Quantifying the Impacts of Climate Change and Human Activities on Runo ﬀ in the Lancang River Basin Based on the Budyko Hypothesis

: Based on the Lancang River Basin (LRB) hydro–meteorological data from 1961 to 2015, this study uses the Mann–Kendall trend test and mutation test to analyze the trend of hydro–meteorological variables, as well as which year the runo ﬀ series changes, respectively. We applied the Choudhury–Yang equation to calculate the climate and catchment landscape elasticity of runo ﬀ . Then we quantiﬁed the impact of climate change and human activities on runo ﬀ change. The results show that: (1) the mean annual precipitation ( P ) in LRB showed an insigniﬁcant decline, the annual potential evapotranspiration ( E 0 ) showed a signiﬁcant increase, and the runo ﬀ depth ( R ) showed a signiﬁcant decrease; (2) the abrupt change of the R occurred in 2005. Both the climate and catchment landscape elasticity of runo ﬀ increased, which means that the hydrological process of LRB became more sensitive to climate changes and human activities; (3) compared with the base period (1961–2004), the reduction of P was the leading cause of runo ﬀ reduction, with a contribution of 45.64%. The contribution of E 0 and human activities to runo ﬀ changes are 13.91% and 40.45%, respectively.


Introduction
Under the impact of climate change and human activities, the water cycle process of many basins worldwide has significantly changed [1]. Since the 21st century, the runoff of China's Haihe, Yellow, and Liaohe rivers decreased significantly, aggravating the conflict between regional water supply and demand [2]. Runoff is an essential form of water resources and is closely related to climate change and human activities. Human activities mainly affect runoff changes through land use/cover changes and engineering construction [3]. Climate change affects the runoff by changing the temporal and spatial distribution of precipitation, surface evaporation, and others [4,5].
Hu-line is an important geographical boundary between China and Southeast Asia. The geographical environment at the end of the line is just the Lancang-Mekong River Basin. Understanding its water resources change under climate change is a geographical science basis for understanding the ecological environment of Southwest China and Lancang-Mekong River Basin. As the Mekong River's upper reaches, the Lancang River (LR) is an important international cross-border river connecting China and Southeast Asia. Its water resources are important for the development of countries in the basin. LR straddles several climatic regimes along latitude direction with huge climate differences and is a sensitive area to global climate change. The lower LR reaches through Southeast Asian countries, regulating the dry season river runoff of downstream countries. The utilization and management of water resources in LR have been highly concerned by organizations and governments worldwide.
the Murray Darling Basin in Australia. Results emphasize the importance of the spatial variation in runoff sensitivity, which is low for the whole basin, and moderate-high in high-yielding catchments. These studies strongly proved that the Choudhury-Yang equation, a water-energy balance equation based on the Budyko hypothesis, can be used worldwide and achieve satisfactory results.
This study aims to quantify the climate and human impacts on runoff using observed runoff data in conjunction with trend analysis and the Budyko hypothesis. First, this study analyzes the trend of hydro-meteorological variables and the abrupt change of runoff series by the Mann-Kendall trend test and mutation test, respectively. Then, the Choudhury-Yang equation is applied to calculate the climate and the catchment landscape elasticity of runoff. Finally, the contribution of climate change and human activities to runoff change are obtained. This study reveals the driving factors of runoff change in LRB and provides some theoretical support for cross-border water resource allocation and basin management in LRB.  [28]. The total length of the mainstream of LR is 2161 km, the catchment area is 167,487 km 2 , and the average altitude is 3058 m. It can be seen from Figure 1, except for the snow peak, the mountains in the upper reaches are generally flat to gently undulating. The mountains and valleys in the middle reaches are sheer, the river bed slope is steep, and the shape of the basin is long and narrow; the terrain of the lower reaches is gentle [29]. The basin terrain decreases from north to south, including almost all of the natural landscapes and climate types in the world except Gobi and desert [30].

Study Area and Data
Water 2020, 12, x FOR PEER REVIEW 3 of 12 Roderick and Farquhar [27] in the Murray Darling Basin in Australia. Results emphasize the importance of the spatial variation in runoff sensitivity, which is low for the whole basin, and moderate-high in high-yielding catchments. These studies strongly proved that the Choudhury-Yang equation, a water-energy balance equation based on the Budyko hypothesis, can be used worldwide and achieve satisfactory results. This study aims to quantify the climate and human impacts on runoff using observed runoff data in conjunction with trend analysis and the Budyko hypothesis. First, this study analyzes the trend of hydro-meteorological variables and the abrupt change of runoff series by the Mann-Kendall trend test and mutation test, respectively. Then, the Choudhury-Yang equation is applied to calculate the climate and the catchment landscape elasticity of runoff. Finally, the contribution of climate change and human activities to runoff change are obtained. This study reveals the driving factors of runoff change in LRB and provides some theoretical support for cross-border water resource allocation and basin management in LRB.

Study Area
LR originates from Qinghai Province, China. It is the name of the Mekong River in China. LRB ranges from 22°05′-33°40′ N to 93°50′-101°30′ E. The Lancang-Mekong River flows through Qinghai, Tibet, and Yunnan in China. It passes through Mengla County, Xishuangbanna, Yunnan Province, and then flows through Myanmar, Laos, Thailand, and Cambodia, finally flowing into the South China Sea at Saigon, Vietnam. It is the largest international river in Southeast Asia [28]. The total length of the mainstream of LR is 2161 km, the catchment area is 167,487 km 2 , and the average altitude is 3058 m. It can be seen from Figure 1, except for the snow peak, the mountains in the upper reaches are generally flat to gently undulating. The mountains and valleys in the middle reaches are sheer, the river bed slope is steep, and the shape of the basin is long and narrow; the terrain of the lower reaches is gentle [29]. The basin terrain decreases from north to south, including almost all of the natural landscapes and climate types in the world except Gobi and desert [30].

Data
The observed runoff data of Yunjinghong hydrometric station in the lower LR reaches from 1961 to 2015 are from the Hydrological Yearbook and National Earth System Science Data Center. The daily meteorological data of 18 stations from 1961 to 2015 are from the National Meteorological Information Center, including precipitation, average temperature, maximum and low temperature, sunshine hours, average wind speed, and relative humidity. Then the potential evapotranspiration was calculated according to the Penman-Monteith equation: where ET 0 is reference evapotranspiration (mm day −1 ), R n is net radiation at the crop surface (MJ m −2 day −1 ), G is soil heat flux density (MJ m −2 day −1 ), T is mean daily air temperature at 2 m height ( • C), U 2 is the wind speed at 2 m height (m s −1 ), e s s is saturation vapor pressure (kPa), e a is actual vapor pressure (kPa), e s − e a is saturation vapor pressure deficit (kPa), ∆ is slope vapor pressure curve (kPa • C −1 ), γ is psychrometric constant (kPa • C −1 ).

Mann-Kendall Trend Test
Mann-Kendall (MK) non-parametric trend test [31,32] is recommended by the World Meteorological Organization. It can effectively distinguish whether a natural process is in natural fluctuation or has a certain change trend. It is widely used in the trend analysis of climate and hydrological series [33]. In this study, we applied the MK test to analyze the hydro-meteorological series trend in LRB from 1961 to 2015, and calculate its magnitude and direction. When the Z value is positive, it indicates an increasing trend, and the negative value indicates a decreasing trend.

Mann-Kendall Mutation Test
Mann-Kendall mutation detection test [34] is an effective method to detect the abrupt year of runoff series. Yan et al. [35], Song et al. [36], Wang et al. [37] have detected the hydro-meteorological series in different regions and obtained reliable results using this method. This study applied this test to detect the runoff depth series mutation from 1961 to 2015 of Yunjinghong hydrometric station. UF, UB are calculated, and the critical value U 0.05 = ±1.96 when the significance level α = 0.05. The mutation point is at the intersection between the UF curve and the UB curve, and is within the critical value range.

Budyko Hypothesis
Budyko hypothesis [38] states that the actual evapotranspiration is a function of precipitation and potential evapotranspiration: where E 0 is potential evapotranspiration (mm), E is actual evaporation (mm), and P is precipitation (mm). According to the Budyko hypothesis, Choudhury [22] and Yang [23] considered the principle of water and energy balance and deduced the Choudhury-Yang water-energy balance equation as Equation (3). The equation is used to represent the mean annual water-energy balance. It describes the Budyko hypothesis by combining dimensional analysis and mathematical reasoning. It is also a useful theoretical tool to evaluate the effect of climate and land-use changes on the water cycle [23].
Water 2020, 12, 3501 where n is the parameter reflecting the characteristics of the catchment landscape, including topography, soil, and vegetation [39]. Over a long-term water cycle, it is reasonable to assume the change in soil water storage (∆S) is zero. Therefore, the long-term water balance equation is P = E + R, combining Equation (3), we derived: where R is the mean annual runoff depth (mm). The elasticity of R for the independent variable x can be written as: R's rainfall elasticity means the change rate of annual R relative to mean annual R, when annual P increased by 1%. Similarly, catchment elasticity can be defined as the variation of R caused by catchment landscape changed per unit [39]. Assuming φ = E 0 /P, the rainfall elasticity (ε P ), the potential evapotranspiration elasticity (ε E 0 ), and the catchment landscape elasticity (ε n ) of R are written as Equations (6)-(8) [13,40]:

Attribution Analysis of Runoff Change
According to the mutation point detected in Section 3.2, the time series is divided into two periods T1 and T2. The mean annual R, E 0 , P, and n of the two periods are calculated respectively. Then, the annual R variation from T1-T2 can be expressed as the difference of the mean R of two periods (∆R), as follows: Similarly, the variation of P, E 0 , n can be described as follows: The ∆R is attributed to the impacts of climate change (∆R climate ) and catchment landscape change (∆R landscape ). Assuming that ∆R landscape is mainly caused by land use/cover change, and ∆R climate includes the impact of precipitation variation on runoff (∆R P ) and potential evapotranspiration variation on runoff (∆R E 0 ). The equations are as follows: Water 2020, 12, 3501 6 of 11 The contribution of each variable to runoff change is calculated as follows: where η P , η E o and η l are the contribution of precipitation, potential evapotranspiration, and human activities to runoff change, respectively. Figure 2 shows the interannual variation of P and E 0 in LRB from 1961 to 2015. Figure 3 shows the interannual variation of R of Yunjinghong hydrometric station from 1961 to 2015. The annual P generally presents a slight downward trend, the annual E 0 presents a slight upward trend, and the annual R presents a relatively apparent downward trend.

Trend of Hydro-Meteorological Variables
The contribution of each variable to runoff change is calculated as follows: where P η , o E η and l η are the contribution of precipitation, potential evapotranspiration, and human activities to runoff change, respectively. Figure 2 shows the interannual variation of P and E0 in LRB from 1961 to 2015. Figure 3 shows the interannual variation of R of Yunjinghong hydrometric station from 1961 to 2015. The annual P generally presents a slight downward trend, the annual E0 presents a slight upward trend, and the annual R presents a relatively apparent downward trend.

Trend of Hydro-Meteorological Variables
The contribution of each variable to runoff change is calculated as follows: where P η , o E η and l η are the contribution of precipitation, potential evapotranspiration, and human activities to runoff change, respectively. Figure 2 shows the interannual variation of P and E0 in LRB from 1961 to 2015. Figure 3 shows the interannual variation of R of Yunjinghong hydrometric station from 1961 to 2015. The annual P generally presents a slight downward trend, the annual E0 presents a slight upward trend, and the annual R presents a relatively apparent downward trend.  Catchment parameter n for each year is calculated by Equation (3). Then set R as the dependent variable and performed regression analysis with three independent variables that affect it, respectively. Results are shown in Table 1 below. R is positively correlated with P, negatively correlated with E 0 and n, and all passed α = 0.001 significant test. Using the MK trend test to detect the annual P, E 0 , and R in LRB, and the results are shown in Table 2. From 1961 to 2015, the Z value of annual P is −1.44, which means that annual P shows a downward trend but not significant; for E 0 is 3.50, which shows an extremely significant increase trend; for R is −3.17, which presents an extremely significant decrease trend.  respectively. Results are shown in Table 1 below. R is positively correlated with P, negatively correlated with E0 and n, and all passed = 0.001 significant test. Using the MK trend test to detect the annual P, E0, and R in LRB, and the results are shown in Table 2. From 1961 to 2015, the Z value of annual P is −1.44, which means that annual P shows a downward trend but not significant; for E0 is 3.50, which shows an extremely significant increase trend; for R is −3.17, which presents an extremely significant decrease trend.

The Climate and Catchment Landscape Elasticity of Runoff
According to MK mutation detection results, the time series is divided into two periods: 1961-2004, which can be regarded as a period of no or less human activities, named the base period; 2005-2015, regarded as a period of human activities. According to the Choudhury-Yang equation, the mean annual P, 0 E , R, n, R/P, 0 / E P , P ε , 0 E ε , and n ε are calculated for the two periods, respectively.

The Climate and Catchment Landscape Elasticity of Runoff
According to MK mutation detection results, the time series is divided into two periods: 1961-2004, which can be regarded as a period of no or less human activities, named the base period; 2005-2015, regarded as a period of human activities. According to the Choudhury-Yang equation, the mean annual P, E 0 , R, n, R/P, E 0 /P, ε P , ε E 0 , and ε n are calculated for the two periods, respectively. Table 3 presented that compared with the base period, P and R during the human activity period decreased by 53.59 mm and 78.54 mm; E 0 and n increased by 48.21 mm and 0.12; the mean annual runoff coefficient R/P decreased by 0.07; the mean annual drought index E 0 /P increased by 0.12, but LRB is still a semi-humid area in general. The absolute value of ε P , ε E 0 , ε n increased by 0.12, 0.12, 0.13, respectively, indicated that R is more sensitive to all three variables in period 2. When annual P, E 0 , n increases by 1%, R will increase by 0.12% and decrease by 0.12%, 0.13%, respectively. The MK trend test Z value of ε P , ε E 0 and ε n are listed in Table 4, and the interannual variation trend of runoff elasticity is shown in Figure 5. Table 4 and Figure 5 both indicated that the ε P in LRB from 1961 to 2015 showed a significant increase trend, ε E 0 showed a significant decreasing trend, and ε n showed an extremely significantly decreasing trend. Moreover, the three elasticity all fluctuated greatly after 2005. runoff coefficient R/P decreased by 0.07; the mean annual drought index 0 / E P increased by 0.12, but LRB is still a semi-humid area in general. The absolute value of P ε , 0 E ε , n ε increased by 0.12, 0.12, 0.13, respectively, indicated that R is more sensitive to all three variables in period 2. When annual P, E0, n increases by 1%, R will increase by 0.12% and decrease by 0.12%, 0.13%, respectively. ε and n ε are listed in Table 4, and the interannual variation trend of runoff elasticity is shown in Figure 5. Table 4 and Figure 5     In general, the absolute values of the climate and catchment elasticity of runoff are increasing, which means that the runoff in LRB is more sensitive to climate change and human activities. In general, the absolute values of the climate and catchment elasticity of runoff are increasing, which means that the runoff in LRB is more sensitive to climate change and human activities.

Quantitative Attribution of the Runoff Change
Based on Equations (9)-(18), calculate ∆R P , ∆R E 0 , ∆R l , ∆R = ∆R P + ∆R E 0 + ∆R l (Runoff depth change derived from Budyko hypothesis), δ = ∆R − ∆R (Difference between calculated value and observed value), η P , η E 0 and η l , as shown in Table 5: As can be seen from Table 5, the difference between the calculated and observed runoff depth change is only 0.76 mm, indicating that the methods used and results obtained in this study are adequate for evaluating the impact of climate change and human activities on runoff changes.
In LRB, compared with the baseline period, climate change and human activities both have reduced runoff in the human activity period.
Among them, precipitation-induced has the most significant impact on runoff changes, with runoff depth reducing by 35.49 mm, accounting for 45.64%; human activities are the second, with runoff depth reduced by 31.46 mm, accounting for 40.45%; potential evapotranspiration is the last, with runoff depth reduced by 10.82 mm, accounting for 13.91%. During the human activity period, runoff variation in LRB is still dominated by precipitation changes, and the contribution rate of climate change to runoff changes is close to 60%.

Conclusions and Discussion
This study takes LRB as the research area, based on the hydro-meteorological data from 1961 to 2015, using the MK trend test and mutation test to analyze the interannual change trend of hydro-meteorological variables and the year of abrupt change of runoff series, respectively. Choudhury-Yang water-energy balance equation based on the Budyko hypothesis is used to quantify the climate change and catchment landscape change elasticity of runoff. The contribution of climate change and human activities to runoff are calculated. The following conclusions are obtained:

1.
During the period 1961 to 2015 in LRB, the precipitation showed a downward trend, but not statistically significant; the potential evapotranspiration showed an extremely significant upward trend; the runoff depth showed an extremely significant downward trend.

2.
The abrupt year of runoff series in LRB from 1961 to 2015 is 2005. Compared with the period before 2005, the precipitation, and runoff depth in the post-abrupt period decreased by 53.9 mm and 78.54 mm, respectively, and the potential evapotranspiration increased by 48.21 mm. Both the climatic and catchment landscape elasticity of runoff increased in absolute value, indicating that the water cycle of LRB became more sensitive to climate changes and human activities.
This study quantified the impact of climate change and human activities on runoff variation in LRB using the Choudhury-Yang equation, which is simple to implement while avoiding the tedious process of model calibration and validation, and uncertainty in model structures and parameter estimations with the hydrological model method. The processes of impacts of climate change and human activities on runoff are presented. The driving factors of runoff change in LRB are revealed; meanwhile, theoretical support for cross-border water resources allocation and basin management in LRB is also provided. Although the data and model are strictly controlled in this study, there are still some uncertainties. Precipitation and potential evapotranspiration data may not represent the accurate situation of the whole basin, for both are interpolated from the station data and the low density of national meteorological stations in the study area. Besides, due to the lack of observed runoff data from multiple stations, the runoff from only one station is used to represent the situation of the whole basin, resulting in a lack of spatial heterogeneity.