Climate Change Impact On Flood Runoff Over Lake Baikal Catchment

Water level and distribution of dissolved and suspended matter of Lake Baikal are strongly affected by river inow during rain-driven oods. This study analyses river ow changes at 44 streamow gauges and related precipitation, evaporation, potential evaporation and soil moisture obtained from ERA5-Land dataset. Based on Sen-Slope trend estimator, Mann–Kendall non-parametric test, and using dominant analyses we estimated inuence of meteorological parameters on river ow during 1979-2019. Using ridge-regression we found signicant relationships between precipitation elasticity of river ow and catchments features. Half of the gauges in eastern part of Selenga river basin showed a signicant decreasing trend of average and maximum river ow (up to -2.9%/year). No changes in central volume date of ood ow have been found. A reduction in rainfall amounts explains more than 60% of runoff decline. Decrease in evaporation is observed where precipitation decrease is 0.8%/y or more. Catchments where the precipitation trends are not as substantial are associated with increasing evaporation as a result of the increase of potential evaporation. Negative trends of precipitation are accompanied by negative trends of soil moisture. Finally, the study reveals sensitivity of the catchments with steep slopes in humid area to precipitation change.


Introduction
The impact of climate change on the hydrological regime of rivers is still a subject of active research in regarding extreme hydrological events such as oods or droughts (Renard et al. 2008). Long-term changes in water cycling in inland areas can considerably alter the hydrological and hydrogeochemical conditions of downstream lakes, wetlands and coastal waters. This is particularly important for the areas recently reported as the hot spots of ecosystem threats. One of them is the Lake Baikal area which contains 20% of the total amount of unfrozen freshwaters of the globe and thus representing unmatched source of freshwater in a drying world (Kasimov et al. 2019). River ow plays an important role for both economic activity and ecological condition of Baikal catchment (BC). Its magnitude, variability and timing re ect runoff-generation processes, including evapotranspiration, soil wetting-drying, permafrost freezing-thawing (Bring et al. 2015), erosion and weathering ), environmental functioning of streams (Goncharov et al. 2020) and wetlands ). Direct monitoring these processes for large areas and long-term period is still hardly possible, particularly in developing countries like Russia and Mongolia (Karthe et al. 2014; Alekseevskii et al. 2015).
BC is the most economically developed part of Mongolia. Its area covers only 19% of the country's territory, whereas 75% (~ 2.5 million people) of total population and practically all industrial production is located here. Absence of the large impoundments within its basin supposes rather natural patterns of river ow ). Mining, industrial and agricultural activities within the Selenga drainage basin affect the ow generation processes which is additionally coverуd by overgrazing of pasture land, soil degradation and deserti cation (Onda et al. 2007; Garmaev et al. 2019). Russian part of BC is subjected to the crisis in the agricultural sector of the Russian economy in the end of 20th century and associated abandonment of cultivated lands. Nevertheless, currently about 130 villages and towns (State report …, 2018) and 3000 km 2 of agricultural land under threat of ood (Kadetova and Radziminovich 2020). Currently, Lake Baikal is regulated as the part of the Irkutsk reservoir built in [1950][1951][1952][1953][1954][1955][1956][1957][1958][1959]. Since 2001, level of Baikal is regulated not only by the Russian standard Regulations for the use of water resources of reservoirs, but also by a separate government decree limiting the permissible range of level uctuations to 1 m, instead of 1.96 m laid down in the design (Abasov et al. 2017). Ful llment of these requirements during low ow period of 1995-2017 led to economic damage, primarily due to a shortage of electricity generation at Angara river hydropower stations.
Long-term low ow period in BC is characterized by spring and summer ood ow reduction and average or above average minimum discharge (Frolova et al. 2017;Sinyukovich and Chernyshov 2019a). A comparison of the periods before and after 1975 in the Selenga basin showed overall trends of a decrease in the maximum (by 5-35%) and average annual runoff (up to 15%), with an increase in the minimum (by 30%), a decrease in the annual precipitation (by 12%) and evaporation despite an increase in potential evaporation (4%) (Frolova et al. 2017). Both a decrease in precipitation and permafrost thawing are considered as reasons for maximum runoff reduction (Törnqvist et al. 2014). Since 1971, a weak (~ 0.2% per year) tendency towards a decrease in the maximum runoff of rainfall oods is also characteristic of the Upper Angara and Barguzin rivers (Sinyukovich and Chernyshov 2017). Projections of the Selenga river ow change vary greatly and show both growth and decrease for different scenarios These processes led to decreased sediment delivery to the Lake Baikal since the middle of the 1970s by 51% for the Selenga River and by 70% for the Upper Angara River and signi cant changes in sediment budget of the Selenga valley sink areas -downstream oodplains and delta ).
Flooding of the wetland-dominated areas of the delta between the distributary channels occurs when Q > 3000 m 3 /s (Aminjafari et al. 2020). Due to the decreased frequency of high discharge events (Q > 1350 m 3 /s; Fig. 1), as well as the decreased magnitude of annual high ow events over the past two decades (Törnqvist et al. 2014), oodplain lakes could remain disconnected from the delta's main channels for longer time intervals, decreasing buffering functions of the delta (Chalov et al. 2017).
The recent estimates show that over 70% of suspended sediments are stored in the delta, including small particles that are important carriers of pollutants (Chalov et al. 2017;Pietroń et al. 2018;Roberts et al. 2020). Additionally, ood regime affects delta's macrophytes which capture from 3 up to 10%, heavy metals ow (Shinkareva et al. 2019). Finally, the impacts of the ambient conditions of riverine in ow to the Lake Baikal are linked to eutrophication of Lake Baikal waters which are in turn are related to 300% increase in chlorophyll concentration since 1979 and development of algae of the genus Spirogyra in the coastal zone (Kravtsova et al. 2014). The main factors determining the development of algae in Lake Baikal are the load of nitrogen and phosphorus (O'Donnell et al. 2017), while an increase in temperature, although it plays a positive role, does not have a signi cant effect. In turn, the phosphorus load by the Selenga River is regulated by the ood ow (Sorokovikova et al. 2018).Contribution of some particular drivers of river ow formation (topography, climate's aridity, features of the seasonal variation of precipitation and evaporation, type of vegetation cover) were never studied in the regional in su cient quantitatively and sophisticated way (Merz et al. 2006;Guastini et al. 2019). At the same time it is supposed that their role can be important in average long-term runoff changes over BC region during 1950-2016 (Grigor'ev et al., 2020). Also, there is a lack of quantitative estimates of the particular climatic drivers impacts on abrupt changes in water delivery to Lake Baikal. Some studies showed importance of This study aims to evaluate long-term ood ow change and related climate variables in the entire Lake Baikal catchment -both withing major and minor tributaries (in total 33 rivers) using more than 40 years of discharge observations and gridded climate data. In particular the study focuses on revealing trends in ood ow and associated climate variables; their relationships -quantifying the contribution of precipitation and evaporation to variability of oods and contribution of potential evaporation and soil moisture to evaporation; studying the connection between the geographical features of the catchments and the response of ood runoff to precipitation uctuations.

Studied area
The climate of BC is continental, with dense snow cover during winter and warm and rainy summers. The average January temperature ranges from − 15°C to -25°C, while the average July temperature is around 14-18°C. At the same time, the average daily temperature can drop below − 40°C and rise above + 30°C. The mean precipitation in the Mongolian part of BC is about 300 mm, and in the Russian part it is more than 520 mm. In the river valleys of the Mongolian part of the basin, precipitation is less than 200 mm. In the mountains, the precipitation increases to 500 mm. In the Russian part of the basin, inequality is even greater -from less than 200 mm in river valleys and coastal areas of Lake Baikal to more than 1200 mm on the slopes of the Khamar Daban and Barguzinsky Ridges. The southern part of the BC is characterized by steppe vegetation, while the northern part is characterized by dense taiga. The main part of BC is occupied by permafrost, mostly discontinuous and insular (The Ecological Atlas ..., 2015). In 1979-2016, the air temperature in the Mongolian and Russian parts of the BC increased by 1.8 °С and by 1 °С correspondingly. Analysis of transformation in the types of underlying surface for 2000-2010 revealed changes in the area of various types of underlying surface around 1000 km 2 , i.e. about 0.2% of the BC area (Dorjsuren et al. 2018b).
We divided BC at 4 regions, according to their hydrographic features and available stream ow data (Fig.   2). The region I includes the upper reach of Selenga river basin above Naushki gauge near Mongolian-Russian border. The largest tributary of Selenga river in rst area is Orkhon river, which occupies its eastern part. The lower part of the Selenga River basin is considered as region II. Within region II, the largest tributaries of the Selenga River are the Zheltura, Temnik (left-bank), Chikoy, Khilok, Uda (rightbank), splited by mountain ranges. To the North of the Temnik basin, catchments of small but waterbearing tributaries of the Baikal's South coast (region III) are located. To the North of the region II Turka, Barguzin and Upper Angara river basins are located (region IV).
Rain-dominated stream ows are most common for Selenga basin. Runoff formation within Mongolia is featured by the presence of altitudinal zoning, areas of runoff formation and disappearance. Runoff forms in the mountains, where it is facilitated by large slopes, low porosity and fracturing of rocks, and permafrost. The territory of the foothill plains is dominated by well permeable and not saturated alluvial and talus deposits. This leads to the absence of water in ow and water loss in the channel for in ltration (Garmaev et  Unfortunately, we were unable to obtain data on water discharge in Mongolia, what causes uneven distribution of gauges across the regions. Region II has 36 gauges available, whereas regions I, III, and IV have 1, 4, and 3 gauges respectively (Fig. 2). The gauges covered about 92% of BC area (excluding lake area) with median size of the considered watersheds is 3450 km 2 . For seven gauges, there are gaps in the observations (primarily for 2002-2007). The share of missing values is 3.5% (Table 1).

Methods
Data processing was carried out by free available soft: programming language Python and GIS QGIS.
We considered period from June to September as ood ow period. River ow was characterized by its volume (Q ood ) normalized by drainage area and expressed in liters per second from square kilometer, daily maximum discharge (Q max ), also expressed in liters per second from square kilometer, and ood period center-volume date (CVD, Julian date) for each year was calculated as the date when half of the total stream ow volume during the ood period passed by the gauge.
Precipitation (P) was characterized by its total amount and amount of heavy precipitation (P 16 -above 16 mm/day) (Sato et al. 2007), and CVD. Evaporation (E), potential evaporation (PET), and soil moisture (W) data were averaged.
Also, for each region, for 1979-2019 average monthly terrestrial water storage change (TWSC) was calculated as (1) Sen's slope was used to determine the magnitude linear trends in hydrometeorological data. This slope is the median over all combinations of record pairs for the entire dataset (Helsel and Hirsch 2002). The statistical signi cance of the linear trends was assessed at α = 5%, 2%, and 1% level using the Mann-Kendall non-parametric test (Kendall 1975 correlation coe cients within 0.5 (Kislov et al. 2015;Duerinck et al. 2016;Zongxing et al. 2016). Based on this assumption, the effect of P on Q ood is taken into account not only through the amount of available moisture, but also by reducing the PET on rainy days.
As part of the procedure for assessing the impact of P on Q ood for each catchment, several multiple regression equations Q ood (P, E) were compiled, where Q ood was always taken for June-September, and P and E were taken with some delay (from 0 to 20 days), taking into account, that precipitation and evaporation from the catchment surface do not immediately affect the value of Q ood . For further analyses, we used the most signi cant relationships based on coe cient of determination (R 2 ). For the same periods, which showed the highest R 2 value, linear trends P, P 16 , E, PET, and W were calculated. The contribution of P to the variability of Q ood was taken equals R 2 of the regression equation Q ood (P).
Similary, the contribution of E to the variability of Q ood equals to the difference R 2 (Q ood (P, E))-R 2 (Q ood (P)).
The contribution of W and PET to the variability of E was calculated in a similar way. The contribution of W to the variability of E was counted as difference R 2 (E (W, PET))-R 2 (E (PET)), and the contribution from PET -as R 2 (E (PET)).
Various catchments might pose a speci c signals of climate changes to ood generation processes (Perdigão and Blöschl 2014). In particular, catchments may differ by the degree of Q ood sensitivity to changes in P expressed through the ratio of the linear trend Q ood (mm / year) to the linear trend P (mm / year) -. Due to the presence of errors in the P series, was calculated only for watersheds with the P trend, which is signi cant at the 5.6 % level. The percentage of area coveraged by permafrost of river basin, forests, non-forest vegetation, bare soil, the average slope of the catchment and the degree of climate humidity -the ratio of the average long-term value of P to E, were considered as the main climatic feature of river basins that affect (Table 2).
The in uence of the features of river basins on the value was assessed using following statistical approach. Firstly, using the correlation matrix, we identi ed the parameters most closely related to , as well as the degree of dependence of these parameters on each other. Further, the graphs of the relationship from the selected characteristics were analyzed in order to nd bouncing points associated with errors in the initial data or other factors not taken into account in the study. Next, a number of parameters have been transformed to linearize the relationship between them and . Finally, the correlation matrix was built again and the nal selection of parameters for the model was carried out on its basis. Since changes in the features of catchments occur as a result of their coevolution, there is a correlation between them. Based on the ideas of cause-and-effect relationships in the formation of river runoff and landscapes, we chose the parameters that determine the value of .
To facilitate the search for the parameters of the regression dependence, all predictors were scaled. A signi cance of particular variables for explaining variability of was evaluated by F-test and t-test (Costa 2017) at 5% level.

Results
Water balance of the ood season. Despite the large spatial variation of amount precipitation during the ood period among 4 regions (from 310 mm in region I to 605 mm in region III), their seasonal distribution in BC is relatively similar. Maximal precipitation occurs in July -August and the minimal takes place in June or September. The September minimum are more pronounced for I and II regions. The maximum evaporation occurs earlier -in June -July with rapid decline in August -September (Fig. 3).
Maximum water discharge occurs after the maximum precipitation across region I and II, probable due to their vast area. Region III is characterized by a maximum water discharge in August, however, in contrast to the Selenga basin, water discharge in June and July practically does not differ from each other.
Maximum of river discharge in region IV occurs in June further followed by decline during the entire season. All four gauges on Selenga river experienced downward trends, moreover, the rate of decrease is more pronounced at the downstream section and vary from − 1.08%/year at Naushki gauge near Mongolian-Russian border to -1.51% / year at Kabansk gauge. It can be seen that higher rates of Q ood change at the lower reaches compared to the upper ones are also typical for the largest tributaries of the Selenga river in region II -the rivers Chikoy, Hilok and, to a lesser extent, Uda. These rivers as well as their tributaries and small rivers of North-East of region II showed maximum magnitude of Q ood reduction. The trends in Chikoy basin are around 0.7-1.4 %/year. Upper reaches of Hilok basin showed 1%/year Q ood trend, whereas lower reaches are dominated by rate around 2%/year. The rate of Q ood decrease for the northernmost tributaries of the Selenga river reaches 1.5-2.9% / year. Similarly, west part of region II is characterized mostly by Q ood reduction, while none of the trends were signi cant. The rate of reduction is about 0.5%/year -up to 1%/year in upper reaches of the basins and at lower reaches is equals 0. Near the mouth of Djida river Q ood trend reaches 0.4%/year (№ 14) -maximum value across all gauges.
Region III had average Q ood reduction rate around 0.6%/year with single gauge experienced statistically signi cant change (№ 38). Insigni cant negative trends were found for the region IV, and the trend rate decreases from the South (around − 0.65%/year) to the North (-0.17%/year). 41 out of 44 gauges showed negative trend of Q max , statistically signi cant at 5%, 2% and 1% level for 17, 9 and 6 gauges correspondingly. Overall, Q max has similar to Q ood rate of reduction and larger coe cient of variation. According to Mann-Kendall test, fewer gauges with signi cant Q max trends compared to Q ood were found. At the same time, unlike Q ood , there is less pronounced change of Q max trend downstream. Thus, the peaks of the maximum discharge have become more pronounced against the average ood peaks (Selenga, Uda, Hilok gauges). However, it is not the case for Chikoj and Turka rivers. Gauges with eld signi cant changes of Q max were determined only for region II. Nevertheless, there was the signi cant decreasing trend for Turka river in region IV.
Small number of gauges (2 out of 44) demonstrated signi cant CVD trend (not shown here). So, after the consideration of eld signi cance herein, no signi cant trends were revealed for any region. 10 of 44 gauges had negative trend and located in North-East part of region II. Gauges with signi cant CVD trends (Itantsa and Bryanka rivers) also showed highest rate of Q ood reduction (<-2.5%/год).
A negative relationship between Q ood decline rate and average elevation of catchment was revealed (pval < 5%). The connection is probably associated with an increase of average Q ood value with an increase in the average catchment height.
Precipitation and evaporation in uence on river ow variation. A strong connection between P and E with Q ood revealed R 2 (P, E) median equals 0.66 (Fig. 5). Precipitation is a most important factor in uencing Q ood in BC. A median R 2 (P) value is 0.63, while minimum 0.34 and maximum 0.79.
The contribution of evaporation is by order of magnitude less important: the median R 2 (E) estimated here is 0.022, with a minimum close to 0 and a maximum of 0.12. Furthermore, the median value of the E / P ratio is 0.9. E and P average values are similar. Low values of R 2 (E) can be due to many different reasons (E may be more changeable where river ow is close to zero; moisture loss from upper soil level may does not affect much river ow formation in June-September; errors in initial E data etc.). We considered two of them: 1) E value during ood season is almost completely determined by the value of P, and 2) the variability of E is much less than the variability of P.
In general, for the BC, there is a weak positive relationship between P and E -the median value of the Pearson correlation coe cient (r) is 0.34. Moreover, more than half of the gauges (25 out of 44) had positive r value. Probably, the sign of r is determined by the main limiting factor of evaporation in the basin -the available moisture (in this case, r > 0) or potential evaporation (r < 0) (Jung et al. 2010). It seems like the small value of R 2 (E) is explained mostly by the low variability of E -the median ratio standard deviation of E to standard deviation of P is 0.28. Thus, E control amount of available for runoff formation water at much less extent that P. Precipitation change. All catchments showed decreasing trends in P with spatial distribution of linear trend rate similar to Q ood . The rate of P decline in region I was 0.65% / year (p-val = 0.1%). Western part of region II showed rate of P decline ranging between − 0.25%/year to -0.45%/year. Though, the trend was signi cant only for one watershed at p-val < 5%. The upper reaches of the Chikoy river are characterized by a linear trend of -0.55 --0.65% / year. The downstream gauges over there have P decline rate up to -0.8-0.9%/year. Hilok basin is characterized P trend rate around − 0.7 --1% / year, with large trend rate located in downstream. The highest rates of P decrease have been shown in the Uda basin --1 --1.5% / year, regardless of the position within the catchment, except for the upper gauge where P rate is -0.7%/year. Three of four catchments of region III have P decline rate about − 0.45% / year. However, a catchment located near Selenga delta (Table 1, №38) showed decline rate around − 1.1% / year. The region IV is characterized by a decrease in the linear trend of P magnitude from south to north -from − 1% / year in the Turka catchment and up to -0.1% in the Upper Angara catchment (Table 3). Overall, the decrease in the precipitation amount is statistically signi cant for I and II regions, with the median p-val across all 44 catchments being 0.03%. Like for Q ood , signi cant changes of P CVD for 1979-2019 was not identi ed -the median value of p-val of the CVD P linear trend was 66.4%, with a minimum of 9%.
Linear trends of P 16 for 40 river basins were negative (Table 3), however, due to the greater variability of P 16 compared to P, they were statistically signi cant only for the regions of the Turka and Kurba rivers (border of regions II and III). Generally, the rate of P 16 decline is less than that for P. The ratio of the median of the P 16 trend to the median of the P trend is 0.92. This could be the reason why the negative trend of Q max is slightly less than for Q ood . regions, however, if in the regions III and IV (increase in E) and in the I area (decrease in E) they are unidirectional, then for the II area they are multidirectional (Table 3).
It is likely that an increase in E in some catchments despite a decrease in P is associated with an increase in PET. Spatial distribution of PET trend is similar to the P, but with opposite sign. That is probably due to small PET value on days with precipitation. Region I showed the growth rate of PET at 0. Connection between precipitation elasticity of river ow and watersheds features. The number of catchment evaluated for connection between precipitation elasticity of river ow ( ) and catchments features (32) was less than that used in P and Q ood determination because of the fact that some catchments demonstrated statistically insigni cant P change rate (below 5.6%), either were too vast, so value averaged over entire basin is not representative (Selenga river's gauges).
The two catchments located in the Northwest of the Baikal basin (Khara-Murin and Snezhnaya) have close to 1-0.95 and 0.8 accordingly. Due to the small size of these basins (up to 3000 km 2 ), this may be because of initial data uncertanities. However, since these catchments related to the humid climates (P/PET ratio is from 1.23 to 2.08) and continuous permafrost region, around 1 is physically possible.
In particular, the amount of precipitation of humid areas is less connected with the amount of evaporation -r between P and E for these three catchments varies from 0.07 to 0.14. Also precipitation, not only themselves form river ow, but also contribute to the melting of permafrost, which leads to an increase in moisture available for the formation of river ow. The median value of was 0.29, with a minimum of 0.03. Such a relatively low sensitivity of Q ood to P, while PET is increasing that should enhance Q ood decline due to P decline, can be explained by relatively arid climate of the considered are with median Hum equal to 1.07. So, a more de nite analysis requires consideration of the water balance of the catchments in other seasons.
The correlation matrix of untransformed features of catchments and is given in Fig. 6.
For further study, we elaborated speci c indices for the catchment parameters demonstrating a monotonic relationship with , aiming to reach linear functions with . In particular, the BS was transformed and S After linearization features and the correlation rate band etween them and will be equal 0. After evaluation the signi cance of the variables by F-test and t-tset, the following features were taken for the model: and BS.
The following model was obtained: explaining about 74% of the variability (Fig. 7).

Discussion
Our results signi cantly extend the previous nding related to climate change impact on hydrological system in the regional Baikal catchment. They suggest signi cant spatial heterogeneity of hydroclimatic development in the region.
June and July in regions II, III, and IV are characterized by highest in a season precipitation but decreasing of TWS. Mostly arid conditions and low snow cover, the main considered driver of these changes might be the melting of seasonally frozen ground (Sazonova et al. 2004;Biskaborn et al. 2019).
Towards the middle of summer, the lower portion of the permafrost is still not subjected to melting. Further replacement of the underground ice by rainwaters is observed. However, this conclusion is based on assumption of unbiased estimate of difference between P and E in ERA5-Land, that may not be a case. ERA5 (and ERA5-Land accordingly) has tendency to overestimate P (Nogueira 2020) and ERA5- The decrease of P, Q ood and Q max is most pronounced over region I, eastern part of the region II, northern part of region III and southern part of the region IV. This pattern is consistent with the early ndings (Antokhina et al. 2019), wherein runoff decrease in the Selenga basin was associated with middle summer precipitation decrease, which is impacted by weakening of the East Asian summer monsoon.
However, we found no evidence of altering temporal pattern of precipitation and river ow during June-September. Also it can be seen that P obtained in this study from ERA5 showed much more rapid P decline then the one obtained from precipitation gauges (Dorjsuren et  The rate of precipitation's change is spatially inhomogeneous and can vary by a factor of two or more for catchments located at a distance of 150-250 km from each other ( Table 3, Fig. 1). The obtained trends and their statistical signi cance are indicators of current changes of ow formation, but cannot be used to predict its changes in the future. Despite the general downward trend of Q ood in the Baikal basin over Page 21/30 the past decades, the Q ood was higher or close to the norm for some current years (2012,2013,2018,2019). Also, according to preliminary data, the river ow was higher or close to the norm in 2020-2021. Multiple linear regression showed that the large values are features of whose catchments that are distinguished by a large slopes and small bare soil area (Fig. 7). Catchments characterized by these features usually have runoff coe cient more than the other ones. Also it can be seen that at the basin scale topography is more important than climate for particular response of ood ow to climate change. These outcome is close to the conclusions from an earlier research examining hydrological drought variation (Van Loon and Laaha 2015). Since depends mostly on terrain features we can assume that relationship between Q and P will keep being linear in changing climate and altering land use.
Our results suggest that precipitation variability remains main factor of rain-driven oods variability in BC, despite both the rise in air temperature and solar radiation (He et al. 2018;Dorjsuren et al. 2018b).
Precipitation regulates both Q and E not only by amount of available water but also by PET rate. So, median correlation coe cient between P and PET among 44 river basins is < − 0.8. The result is similar to what was obtained on a global scale (Jung et al. 2010) where W was considered as main factor of E change. The conclusion also suggests that accuracy of P in climate projections is the key factor for assessment of future water resources in BC.

Conclusion
The nonstationarity of the ood ow in the Baikal basin, as well as the peculiarities of the catchments' response to climatic changes, depending on their characteristics, should be taken into account when planning economic activities in this region. In particular, this is crucially important due to future possible hydropower projects development and Lake Baikal water level regulation. ϵ p ϵ p 1. The ood runoff over Baikal catchment is one of the most important environmental drivers effecting pristine ecosystem of the largest freshwater reservoir of the World. From 1979 to 2018, the Q ood and Q max showed decreasing trend under the in uence of a precipitation decreasing. The most substantial changes were revealed in the northeastern part of the Selenga basin with the rates up to 1.5-2%/year. There was no clear signal of altering river ow timing during June-September with slight domination of upward CVD river ow trends. The decrease of precipitation affected the entire catchment area of Lake Baikal. A P 16 (P> 16 mm / day) did not decrease as much (by 8% less) than the total precipitation, i.e. their distribution became more uneven. A statistically signi cant negative trend of evaporation was revealed for 15 catchments (up to -1.03%/y), and a positive one for another 15 (up to 0.35%/y). Statistically most signi cant changes occurred with soil moisture and potential evaporation. A signi cant decline of W was revealed for 43 from 44 river basins with exception of Upper Angara basin. An increase of PET has place over entire BC with p-val<1%.
2. Q ood showed a close relationship to P -the median value of the coe cient of determination was 0.63. The impact of E variation on Q ood variation was rather neglectable. Due to the comprehensive impact of the increasing potential evaporation and decreasing soil moisture, the amount of evaporation has changed over the studied period. A decrease in evaporation is typical for catchments that have experienced a decrease in P by more than 0.8% per year and where the amount of available moisture can limit the evaporation.
3. Statistical analysis revealed that Q ood of watersheds with large steep slopes and small bare soil area are most sensitive to alter of P. Since depends mostly on terrain features, rst of all on slope, we can assume that relationship between Q and P will keep being linear in changing climate and altering land use.      Determination coe cient of linear regression between Q ood and P, E.

Figure 6
The correlation matrix of untransformed features of catchments and εp.