Water supply is an important freshwater ecosystem service provided by ecosystems. Water shortages resulting from spatio-temporal heterogeneity of climate condition or human activities present serious problems in the Guizhou Province of southwest China. This study aimed to analyze the spatio-temporal changes of water supply service using the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model, explore how climate and land-use changes impact water supply provision, and discuss the impact of parameters associated with climate and land-use in the InVEST model on water supply in the region. We used data and the model to forecast trends for the year 2030 and found that water supply has been declining in the region at the watershed scale since 1990. Climate and land-use change played important roles in affecting the water supply. Water supply was overwhelmingly driven by the reference evapotranspiration and annual average precipitation, while the plant evapotranspiration coefficients for each land-use type had a relatively small effect. The method for sensitivity analysis developed in this study allowed exploration of the relative importance of parameters in the InVEST water yield model. The Grain-for-Green project, afforestation, and urban expansion control should be accelerated in this region to protect the water supply.

Introduction

The supply of water provides the foundation for human survival and development (e.g. drinking, hydropower production, industrial use, and irrigation) (Liquete et al., 2011; Fu et al., 2014). However, water shortage due to the change in natural conditions and human activities in many countries and regions is a serious problem (López-Moreno et al., 2014). Therefore, there is a significant need for accurate water supply provision assessment at the global and regional scales (Bangash et al., 2012, 2013; Boithias et al., 2014).

Climate and land-use change have caused great impacts on ecosystems and their associated ecosystem services over the past few decades (Briner et al., 2013; Su & Fu, 2013; Rocca et al., 2014; Shoyama & Yamagata, 2014). Changes in the global mean surface temperature and precipitation have affected the hydrological cycle, thereby impacting hydrological ecosystem services (e.g. water supply, water purification and erosion control) (Terrado et al., 2014). The water quantity and quality has also been affected by land-use change with effects on evapotranspiration, infiltration rates, and runoff quantity and timing (Millennium Ecosystem Assessment (MA), 2005). Recent studies have suggested that climate and land-use change were important factors in driving the change of water supply (Garmendia et al., 2012; Lu et al., 2013; Nelson et al., 2013). Water supply estimates are highly sensitive to climate (Marquès et al., 2013). For instance, drinking water and hydropower production were highly threatened in dry years and with precipitation variation (Chiang et al., 2014; Terrado et al., 2014). Additionally, urban expansion has decreased the evapotranspiration and infiltration rates, thereby having a negative impact on the water supply (Hoyer & Chang, 2014). The conversion of pastureland and rangelands to fast-growing exotic plantations has resulted in a decline of water availability (Garmendia et al., 2012). Although changes in the water supply resulting from climate and land-use changes have been quantified in many countries and regions (Liu et al., 2013; Getnet et al., 2014; López-Moreno et al., 2014; Molina-Navarro et al., 2014), few studies have addressed the provision level of water supply and explored the role of climate and land-use change on water supply changes in mountainous areas with strong human activity.

Guizhou Province lies in a mountainous area in southwest China. Water shortages resulting from spatio-temporal heterogeneity of climate condition and human activities have become serious problems limiting economic and social development in the region (Yu et al., 2009). Guizhou Province has a typical monsoon climate. Temperature and precipitation are different at different times and places due to climate features and topography (Wang & Meng, 2007; Ge et al., 2014). Additional changes have resulted from conservation projects such as the Grain-for-Green project (the conversion from farmland to forestland and shrub land in the gully and hilly areas) and the forest protection programs in the upper reaches of the Yangtze River and the Pearl River began to be implemented in the late 1980s, which brought about a great change in land-use (particularly for arid land and forestland) (Peng et al., 2006; Zhang et al., 2011). However, little is known about water supply change under the influence of climate condition and human activities. This knowledge is essential to assess the effects of climate and land-use change on water supply in this region and is required for the sustainable utilization of clean water and a reduction in the potential negative impacts of human activity on water supply.

In this paper we assess the change in water supply in Guizhou Province, China. Our objectives were to: (1) quantify spatio-temporal variation of water supply from 1990 to the future year 2030 using the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) water yield model; (2) discuss the effect of climate and land-use change on water supply provision; (3) assess the sensitivity of water supply change to parameters associated with climate and land-use using the method of sensitivity analysis.

Methodology

Study area

The Guizhou Province (24°37′–29°13′ N, 103°36′–109°35′E), which is situated in the southwest of China and upstream of the Yangtze River and Pearl River (Figure 1), was selected as a case study in this study. It covers an area of 176,000 km2. The climate is monsoonal with great fluctuations in temperature and precipitation. Droughts and floods occur frequently. Annual precipitation varies from 850 mm in the northwest part to 1,600 mm in the southeast part and annual temperatures range from 4 °C in January to 14 °C in July. The region is a typical mountainous area and the elevation varies between 154 and 2,859 m. The region is divided into Chishuihe, Wujiang, Nanpanjiang, Niulanjiang-Hengjiang, Hongshuihe, Beipanjiang, Duliujiang, and Yuanjiang watersheds. This region is one of the most vulnerable and rapidly developing regions in China (Xu et al., 2011; Zhang et al., 2012). The rapid growth of economy and population have caused great pressure on the natural environment. At the same time, projects such as the Grain-for-Green project and forest protection programs were implemented in the late 1980s, and aimed to improve the quality of the environment (Peng et al., 2006; Zhang et al., 2011). Therefore, if these programs are successful, then natural vegetation will increase in the future.
Fig. 1.

Location of the study area.

Fig. 1.

Location of the study area.

Data

The land-use maps for 1990 and 2010 of the region were extracted from Landsat Thematic Mapper/Enhanced Thematic Mapper (TM/ETM) (30 m × 30 m) (National Aeronautics and Space Administration, http://www.nasa.gov/) and the land-use types were divided into forestland, shrub land, grassland, arid land, paddy field, built-up land, water body and unused land in ArcGIS 10.1. The annual average precipitation was derived from the China Meteorological Administration (www.cdc.nmic.cn) and the soil data was derived from the China Soil Scientific Database (www.soil.csdb.cn). The watershed boundary was extracted from the Digital Elevation Model (30 m × 30 m) using ArcGIS 10.1. The root depth and plant evapotranspiration coefficient are available as recommended parameters in the InVEST water yield model (Table 1).

Table 1.

Parameters for root depth and plant evapotranspiration coefficient.

Land-use types Forest-land Shrub land Grass-land Arid land Paddy field Built-up land Water body Unused land 
Root depth (mm) 7,000 5,000 1,700 600 500 1,000 10 
Plant evapotranspiration coefficient 0.8 0.65 0.7 0.75 0.1 0.2 
Land-use types Forest-land Shrub land Grass-land Arid land Paddy field Built-up land Water body Unused land 
Root depth (mm) 7,000 5,000 1,700 600 500 1,000 10 
Plant evapotranspiration coefficient 0.8 0.65 0.7 0.75 0.1 0.2 

Water yield modeling

InVEST, developed by the Natural Capital Project, is a suite tool to map and estimate the ecosystem services change (www.naturalcapitalproject.org). This model currently includes 17 distinct models that analyze different aspects of marine, freshwater and terrestrial environments (Sharp et al., 2014). The freshwater yield model in the InVEST model was used to map and quantify the supply of freshwater.

The Budyko curve and annual average precipitation were used to calculate the water yield in the InVEST model (Budyko, 1974). 
formula
1
where Y is the annual water yield for each pixel. is the annual actual evapotranspiration for each pixel with given land-use type, and P is the annual precipitation on that pixel. The evapotranspiration partition of the water balance is calculated by the approximation of the Budyko curve as developed by Zhang et al. (2001). 
formula
2
where is the plant-available water coefficient for expected precipitation, and R is the Budyko dryness index for each pixel with given land-use type. 
formula
3
where is the reference evapotranspiration and is calculated based on the Hargreaves equation (Hargreaves & Allen, 2003) and k is the plant evapotranspiration coefficient for each pixel associated with the land-use type. 
formula
4
where is the volumetric (mm) plant available water content and is calculated based on the method of Zhou et al. (2005) and is an empirical constant that presents the seasonal rainfall distribution and rainfall depths.

Prediction of climate change

NCC/GU-WG version 2.0 is a weather generator developed by the National Climate Center of China and Gothenburg University for the Assessment of Climate Impact in China (www.climatechange-data.cn). The model, in this study, was employed to forecast the precipitation and temperature in 2030 based on two states using the first order Markov chain and two parameter Gamma distribution (Liao et al., 2004). First, the daily precipitation, maximum temperature and minimum temperature for 2030 were simulated by the model. Second, the annual precipitation was calculated based on the daily precipitation. Finally, the Hargreaves equation (Hargreaves & Allen, 2003) was used to calculate the reference evapotranspiration.

Prediction of land-use change

The Conversion of Land Use and its Effects model (Verburg et al., 2002; Verburg & Overmars, 2009) predicts land-use change through the logistic regression of land use and its driving factors (Gibreel et al., 2014). This model includes spatial policies and restrictions, land-use type specific conversion settings, land-use demands, and location characteristics (Verburg et al., 2004; Verburg et al., 2006). We used this model with 0.7, 0.6, 0.7, 0.6, 0.7, 1, 0.9 and 0.7 values for the land-use type specific conversion settings assigned to paddy field, arid land, forestland, shrub land, grassland, built-up land, water body and unused land, respectively. Areas of natural conservation were considered based on spatial policies and restrictions. The Markov chain was used to calculate future land-use demands from 2010 to 2030 based on the transition matrix of 1990 to 2010. Regression analysis was used to estimate the contribution of different location characteristics (potential factors) to the suitability of a location for a specific land-use type (Luo et al., 2010).

Sensitivity analysis

Sensitivity analysis in this study was based on Scardi's method (Scardi & Harding, 1999) and analyzed the relative importance of parameters associated with climate and land-use in the InVEST water yield model by comparison of the outputs dependent on variations in the input parameters. We selected ±100%, ± 80%, ±60%, ±40%, ±20%, ±10% and ±5% of the mean value of each parameter to test sensitivity and determine the parameters with relatively large effects on water supply.

Results

Past changes in water supply

The water supply across the whole region decreased from 559 mm to 545 mm (−2.5%) between 1990 and 2010. At the watershed scale, water supply decreased in all but the Wujiang, Hongshuihe, and Niulanjiang-Hengjiang watersheds (Table 2).

Table 2.

Water supply for 1990, 2010 and 2030 at watershed scale, actual and predicted values.

 1990 2010 2030 Change (1990–2010)
 
Change (2010–2030)
 
  mm mm mm mm mm 
Entire study area 559 545 519 −14 −2.50 −26 −4.77 
Wujiang 528 544 560 16 3.03 16 2.94 
Yuanjiang 592 574 498 −18 −3.04 −76 −13.24 
Hongshuihe 547 578 435 31 5.67 −143 −24.74 
Duliujiang 578 507 435 −71 −12.28 −72 −14.20 
Nanpanjiang 495 423 340 −72 −14.55 −83 −19.62 
Beipanjiang 683 618 702 −65 −9.52 84 13.59 
Niulanjiang-Hengjiang 187 320 72 133 71.12 −248 −77.50 
Chishuihe 539 486 383 −53 −9.83 −103 −21.19 
 1990 2010 2030 Change (1990–2010)
 
Change (2010–2030)
 
  mm mm mm mm mm 
Entire study area 559 545 519 −14 −2.50 −26 −4.77 
Wujiang 528 544 560 16 3.03 16 2.94 
Yuanjiang 592 574 498 −18 −3.04 −76 −13.24 
Hongshuihe 547 578 435 31 5.67 −143 −24.74 
Duliujiang 578 507 435 −71 −12.28 −72 −14.20 
Nanpanjiang 495 423 340 −72 −14.55 −83 −19.62 
Beipanjiang 683 618 702 −65 −9.52 84 13.59 
Niulanjiang-Hengjiang 187 320 72 133 71.12 −248 −77.50 
Chishuihe 539 486 383 −53 −9.83 −103 −21.19 

There was a similar spatial pattern of water supply for 1990 and 2010, where the high value (>601 mm) of water supply was mainly located in central and east parts of Beipanjiang, west and north parts of Yuanjiang, the west part of Duliujiang, the east part of Hongshuihe and the south part of Wujiang, while the low value (<400 mm) of water supply was mainly located in the Niulanjiang-Hengjiang region, the west part of Wujiang and the east part of Duliujiang and Yuanjiang (Figure 2).
Fig. 2.

Spatial pattern of water supply in 1990 (left) and 2010 (right) at watershed scale.

Fig. 2.

Spatial pattern of water supply in 1990 (left) and 2010 (right) at watershed scale.

The change in the water supply from 1990 to 2010 was positive in the Niulanjiang-Hengjiang and Hongshuihe regions, the north and east parts of Wujiang, and the west part of Yuanjiang, and was negative in Nanpanjiang and Duliujiang, the north and southeast parts of Yuanjiang, most parts of Beipanjiang, and the west part of Wujiang and Chishuihe. The large increase (>10%) was located in the south and north parts of Wujiang, the east part of Chishuihe and the entire Niulanjiang-Hengjiang region, while the large decrease (>−10%) included the west and south parts of Chishuihe, the west part of Nanpanjiang and Beipanjiang, the south and east parts of Duliujiang, the west part of Wujiang and the southeast part of Yuanjiang (Figure 3).
Fig. 3.

Spatial pattern of water supply change from 1990 to 2010 at the watershed scale.

Fig. 3.

Spatial pattern of water supply change from 1990 to 2010 at the watershed scale.

Future change in water supply

According to the predictions of our model, the water supply across the whole region will decrease (−4.77%) from 2010 to 2030. At the watershed scale, water supply will decrease in most watersheds except for Wujiang and Beipanjiang (Table 2).

Regions predicted to have a high value (>700 mm) of water supply in 2030 were located mainly in the south and northeast parts of Wujiang, the southwest and north parts of Yuanjiang, the northwest part of Duliujiang, the west and east parts of Beipanjiang, and the east part of Hongshuihe. Regions predicted to have a low value (<400 mm) included the west part of Chishuihe and Wujiang, the east part of Yuanjiang and Duliujiang, the entire Niulanjiang-Hengjiang region, and the south part of Nanpanjiang (Figure 4).
Fig. 4.

Predicted spatial pattern of water supply for 2030 at the watershed scale.

Fig. 4.

Predicted spatial pattern of water supply for 2030 at the watershed scale.

The calculated predicted trends in water supply from 2010 to 2030 showed positive change in the north and south parts of Wujiang, the west part of Yuanjiang and Duliujiang, and most parts of Beipanjiang, while a negative change was predicted for the west part of Wujiang, the entire Niulanjiang-Hengjiang, Chishuihe and Nanpanjiang regions, the east part of Yuanjiang and Duliujiang, the west part of Hongshuihe, and the south part of Beipanjiang (Figure 5).
Fig. 5.

Calculated prediction of changes in water supply at the watershed scale from 2010 to 2030.

Fig. 5.

Calculated prediction of changes in water supply at the watershed scale from 2010 to 2030.

Sensitivity analysis

The reference evapotranspiration and annual average precipitation had relatively large effects on water supply in the region, while the plant evapotranspiration coefficient for each land-use type had a relatively small effect on water supply. Moreover, the reference evapotranspiration and annual average precipitation showed a negative and positive effect on water supply, respectively. Significant differences were seen in the effects of the plant evapotranspiration coefficient for different land-use types on water supply. For instance, the plant evapotranspiration coefficients for forestland, shrub land and arid land had a relatively large negative effect on water supply, while the plant evapotranspiration coefficients for other land-use types had a relatively small negative effect (Figure 6).
Fig. 6.

Sensitivity analysis of parameters in the InVEST water yield model.

Fig. 6.

Sensitivity analysis of parameters in the InVEST water yield model.

Discussion

Effects of climate and land-use change on water supply

The water supply change in the study area was deeply affected by climate change. Although precipitation and evaporation change for 1990–2010 showed differences compared to the predicted values for 2010–2030, the difference between precipitation and evaporation showed a continuous decrease for both the 1990–2010 and 2010–2030 time periods (Table 3). Thus, a similar variation trend was found between climate change and water supply change (Tables 2 and 3). This suggested that climate change has a significant effect on water supply change in this region. In addition, climate affected the spatial pattern of water supply. For example, the provision of water supply in Niulanjiang-Hengjiang was low due to a low value for precipitation and evaporation from 1990 to 2010, while a high value of precipitation and low value of evaporation resulted in a relatively high level of water supply provision in the east part of Beipanjiang (Figure 7).
Table 3.

Predicted and actual precipitation and evaporation for 1990, 2010 and 2030 (mm).

 1990 2010 2030 Change (1990–2010) Change (2010–2030) 
Precipitation 1,031 1,011 1,130 −20 119 
Evaporation 474 469 602 −5 133 
Difference between precipitation and evaporation 557 542 528 −15 −14 
 1990 2010 2030 Change (1990–2010) Change (2010–2030) 
Precipitation 1,031 1,011 1,130 −20 119 
Evaporation 474 469 602 −5 133 
Difference between precipitation and evaporation 557 542 528 −15 −14 
Fig. 7.

Precipitation and evaporation for 1990, 2010, and 2030 at the watershed scale.

Fig. 7.

Precipitation and evaporation for 1990, 2010, and 2030 at the watershed scale.

The land-use changes had significant effects on the water supply by affecting rainfall and evaporation (Dong et al., 2013). The increase of forestland increased evaporation and interception, while urbanization and agricultural development decreased evaporation and soil moisture content (Costa et al., 2003; Bari et al., 2005; Nalk & Jay, 2005; Zheng et al., 2009). The decline of water supply occurred mostly in regions where there was an increase in forestland and a decrease in arid land from 1990 to 2010, while the water supply increase in the northeast and south parts of Wujiang was related to rapid urbanization and agricultural development (Figures 8 and 9).
Fig. 8.

Calculated land-use change from 2010 to 2030.

Fig. 8.

Calculated land-use change from 2010 to 2030.

Fig. 9.

Spatial pattern of land-use for 1990, 2010, and 2030.

Fig. 9.

Spatial pattern of land-use for 1990, 2010, and 2030.

Sensitivity analysis

Our study suggested that the reference evapotranspiration and annual average precipitation played dominant roles in water supply change in this region, consistent with the studies of Sánchez-Canales et al. (2012) and Hoyer & Chang (2014). However, this study also found that there was a relatively small effect due to plant evapotranspiration coefficients for different land-use types compared to the larger effects of evapotranspiration and precipitation on water supply. The plant evapotranspiration coefficients for forestland, shrub land and arid land had greater effects on water supply than the plant evapotranspiration coefficients for other land-use types. Forestland, shrub land and arid land were the primary land-use types in the region, while grassland, built-up land, water body, unused land and paddy field made up a smaller component of the study area. Scardi's method (Scardi & Harding, 1999) allowed us to explore the relative importance of each parameter to water supply change in the InVEST water yield model.

Implications for environmental management

The InVEST water yield model provides spatial explicit information for environmental managers (Vigerstol & Aukema, 2011). The estimates for water supply at the sub-watershed scale are helpful for land-use and socio-economic policy-making for different sub-watersheds, which is crucial for water resource management. For instance, the increase of water supply in the south part of Wujiang resulting from rapid urbanization may increase flood risk. To minimize this risk, this region should control urban expansion and increase natural vegetation. Similarly, the northeast part of Wujiang is predicted to have an increase of water supply due to agricultural development. Therefore, the Grain-for-Green project should be accelerated in this region. In addition, effective policy should monitor afforestation in this region, which can increase negative effects, including flood and drought, due to climate change.

Limitations

Water supply assessment, in a water yield model, is based on the annual average precipitation level, which neglects extreme and sub-annual changes of water supply. In addition, the water balance based on Budyko hypothesis, in this model, is deeply affected by land use. This model does not consider the effects of complex land-use patterns on water balance.

Conclusion

We used the InVEST water yield model to analyze the spatio-temporal variation of water supply in Guizhou Province. This study showed that water supply over the whole region is predicted to decrease from 1990 to 2030. There were differences in the water supply change at the watershed scale. Effects of climate and land use played important roles in water supply change. The reference evapotranspiration and annual average precipitation had relatively large effects on water supply in the region, while the plant evapotranspiration coefficient for each land-use type had a relatively small effect on water supply. We also found significant differences in water supply due to the effects of the plant evapotranspiration coefficient on different land-use types.

Conflicts of interest

The authors declare no conflict of interest.

Acknowledgment

This work was supported by the National Natural Science Foundation of China (No. 41361091).

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