Multiple spatio-temporal patterns of vegetation coverage and its relationship with climatic factors in a large dam-reservoir-river system

Ecological Engineering 138 (2019) 188–199

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Multiple spatio-temporal patterns of vegetation coverage and its relationship with climatic factors in a large dam-reservoir-river system

T

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Pingping Zhanga, Yanpeng Caia,b,c, , Wei Yanga, Yujun Yia, Zhifeng Yanga, Qiang Fud a

State Key Laboratory of Water Environment Simulation, School of Environment, Beijing Normal University, Beijing 100875, China Institute of Environmental and Ecological Engineering, Guangdong University of Technology, Guangzhou 510006, China c Institute for Energy, Environment and Sustainable Communities, University of Regina, 120, 2 Research Drive, Regina, Saskatchewan S4S 7H9, Canada d School of Water Conservancy & Civil Engineering, Northeast Agricultural University, Harbin, Heilongjiang 150030, China b

A R T I C LE I N FO

A B S T R A C T

Keywords: Normalized difference vegetation index (NDVI) Climatic factors (CFs) Spatio-temporal patterns Correlation analysis Cascade hydropower stations Dam-reservoir-river system

Along with the development of large-scale hydropower projects, many changes are arising both on the river channel and the ecosystems, which leads to the formation of a dam-reservoir-river system. The system is of significance to China due to the large-scale utilization of hydropower. However, such projects may cause significant impacts on local vegetation dynamics, which is of importance to local decision makers and water resources managers. In this research, the spatio-temporal variations of normalized difference vegetation index (NDVI) and climatic factors (CFs) under multiple time scales (i.e., annual, seasonal, and wet/dry periods) and the spatio-temporal pattern of the correlation of NDVI-CFs under the scales of annual and wet season were analyzed in the system by using the statistical methods. The response of NDVI to CFs in the basin was explored at the monthly scale. The main results indicated that from 1999 to 2013, the annual average NDVI had a significant overall upward trend. The distributions of the NDVI and its trend had the spatial and seasonal differences, with majority areas showing medium-high and high vegetation cover and a slight improvement in the grade of vegetation cover change of more than 40% of the total area. In the basin, temperature (T) and evapotranspiration (ET0) showed an upward trend, and precipitation (P) and the humidity index (HI) were the opposite. The corrections of NDVI-T and NDVI-ET0 were positive, and that of NDVI-P and NDVI-HI were negative. Also, the vegetation growth was mainly affected by the amount of P, and its time-lag period to the P was two months. In the scale of the eight cascade hydropower stations, the vegetation coverage and the correlation of NDVI-CFs around the Jinanqiao and Longkaikou hydropower stations were the best, and those near the Ludila and Guanyinyan were the worst, indicating that the vegetation coverage was affected by the cascade hydropower stations development. Therefore, the results may be beneficial for sustainable watershed ecosystem management in the system, as well as ecological development planning around the cascade hydropower stations.

1. Introduction Investigating the variations in both climate and vegetation and their interactions is critical to improve the projection of future ecosystem dynamics (Liu and Menzel, 2016). In recent years, rapid growth of economies is increasingly driving up water consumptions and land utilization (Ren et al., 2017; Tan et al., 2016, 2017). Especially, increasing attention has been paid to sustainable development of hydropower projects. One of the key issues of such projects is the protection of indigenous ecosystems, particularly for those watersheds with a number of cascade hydropower stations (CHSs). For example, CHSs in Jinsha River (i.e., the upper reach of the Yangtze River in China) has been implemented gradually since 2005. The cascade hydropower

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development zone in the river comprises one high-latitude reservoir with eight dams, which can be considered a dam-reservoir-river (DRR) system. With the gradual completion of the CHSs project, many indigenous territorial ecosystems will be significantly disturbed by human activities, resulting in potential ecological degradation (Jiang et al., 2015; Shan et al., 2016). Also, many negative ecological environmental problems caused by the construction and operation of CHSs will be more complex than those induced by a single hydropower station (Fu, 2009). To solve these problems, vegetation plays a primary role in energy conversion and material circulation within ecosystems, vegetation cover is thus a visual sign of ecosystem health (Li et al., 2013). Dynamic change of vegetation cover is an effective indicator for the ecosystem health and affected by many natural and anthropogenic

Corresponding author at: State Key Laboratory of Water Environment Simulation, School of Environment, Beijing Normal University, Beijing 100875, China. E-mail address: [email protected] (Y. Cai).

https://doi.org/10.1016/j.ecoleng.2019.07.016 Received 19 December 2018; Received in revised form 9 July 2019; Accepted 16 July 2019 0925-8574/ © 2019 Elsevier B.V. All rights reserved.

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Fig. 1. Location and elevation of the study area within China. Note: a-d is the weather station of Daocheng, Lijiang, Huaping and Panzhihua, respectively. A-H is the hydropower station of Longpan (LP), Liangjiaren (LJR), Liyuan (LY), Ahai (AH), Jinanqiao (JAQ), Longkaikou (LKK), Ludila (LDL), and Guanyinyan (GYY), respectively.

et al., 2012; Du et al., 2013). Also, there are many studies that have been conducted on the vegetation coverage variations of long sequence and their relationships with relevant CFs in the Jinsha River Basin. The spatio-temporal variations of NDVI were investigated in the Jinsha River Basin through the trend analysis method (Wang et al., 2012; Zhou, 2012). Jiang et al. (2014) adopted the trend and correlation analysis methods to explore the characteristics of NDVI and its relationships with precipitation and temperature from 1999 to 2012 in the lower reach of the Jinsha River. Ding (2017) explored the relationship between the changes of NDVI and several environmental factors from 2001 to 2010 in the lower reach of Jinsha River through the methods of correlation analysis, spatial statistical analysis, and overlay analysis. Based on the employment of many statistical methods (e.g., linear regression models, correlation coefficient, and Mann-Kendall test), many studies have provided valuable information regarding the dynamics of NDVI and CFs and their interactions at regional and global scales. However, limited effort has been made to analyze spatiotemporal variations in vegetation coverage and CFs, relationships between vegetation coverage and CFs, and response of vegetation coverage to CFs under multiple time scales within an individual DRR system. In this research, the spatio-temporal variations of NDVI and CFs (i.e., temperature, precipitation, evapotranspiration, and humidity index), the spatio-temporal pattern of the correlation of NDVI-CFs, and the response of NDVI to CFs will be analyzed in the DRR system of the Jinsha River Basin and the associated eight CHSs. Statistical methods will be adopted, including the Mann-Kendall (MK) trend test, the least square, and Pearson’s correlation analysis. Then, Objective of this research is to: (i) investigate the spatio-temporal variations of vegetation coverage and climatic situation from 1999 to 2013 under multiple time scales (i.e., annual, seasonal, and wet/dry periods), (ii) explore the spatial patterns of the correlation coefficient and significance test of NDVI-CFs under annual and wet season scales, and (iii) examine the

factors (Kulakowski et al., 2011). Normalized difference vegetation index (NDVI), an effective indicator of vegetation growth and coverage, has been widely employed to describe the spatio-temporal characteristics of vegetation coverage and the associated spatial patterns (Gao et al., 2012; Liu and Menzel, 2016; Wang et al., 2012; Zheng et al., 2017). Previously, the NDVI has been an effective description of vegetation dynamics, and it has been used to study relationships between climatic factors (CFs) and vegetation activity (Sun et al., 2013; Zewdie et al., 2017). Moreover, the Chinese government has listed the Jinsha River as an important area for ecological restoration (Luo and Wang, 2006; Yang et al., 2003). Therefore, to develop a sustainable DRR system, investigation of the variability of CFs, NDVI, and their complex interactions is necessary. Numerous studies have attempted to establish the correlations between NDVI and relevant CFs to investigate ecosystem variations under climatic impacts (Gao et al., 2013; Gillespie et al., 2018; Liu and Menzel, 2016; Pang et al., 2016; Vahagn et al., 2019; Zheng et al., 2017). In southwest China, there were a great deal of literatures to investigate the NDVI changes and their responses to the CFs through the statistical methods (e.g., trend analysis, change vector analysis, Pearson correlation, and Partial correlation analysis). For example, Zhang et al. (2011) analyzed the relationships between the NDVI and CFs. Wang (2014) explored the vegetation cover change and its response to the climate characteristics at multiple time scales. Zhao et al. (2018) explored the NDVI changes and their responses to changes in dry and wet weather under various time scales. In a dry-hot river valley of the Jinsha River (i.e., a dry-hot shrub landscape river valley located in a humid climate within a tropical or subtropical zone; Ding et al., 2011), an enormous amount of studies are focusing on an optimal configuration of streamflow stations (Wang et al., 2015a), the long-term scheduling of large CHSs (Wang et al., 2015b), succession characteristics of soil erosion (Lin et al., 2014), and variation features of runoff and sediment yields based on various vegetation types and rainfall (Song 189

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et al., 2016; Mao et al., 2012). Therefore, the T, P, evapotranspiration (ET0), and humidity index (HI) were chosen as the CFs. The ET0 is the amount of evaporation and transpiration from a grass. Penman calculates the ET0 of a hypothetical reference crop according to the FAO-56 Penman-Monteith equation (Allen et al., 1994). This is a simplification of the original Penman-Monteith equation and has been widely utilized. In this paper, the FAO Penman-Monteith equation, improved by Allen et al. (1998), was adopted by Tabari and Hosseinzadeh Talaee (2013). The ET0 were calculated through using “SPEI” (i.e., Standardized Precipitation-Evapotranspiration Index) package in the R environment, and the involved parameters included the mean minimum temperature, mean maximum temperature, mean wind speed, sunshine duration, and the latitude and elevation of the weather stations. Comparatively, HI is a relative index that reflects the degree of dry and wet, and it can objectively reflect the water and heat balance of an area (He et al., 2016; Wang et al., 2014b). The calculation equation can be obtained from the document of China Meteorological Administration (2005). The higher the HI of a region, the more humid the climate is, or vice versa. Generally, the HI can be obtained using the ratio of P to ET0. Data for the four CFs was obtained from twenty weather stations, and they were processed under multiple time scales. The multiple time scales included seven situations, i.e., annual, spring (March to May), summer (June to August), autumn (September to November), winter (December to February), wet season (May to October), and dry season (November to April). Then the processed data was used for spatial interpolation. Kriging, a geostatistical interpolation method, is used for the spatial interpolation analysis of the T and P. The Empirical Bayesian Kriging (EBK) interpolation method is frequently used for small data sets because the standard error prediction is more accurate than other Kriging methods. Then the EBK interpolation method was adopted to mesh into the 1 km resolution for the four CFs. The optimum criterion of the interpolation method was that the mean standardized was near to zero, the root mean square was close to the average standard error, and the root mean square standardized was close to 1.

response of NDVI to CFs under a monthly scale. Also, this research presents two potential innovations: a) an exploration of the spatiotemporal variations of NDVI and CFs and a presentation of the spatiotemporal patterns of the correlation of NDVI-CFs in the context of a DRR system, and b) a multiple scale analysis of the spatio-temporal variations of NDVI and CFs, and a comparison analysis of the spatiotemporal patterns of the correlation of NDVI-CFs between annual and wet season scales. 2. Materials and methods 2.1. Overview of the study area The Jinsha River flows across rugged mountain cliffs and two terrain steps with a total length of 3486 km and has an elevation difference up to 5142 m, and it accounts for 50% of the upper Yangtze River Basin (Du et al., 2013). The location and elevation of the study area is shown in Fig. 1. In the DRR system, the middle reach of the Jinsha River Basin is the catchment area from the Shigu to Panzhihua hydrological station. The basin is located at southwest China and flows through 22 counties in seven cities of two provinces Sichuan and Yunnan (Ma et al., 2009). The altitude range of the basin is between 969 m and 5910 m above the sea level. The basin has a vertical topography and geomorphology, and it is a typical arid valley area (He et al., 2015). The climate of the basin is diverse, with subtropical to temperate zones and sufficient sunlight and obvious dry and wet seasons (Ma et al., 2009; Wang et al., 2014a). Water resources for power generation are abundant, with a theoretical reserve of 1538 million kW, accounting for 26% of the total reserves in the main stream of the Jinsha River (Lin, 2012). The basin includes four weather stations: Daocheng, Lijiang, Huaping, and Panzhihua. The other sixteen weather stations around the basin are Batang, Litang, Deqin, Gongshan, Xianggelila, Weixi, Baoshan, Dali, Jingdong, Chuxiong, Yuanmou, Huili, Yanyuan, Xichang, Muli, and Jiulong. In addition, within this DRR system the eight CHSs consist of Longpan (LP), Liangjiaren (LJR), Liyuan (LY), Ahai (AH), Jinanqiao (JAQ), Longkaikou (LKK), Ludila (LDL), and Guanyinyan (GYY). At present, the LP and LJR hydropower stations are still being planned. The construction time for the LY, AH, JAQ, LKK, LDL, and GYY hydropower stations was 2008, 2008, 2005, 2008, 2009, and 2006, respectively.

2.3. Methods To explore the CFs that drive the changes in vegetation indicated by the NDVI in the DRR system, the NDVI of each pixel was calculated, and the CFs were interpolated under multiple time scales. Also, Person’s correlation coefficient under annual and wet season scales and the timelag coefficient at the monthly scale were calculated for the NDVI values and CFs. To further analyze the vegetation coverage and the correlation of NDVI-CFs around the CHSs, round buffers were drawn around the CHSs. The radius of each round buffer was 1, 2, 3, 5, 10, 15, and 20 km, respectively. The intra- and inter-annual changes of NDVI were calculated using Eqs. (1) and (2) respectively. The difference method was chosen to quantify the change in NDVI over different years. This was done by using the NDVI value of all the pixels of the current year minus that of the previous year (Jiang et al., 2014). Then the NDVI values of all pixels from 1999 to 2013 were obtained. The change value of NDVI was calculated using the average method.

2.2. Data source As one of the most widely used indictors for vegetation cover monitoring, NDVI includes multiple available datasets such as MODIS, AVHRR, SPOT-VGT, and TM NDVIs. Among them, SPOT-VGT data has been widely utilized in China for the dynamic monitoring of vegetation cover under multiple spatio-temporal scales (Jiang et al., 2014). In this research, SPOT-VGT data was acquired from the Cold and Arid Regions Sciences Data Center in Lanzhou (http://westdc.westgis.ac.cn) and the Belgian Flemish Technology Image processing center (http://free.vgt. vito.be/). Also, the time sequences used were January 1999 to July 2008 and August 2008 to December 2013, respectively. The data set was maximized to synthetic NDVI data on the 10th day after the pretreatment of atmospheric correction, radiation correction, and geometric correction. The spatial and time resolutions were 1 km and ten days, respectively. Monthly NDVI data were obtained through adopting the maximum value composites method, and the annual NDVI value was calculated using a 12-month average of monthly NDVI values (Pang et al., 2016; Jiang et al., 2014; Song and Ma, 2007). Meteorological data was provided by the China Meteorological Science Data Sharing Service network (http://data.cma.cn/). The data included the mean temperature (T), the mean minimum temperature, the mean maximum temperature, the mean wind speed, the sunshine duration, and the amount of precipitation (P) monthly from 1999 to 2013. Most studies showed that vegetation dynamics was significantly related to climate change, particularly water and heat conditions (He

15

NDVII =

∑ NDVIij/15 j=1

(1)

where i is the month of 1–12, j is the year of 1–15, NDVIi is multi-year mean value of the i month, NDVIij is the value of the i month in the j year. 12

NDVIj =

∑ NDVIij/12 i=1

(2)

where i is the month of 1–12, j is the year of 1–15, NDVIj is annual NDVI value of the j year, NDVIij is the value of the i month in the j year. The Mann-Kendal (MK) trend test was used to analyze the 190

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3. Results and discussions

significant change and break point of the annual average NDVI sequence from 1999 to 2013. It is a non-parametric statistical approach and is widely used for trend analysis in meteorological time series. Also, it is commonly applied for testing the significance of changes in NDVI trends, and a considerate number of documents have provided specific information regarding the MK test and its formulation (Liu and Menzel, 2016; Qiu and Cao, 2011; Wang and Jing, 2015; Xu et al., 2010; Zewdie et al., 2017). The range of the MK statistic (i.e., UF) is (−∞, +∞). If UF > 0, the NDVI shows an increasing trend, and vice versa. Comparatively, if |UF| > UF0.05/2 = 1.96, the trend of NDVI is changed significantly. If the MK time series is x = xn, xn−1, …xn, making UB = −UF, then the break point of the NDVI sequence can be obtained. To reflect the spatio-temporal variation of the trend of vegetation coverage, the trend analysis method (i.e., regression trend slope, θ ) was used to simulate the annual average NDVI from 1999 to 2013 at the pixel scale (Jiang et al., 2014; Qiu and Cao, 2011; Song and Ma, 2007; Wang and Jing, 2015). The formula can be presented as follows: n

θ=

n

3.1. Spatio-temporal variations of NDVI in the DRR system 3.1.1. Intra- and inter-annual changes of NDVI Intra-annual changes and differences of monthly average NDVI in the DRR system can be shown in Fig. S1. The change range of monthly average NDVI was 0.45–0.63. The mean value of NDVI was 0.54, and it was similar with that in the lower reach of the Jinsha River Basin (Jiang et al., 2014). The monthly average NDVI in the basin was high on the whole, and the high values mainly concentrated in July to December, which were all greater than 0.54. The maximum value appeared in September and October. The NDVI values from January to June were below the mean value, and the minimum value appeared in March and April. The trend of intra-annual change was descend-rise-descend, and the lowest value rose from March or April in per year to the highest in September and October. The difference diagram in NDVI reflects the monthly variations of NDVI in the basin (Fig. S1b). A positive number of the difference value of NDVI indicated the rise of monthly average NDVI, and a negative value of that indicated a decline. The NDVI rose from May and was the maximum in June, indicating that the month of May was the time of the rapid growth of vegetation. In November, the difference value of NDVI shifted from positive to negative, which showed that the vegetation coverage was the highest in October. It then continued to decline and reached the minimum in March, indicating that the vegetation coverage was lowest in March. Inter-annual variation of yearly NDVI in the DRR system is shown in Fig. 2. Within the 15-year time span, the annual average NDVI in the basin displayed a significant upward trend, and the linear fitting growth rate was 0.049/10a. Similarly, the previous studies have reported that the NDVI had the increase trend in the Jinsha River Basin (Ding, 2017; Jiang et al., 2014; Liu and Liu, 2014; Wang et al., 2012; Zhou, 2012). The increase trend of NDVI might be the dual effects of the comprehensive management of ecological construction projects (e.g., Natural Forest Protection Project and Conversion of Farmland to Forestry Project) and climate change (Jiang et al., 2014; Wang et al., 2012). The minimum value of NDVI appeared in the year of 2000 (0.49), which indicated that the vegetation coverage in the year of 2000 was the lowest in the fifteen years. The maximum value of NDVI appeared in the years of 2011 and 2013, and the values were both 0.57, indicating the vegetation coverage were higher than that in the other year. The lowest vegetation coverage in the year of 2000 might be related with the human activities (e.g., the project of CHSs, population density, and land use change) and climate change (Jiang et al., 2014; Wen et al., 2017; Zewdie et al., 2017). In the 20 km buffer of the eight CHSs, the annual

n

n × ∑ j = 1 (j × NDVIj ) − ∑ j = 1 j ∑ j = 1 NDVIj n

(

n

n × ∑ j=1 j2 − ∑ j=1 j

)

2

(3)

where θ is the regression trend slope, if θ>0, NDVI shows an upward trend, and vice versa. n is the length of time series (i.e., n = 15), i is the month of 1–12, j is the year of 1–15, NDVIij is the value of the i month in the j year. According to the range of the trend slope, seven grades of vegetation coverage change can be defined (Qiu and Cao, 2011; Song and Ma, 2007; Wang and Jing, 2015). The grades can be severe degradation (θ < − 0.012 ), moderate degradation (− 0.012 ≤ θ < − 0.009), slight degradation (− 0.009 ≤ θ < − 0.003), basically unchanged (− 0.003 ≤ θ < 0.003), slight improvement (0.003 ≤ θ < 0.009 ), moderate improvement (0.009 ≤ θ < 0.015), and significant improvement (θ > 0.015). For the trend analysis of the CFs, the least square method was used to obtain the tendency rate of the four CFs from the twenty weather stations under multiple time scales (Liu et al., 2012). Then the tendency rate was used for supporting the spatial interpolation to obtain the spatial distribution of the trend changes of the CFs. To explore the CFs that drive the changes in vegetation coverage indicated by the NDVI, a spatial interpolation of the CFs was made to have uniform grid data with NDVI. Then the Pearson's correlation coefficients were calculated between NDVI and CFs of each pixel under annual and wet season scales, and two-tailed p values were used to test the significance of these correlations (Gao et al., 2012; Pang et al., 2016; Zhang et al., 2015). If p < 0.01, the correlation is extremely significant. If 0.01 ≤ p < 0.05, the correlation is significant. Other p values mean that the correlation is not significant. Climate has an inevitable impact on the growing environment and the condition of vegetation. Additionally, vegetation has certain adaptability to climate change. When climate change reach a certain degree, the vegetation coverage will change. Thus, the response of NDVI to climate change may exist as a certain lag. The lag correlation coefficient was chosen to characterize the response of NDVI to CFs (Zhang et al., 2015), which can be shown in Eq. (4). In this research, R0, R1, R2, and R3 were calculated to analyze the time-lag effect of NDVI on CFs in the basin. If R* = Rn, the lag period of the NDVI response to changes in CFs is n months.

R* = max{R 0, R1, R2, …, Rn−1, Rn}

(4)

where R* is the lag correlation coefficient, R0 is correlation coefficient of CFs and NDVI in the same period, R1 − Rn is the correlation coefficient of NDVI lagged the CFs by 1-n months. Fig. 2. Inter-annual variations of yearly NDVI in the DRR system. 191

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average NDVI fluctuated upward. The linear fitting growth rates of the LP, LJR, LY, AH, JAQ, LKK, LDL, and GYY hydropower stations were 0.061/10a, 0.065/10a, 0.057/10a, 0.060/10a, 0.079/10a, 0.077/10a, 0.024/10a, and 0.003/10a, respectively. The annual average NDVI near the LDL and GYY were lower than those near the other hydropower stations and declined rapidly in 2008. The decline of the NDVI between 2005 and 2010 around LY, AH, JAQ, LKK, LDL and GYY hydropower stations might be related with the construction of the CHSs. Similarly, Jiang et al. (2014) showed that the tendency rates of NDVI around the Wudongde, Baihetan, and Xiluodu hydropower stations were negative or very small positive, and then the constructions of hydropower stations had an impact on the vegetation coverage. The MK test of the yearly NDVI in the DRR system is shown in Fig. S2. The annual average NDVI in the basin decreased from 1999 to 2001, increased from 2001 to 2013, and increased significantly after 2005. The NDVI sequence generally displayed a rising trend, and the break point in 2003 showed that vegetation cover had the significant improvement in 2003. Under the eight CHSs scales, the annual average NDVI near the LP hydropower station increased from 1999 to 2013 and increased significantly after 2002. The trend of the annual average NDVI in other CHSs was similar to that in the basin overall. The break point of NDVI in the eight CHSs was different, and the time was from 2002 to 2004. The degree of significant change of vegetation cover declined in the LDL and GYY hydropower stations beginning in 2008. Generally, the trend of the annual average NDVI was consistent in the DRR system, as confirmed by linear fitting and the MK test.

averaged to get the multiple annual-season mean NDVI, and the spatial change image was obtained under multiple time scales (Fig. 3). The minimum, maximum and mean values of NDVI under multiple scales in the basin are shown in Table S1. The distribution of the mean NDVI had obvious spatial and annual-season differences, which was consistent with the previous researches in southwest China, such as Hua et al. (2008), Wang et al. (2012), and Zhang et al. (2009). Under the annual scale, the minimum, maximum and average values of NDVI in the DRR system were 0.048, 0.739, and 0.540, respectively. The high values of the NDVI was mainly distributed in the central and southern of the basin. The low values of the NDVI were mainly distributed in the north of the basin and the vicinity of the Daocheng weather station and the LP hydropower station. The NDVI values around the eight CHSs were generally low, with the lowest vegetation coverage around the LP followed by the GYY and LDL hydropower stations. Under the spring scale, the minimum, maximum and mean values of NDVI in the system were 0.053, 0.769, and 0.504, respectively. The values of NDVI were generally lower in spring than that under the annual scale. The vegetation coverage in the vicinity of the Lijiang was higher than that near the Daocheng, Huaping, and Panzhihua weather stations. Among the eight CHSs, the vegetation coverage near the LP, GYY, and LDL hydropower stations was relatively low. Under summer scale, the minimum, maximum and mean values of NDVI in the system were 0.097, 0.850, and 0.648, respectively. The NDVI in summer was obviously higher than that under annual and spring scales, and Zhang et al. (2009) also showed that the vegetation coverage was better in summer than the other seasons. The vegetation coverage in most areas was high, and that near the LP and LDL hydropower stations were relatively low. Under the autumn scale, the minimum, maximum and mean NDVI values in

3.1.2. Spatio-temporal distribution of NDVI under multiple time scales The annual-season mean NDVI of each pixel from 1999 to 2013 was

Fig. 3. Spatial distribution of NDVI under multiple temporal scales in the basin from 1999 to 2013. 192

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NDVI grade of majority areas was medium-high vegetation cover in the 10 km buffer of the LY, AH, JAQ, and LKK hydropower stations, indicating that there was higher vegetation coverage around these hydropower stations than that of the others. For the LP and LJR hydropower stations, there were part of middle-low vegetation cover in the 5 km buffer. The vegetation coverage was lower near the LDL and GYY hydropower stations with medium vegetation cover in the 3 km buffer. Thus, the sequence of vegetation coverage in the 20 km buffer of the eight CHSs from low to high under the annual scale was in the following order: GYY, LDL, LP, LJR, LY, LKK, AH, and JAQ hydropower stations. The vegetation coverage was the highest near the JAQ and was relatively poor around the GYY and LDL hydropower stations.

the system were 0.072, 0.717, and 0.672, respectively. The NDVI spatial distribution in autumn was similar to that in summer, and the vegetation coverage around the Daocheng weather station in autumn was lower than that in summer. Under the winter scale, the minimum, maximum and mean values of NDVI in the system were 0.051, 0.913, and 0.580, respectively. In the north of the basin, the NDVI spatial distribution was similar to that in spring, and the vegetation coverage near the LDL and GYY hydropower stations in winter was higher than that in spring. Under the wet season scale, the minimum, maximum and mean NDVI values in the system were 0.109, 0.851, and 0.691, respectively. The vegetation coverage of the basin was generally high, with the exception of that near the LP hydropower station. Under the dry season scale, the minimum, maximum and mean NDVI values in the system were 0.075, 0.913, and 0.611, respectively. The vegetation coverage was relatively low in the north of the basin. The vegetation coverages around the LP, GYY, and LDL were lower than that around the other hydropower stations. In summary, the NDVI distribution had obvious spatial and seasonal differences from 1999 to 2013. The NDVI spatial distribution was better in summer, autumn, and the wet season than that under the other scales, indicating that the vegetation growth period was mainly in summer and autumn. Vegetation coverages around LP, GYY, and LDL were lower than those around the other hydropower stations. To analyze the vegetation cover in the basin, the NDVI was reclassified. The classification included four grades, which were low vegetation cover (i.e., I, the value of NDVI < 0.3), medium vegetation cover (i.e., II, 0.3 ≤ the value of NDVI < 0.5), medium-high vegetation cover (i.e., III, 0.5 ≤ the value of NDVI < 0.65), and high vegetation cover (i.e., IV, the value of NDVI ≥ 0.65) (Jiang et al., 2014). The classification statistics of NDVI under multiple time scales in the basin are shown in Table 1. Under the annual and spring scales, medium-high vegetation cover was 62.81% and 54.85% of the total area of the basin, respectively. Similarly, Jiang et al. (2014) showed that the area rate of the medium-high vegetation cover was 60.50% under annual scale from 1999 to 2012 in the upper reach of the Jinsha River Basin. Under the scales of summer, autumn, winter, wet season, and dry season, more than 60% of the total area of the basin was high vegetation cover. Thus, the majority areas of the basin were medium-high and high vegetation cover under multiple time scales. To further analyze the vegetation cover around the eight CHSs, the NDVI spatial distribution around the eight CHSs with multiple buffers is shown in Fig. S3. The NDVI distribution around the CHSs had obvious seasonal differences. Under multiple temporal scales, the NDVI grade of majority areas was the medium-high and high vegetation cover under the scales of summer, autumn, and wet season, indicating the vegetation coverages in summer, autumn, and wet season were higher than that under the other scales. The wet season was the vegetation growing period, and the vegetation coverage was the best among all the temporal scales. The NDVI grade around the CHSs was different under the multiple buffers. Among the eight CHSs under the annual scale, the

3.1.3. Spatio-temporal pattern of the trend of NDVI under multiple time scales The spatial distribution of the trend of NDVI (θ ) in 51,248 pixels under multiple time scales in the basin is shown in Fig. 4, and the data statistics of the trend of NDVI in the basin is shown in Table S2. The trend of NDVI had the spatial and seasonal differences. Under the annual scale, the θ value was higher in the central part of the basin than in north and south. Negative θ values were less in the summer, autumn, and wet seasons than in the other temporal scales. Generally, NDVI in more than 90% of the total area of the basin showed an upward trend under multiple time scales, which was similar with the report of Liu and Liu (2014) indicating that 2.80% of the θ value was less than zero from 2001 to 2010 in the middle reach of the Jinsha River Basin. According to the seven grades of vegetation coverage changes under multiple time scales, the degree of vegetation coverage changes is shown in Table 2. The degree of vegetation changes in more than 40% of the total area of the basin showed a slight improvement under multiple time scales. Particularly, under the annual scale, the rate of the slight improvement was 63.335%, indicating that vegetation coverage increased from 1999 to 2013. To further analyze the degree of vegetation cover changes around the eight CHSs, the spatial distributions of the trend of NDVI around the CHSs with multiple buffers are shown in Fig. S4. The degree of vegetation cover changes around the CHSs had the seasonal differences and was also different with the multiple buffers. The seasonal difference of θ around the CHSs was consistent with that in the basin. In the 20 km buffer surrounding the eight CHSs, there was basically no severe degradation areas. The grades of vegetation cover change in majority areas were basically unchanged, slight improvement, moderate improvement, and obvious improvement, indicating that the vegetation coverage in majority areas around the eight CHSs increased from 1999 to 2013. Among the eight CHSs under the annual scale, the area of slight degradation was the largest in the 5 km buffer of the LDL, followed by the GYY hydropower station. Generally, in the 20 km buffer of the eight CHSs, the sequence of the degree of vegetation cover changes from bad to good under the annual scale was in the following order: GYY, LDL, LJR, LP, AH, LY, LKK, and JAQ hydropower stations. In addition, under multiple time scales, the NDVI values in some areas had a declining trend of different degrees around the CHSs, of which the decline degree of the NDVI was the most obvious near the GYY and LDL hydropower stations, especially around the GYY hydropower station. The results also verified that the development of the CHSs had a certain influence on the surrounding vegetation cover (Jiang et al., 2014; Wang et al., 2008; Wen et al., 2017).

Table 1 The classification statistics of NDVI under multiple time scales in the basin. Scales

Annual Spring Summer Autumn Winter Wet season Dry season

Percentage (%) Ⅰ

Ⅱ

III

IV

2.24 3.94 0.24 0.19 3.09 0.11 2.00

16.56 25.59 3.97 4.34 10.93 3.12 8.49

62.81 54.85 27.38 17.36 35.24 11.57 29.04

18.39 15.62 68.41 78.11 50.74 85.20 60.47

3.2. Spatio-temporal variation of climatic factors under multiple time scales Empirical Bayesian Kriging (EBK) was used to make spatial interpolations of the four CFs (i.e., T, P, ET0, and HI), then the spatial variation and trend distribution of the CFs were analyzed under multiple time scales. The ranges of the variation and tendency rate of CFs under multiple time scales are shown in Tables S3 and S4, respectively. Under multiple time scales, the ranges of the variation and tendency

Note: The grade of I-IV is the value of NDVI < 0.3, 0.3 ≤ the value of NDVI < 0.5, 0.5 ≤ the value of NDVI < 0.65, the value of NDVI ≥ 0.65, respectively. 193

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Fig. 4. Spatial distribution of the trend of NDVI under multiple temporal scales in the basin from 1999 to 2013.

low in north and high in south of the basin, and it showed no obvious seasonal differences. The spatial distribution of P in the basin was uneven under the annual scale. The P was less in the north and higher in the southwest of the basin. In wet season, the P was mainly concentrated in the south of the basin. Normally, the higher P occurred in summer, autumn, and wet season than under the other temporal scales. The spatial distributions of ET0 were similar under the scales of summer and wet season. The ET0 was lower in north and higher in southeast of the basin. Under the annual scale, the HI in northeast of the basin was the lowest, most of lower HI was concentrated in southeast, and HI in southwest was higher. Higher HI happened in summer, autumn, and wet season than the other temporal scales. The HI in wet season was similar with that in summer, and the HI was higher in south and lower in north of the basin. For the spatial distribution of the tendency rate of CFs, the T in southeast increased more rapidly than

rate of the four CFs had a seasonal difference. The four CFs in summer were higher than that under the other temporal scales. The tendency rate ranges of T and HT0 were all the positive values under multiple time scales, indicating that T and HT0 generally increased from 1999 to 2013. The tendency rate ranges of P and HI were negative values except that under the dry season scale, indicating that P and HI decreased overall from 1999 to 2013. Also, in southwest China, Zhang et al (2011) showed that the T had an increase trend, and the P had a decrease trend from 1982 to 2006. Wang (2014) found that the T had an increase trend, the most obvious upward trend was in summer, and the P decreased from 1999 to 2012. The spatial variations and trend distributions of CFs under multiple time scales are shown in Figs. S5 and S6, respectively. The variations and trend distributions of the CFs had spatial and seasonal differences, which was similar with the research of Wang (2014). For the spatial distributions of the CFs, the value of T was

Table 2 The percentage of degree of vegetation coverage changes of the basin under multiple time scales. Grades

I1 II1 III1 IV1 V1 VI1 VII1

Percentage (%) Annual

Spring

Summer

Autumn

Winter

Wet season

Dry season

0.008 0.026 1.087 30.308 63.335 5.222 0.014

0.334 0.293 2.630 28.331 59.975 8.199 0.238

0.049 0.097 1.598 20.807 61.770 14.422 1.257

0.008 0.015 0.535 12.430 67.741 18.570 0.701

0.322 0.466 4.455 43.840 41.412 7.731 1.774

0.039 0.080 1.198 16.106 65.431 16.886 0.260

0.181 0.365 2.572 41.707 45.327 8.736 1.112

Note: The grade of Ⅰ1-VII1 is severe degradation, moderate degradation, slight degradation, basically unchanged, slight improvement, moderate improvement, and significant improvement, respectively. 194

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NDVI-P and NDVI-HI was stronger than that under the annual scale. Generally, the spatial distributions of the r and p showed differences under the annual and wet season scales, and the NDVI was mainly affected by the amount of P. Similarly, Sun et al. (2013) showed that the P was the main factor affecting the vegetation cover in the entire Tibetan Plateau from 1982 to 2006. Ding (2017) showed that the NDVI was more affected by the P than T in the lower reach of the Jinsha River Basin. While, Mao et al. (2012) showed that the NDVI-T correlation was stronger than NDVI-P correlation in most stations of the northeast China, which was not the same with that in the DRR system. Then, the main impact factor of NDVI had the regional differences due to the different vegetation types (Zhang et al., 2011). To further analyze the correlation of NDVI-CFs around the eight CHSs, the spatial distributions of the r and p of the NDVI-CFs around the eight CHSs with multiple buffers are shown in Figs. S7–S10. Under the scales of annual and wet season, the distributions of the r and p had spatial and seasonal differences around the eight CHSs. The spatial distribution of the r of NDVI-ET0 was similar to that of NDVI-T, and the spatial distribution of the r of NDVI-HI was similar to that of NDVI-P. Under the annual scale, the correlations of the NDVI-T around the CHSs were basically positive in majority areas, with the exception of the LDL and GYY hydropower stations. The correlations of the NDVI-T in some areas near the LDL and GYY hydropower stations were negative. The value of T around that two hydropower stations was highest among all eight CHSs, indicating excessive T was not suitable for vegetation growth. Also, the correlation of NDVI-T in most areas around the eight CHSs did not pass the significance test. The NDVI-P in majority areas around the CHSs was a negative correlation. The correlation of NDVI-P in part area near the LDL and GYY hydropower stations was positive. The amount of P around the GYY hydropower station was lowest among the eight CHSs. The value of p around the LDL followed the LKK hydropower station (i.e., LKK had the highest P among the eight CHSs), indicating that under the different P, the consistent of NDVI-P correlation between the two hydropower stations might be affected by regional climate. The correlation of NDVI-P in majority areas around the CHSs, with the exception of the LDL and GYY hydropower stations, passed the significance test of 0.05 and 0.01, and the area that passed the significance test at 0.01 was largest in the JAQ and LKK hydropower stations. The correlation of NDVI-ET0 and NDVI-HI in portions of areas around the CHSs, with the exception of the LDL and GYY hydropower stations, passed the significance test at 0.05 and 0.01. Under the wet season scale, the correlation of NDVI-T around the CHSs was basically positive and passed the significance test at 0.01 and 0.05 in majority areas of the JAQ and LKK hydropower stations. The correlation of NDVI-P around the CHSs was basically negative and passed the significance test at 0.01 in majority areas of the JAQ and LKK hydropower stations. The correlation of NDVI-CFs in the 20 km buffer of the JAQ and LKK hydropower stations showed no difference, and majority areas passed the significance test at 0.01 and 0.05. The correlation of NDVI-CFs in the 20 km buffer of the LDL and GYY hydropower stations showed a difference, and small areas passed the significance test at 0.01 and 0.05. Generally, in wet season, the consistency of the correlation of NDVIP and NDVI-HI was stronger than that under the annual scale. Under the scales of annual and wet season, the correlation of the NDVI-CFs displayed differences in the eight CHSs. The correlation of the NDVI-CFs in the 20 km buffer of the JAQ and LKK hydropower stations was the best, and that of LDL and GYY hydropower stations was the worst. The difference of the correlations of NDVI-CFs around the eight CHSs might be influenced by local climate resulted from the CHSs development. The results are consistent with previous studies indicating the hydropower stations development had an impact on local climate and the vegetation cover (Jiang et al., 2014; Li et al., 2017; Sun et al., 2019).

that in north of the basin. The P in north increased more rapidly than that in south of the basin. The ET0 showed an increasing trend under multiple time scales, and it increased more rapidly in the south than in the north of the basin. The spatial and seasonal distribution of the tendency rate of HI were similar to that of P, indicating that HI was mainly affected by the amount of P. Generally, from 1999 to 2013, the T and ET0 showed an upward trend, and the P and HI showed a downward trend. Wang et al. (2014b) also showed that the HI decreased from 1960 to 2011 in southwest China. The distribution of CFs had spatial and seasonal differences. The change of HI appeared to be mainly affected by the amount of P, and the consistency of the spatial variation between HI and P was more obvious under the wet season than the other temporal scales. In addition, the variations of the CFs in the eight CHSs were investigated at the annual and wet season scales. The spatial variation of ET0 was similar to that of T. The sequence of T in the CHSs from high to low was in the following order: GYY, LDL, LKK, JAQ, LP, LJR, AH, and LY hydropower stations. The P showed differences at the annual and wet season scales. Among the eight CHSs, the P in the LDL and LKK hydropower stations were largest under both the temporal scales. The P in the GYY hydropower station was the smallest under the annual scale. Generally, among the eight CHSs under the annual scale, the sequence of P from large to small was in the following order: LKK, LDL, JAQ, LY, LP, LJR, AH, and GYY hydropower stations. Also, under the wet season scale, the sequence was in the following order: LKK, LDL, GYY, JAQ, LP, LJR, AH, and LY hydropower stations. The trend variation of the CFs in the eight CHSs was consistent with the entire basin, representing that in the eight CHSs, the T and ET0 showed an upward trend, and the P and HI showed a downward trend. 3.3. Spatio-temporal pattern of the correlation between NDVI and climatic factors under the scales of annual and wet season The spatial patterns of the correlation coefficient (r) and significant coefficient (p) of NDVI-CFs under the annual scale are shown in Fig. 5. The r of the NDVI-CFs had spatial differences at the annual scale. The negative value of r of NDVI-T was mainly distributed in south of the basin, showing the correlation in north and middle was positive basically and that in south was negative partly. The correlation of NDVI-P in north was basically negative, and that in the south was partially positive, indicating that a moderate amount of rain was suitable for plant growth. In majority areas, the spatial distribution of the r of NDVI-ET0 was similar with that of NDVI-T. The spatial distribution of the r of NDVI-HI was similar with that of NDVI-P. The spatial distribution of the p of NDVI-CFs had difference, the correlation of NDVI-T in a small part area passed the significance test values of 0.05 and 0.01. The correlation of NDVI-P in majority areas passed a 0.01 significance test, indicating that P had a great influence on the vegetation growth. The area that passed the significance test of the correlation of NDVI-ET0 and NDVI-HI was larger than that of NDVI-T. The order of the correlation of CFs with NDVI was P > HI > ET0 > T, which showed that the P had the greatest impact on the vegetation growth. The spatial pattern of the correlation coefficient (r) and significant coefficient (p) of NDVI-CFs under wet season scale is shown in Fig. 6. The correlation of NDVI-CFs had spatial differences at the wet season scale, and the correlation in majority areas of the basin was consistent. Generally, in most areas the spatial distributions of the r of NDVI-T and NDVI-ET0 were similar and the correction for both was positive. Also, the spatial distributions of the r of NDVI-P and NDVI-HI were similar and the correction for both was negative. The spatial distribution of the p of the NDVI-CFs at the wet season was different than that at the annual scale. The area that passed the significance test of the correlation of NDVI-T was larger in wet season than that under the annual scale. The area that passed the significance test of the correlation of NDVI-P was smaller under the wet season scale than under annual scale. In wet season, the consistency of the spatial distributions of r and p of the 195

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Fig. 5. Spatial pattern of the correlation coefficient (r) and significant coefficient (p) of NDVI-climatic factors under the annual scale from 1999 to 2013.

interactions from 1999 to 2013 in a large dam-reservoir-basin (DRR) system. From 1999 to 2013, the monthly average NDVI was high, and the high values were mainly in the period from July to December. The NDVI in the basin had a significant upward trend overall, and vegetation cover provided the obvious improvement beginning in 2003. Under the multiple temporal scales, the NDVI distribution showed spatial and seasonal differences. The vegetation growth period was mainly in summer and autumn. The majority areas of the basin were medium-high and high vegetation cover under multiple time scales. The NDVI in more than 90% of the total area of the basin showed an upward trend under multiple temporal scales. The grade of vegetation cover change in more than 40% of the total area of the basin was a slight improvement under multiple time scales. For the climatic factors (CFs) in the basin, the temperature (T) and evapotranspiration (ET0) showed an upward trend. The precipitation (P) and the humidity index (HI) had a downward trend from 1999 to 2013. The correlation of NDVI-CFs of the basin had spatial differences under the annual and wet season scales. In most areas of the basin, the spatial distributions of r of NDVI-T and NDVI-ET0 were similar, and the correction for both was positive. The spatial distributions of r of NDVI-P and NDVI-HI were similar, and the correction for both was negative. The order of the correlation of the CFs on NDVI was P > HI > ET0 > T, which showed that the amount of P had the greatest impact on vegetation growth. In wet season, the consistency of the spatial distribution of the correlation of NDVI-P and NDVI-HI was stronger than that under the annual scale. Also, the correlation of NDVIT, NDVI-ET0 and NDVI-HI displayed no time-lag effect in most months.

3.4. Response of NDVI to climatic factors under the monthly scale The correlation of NDVI-CFs of the basin under the monthly scale is shown in Table 3. The correlation of NDVI-T was positive and had no obvious time-lag effect in most months except in August, September, October, and November. The NDVI in August had a significant positive correlation with the T in July, indicating that the multi-year average T in July and August was basically flat. The NDVI in October had a positive correlation with the T in August, and the NDVI in November had a significant positive correlation with the T in August. The correlation of NDVI-P was negative and had an obvious time-lag effect in most months, and the lag period in the NDVI in response to the P was two months. The correlation of NDVI-P2 in August, September, October, and November was negative, and the P in June, July, August and September was abundant, indicating that excessive P was not conducive to vegetation growth. The NDVI had a significant positive correlation with the ET0 and a negative correlation with the HI. Also, the correlation of NDVI-ET0 and NDVI-HI had no time-lag effect. Generally, the time-lag of vegetation growth in response to the P was two months. The results were similar with the previous studies carried out in southwest China indicating that the growth of vegetation had obvious lagging effect on the amount of P (Ding, 2017; Zhang et al., 2009; Zhang et al., 2015; Zhao et al., 2018). 4. Conclusions This research was conducted to explore vegetation and climate 196

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Fig. 6. Spatial pattern of the correlation coefficient (r) and significant coefficient (p) of NDVI-climatic factors under the wet season scale from 1999 to 2013.

The NDVI around the Liyuan (LY), Ahai (AH), Jinanqiao (JAQ), Longkaikou (LKK), LDL, and GYY hydropower stations declined in varying degrees between 2005 and 2010. Vegetation coverage was the highest in the 20-km buffer of the JAQ hydropower station and was relatively poor around the GYY and LDL hydropower stations. For the

The correlation of NDVI-P had obvious time-lag effect in most months, and the time-lag period was two months. For the vegetation coverage around the eight CHSs, the annual average NDVI generally increased, and the NDVI in Guanyinyan (GYY) and Ludila (LDL) was lower than that in the other hydropower stations.

Table 3 The correlation of NDVI-climatic factors of the basin under the monthly scale. Correlation

NDVI-T0 NDVI-T1 NDVI-T2 NDVI-T3 NDVI-P0 NDVI-P1 NDVI-P2 NDVI-P3 NDVI-E0 NDVI-E1 NDVI-E2 NDVI-E3 NDVI-HI0 NDVI-HI1 NDVI-HI2 NDVI-HI3

The months of NDVI 1

2

3

4

5

6

7

8

9

10

11

12

0.424 −0.036 −0.077 −0282 −0.587* 0.291 −0.313 0.061 0.681** 0.416 0.408 −0.002 −0.599* 0.255 −0.314 0.084

0.468 −0.033 0.009 0.058 −0.048 −0.34 −0.057 −0.083 0.699** 0.169 0.507 0.131 −0.049 −0.318 −0.071 −0.079

0.6* 0.247 0.172 0.177 −0.269 0.182 −0.265 0.068 0.628* 0.345 0.261 0.375 −0.281 0.182 −0.269 0.060

0.038 0.003 0.178 0.186 0.025 0.215 0.012 −0.072 0.194 0.014 0.334 0.188 −0.003 0.210 0.008 −0.079

0.414 −0.561* 0.127 −0.119 −0.327 0.456 0.147 0.270 0.473 −0.445 0.112 0.234 −0.427 0.447 0.129 0.265

0.06 −0.332 −0.148 −0.124 −0.292 0.532* −0.091 0.162 0.421 −0.275 0.211 0.008 −0.35 0.494 −0.095 0.146

−0.054 −0.505 −0.46 −0.119 −0.351 0.046 0.478 0.145 0.489 −0.265 −0.36 0.077 −0.416 0.116 0.42 0.136

0.245 0.529* 0.226 0.138 −0.491 −0.2 −0.241 0.133 0.796** 0.199 0.246 0.030 −0.604* −0.201 −0.241 0.11

0.193 0.497 0.438 0.401 −0.3 −0.653** −0.018 −0.171 0.405 0.293 −0.031 0.433 −0.363 −0.608* −0.035 −0.243

−0.16 0.164 0.419 0.318 −0.329 −0.422 −0.533* −0.124 0.241 0.167 0.147 −0.248 −0.309 −0.404 −0.484 −0.045

0.137 0.019 0.446 0.653** −0.628* −0.387 −0.189 −0.610* 0.767** 0.203 0.218 0.303 −0.620* −0.343 −0.216 −0.589*

0.313 −0.024 −0.15 0.430 −0.265 −0.235 0.142 −0.159 0.591* 0.209 −0.124 0.191 −0.288 −0.203 0.208 −0.172

Note: * is the significant correlation on the 0.05 level (bilateral), ** is the significant correlation on the 0.01 level (bilateral). 197

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spatial distribution of NDVI under multiple time scales, the NDVI had a declining trend around the CHSs, of which the degree of decline of the NDVI was most obvious in the vicinity of the GYY and LDL hydropower stations, especially near the GYY hydropower station. For the CFs among the eight CHSs under annual and wet season scales, the spatial variation of the T was similar to that of the ET0. The P showed a difference under the annual and wet season scales. Generally, the trend variations in the CFs around the eight CHSs were consistent with trend variations of the entire basin. Also, the correlations of NDVI-CFs in the 20-km buffer of the JAQ and LKK hydropower stations were the best, and that of the LDL and GYY hydropower stations were the worst. The differences in the spatio-temporal distribution of NDVI and the correlations of NDVI-CFs around the eight CHSs might have been influenced by the local climate due to the CHSs development. The findings of this study provide a close look at spatio-temporal changes in vegetation activities and their correlation with and response to climate factors during 1999–2013 in a large DRR system. The DRR system is a unique geographical region that has undergone significant changes in the social and natural environments, and these changes affect the surrounding ecosystems. These results will be beneficial for the development of a sustainable watershed ecosystem management plan in the DRR system, as well to assist ecological development planning around the CHSs.

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