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Extended growing season reduced river runoff in Luanhe River basin
Journal Pre-proofs Research papers Extended growing season reduced river runoff in Luanhe River basin Xiaojun Geng, Xuancheng Zhou, Guodong Yin, Fanghua Hao, Xuan Zhang, Zengchao Hao, Vijay P. Singh, Yongshuo H. Fu PII: DOI: Reference:
S0022-1694(19)31273-9 https://doi.org/10.1016/j.jhydrol.2019.124538 HYDROL 124538
To appear in:
Journal of Hydrology
Received Date: Revised Date: Accepted Date:
27 August 2019 27 November 2019 31 December 2019
Please cite this article as: Geng, X., Zhou, X., Yin, G., Hao, F., Zhang, X., Hao, Z., Singh, V.P., Fu, Y.H., Extended growing season reduced river runoff in Luanhe River basin, Journal of Hydrology (2019), doi: https://doi.org/ 10.1016/j.jhydrol.2019.124538
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© 2019 Published by Elsevier B.V.
Extended growing season reduced river runoff in Luanhe River basin
Xiaojun Genga, Xuancheng Zhoua, Guodong Yina, Fanghua Haoa*, Xuan Zhanga*, Zengchao Haoa, Vijay P. Singhb, and Yongshuo H. Fua*
a College
of Water Sciences, Beijing Normal University, Beijing 100875, China.
b Department
of Biological and Agricultural Engineering, Texas A&M University, College
Station, Texas, USA.
Corresponding author: Yongshuo H. Fu ([email protected]) Fanghua Hao ([email protected]) and Xuan Zhang ([email protected])
Highlights:
Climate warming significantly extended growing season by 0.60 ± 0.08 d per year in Luanhe River basin..
The extended growing season mainly related to the earlier start of growing season (SOS, -0.40 ± 0.05 d per year).
The results confirmed the primary regulation of river runoff by precipitation, but the length of growing season and vegetation growth also played a key role in the changes of runoff.
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Abstract Global warming has substantially altered vegetation growth and phenology in temperate biomes worldwide, whose impacts on river runoff have not been fully understood, especially at the watershed scale. Investigating the relationships among phenological shift, changes in vegetation growth, and river runoff is essential to improve our understanding of the ecosystem response to ongoing climate change. Using in-situ runoff records, phenological dates that were extracted from the normalized difference vegetation index (NDVI) over the period 1982-2015, and grey relational analysis, this study investigated the relationships between changes in river runoff and phenological variables in Luanhe River basin, China. It was found that the growing season in the Luanhe basin was significantly extended by 0.60 ± 0.08 d per year associated with climate warming over the period 1982-2015, which is mainly related to the earlier start of growing season (SOS, 0.40 ± 0.05 d per year), rather than the delayed end of growing season (EOS, 0.11 ± 0.01 d per year). The vegetation growth, defined as the average growing season NDVI (GS_NDVI), significantly increased in more than 88.4% of the study area, on average the GS_NDVI increased by 1.30 ± 0.03×10-3 d per year. On the other hand, runoff significantly reduced by 1.42 mm per year over the study period, but large differences were observed between upper and middle-lower reaches region of the Luanhe basin. The grey relational analysis confirmed the primary regulation of river runoff by precipitation, but the length of growing season and vegetation growth also played a key role in the changes of runoff. These results provide new insights into the interaction between vegetation dynamics and water balance at the watershed scale, and highlight to couple the phenological processes into eco-hydrological models to improve the modelling accuracy. Key words: climate change; phenology shifts; vegetation growth; river runoff; watershed scale
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1 Introduction In temperate and boreal regions, vegetation phenology is particularly sensitive to climate change (Chuine et al., 2004; Schwartz, 2013). Recent climate warming has extended the growing season mainly due to the advanced start of season (SOS) and the delayed end of season (EOS) in the Northern Hemisphere (Menzel et al., 2006; Peñuelas and Filella, 2001; Piao et al., 2019; Fu et al., 2015), and has subsequently affected the ecosystem structure and function (Piao et al., 2017; Thackeray et al., 2016; Zohner et al., 2018). Extended growing season results in higher carbon assimilation and increases the water retention of terrestrial ecosystems (Lu et al., 2013; Piao et al., 2007; Williams et al., 2012). However, to our knowledge, how the phenological shift affects the regional water cycle, especially at the watershed scale, has not been well investigated. Hence, it is essential to investigate the water cycle response to climate warming by analyzing the interaction between phenological shift and river runoff at the watershed scale (Peñuelas et al., 2009; Zeng et al., 2018). It is widely accepted that water cycle processes are mainly controlled by changes of climatic variables, such as precipitation and radiation, as well as land use and land cover change (Chiew et al., 2009; Hundecha and Bárdossy, 2004; Singh et al., 2014; Williams et al., 2012). Climate warming may directly cause the frequency and intensity changes in precipitation, accelerate the transfer of water from the earth's surface to the atmosphere, and lead to higher drought stress, and thus reduce river runoffs (Hao et al., 2018; Prudhomme et al., 2014). Except for climate variables, recent studies have found that warming-induced vegetation growth has affected evapotranspiration in river basins, and thus has a great impact on water yield and river runoff (Goulden and Bales, 2014; Li et al., 2018; Richardson et al., 2013; Thompson et al., 2011; Yang et al., 2019; Zeng et al., 2018). For example, Lemordant et al. (2018) found that compared
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with the changes of precipitation or radiation, the physiological response to CO2 and water use efficiency of vegetation growth played a more pivotal role affecting terrestrial water cycles based on earth system models. In general, few studies have investigated the influence of vegetation growth on river runoff, especially using in situ records, at the watershed scale. Vegetation phenology also contributes to the shift of river runoff. Recent studies found that in a temperate deciduous forest, advanced spring green-up dates caused higher evapotranspiration and reduced river runoff (Kim et al., 2018) where the spatial resolution was small, i.e. based only on a small catchment scale (~0.8 km2). How phenological shifts affect river runoff at a large scale, especially at the large watershed scale, is still unclear. Furthermore, climate warming has extended the vegetation growing season, but the contribution of spring and autumn phenology has been reported inconsistently among studies. Some studies found that the spring phenology contributed more to the length of growing season (Chen et al., 2000; Linderholm, 2006; Menzel and Fabian, 1999), however, other studies reported that autumn phenology played a more decisive role than spring phenology (Wu et al., 2013). Overall, how spring and autumn phenology affect the length of growing season (GSL) and subsequently hydrological processes of the watershed ecosystem remains unclear. One possible reason is that large uncertainty exists when extracting remote sensing-based phenology dates using a single method (Cong et al., 2012; White et al., 2009). Using multiple methods to accurately extract phenology dates are thus necessary to apply in the phenology-river runoff analysis. We, therefore, applied five methods to determine the start and the end of growing season (SOS and EOS) using the normalized difference vegetation index (NDVI) over the period 19822015 in Luanhe River basin, a typical semi-arid watershed in temperate China. Grey relational analysis (GRA) was applied to detect the relationships among phenology, vegetation growth,
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climatic variables, and river runoff. The objectives of this study were to (a) determine the temporal shifts of vegetation phenology and growth and river runoff, (b) investigate the relative contribution of spring and autumn phenology to vegetation growth, and (c) explore the relationships between trends in phenology, vegetation growth, and river runoff over the period 1982-2015. 2 Materials and Methods 2.1 Study area Luanhe River basin, with a total area of 38,000 km2, is located in arid and semi-arid areas of North China (115°27′E-118°56′E, 40°N-42°41′N) (Figure 1a). The Luanhe River flows from northwest to southeast with 12 main sub-basins (Figure 1b and Figure A.1). The annual average temperature and precipitation during period of 1982-2015 were 7.0 ± 2.6℃ and 488.4 ± 80.7 mm, respectively, but a large spatial heterogeneity exists. In recent decades, the land use types in the area have not changed much, and mainly covered by forest (42.4%) and grassland (46.3%) (Table A.1). The whole river basin was separated into two regions, based on different land covers in year of 2015 (Figure 1c). The upper reaches region (UR) is mainly covered by temperate and meadow grassland, while the middle-lower reaches region (MLR) is mainly covered by temperate forests (Figure 1d). 2.2 Data and processing 2.2.1 DEM and land use data. The Digital Elevation Model (DEM) of the watershed (30 m × 30 m) was provided by the Data Center for Resources and Environmental Sciences, Chinese Academy of Sciences (RESDC)
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(http://www.resdc.cn). Land use data with a spatial resolution of 300 m in year of 2015 was collected from the European Space Agency (http://www.esa-landcover-cci.org). 2.2.2 Meteorological and hydrological data. Monthly precipitation, mean air temperature, and solar radiation data (1982-2015) from 13 meteorological stations within the river basin were collected from the Chinese Daily Surface weather database (http://data.cma.cn) (Figure. 1b). The Kriging method was applied for spatial interpolation, and then annual series of meteorological variables were obtained for sub-basins in order to analyze temporal trends and relationships between runoff and related factors. The observed data of annual runoff was collected from hydrometric stations in 12 typical sub-basins of Luanhe River basin (Figure. 1b), including 4 sub-basins in the UR (S1~S4) and 8 sub-basins in the MLR (S5~S12) (Figure A.1). The distributions of meteorological and hydrometric stations are shown in Figure 1b. 2.2.3 NDVI dataset. The data of vegetation growth and long-term phenological records (1982-2015) were extracted from the bi-monthly 8-km Global Inventory Modeling and Mapping Studies (GIMMS) NDVI3g (third generation normalized difference vegetation index) data set from Advanced Very High Resolution Radiometer (AVHRR) (Tucker et al., 2005; Tucker et al., 2004) (https://nex.nasa.gov/nex/projects/1349/). The growing season length (GSL) was defined as the time interval between the start of growing season (SOS) and the end of growing season (EOS). Vegetation growth was defined as the mean NDVI over the growing season.
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2.2.4 Estimation of SOS and EOS. Five commonly used methods (including Gaussian-Midpoint method, Spline-Midpoint method, HANTS-Maximum method, Polyfit-Maximum method, and Timesat-SG method) were applied to determine SOS and EOS. The mean values of SOS and EOS across the five methods were applied for each pixel. The details of the five methods are provided in the appended Text A.1. In general, three steps were involved for extracting the phenological dates: (1) eliminating the influence of errors and noises caused by orbital drift, calibration, viewing geometry, stratospheric volcanic aerosols, and snow cover; (2) using different methods to filter and interpolate NDVI, and using thresholds or maximum/minimum rate of temporal NDVI change to determine the phenological dates; and (3) distinguishing and removing the abnormal points based on a five-point median-value moving average method. 2.3 Methods Min-max normalization was used to process the time series data, to compare temporal dynamics of different indicators with large different absolute magnitudes (Jia et al., 2011). Grey relational analysis (GRA) is a method of the geometric proximity between different discrete sequences within a system (Huang and Huang, 1996; Deng, 1989), which was applied to determine the correlation among climatic variables (i.e. precipitation, temperature, solar radiation), ecological variables (i.e. SOS, EOS, GSL, GS_NDVI) and river runoff. The proximity of two series is described by the grey relational degree (GRD), a higher GRD (usually >0.6) represents a greater influence of the parameter on the reference series (Fu et al., 2001; Ip et al., 2009; Xu et al., 2011). The GRD was calculated as follows (Deng, 1989):
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Runoff series were named as reference sequences, labeled as 𝑋0(𝑡), and the climatic and ecological variables as influence sequences, labeled as 𝑋𝑖(𝑡). 𝑋′0(𝑡) and 𝑋′𝑖(𝑡) represent the normalized variables of 𝑋0(𝑡) and 𝑋𝑖(𝑡) through min-max normalization. The GRD of influence of each series 𝑋𝑖(𝑡) on reference series 𝑋0(𝑡) was calculated following the formulations given below: 1
𝑛
𝐺𝑅𝐷𝑖 = 𝑛∑𝑡 = 1𝐾(𝑡)
𝐾(𝑡) =
𝑚𝑖𝑛 (∆X(t)) + 𝜌 ∗ max (∆X(t)) ∆X(t) + 𝜌 ∗ max (∆X(t))
∆X(t) =
|(𝑋′𝑖(𝑡) ― 𝑋′0(𝑡))|
(1)
(2) (3)
where ρ is the grey parameter that ranged from 0 to 1, and is often assigned a value of 0.5. 3 Results 3.1 Changes of climatic variables During the period 1982-2015, the whole region of Luanhe River basin (WR) was getting warming and drying, with temperature significantly increasing by 0.03 ± 0.002 ℃ per year (℃/y) and precipitation decreasing by 1.35 ± 0.19 mm per year (mm/y), and the radiation was increasing by 0.19 ± 0.03 W·m-2 per year (Figure 2). These trends were consistent in all subbasins (Figure A.2), but the warming trend was stronger in UR than in MLR (0.04 ± 0.002 and 0.02 ± 0.001 ℃/y, UR and MLR, respectively), while the drying trend was more obvious in MLR than in UR, on average more than two times reduction in precipitation was observed in UR (-0.76 ± 0.05 and -1.66 ± 0.02 mm/y, UR and MLR, respectively). 8
3.2 Changes in phenology and vegetation growth in Luanhe River basin 3.2.1 Phenological dynamics The mean date of SOS in the Luanhe River basin is 121 ± 4.5 (Day of year, DoY) over the period 1982-2015. The SOS was advanced by an average of -0.40 ± 0.05 days per year (d/y), and around 75.4% of the study area experienced advanced trends, with roughly 24.0% of them statistically significant at p-value < 0.05 (Figure 3a). However, large spatial difference of SOS exists, and the earlier SOS was mainly observed in MLR (-0.61 ± 0.06 d/y), whereas the SOS in UR was even delayed (0.21 ± 0.06 d/y) (Figure 3b). The mean date of EOS is 291 ± 2.8 (DoY) over the whole area, and the EOS was delayed by an average of 0.11 ± 0.01 d/y over the study period 1982-2015. The delayed trend was observed in 81.1% of the study area, and 53.5% of which was significant at p-value < 0.05 (Figure 3c). Although delayed EOS existed in both UR and MLR, its value was different in the two regions. The EOS delay in MLR (0.14 ± 0.01 d/y) was more than twice that in UR (0.06 ± 0.01 d/y) (Figure 3d). The spatial distribution of annual trend of GSL is shown in Figure 3e. The extension of GSL was extensively observed across 76.0% of the study area, with 43.0% of the pixels showing significant extension (p-value < 0.05，Figure 3e). The mean extension of GSL across the whole basin was 0.60 ± 0.08 d/y. Comparing different regions, it was found that the significant extension pixels were mainly distributed in MLR (0.85 ± 0.10 d/y), while there was no obvious change in UR (0.002 ± 0.07 d/y) (Figure 3f).
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3.2.2 Growth dynamics and its correlation with phenology Using the GS_NDVI as proxy of vegetation growth, it was found that vegetation growth substantially increased over the Luanhe River basin during the period 1982-2015, increasing trends were found in 88.4% areas, with mean annual trend (1.30 ± 0.03) ×10-3, and 78.4% of the trends were statistically significant at p-value < 0.05 (Figure 3g). Consistent with phenological changes, there were large spatial variances between UR and MLR. Although the overall GS_NDVI in UR exhibited an increasing trend (0.55 ± 0.05) ×10-3 per year, there were still 23% pixels with decreasing vegetation growth, whereas GS_NDVI in MLR increased significantly at (1.6 ± 0.04) ×10-3 per year, nearly three times as large as that in UR (Figure 3h). In addition, it was noted that the GS_NDVI was not the same everywhere in UR, so we further divided the UR into two regions according to the GSL, i.e. extended vs. shortened. Compared with GSL extended areas, both the temperature and precipitation in GSL shortened areas were lower, and the changes of both temperature and precipitation were significantly higher (p-value < 0.05, Figure A.3). All these changes were likely to affect the vegetation growth and lead to the regional differences of growing season in UR. GS_NDVI was significantly correlated with SOS and GSL (p-value < 0.05), but not with EOS (p-value = 0.10) in the whole region (WR) (Figure 4). However, significant correlations were observed only in MLR rather than in UR, suggesting other factors, such as climate and peak NDVI, may determine the vegetation growth in UR. On the contrary, in MLR, SOS, EOS, and GSL were all significantly correlated with vegetation growth, especially GSL, where the coefficient of determination was larger (R2 = 0.48) than for SOS (R2 = 0.34) and EOS (R2 = 0.13).
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3.3 Changes of river runoff and its association with phenology 3.3.1 Changes of river runoff To compare the temporal trend of annual runoff, this study used annual runoff anomaly (Figure 5). Across all the 12 sub-basins, runoff decreased significantly at a rate of -1.42 mm/y (p-value < 0.05) from 1982 to 2015. The significantly reduced runoff was found in both UR and MLR, but the reduction in MLR was around 4 times higher than in UR; the average trends of the four sub-basins in UR and the eight sub-basins in MLR were -0.45 mm/y and -1.90 mm/y, respectively (p-value < 0.05，Figure 5). Furthermore, it was found that river runoff in the whole region or sub-basins was generally lower than the multi-year average after the year 2000. 3.3.2 Correlation between shifts in river runoff and phenology Linear trends of key factors affecting river runoff (Figure 6a-c) and grey relational analysis (GRA) among them (Figure 6d) are shown in Figure 6. The runoff in WR showed a negative association with either GSL or GS_NDVI (Figure 6a) and the negative association was much stronger in MLR than in UR (Figure 6b and 6c). Combining with the grey relational degree, it was found that the association between the change of precipitation and that of river runoff was the strongest (GRD = 0.75,, Figure 6d). It was also observed that both GSL and GS_NDVI showed a relatively higher association with river runoff (GRD = 0.59 and 0.64, respectively), rather than other climatic variables (Figure 6d). These relationships varied between UR and MLR. The change of river runoff in UR was most related to the change of GS_NDVI (GRD = 0.75), even slightly more strongly than to precipitation (GRD = 0.72) (Figure 6d). However, precipitation change still had the highest correlation with river runoff (GRD = 0.77) in MLR, and GSL and GS_NDVI were strongly associated with runoff (GRD = 0.59 and 0.60,
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respectively) (Figure 6d). These findings suggested that both climate and ecological variables were associated with changes of river runoff. The grey relational analysis between SOS/EOS and river runoff (Figure A.4) was also applied. Both precipitation and phenological shifts showed a relatively higher association with river runoff than other climatic factors among which the contribution of SOS (GRD = 0.66) was more significant than EOS (GRD = 0.64) (Figure A.4d).
4 Discussion 4.1 Phenological shift and spatial heterogeneity Earlier SOS and later EOS, and extended growing season were found in the Luanhe River basin over the period 1982-2015 when significant warming trend was detected. It was further found that the extended growing season promoted the vegetation growth in the basin. Such results are consistent with previous studies in temperate areas in the Northern Hemisphere (Cong et al., 2012; Gong et al., 2015; Linderholm, 2006), whereas higher vegetation growth largely relied on the earlier SOS rather than EOS in the basin. In addition, higher vegetation growth was found in MRL but not in UR. This was mainly because a delayed, rather advanced SOS, found in UR. Due to small changes of land use in the study area over the past years (Table A.1), such a difference in phenology shift is likely attributed to the different vegetation types (Figure 1d) (Liu et al., 2016a; Richardson et al., 2013; Wang et al., 2017; Zhu et al., 2012). The mechanisms of phenology dynamics between grassland and forest are different. Grassland phenology usually depends on the combined effect of temperature and precipitation, and it was more sensitive to precipitation compared with forests (Chuai et al., 2018). In UR where grasslands are dominant, 12
the spring phenology may be determined by precipitation rather temperature (Yuan et al., 2007), and thus a reduced precipitation would result in a delayed SOS and the legacy effect on the vegetation growth. On the other hand, in MLR where forest vegetation is dominant, the large root system of trees may buffer the water stress during spring before SOS and during the growing season (Brunner et al., 2015; Gargallo-Garriga et al., 2015), and thus a warming spring would promote SOS and extend the growing season and vegetation growth. It needs to be however noted that the phenology of forest is also influenced by precipitation and radiation/photoperiod (Fu et al., 2015; Fu et al., 2019; Hänninen, 2016), and a gradually increasing weight of other climatic variables on the forest phenology may be expected under future climate change. 4.2 Phenology shift contribution to GS_NDVI This study used GS_NDVI as proxy of vegetation growth, and found an overall enhanced tendency of vegetation growth over the past 34 years in the Luanhe River basin. This is consistent with previous studies that reported that a longer GSL could induce higher vegetation growth (Park et al., 2016; Piao et al., 2007). Changes of SOS and EOS resulting in a longer GSL have been widely reported (Lucht et al., 2002; Menzel and Fabian, 1999; Piao et al., 2007; Tucker et al., 2001; Vesala et al., 2010), but their contributions to vegetation growth vary (Barichivich et al., 2013; Vesala et al., 2010; Wu et al., 2013). In this study, it was found that the advanced SOS contributed more to the enhanced GS_NDVI than did the postponed EOS (Figure 4). This may be attributed to different seasonal temperature changes (Garonna et al., 2014; Piao et al., 2007). According to IPCC (2013) report, the warming trends in winter and spring was significantly higher than autumn. Although autumn phenology was not as well-documented as
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spring phenology, recent studies indicated the critical role of delayed autumn in prolonging the growing season (Garonna et al., 2014; Jeong et al., 2011; Zhu et al., 2012). However, that mainly happened in shrubs-covered lands with short GSL (Zhu et al., 2012), or where vegetation growth depends more on photoperiod and temperature (Garonna et al., 2014; Liu et al., 2016b). Furthermore, to better understand the contributions of phenological shift to GS_NDVI, vegetation types and changes of environmental factors were also considered (Garonna et al., 2014; Peng et al., 2013). This study found significantly different GS_NDVI between UR and MLR of the basin, which may be attributed to the deeper root system of forest in MLR and its better capability of resistance to drought stress than grassland in UR (Filoso et al., 2017; Liu et al., 2018; Wang et al., 2011). 4.3 Vegetation impacts on river runoff and its spatial heterogeneity Previous studies have reported that shifted regimes in precipitation and radiation under climate warming were the key factors impacting hydrological processes (Nohara et al., 2006; Ragettli et al., 2016; Revi et al., 2014). Consistent with these studies, it was found that precipitation was strongly associated with river runoff, but, interestingly, SOS and GSL, as well as GS_NDVI, were all strongly associated with river runoff. Specifically, vegetation growth played a more important role in UR than precipitation (Figure 6), and the contribution of SOS to runoff was second to precipitation, but obviously higher than that of other climatic factors (Figure A.4). Forest covered areas (e.g. MLR in this study) usually have higher water interception and transpiration compared with grassland covered areas (e.g. UR in this study) (James et al., 2003; Yamazaki et al., 2004). Due to earlier spring growth onset and longer vegetation greening, vegetation water use may lead to aggravated decrease of soil water, even
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groundwater, resulting in water stress in spring. Therefore, precipitation would first replenish soil deficit and recharge groundwater, thus reduce water discharge of the river basin (Kim et al., 2018; Wang et al., 2019). Additionally, the spring phenology effect may also promote the process of water transport from land surface to the atmosphere substantially (Huntington, 2008; Kim et al., 2018; Piao et al., 2019; Wang et al., 2016; Zeng et al., 2018). As transpiration of vegetation canopy is the main contribution of evapotranspiration in arid and semi-arid areas (Jasechko et al., 2014; Wang et al., 2010; Yamazaki et al., 2004), intensified evapotranspiration might be expected, and would subsequently result in the reduction of river runoff. Indeed, using the gridded dataset, the evapotranspiration was found significantly increased over the study period in the river basin (Figure A.5). Therefore, the intensified processes of surface water recharge of soil and groundwater, vegetation evapotranspiration, combined with precipitation reduction, jointly led to the decreasing of river runoff. In addition, land cover is a key factor affecting runoff, with the promotion of afforestation, researches on impacts of land use or vegetation type change on surface runoff are still of great importance (Yang et al., 2019). 5 Conclusion and implications Our results confirmed that river runoff was regulated by both climatic and phenological variables at the watershed scale (Hwang et al., 2018). The prominent effects of vegetation growth and phenological shift on river runoff were revealed in Luanhe River basin. However, research at the watershed scale is still lacking, considering that Luanhe River basin is located in a typical semi-arid area. In addition, large-scale research cannot effectively reveal the underlying mechanisms of the impacts of phenological shift and vegetation growth on river runoff. Thus, it is necessary to further assess on other watersheds in different climate regions and conduct eco-
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physiological experiments which is of great practical significance to cope with climate change and strengthen water resources and ecosystem management in watersheds. Additionally, river water withdrawal may affect the estimation of runoff, and subsequently increase uncertainty in our analysis, and thus the role of vegetation phenology on runoff might be overestimated. Whereas, over the Luanhe river basin, considering the fact that forest and grassland are the major vegetation types and the cover of cropland is minor, and the ground water is the main source of cropland requirement in the study area, we may expect an insignificant effect of river water withdrawal on Luanhe runoff. Furthermore, although not included in this study, it is worthy of strengthening the impacts of land use change on surface runoff, which is of great significance for resource and environment management under the intensified human activities in the future. Vegetation phenology is particularly sensitive to climate change, and a dramatic shift in phenology would be expected under future climate change, and subsequently impact terrestrial water cycles. However, it was found that different extraction methods generate large uncertainties in phenological date estimation, due to irregular or asymmetric NDVI series, or their sensitivity to NDVI values of the non-growing season affected by snow cover (Cong et al., 2012; Liu et al., 2016). Therefore, the fusion of multiple methods is necessary to make the results more reliable. In addition, researches on physiological processes are required to further improve the accuracy of remote sensing information. Furthermore, the river runoff may be greatly underestimated by hydrological models without considering the vegetation dynamics and its’ response to climate change. This study highlights the importance of accurate estimation of vegetation phenology in the hydrological models, especially in semi-arid and arid areas, because vegetation may be more sensitive to precipitation changes in these areas. Current phenology models are generally based on statistical
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methods, especially the autumn phenology models, and process-based models that based on ecophysiological processes (Hänninen et al, 2019) are thus needed. Additionally, coupled with hydrological models will help improve model accuracy and our understanding of the terrestrial water cycles under future climate change. Acknowledgments This study was supported by the General program of National Nature science foundation of China (Grant No. 31770516), the National Key Research and Development Program of China (2017YFA06036001), and the 111 Project (B18006) and Fundamental Research Funds for the Central Universities (2018EYT05). The authors gratefully acknowledge all members of the Hydrological Yearbook of the People’s Republic of China for providing the in-situ runoff data. Y.H.F. designed the research and drafted the paper; X.J.G and X.C.Z performed the analysis and all authors contributed to the interpretation of the results and to the text.
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Figure 1 Study area. a is the location of Luanhe River basin in China. b is the topography and the distribution of both meteorological and hydrometric sites. c is the distribution of river networks and regions division. d is the current land cover of Luanhe River basin.
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Figure 2 Annual trend of climatic variables by regions. WR is the whole region of Luanhe River basin, UR is upper reaches region, and MLR is middle-lower reaches region. The error bar denotes one standard error.
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Figure 3 Annual trend of phenology and growth in Luanhe River basin from 1982 to 2015. The subpanels are spatial and frequency distributions of annual trend of SOS (a and b), EOS (c and d), GSL (e and f) and GS_NDVI (g and h), respectively. The grids with black dots in a, c, e, and g indicate the level of significance is within 0.05. The subpanels in b, d, f, and h also show the differences of Mean ± SE
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between upper reaches region (UR) and middle-lower reaches region (MLR). The error bar denotes one standard error.
Figure 4 Linear correlations between GS_NDVI and (a) SOS, (b) EOS and (c) GSL by regions. The colored values indicate the slope, R2 and p-values of each linear fitting line. WR is the whole region of Luanhe River basin, UR is upper reaches region, and MLR is middle-lower reaches region.
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Figure 5 Temporal changes of anomalies in annual river runoff from 1982 to 2015 based on subbasins. Point values are detected by comparing each year with the multi-year average. The black solid line is the mean trend of the whole river basin with light blue shading area of 95% confidence interval. The table at the top right shows the runoff trend by regions. WR is the whole region of Luanhe River basin, UR is upper reaches region (including sub-basins of S1, S2, S3 and S4), and MLR is middle-lower reaches region (including sub-basins of S5, S6, … S12).
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Figure 6 Linear trend of normalized key factors affecting runoff and grey relational degrees between them. Subpanels a, b, and c are linear trends of runoff, GSL, and GS_NDVI in different regions. The linear regression is based on normalized data, plotted as the lines, with the shaded area of 95% confidence interval. d is grey relational degrees by regions. Color from shallow to deep indicates the correlation from small to large. WR is the whole region of Luanhe River basin, UR is upper reaches region, and MLR is middle-lower reaches region.
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Author Contribution Y.H.F. designed the research and drafted the paper; X.J.G and X.C.Z performed the analysis and all authors contributed to the interpretation of the results and to the text.
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Highlights:
Climate warming significantly extended growing season by 0.60 ± 0.08 d per year in Luanhe River basin.
The extended growing season mainly related to the earlier start of growing season (SOS, -0.40 ± 0.05 d per year).
The results confirmed the primary regulation of river runoff by precipitation, but the length of growing season and vegetation growth also played a key role in the changes of runoff.
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S0022-1694(19)31273-9 https://doi.org/10.1016/j.jhydrol.2019.124538 HYDROL 124538
To appear in:
Journal of Hydrology
Received Date: Revised Date: Accepted Date:
27 August 2019 27 November 2019 31 December 2019
Please cite this article as: Geng, X., Zhou, X., Yin, G., Hao, F., Zhang, X., Hao, Z., Singh, V.P., Fu, Y.H., Extended growing season reduced river runoff in Luanhe River basin, Journal of Hydrology (2019), doi: https://doi.org/ 10.1016/j.jhydrol.2019.124538
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© 2019 Published by Elsevier B.V.
Extended growing season reduced river runoff in Luanhe River basin
Xiaojun Genga, Xuancheng Zhoua, Guodong Yina, Fanghua Haoa*, Xuan Zhanga*, Zengchao Haoa, Vijay P. Singhb, and Yongshuo H. Fua*
a College
of Water Sciences, Beijing Normal University, Beijing 100875, China.
b Department
of Biological and Agricultural Engineering, Texas A&M University, College
Station, Texas, USA.
Corresponding author: Yongshuo H. Fu ([email protected]) Fanghua Hao ([email protected]) and Xuan Zhang ([email protected])
Highlights:
Climate warming significantly extended growing season by 0.60 ± 0.08 d per year in Luanhe River basin..
The extended growing season mainly related to the earlier start of growing season (SOS, -0.40 ± 0.05 d per year).
The results confirmed the primary regulation of river runoff by precipitation, but the length of growing season and vegetation growth also played a key role in the changes of runoff.
1
Abstract Global warming has substantially altered vegetation growth and phenology in temperate biomes worldwide, whose impacts on river runoff have not been fully understood, especially at the watershed scale. Investigating the relationships among phenological shift, changes in vegetation growth, and river runoff is essential to improve our understanding of the ecosystem response to ongoing climate change. Using in-situ runoff records, phenological dates that were extracted from the normalized difference vegetation index (NDVI) over the period 1982-2015, and grey relational analysis, this study investigated the relationships between changes in river runoff and phenological variables in Luanhe River basin, China. It was found that the growing season in the Luanhe basin was significantly extended by 0.60 ± 0.08 d per year associated with climate warming over the period 1982-2015, which is mainly related to the earlier start of growing season (SOS, 0.40 ± 0.05 d per year), rather than the delayed end of growing season (EOS, 0.11 ± 0.01 d per year). The vegetation growth, defined as the average growing season NDVI (GS_NDVI), significantly increased in more than 88.4% of the study area, on average the GS_NDVI increased by 1.30 ± 0.03×10-3 d per year. On the other hand, runoff significantly reduced by 1.42 mm per year over the study period, but large differences were observed between upper and middle-lower reaches region of the Luanhe basin. The grey relational analysis confirmed the primary regulation of river runoff by precipitation, but the length of growing season and vegetation growth also played a key role in the changes of runoff. These results provide new insights into the interaction between vegetation dynamics and water balance at the watershed scale, and highlight to couple the phenological processes into eco-hydrological models to improve the modelling accuracy. Key words: climate change; phenology shifts; vegetation growth; river runoff; watershed scale
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1 Introduction In temperate and boreal regions, vegetation phenology is particularly sensitive to climate change (Chuine et al., 2004; Schwartz, 2013). Recent climate warming has extended the growing season mainly due to the advanced start of season (SOS) and the delayed end of season (EOS) in the Northern Hemisphere (Menzel et al., 2006; Peñuelas and Filella, 2001; Piao et al., 2019; Fu et al., 2015), and has subsequently affected the ecosystem structure and function (Piao et al., 2017; Thackeray et al., 2016; Zohner et al., 2018). Extended growing season results in higher carbon assimilation and increases the water retention of terrestrial ecosystems (Lu et al., 2013; Piao et al., 2007; Williams et al., 2012). However, to our knowledge, how the phenological shift affects the regional water cycle, especially at the watershed scale, has not been well investigated. Hence, it is essential to investigate the water cycle response to climate warming by analyzing the interaction between phenological shift and river runoff at the watershed scale (Peñuelas et al., 2009; Zeng et al., 2018). It is widely accepted that water cycle processes are mainly controlled by changes of climatic variables, such as precipitation and radiation, as well as land use and land cover change (Chiew et al., 2009; Hundecha and Bárdossy, 2004; Singh et al., 2014; Williams et al., 2012). Climate warming may directly cause the frequency and intensity changes in precipitation, accelerate the transfer of water from the earth's surface to the atmosphere, and lead to higher drought stress, and thus reduce river runoffs (Hao et al., 2018; Prudhomme et al., 2014). Except for climate variables, recent studies have found that warming-induced vegetation growth has affected evapotranspiration in river basins, and thus has a great impact on water yield and river runoff (Goulden and Bales, 2014; Li et al., 2018; Richardson et al., 2013; Thompson et al., 2011; Yang et al., 2019; Zeng et al., 2018). For example, Lemordant et al. (2018) found that compared
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with the changes of precipitation or radiation, the physiological response to CO2 and water use efficiency of vegetation growth played a more pivotal role affecting terrestrial water cycles based on earth system models. In general, few studies have investigated the influence of vegetation growth on river runoff, especially using in situ records, at the watershed scale. Vegetation phenology also contributes to the shift of river runoff. Recent studies found that in a temperate deciduous forest, advanced spring green-up dates caused higher evapotranspiration and reduced river runoff (Kim et al., 2018) where the spatial resolution was small, i.e. based only on a small catchment scale (~0.8 km2). How phenological shifts affect river runoff at a large scale, especially at the large watershed scale, is still unclear. Furthermore, climate warming has extended the vegetation growing season, but the contribution of spring and autumn phenology has been reported inconsistently among studies. Some studies found that the spring phenology contributed more to the length of growing season (Chen et al., 2000; Linderholm, 2006; Menzel and Fabian, 1999), however, other studies reported that autumn phenology played a more decisive role than spring phenology (Wu et al., 2013). Overall, how spring and autumn phenology affect the length of growing season (GSL) and subsequently hydrological processes of the watershed ecosystem remains unclear. One possible reason is that large uncertainty exists when extracting remote sensing-based phenology dates using a single method (Cong et al., 2012; White et al., 2009). Using multiple methods to accurately extract phenology dates are thus necessary to apply in the phenology-river runoff analysis. We, therefore, applied five methods to determine the start and the end of growing season (SOS and EOS) using the normalized difference vegetation index (NDVI) over the period 19822015 in Luanhe River basin, a typical semi-arid watershed in temperate China. Grey relational analysis (GRA) was applied to detect the relationships among phenology, vegetation growth,
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climatic variables, and river runoff. The objectives of this study were to (a) determine the temporal shifts of vegetation phenology and growth and river runoff, (b) investigate the relative contribution of spring and autumn phenology to vegetation growth, and (c) explore the relationships between trends in phenology, vegetation growth, and river runoff over the period 1982-2015. 2 Materials and Methods 2.1 Study area Luanhe River basin, with a total area of 38,000 km2, is located in arid and semi-arid areas of North China (115°27′E-118°56′E, 40°N-42°41′N) (Figure 1a). The Luanhe River flows from northwest to southeast with 12 main sub-basins (Figure 1b and Figure A.1). The annual average temperature and precipitation during period of 1982-2015 were 7.0 ± 2.6℃ and 488.4 ± 80.7 mm, respectively, but a large spatial heterogeneity exists. In recent decades, the land use types in the area have not changed much, and mainly covered by forest (42.4%) and grassland (46.3%) (Table A.1). The whole river basin was separated into two regions, based on different land covers in year of 2015 (Figure 1c). The upper reaches region (UR) is mainly covered by temperate and meadow grassland, while the middle-lower reaches region (MLR) is mainly covered by temperate forests (Figure 1d). 2.2 Data and processing 2.2.1 DEM and land use data. The Digital Elevation Model (DEM) of the watershed (30 m × 30 m) was provided by the Data Center for Resources and Environmental Sciences, Chinese Academy of Sciences (RESDC)
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(http://www.resdc.cn). Land use data with a spatial resolution of 300 m in year of 2015 was collected from the European Space Agency (http://www.esa-landcover-cci.org). 2.2.2 Meteorological and hydrological data. Monthly precipitation, mean air temperature, and solar radiation data (1982-2015) from 13 meteorological stations within the river basin were collected from the Chinese Daily Surface weather database (http://data.cma.cn) (Figure. 1b). The Kriging method was applied for spatial interpolation, and then annual series of meteorological variables were obtained for sub-basins in order to analyze temporal trends and relationships between runoff and related factors. The observed data of annual runoff was collected from hydrometric stations in 12 typical sub-basins of Luanhe River basin (Figure. 1b), including 4 sub-basins in the UR (S1~S4) and 8 sub-basins in the MLR (S5~S12) (Figure A.1). The distributions of meteorological and hydrometric stations are shown in Figure 1b. 2.2.3 NDVI dataset. The data of vegetation growth and long-term phenological records (1982-2015) were extracted from the bi-monthly 8-km Global Inventory Modeling and Mapping Studies (GIMMS) NDVI3g (third generation normalized difference vegetation index) data set from Advanced Very High Resolution Radiometer (AVHRR) (Tucker et al., 2005; Tucker et al., 2004) (https://nex.nasa.gov/nex/projects/1349/). The growing season length (GSL) was defined as the time interval between the start of growing season (SOS) and the end of growing season (EOS). Vegetation growth was defined as the mean NDVI over the growing season.
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2.2.4 Estimation of SOS and EOS. Five commonly used methods (including Gaussian-Midpoint method, Spline-Midpoint method, HANTS-Maximum method, Polyfit-Maximum method, and Timesat-SG method) were applied to determine SOS and EOS. The mean values of SOS and EOS across the five methods were applied for each pixel. The details of the five methods are provided in the appended Text A.1. In general, three steps were involved for extracting the phenological dates: (1) eliminating the influence of errors and noises caused by orbital drift, calibration, viewing geometry, stratospheric volcanic aerosols, and snow cover; (2) using different methods to filter and interpolate NDVI, and using thresholds or maximum/minimum rate of temporal NDVI change to determine the phenological dates; and (3) distinguishing and removing the abnormal points based on a five-point median-value moving average method. 2.3 Methods Min-max normalization was used to process the time series data, to compare temporal dynamics of different indicators with large different absolute magnitudes (Jia et al., 2011). Grey relational analysis (GRA) is a method of the geometric proximity between different discrete sequences within a system (Huang and Huang, 1996; Deng, 1989), which was applied to determine the correlation among climatic variables (i.e. precipitation, temperature, solar radiation), ecological variables (i.e. SOS, EOS, GSL, GS_NDVI) and river runoff. The proximity of two series is described by the grey relational degree (GRD), a higher GRD (usually >0.6) represents a greater influence of the parameter on the reference series (Fu et al., 2001; Ip et al., 2009; Xu et al., 2011). The GRD was calculated as follows (Deng, 1989):
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Runoff series were named as reference sequences, labeled as 𝑋0(𝑡), and the climatic and ecological variables as influence sequences, labeled as 𝑋𝑖(𝑡). 𝑋′0(𝑡) and 𝑋′𝑖(𝑡) represent the normalized variables of 𝑋0(𝑡) and 𝑋𝑖(𝑡) through min-max normalization. The GRD of influence of each series 𝑋𝑖(𝑡) on reference series 𝑋0(𝑡) was calculated following the formulations given below: 1
𝑛
𝐺𝑅𝐷𝑖 = 𝑛∑𝑡 = 1𝐾(𝑡)
𝐾(𝑡) =
𝑚𝑖𝑛 (∆X(t)) + 𝜌 ∗ max (∆X(t)) ∆X(t) + 𝜌 ∗ max (∆X(t))
∆X(t) =
|(𝑋′𝑖(𝑡) ― 𝑋′0(𝑡))|
(1)
(2) (3)
where ρ is the grey parameter that ranged from 0 to 1, and is often assigned a value of 0.5. 3 Results 3.1 Changes of climatic variables During the period 1982-2015, the whole region of Luanhe River basin (WR) was getting warming and drying, with temperature significantly increasing by 0.03 ± 0.002 ℃ per year (℃/y) and precipitation decreasing by 1.35 ± 0.19 mm per year (mm/y), and the radiation was increasing by 0.19 ± 0.03 W·m-2 per year (Figure 2). These trends were consistent in all subbasins (Figure A.2), but the warming trend was stronger in UR than in MLR (0.04 ± 0.002 and 0.02 ± 0.001 ℃/y, UR and MLR, respectively), while the drying trend was more obvious in MLR than in UR, on average more than two times reduction in precipitation was observed in UR (-0.76 ± 0.05 and -1.66 ± 0.02 mm/y, UR and MLR, respectively). 8
3.2 Changes in phenology and vegetation growth in Luanhe River basin 3.2.1 Phenological dynamics The mean date of SOS in the Luanhe River basin is 121 ± 4.5 (Day of year, DoY) over the period 1982-2015. The SOS was advanced by an average of -0.40 ± 0.05 days per year (d/y), and around 75.4% of the study area experienced advanced trends, with roughly 24.0% of them statistically significant at p-value < 0.05 (Figure 3a). However, large spatial difference of SOS exists, and the earlier SOS was mainly observed in MLR (-0.61 ± 0.06 d/y), whereas the SOS in UR was even delayed (0.21 ± 0.06 d/y) (Figure 3b). The mean date of EOS is 291 ± 2.8 (DoY) over the whole area, and the EOS was delayed by an average of 0.11 ± 0.01 d/y over the study period 1982-2015. The delayed trend was observed in 81.1% of the study area, and 53.5% of which was significant at p-value < 0.05 (Figure 3c). Although delayed EOS existed in both UR and MLR, its value was different in the two regions. The EOS delay in MLR (0.14 ± 0.01 d/y) was more than twice that in UR (0.06 ± 0.01 d/y) (Figure 3d). The spatial distribution of annual trend of GSL is shown in Figure 3e. The extension of GSL was extensively observed across 76.0% of the study area, with 43.0% of the pixels showing significant extension (p-value < 0.05，Figure 3e). The mean extension of GSL across the whole basin was 0.60 ± 0.08 d/y. Comparing different regions, it was found that the significant extension pixels were mainly distributed in MLR (0.85 ± 0.10 d/y), while there was no obvious change in UR (0.002 ± 0.07 d/y) (Figure 3f).
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3.2.2 Growth dynamics and its correlation with phenology Using the GS_NDVI as proxy of vegetation growth, it was found that vegetation growth substantially increased over the Luanhe River basin during the period 1982-2015, increasing trends were found in 88.4% areas, with mean annual trend (1.30 ± 0.03) ×10-3, and 78.4% of the trends were statistically significant at p-value < 0.05 (Figure 3g). Consistent with phenological changes, there were large spatial variances between UR and MLR. Although the overall GS_NDVI in UR exhibited an increasing trend (0.55 ± 0.05) ×10-3 per year, there were still 23% pixels with decreasing vegetation growth, whereas GS_NDVI in MLR increased significantly at (1.6 ± 0.04) ×10-3 per year, nearly three times as large as that in UR (Figure 3h). In addition, it was noted that the GS_NDVI was not the same everywhere in UR, so we further divided the UR into two regions according to the GSL, i.e. extended vs. shortened. Compared with GSL extended areas, both the temperature and precipitation in GSL shortened areas were lower, and the changes of both temperature and precipitation were significantly higher (p-value < 0.05, Figure A.3). All these changes were likely to affect the vegetation growth and lead to the regional differences of growing season in UR. GS_NDVI was significantly correlated with SOS and GSL (p-value < 0.05), but not with EOS (p-value = 0.10) in the whole region (WR) (Figure 4). However, significant correlations were observed only in MLR rather than in UR, suggesting other factors, such as climate and peak NDVI, may determine the vegetation growth in UR. On the contrary, in MLR, SOS, EOS, and GSL were all significantly correlated with vegetation growth, especially GSL, where the coefficient of determination was larger (R2 = 0.48) than for SOS (R2 = 0.34) and EOS (R2 = 0.13).
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3.3 Changes of river runoff and its association with phenology 3.3.1 Changes of river runoff To compare the temporal trend of annual runoff, this study used annual runoff anomaly (Figure 5). Across all the 12 sub-basins, runoff decreased significantly at a rate of -1.42 mm/y (p-value < 0.05) from 1982 to 2015. The significantly reduced runoff was found in both UR and MLR, but the reduction in MLR was around 4 times higher than in UR; the average trends of the four sub-basins in UR and the eight sub-basins in MLR were -0.45 mm/y and -1.90 mm/y, respectively (p-value < 0.05，Figure 5). Furthermore, it was found that river runoff in the whole region or sub-basins was generally lower than the multi-year average after the year 2000. 3.3.2 Correlation between shifts in river runoff and phenology Linear trends of key factors affecting river runoff (Figure 6a-c) and grey relational analysis (GRA) among them (Figure 6d) are shown in Figure 6. The runoff in WR showed a negative association with either GSL or GS_NDVI (Figure 6a) and the negative association was much stronger in MLR than in UR (Figure 6b and 6c). Combining with the grey relational degree, it was found that the association between the change of precipitation and that of river runoff was the strongest (GRD = 0.75,, Figure 6d). It was also observed that both GSL and GS_NDVI showed a relatively higher association with river runoff (GRD = 0.59 and 0.64, respectively), rather than other climatic variables (Figure 6d). These relationships varied between UR and MLR. The change of river runoff in UR was most related to the change of GS_NDVI (GRD = 0.75), even slightly more strongly than to precipitation (GRD = 0.72) (Figure 6d). However, precipitation change still had the highest correlation with river runoff (GRD = 0.77) in MLR, and GSL and GS_NDVI were strongly associated with runoff (GRD = 0.59 and 0.60,
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respectively) (Figure 6d). These findings suggested that both climate and ecological variables were associated with changes of river runoff. The grey relational analysis between SOS/EOS and river runoff (Figure A.4) was also applied. Both precipitation and phenological shifts showed a relatively higher association with river runoff than other climatic factors among which the contribution of SOS (GRD = 0.66) was more significant than EOS (GRD = 0.64) (Figure A.4d).
4 Discussion 4.1 Phenological shift and spatial heterogeneity Earlier SOS and later EOS, and extended growing season were found in the Luanhe River basin over the period 1982-2015 when significant warming trend was detected. It was further found that the extended growing season promoted the vegetation growth in the basin. Such results are consistent with previous studies in temperate areas in the Northern Hemisphere (Cong et al., 2012; Gong et al., 2015; Linderholm, 2006), whereas higher vegetation growth largely relied on the earlier SOS rather than EOS in the basin. In addition, higher vegetation growth was found in MRL but not in UR. This was mainly because a delayed, rather advanced SOS, found in UR. Due to small changes of land use in the study area over the past years (Table A.1), such a difference in phenology shift is likely attributed to the different vegetation types (Figure 1d) (Liu et al., 2016a; Richardson et al., 2013; Wang et al., 2017; Zhu et al., 2012). The mechanisms of phenology dynamics between grassland and forest are different. Grassland phenology usually depends on the combined effect of temperature and precipitation, and it was more sensitive to precipitation compared with forests (Chuai et al., 2018). In UR where grasslands are dominant, 12
the spring phenology may be determined by precipitation rather temperature (Yuan et al., 2007), and thus a reduced precipitation would result in a delayed SOS and the legacy effect on the vegetation growth. On the other hand, in MLR where forest vegetation is dominant, the large root system of trees may buffer the water stress during spring before SOS and during the growing season (Brunner et al., 2015; Gargallo-Garriga et al., 2015), and thus a warming spring would promote SOS and extend the growing season and vegetation growth. It needs to be however noted that the phenology of forest is also influenced by precipitation and radiation/photoperiod (Fu et al., 2015; Fu et al., 2019; Hänninen, 2016), and a gradually increasing weight of other climatic variables on the forest phenology may be expected under future climate change. 4.2 Phenology shift contribution to GS_NDVI This study used GS_NDVI as proxy of vegetation growth, and found an overall enhanced tendency of vegetation growth over the past 34 years in the Luanhe River basin. This is consistent with previous studies that reported that a longer GSL could induce higher vegetation growth (Park et al., 2016; Piao et al., 2007). Changes of SOS and EOS resulting in a longer GSL have been widely reported (Lucht et al., 2002; Menzel and Fabian, 1999; Piao et al., 2007; Tucker et al., 2001; Vesala et al., 2010), but their contributions to vegetation growth vary (Barichivich et al., 2013; Vesala et al., 2010; Wu et al., 2013). In this study, it was found that the advanced SOS contributed more to the enhanced GS_NDVI than did the postponed EOS (Figure 4). This may be attributed to different seasonal temperature changes (Garonna et al., 2014; Piao et al., 2007). According to IPCC (2013) report, the warming trends in winter and spring was significantly higher than autumn. Although autumn phenology was not as well-documented as
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spring phenology, recent studies indicated the critical role of delayed autumn in prolonging the growing season (Garonna et al., 2014; Jeong et al., 2011; Zhu et al., 2012). However, that mainly happened in shrubs-covered lands with short GSL (Zhu et al., 2012), or where vegetation growth depends more on photoperiod and temperature (Garonna et al., 2014; Liu et al., 2016b). Furthermore, to better understand the contributions of phenological shift to GS_NDVI, vegetation types and changes of environmental factors were also considered (Garonna et al., 2014; Peng et al., 2013). This study found significantly different GS_NDVI between UR and MLR of the basin, which may be attributed to the deeper root system of forest in MLR and its better capability of resistance to drought stress than grassland in UR (Filoso et al., 2017; Liu et al., 2018; Wang et al., 2011). 4.3 Vegetation impacts on river runoff and its spatial heterogeneity Previous studies have reported that shifted regimes in precipitation and radiation under climate warming were the key factors impacting hydrological processes (Nohara et al., 2006; Ragettli et al., 2016; Revi et al., 2014). Consistent with these studies, it was found that precipitation was strongly associated with river runoff, but, interestingly, SOS and GSL, as well as GS_NDVI, were all strongly associated with river runoff. Specifically, vegetation growth played a more important role in UR than precipitation (Figure 6), and the contribution of SOS to runoff was second to precipitation, but obviously higher than that of other climatic factors (Figure A.4). Forest covered areas (e.g. MLR in this study) usually have higher water interception and transpiration compared with grassland covered areas (e.g. UR in this study) (James et al., 2003; Yamazaki et al., 2004). Due to earlier spring growth onset and longer vegetation greening, vegetation water use may lead to aggravated decrease of soil water, even
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groundwater, resulting in water stress in spring. Therefore, precipitation would first replenish soil deficit and recharge groundwater, thus reduce water discharge of the river basin (Kim et al., 2018; Wang et al., 2019). Additionally, the spring phenology effect may also promote the process of water transport from land surface to the atmosphere substantially (Huntington, 2008; Kim et al., 2018; Piao et al., 2019; Wang et al., 2016; Zeng et al., 2018). As transpiration of vegetation canopy is the main contribution of evapotranspiration in arid and semi-arid areas (Jasechko et al., 2014; Wang et al., 2010; Yamazaki et al., 2004), intensified evapotranspiration might be expected, and would subsequently result in the reduction of river runoff. Indeed, using the gridded dataset, the evapotranspiration was found significantly increased over the study period in the river basin (Figure A.5). Therefore, the intensified processes of surface water recharge of soil and groundwater, vegetation evapotranspiration, combined with precipitation reduction, jointly led to the decreasing of river runoff. In addition, land cover is a key factor affecting runoff, with the promotion of afforestation, researches on impacts of land use or vegetation type change on surface runoff are still of great importance (Yang et al., 2019). 5 Conclusion and implications Our results confirmed that river runoff was regulated by both climatic and phenological variables at the watershed scale (Hwang et al., 2018). The prominent effects of vegetation growth and phenological shift on river runoff were revealed in Luanhe River basin. However, research at the watershed scale is still lacking, considering that Luanhe River basin is located in a typical semi-arid area. In addition, large-scale research cannot effectively reveal the underlying mechanisms of the impacts of phenological shift and vegetation growth on river runoff. Thus, it is necessary to further assess on other watersheds in different climate regions and conduct eco-
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physiological experiments which is of great practical significance to cope with climate change and strengthen water resources and ecosystem management in watersheds. Additionally, river water withdrawal may affect the estimation of runoff, and subsequently increase uncertainty in our analysis, and thus the role of vegetation phenology on runoff might be overestimated. Whereas, over the Luanhe river basin, considering the fact that forest and grassland are the major vegetation types and the cover of cropland is minor, and the ground water is the main source of cropland requirement in the study area, we may expect an insignificant effect of river water withdrawal on Luanhe runoff. Furthermore, although not included in this study, it is worthy of strengthening the impacts of land use change on surface runoff, which is of great significance for resource and environment management under the intensified human activities in the future. Vegetation phenology is particularly sensitive to climate change, and a dramatic shift in phenology would be expected under future climate change, and subsequently impact terrestrial water cycles. However, it was found that different extraction methods generate large uncertainties in phenological date estimation, due to irregular or asymmetric NDVI series, or their sensitivity to NDVI values of the non-growing season affected by snow cover (Cong et al., 2012; Liu et al., 2016). Therefore, the fusion of multiple methods is necessary to make the results more reliable. In addition, researches on physiological processes are required to further improve the accuracy of remote sensing information. Furthermore, the river runoff may be greatly underestimated by hydrological models without considering the vegetation dynamics and its’ response to climate change. This study highlights the importance of accurate estimation of vegetation phenology in the hydrological models, especially in semi-arid and arid areas, because vegetation may be more sensitive to precipitation changes in these areas. Current phenology models are generally based on statistical
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methods, especially the autumn phenology models, and process-based models that based on ecophysiological processes (Hänninen et al, 2019) are thus needed. Additionally, coupled with hydrological models will help improve model accuracy and our understanding of the terrestrial water cycles under future climate change. Acknowledgments This study was supported by the General program of National Nature science foundation of China (Grant No. 31770516), the National Key Research and Development Program of China (2017YFA06036001), and the 111 Project (B18006) and Fundamental Research Funds for the Central Universities (2018EYT05). The authors gratefully acknowledge all members of the Hydrological Yearbook of the People’s Republic of China for providing the in-situ runoff data. Y.H.F. designed the research and drafted the paper; X.J.G and X.C.Z performed the analysis and all authors contributed to the interpretation of the results and to the text.
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Figure 1 Study area. a is the location of Luanhe River basin in China. b is the topography and the distribution of both meteorological and hydrometric sites. c is the distribution of river networks and regions division. d is the current land cover of Luanhe River basin.
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Figure 2 Annual trend of climatic variables by regions. WR is the whole region of Luanhe River basin, UR is upper reaches region, and MLR is middle-lower reaches region. The error bar denotes one standard error.
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Figure 3 Annual trend of phenology and growth in Luanhe River basin from 1982 to 2015. The subpanels are spatial and frequency distributions of annual trend of SOS (a and b), EOS (c and d), GSL (e and f) and GS_NDVI (g and h), respectively. The grids with black dots in a, c, e, and g indicate the level of significance is within 0.05. The subpanels in b, d, f, and h also show the differences of Mean ± SE
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between upper reaches region (UR) and middle-lower reaches region (MLR). The error bar denotes one standard error.
Figure 4 Linear correlations between GS_NDVI and (a) SOS, (b) EOS and (c) GSL by regions. The colored values indicate the slope, R2 and p-values of each linear fitting line. WR is the whole region of Luanhe River basin, UR is upper reaches region, and MLR is middle-lower reaches region.
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Figure 5 Temporal changes of anomalies in annual river runoff from 1982 to 2015 based on subbasins. Point values are detected by comparing each year with the multi-year average. The black solid line is the mean trend of the whole river basin with light blue shading area of 95% confidence interval. The table at the top right shows the runoff trend by regions. WR is the whole region of Luanhe River basin, UR is upper reaches region (including sub-basins of S1, S2, S3 and S4), and MLR is middle-lower reaches region (including sub-basins of S5, S6, … S12).
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Figure 6 Linear trend of normalized key factors affecting runoff and grey relational degrees between them. Subpanels a, b, and c are linear trends of runoff, GSL, and GS_NDVI in different regions. The linear regression is based on normalized data, plotted as the lines, with the shaded area of 95% confidence interval. d is grey relational degrees by regions. Color from shallow to deep indicates the correlation from small to large. WR is the whole region of Luanhe River basin, UR is upper reaches region, and MLR is middle-lower reaches region.
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Author Contribution Y.H.F. designed the research and drafted the paper; X.J.G and X.C.Z performed the analysis and all authors contributed to the interpretation of the results and to the text.
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Highlights:
Climate warming significantly extended growing season by 0.60 ± 0.08 d per year in Luanhe River basin.
The extended growing season mainly related to the earlier start of growing season (SOS, -0.40 ± 0.05 d per year).
The results confirmed the primary regulation of river runoff by precipitation, but the length of growing season and vegetation growth also played a key role in the changes of runoff.
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