Spatial Variation and Trends in PDSI and SPI Indices and Their Relation to Streamflow in 10 Large Regions of China

1FEBRUARY 2010 Z H A I E T A L. 649 Spatial Variation and Trends in PDSI and SPI Indices and Their Relation to Streamflow in 10 Large Regions of China JIANQING ZHAI State Key Laboratory of Lake Science and Environment, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing, and National Climate Center, China Meteorological Administration, and Graduate University of Chinese Academy of Sciences, Beijing, China BUDA SU National Climate Center, China Meteorological Administration, Beijing, China VALENTINA KRYSANOVA AND TOBIAS VETTER Potsdam Institute for Climate Impact Research, Potsdam, Germany CHAO GAO State Key Laboratory of Lake Science and Environment, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing, and Graduate University of Chinese Academy of Sciences, Beijing, China TONG JIANG National Climate Center, China Meteorological Administration, Beijing, and Nanjing University of Information Sciences and Technology, Nanjing, China (Manuscript received 9 December 2008, in final form 31 July 2009) ABSTRACT Time series of the average annual Palmer drought severity index (PDSI) and standardized precipitation index (SPI) were calculated for 483 meteorological stations in China using monthly data from 1961 to 2005. The time series were analyzed for 10 large regions covering the territory of China and represented by seven river basins and three areas in the southeast, southwest, and northwest. Results show that the frequencies of both dry and wet s for the whole period are lower for southern basins than for the northern ones when estimated by PDSI but very similar for all basins when calculated by SPI. The frequencies of dry and wet s calculated for 5- and 15-yr subperiods by both indices show the upward dry trends for three northeastern basins, Songhuajiang, Liaohe, and Haihe; a downward dry trend for the northwest region; a downward wet trend for the Yellow River basin; and an upward wet trend for the northwest region. Trend detection using PDSI indicates statistically significant negative trends for many stations in the northeastern basins (Songhuajiang, Liaohe, Haihe, and Yellow) and in the middle part of the Yangtze, whereas statistically significant positive trends were found in the mountainous part of the northwest region and for some stations in the upper and lower Yangtze. A moderately high and statistically significant correlation between the percentage of runoff anomaly (PRA) and the annual average PDSI and SPI was found for six large rivers. The results confirm that PDSI and SPI indices can be used to describe the tendency of dryness and wetness severity and for comparison in climate impact assessment. Corresponding author address: Buda Su, National Climate Center, China Meteorological Administration 46, Zhongguancun Nandajie Haidian, Beijing 100081, China. E-mail: subd@cma.gov.cn DOI: 10.1175/2009JCLI2968.1 Ó 2010 American Meteorological Society

650 J O U R N A L O F C L I M A T E VOLUME 23 1. Introduction Drought is one of the major natural hazards that cause billions of dollars of losses to agricultural communities throughout the world each (Narasimhan and Srinivasan 2005). To evaluate climatic drought, many different types of indices have been devised, including the rainfall anomaly index, the Palmer drought severity index (PDSI), the Bhalme Mooley index, and the standardized anomaly index (Palmer 1965; McKee et al. 1993; Akinremi et al. 1996). Different indices to describe dry and wet variation are used worldwide (Akinremi et al. 1996; Bordi et al. 2004; Bayarjargal et al. 2006; Bhuiyan et al. 2006). Among them, PDSI is a widely used index for agro-climatological analysis (Palmer 1965; Alley 1984). In addition, the standardized precipitation index (SPI) (McKee et al. 1993) is often used to monitor moisture supply conditions. China is a country with a severe water resource shortage. The total volume of freshwater resources in China is 2.8 trillion m 3, which accounts for almost 6% of the freshwater in the world. However, because of high population density (Xinhua Net 2004), per capita water availability in China is low: only 25% of the world average. Moreover, uneven spatial and seasonal distribution of water resources leads to recurrent water stress in the northern regions of China and results in frequent floods in southern China. The climatic conditions that have the greatest impact on society are extreme events. During the last few decades many regions in China have repeatedly suffered massive damage due to catastrophic flooding or prolonged droughts. The recurrent nature of floods and droughts requires a thorough analysis of trends in extreme events to provide better understanding of the spatial and temporal characteristics of climatic hazards over China. Recently, several studies were performed using different climatic and hydrological indices to study changes in agricultural water demand and soil moisture in the regions of China. It was recognized that soil moisture has an increasing trend in southeast China, and surface runoff has an increasing trend in mountainous areas of southwest, northeast, and some areas along the southern coast of China (An and Xing 1985; Zhu et al. 1998; Zhang et al. 1998; Tao et al. 2003; Wang et al. 2007). Wei and Ma (2003) calculated the monthly PDSI, surface moisture index, and percentage of precipitation anomaly from 1951 to 1999 using 160 international exchange meteorological stations in China, and they found that PDSI describes the drought severity more precisely than a percentage of precipitation anomaly in the regions with higher actual evaporation. They also revealed that a relatively moist period and a wet summer period became dryer in northern China after the 1980s (Wei et al. 2003a,b). Ren et al. (2002) investigated the impacts of human activities on river runoff in the northeastern area of China and found that, besides climatic driving forces, the increasing water consumption causes runoff decrease in this region. Although the aforementioned studies already investigated climatic droughts in the regions of China, there has been no systematic analysis on spatial variation and trends in drought indices and their relation to streamflow, which is directly connected to agricultural drought in large river basins of China (Shen and He 1996). Therefore, this study is focused on the analysis of spatial variability and trends in PDSI and SPI in 10 large regions of China. These 10 regions are seven large river basins and three areas in the southeast, southwest, and northwest, each combining several river basins. The aim is to identify tendencies in dry and wet conditions during recent decades over large regions in China. The data and analytical methods used for the study are described in section 2. The results of analysis on frequency of dry and wet s in the 10 regions, trends in PDSI and SPI in 10 regions, and correlation between the two indices and the percentage of runoff anomaly (PRA) in eight large rivers representing 8 of the 10 regions are presented in section 3. 2. Data and methods a. Study area and data sources China s territory spans many degrees of latitude and has complicated terrain, and therefore climate varies sharply in the country. Current research was carried out for the whole territory of China subdivided in 10 regions: seven large river basins (the Songhuajiang, Liaohe, Haihe, Yellow, Huaihe, Yangtze, and Pearl River basin) and three areas in southeast, southwest, and northwest combining several river basins (later referred as regions or by names; see Fig. 1 and Table 1). The annual precipitation averaged for the regions ranges from 161.2 (northwest) to 1787.4 mm (southeast), and average annual actual evapotranspiration estimated from a simple water balance equation as a difference between average annual precipitation and average annual runoff (Zhang and Wang 2007) ranges from 125.8 (northwest) to 734 mm (Pearl) (Table 1). It is clear that it is hard or impossible to estimate severity of climatic hazards for such a wide territory directly from the local observed precipitation data. Data from 752 meteorological stations in China were provided by the National Climate Center of the China Meteorological Administration. Of these, 483 stations have been selected for this study based on the following criteria: data quality, continuity, homogeneity, and the

1FEBRUARY 2010 Z H A I E T A L. 651 FIG. 1. The 10 regions and meteorological stations in China. The solid dots represent meteorological stations; numbers denote the 10 regions: 1 5 Songhuajiang River basin; 2 5 Liaohe River basin; 3 5 Haihe River basin; 4 5 Yellow River basin; 5 5 Huaihe River basin; 6 5 Yangtze River basin; 7 5 Pearl River basin; 8 5 southeast region; 9 5 southwest region; 10 5 northwest inland region. length of data record. The selected stations have uninterrupted observational daily precipitation data and daily maximum, mean, and minimum temperature data covering the period from 1961 to 2005. The density of selected stations is much higher in the eastern part of China, and they are more evenly distributed there compared to the western part of the country (Fig. 1 and Table 1). The annual runoff data for regions 1 8 (Table 1) were provided by the Ministry of Water Resources of China (see overview in Table 2). The Minjiang River belongs to the southeast basin. Only for the Yellow and Yangtze Rivers the drainage areas of the investigated gauges (Table 2) are soundly comparable with the total region area; in four cases they represent 37% 61% of the total region area, and in two cases they represent a small part of the total region area: 14% of the Haihe River basin (because of actual data availability: there are no longterm runoff data for the main gauge stations in the lower reaches) and 22% of the southeast region (no one major river there, only relatively small rivers in this coastal region). Unfortunately, no time series on human water use for purposes such as irrigation, industry, or municipal water were available for the eight regions for the time period of this study. TABLE 1. Main characteristics of the 10 regions in China represented by seven large river basins and three areas in southeast, southwest, and northwest combining several river basins. Region No. Name of the river basins Area (10 3 km 2 ) Number of meteorological stations included Average annual precipitation (mm) Average annual evapotranspiration (mm) Average annual evapotranspiration in % to average annual precipitation 1 Songhuajiang River basin 934.8 47 504.8 358.3 71 2 Liaohe River basin 314.2 35 545.2 400 73 3 Haihe River basin 320.1 30 534.8 425.4 80 4 Yellow River basin 795.1 50 447.1 364.2 81 5 Huaihe River basin 330 32 838.5 599.9 72 6 Yangtze River basin 1800 113 1086.6 533.3 49 7 Pearl River basin 579 58 1549.7 734 47 8 Southeast River basins 244.6 27 1787.4 702.1 39 9 Southwest River basins 844.1 23 1088.2 404.1 37 10 Northwest River basins 3362.9 68 161.2 125.8 78

652 J O U R N A L O F C L I M A T E VOLUME 23 Region No. TABLE 2. Hydrologic gauge stations and period of availability of water discharge time series in eight large rivers in China. Rivers Name of the gauge station Catchment area (10 3 km 2 ) Catchment area in % of the corresponding region area Period of availability of water discharge time series 1 Songhuajiang River Jiamusi 528 56 1961 2004 2 Liaohe River Tieling 121 39 1961 2004 3 Haihe River Yanchi 44 14 1963 99 4 Yellow River Huayuankou 730 92 1961 2000 5 Huaihe River Wujiadu 121 37 1961 2004 6 Yangtze River Datong 1705 95 1961 2004 7 Pearl River Gaoyao 352 61 1961 2004 8 Minjiang River Zhuqi 55 22 1961 2004 Field capacities required for the PDSI calculation in China were provided by the Potsdam institute for Climate Impact Research. They originate from the Food and Agriculture Organization (FAO) digitized soil map of the world (FAO 1991; Zobler 1986). b. Drought indices The nature of PDSI and SPI indices allows for comparing the frequency of dry and wet events among regions with different climate and soil characteristics. They can measure how much precipitation and soil moisture conditions for a certain time period have deviated from the historically established norms based on 30 s or more of continuous precipitation record (Edwards and McKee 1997). PDSI is a hydrological index based on the supply-anddemand concept of the water balance equation (Palmer 1965). Computation of PDSI begins with the determination of monthly departure of moisture from normal by estimating the gaps between actual precipitation and precipitation that is climatically appropriate for existing conditions (CAFEC-P). The CAFEC-P can be obtained from the basic terms of the water balance equation, which deducts the expected supply from the expected demand factors to get the water demand that must be met by precipitation. Parameters of the CAFEC include evapotranspiration, soil recharge, runoff, and moisture loss from the surface layer. The monthly moisture anomalies are then converted into the indices of moisture anomaly by multiplying by a weighting factor. Finally, dryness or wetness severity is deduced from the moisture anomaly index and the PDSI of the previous month. Theoretically, PDSI is a standardized measure, ranging from about 26.0 to 16.0. Palmer selected the classification scale of moisture conditions (Table 3) based on his study areas in Iowa and Texas (Palmer 1965). SPI was developed by McKee et al. (1993) for the quantifying precipitation deficit for multiple time scales and identifying dry and wet events and their severity (Moreira et al. 2006). It is a meteorological index. The calculation of SPI is based on the long-term precipitation record for any location (Hayes et al. 1999). This long-term precipitation record is fitted to a gamma distribution, and the cumulative probability of an observed precipitation event for each time scale of interest is deduced. The value of SPI can be obtained by transformation of the cumulative probability to the standard normal random variable with mean of zero and variance of one. Positive SPI values indicate higher than median precipitation, and negative values indicate lower than median precipitation. McKee et al. (1993) suggested the classification system to define drought or wet intensities (see Table 3). For the purpose of this study, PDSI was first calculated for monthly time scale and then annually averaged, and 12-month values of SPI were calculated for each of the 483 meteorological stations. The frequencies of dry and wet s were calculated for every station in the period using the common predefined thresholds for PDSI and SPI (Table 3) and then averaged for all stations in the regions for the whole period and for 5- and 15-yr subperiods from 1961 until 2005 and analyzed. c. Trend and correlation analyses Trends in annual PDSI and SPI for 483 meteorological stations were analyzed by generalized least squares and linear regression models, and the relationships between the two indices and PRA in 8 of the 10 regions were compared. Since the PDSI values are highly influenced by TABLE 3. Classifications of the dryness/wetness degree in accordance with the PDSI and SPI definitions. Categories PDSI classifications SPI classifications dry #24.0 #22.0 Severely dry 23.99 ;23.0 21.99 ;21.5 Moderately dry 22.99 ;22.0 21.49 ;21.0 Near normal 21.99 ; 1.99 20.99 ; 0.99 Moderately wet 2.0 ; 2.99 1.0 ; 1.49 Severely wet 3.0 ; 3.99 1.5 ; 1.99 wet $4.0 $2.0

1FEBRUARY 2010 Z H A I E T A L. 653 the value of the previous month, the time series contain nonrandom patterns of behavior. Existing positive series correlation will increase the possibility of rejecting the null hypothesis of no trend, while negative series correlation will decrease the possibility of rejecting the null hypothesis. Thus, the changing detection technique used to estimate a monotonic linear trend in the study included two functions from the R environment, that is, the linear model for fitting linear regression models with normal error distribution and the generalized least squares model for fitting linear regression models with a variety of correlated errors. SPI trends were investigated using the linear model (LM) from the R software. It was used for fitting the linear regression with normal error distribution, as SPI time series are normally distributed. In the case of PDSI, a simple linear regression cannot be used, as the Palmer index is a hydrological index taking into account soil moisture, and PDSI for a subsequent month is dependent on PDSI of the current and previous months. As a consequence, the errors from a regression model are unlikely to be independent. Therefore, the autocorrelation of obtained time series was analyzed in advance and taken into account. In most cases an autoregressive model of the second order was found. Therefore, the generalized least squares model (GLS) with an automatic calculation of the autoregressive order using the R function (AR) was used for analyzing the PDSI trends. All the trend results in the study were evaluated at the 10% and 5% levels of significance to ensure an effective exploration of the trend characteristics of the target area. The trend analysis was done for all stations separately. Because of the uneven distribution of stations in the western part of the country the interpolation of the resulting trends for the whole country was not done. The PRA for i in the period T was calculated using the formula PRA i 5 (X X) i, X where X i is the average runoff in i, and X is the average runoff of all s in the period T. The relationships between the two indices and the PRA in 8 of the 10 regions were investigated by calculation of the correlation coefficients. The correlation analysis was performed between the time series of PRA and the annual average PDSI and SPI time series. 3. Results The results of frequency analysis for the whole period are presented in section 3a. Section 3b includes results of qualitative trend analysis based on frequencies in subperiods, and section 3c describes statistical trends found in time series of PDSI and SPI for the whole period. Finally, section 3d presents the comparison of PRA and time series of both PDSI and SPI indices using correlation analysis. a. Frequencies of dry and wet s in the regions Figure 2 shows the number of dry s according to PDSI (Fig. 2a) and SPI (Fig. 2b) in the period 1961 2005 for 483 stations. Stations belonging to the category 9 14 in Fig. 2a have 9 to 14 s with average annual PDSI # 22.0 (dry s according to threshold; Table 3), and stations belonging to the category 9 11 in Fig. 2b have 9 to 11 s with an average annual SPI # 21.0 (dry s according to threshold; Table 3). A higher density of stations in the northern part of China (regions 1 4 and 10) have 9 or more dry s in the period, that is, have a dry frequency of more than 20%, while the five regions in the south practically do not have stations in this category according to PDSI. In contrast, the patterns for SPI are different: the stations in the highest category 9 11 are mixed over all regions; that is, the probability of occurrence of dry is similar in the regions without any distinct spatial variation. The frequencies of dry and wet s for the whole period according to both indices, averaged for the 10 regions, are presented in Table 4. One can see distinctly lower frequencies of dry s according to PDSI (Table 4a) for regions 6 9 (southern part). Also, the frequencies of wet s according to PDSI show similar results: lower for the southern regions 6 9. The conclusion is that the water balance in regions 6 9 has lower dispersion; that is, the tails of distribution function are smaller. The results obtained with SPI are different. For all regions the frequencies of dry s are quite similar, ranging from 15.4 to 17.5. And the frequencies of wet s are similar as well, ranging from 15.2 to 17.0. Most probably, this results from the definition of SPI, which is a standardized index, and averaging over stations leads to such similar results. Based on the analysis of data across Colorado, McKee et al. (1993) determined that the SPI is in moderate drought 9.2% of the time, in severe drought 4.4% of the time, and in extreme drought 2.3% of the time, that is, 15.9% of the time in drought. We can conclude from this qualitative analysis that the frequencies of both dry and wet s for the whole period according to PDSI are lower for southern regions than for northern ones. The frequencies of both dry and wet s, estimated for the regions for the whole period according to SPI, are similar for all regions. The next step is to analyze trends in the both indices in the period 1961 2005. The trends were analyzed first qualitatively and then statistically.

654 J O U R N A L O F C L I M A T E VOLUME 23 FIG. 2. Number of dry s according to (a) PDSI and (b) SPI for meteorological stations in China during the period 1961 2005. b. Qualitative trend analysis in frequencies of dry and wet s The frequencies of dry and wet s according to both indices were calculated for 5- and 15-yr time scales for the 10 regions over the period 1961 2005 to analyze possible trends qualitatively (see Tables 5 and 6). The dry frequencies in the regions for 5-yr time scales vary from 0.4 to 48 for PDSI and from 2.1 to 34 for SPI. The wet frequencies vary from 0 to 33.8 for PDSI and from 2 to 35.4 for SPI. It is evident that the frequencies of dry s, according to PDSI, in the most recent two 5-yr periods in regions 1 4 (the Songhuajiang, Liaohe, Haihe, and Yellow River basins) are notably higher than in practically all previous 5-yr periods for these basins (Table 5a). The trends indicated in the last columns of Tables 5 and 6 are based on the qualitative analysis of frequencies for 15-yr time scales. An upward trend in dry frequencies according to PDSI for 15-yr time scales in regions 1 4 (indicated by upward arrows; Table 5a) is obvious, with at least a 45% increase in the subsequent periods. Phenomena such as drought in north China have become more serious since the 1980s, as also reported in the previous study by Wei

1FEBRUARY 2010 Z H A I E T A L. 655 TABLE 4. Frequencies of dry and wet s in the period 1961 2005 (in %) according to (a) PDSI and (b) SPI categories (see Table 3) in 10 regions of China. a. PDSI categories River basins Moderately dry Severely dry dry and severely dry s Total dry s Moderately wet Severely wet wet and severely wet s 1 Songhuajiang 8.6 4.1 2.1 6.2 14.8 7.2 3.7 1.6 5.4 12.5 2 Liaohe 8.0 4.2 2.0 6.2 14.2 8.5 3.0 1.3 4.3 12.8 3 Haihe 9.0 4.4 2.5 7.0 16.0 7.7 3.6 2.7 6.4 14.1 4 Yellow 8.7 4.2 1.5 5.7 14.4 7.7 3.0 2.2 5.1 12.8 5 Huaihe 9.2 3.6 0.7 4.3 13.5 6.1 3.1 1.4 4.5 10.6 6 Yangtze 5.6 1.4 0.4 1.7 7.3 5.3 1.1 0.3 1.4 6.7 7 Pearl 6.0 0.7 0.0 0.7 6.7 3.0 1.1 0.4 1.6 4.6 8 Southeast 6.0 1.7 0.1 1.8 7.8 4.8 0.8 0.5 1.3 6.1 9 Southwest 5.4 1.7 0.5 2.2 7.6 4.3 1.4 0.3 1.6 5.9 10 Northwest 10.6 2.9 0.5 3.4 14.0 5.3 2.3 1.5 3.8 9.2 b. SPI categories River basins Moderately dry Severely dry dry and severely dry s Total dry s Moderately wet Severely wet wet and severely wet s 1 Songhuajiang 9.2 4.6 2.4 7.0 16.1 9.0 4.4 2.6 7.0 16.0 2 Liaohe 10.2 4.8 1.5 6.3 16.4 9.8 4.6 2.5 7.1 17.0 3 Haihe 10.2 5.3 1.9 7.3 17.5 9.1 4.2 2.2 6.4 15.6 4 Yellow 9.3 4.7 2.2 6.9 16.2 9.0 4.5 2.5 7.0 16.0 5 Huaihe 9.8 4.8 1.8 6.7 16.5 9.7 3.5 2.6 6.1 15.8 6 Yangtze 9.3 4.7 2.0 6.7 16.0 9.4 4.4 2.5 6.8 16.2 7 Pearl 8.5 4.5 2.7 7.2 15.7 10.0 4.4 1.9 6.4 16.4 8 Southeast 7.5 5.1 2.9 7.9 15.4 9.5 4.5 1.8 6.3 15.8 9 Southwest 8.9 5.6 2.2 7.8 16.7 8.8 4.3 2.1 6.4 15.2 10 Northwest 9.8 4.4 2.2 6.7 16.4 9.2 4.7 2.1 6.8 16.0 Total wet s Total wet s and Ma (2003). The downward trend in dry s based on PDSI can be confirmed only for the northwest region (a downward arrow; Table 5a). An obvious downward trend in the frequencies of wet s according to PDSI is visible in the Haihe and Yellow River basins based on 15-yr time scales (Table 5b), and an upward trend in the frequencies of wet s according to PDSI is observed in the northwest region (Table 5b). According to the analysis, an evident signal of departure from dryness to wetness can be stated for the arid northwest inland river basins since the mid-1980s. It is consistent with the conclusion of another study (Shi et al. 2003) that the climate of northwest China has shifted from dry to wet after the 1980s. Similar results were found for trends in the frequencies according to SPI (Table 6). An upward (though less pronounced compared to that of PDSI) trend in dry s is also visible in regions 1 3 (Table 6a). Simultaneously, there is a lack of wet trends in these regions. The downward trend in dry s based on SPI can be confirmed only for the northwest region (Table 6a) the same as for PDSI. An obvious downward trend in the frequencies of wet s according to SPI is observed for the Yellow River basin based on 15-yr time scales (Table 6b), and an upward trend in the frequencies of wet s according to SPI is visible in the southeast and northwest regions. The averages for the 10 regions (i.e., for the whole of China) shown in lower rows of Tables 5 and 6 indicate an upward trend in dry frequencies (especially since 1996) and a downward trend in wet frequencies according to PDSI but no overall trends according to SPI. It can be concluded that frequencies of dry s calculated in 15-yr subperiods from 1961 until 2005 by both indices show the upward trends for three northern basins (the Songhuajiang, Liaohe, and Haihe) and a downward trend for the northwest region. The frequencies of wet s calculated for 15-yr subperiods from 1961 until 2005 by both indices show a downward trend for the Yellow River and an upward trend for the northwest region. This qualitative analysis is supplemented by a more rigorous statistical analysis of trends in the next section. c. Statistical trend analysis in PDSI and SPI time series The spatial patterns of the trends of both PDSI and SPI indices over regions are displayed in terms of the levels of significance of trends for individual stations at the 0.05 # p, 0.1 and p, 0.05 levels (Fig. 3).

656 J O U R N A L O F C L I M A T E VOLUME 23 TABLE 5. Frequencies of (a) dry and (b) wet s in every 5- and 15-yr periods (in %) according to PDSI in 10 regions of China. a. Frequency of dry s River basins 1961 65 1966 70 1971 75 1976 80 1981 85 1986 90 1991 95 1996 2000 2001 05 1961 75 1976 90 1991 2005 Trend 1 Songhuajiang 1.3 13.2 8.9 32.3 6.8 5.5 3.0 23.0 39.2 7.8 14.9 21.7 [ 2 Liaohe 4.0 10.9 2.3 1.7 14.9 10.9 8.0 26.9 48.0 5.7 9.1 27.6 [ 3 Haihe 6.0 6.7 8.0 4.0 31.3 2.0 18.0 33.3 34.7 6.9 12.4 28.7 [ 4 Yellow 3.7 7.8 6.7 5.2 15.6 8.2 10.7 44.1 28.2 6.1 9.6 27.7 [ 5 Huaihe 4.1 15.9 0.7 14.5 18.6 15.9 11.0 24.8 15.9 6.9 16.3 17.2 6 Yangtze 8.7 6.7 3.9 11.7 3.0 6.6 5.8 8.7 10.4 6.4 7.1 8.3 7 Pearl 8.4 6.7 0.4 4.6 1.1 8.1 5.6 5.6 20.4 5.1 4.6 10.5 8 Southeast 9.3 15.0 8.6 15.0 0.7 2.1 1.4 2.1 15.7 11.0 6.0 6.4 9 Southwest 2.6 6.1 8.7 9.6 13.9 4.4 7.0 3.5 13.0 5.8 9.3 7.8 10 Northwest 27.5 14.9 12.5 14.3 17.6 9.3 3.3 14.0 12.5 18.3 13.7 10.0 Y Average 7.6 10.4 6.1 11.3 12.3 7.3 7.4 18.6 23.8 8.0 10.3 16.6 b. Frequency of wet s River basins 1961 65 1966 70 1971 75 1976 80 1981 85 1986 90 1991 95 1996 2000 2001 05 1961 75 1976 90 1991 2005 Trend 1 Songhuajiang 22.1 6.8 8.5 0.9 22.6 26.0 13.6 8.1 4.3 12.5 16.5 8.7 2 Liaohe 16.0 9.1 16.6 12.0 6.9 29.7 15.4 4.6 5.1 13.9 16.2 8.4 3 Haihe 28.7 22.7 16.7 20.7 3.3 20.0 7.3 4.7 2.7 22.7 14.7 4.9 Y 4 Yellow 30.4 24.4 4.1 17.4 14.8 11.5 3.7 1.1 7.8 19.6 14.6 4.2 Y 5 Huaihe 33.8 0.7 13.8 4.8 14.5 5.5 6.2 1.4 14.5 16.1 8.3 7.4 6 Yangtze 7.8 6.9 7.8 5.8 8.7 6.7 6.6 5.5 4.6 7.5 7.1 5.5 7 Pearl 2.1 0.7 16.5 1.4 8.4 1.8 2.5 4.9 2.8 6.4 3.9 3.4 8 Southeast 1.4 0.0 20.0 5.0 1.4 5.7 2.1 14.3 5.0 7.1 4.0 7.1 9 Southwest 4.4 3.5 7.8 6.1 7.8 5.2 4.4 10.4 3.5 5.2 6.4 6.1 10 Northwest 2.4 5.1 6.0 7.2 5.1 18.2 11.9 8.4 18.2 4.5 10.1 12.8 [ Average 14.9 8.0 11.8 8.1 9.3 13.0 7.4 6.3 6.8 11.6 10.2 6.8

1FEBRUARY 2010 Z H A I E T A L. 657 TABLE 6. Frequencies of (a) dry and (b) wet s in every 5- and 15-yr periods (in %) according to SPI in 10 regions of China. a. Frequency of dry s River basins 1961 65 1966 70 1971 75 1976 80 1981 85 1986 90 1991 95 1996 2000 2001 05 1961 75 1976 90 1991 2005 Trend 1 Songhuajiang 5.1 15.7 15.7 29.4 11.1 10.2 4.7 19.2 34.0 12.2 16.9 19.3 [ 2 Liaohe 4.0 21.1 8.6 9.7 25.1 16.0 11.4 18.9 33.1 11.2 17.0 21.1 [ 3 Haihe 10.0 21.3 14.7 10.7 23.3 13.3 11.3 28.7 24.0 15.3 15.8 21.3 [ 4 Yellow 10.0 21.5 22.2 5.9 19.3 14.1 11.9 21.9 19.3 17.9 13.1 17.7 5 Huaihe 5.5 24.8 2.1 15.9 21.4 21.4 15.2 17.9 24.1 10.8 19.5 19.1 6 Yangtze 16.8 20.4 16.6 18.2 10.8 18.9 12.7 12.2 17.5 17.9 16.0 14.2 7 Pearl 23.2 15.4 6.0 13.0 7.4 24.2 16.1 8.8 27.4 14.9 14.9 17.4 8 Southeast 18.6 22.1 16.4 24.3 2.1 10.0 10.0 8.6 26.4 19.0 12.1 15.0 9 Southwest 17.4 19.1 15.7 17.4 21.7 20.9 19.1 7.0 12.2 17.4 20.0 12.8 10 Northwest 26.0 23.0 14.3 17.9 17.6 17.3 9.6 14.0 8.1 21.1 17.6 10.5 Y Average 13.7 20.5 13.2 16.2 16.0 16.6 12.2 15.7 22.6 15.8 16.3 16.8 b. Frequency of wet s River basins 1961 65 1966 70 1971 75 1976 80 1981 85 1986 90 1991 95 1996 2000 2001 05 1961 75 1976 90 1991 2005 Trend 1 Songhuajiang 24.3 10.6 12.3 2.6 19.6 25.5 26.8 14.9 7.7 15.7 15.9 16.5 2 Liaohe 35.4 16.6 12.6 10.3 2.9 26.3 23.4 21.7 3.4 21.5 13.1 16.2 3 Haihe 30.7 16.7 17.3 23.3 2.0 10.7 15.3 17.3 6.7 21.6 12.0 13.1 4 Yellow 34.8 20.0 9.6 20.7 14.1 13.7 8.9 7.0 14.8 21.5 16.2 10.2 Y 5 Huaihe 33.8 6.9 17.9 12.4 8.3 7.6 16.6 9.7 29.0 19.5 9.4 18.4 6 Yangtze 14.3 15.0 16.5 13.8 20.5 12.0 14.7 23.9 15.4 15.3 15.5 18.0 7 Pearl 13.0 13.3 25.3 14.7 19.3 6.7 20.0 16.8 18.3 17.2 13.6 18.4 8 Southeast 10.7 5.7 19.3 12.9 12.9 11.4 20.7 32.1 16.4 11.9 12.4 23.1 [ 9 Southwest 10.4 12.2 20.9 7.8 15.7 7.8 13.0 16.5 32.2 14.5 10.4 20.6 10 Northwest 6.9 10.2 12.8 13.4 13.7 20.0 22.4 22.4 22.4 10.0 15.7 22.4 [ Average 21.4 12.7 16.5 13.2 12.9 14.2 18.2 18.2 16.6 16.9 13.4 17.7

658 J O U R N A L O F C L I M A T E VOLUME 23 FIG. 3. Spatial distributions of the trends of (a) PDSI and (b) SPI indices in the 10 regions in China. The statistically significant negative trends (red and yellow dots) indicate an increasing frequency of dry s, and the statistically significant positive trends (blue and light blue dots) indicate an increasing frequency of wet s. Among the 483 stations, those with insignificant trends (p. 0.1) are not shown. The positive and negative trends in PDSI and SPI, which represent wetter and dryer trends, respectively, were detected by calculating the slope and level of significance using the generalized least squares and linear regression models described in section 2c. Trend detection using PDSI indicates statistically significant negative trends (increasing frequency of dry s) for many stations in three northeastern river basins: the Songhuajiang, Liaohe, and Haihe (43%, 57%, and 80% of all stations, respectively); in the Yellow River basin (70%), and in the middle part of the Yangtze River basin. However, many fewer stations in these regions have statistically significant negative trends in SPI (4%, 26%, 27%, 19% in regions 1 4, respectively), meaning that more frequent droughts do not only result from lower precipitation but also from other factors influencing water balance conditions in these basins (as PDSI is based on the water balance equation). Statistically significant positive trends (increasing frequency of wet s) were found in the northwestern mountainous part of the northwest region (58 and 53%

1FEBRUARY 2010 Z H A I E T A L. 659 of all station for PDSI and SPI, respectively), and for some stations in the upper and lower Yangtze River according to both PDSI and SPI indices. The main difference between the trends in PDSI and SPI is that many fewer stations show statistically significant trends in SPI compared to PDSI. These differences are quite natural and can be explained taking into account conceptualization of the methods. Namely, the meteorological index SPI is calculated only from the precipitation records, whereas the hydrological index PDSI is based on the supply-and-demand concept of the water balance equation taking into account precipitation, temperature, and available water content of soil. Therefore, it is clear that the level of significance of trends in PDSI and SPI may be different. A similar difference in the trend analysis results with PDSI and SPI for the Elbe River basin in Germany was found (Krysanova et al. 2008). d. Relationships between PRA and indices A previous study on annual runoff of the main rivers in China for the period 1951 2000 (Zhang et al. 2008) has shown a generally decreasing trend with statistically significant trends in north China, especially for the Songhuajiang, Liaohe, Haihe, and Yellow Rivers. Unfortunately, this study did not look at human influences (such as changing withdrawals from the groundwater and rivers) on river runoff. Another study (Ren et al. 2002) was devoted specifically to the impacts of human activities on river runoff in the northeastern area of China, and it found that, besides climatic driving forces, the increasing water consumption causes runoff decrease in this region. In our study the trends in river runoff and the time series of the PRA were analyzed for eight rivers representing regions 1 8 (see Table 2) and compared with time series of the PDSI and SPI averaged over the corresponding catchment areas. The data on human water consumption for the eight rivers, which would allow performing a more comprehensive analysis, were not available for our study. Therefore the focus was only on pure relationships between PRA and both indices without considering human factor. Trends in river runoff and PRA for eight rivers during 1961 2005 were tested by the GLS from the R software with an automatic calculation of the autoregressive order. The results show that the statistically significant negative trend in river runoff can be confirmed at the 0.001 level for two rivers: the Haihe River (negative slope 520.28 3 10 8 m 3 yr 21 ) and the Yellow River (negative slope 529.33 3 10 8 m 3 yr 21 ). It is worth mentioning that the annual runoff at the gauge station of the Haihe River basin is much smaller than that of the Yellow River because of the difference in their catchment areas (Table 2), therefore the slopes also differ significantly. Obviously, the negative trends in the PRA are also statistically significant at the 0.001 level for the Haihe and Yellow Rivers. After that, time series of PRA and the basin-averaged PDSI and SPI in eight river basins were compared graphically. Figures 4 and 5 show that in most cases both PDSI and SPI closely follow the PRA trend during the whole period 1961 2005. The decreasing trends for PRA and PDSI are visible for the Haihe River and the Yellow River (Figs. 4 and 5). The correlation coefficients between PRA and both indices are above 0.66 and statistically significant at the 0.01 level in six of eight river basins (except the Haihe and Minjiang) (Table 7). In four cases, the Songhuajiang, Liaohe, Huaihe, and Pearl, the correlation coefficients are relatively very high, 0.78 on average, and all above 0.72. The correlation is not significant in only one case: between PRA and SPI in the Haihe River. Almost all correlation coefficients between PRA and SPI are higher than that of PRA and PDSI. However, in the interpretation one should take into account different temporal and spatial scales considered in the conceptualization of these indices and PRA and not overestimate the found connection between them. It can be concluded that a relatively high and statistically significant correlation between the percentage of runoff anomaly and the annual average PDSI and SPI indices was found for six large rivers, that is, the Songhuajiang, Liaohe, Yellow, Huaihe, Yangtze, and Pearl. Therefore, PDSI and SPI can directly indicate streamflow variation and could be useful for evaluating water resources availability in China and elsewhere. However, one should take into account that trends in river runoff may result not only from climate variability but also from human impacts such as increasing water use for irrigation. The latter was not evaluated in this study as explained before. 4. Discussion and conclusions Using PDSI and SPI indices for expressing the severity of dry and wet conditions using different approaches, this study investigated the spatial variation and trends in dry and wet conditions in 10 regions covering the whole territory of China during the period of 1961 2005. To identify the efficiency of both indices to detect hydrological drought, the correlation between the PRA and both indices was analyzed. According to the PDSI results, some meteorological stations in north China had 20% (or more) dry s during the period of 1961 2005, and almost all stations in south China have dry frequencies no higher than 10%. In accordance with the regionally averaged PDSI, the frequencies of both dry and wet s for the whole

660 J O U R N A L O F C L I M A T E VOLUME 23 FIG. 4. Time series of the percentage of runoff anomaly (dark lines) and annual average PDSI (dotted lines) for eight river basins in 1961 2005, and the corresponding regression lines. period are lower for southern regions than for the northern ones. According to the regionally averaged SPI, the frequencies of both dry and wet s for the whole period are very similar for all regions no differences between the regions were found in this case. However, in individual river basins SPI is able to explain the spatial distribution of dry or wet severity. In the Yangtze River basin, for example, the distribution of stations with a high frequency of dryness confirms that the drought situation is less severe in the upper reaches than in the middle reaches of the river (see also similar results in Su et al. 2007, 2008). Trend analysis revealed spatiotemporal dynamics of dry and wet s in China. Most river basins in north

1FEBRUARY 2010 Z H A I E T A L. 661 FIG. 5. Time series of the percentage of runoff anomaly (dark lines) and 12-month values of SPI (dotted lines) for eight river basins in 1961 2005, and the corresponding regression lines. China show a trend of increasing droughts since the mid- 1990s, whereas the northwest region shows more wet s starting the mid-1980s. The frequencies of dry s calculated for 15-yr subperiods from 1961 until 2005 by both indices show the upward trends for three northern regions (Songhuajiang, Liaohe, and Haihe) and a downward trend for the northwest region. The frequencies of wet s calculated for 15-yr periods from 1961 until 2005 by both indices show a downward trend for the Yellow River and an upward trend for the northwest region. Some of those phenomena also have been reported by Wei and Ma (2003).

662 J O U R N A L O F C L I M A T E VOLUME 23 TABLE 7. Correlation coefficients between the percentage of runoff anomaly and annual average PDSI and SPI calculated over the catchment areas above the hydrological gauge stations. Indices Songhuajiang River Liaohe River Haihe River Yellow River Huaihe River Yangtze River Pearl River Minjiang River PDSI 0.84* 0.72* 0.48* 0.70* 0.76* 0.66* 0.72* 0.51* SPI 0.85* 0.75* 0.04 0.72* 0.79* 0.76* 0.82* 0.47* * Correlation is significant at the 0.01 level (2-tailed). The trend detection using PDSI indicates statistically significant negative trends (increasing frequency of dry s) for many stations in three northeastern regions (Songhuajiang, Liaohe, and Haihe), in the Yellow River basin, and in the middle part of the Yangtze basin. However, many fewer stations in these basins have statistically significant negative trends in SPI. This may indicate that more frequent droughts are resulting not only from lower precipitation but also from other factors influencing water balance conditions in these basins. Statistically significant positive trends were found in the northern part of the northwest region and for some stations in the upper and lower Yangtze River according to both PDSI and SPI indices. To understand the relation between indices and river runoff, the correlation coefficients between the PRA and PDSI or SPI were calculated. A moderately high (.0.66) and statistically significant correlation between PRA and the annual average PDSI and SPI was found for six large rivers (Songhuajiang, Liaohe, Yellow, Huaihe, Yangtze, and Pearl). An interesting finding is that the correlation between PRA and SPI for these six cases is higher than that of PRA and PDSI. The moderately high correlation reveals that the both indices PDSI and SPI can reflect, to some extent, the long-term runoff variation and trends. This indicates that runoff in these rivers is generated primarily from precipitation. The SPI and PDSI indices were also used to explain that annual runoff in the main rivers decreased in China from 1951 to 2000 (Zhang et al. 2008). Unfortunately, no time series on human water withdrawal for purposes such as irrigation, industry, and municipal water were available for this period. This would enable a deeper and more comprehensive analysis of the runoff trends and could be the subject of future study. Both PDSI and SPI can reveal dryness and wetness characteristics over large regions of China, although the sensitivities of the two methods are different. These differences can be explained taking into account different conceptualization of the two methods (see above and in Krysanova et al. 2008). We propose to use both indices to detect the dryness and wetness tendencies and variation of water resources in China. 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