Variability of spatial patterns of autocorrelation and heterogeneity embedded in precipitation Free

All water enters the land phase of the hydrologic cycle as precipitation (Dingman 2002). Thus, to assess, predict and forecast hydrologic responses in water resource management, a hydrologist needs to understand the amount and distribution of the precipitation. As an essential tool for the hydrologist, the hydrological model is very sensitive to the precipitation input (Teegavarapu et al. 2012; Alizadeh et al. 2018; Herrnegger et al. 2018). The precipitation in the hydrological model should be spatially continuous data or converted areal precipitation for the description of the flow generation process (Syed et al. 2003; Kobold & Sušelj 2005; Gabellani et al. 2007; Cole & Moore 2008; Moulin et al. 2009; Pechlivanidis et al. 2017; Zou et al. 2017; Herrnegger et al. 2018). Although radar (NEXRAD) based precipitation estimation (Vieux 2001) can provide data with significant spatial resolution compared to point precipitation gauge measurements (Teegavarapu et al. 2012), indirect estimation of continuous surface precipitation based on the measurement of related ancillary variables must also be calibrated and validated by direct ground-based measurement through spatial interpolation methods. Spatial interpolation of precipitation data is generally classified into deterministic and geostatistical groups (Ly et al. 2011; Teegavarapu et al. 2012). The most frequently used of both the deterministic (e.g., Thiessen polygon and inverse distance weighting) and the geostatistical (e.g., Kriging) methods are based on the spatial characteristics of precipitation. The spatial characteristics of precipitation are mainly composed of spatial autocorrelation and spatial heterogeneity. According to the assumption of high spatial autocorrelation in precipitation (Gilbert & Lowell 1997), each grid point is assigned with the value of the nearest rain gauge in a Thiessen polygons scheme (Thiessen 1911), and the influence of the measured point data is weighted according to the distance from the sampled point to the estimated point in an inverse distance weighting scheme (Shepard 1968; Wei & McGuinness 1973; Simanton & Osborn 1980; ASCE 1996; Farajzadeh & Alizadeh 2018). Spatial heterogeneity, quantified by the semivariogram, has been considered in geostatistical and variance-dependent stochastic surface interpolation methods (Krige 1951; Zimmerman et al. 1999; Grayson & Bloschl 2001; Vieux 2001; Ly et al. 2011). The variation in the precipitation spatial pattern impacts the implementation of the interpolation methods of point precipitation for areal precipitation. Although the spatiotemporal pattern of precipitation has received increasing attention in water cycle studies through point or interpolated areal precipitation data series (Larson & Peck 1974; Xu & Singh 1998; Vieux 2001; Xing et al. 2015), there are few studies on the variations in the spatial autocorrelation and spatial heterogeneity of precipitation.

Time-variant parameters in hydrological models have been reported to improve the accuracy of the model results (Merz et al. 2011; Peel & Blöschl 2011; Brigode et al. 2013; Jeremiah et al. 2013; Westra et al. 2014; Patil & Stieglitz 2015; Thirel et al. 2015; Deng et al. 2016). As precipitation is the largest source of uncertainty in the hydrological model for forecasting (Yatheendradas et al. 2008; Villarini et al. 2010; Hapuarachchi et al. 2011), time-variant spatial interpolation methods of areal precipitation could also be developed to improve the reliability of the results from the hydrological model. However, the stationary or nonstationary conditions for the spatial interpolation method should be tested first. Therefore, the aim of this study is to analyse the variability of the spatial autocorrelation and spatial heterogeneity of precipitation to test the suitability of the spatial interpolation method in a changing environment. If the spatial autocorrelation and spatial heterogeneity of precipitation are nonstationary, finding a suitable successor for spatial interpolation methods of areal precipitation is crucial for the adaptation to changing environment.

As the extreme precipitation in the upstream area of the Changjiang river basin is inflow to the Three Gorges Reservoir and the main source of flood for the middle and downstream area, the extreme precipitation in this area is important for the reservoir operation and flood control, and is chosen for our case study. The spatial autocorrelation indexes are specified by Moran's I, the Getis–Ord general G, and the local Getis–Ord G_i statistics G_i^⃰. Spatial heterogeneity is quantified by the semivariogram, and the trends of all indexes are tested by Mann–Kendall (MK) test. Therefore, the framework of the test can be as shown in Figure 1.

The upstream part of the Changjiang river has a total length of 4,504 km and drainage area of 1 million km², accounting for 70% of the entire river length and 55.5% of the total river basin, from the headwater to the Yichang gauge station in Hubei province. The upper mainstream is fed by many large tributaries, such as the Yalongjiang, Minjiang, Jialingjiang and Wujiang rivers. According to the statistics of the Yangtze River Water Resources Commission (CCYRA 2006), by 2005, the number of dams in the upper reaches of the Changjiang river was 12,929. The Three Gorges Dam, the world's largest hydropower project to date, is located 44 km upstream of the Yichang hydrological gauge station. It has a subtropical humid monsoon climate, with an annual average temperature of 15–18 °C and a mean annual precipitation of 1,232 mm (Lu & Cao 2010). Owing to the large geographic extent of the upstream part of the Changjiang river basin (24.5°N–27.8°N; 90.6°E–111.5°E), the precipitation spatial autocorrelation and heterogeneity have an irregular distribution. If the spatially uneven distribution pattern of precipitation in this upstream area changes, the instream flow to all the reservoirs located in the middle and downstream areas will be impacted. Therefore, the variability of the precipitation spatial pattern is critical for water resource management of the whole Changjiang river basin.

The flood control storage of the Three Gorges Reservoir is 221.5 × 10⁸m³, which is based on the annual maximum 7-day flood volume (AM7FV) series. Thus, the AM7FV is the most important index for flood forecasting in the upstream area of the Changjiang river basin and for the operation of the Three Gorges reservoir. For example, the typical flood event for the Three Gorges Reservoir design occurred in 1870 and is attributed to the heavy precipitation from July 13th to 19th. The corresponding 7-day precipitation with AM7FV (P-AM7FV) is selected for the analysis of its spatial pattern variability. Although there are 169 rainfall gauge stations located in the upstream area of the Changjiang river basin, only 53 of them were not interrupted between 1960 and 2008 (as shown in Figure 2), and they were selected for our study. The daily runoff data of the Yichang station from 1960 to 2008 were provided by the Bureau of Hydrology of the Changjiang (Yangtze river) Water Resources Commission of China. Daily precipitation data in the 53 rainfall gauge stations were collected from the website of the China Meteorological Bureau (http://data.cma.cn/). These stations have a relatively uniform distribution over the upstream area of the Changjiang river basin, as presented in Figure 2.

The spatial patterns of precipitation include spatial autocorrelation and spatial heterogeneity. Spatial autocorrelation was conducted by Moran's I statistic with global clustering. The global Moran's I statistic can provide a single index that summarises the local precipitation patterns of the study area. To identify the level of clustering at each of the 53 studied rainfall gauge stations, the Getis–Ord general G test and the local Getis–Ord G_i* were employed to determine the high clusters (stations clustered owing to high precipitation) or low clusters (stations clustered owing to low precipitation). As the semivariogram range quantifies the degree of similarity/dissimilarity of a continuously varying attribute between a pair of samples separated by a certain distance (Isaaks & Srivastava 1989), it has been used to analyse the spatial heterogeneity of precipitation. Then, we performed the MK test to detect the spatial autocorrelation and heterogeneity trends in precipitation at the scale of the whole study area.

Moran's I is a global statistical index that summarises the values of autocorrelation over the entire study area (Luković et al. 2015). A high value of precipitation may cause serious flood events, while a low value of precipitation involves a lower possibility of disaster event occurrence. Thus, it is necessary to discriminate between those patterns that have high-precipitation or low-precipitation dominant values.

As there are limited gauge stations, the empirical variogram cannot offer values for each distance h, but several empirical variogram models are able to estimate these variances. The Gaussian, spherical and exponential models for the semivariogram are broadly used and are adopted for spatial heterogeneity analysis in our study as these models adequately fit the variogram values with only a few parameters (Berne et al. 2004; Diggle & Ribeiro 2007).

There are three characteristic parameters in the fitted semivariogram, namely, range, sill and nugget. The ‘nugget variance’ (C₀) is the random variation that usually results from either measurement errors or variation in the studied variable, which is impossible to detect in the sample range (Trangmar et al. 1986; Cressie 1993). C₀+C is called the ‘sill’ and represents the total sample variability at the semivariogram level for the patterned data. The structure variance (C) is the difference between the sill and nugget. The spatial dependence is often defined as (C/[C + C₀]), and it relates the small-scale variability to the large-scale variability (He et al. 2015). The ‘range’ referring to a is the maximum distance over which the measured precipitation properties exhibit significant spatial autocorrelation. The ranges of the exponential, Gaussian and spherical models are often defined by the semivariogram, which is 95% of the sill (Bardossy 1997). Thus, their ranges are 3a for the exponential, for the Gaussian, and a for the spherical models.

The non-parametric MK test (Mann 1945; Kendall 1975) has been widely used to assess the significance of the trends in hydro-meteorological time series (Liu et al. 2010; Rajah et al. 2014; Rashid et al. 2015; Wang et al. 2017; Yang et al. 2017; Foulon et al. 2018; Sun & Fang 2018), and it has also been recommended by the World Meteorological Organization (WMO) (Yue et al. 2002). Therefore, we applied the MK to detect trends in the spatial autocorrelation and heterogeneity of precipitation in this study.

U>U_1−α/2 indicates a positive trend and U<−U_1-α/2 represents a negative trend in the time series at a significant level of α. Otherwise, there is no trend in the time series at a significant level of α.

The spatial autocorrelation of precipitation was assessed using Moran's I in the upstream part of the Changjiang river when the AM7FV happened (P-AM7FV). The results of the Moran's I analysis showed that the values were greater than 0 for every year from 1960 to 2008 (shown in Figure 3(a)). There was a positive spatial autocorrelation in the study area. P-AM7FV data were clustered but not dispersed. The degree of spatial autocorrelation was also assessed by the Z values of the normal distribution. It was found that the Z scores in 1961, 1962, 1963 and 1992 were less than 1.96 (shown in Figure 3(b)), and thus, the clustered patterns in these four years could have been the result of random chance at a 5% significance level. The other 45 years exhibited tendencies towards positive spatial autocorrelations and represented clustered patterns. To show the difference between the clustered and random patterns, the Moran's index, Z score, and p value from the Moran's I test are shown in Figure 4, which presents the lowest Z(I) in 1992 and the highest Z(I) in 1993. Figure 4(a) shows that the Moran's I reported for the P-AM7FV in 1992 was 0.02, which indicates a random pattern. Similar situations were found in 1961, 1962 and 1963. Figure 4(b) shows that Moran's I is 0.60 in 1993, which indicates a high positive spatial autocorrelation or a highly clustered pattern. Given the Z(I) score of 8.55 in 1993, there is less than a 5% probability that such a clustered pattern could have resulted from chance (p < 0.0001).

The Getis–Ord general G discriminates between spatial patterns that have dominant high values or low values. Positive G values, which are found in Figure 5, indicate that high values within other high values dominate the spatial autocorrelation pattern in our study area. The Z(G) scores in Figure 5(b) from the test of statistical significance at a level of 0.05 show that the P-AM7FV features with high values tended to cluster in the upstream area, except in 1961, 1963, 1975, 1992 and 1996.

We also trace the Moran's I and Getis–Ord general G values over time (as shown in Figures 3 and 5). Statistically significant increasing trends in Moran's I were observed in the upstream area of the Changjiang river with an MK U value of 2.13 at a significance level of 0.05, as presented in Table 1. However, the series of general G did not pass the trend test. Therefore, the trend of the spatial cluster pattern increased significantly for P-AM7FV data from 1960 to 2008, while the trend of the high-value spatial pattern for the P-AM7FV did not increase significantly. According to the statistic's structure and the characteristics of Moran's I and the Getis–Ord general G (Getis & Ord 1992), P-AM7FV data values from 1960 to 2008 tended to increase by the same proportion rather than to increase by the same absolute amount.

Both the global statistic Moran's I and the general G include all the precipitation gauge stations and provide an overall measure of spatial autocorrelation. The spatial autocorrelation associated with one particular spatial gauge station can be assessed through the hot spot analysis tool, which is calculated by the local Getis–Ord G* statistic (Equation (4)). The hot spot analysis leads to spatial cluster identification and spatial filtering. Z(G_i*) from the test of statistical significance at the 0.05 significance level was used to determine whether features with high or low values tended to cluster. Clusters of precipitation gauge stations with high P-AM7FV values were considered hot spots, whereas clusters of precipitation gauge stations with low P-AM7FV values were considered cold spots. A ring map, which is a spatial visualisation tool combing temporal and spatial data for more meaningful interpretation of trends (Huang et al. 2008; Chan et al. 2013), was used to explore the hot and cold spots of P-AM7FV (Figure 6). The centre of Figure 6 is the digital elevation model (DEM) distribution map of the upstream part of the Changjiang river basin. The ring map is surrounded by a set of concentric, segmented rings that possess a circular shape indicating Z(G_i*) values. The four shades represent significant hot spots (Z(G_i*) > 1.96), no significant hot spots (0 ≤ Z(G_i*) ≤ 1.96), no significant cold spots (−1.96 ≤ Z(G_i*) < 0) and significant cold spots (Z(G_i*) < −1.96). Each ring displays a Z(G_i*) value that represents high or low clusters of P-AM7FV at a particular location. There are 49 rings created corresponding to the number of years considered in our study. The inner ring displays the Z(G_i*) value from 1960, whereas the outer ring displays the Z(G_i*) value from 2008. The 53 spokes surrounding the base map represent the 53 precipitation gauge stations.

It is clearly noticeable that the Z(G_i*) values in the east and southeast parts of the ring map mainly show the hot spots in most of the years, while there are cold spots in the west area with higher elevations (higher DEM). According to the distribution of the precipitation gauge station's location shown in Figure 2, the east and southeast parts of the ring map mainly cover the Three Gorges Reservoir area. The west area covers the Jinshajiang river sub-basin. The south and south-west parts of the ring map, which represent the confluence area of the Jinshajiang and Daduhe river basins, shows Z(G_i*) values from negative to highly positive. In other words, the cold spots of P-AM7FV are becoming hot spots over time. However, the hot spots have tended to change to cold spots in the northeast part of the ring map. The distribution of the hot and cold spots is associated with the distribution of the elevations. The high (west) and low (east) elevational areas are the cold and hot spot areas, respectively. However, the transition zone between the high and low elevation areas is divided into two parts. The first part, located in the upper area of the ring map or at the north of the transition zone, is where the transition occurs with spot changes from hot to cold, whereas the other part, located to the south of the transition zone, has shown a tendency to change spots from cold to hot.

To estimate the semivariogram parameters of the three semivariogram models (Gaussian, spherical and exponential models), the observed precipitation datasets from the 53 gauge stations are used to optimise the RMSE values for the model's goodness of fit for every year. RMSE is a measure to describe the semi-variance difference between the observed and modelled values. The semi-variance can be determined through Equation (5). To ensure enough data for the improvement of the stability of the semivariogram parameter estimation, the value h in Equation (5) is set to 35 km, according to the maximum separation distance between the pairs of observation gauges in our study (Journel & Huijbregts 1978; Burrough 1991) and the minimum N(h) value is 30 in Equation (5). Boxplots are utilised to show graphically the RMSE from 1960 to 2008 (shown in Figure 7). Figure 7 indicates that the performances of all three semivariogram models are acceptable for describing the spatial heterogeneity of the P-AM7FV.

As the characteristic parameters C₀ (random variation), C₀+C (total sample variability), C/[C + C₀] (relates small-scale variability to large-scale variability), and range (exhibits significant spatial autocorrelation) fully quantify the spatial heterogeneity, the trend of spatial heterogeneity in P-AM7FV was tested by the calibrated parameters of the semivariogram model from 1960 to 2008. As seen in Figure 8, all the parameters of all the models changed every year, and thus it was indispensable to validate the parameters of the models for the precipitation interpolation. However, the parameter values from the three models and their trends were similar to their spatial heterogeneity simulation.

The random variation C₀ is typically used to model the white noise effect in spatial heterogeneity analyses. Without considering the measurement errors in the spatial heterogeneity, the nugget C₀ only refers to microscale variations (Cressie 1993). The P-AM7FV in the upstream area of the Changjiang river was mainly influenced by the interactions between monsoons and elevation, while the effects of monsoons on precipitation varied owing to climate change (Niu et al. 2015; Wang et al. 2018). As a result, the trend in random variation was a significantly increasing trend at a significance level of 0.05 (as presented in Table 2). The maximum variability of P-AM7FV C₀ + C (sill) was composed of the spatial random variations and structure variations, and it showed a non-significant increasing trend at a significance level of 0.05 (as shown in Figure 8(b)). The spatial structure variation C represents the amount of the observed variation that can be explained by the distance between observations. As the distance can be interpreted as the surrogate of different spatial effects of the monsoons on the P-AM7FV, the C can be physically attributed to the spatial distributions and intensities of the monsoons. If the ratio of C/[C + C₀] is used to define distinct classes of spatial structure or dependence of the P-AM7FV, the P-AM7FVs can be strongly, moderately or weakly dependent spatially when the ratios are >75%, between 25% and 75%, and ≤25%, respectively (Cambardella et al. 1994). From Figure 8(c), it is evident that 41%, 53%, and 61% of the years are strongly dependent spatially on the Gaussian, spherical and exponential models, respectively, and fewer than 10% of the years are weakly dependent spatially on the P-AM7FV in all the models. However, their spatial dependence decreased non-significantly from 1960 to 2008 at a significance level of 0.05 (shown in Table 2). Therefore, the influences of the monsoons on the P-AM7FV were decreasing, while the randomness of the precipitation, including the effects of geomorphology and local climate facts, were increasing at the microscale.

The range is the maximum length scale of the spatial autocorrelation. The ranges fluctuate in the three models (as shown in Figure 8(d)), resulting in non-constant autocorrelation distances. As the influences of the monsoon on the P-AM7FV were decreasing, the importance of local elevations to the P-AM7FV was relatively enhanced. Range had a significant increasing trend. Therefore, the degree of global spatial autocorrelation represented by Moran's I had a significant increasing trend, which may have been partly related to the significant increasing trend of the range in spatial autocorrelation. However, both the randomness, with a significant increasing trend, and the spatial dependence, with a non-significant decreasing trend, may be attributable to changes in the distribution of hot and cold spots at the microscale.

As the summer monsoons are the intrinsic variation factors in the spatial pattern of the P-AM7FV, it is necessary to understand further the trends of the summer monsoons in the upstream area of the Changjiang river basin. The P-AM7FV in the study area was mainly influenced by the South Asia summer monsoon (SASM), Plateau summer monsoon, and elevation. The SASMs are often quantified by the dynamically normalised seasonality index (Li & Zeng 2002) while the Plateau summer monsoon index (PSMI) is based on the height departure values of the low-pressure systems in the surface layer over the plateau (Tang et al. 1984). The SASM sector (5°N–22.5°N, 35°E–97.5°E) is composed of two independent components, the SWASM (2.5°N–20°N, 35°E–70°E) and the SEASM (2.5°N–20°N, 70°E–110°E), with different relations than the monsoon rainfall in South Asia (Li & Zeng 2002). PSM refers to the centre position (32.5°N, 90°E) and the four positions (32.5°N, 80°E; 25°N, 90°E; 32.5°N, 100°E; 40°N, 90°E) around the centre (Tang et al. 1984). Higher index values for the SWASM, SEASM, and PSM mean that higher precipitation happened in the upstream area of the Changjiang river basin. Lower index values mean lower precipitation. The trend of the SEASM was significantly decreasing, while the SEASM and PSM had non-significant increasing trends. If the SWASMI, SEASMI and PSM are normalised and the trend of their average values (AVSWEP) is tested, they show a non-significant decreasing trend, which consists of the AM7FV trend and the range and spatial structure variation trends (as presented in Table 3). As the sources of the summer monsoons are different, their trends are different, especially under climate change. Their impacts on the extents and intensities of precipitation are also different every year. Therefore, there are more uncertainties in the variation of the spatial pattern of P-AM7FV at the microscale than that at the global scale.

This study evaluated the variation in spatial autocorrelation and heterogeneity of the precipitation in the upstream area of the Changjiang river basin, China. The spatial autocorrelation of P-AM7FV represented by Moran's I had a statistically significant increasing trend over the whole study area. The range of spatial heterogeneity also had a significant increasing trend. Spatial interpolation schemes for precipitation depending on the spatial autocorrelation and variance can lead to better estimation and lower error in the spatial distribution of the areal precipitation for the lumped hydrologic model. However, owing to the changing effects of summer monsoons on precipitation, the random variation in the spatial heterogeneity analysis had a significant increasing trend. It may have impacted the accuracy of the spatial interpolation schemes at the microscale and further reduced the reliability of the distributed hydrological model with the input of local or microscale P-AM7FV in the upstream area of the Changjiang river basin. Also, the effects of duration of precipitation (e.g., 3 days or 15 days) on their spatial patterns of autocorrelation and heterogeneity will be studied in the future.

The authors gratefully acknowledge the financial support from the National Natural Science Foundation of China (No. 51579183, 91647106, 51879194 and 51525902), the Science and Technology Program of Guangzhou City (No. 201707010072) and the 111 Project (No. B18037).