Responses of water environmental indicators to climate conditions in the middle and lower reaches of Lijiang River

: With global climate change and increasingly extreme weather conditions, the water environment of the Lijiang River Basin is facing huge threats. Past studies have mostly focused 16 on large-scale areas or have regional characteristics. Therefore, this study is based on the 17 meteorological, hydrological, and water quality data of the Lijiang River from 2012 to 2018, 18 using the analysis method Spearman's rank correlation coefficient, sensitivity, and contribution 19 rate to quantitative analysis of the relationship between climate conditions and water 20 environment indicators. The results show that the oxidation and alkalinity of the water in the 21 Lijiang River Basin gradually increase, and the intensity becomes stronger as it goes 22 downstream. DO increase and the concentrations of COD Mn , BOD 5 , and NH 4 -N all decreased, 23 and water quality improved year by year. The input of external pollution has led to an upward 24 trend in TP in Yangshuo. DO is positively correlated with wind speed and negatively correlated 25 with other climate indicators. NH 4 -N and TP are mainly affected by precipitation, streamflow, 26 humidity, and sunshine duration, only sunshine duration is negatively correlated. Pollutants 27 from Guilin to Yangshuo on both sides of the Lijiang River were carried by the surface runoff 28 into the water body contain a certain amount of organic matter and acidic matter. Water environment indicators are not very sensitive to precipitation and streamflow, humidity and 30 wind speed have higher sensitivity. Water temperature and sunshine duration have a positive 31 effect on reducing NH 4 -N and TP. Various climate conditions can help reduce organic matter in 32 the water body where there are few external sources and the opposite contribution with external 33 sources. No climate condition can dominate one water environment indicator of two stations at 34 the same time. The difference between Yangshuo and Guilin is mostly due to the input of 35 external sources on both sides of the Lijiang River, which leads to the difference in sensitive 36 climate conditions. Construction of non-point source pollution reduction facilities and sewage 37 treatment measures are very necessary. 38

The monitoring stations are located in Guilin and Yangshuo (Figure 1), including daily 111 maximum temperature, minimum temperature, precipitation, sunshine duration, wind speed, 112 humidity, streamflow, and other indicators, the data scale is a daily scale. Water quality data 113 were obtained by combining manual and automatic monitoring. The stations are shown in Table  114 1, including environmental factors such as water temperature, pH, dissolved oxygen (DO), 115 oxidation-reduction potential (ORP), etc., and water quality indicators such as ammonia 116 nitrogen (NH4-N), total phosphorus (TP), etc., the data scale is monthly. Due to the different 117 data scales, the meteorological and hydrological data are downscaled in this study, and the 118 calculated monthly data are daily averages. 119

Trend analysis 123
In order to analyze the temporal and spatial evolution of climate indicators in the study 124 area, we used the unary linear regression trend method to analyze the change trend and intensity 125 on the time scale of the data of different monitoring stations in the Lijiang River Basin. The 126 slope of the regression equation reflects the changing trend of the climate index. A slope above 127 0 indicates that the index is on the rise in the time range, and vice versa is a downward trend. 128 The magnitude of the slope can reflect the magnitude of the increase or decrease of the index. 129 The calculation formula is as follows: 130 Where θ is the changing trend, i is the time (month), n is the study period, and C is the climate 132 index at the time i. 133

Correlation analysis 134
Climate conditions impact water quality indicators differently. In this study, correlation 135 analysis methods are used to quantify the correlation between the two indicators. Due to the 136 poor regularity of the measured data and occasional outliers, the Spearman rank correlation 137 coefficient method is adopted. This method does not require the distribution of the original 138 variables and has a wider application range than the Pearson correlation coefficient (Schober 139 and Schwarte 2018). While the Spearman correlation coefficient has an outlier value, the rank 140 of the outlier usually does not change significantly, so the influence on the Spearman correlation 141 coefficient is very low. For the original data with a sample size of n, the correlation coefficient 142 Where xi, yi are the average of the descending position of each raw data. 145

Sensitivity of water environmental indicators to climate conditions 146
Sensitivity can quantitatively reflect the impact of relative changes in climate conditions 147 on the relative changes in the concentration of water quality indicators. This study uses the 148 sensitivity coefficient defined by McCuen (1974) to analyze the sensitivity of different water 149 quality indicators in the Lijiang River Basin to climate conditions, calculated as follows: 150 Where S(Cl) is the sensitivity coefficient of water quality indicators to climate conditions. The 152 higher the absolute value of the sensitivity coefficient, the larger the impact of relative changes 153 in climate conditions on relative changes in water quality indicators. WQ and Cl represent water 154 quality indicators and climate conditions, respectively. The advantage of the sensitivity 155 coefficient is that it is dimensionless and can be compared between a variety of different 156 dimensions. 157

Contribution rate 158
The contribution rate of climate conditions is obtained by multiplying the sensitivity 159 coefficient and the relative change rate of the indicator over the years, and the calculation 160 formula is as follows: 161 Where ConCl is the contribution rate of climate condition Cl to the change of water environment 163 index WE, RCCl is the multi-year relative change rate of climate conditions, n is the number of 164 months, aCl is the changing trend of climate condition, ̅ is the monthly average of climate 165 conditions. 166 warming; on the other hand, the increase in sunshine duration leads to higher absorption of heat, 174 which increases the air temperature and water temperature. The difference between the 2 monitoring stations of the 3 indicators is unclear, the temperature is a little bit higher in 176

Results and discussion
Yangshuo than in Guilin due to the lower latitude. The air temperature in Guilin exceeded 177 Yangshuo in the period 2016~2018 whereas the water temperature did not appear the same 178 phenomenon, indicating that the heat island effect caused by urbanization primarily affects air 179 temperature. 180 181 Figure 2 The evolution trend of climate conditions 182 Precipitation and streamflow have decreased with time. As a rain source basin, streamflow 183 and precipitation are highly consistent, the difference between Guilin and Yangshuo is primarily 184 reflected in the amount of increased streamflow caused by each rain. There is not a significant 185 difference in precipitation between Guilin and Yangshuo, thus we analyze the correlation 186 between precipitation and streamflow ( Figure 3), correlation coefficients (R) between 187 precipitation and streamflow are 0.871 and 0.829 at Guilin and Yangshuo, respectively, show 188 high correlation. However, R between Guilin precipitation and Yangshuo streamflow is 0.881, 189 higher than that of the same stations, which means the precipitation has a higher influence on 190 the downstream than the local area. Yangshuo also received discharges and runoff from other 191 tributaries and their basins, make it a higher flow than Guilin. Guilin is 1.78 times that of Yangshuo and the average humidity is only 0.9 times that of 200 Yangshuo. Guilin station is located in Guilin City while Yangshuo station is located in a county 201 town, urbanization can lead to a significant wind reduction in urban areas (Peng et al. 2018), 202 and the city is relatively drier than a rural area. These climatic differences caused by 203 urbanization will also have some effect on water quality. 204 is located in a subtropical region, and the annual humidity changes are not significant. Therefore, 306

Water quality indicators
although the sensitivity of CODMn and BOD5 to humidity is extremely sensitive, these two 307 indicators will not cause a huge change in water quality due to sudden changes in humidity. 308 Table 3  The sensitivity of water environment indicators to temperature has significant spatial 311 differences. Almost all indicators in Yangshuo have high sensitivity to temperature, while 312 Guilin has very low sensitivity to temperature except for TP and ORP. It is worth noting that 313 the sensitivity of TP to air temperature and water temperature is opposite. This is because rising 314 water temperatures will accelerate phosphorus consumption and deposition, and rising air 315 temperatures will bring more exogenous phosphorus to the river. calculated using formula (4), as shown in Figure 6. Those with contribution rates in excess of 323 5% include streamflow to BOD5 at Yangshuo, sunshine duration to BOD5 at Guilin, water 324 temperature to CODMn at Yangshuo, water temperature to DO at Yangshuo, sunshine duration 325 and water temperature to NH4-N at Yangshuo, sunshine duration and water temperature to TP 326 at Guilin, and wind speed to TP at Yangshuo. All of them are negative values except for the last 327 one. It seems that the growth of climate indicators may cause a more obvious decline in some 328 water environment indicators. 329 Water temperature and sunshine duration have a positive impact on the reducing NH4-N 330 and TP, with a contribution rate ranging from 2.42% to 8.77%. Wind speed is the main 331 contribution of climate conditions to the increase of NH4-N and TP in the water body. Relatively, 332 the contribution to Yangshuo is greater than that of Guilin. Humidity's contribution to NH4-N 333 and TP is positive in Yangshuo and negative in Guilin, which is caused by the difference in 334 exogenous phosphorus. In addition, air temperature is also a big boost to the growth of TP in 335 the water bodies, with the contribution rates of Guilin and Yangshuo reaching 3.56% and 1.64%,

359
The difference in the contribution rate of the climate conditions between Yangshuo and 360 Guilin to the water environment is shown in Table 5. Under normal circumstances, lower 361 CODMn, BOD5, NH4-N, and TP, higher DO always means better water quality. Comparing with 362 the change trend of climate conditions (Figure 2), indicators with a risk of deterioration are 363 marked with "*" (Table 5). These differences are mainly due to the different pollution in the 364 two places, therefore the impact of the climate conditions is different. 365 Through data analysis, it can be seen that the difference between Yangshuo and Guilin is 368 mainly due to the input of external sources on both sides of the Lijiang River, which leads to 369 the difference in sensitive climate conditions. Sunshine duration, humidity, and precipitation all 370 indirectly affect the water quality of the Lijiang River by affecting the pollutants on the shore. 371 Therefore, the construction of non-point source pollution reduction facilities such as 372 constructed wetlands on both banks of the Lijiang River can effectively cope with the 373 deterioration of water quality in the lower reaches of the Lijiang River caused by climate change. 374 As a world-famous tourist city, Guilin has many foreign tourists all year round, the Lijiang 375 River from Guilin to Yangshuo is the most frequent tourist route, thus this section has suffered 376 the great risk of deteriorating the quality of the water environment by the sewage discharge. 377 The construction of sewage treatment measures along the river is also very necessary. 378