# Study on the Impact of Future Climate Change on Extreme Meteorological and Hydrological Elements in the Upper Reaches of the Minjiang River

Authors: Ting Chen; Yao Ye; Kebi Yang; Xu Zhang; Tianqi Ao
Journal: Advances in Meteorology
 (2023)

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## Abstract

Global warming increases global average precipitation and evaporation, causing extreme climate and hydrological events to occur frequently. Future changes in temperature, precipitation, and runoff from 2021 to 2050 in the upper reaches of the Minjiang River were analyzed using a distributed hydrological model, the SWAT (Soil and Water Assessment Tool), under a future climate scenario. Simultaneously, future variation characteristics of extreme climate hydrological elements in the upper reaches of the Minjiang River were analyzed using extreme climate and runoff indicators. The research shows that the frequency and intensity of the extreme temperature warming index will increase, while those of the extreme temperature cooling index will increase and then weaken in the upper reaches of the Minjiang River under a future climate scenario. The duration of precipitation, the intensity of continuous heavy precipitation, and the frequency of heavy precipitation will increase, whereas the intensity of short-term heavy precipitation and the frequency of heavy precipitation will decrease. However, spatial distribution of flood in the upper reaches is different, and thus flood risk in the upstream source area will still tend to increase. Particular attention should be given to the increase in autumn flood risk in the upper reaches of the Minjiang River.


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## Body

## 1. Introduction

Global warming will aggravate the global hydrological cycle and increase global average precipitation and evaporation [1]. Simultaneously, precipitation variability may change, exerting direct effects on evaporation, runoff, and soil humidity. The extreme hydrological events, such as floods and droughts, increase the risk of water disasters, which have become major challenges to human survival [2]. Under the background of climate warming, extreme climate and hydrological events occur frequently in China [3–10]. The change in extreme climate events and the impact of change in hydrological and water resources caused by extreme climate events on human life and production have attracted increasing attention [8, 11]. Research on the changing trend, occurrence mechanism, response, and prediction of hydrological extreme events in river basins under the background of climate change is important to understand. It is significant for scientifically understanding the spatiotemporal evolution law of the land-water cycle under the background of global climate change. It has an important application value in basin flood control planning and design, large-scale hydropower development planning and operation management, regional disaster prevention and reduction, ecological environment protection, and sustainable economic and social development.The study of the impact of climate change on water resources focuses on the analysis of the water cycle and water resources trends under climate change [12]. Trends in water resources under climate change first need to provide more accurate climate change scenario data for hydrological simulation. Climate change scenarios are based on several scientific assumptions and use a long series of historical information to establish a continuous and consistent predictive description of future climate including temperature, precipitation, and other elements. Currently, the GCM (Global Climate Model) output method for developing future climate change scenario design is most widely used, but the basin-scale hydrological models do not match the large grid data of GCMs. So, downscaling of future climate change scenarios is needed [13, 14]. The main types of downscaling methods are statistical downscaling, dynamical downscaling, and combined statistical-dynamical downscaling. Statistical downscaling has been increasingly used to predict future climate scenarios such as precipitation and temperature due to the lower operational resource requirements and higher accuracy [15]. Statistical downscaling refers to the use of historical observations to establish statistical relationships between regional observations and large-scale climate elements and to verify such statistical relationships using other regional observations. The frequently used statistical downscaling models are ASD (automated statistical downscaling) and SDSM (statistical downscaling model), among which SDSM has less bias and is convenient to use and is one of the most widely used statistical downscaling models [16]. Gulacha et al. [17] assessed that the model and the observed showed a good fit in the Wami-Ruvu River Basin of Tanzania, and the SDSM’s R2 values between raw and model for temperature ranged from 0.42 to 0.98. Tukimat et al. [18] found that the SDSM successfully provided long-term climate pattern at the gauged stations with an R value close to 1.0 in Kuantan River Basin of Malaysia. Dehghan et al. [19] assessed that there is a good agreement between the simulated and the observed precipitation, and R2 for precipitation is greater than 0.63.Research shows that the impact of climate change on the hydrological cycle is regional [20–30]. Pandey et al. [20] assessed that there is a high consensus for increase in temperature but higher uncertainty with respect to precipitations in Mahakali of Nepal. Under the projected changes, the average annual streamflow was simulated to increase gradually from the near to far future under both RCPs (Representative Concentration Pathways). Bajracharya et al. [21] found that the extreme projection of a RCP 8.5 scenario shows that the average annual temperature of the basin is expected to increase by more than 4°C in the Kaligandaki Basin of Nepal. Likewise, the average annual precipitation in the basin is projected to increase by as much as 26% during the late century under a RCP 8.5 scenario. The synergetic effect of an increase in temperature and precipitation shows the aggravated effect on the discharge and water yield with an increase of more than 50% at the outlet of the basin. Fonseca and Santos [22] simulated the potential effects of climate change on the hydrology in the Tâmega River Basin, Northern Portugal, experiencing a Mediterranean climate. The annual precipitation over the Tâmega River Basin exhibits weakly decreasing trends across the entire future period. On the other hand, temperatures show consistently warming trends throughout the basin for the future period, with a mean warming rate of 0.03°C per year. As a result, the mean annual flow rate decreased at all hydrometric stations by about 0.25 m3s−1 per year, with increased flow rates in winter when compared to the historical period but significantly lower flow rates in summer. Meaurio et al. [23] assessed the climate change impact on river discharge in the Bay of Biscay, Spain. It was found that trends for extreme flows show an increase in the duration of low flows.In China, Yu et al. [24] pointed out that while the precipitation in Northern China is decreasing, the change in precipitation exhibits the form of “waterlogging in the south and drought in the north.” The probability of flood disaster in the Yangtze River Basin is considerably higher than that in other regions of China. Since the 20th century, more than 20 floods have occurred in the Yangtze River Basin. Among these, the floods of 1905, 1931, 1954, 1988, 2010, and 2017 were the most severe [24]. Wang et al. [25] reported that the concentrated distribution and high frequency of large-area rainstorms and floods occur in the monsoon region of Eastern China. Meanwhile, in Western China’s arid and semiarid regions, disastrous floods are mostly caused by short-term local rainstorms, and small and medium-sized rivers can form high peak flow, causing serious disasters to local regions.The Minjiang River Basin is a first-class tributary in the upper reaches of the Yangtze River. It is located to Southwest China and is in the southeast edge of the Qinghai-Tibet Plateau. Under the background of the large terrain of the Qinghai-Tibet Plateau and the comprehensive influences of the large, medium, and small complex terrain, the valley is steep, hydropower energy is abundant, and weather and climate disasters, such as rainstorms and mountain torrents, occur frequently. This area is sensitive to climate change and extreme hydrometeorological events. Previous studies on the Minjiang River Basin focused on the response of this basin to climate change. For example, Liang et al. [26] found that the temporal variation characteristic of the upper reaches of the Minjiang River is as follows: annual average precipitation exhibits a downward trend due to the reduction in summer precipitation. Meanwhile, the spatial distribution characteristic is that high-altitude areas exhibit an increasing trend, whereas low-altitude areas present a decreasing trend. The annual runoff in the upper reaches of the Minjiang River shows a significant downward trend from 1937 to 2018, and it may continue to demonstrate a downward trend in the future. Huang et al. [27] found that the average temperature in the upper reaches of the Minjiang River shows an upward trend, whereas precipitation and annual runoff exhibit a downward trend. The trend of the average temperature has an evident positive correlation with precipitation, particularly in spring and autumn. The decrease in water inflow during spring and the extension of the duration of the low-flow season during autumn will considerably impact irrigation and urban water supply. Huang et al. [28] demonstrated that climate change scenario analysis combined with the Soil and Water Assessment Tool (SWAT) hydrological model can effectively simulate the effect of climate change on runoff. The influence of precipitation change on runoff is greater than that of temperature change. The impact of temperature change on runoff is more evident in dry years than in wet years. Chen et al. [29] showed that the overall temperature in the Minjiang River Basin exhibited a trend of decreasing the number of extreme cold days while increasing the number of extreme warm days. In terms of spatial distribution, the high value of the extreme cold event index in the basin was mostly recorded in the upstream. The spatial distribution of extreme precipitation indicators in the Minjiang River Basin is highly uneven, as manifested in the high value of extreme precipitation indicators mostly appearing in the middle and lower reaches of the basin. From the analysis of the changing trend, the average characteristics of the Minjiang River Basin range from short-term to sustained extreme precipitation [30].In summary, research on the change in extreme climate events in the upper reaches of the Minjiang River mostly used historical data to analyze temperature and precipitation extreme events. Meanwhile, there is less research on future changes in extreme events, particularly hydrological extreme events. In the current study, the hydrological model (i.e., SWAT) is used to simulate runoff change in the upper reaches of the Minjiang River under a future climate scenario. Simultaneously, the change characteristics of extreme climate hydrological elements in the upper reaches of the Minjiang River under a future climate scenario are analyzed using extreme climate and runoff indices. The study analyzes the future trends of hydrometeorological element extremes under climate scenarios, providing useful support for theoretical studies of potential drought and flood threats in the upper reaches of the Minjiang River, as well as a reference for future water conservancy project design in the upper reaches of the Minjiang River.


## 2. Materials and Methods

### 2.1. Study Area and Data Sources

The upper reaches of the Minjiang River Basin are located between 102° 59′–104° 14′ E and 26° 33′–33° 16′ N and have a drainage area of about 23,000 km2 (Figure 1). The upper reaches of the Minjiang River are sensitive to climate change and frequent natural disasters due to the high intensity of water resource development, the reduction of forest coverage, the degradation of ecological functions, and serious soil and water losses [29, 30].Figure 1
The drainage system in the upper reaches of the Minjiang River.The Digital Elevation Model (DEM) data in this study were obtained from the geospatial data cloud (https://www.gscloud.cn/search) with a resolution of 1 km (Figure 2(a)). Land use data were obtained from the International Geosphere-Biosphere Programme (IGBP), which uses the United States Geological Survey (USGS) method to classify land use into 17 categories. The land use types of the Minjiang River were mainly classified into 7 categories (Figure 2(b)). Soil data are from the Harmonized World Soil Database (HWSD) at 1 km resolution and can be downloaded from the Food and Agriculture Organization of the United Nations. SPAW was used to calculate the parameters required for the SWAT soil database, which contains 21 soil types in the assessment area of this study (Figure 2(c) and Table 1).Figure 2
(a) DEM, (b) land use, and (c) soil cover maps of the Minjiang River.
(a)(b)(c)Table 1
Soil types of the Minjiang River.
NoName1Water bodies2Rock outcrops3Haplic Luvisols4Eutric Cambisols5Luvisols6Dystric Cambisols7Alisols8Lithic Leptosols9Leptosols10Mollic Leptosols11Gelic Cambisols12Gelic Leptosols13Mollic Gleysols114Mollic Gleysols15Haplic Phaeozems16Calcaric Regosols17Eutric Regosols18Dystric Regosols19Leptosols20Chromic Luvisols21Haplic Luvisols22Haplic Greyzems23Calcaric Cambisols24Calcic Luvisols25Calcaric Cambisols26Dystric Podzoluvisols27Haplic Luvisols


## 2.1. Study Area and Data Sources

The upper reaches of the Minjiang River Basin are located between 102° 59′–104° 14′ E and 26° 33′–33° 16′ N and have a drainage area of about 23,000 km2 (Figure 1). The upper reaches of the Minjiang River are sensitive to climate change and frequent natural disasters due to the high intensity of water resource development, the reduction of forest coverage, the degradation of ecological functions, and serious soil and water losses [29, 30].Figure 1
The drainage system in the upper reaches of the Minjiang River.The Digital Elevation Model (DEM) data in this study were obtained from the geospatial data cloud (https://www.gscloud.cn/search) with a resolution of 1 km (Figure 2(a)). Land use data were obtained from the International Geosphere-Biosphere Programme (IGBP), which uses the United States Geological Survey (USGS) method to classify land use into 17 categories. The land use types of the Minjiang River were mainly classified into 7 categories (Figure 2(b)). Soil data are from the Harmonized World Soil Database (HWSD) at 1 km resolution and can be downloaded from the Food and Agriculture Organization of the United Nations. SPAW was used to calculate the parameters required for the SWAT soil database, which contains 21 soil types in the assessment area of this study (Figure 2(c) and Table 1).Figure 2
(a) DEM, (b) land use, and (c) soil cover maps of the Minjiang River.
(a)(b)(c)Table 1
Soil types of the Minjiang River.
NoName1Water bodies2Rock outcrops3Haplic Luvisols4Eutric Cambisols5Luvisols6Dystric Cambisols7Alisols8Lithic Leptosols9Leptosols10Mollic Leptosols11Gelic Cambisols12Gelic Leptosols13Mollic Gleysols114Mollic Gleysols15Haplic Phaeozems16Calcaric Regosols17Eutric Regosols18Dystric Regosols19Leptosols20Chromic Luvisols21Haplic Luvisols22Haplic Greyzems23Calcaric Cambisols24Calcic Luvisols25Calcaric Cambisols26Dystric Podzoluvisols27Haplic Luvisols


## 3. Methodology

This study analyzes the impact of future climate change on extreme hydrometeorological elements in the upper reaches of the Minjiang River. The statistical downscaling model (SDSM) is selected for future climate scenario analysis, and the SWAT model is used for hydrological simulation.


### 3.1. SDSM

The SDSM model scales down by establishing statistical relationships between large-scale climate predictors and observations.When simulating precipitation, the large-scale climate forecaster is used to first simulate the probability of precipitation on a particular day and then the amount of precipitation on that rainy day:(1)Wi=a0+∑j=0najxj+ai−1Wi−1,where Wi and Wi−1 represent the precipitation probability of days i and i − 1, respectively, and xj is the j-th predictor and regression coefficient.The occurrence of precipitation is determined by a random numberr (0 ≤ r ≤ 1) that follows a uniform distribution. If r ≤ Wi, then precipitation will occur on that day. When precipitation occurs on a certain day, a multiple exponential regression function will be used to simulate precipitation on that day:(2)Ri=expβ0+∑j=1nβjxj+εi,where Ri is precipitation on the i-th day, β0 and βj are the regression coefficients, xj is the prediction factor on the j-th day, and εi is the error.The major steps in using SDSM include quality control, downscaling prediction factor screening, model correction, weather generator, and model evaluation. The original meteorological observation data collected from meteorological stations may be missing. Therefore, quality control should be implemented to identify missing data, outliers, and suspicious incomplete data, improving the quality of model output [6, 31].Referring to Wilby [31], the prediction factors of temperature and precipitation (Table 2) and rainfall station forecast factors are selected.Table 2
Prediction factors of temperature and precipitation in the study area.
StationMaximum temperatureMinimum temperaturePrecipitationMaoxianmslp; p1_u; p8_u; p850; s500; s850; tempmslp; p1_u; p1_z; p5_z; p500; p850; s850; shum; tempmslp; p1_v; p5zh; p1zh; p5_v; p5_z; p5zh; p8_v; prcp; s500; shum; tempSongpanmslp; p1_v; p5_u; p500; p8_v; p850; prcp; s850; shum; tempmslp; p1_up1_v; p1zhp5_v; s500; s850; shum; temp;mslp; p1_v; p1zh; p5_v; p500; p8_z; p850; p8zh; prcp; tempWenchuanmslp; p1zh; p500; p8_z; s850; tempmslp; p1_z; p500; p8zh; s850; shum; tempp1_u; p1_v; p1zh; p5_u; p5_z; p500; p5zh; p8_u; p8zh; s500; shumLixianmslp; p1_v; p850; s500; s850; tempmslp; p1_z; p500; s850; shum; tempmslp; p1_f; p1_u; p1zh; p5_z; p500; p5zh; p8_u; p8zh; prcp; shum; tempHeishuimslp; p1_v; p5_u; p5_z; p500; p5zh; prcp; s850; s500; shum; tempmslp; p1zh; p5_v; p500; s850; shum; tempp1_v; p1zh; p5_u; p5_z; p500; p8_ugl; p8zh; shum; tempThe temperature and precipitation variables under three climate scenarios in Coupled Model Intercomparison Project Phase 5 (CMIP5) by SDSM are downscaled as shown in Table3. CMIP5 has designed “Representative Concentration Pathways” (RCPs) for the emissions of four types of greenhouse gases and aerosols [32–35], namely, RCP2.6, RCP4.5, RCP6.0, and RCP8.5. Each scenario includes a set of greenhouse gas, aerosol, and other gas emissions.Table 3
Typical concentration path of future climate change recommended in the IPCC fifth assessment report.
Climate scenarioRadiation forcing in 2010–2100The emissions of greenhouseRCP2.6It reaches the forced peak of 3 W/m2 in the medium term and then decreases to 2.6 W/m2by 2100ReliefRCP4.5Increases to 4.5 W/m2ModerateRCP8.5Increases to 8.5 W/m2Aggravate


### 3.2. SWAT Model

The SWAT model is a semidistributed watershed hydrological model developed by the United States Department of Agriculture-Agricultural Research Service (USDA-ARS). The watershed delineation tool in SWAT delineates the whole study basin into several subbasins in accordance with the characteristics of topographic factors and river network distribution. On this basis, hydrological response units are divided in accordance with the land use type, soil type, and slope area threshold of the basin. Runoff is calculated separately. Finally, the total runoff of an outlet section is obtained through river confluence routing.


### 3.3. SUFI-2

SWAT-CUP makes the calibrating procedure easy to users[8, 36]. Sequential Uncertainty FItting ver. 2 (SUFI-2) is selected to calibrate the parameters in this study.In SUFI-2, parameter uncertainty accounts for all sources of uncertainties such as uncertainty in driving variables (e.g., rainfall), conceptual model, parameters, and measured data. The degree to which all uncertainties are accounted for is quantified by a measure referred to as the P factor, which is the percentage of measured data bracketed by the 95% prediction uncertainty (95PPU). As all the processes and model inputs such as rainfall and temperature distributions are correctly manifested in the model output (which is measured with some error)—the degree to which we cannot account for the measurements—the model is in error and is hence uncertain in its prediction. Therefore, the percentage of data captured (bracketed) by the prediction uncertainty is a good measure to assess the strength of our uncertainty analysis. The 95PPU is calculated at the 2.5% and 97.5% levels of the cumulative distribution of an output variable obtained through Latin hypercube sampling, disallowing 5% of the very bad simulations. As all forms of uncertainties are reflected in the measured variables (e.g., discharge), the parameter uncertainties generating the 95PPU account for all uncertainties. Reducing the total uncertainty into its various components is highly interesting but quite difficult to do, and to the best of the authors’ knowledge, no reliable procedure yet exists.


### 3.4. SWAT Model Development

In this study, the 1969–1980 is the calibration period and 1981–1987 is the verification period. The Nash–Sutcliffe model efficiency coefficient (NSE) and the coefficient of determination (R2) are selected as indices for evaluating the simulation effect of daily runoff in the upper reaches of the Minjiang River. Simultaneously, the simulation effect of the SWAT model on extreme high and low flows is evaluated using the correlation coefficient.


#### 3.4.1. Extreme Climate and Flow Indicators

The extreme temperature and precipitation indicators [37] recommended by the World Meteorological Organization are selected in this study. These indicators can reflect the intensity, frequency, and duration of extreme temperature and precipitation elements [38]. A detailed introduction of these indicators is provided in Table 4. This study also selects extreme runoff indicators to reflect changes in extreme hydrological factors. Extreme runoff indicators are classified into absolute and relative indicators. Absolute indicators refer to the maximum daily flood volume. Relative indicators refer to the 1% (Q1), 5% (Q5), 10% (Q10), 99% (Q99), 95% (Q95), and 90% (Q90) quantile runoff. Q1, Q5, and Q10 represent low-flow extreme runoff, while Q99, Q95, and Q90 represent extreme high-flow runoff [39]. Simultaneously, this study calculates the hydrological frequency of extreme high and low flows under a future climate scenario and the historical period, in which the annual maximum daily flow can represent extreme high flow while the annual minimum monthly flow can represent extreme low flow [38].Table 4
Definitions of indices.
IndexIndex titleIndex definitionUnitExtreme temperature indicesTX10pCold daysAnnual total days when maximum daily temperature is less than the 10th percentile value in 1961–2016DaysTN10pCold nightsAnnual total days when minimum daily temperature is less than the 10th percentile value in 1961–2016DaysTX90pWarm daysAnnual total days when maximum daily temperature is more than the 10th percentile value in 1961–2016DaysTN90pWarm nightsAnnual total days when minimum daily temperature is more than the 10th percentile value in 1961–2016DaysTXnMinimum TmaxThe minimum of daily maximum temperature in each month°CTNnMinimum TminThe minimum of daily minimum temperature in each month°CTXxMaximum TmaxThe minimum of daily maximum temperature in each month°CTNxMaximum TminThe maximum of daily minimum temperature in each month°CExtreme precipitation indicesCWDConsecutive wet daysMaximum number of consecutive days with RR ≥ 1 mmDaysR10 mmNumber of heavy precipitation daysAnnual count of days when RR ≥ 10 mmdaysR25 mmNumber of very heavy precipitation daysAnnual count of days when RR ≥ 25 mmDaysRX1DAYMaximum 1-day precipitationAnnual maximum 1-day precipitationmmRX5DAYMaximum 5-day precipitationAnnual maximum 5-day precipitationmmExtreme flow indicesAMMaximum 1-day flowAnnual maximum 1-day flowm³/sIMMinimum monthly flowAnnual minimum monthly flowm³/sQ9090% quantile runoff10% of the daily average runoff series in the year exceed this valuem³/sQ9595% quantile runoff5% of the daily average runoff series in the year exceed this valuem³/sQ9999% quantile runoff1% of the daily average runoff series in the year exceeds this valuem³/sQ1010% quantile runoffMore than 90% of the annual average daily runoff of the seriesm³/sQ55% quantile runoff95% of the daily average runoff series in the year exceed this valuem³/sQ11% quantile runoff99% of the daily average runoff series in the year exceed this valuem³/s


## 3.1. SDSM

The SDSM model scales down by establishing statistical relationships between large-scale climate predictors and observations.When simulating precipitation, the large-scale climate forecaster is used to first simulate the probability of precipitation on a particular day and then the amount of precipitation on that rainy day:(1)Wi=a0+∑j=0najxj+ai−1Wi−1,where Wi and Wi−1 represent the precipitation probability of days i and i − 1, respectively, and xj is the j-th predictor and regression coefficient.The occurrence of precipitation is determined by a random numberr (0 ≤ r ≤ 1) that follows a uniform distribution. If r ≤ Wi, then precipitation will occur on that day. When precipitation occurs on a certain day, a multiple exponential regression function will be used to simulate precipitation on that day:(2)Ri=expβ0+∑j=1nβjxj+εi,where Ri is precipitation on the i-th day, β0 and βj are the regression coefficients, xj is the prediction factor on the j-th day, and εi is the error.The major steps in using SDSM include quality control, downscaling prediction factor screening, model correction, weather generator, and model evaluation. The original meteorological observation data collected from meteorological stations may be missing. Therefore, quality control should be implemented to identify missing data, outliers, and suspicious incomplete data, improving the quality of model output [6, 31].Referring to Wilby [31], the prediction factors of temperature and precipitation (Table 2) and rainfall station forecast factors are selected.Table 2
Prediction factors of temperature and precipitation in the study area.
StationMaximum temperatureMinimum temperaturePrecipitationMaoxianmslp; p1_u; p8_u; p850; s500; s850; tempmslp; p1_u; p1_z; p5_z; p500; p850; s850; shum; tempmslp; p1_v; p5zh; p1zh; p5_v; p5_z; p5zh; p8_v; prcp; s500; shum; tempSongpanmslp; p1_v; p5_u; p500; p8_v; p850; prcp; s850; shum; tempmslp; p1_up1_v; p1zhp5_v; s500; s850; shum; temp;mslp; p1_v; p1zh; p5_v; p500; p8_z; p850; p8zh; prcp; tempWenchuanmslp; p1zh; p500; p8_z; s850; tempmslp; p1_z; p500; p8zh; s850; shum; tempp1_u; p1_v; p1zh; p5_u; p5_z; p500; p5zh; p8_u; p8zh; s500; shumLixianmslp; p1_v; p850; s500; s850; tempmslp; p1_z; p500; s850; shum; tempmslp; p1_f; p1_u; p1zh; p5_z; p500; p5zh; p8_u; p8zh; prcp; shum; tempHeishuimslp; p1_v; p5_u; p5_z; p500; p5zh; prcp; s850; s500; shum; tempmslp; p1zh; p5_v; p500; s850; shum; tempp1_v; p1zh; p5_u; p5_z; p500; p8_ugl; p8zh; shum; tempThe temperature and precipitation variables under three climate scenarios in Coupled Model Intercomparison Project Phase 5 (CMIP5) by SDSM are downscaled as shown in Table3. CMIP5 has designed “Representative Concentration Pathways” (RCPs) for the emissions of four types of greenhouse gases and aerosols [32–35], namely, RCP2.6, RCP4.5, RCP6.0, and RCP8.5. Each scenario includes a set of greenhouse gas, aerosol, and other gas emissions.Table 3
Typical concentration path of future climate change recommended in the IPCC fifth assessment report.
Climate scenarioRadiation forcing in 2010–2100The emissions of greenhouseRCP2.6It reaches the forced peak of 3 W/m2 in the medium term and then decreases to 2.6 W/m2by 2100ReliefRCP4.5Increases to 4.5 W/m2ModerateRCP8.5Increases to 8.5 W/m2Aggravate


## 3.2. SWAT Model

The SWAT model is a semidistributed watershed hydrological model developed by the United States Department of Agriculture-Agricultural Research Service (USDA-ARS). The watershed delineation tool in SWAT delineates the whole study basin into several subbasins in accordance with the characteristics of topographic factors and river network distribution. On this basis, hydrological response units are divided in accordance with the land use type, soil type, and slope area threshold of the basin. Runoff is calculated separately. Finally, the total runoff of an outlet section is obtained through river confluence routing.


## 3.3. SUFI-2

SWAT-CUP makes the calibrating procedure easy to users[8, 36]. Sequential Uncertainty FItting ver. 2 (SUFI-2) is selected to calibrate the parameters in this study.In SUFI-2, parameter uncertainty accounts for all sources of uncertainties such as uncertainty in driving variables (e.g., rainfall), conceptual model, parameters, and measured data. The degree to which all uncertainties are accounted for is quantified by a measure referred to as the P factor, which is the percentage of measured data bracketed by the 95% prediction uncertainty (95PPU). As all the processes and model inputs such as rainfall and temperature distributions are correctly manifested in the model output (which is measured with some error)—the degree to which we cannot account for the measurements—the model is in error and is hence uncertain in its prediction. Therefore, the percentage of data captured (bracketed) by the prediction uncertainty is a good measure to assess the strength of our uncertainty analysis. The 95PPU is calculated at the 2.5% and 97.5% levels of the cumulative distribution of an output variable obtained through Latin hypercube sampling, disallowing 5% of the very bad simulations. As all forms of uncertainties are reflected in the measured variables (e.g., discharge), the parameter uncertainties generating the 95PPU account for all uncertainties. Reducing the total uncertainty into its various components is highly interesting but quite difficult to do, and to the best of the authors’ knowledge, no reliable procedure yet exists.


## 3.4. SWAT Model Development

In this study, the 1969–1980 is the calibration period and 1981–1987 is the verification period. The Nash–Sutcliffe model efficiency coefficient (NSE) and the coefficient of determination (R2) are selected as indices for evaluating the simulation effect of daily runoff in the upper reaches of the Minjiang River. Simultaneously, the simulation effect of the SWAT model on extreme high and low flows is evaluated using the correlation coefficient.


### 3.4.1. Extreme Climate and Flow Indicators

The extreme temperature and precipitation indicators [37] recommended by the World Meteorological Organization are selected in this study. These indicators can reflect the intensity, frequency, and duration of extreme temperature and precipitation elements [38]. A detailed introduction of these indicators is provided in Table 4. This study also selects extreme runoff indicators to reflect changes in extreme hydrological factors. Extreme runoff indicators are classified into absolute and relative indicators. Absolute indicators refer to the maximum daily flood volume. Relative indicators refer to the 1% (Q1), 5% (Q5), 10% (Q10), 99% (Q99), 95% (Q95), and 90% (Q90) quantile runoff. Q1, Q5, and Q10 represent low-flow extreme runoff, while Q99, Q95, and Q90 represent extreme high-flow runoff [39]. Simultaneously, this study calculates the hydrological frequency of extreme high and low flows under a future climate scenario and the historical period, in which the annual maximum daily flow can represent extreme high flow while the annual minimum monthly flow can represent extreme low flow [38].Table 4
Definitions of indices.
IndexIndex titleIndex definitionUnitExtreme temperature indicesTX10pCold daysAnnual total days when maximum daily temperature is less than the 10th percentile value in 1961–2016DaysTN10pCold nightsAnnual total days when minimum daily temperature is less than the 10th percentile value in 1961–2016DaysTX90pWarm daysAnnual total days when maximum daily temperature is more than the 10th percentile value in 1961–2016DaysTN90pWarm nightsAnnual total days when minimum daily temperature is more than the 10th percentile value in 1961–2016DaysTXnMinimum TmaxThe minimum of daily maximum temperature in each month°CTNnMinimum TminThe minimum of daily minimum temperature in each month°CTXxMaximum TmaxThe minimum of daily maximum temperature in each month°CTNxMaximum TminThe maximum of daily minimum temperature in each month°CExtreme precipitation indicesCWDConsecutive wet daysMaximum number of consecutive days with RR ≥ 1 mmDaysR10 mmNumber of heavy precipitation daysAnnual count of days when RR ≥ 10 mmdaysR25 mmNumber of very heavy precipitation daysAnnual count of days when RR ≥ 25 mmDaysRX1DAYMaximum 1-day precipitationAnnual maximum 1-day precipitationmmRX5DAYMaximum 5-day precipitationAnnual maximum 5-day precipitationmmExtreme flow indicesAMMaximum 1-day flowAnnual maximum 1-day flowm³/sIMMinimum monthly flowAnnual minimum monthly flowm³/sQ9090% quantile runoff10% of the daily average runoff series in the year exceed this valuem³/sQ9595% quantile runoff5% of the daily average runoff series in the year exceed this valuem³/sQ9999% quantile runoff1% of the daily average runoff series in the year exceeds this valuem³/sQ1010% quantile runoffMore than 90% of the annual average daily runoff of the seriesm³/sQ55% quantile runoff95% of the daily average runoff series in the year exceed this valuem³/sQ11% quantile runoff99% of the daily average runoff series in the year exceed this valuem³/s


## 3.4.1. Extreme Climate and Flow Indicators

The extreme temperature and precipitation indicators [37] recommended by the World Meteorological Organization are selected in this study. These indicators can reflect the intensity, frequency, and duration of extreme temperature and precipitation elements [38]. A detailed introduction of these indicators is provided in Table 4. This study also selects extreme runoff indicators to reflect changes in extreme hydrological factors. Extreme runoff indicators are classified into absolute and relative indicators. Absolute indicators refer to the maximum daily flood volume. Relative indicators refer to the 1% (Q1), 5% (Q5), 10% (Q10), 99% (Q99), 95% (Q95), and 90% (Q90) quantile runoff. Q1, Q5, and Q10 represent low-flow extreme runoff, while Q99, Q95, and Q90 represent extreme high-flow runoff [39]. Simultaneously, this study calculates the hydrological frequency of extreme high and low flows under a future climate scenario and the historical period, in which the annual maximum daily flow can represent extreme high flow while the annual minimum monthly flow can represent extreme low flow [38].Table 4
Definitions of indices.
IndexIndex titleIndex definitionUnitExtreme temperature indicesTX10pCold daysAnnual total days when maximum daily temperature is less than the 10th percentile value in 1961–2016DaysTN10pCold nightsAnnual total days when minimum daily temperature is less than the 10th percentile value in 1961–2016DaysTX90pWarm daysAnnual total days when maximum daily temperature is more than the 10th percentile value in 1961–2016DaysTN90pWarm nightsAnnual total days when minimum daily temperature is more than the 10th percentile value in 1961–2016DaysTXnMinimum TmaxThe minimum of daily maximum temperature in each month°CTNnMinimum TminThe minimum of daily minimum temperature in each month°CTXxMaximum TmaxThe minimum of daily maximum temperature in each month°CTNxMaximum TminThe maximum of daily minimum temperature in each month°CExtreme precipitation indicesCWDConsecutive wet daysMaximum number of consecutive days with RR ≥ 1 mmDaysR10 mmNumber of heavy precipitation daysAnnual count of days when RR ≥ 10 mmdaysR25 mmNumber of very heavy precipitation daysAnnual count of days when RR ≥ 25 mmDaysRX1DAYMaximum 1-day precipitationAnnual maximum 1-day precipitationmmRX5DAYMaximum 5-day precipitationAnnual maximum 5-day precipitationmmExtreme flow indicesAMMaximum 1-day flowAnnual maximum 1-day flowm³/sIMMinimum monthly flowAnnual minimum monthly flowm³/sQ9090% quantile runoff10% of the daily average runoff series in the year exceed this valuem³/sQ9595% quantile runoff5% of the daily average runoff series in the year exceed this valuem³/sQ9999% quantile runoff1% of the daily average runoff series in the year exceeds this valuem³/sQ1010% quantile runoffMore than 90% of the annual average daily runoff of the seriesm³/sQ55% quantile runoff95% of the daily average runoff series in the year exceed this valuem³/sQ11% quantile runoff99% of the daily average runoff series in the year exceed this valuem³/s


## 4. Results and Discussion

### 4.1. Simulation Results of SDSM

The results show that the spatial correlation between large-scale variables simulated and measured using the second-generation Canadian Earth System Model (CANESM2) is 0.76 for all stations in Southwest China, where the upper reaches of the Minjiang River are located. CANESM2 can simulate more accurate large-scale variables, such as the upper atmospheric field and atmospheric circulation, with a resolution of 2.5° × 2.5°; however, it lacks regional climate information.The large-scale and low-resolution variables of CANESM2 simulation are reduced to a regional scale by SDSM. The analysis shows that the correlation coefficient between the simulated and measured daily maximum and minimum temperatures is about 0.9, while the correlation coefficient between the simulated and measured daily precipitation is between 0.4 and 0.5 (Table5), indicating that analyzing extreme meteorological elements is feasible by using temperature and precipitation data after SDSM downscaling.Table 5
Correlation coefficients of measured and simulated daily maximum and minimum temperature and precipitation during the verification period.
StationMaximum temperatureMinimum temperaturePrecipitationMaoxian0.9∗∗∗0.9∗∗∗0.4∗∗Songpan0.9∗∗∗0.8∗∗∗0.4∗∗Wenchuan0.9∗∗∗0.9∗∗∗0.5∗∗Lixian0.9∗∗∗0.9∗∗∗0.5∗∗Heishui0.9∗∗∗0.9∗∗∗0.5∗∗∗∗indicates passing the 95% significance test; ∗∗∗indicates passing the 99% significance test.The average absolute error between the simulation and actual measurements during different seasons in the verification period is calculated (Table6). The average absolute error of the highest and lowest temperatures exhibits minimal seasonal variation, and the daily average absolute error of precipitation in summer is higher than that in other seasons. In general, using SDSM for downscaling research in the upper reaches of the Minjiang River is feasible.Table 6
Mean absolute error of variables during the validation period.
VariablesSeasonsSpringSummerAutumnWinterMaximum temperature (°C)1.41.41.41.4Minimum temperature (°C)1.41.31.41.5Precipitation (mm)1.32.21.30.4


### 4.2. Simulation Results of SWAT

The SWAT model was used to simulate the daily runoff in the upper Minjiang River, and the optimal values of the model parameters are shown in Table7. The results showed that R2 was 0.87 and the NSE was 0.86 in the calibration period, R2 was 0.79, and the NSE was 0.77 in the validation period, indicating that it is feasible to simulate the runoff of the upper Minjiang River using the SWAT model (Figure 3). From the simulation results, it can be seen that the SWAT model is able to simulate the seasonal distribution characteristics of runoff. SWAT simulates runoff more accurately in the flood season, and the model simulates slightly lower runoff in the dry season, which may be related to the greater contribution of high flows in the flood season to the simulation error evaluation [7, 8].Table 7
Sensitivity ranks and optimal values of the model parameters.
Parameter nameBrief descriptionOptimal valueT-statP valuev__GW_DELAY.gwGroundwater delay (days)13.0634.630v__ALPHA_BF.gwBaseflow alpha factor (days)0.63−7.460v__TLAPS.subTemperature lapse rate (°C/km)18.311.530.13r__SOL_K(1).sol_1Saturated hydraulic conductivity0.721.230.22v__NPERCO.bsnNitrogen percolation coefficient9.721.20.23v__CH_N2.rteManning’s n value for main channel−5.34−1.160.25v__SPEXP.bsnExp. re-entrainment parameter for channel sediment routing1.45−0.960.34v__GW_REVAP.gwGroundwater “revap” coefficient0.02−1.040.3v__REVAPMN.gw_1Threshold water depth in the shallow aquifer for “revap” (mm)2.720.690.49v__EPCO.hruPlant uptake compensation factor1.270.660.51v__PHOSKD.bsnPhosphorus soil partitioning coefficient147.570.440.66r__SOL_ALB(1).solMoist soil albedo0.220.420.68v__CH_K2.rteChannel effective hydraulic conductivity (mm/hr)−489.23−0.420.68v__ESCO.hruSoil evaporation compensation factor1.09−0.310.76a__GWQMN.gwThreshold depth of the water in the shallow aquifer required for return flow to occur910.29−0.290.77v__SMFMN.bsnMelt factor for snow on December 21 (mm H2O/°C-day)11.660.250.8r__CN2.mgtSCS streamflow curve number−0.58−0.210.84v__SMFMX.bsnMelt factor for snow on June 21 (mm H2O/°C-day)−0.880.130.9r__SOL_AWC(1).solAvailable water capacity (mm H2O/mm soil)−3.88−0.040.97v__TIMP.bsnSnow pack temperature lag factor0.760.030.98v__SURLAG.bsnSurface runoff lag time (days)719.80.010.99r_ means an existing parameter value is multiplied by (1þ given value); v_ means the existing parameter value is to be replaced by given value; the detailed description of the parameters was introduced as technical report [36, 40].Figure 3
Daily runoff hydrograph of the Zipingpu hydrometric station during calibration and verification period.The simulated effects of extreme high flows and extreme low flows in the upper Min River from 1969 to 1987 simulated by SWAT were calculated, where the annual maximum daily flows were used to represent extreme high flows, and the annual minimum monthly flows were used to represent extreme low flows. The simulation results show that the correlation coefficient between the simulated and measured annual maximum 1-day (AM) flow is 0.75, and the correlation coefficient between the simulated and measured minimum monthly flow (IM) is 0.82, indicating that the SWAT model for the upper Minjiang River can be used to carry out studies of extreme runoff.


### 4.3. Variation Characteristics of Temperature, Precipitation, and Runoff under Future Scenarios

As shown in Figure4, the monthly average temperature rises by 2°C to 3°C. Temperature increase is most evident from June to August in summer and December to January in winter. Comparing the three scenarios, temperature rise is most apparent in the RCP8.5 scenario. Precipitation decreases from April to May in spring compared with the historical period, and it increases in other months compared with the historical period, particularly from July to August in summer, with an increase of 27–40 mm. The increase in runoff is most evident from November to April of the following year, while runoff from June to July is less than that in the historical period, i.e., more than 10% less than that in the historical period. From June to July in summer, temperature and precipitation increase, whereas runoff decreases, indicating that the rising range of precipitation cannot compensate for the impact of the rising evapotranspiration caused by increasing temperature. From April to May in spring, temperature and runoff increase, whereas precipitation decreases, indicating that the impact of temperature increase on the snow-melting process is greater than that on evapotranspiration in spring. The increase in melting snow amount increases runoff. The decrease in runoff from June to July in summer will affect agricultural irrigation water and may lead to agricultural production reduction. It may also affect downstream water diversion and the ecological environment.Figure 4
Changes in monthly average temperature, precipitation, and runoff compared with the base year under RCP2.6, RCP4.5, and RCP8.5 from 2021 to 2050.


## 4.1. Simulation Results of SDSM

The results show that the spatial correlation between large-scale variables simulated and measured using the second-generation Canadian Earth System Model (CANESM2) is 0.76 for all stations in Southwest China, where the upper reaches of the Minjiang River are located. CANESM2 can simulate more accurate large-scale variables, such as the upper atmospheric field and atmospheric circulation, with a resolution of 2.5° × 2.5°; however, it lacks regional climate information.The large-scale and low-resolution variables of CANESM2 simulation are reduced to a regional scale by SDSM. The analysis shows that the correlation coefficient between the simulated and measured daily maximum and minimum temperatures is about 0.9, while the correlation coefficient between the simulated and measured daily precipitation is between 0.4 and 0.5 (Table5), indicating that analyzing extreme meteorological elements is feasible by using temperature and precipitation data after SDSM downscaling.Table 5
Correlation coefficients of measured and simulated daily maximum and minimum temperature and precipitation during the verification period.
StationMaximum temperatureMinimum temperaturePrecipitationMaoxian0.9∗∗∗0.9∗∗∗0.4∗∗Songpan0.9∗∗∗0.8∗∗∗0.4∗∗Wenchuan0.9∗∗∗0.9∗∗∗0.5∗∗Lixian0.9∗∗∗0.9∗∗∗0.5∗∗Heishui0.9∗∗∗0.9∗∗∗0.5∗∗∗∗indicates passing the 95% significance test; ∗∗∗indicates passing the 99% significance test.The average absolute error between the simulation and actual measurements during different seasons in the verification period is calculated (Table6). The average absolute error of the highest and lowest temperatures exhibits minimal seasonal variation, and the daily average absolute error of precipitation in summer is higher than that in other seasons. In general, using SDSM for downscaling research in the upper reaches of the Minjiang River is feasible.Table 6
Mean absolute error of variables during the validation period.
VariablesSeasonsSpringSummerAutumnWinterMaximum temperature (°C)1.41.41.41.4Minimum temperature (°C)1.41.31.41.5Precipitation (mm)1.32.21.30.4


## 4.2. Simulation Results of SWAT

The SWAT model was used to simulate the daily runoff in the upper Minjiang River, and the optimal values of the model parameters are shown in Table7. The results showed that R2 was 0.87 and the NSE was 0.86 in the calibration period, R2 was 0.79, and the NSE was 0.77 in the validation period, indicating that it is feasible to simulate the runoff of the upper Minjiang River using the SWAT model (Figure 3). From the simulation results, it can be seen that the SWAT model is able to simulate the seasonal distribution characteristics of runoff. SWAT simulates runoff more accurately in the flood season, and the model simulates slightly lower runoff in the dry season, which may be related to the greater contribution of high flows in the flood season to the simulation error evaluation [7, 8].Table 7
Sensitivity ranks and optimal values of the model parameters.
Parameter nameBrief descriptionOptimal valueT-statP valuev__GW_DELAY.gwGroundwater delay (days)13.0634.630v__ALPHA_BF.gwBaseflow alpha factor (days)0.63−7.460v__TLAPS.subTemperature lapse rate (°C/km)18.311.530.13r__SOL_K(1).sol_1Saturated hydraulic conductivity0.721.230.22v__NPERCO.bsnNitrogen percolation coefficient9.721.20.23v__CH_N2.rteManning’s n value for main channel−5.34−1.160.25v__SPEXP.bsnExp. re-entrainment parameter for channel sediment routing1.45−0.960.34v__GW_REVAP.gwGroundwater “revap” coefficient0.02−1.040.3v__REVAPMN.gw_1Threshold water depth in the shallow aquifer for “revap” (mm)2.720.690.49v__EPCO.hruPlant uptake compensation factor1.270.660.51v__PHOSKD.bsnPhosphorus soil partitioning coefficient147.570.440.66r__SOL_ALB(1).solMoist soil albedo0.220.420.68v__CH_K2.rteChannel effective hydraulic conductivity (mm/hr)−489.23−0.420.68v__ESCO.hruSoil evaporation compensation factor1.09−0.310.76a__GWQMN.gwThreshold depth of the water in the shallow aquifer required for return flow to occur910.29−0.290.77v__SMFMN.bsnMelt factor for snow on December 21 (mm H2O/°C-day)11.660.250.8r__CN2.mgtSCS streamflow curve number−0.58−0.210.84v__SMFMX.bsnMelt factor for snow on June 21 (mm H2O/°C-day)−0.880.130.9r__SOL_AWC(1).solAvailable water capacity (mm H2O/mm soil)−3.88−0.040.97v__TIMP.bsnSnow pack temperature lag factor0.760.030.98v__SURLAG.bsnSurface runoff lag time (days)719.80.010.99r_ means an existing parameter value is multiplied by (1þ given value); v_ means the existing parameter value is to be replaced by given value; the detailed description of the parameters was introduced as technical report [36, 40].Figure 3
Daily runoff hydrograph of the Zipingpu hydrometric station during calibration and verification period.The simulated effects of extreme high flows and extreme low flows in the upper Min River from 1969 to 1987 simulated by SWAT were calculated, where the annual maximum daily flows were used to represent extreme high flows, and the annual minimum monthly flows were used to represent extreme low flows. The simulation results show that the correlation coefficient between the simulated and measured annual maximum 1-day (AM) flow is 0.75, and the correlation coefficient between the simulated and measured minimum monthly flow (IM) is 0.82, indicating that the SWAT model for the upper Minjiang River can be used to carry out studies of extreme runoff.


## 4.3. Variation Characteristics of Temperature, Precipitation, and Runoff under Future Scenarios

As shown in Figure4, the monthly average temperature rises by 2°C to 3°C. Temperature increase is most evident from June to August in summer and December to January in winter. Comparing the three scenarios, temperature rise is most apparent in the RCP8.5 scenario. Precipitation decreases from April to May in spring compared with the historical period, and it increases in other months compared with the historical period, particularly from July to August in summer, with an increase of 27–40 mm. The increase in runoff is most evident from November to April of the following year, while runoff from June to July is less than that in the historical period, i.e., more than 10% less than that in the historical period. From June to July in summer, temperature and precipitation increase, whereas runoff decreases, indicating that the rising range of precipitation cannot compensate for the impact of the rising evapotranspiration caused by increasing temperature. From April to May in spring, temperature and runoff increase, whereas precipitation decreases, indicating that the impact of temperature increase on the snow-melting process is greater than that on evapotranspiration in spring. The increase in melting snow amount increases runoff. The decrease in runoff from June to July in summer will affect agricultural irrigation water and may lead to agricultural production reduction. It may also affect downstream water diversion and the ecological environment.Figure 4
Changes in monthly average temperature, precipitation, and runoff compared with the base year under RCP2.6, RCP4.5, and RCP8.5 from 2021 to 2050.


## 5. Analysis of the Variation Characteristics of Extreme Temperature Index

### 5.1. Relative Indices (TX10p, TN10p, TX90p, and TN90p)

Table8 provides the comparison of extreme temperature index values in the upper reaches of the Minjiang River between the historical period and future climate scenarios. Tx10p and Tn10p mostly increase, particularly in the RCP8.5 scenario. The increase in Tn90p and Tx90p is more evident in the future climate scenario, particularly in the RCP2.6 scenario. These findings show that the relative indicators in the upper reaches of the Minjiang River are largely increasing in the future climate scenario, in which the increase of warm indicators is more evident. Meanwhile, the increase of cold indicators is more apparent in the RCP8.5 scenario, and the increase of warm indicators is more noticeable in RCP2.6.Table 8
Comparison of extreme temperature index values in the upper reaches of the Minjiang River in the historical period and future climate scenario from 2021 to 2050 (unit: °C or days).
Index classificationIndex abbreviationMaoxianSongpanWenchuanLixianHeishuiHistoricalRCP2.6RCP 4.5RCP 8.5HistoricalRCP 2.6RCP 4.5RCP 8.5HistoricalRCP 2.6RCP 4.5RCP 8.5HistoricalRCP 2.6RCP 4.5RCP 8.5HistoricalRCP 2.6RCP 4.5RCP 8.5Relative indexTX10p36.065.672.888.835.926.533.842.035.560.970.478.535.560.368.375.136.026.132.940.2TN10p35.335.540.446.536.134.840.645.435.535.939.044.136.035.540.844.036.235.540.243.9TX90p35.285.143.924.236.559.048.429.336.158.454.336.136.059.053.939.935.860.354.539.7TN90p35.373.512110735.179.775.064.436.310197.884.736.099.595.584.536.075.272.858.5Extremal indexID04.52.22.01.53.81.61.91.81.00.00.01.02.91.61.51.52.51.01.01.0FD075.049.052.550.216315315215134.29.38.56.877.561.962.957.7125109108105SU2547.965.668.668.717.049.251.752.699.913313513677.810910911053.087.991.488.5TR204.551.353.857.80.03.53.94.617.850.351.253.23.218.819.322.70.03.43.44.2


### 5.2. Extreme Indices (TXn, TNn, TXx, and TNx)

TXn and TNn in the upper reaches of the basin will rise under the future climate scenario, particularly in RCP8.5. TXx and TNx will rise in the future climate scenario. The rise of TXx is most evident in the RCP8.5 scenario, while the rise of TNx is most apparent in the RCP2.6 scenario. These findings show that among the extreme value indices in the upper reaches of the Minjiang River, the cold index decreases, whereas the warm index increases, and the change is more evident in the RCP8.5 scenario.


## 5.1. Relative Indices (TX10p, TN10p, TX90p, and TN90p)

Table8 provides the comparison of extreme temperature index values in the upper reaches of the Minjiang River between the historical period and future climate scenarios. Tx10p and Tn10p mostly increase, particularly in the RCP8.5 scenario. The increase in Tn90p and Tx90p is more evident in the future climate scenario, particularly in the RCP2.6 scenario. These findings show that the relative indicators in the upper reaches of the Minjiang River are largely increasing in the future climate scenario, in which the increase of warm indicators is more evident. Meanwhile, the increase of cold indicators is more apparent in the RCP8.5 scenario, and the increase of warm indicators is more noticeable in RCP2.6.Table 8
Comparison of extreme temperature index values in the upper reaches of the Minjiang River in the historical period and future climate scenario from 2021 to 2050 (unit: °C or days).
Index classificationIndex abbreviationMaoxianSongpanWenchuanLixianHeishuiHistoricalRCP2.6RCP 4.5RCP 8.5HistoricalRCP 2.6RCP 4.5RCP 8.5HistoricalRCP 2.6RCP 4.5RCP 8.5HistoricalRCP 2.6RCP 4.5RCP 8.5HistoricalRCP 2.6RCP 4.5RCP 8.5Relative indexTX10p36.065.672.888.835.926.533.842.035.560.970.478.535.560.368.375.136.026.132.940.2TN10p35.335.540.446.536.134.840.645.435.535.939.044.136.035.540.844.036.235.540.243.9TX90p35.285.143.924.236.559.048.429.336.158.454.336.136.059.053.939.935.860.354.539.7TN90p35.373.512110735.179.775.064.436.310197.884.736.099.595.584.536.075.272.858.5Extremal indexID04.52.22.01.53.81.61.91.81.00.00.01.02.91.61.51.52.51.01.01.0FD075.049.052.550.216315315215134.29.38.56.877.561.962.957.7125109108105SU2547.965.668.668.717.049.251.752.699.913313513677.810910911053.087.991.488.5TR204.551.353.857.80.03.53.94.617.850.351.253.23.218.819.322.70.03.43.44.2


## 5.2. Extreme Indices (TXn, TNn, TXx, and TNx)

TXn and TNn in the upper reaches of the basin will rise under the future climate scenario, particularly in RCP8.5. TXx and TNx will rise in the future climate scenario. The rise of TXx is most evident in the RCP8.5 scenario, while the rise of TNx is most apparent in the RCP2.6 scenario. These findings show that among the extreme value indices in the upper reaches of the Minjiang River, the cold index decreases, whereas the warm index increases, and the change is more evident in the RCP8.5 scenario.


## 6. Analysis of the Variation Characteristics of Extreme Precipitation

### 6.1. Sustainability Index: Crop Water Demand (CWD)

Table9 presents the comparison of extreme precipitation index values in the upper reaches of the Minjiang River in the historical period and future climate scenario. Compared with the historical period, CWD increased in the future climate scenario, with RCP4.5 increasing most significantly in the Maoxian station and RCP8.5 increasing most significantly in the other stations. These findings show that continuous precipitation in the upper reaches of the Minjiang River will further increase under the future climate scenario.Table 9
Comparison of extreme precipitation index values in the upper reaches of the Minjiang River in the historical period and future climate scenario from 2021 to 2050 (unit: mm or days).
Index classificationIndex abbreviationMaoxianSongpanWenchuanLixianHeishuiHistoricalRCP 2.6RCP 4.5RCP 8.5HistoricalRCP 2.6RCP 4.5RCP 8.5HistoricalRCP 2.6RCP 4.5RCP 8.5HistoricalRCP 2.6RCP 4.5RCP 8.5HistoricalRCP 2.6RCP 4.5RCP 8.5Sustainability indexCWD4.15.65.75.56.17.57.67.85.18.38.39.45.57.47.47.77.06.26.36.3Threshold indexR10 mm9.123.723.724.918.030.731.534.62.519.120.523.111.118.118.019.723.532.832.233.7R25 mm1.10.10.00.01.50.00.00.00.20.00.00.00.80.00.00.02.60.30.30.3Absolute value indexRX1DAY33.019.919.920.429.619.219.319.625.117.617.217.527.016.716.517.133.723.423.523.6RX5DAY41.148.748.950.652.951.553.155.847.846.747.649.742.344.043.145.061.861.962.663.6


### 6.2. Threshold Indices (R10 mm and R25 mm)

Under the future climate scenario, R25 mm largely decreases, particularly in the Songpan, Wenchuan, and Lixian stations. R10 mm is largely rising, particularly in the RCP8.5 scenario. This result shows that under the future climate scenario, the frequency of strong precipitation in the upper reaches of the Minjiang River will increase, while the frequency of heavy precipitation will decrease.


### 6.3. Absolute Value Indices (RX1DAY and RX5DAY)

Under the future climate scenario, RX1DAY decreases, particularly in RCP2.6 and RCP4.5, whereas RX5DAY increases, particularly in RCP8.5. This result shows that the intensity of continuous heavy precipitation in the upper reaches of the Minjiang River increases, whereas the intensity of short-term heavy precipitation decreases.


### 6.4. Analysis of the Variation Characteristics of the Extreme Runoff Index Value

Table10 provides the change rate of extreme runoff in the upper reaches of the Minjiang River relative to the historical period under the future climate scenario. Under the future climate scenario, AM in the upper reaches of the Minjiang River exhibits a downward trend, i.e., a reduction of 32%–42%. Among these, the decrease in Heishui station is the most evident, followed by that in Zipingpu station. When different future climate scenarios are compared, RCP4.5 presents the most noticeable downward trend, followed by RCP2.6. Q10, Q5, and Q1, which represent the extreme runoff of low flow and exhibit increasing trends, indicating that the risk of drought in the upper reaches of the Minjiang River will be weakened under different discharge scenarios in the future.Table 10
Variation of extreme runoff in the upper reaches of the Minjiang River from 2021 to 2050 (%).
HeishuiShabaZipingpuRCP2.6RCP4.5RCP8.5RCP2.6RCP4.5RCP8.5RCP2.6RCP4.5RCP8.5AM−41.1−41.8−38.6−34.3−35.1−32.5−36.5−36.7−34.8Q9019.517.523.327.12430.22.50.64.2Q955.23.38.310.69.513.2−9.1−9.3−6.3Q99−17.7−18.7−15.6−13.7−14.4−11.5−24−23.7−21.2Q10102.4121.8130.571.182.693.8464855.4Q575.289.899.552.756.670.329.72639.5Q151.160.967.929.333.142.216.41426.2Q90 and Q95 of Heishui and Shaba stations show an increasing trend, whereas Q99 shows a decreasing trend. Q90 of Zipingpu station presents a weak upward trend (i.e., less than 5%), and Q95 and Q99 exhibit a downward trend. These results show that the overall flood risk in the upper reaches of the Minjiang River is weakened. However, the spatial distribution in the upper reaches is different, and flood risk in the upstream source area demonstrates an increasing trend.Figure5 shows the changes in Q90, Q95, and Q99 at Zipingpu station from May to October under the historical period and future climate scenario. The overall characteristics of Q90, Q95, and Q99 that reflect the peak runoff are as follows: they decrease from June to July, decrease weakly in May, and increase in other months. Combined with the runoff wet season in the upper reaches of the Minjiang River from May to October, extreme flood events from June to July will be reduced under the future climate scenario, whereas the extreme flood risk from August to October will increase. Particular attention should be given to the increase in autumn flood risk in the upper reaches of the Minjiang River.Figure 5
Variation of quantile runoff from May to October in the upper reaches from 2021 to 2050 (%).


### 6.5. Hydrological Frequency Analysis of Extreme Runoff under Future Climate Scenarios

Daily runoff under different future climate scenarios is simulated using the SWAT model. Annual maximum daily runoff and annual minimum monthly runoff are selected for hydrological frequency analysis. Annual maximum daily runoff can reflect the risk of flood, while annual minimum monthly runoff can reflect the risk of drought.Hydrological frequency is analyzed using annual maximum daily runoff under the future climate scenario and the historical period. As indicated in Figure6 and Table 11, the Monte Carlo simulation [28] shows that the optimal frequency distribution line of RCP2.6 and RCP4.5 in the historical period is generalized logic distribution (GLO), while that of RCP8.5 is Wakeby distribution.Figure 6
Annual maximum daily runoff with different return periods.Table 11
Return period of annual maximum daily runoff.
Optimal distributionReturn period10 years20 years100 yearsHistoricalGLO2275.22537.03259.0RCP2.6GLO1276.81335.91471.9RCP4.5GLO1298.11373.71559.3RCP8.5Wakeby1352.51437.31610.4By using the optimal frequency distribution, the annual maximum daily runoff under different discharge scenarios is calculated to be reduced compared with that of the historical period. The annual maximum daily runoff with a 20-year return period under RCP2.6, RCP4.5, and RCP8.5 discharge scenarios is reduced by 47.3%, 45.9%, and 43.3%, respectively, compared with that of the historical period. Meanwhile, the annual maximum daily runoff with a 100-year return period is reduced by 54.8%, 52.2%, and 50.6%, respectively, compared with that of the historical period. This result shows that the risk of flood under different discharge scenarios is lower than that in the historical period.Hydrological frequency is analyzed using annual minimum monthly runoff under the future climate scenario and the historical period. As indicated in Figure7 and Table 12, Monte Carlo simulation [28] is used to determine that the optimal frequency distribution line of RCP2.6 and RCP8.5 in the historical period is generalized logistic distribution (GLO), while that of RCP4.5 is generalized extreme value distribution (GEV).Figure 7
Annual minimum daily runoff with different return periods.Table 12
Annual minimum monthly runoff under three discharge scenarios and the historical period (m³/s).
Optimal distributionReturn period10 years20 years100 yearsHistoricalGLO122.6136.7175.7RCP2.6GLO161.8179.1222.5RCP4.5GEV162.5176.2200.4RCP8.5GLO176.3198.6258.3Under different discharge scenarios, the annual minimum monthly runoff increases compared to historical period. Under RCP2.6, RCP4.5, and RCP8.5 discharge scenarios, the annual minimum monthly runoff with a 20-year return period increases by 30.9%, 28.8%, and 45.2%, respectively, compared with that of the historical period. Meanwhile, the annual minimum monthly runoff with a 100-year return period increases by 26.6%, 14.1%, and 47.0%, respectively, compared with that of the historical period. Therefore, the risk of drought in the upper reaches of the Minjiang River is reduced under different emission scenarios.


## 6.1. Sustainability Index: Crop Water Demand (CWD)

Table9 presents the comparison of extreme precipitation index values in the upper reaches of the Minjiang River in the historical period and future climate scenario. Compared with the historical period, CWD increased in the future climate scenario, with RCP4.5 increasing most significantly in the Maoxian station and RCP8.5 increasing most significantly in the other stations. These findings show that continuous precipitation in the upper reaches of the Minjiang River will further increase under the future climate scenario.Table 9
Comparison of extreme precipitation index values in the upper reaches of the Minjiang River in the historical period and future climate scenario from 2021 to 2050 (unit: mm or days).
Index classificationIndex abbreviationMaoxianSongpanWenchuanLixianHeishuiHistoricalRCP 2.6RCP 4.5RCP 8.5HistoricalRCP 2.6RCP 4.5RCP 8.5HistoricalRCP 2.6RCP 4.5RCP 8.5HistoricalRCP 2.6RCP 4.5RCP 8.5HistoricalRCP 2.6RCP 4.5RCP 8.5Sustainability indexCWD4.15.65.75.56.17.57.67.85.18.38.39.45.57.47.47.77.06.26.36.3Threshold indexR10 mm9.123.723.724.918.030.731.534.62.519.120.523.111.118.118.019.723.532.832.233.7R25 mm1.10.10.00.01.50.00.00.00.20.00.00.00.80.00.00.02.60.30.30.3Absolute value indexRX1DAY33.019.919.920.429.619.219.319.625.117.617.217.527.016.716.517.133.723.423.523.6RX5DAY41.148.748.950.652.951.553.155.847.846.747.649.742.344.043.145.061.861.962.663.6


## 6.2. Threshold Indices (R10 mm and R25 mm)

Under the future climate scenario, R25 mm largely decreases, particularly in the Songpan, Wenchuan, and Lixian stations. R10 mm is largely rising, particularly in the RCP8.5 scenario. This result shows that under the future climate scenario, the frequency of strong precipitation in the upper reaches of the Minjiang River will increase, while the frequency of heavy precipitation will decrease.


## 6.3. Absolute Value Indices (RX1DAY and RX5DAY)

Under the future climate scenario, RX1DAY decreases, particularly in RCP2.6 and RCP4.5, whereas RX5DAY increases, particularly in RCP8.5. This result shows that the intensity of continuous heavy precipitation in the upper reaches of the Minjiang River increases, whereas the intensity of short-term heavy precipitation decreases.


## 6.4. Analysis of the Variation Characteristics of the Extreme Runoff Index Value

Table10 provides the change rate of extreme runoff in the upper reaches of the Minjiang River relative to the historical period under the future climate scenario. Under the future climate scenario, AM in the upper reaches of the Minjiang River exhibits a downward trend, i.e., a reduction of 32%–42%. Among these, the decrease in Heishui station is the most evident, followed by that in Zipingpu station. When different future climate scenarios are compared, RCP4.5 presents the most noticeable downward trend, followed by RCP2.6. Q10, Q5, and Q1, which represent the extreme runoff of low flow and exhibit increasing trends, indicating that the risk of drought in the upper reaches of the Minjiang River will be weakened under different discharge scenarios in the future.Table 10
Variation of extreme runoff in the upper reaches of the Minjiang River from 2021 to 2050 (%).
HeishuiShabaZipingpuRCP2.6RCP4.5RCP8.5RCP2.6RCP4.5RCP8.5RCP2.6RCP4.5RCP8.5AM−41.1−41.8−38.6−34.3−35.1−32.5−36.5−36.7−34.8Q9019.517.523.327.12430.22.50.64.2Q955.23.38.310.69.513.2−9.1−9.3−6.3Q99−17.7−18.7−15.6−13.7−14.4−11.5−24−23.7−21.2Q10102.4121.8130.571.182.693.8464855.4Q575.289.899.552.756.670.329.72639.5Q151.160.967.929.333.142.216.41426.2Q90 and Q95 of Heishui and Shaba stations show an increasing trend, whereas Q99 shows a decreasing trend. Q90 of Zipingpu station presents a weak upward trend (i.e., less than 5%), and Q95 and Q99 exhibit a downward trend. These results show that the overall flood risk in the upper reaches of the Minjiang River is weakened. However, the spatial distribution in the upper reaches is different, and flood risk in the upstream source area demonstrates an increasing trend.Figure5 shows the changes in Q90, Q95, and Q99 at Zipingpu station from May to October under the historical period and future climate scenario. The overall characteristics of Q90, Q95, and Q99 that reflect the peak runoff are as follows: they decrease from June to July, decrease weakly in May, and increase in other months. Combined with the runoff wet season in the upper reaches of the Minjiang River from May to October, extreme flood events from June to July will be reduced under the future climate scenario, whereas the extreme flood risk from August to October will increase. Particular attention should be given to the increase in autumn flood risk in the upper reaches of the Minjiang River.Figure 5
Variation of quantile runoff from May to October in the upper reaches from 2021 to 2050 (%).


## 6.5. Hydrological Frequency Analysis of Extreme Runoff under Future Climate Scenarios

Daily runoff under different future climate scenarios is simulated using the SWAT model. Annual maximum daily runoff and annual minimum monthly runoff are selected for hydrological frequency analysis. Annual maximum daily runoff can reflect the risk of flood, while annual minimum monthly runoff can reflect the risk of drought.Hydrological frequency is analyzed using annual maximum daily runoff under the future climate scenario and the historical period. As indicated in Figure6 and Table 11, the Monte Carlo simulation [28] shows that the optimal frequency distribution line of RCP2.6 and RCP4.5 in the historical period is generalized logic distribution (GLO), while that of RCP8.5 is Wakeby distribution.Figure 6
Annual maximum daily runoff with different return periods.Table 11
Return period of annual maximum daily runoff.
Optimal distributionReturn period10 years20 years100 yearsHistoricalGLO2275.22537.03259.0RCP2.6GLO1276.81335.91471.9RCP4.5GLO1298.11373.71559.3RCP8.5Wakeby1352.51437.31610.4By using the optimal frequency distribution, the annual maximum daily runoff under different discharge scenarios is calculated to be reduced compared with that of the historical period. The annual maximum daily runoff with a 20-year return period under RCP2.6, RCP4.5, and RCP8.5 discharge scenarios is reduced by 47.3%, 45.9%, and 43.3%, respectively, compared with that of the historical period. Meanwhile, the annual maximum daily runoff with a 100-year return period is reduced by 54.8%, 52.2%, and 50.6%, respectively, compared with that of the historical period. This result shows that the risk of flood under different discharge scenarios is lower than that in the historical period.Hydrological frequency is analyzed using annual minimum monthly runoff under the future climate scenario and the historical period. As indicated in Figure7 and Table 12, Monte Carlo simulation [28] is used to determine that the optimal frequency distribution line of RCP2.6 and RCP8.5 in the historical period is generalized logistic distribution (GLO), while that of RCP4.5 is generalized extreme value distribution (GEV).Figure 7
Annual minimum daily runoff with different return periods.Table 12
Annual minimum monthly runoff under three discharge scenarios and the historical period (m³/s).
Optimal distributionReturn period10 years20 years100 yearsHistoricalGLO122.6136.7175.7RCP2.6GLO161.8179.1222.5RCP4.5GEV162.5176.2200.4RCP8.5GLO176.3198.6258.3Under different discharge scenarios, the annual minimum monthly runoff increases compared to historical period. Under RCP2.6, RCP4.5, and RCP8.5 discharge scenarios, the annual minimum monthly runoff with a 20-year return period increases by 30.9%, 28.8%, and 45.2%, respectively, compared with that of the historical period. Meanwhile, the annual minimum monthly runoff with a 100-year return period increases by 26.6%, 14.1%, and 47.0%, respectively, compared with that of the historical period. Therefore, the risk of drought in the upper reaches of the Minjiang River is reduced under different emission scenarios.


## 7. Results and Discussion

### 7.1. Discussion

The research shows that precipitation from April to May in spring in the upper reaches of the Minjiang River is lower than that in the historical period, and the duration of the low-flow season is prolonged due to the reduction of water inflow in spring, which will affect irrigation and urban water supply. In the future, temperature and precipitation will increase, whereas runoff will decrease from June to July in summer. Meanwhile, precipitation will decrease and runoff will increase from April to May in spring. Several previous studies have shown that runoff change exhibits a significant positive correlation with precipitation, and the effect of temperature on runoff is more evident in dry years than in wet years [27]. This finding indicates that the effects of temperature on snow melting in spring and evapotranspiration in summer will further increase in the upper reaches of the Minjiang River under future climate change.Under the future climate scenario, the cold index frequency of extreme temperature increases and intensity decreases. In accordance with the fact that extreme cold events in the historical period mostly occur in the upper reaches [19], extreme cold events in the upper reaches of the Minjiang River will further increase in the future. The intensity of continuous heavy precipitation under the future climate scenario will increase, and this finding is consistent with the overall change trend of the Minjiang River Basin in the historical period [20]. It indicates that continuous extreme precipitation events in the upper reaches of the Minjiang River will further increase in the future.The research shows that the occurrence of flood events is typically related to the occurrence of short-term extreme heavy precipitation, and it is similar to the results of previous studies [7]. In the future scenario, the intensity and frequency of short-term heavy precipitation decrease, reducing the risk of extreme flood. The research shows that drought risk is related to precipitation during the dry season. In the future climate scenario, an increase in precipitation during the dry season in the upper reaches of the Minjiang River reduces the risk of drought in that area.At present, research on the impact of climate change on industries is mostly based on climate and hydrological models, including some uncertainties [8, 11], such as the simplification of physical processes by climate and hydrological models, the introduction of parametric processes by climate models, and future climate scenario assumptions. In consideration of the aforementioned characteristics, the current study selects three scenarios (low, medium, and high) in the typical concentration path recommended in the fifth assessment report of the Intergovernmental Panel on Climate Change (IPCC) that can reflect the degree of radiation force under different emission reduction strategies in the future. In the next step, multiple climate models will be considered for uncertainty analysis.


### 7.2. Results

Under the future climate scenario in the upper reaches of the Minjiang River, the frequency and intensity of the extreme temperature warming index will increase while those of the cold index will increase and weaken.Under the future climate scenario in the upper reaches of the Minjiang River, the duration of precipitation, the intensity of continuous heavy precipitation, and the frequency of strong precipitation increase, whereas the intensity of short-term heavy precipitation and the frequency of heavy precipitation decrease.Monte Carlo simulation is used to determine that for the RCP2.6 and RCP4.5 climate scenarios under the historical period, the optimal frequency distribution line of annual maximum daily flow is GLO, while that for the RCP8.5 climate scenario is Wakeby distribution. By using the optimal frequency distribution line, the annual maximum daily flow under different climate scenarios is calculated to be lower than that in the historical period, indicating that the risk of flood in the upper reaches of the Minjiang River under different climate scenarios is lower than that in the historical period.Monte Carlo simulation is used to determine that for the RCP2.6 and RCP8.5 climate scenarios under the historical period, the optimal frequency distribution line of annual minimum monthly flow is GLO, while that for RCP4.5 is GEV. By using the optimal distribution line, the annual minimum monthly flow under different climate scenarios is determined to increase compared with that in the historical period, indicating that the risk of drought in the upper reaches of the Minjiang River under different climate scenarios is reduced.The indicators that represent low flow runoff exhibit an increasing trend, indicating that drought risk in the upper reaches of the Minjiang River is reduced. The indicators that represent high water runoff mostly demonstrate an increasing trend at Heishui and Shaba stations, while those at Zipingpu station present a decreasing trend, indicating that the overall flood risk in the upper reaches of the Minjiang River is reduced. Meanwhile, spatial distribution in the upper reaches is different, and thus flood risk in the upstream source area still exhibits an increasing trend.The distribution characteristics of the indicators that represent high water runoff in the year show that extreme runoff in the upper reaches of the Minjiang River will present an upward trend from August to October in the future. Particular attention should be given to the increase in autumn flood risk in the upper reaches of the Minjiang River.


## 7.1. Discussion

The research shows that precipitation from April to May in spring in the upper reaches of the Minjiang River is lower than that in the historical period, and the duration of the low-flow season is prolonged due to the reduction of water inflow in spring, which will affect irrigation and urban water supply. In the future, temperature and precipitation will increase, whereas runoff will decrease from June to July in summer. Meanwhile, precipitation will decrease and runoff will increase from April to May in spring. Several previous studies have shown that runoff change exhibits a significant positive correlation with precipitation, and the effect of temperature on runoff is more evident in dry years than in wet years [27]. This finding indicates that the effects of temperature on snow melting in spring and evapotranspiration in summer will further increase in the upper reaches of the Minjiang River under future climate change.Under the future climate scenario, the cold index frequency of extreme temperature increases and intensity decreases. In accordance with the fact that extreme cold events in the historical period mostly occur in the upper reaches [19], extreme cold events in the upper reaches of the Minjiang River will further increase in the future. The intensity of continuous heavy precipitation under the future climate scenario will increase, and this finding is consistent with the overall change trend of the Minjiang River Basin in the historical period [20]. It indicates that continuous extreme precipitation events in the upper reaches of the Minjiang River will further increase in the future.The research shows that the occurrence of flood events is typically related to the occurrence of short-term extreme heavy precipitation, and it is similar to the results of previous studies [7]. In the future scenario, the intensity and frequency of short-term heavy precipitation decrease, reducing the risk of extreme flood. The research shows that drought risk is related to precipitation during the dry season. In the future climate scenario, an increase in precipitation during the dry season in the upper reaches of the Minjiang River reduces the risk of drought in that area.At present, research on the impact of climate change on industries is mostly based on climate and hydrological models, including some uncertainties [8, 11], such as the simplification of physical processes by climate and hydrological models, the introduction of parametric processes by climate models, and future climate scenario assumptions. In consideration of the aforementioned characteristics, the current study selects three scenarios (low, medium, and high) in the typical concentration path recommended in the fifth assessment report of the Intergovernmental Panel on Climate Change (IPCC) that can reflect the degree of radiation force under different emission reduction strategies in the future. In the next step, multiple climate models will be considered for uncertainty analysis.


## 7.2. Results

Under the future climate scenario in the upper reaches of the Minjiang River, the frequency and intensity of the extreme temperature warming index will increase while those of the cold index will increase and weaken.Under the future climate scenario in the upper reaches of the Minjiang River, the duration of precipitation, the intensity of continuous heavy precipitation, and the frequency of strong precipitation increase, whereas the intensity of short-term heavy precipitation and the frequency of heavy precipitation decrease.Monte Carlo simulation is used to determine that for the RCP2.6 and RCP4.5 climate scenarios under the historical period, the optimal frequency distribution line of annual maximum daily flow is GLO, while that for the RCP8.5 climate scenario is Wakeby distribution. By using the optimal frequency distribution line, the annual maximum daily flow under different climate scenarios is calculated to be lower than that in the historical period, indicating that the risk of flood in the upper reaches of the Minjiang River under different climate scenarios is lower than that in the historical period.Monte Carlo simulation is used to determine that for the RCP2.6 and RCP8.5 climate scenarios under the historical period, the optimal frequency distribution line of annual minimum monthly flow is GLO, while that for RCP4.5 is GEV. By using the optimal distribution line, the annual minimum monthly flow under different climate scenarios is determined to increase compared with that in the historical period, indicating that the risk of drought in the upper reaches of the Minjiang River under different climate scenarios is reduced.The indicators that represent low flow runoff exhibit an increasing trend, indicating that drought risk in the upper reaches of the Minjiang River is reduced. The indicators that represent high water runoff mostly demonstrate an increasing trend at Heishui and Shaba stations, while those at Zipingpu station present a decreasing trend, indicating that the overall flood risk in the upper reaches of the Minjiang River is reduced. Meanwhile, spatial distribution in the upper reaches is different, and thus flood risk in the upstream source area still exhibits an increasing trend.The distribution characteristics of the indicators that represent high water runoff in the year show that extreme runoff in the upper reaches of the Minjiang River will present an upward trend from August to October in the future. Particular attention should be given to the increase in autumn flood risk in the upper reaches of the Minjiang River.



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*Source: 9458678-2023-02-09.xml*