min et al. 2003; Kim et al. 2009); and China. For region with an elevation descending from south to north example, Bordi et al. (2004) applied SPI to analyze the that varies from 2,079 to 16 m. The annual rainfall is from spatiotemporal variability of dry and wet periods during the 1,300 to 1,600 mm falling mainly from April to September, last 50 years in eastern China. They found that the northern and the annual average temperature is 16–18 °C. part of eastern China had been experiencing dry conditions Hunan Province has a dense hydrological network that more frequently since the 1970s, and they concluded that the can be divided into five main components: the Dongting cycles ranged from 4 to 16 years. Zhai et al. (2010) also used Lake Region, and four major river basins including the SPI and PDSI to investigate the spatial variation and trends of Basin, the Zi River Basin, the dry and wet conditions in 10 large regions covering the ter- Basin, and the Basin (Fig. 1). , ritory of China from 1961 to 2005. They found that the fre- which is located in northern Hunan Province, is the second quencies of occurrence of both dry and wet years for the largest fresh water lake in China. The four major rivers whole period were lower for the southern region than for the originate from the mountains, run from south-to north or northwest. In particular, an increasing frequency of wet years west-to east across Hunan Province and flow into the was detected in the upper and lower reaches of the Yangtze Dongting Lake. Hunan is one of the Chinese provinces that River. Zhang et al. (2008) explored the changes of dry/wet frequently suffer from severe flooding and drought disas- episodes in the Basin in South China and indi- ters. Affected by the monsoon circulation and complex cated that the Pearl River Basin has become drier during the topography, the temporal and spatial distribution of pre- rainy season and wetter in winter. However, very few rele- cipitation is uneven, with 69 % of the annual precipitation vant studies have been carried out in central China, which is concentrated from April to September (Li et al. 2000), and one of the major grain-producing areas in China, that play an heavy rains always occur in some local areas. indispensable role in ensuring the nation’s food security. The daily precipitation dataset used in this study covers SPI has also been widely used for drought monitoring the period from 1951 to 2007. It was collected from 21 rain and management (e.g., Mckee et al. 1993; Hayes et al. gauge stations in Hunan Province and was provided by the 1999; Cancelliere et al. 2007). National Oceanic and National Climate Center of the China Meteorological Atmospheric Administration (NOAA), especially, has Administration. The distribution of these rain gauge sta- provided the products of successive SPI maps of the United tions is shown in Fig. 1. Daily river discharge data from States (NOAA National Climatic Data Center 2011). five hydrological stations along the Xiang River were Although the SPI was originally developed for drought provided by the Hunan Hydrographic Office. All the sta- detection and monitoring, it can also be applied to indentify tions selected have complete records of rainfall and wetter than normal conditions. Seiler et al. (2002) analyzed streamflows. 123 Stoch Environ Res Risk Assess

Fig. 1 Location of Hunan Province and spatial distribution of the rain gauge and hydrological stations

3 Methodology Lloyd-Hughes and Saunders (2002) described the detailed calculation of SPI. The most commonly used 3.1 Calculation of SPI distribution for SPI calculation is the two-parameter gamma distribution with a shape and scale parameter, The SPI was developed by Mckee et al. (1993) to quantify which is defined by its probability density function: precipitation anomaly with respect to long-term normal Zx conditions for multiple time scales. SPI can be calculated at 1 a1 x=b GðxÞ¼ a x e dx for x [ 0 ð1Þ various time scales on which precipitation deficits/sur- b sðaÞ 0 pluses can affect different aspects of the hydrologic cycle, which is the main advantage of the SPI. This advantage is where a is the shape parameter, b is the scale parameter, x crucial because it can reflect the natural lags in the is the precipitation value and sðaÞ is the gamma function. response of different water sources, such as river discharge The gamma distribution is undefined for x = 0, but the and storage, to precipitation anomalies (Paulo et al. 2003). precipitation may have zero value, so the cumulative

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Table 1 Dryness/wetness categories according to SPI values where, similar to the MK test, 8 Code Category SPI values <> þ1 h [ 0 SgnðhÞ¼ 0 h ¼ 0 ð4Þ 1 Extremely wet C2.0 :> 2 Severely wet 1.50 to 1.99 1 h\0 3 Moderately wet 1.00 to 1.49 The most probable change point is found where its value is 4 Near normal 0.99 to -0.99 5 Moderately dry -1.00 to -1.49 Kt ¼ max Ut;T ð5Þ 6 Severely dry -1.50 to -1.99 K 7 Extremely dry B-2.0 and the significance probability associated with value t is evaluated as 6K2 probability distribution given a zero value is derived as p ¼ 2 exp t ð6Þ T3 þ T2 follows: Given a certain significance level a,ifp \ a, we reject the HðxÞ¼q þð1 qÞ GðxÞð2Þ null hypothesis and conclude that xt is a significant change where q is the probability of the zero precipitation value. point at level a. The cumulative probability distribution is then transformed into the standard normal distribution to calculate SPI. The value of SPI indicates the strength of the anomaly. Mckee 4 Results et al. (1993) suggested a classification system to define the intensity of dry/wet phases (Table 1). 4.1 Frequency of occurrence of dry and wet years in the region 3.2 Statistical methods for trend and change point analysis The annual SPI for 21 rain gauge stations from 1951 to 2007 was calculated to demonstrate the frequency of The non-parametric Mann–Kendall (MK) statistical test occurrence of dry and wet conditions and their spatial (Mann 1945; Kendall 1975) was applied in this work to study distributions in Hunan Province. According to the thresh- the trends of SPI. This test is widely used to detect trends in old values in Table 1, a dry year was defined as having an hydrology and climatology because it is robust against non- average annual SPI B-1.0, and a wet year an SPI C 1.0. normal distributions and is insensitive to missing values. As Figure 2 shows the frequencies of occurrence of dry years described by Du et al. (2011), the null hypothesis H0 of the (Fig. 2a) and wet years (Fig. 2b). Stations belonging to the test is that there is no trend in the population from which the category 0.182–0.220 in Fig. 2a experienced a dry condi- data set X is drawn. The alternative hypothesis H1 is that a tion in 18.2–22 % of all years during the study period, and monotonic trend exists in X. The null hypothesis H0 should stations belonging to the category 0.167–0.184 in Fig. 2b be rejected if jjZ Z1a=2 at the a level of significance. The Z experienced a wet condition in 16.7–18.4 % of all years. value is a standard normal variable that can be related to the The results indicate that stations with a higher frequency of significance level of a specific trend. dry years are primarily distributed in the upper reaches of The non-parametric Pettitt test developed by Pettitt the Xiang, Zi, Yuan, and Li rivers, whereas stations with a (1979) is applied in this study to identify the significant relatively higher frequency of wet years were mainly change point in the SPI time series. The Pettitt test is a located in the middle and lower reaches of these same rank-based and distribution-free test for detecting a sig- rivers and in the Dongting Lake region. nificant change in the mean of a time series when the exact time of the change is unknown (Zhang and Lu 2009). The 4.2 Trend analysis of dry and wet conditions Pettitt test considers a sequence of random variables and divides it into two groups represented by x1; x2; ...; xt and To further study the temporal patterns of precipitation xtþ1; xtþ2; ...; xT . If each group has a common distribution anomalies, Mann–Kendall statistical test was employed to function, i.e., F1ðxÞ, F2ðxÞ and F1ðxÞ 6¼ F2ðxÞ, then the analyze the trends for SPI of each season. This is done change point is identified at t. To achieve the identification through calculating a 3-month SPI at February, May, of change point, a statistical index Ut is defined as follows: August, and November (for winter, spring, summer, and Xt XT fall respectively). The 3-month precipitation at month t was Ut;T ¼ Sgnðxi xjÞ; 1 t T ð3Þ calculated using rainfall data at months t - 2, t - 1 and i¼1 j¼1 t (Chen et al. 2009). Thus, the 3-month SPI value of May,

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a b

Shimen Sangzhi Nanxian

Changde Yuanjiang Pingjiang

Yuanling Jishou Anhua

Zhijiang Shuangfeng Nanyue

Hengyang Wugang

Tongdao Lingling

Chenzhou Daoxian

Frequency of dry year Frequency of wet year .120 - .135 .120 - .127 .135 - .179 .127 - .146 .179 - .182 .146 - .167 0408012016020 0408012016020 .182 - .220 Kilometers .167 - .184 Kilometers

Fig. 2 Frequencies of occurrence of dry years and wet years in Hunan Province for the 1951–2007 period. The frequency was calculated as percentage according to the 12-month SPI for each year; a dry year a was defined when SPI B-1.0 and a wet year b SPI C 1.0 for instance, was based on the sum of precipitation of the western region, although the trends were not significant March through May, while that of August was based on the at the [95 % confidence level (Fig. 3e). sum of June–August precipitation. Since the Mann–Kendall tests showed a generally dry- The spatial distribution patterns of the annual and sea- ing tendency in the spring and autumn and a wet tendency sonal SPI trends are shown in Fig. 3. Positive and negative in the summer and winter, the Pettitt test was further used trends, which represent trends toward wetter and drier to analyze the 3-month (seasonal) SPI series for the five conditions, respectively, were detected. It can be seen that regions in the province, in order to detect the change points there were considerably different trends for different sea- or transition years when the significant changes began. sons. All the rain gauge stations were characterized by Table 2 lists these change points in all seasons and the decreasing SPI trends in spring (Fig. 3a) and increasing approximate significance probability p for the corre- SPI trends in summer (Fig. 3b), and a few locations in sponding change point. It is found that the change years are central Hunan Province were characterized by trends sig- different in different regions and different seasons. nificant at the[90 % confidence level, which indicates that According to Table 2, the change points of spring and a drying tendency dominates Hunan Province in spring and summer in the Dongting Lake region occurred in 1978 and a wet tendency prevails in summer. These trends can also 1972, respectively, but that of autumn and winter were in be identified in autumn and winter. Figure 3c shows that 1990 and 1988, respectively. The change years of all sea- the major parts of Hunan Province, except three stations in sons in the Xiang River Basin were all in the 1970s and the the Dongting Lake region, were characterized by decreas- results are statistically significant at the [95 % confidence ing SPI trends in the autumn season, whereas Fig. 3d level, except that of winter. In the Zi, Yuan, and Li River shows that almost all the stations were characterized by Basins, the change points of the four seasons occurred from increasing SPI trends in winter. The stations located in the 1974 to 1990, and most of the results passed the 95 % Yuan River Basin show a significant drying tendency in confidence level. On the whole, the change points of the autumn and a wet tendency in winter (p \ 0.1). When 3-month SPI of every season detected in Hunan Province using the 12-month SPI to detect the annual trends, wet varied between 1970 and 1990. The results fit the general trend signals were mainly identified in the northeastern part picture described by He (2000), that is, the frequency of of Hunan Province, and a drying tendency was observed in occurrence of flood and drought disasters in Hunan

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Fig. 3 Spatial distribution of seasonal and annual SPI trends. Red negative and positive trends, respectively: a March–May (spring), upright triangle and red downward triangle indicate significant b June–August (summer), c September–November (autumn), negative (drying) and positive (wet) trends, respectively; Black d December–February (winter), e January–December (annual). (Color upright triangle and black downward triangle indicate non-significant figure online)

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Table 2 Change point of the seasonal SPI series District Pettitt test for change points Spring Summer Autumn Winter

Change KT p Change KT p Change KT p Change KT p point (year) point (year) point (year) point (year)

Dongting Lake region 1978 225 0.106 1972* 246 0.068 1990 137 0.391 1988** 264 0.025 Xiang River Basin 1970** 343 0.024 1972** 320 0.038 1977** 248 0.046 1978 200 0.170 Zi River Basin 1981** 386 0.009 1985* 286 0.074 1990 184 0.282 1978** 290 0.050 Yuan River Basin 1977** 457 0.001 1985* 278 0.085 1990** 315 0.009 1978** 341 0.006 Li River Basin 1977 183 0.207 1974 92 0.638 1990** 248 0.046 1988** 379 0.001 * Significant level p \ 0.1, ** significant level p \ 0.05

Province shows a general V-shaped distribution in the last 50 years of the 20th century, with a decreasing trend from the 1950s–1970s, and a sharp increasing trend after 1990.

4.3 Relationship between SPI at multiple time scales of SPI and river discharge

Although SPI is widely used, there is only a limited number of empirical studies that provide evidence on the relevance of the temporal scale of SPI for hydrological drought/flood monitoring in surface water resources. River discharge is an important indicator of hydrological droughts and floods, Fig. 4 Correlation between standardized river discharge and SPI at different time scales. The five curves (I, II, III, IV and V) represent the and rainfall certainly has an impact on river discharge correlations obtained from five pairs of rain gauges and hydrological through runoff. stations: I Changsha (rain gauge station) versus Langli (hydrological Considering the length of records and relative proximity, station), II versus Hengyang, III Lingling versus Laobutou, five rain gauge stations, including Changsha, Hengyang, IV Daoxian versus Daoxian, V Shuangfeng versus Shuangfeng Lingling, Daoxian, and Shuangfeng, and five hydrological stations, including Langli, Hengyang, Laobutou, Daoxian, 4.4 Dry/wet period detection and drought/flood event and Shuangfeng, which are all located in the Xiang River monitoring with SPI at multiple time scales Basin, were selected to analyze the correlation between standardized river discharge and SPI at different time scales. The time series of SPI at multiple time scales of 2, 6, 12, Figure 4 shows the Pearson correlation coefficients between and 24 months were calculated to determine their potential the SPI series at different time scales (1–12 months) and the usefulness for detecting dry/wet periods and monitoring standardized river discharge. It is apparent that the correla- drought/flood risk. The SPI series for the Dongting Lake tions are positive, but there are important differences with Region at time scales of 2, 6, 12, and 24 months are pre- regard to the time scales. Higher correlations were obtained sented in Fig. 5, along with the past occurrence of major with the SPI at shorter time scales, and the maximum cor- flood and drought events in the region. relation is found at the time scale of 2 months. It can be seen According to the definition, for SPI at short time scales, that the correlation of Shuangfeng station (located in the the precipitation of each new month has a substantial upper reaches of the Xiang River) was above 0.7. The larger impact on the accumulative precipitation of that period, the time scale, the lower the correlation. At the 12-month and thus has more chance to influence the value of SPI, time scale, the correlations fell below 0.5. The results indi- making it fluctuate above and below zero frequently. As cate that river discharges in the study catchment are more the time scale becomes longer, monthly precipitation associated with the precipitation of current and previous makes less contribution to the total amount and also the months than of longer periods. As for the spatial aspect, the value of SPI. Therefore, the SPI at short time scales reflects correlation coefficients of the stations in the upper reaches near-term precipitation and ignores the overall character- (III, IV, V) were higher than that of the stations in the middle istics of precipitation within a relatively long period; while and lower reaches (I, II). with long time scale, SPI value responses more slowly and 123 Stoch Environ Res Risk Assess

a 1954 Flood 1998 Flood

1960 Drought 1985 Drought 1988 Drought

b 1954 Flood 1998 Flood

1985 Drought 1960 Drought 1988 Drought

c 1954 Flood

1998 Flood

1988 Drought

1960 Drought 1985 Drought

d 1954 Flood 1998 Flood

1988 Drought 1960 Drought 1985 Drought

123 Stoch Environ Res Risk Assess b Fig. 5 SPI values and major drought/flood events in the Dongting However, the 1988 drought event was best represented Lake region from 1951–2007: a 2-month SPI, b 6-month SPI, with the 6-month SPI series because it occurred only in the c 12-month SPI, d 24-month SPI spring and had a relatively short duration (Fig. 5b).

stably to changes in daily precipitation, revealing clear 5 Conclusion and discussion periods of annual and multiple-year dry and wet conditions. Figure 5 shows the variation of the SPI over 2-, 6-, 12-, SPI is a valuable tool for assessing the spatiotemporal and 24-month intervals from 1951 to 2007 in the Dongting variability of dryness/wetness due to its capacity to repre- Lake region. The results confirm the statements discussed sent precipitation anomaly. Meanwhile it would also be a above. At short time scales SPI shows a high frequency of potential tool for monitoring hydrological conditions and change between dry and wet periods. With increasing time drought/flood risk given the advantage that it can be cal- scales, the dry and wet periods show a lower frequency of culated at multiple time scales. change and a longer duration. At the time scale of This paper has applied SPI to identify the frequencies of 2 months, the average duration of dry periods was occurrence of dry and wet years and to reveal trends of dry 3.26 months and average duration of wet periods was and wet conditions in Hunan Province over a 57-years 3.53 months. At the time scale of 6 months, the average period. Moreover, this paper analyzed the potential use- durations of dry and wet periods were 5.18 and fulness of SPI for detecting dry/wet periods and monitoring 5.40 months, respectively. At the time scale of 12 months, drought/flood events in this area. The results indicate that the mean duration was 12.2 months for the dry periods and the upper reaches of the Xiang, Zi, Yuan and Li Rivers 11.6 months for the wet periods. The longest average experienced dry conditions more frequently during durations of dry and wet periods were 18.89 months and 1951–2007, whereas the middle and lower reaches of the 14.95 months, respectively, at the time scale of 24 months. Xiang, Zi, Yuan, and Li Rivers and the Dongting Lake At shorter time scales (i.e., 2, 6 months), the SPI values region had a higher frequency of wet years. Trend analysis fluctuated frequently above and below the zero line, and reveals that a general drying tendency can be observed in there was no extended dry or wet period. The 24-month SPI the spring and autumn seasons, and a wet tendency prevails series shows two well-defined wet cycles, with two major in the summer and winter seasons. Significant change floods in 1954 and 1998 associated with these cycles. points in the SPI series were detected in 1970–1990. A Likewise, main dry periods are more clearly shown in SPI robust relationship was found between the river discharge at the time scales longer than 6 months. and SPI at different time scales, and the maximum corre- The performance of SPI at various time scales in rep- lation was found at the 2-month time scale at most stations resenting individual drought and flood events appears dif- in the catchment of the Xiang River. The result is consis- ferent from identifying dry/wet periods. As has been tent with what Vicente-Serrano et al. (2005) have observed discussed, for identifying dry/wet periods, SPI at the longer in Spain, while differs from other previous studies (e.g. time scales show greater utility, but performed poorly at McKee et al. 1993; Hayes et al. 1999; Zhai et al. 2010; time scales less than 6 months. For flood events however, Tabrizi et al. 2010). Such difference is expected as most of as shown in Fig. 5, the two major events in 1954 and 1998 the previous studies examined the relation between annual matched exactly with the two highest values of SPI at the streamflow anomaly and SPI with a focus on long term 2-month time scale, which is the same time scale of the water resource variability; while this study explores the maximum SPI correlation with river discharge. The SPI usefulness of SPI for monitoring flood events based on the peaks at longer time scales are not clearly associated with relation between SPI and standardized monthly average these two events, because the averaging effect of long-term discharge. The monthly average discharge is more deter- accumulated precipitation obscured the signal of extreme mined by current and previous precipitation of a shorter precipitation over a short period. This highlights the use- period, thus shows higher correlation with SPI of short time fulness of SPI at the 2-month time scale in representing scales. flood events. In addition, spatial variability of the relation between The most suitable time scale for detecting drought may SPI and river discharge was also observed. The correlation differ due to the magnitude or duration of each drought coefficient was higher in the upper reaches of the rivers events. For example, in 1960 and 1985, the Dongting Lake than in the middle and lower reaches. An explanation of region suffered extremely severe multi-seasonal droughts this result might be that the upper reaches of the rivers in that lasted through the summer to autumn in 1960 and Hunan Province are located within mountainous areas, in from the spring to autumn in 1985. And these events are which precipitation is relatively more concentrated and the most clearly seen in the 24–month SPI series (Fig. 5d). generation of runoff is more intense and rapid. While, the 123 Stoch Environ Res Risk Assess terrain in the middle and lower reaches of these rivers is IPCC (2007) Climate change: fourth assessment report of the relatively flat and farmland and ponds are widely spread, intergovernmental panel on climate change. Cambridge Univer- sity Press, Cambridge retaining part of the precipitation, and weakening the cor- Kendall MG (1975) Rank correlation methods. Griffin, London relation between SPI and river discharge. Kim DW, Byun HR, Choi KS (2009) Evaluation, modification, and With respect to identifying the dry and wet periods from application of the effective drought index to 200-year drought the evolution of the SPI, it is obvious that the dry/wet climatology of Seoul, Korea. 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