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Article

Weakened Snowmelt Contribution to Floods in a Climate-Changed Tibetan Basin

by
Liting Niu
1,2,3,
Jian Wang
1,*,
Hongyi Li
1,4 and
Xiaohua Hao
1,4
1
Key Laboratory of Remote Sensing of Gansu Province, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China
2
University of Chinese Academy of Sciences, Beijing 100049, China
3
Gansu Science Institute of Soil and Water Conservation, Lanzhou 730020, China
4
Heihe Remote Sensing Experimental Research Station, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China
*
Author to whom correspondence should be addressed.
Water 2025, 17(4), 507; https://doi.org/10.3390/w17040507
Submission received: 30 December 2024 / Revised: 1 February 2025 / Accepted: 9 February 2025 / Published: 11 February 2025
(This article belongs to the Special Issue Impacts of Climate Change on Extreme Hydro-Climatic Events: Droughts and Floods)
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Abstract

:
Climate warming has led to changes in floods in snow-packed mountain areas, but how snowmelt contributes to floods in the high-altitude Tibetan Plateau remains to be studied. To solve this problem, we propose a more reasonable method for evaluating snowmelt’s contributions to floods. We use a distributed hydrological model with the capability to track snowmelt paths in different media, such as snowpack, soil, and groundwater, to assess snowmelt’s contribution to peak discharge. The study area, the Xiying River basin, is located northeast of the Tibetan Plateau. Our results show that in the past 40 years, the average annual air temperature in the basin has increased significantly at a rate of 0.76 °C/10a. The annual precipitation (precipitation is the sum of rainfall and snowfall) decreased at a rate of 5.59 mm/10a, while the annual rainfall increased at a rate of 11.01 mm/10a. These trends were not obvious. The annual snowfall showed a significant decrease, at a rate of 14.41 mm/10a. The contribution of snowmelt to snowmelt-driven floods is 85.78%, and that of snowmelt to rainfall-driven floods is 10.70%. Under the influence of climate change, the frequency of snowmelt-driven floods decreased significantly, and flood time advanced notably, while the intensity and frequency of rainfall-driven floods slowly decreased in the basin. The causes of the change in snowmelt-driven floods are the significant increase in air temperature and the noticeable decrease in snowfall and snowmelt runoff depth. The contribution of snowmelt to rainfall-driven floods slowly weakened, resulting in a slight decrease in the intensity and frequency of rainfall-driven floods. The results also indicate that rising air temperature could decrease snowmelt-driven floods. In snow-packed mountain areas, rainfall and snowmelt together promote the formation of and change in floods. While rainfall dominates peak discharge, snowpack and snowmelt play a significant role in the formation and variability of rainfall-driven floods. The contributions of snowmelt and rainfall to floods have changed under the influence of climate change, which is the main cause of flood variability. The changed snowmelt adds to the uncertainties and could even decrease the size and frequency of floods in snow-packed high mountain areas. This study can help us understand the contributions of snowmelt to floods and assess the flood risk in the Tibetan Plateau under the influence of climate change.

1. Introduction

Floods are one of the most destructive natural disasters in the world, and are receiving increasing attention [1,2,3]. Climate change, particularly increased air temperature, is altering the hydrological cycle [4,5]. This results in an increase in the magnitude and frequency of extreme weather events, which leads to changes in river flow [6], impacts the formation, frequency, and intensity of floods [7,8,9,10], and poses an increased flood risk [11].
Snow-packed mountain areas face serious flood risks. The frequency and intensity of floods have increased significantly in some mountain regions, including high latitudes in Europe [12,13], North America [14,15], and the Tibetan Plateau [16,17]. Several studies predict that flood size and frequency will increase in many parts of the world in the future [18,19,20]. Some studies show inapparent changes in floods with warming, both in historical observations and climate projections [21,22,23]. As the highest plateau, the Tibetan Plateau is at an increased risk of snowmelt floods [24].
Snowmelt and rainfall are the two major contributors to floods in high mountain areas, such as the Tibetan Plateau. Snow-cover characteristics before floods and rainfall events determine the contributions of snowmelt and rainfall to peak discharge, further influencing flood intensity [7,25]. The heat carried by heavy rainfall accelerates snow melting, and the combination of rainfall runoff and snowmelt can cause rapid hydrological responses [26,27]. The snowmelt runoff also relates to the air temperature and soil environment [1,21]. Air temperature during snowmelt periods affects the changes in the storage, evolution, and physical properties of snowpacks [27,28,29]. Soil moisture and soil freezing characteristics before snowmelt influence the infiltration of snowmelt water into soil, leading to changes in snowmelt runoff [30,31,32]. As the above analysis shows, snowmelt can contribute to flood events through complex environmental interactions. Under the climate-changed conditions, these interactions will be changed, since the increased air temperature will change the scale of snowmelt, and the soil moisture conditions and fluctuant rainfall will also influence the peak discharge.
The interaction between complex terrain and hydrometeorological factors has rendered the cold and arid regions susceptible to climate change [33], so it is important to study the contribution of snowmelt to floods in climate-changed Tibetan basins. However, little is known on this point. The studies to date have usually focused on snowmelt’s contribution to the total discharge, but not floods. As these studies report, the contribution of snowmelt to runoff is 22–49% during the typical snowmelt period in the Tianshan Mountains [34]; snowmelt accounts for 32.2% of runoff in the Naqu River basin in the Tibetan Plateau [35]; the meltwater of the Beas River in northern India accounts for 50% of the total discharge [36]. Some studies have used temperature index models to estimate snowmelt’s contributions to peak discharge [37], but temperature index models are highly regional with much uncertainty. Physically based hydrological models can simulate processes such as infiltration, evaporation, lateral flow, refreezing, and vegetation suction after snowmelt release, evaluating the contribution of snowmelt to runoff more reasonably. They are used to estimate snowmelt’s contribution to discharge, but not to flood events, in the Canadian Arctic [38,39], the western United States [40], the Ganges River basin in the Himalayas [41], and the Babao River basin in the Tibetan Plateau [42]. However, physical models have yet to be used specifically to assess snowmelt’s contributions to floods.
To accurately analyze snowmelt’s contribution to floods in the climate-changed Tibetan basins, a distributed cold hydrological model with the function of tracing snowmelt paths was used to evaluate snowmelt’s contribution to floods in the past 40 years. The impact of snowmelt on floods was analyzed by studying the changes in the contributions of snowmelt and rainfall to floods.

2. Methodology

2.1. Methods

2.1.1. Evaluation of Snowmelt’s Contributions to Floods

We evaluated snowmelt’s contribution to floods using the Geomorphology-Based Eco-hydrological Model (GBEHM). The GBEHM is a physics-based ecohydrological model capable of simulating snow accumulation, melting, and runoff generation, originally developed by Yang et al. [43] and later improved in the snow module by Li et al. [42]. The improved model uses a specific separation method to iteratively calculate the proportions of snowmelt in snowpacks, soil layers, and underlying aquifers, tracking the flow paths of snowmelt and evaluating snowmelt’s contributions to runoff [42,44]. The key to evaluating snowmelt’s contribution to floods is to estimate snowmelt concentrations in different soil layers. Using the snowmelt paths tracking method of this model, the total contribution was evaluated by calculating snowmelt’s contributions to floods from surface water, lateral flow, and base flow separately.
The GBEHM calculates snowmelt water in different media according to the following mass balance equation of snowmelt [42]:
f i t + Δ t ( W i t + Δ t + q l i t + Δ t + T i t + Δ t ) = f u i t U i t − f u i + 1 t U i + 1 t + f i t W i t + f s i t M i t
In Equation (1), f indicates the proportion of snowmelt water in the total liquid water in a soil layer, i is the ith soil layer, t and (t + Δt) represent the time step, W means the total liquid water in a soil layer, qli represents the lateral flow from the ith soil layer, T indicates the water pumped by plant roots in a soil layer, fu means the proportion of snowmelt water in liquid water transferred between different soil layers, U represents the liquid water flux between different soil layers, fuU is the flux of snowmelt water between different soil layers, fs indicates the proportion of snowmelt water in meltwater from frozen soil in a soil layer, M means the meltwater released from frozen soil in a soil layer, and fsM indicates the changes in snowmelt water due to freezing or thawing of the soil.
We used the Nash coefficient (NSE), Percent Bias (PBIAS), Root Mean Square Error (RMSE), Determination coefficient (R2), and Mean Absolute Error (MAE) to evaluate the performance of simulated data by the GBEHM. The formulas are as follows:
N S E = 1 − ∑ t = 1 T ( V m t − V o t ) 2 ∑ t = 1 T ( V o t − V ¯ o ) 2
P B I A S = ∑ t = 1 T V m t − V o t V o t ∗ 100
R M S E = ∑ t = 1 T ( V m t − V o t ) 2 T
R 2 = 1 − ∑ t = 1 T ( V m t − V o t ) 2 ∑ t = 1 T ( V o ¯ − V o t ) 2
M A E = 1 T ∑ t = 1 T ( V o t − V m t )
In Equations (2)–(6), V m , V o , V ¯ o , and V o t represent simulated data, observed data, the average of observed data, and observed data at time t, respectively.

2.1.2. Extraction of Flood Events

The Peak Over Threshold method (POT) [45] was used to extract the peak discharge events of daily runoff observed at the Jiutiaoling gauge from 1980 to 2018, and the peak discharge events were used to represent the flood events. Compared with the Annual Maximum Flood method (AMF) [46], the POT method can select events more comprehensively [45] and analyze variation trends of flood scale and frequency [29]. We applied the percentile threshold method to select peak discharge events, taking the 98th percentile point of daily runoff as the POT threshold. To ensure the independence of each peak discharge event, the following principles were followed in the selection process: (1) if there are multiple peaks in an event, only the largest peak is selected; (2) there should be at least a five-day interval between two consecutive peak discharge events [47].

2.1.3. Trend Analysis

The nonparametric Mann–Kendall (MK) test [48] was used to assess trends in peak discharge, air temperature, precipitation, runoff, snowmelt discharge, snowmelt runoff contribution, and snowmelt contribution to floods over the past 40 years at the 0.05 confidence level. The MK trend method is a commonly used nonparametric statistical method for detecting trend changes in time series data [49]. The method is based on the comparison of rank sums, which does not require assumptions about the statistical distribution of time series [29] and is widely used in climatology and hydrology [50].
The MK trend method determines the trend characteristics of indicators by calculating the statistical variable S, variance Var(S) to derive the value of the normal statistical variable Z.
For the sequence Xt (t = 1, 2, …, n), the principle is as hereunder mentioned [51]:
The statistical variable S:
S = ∑ k = 1 n − i ∑ j = k + 1 n sgn ( X j − X k )
In Equation (7), Xj and Xk indicate the corresponding years of data, and sgn indicates the sign function.
The expectation and variance of S are calculated:
E ( S ) = 0
V a r ( S ) = n ( n − 1 ) ( 2 n + 5 ) / 18
In Equation (9), Var(S) indicates the variance.
Therefore, the standardized test statistic Z can be constructed as:
Z = S − 1 V a r ( S ) ⋯ ⋯ ( s > 0 ) 0     ⋯ ⋯ ⋯ ⋯ ⋯ ( s = 0 ) S + 1 V a r ( S ) ⋯ ⋯ ( s < 0 )
The three commonly used significance test alpha values are 0.05, 0.01, and 0.001, which correspond to passing the 95%, 99%, and 99.9% significance tests.
The Poisson regression (PR) was used to detect the trend in the frequency of floods. PR is a generalized linear model suitable for describing the times of random events occurring in unit time or space and is often used to assess trends in flood frequency [15,29].
The probability distribution of random variable X subject to Poisson distribution is:
P ( X = k ) = λ k k ! e − λ ( k = 0 , 1 , 2 , … )
In Equation (11), X and k indicate the times of events, and λ represents the average incidence of each event.
To explore the changing characteristic of the frequency of floods over time in the watershed, we established the following Poisson regression model for the average of flood series. We tested the trend at the 0.05 confidence level:
ln ( λ ) = β 0 + β 1   t ( t = 1980 , 1981 , … , 2018 )
where λ indicates the average frequency of floods, and t represents t year.

2.2. Research Area

The Xiying River is a tributary of the Shiyang River basin, originating from Lenglong Mountain in the eastern part of the Qilian Mountains. The study area covers the basin above the Jiutiaoling gauge with an area of 1070 km2, located northeast of the Tibetan Plateau (Figure 1). The elevation in the region varies from 2347 to 4695 m, and the terrain is high in the southwest and low in the northeast. The area has strong solar radiation, abundant sunshine, and a large diurnal temperature difference. Precipitation is scarce, and evaporation is high in this region. The soil texture types in the area are mainly loam and sandy loam. The Xiying River Basin has typical high-altitude climate characteristics, with an average annual temperature of −3.10 °C, an average annual precipitation of 624 mm, and an average annual runoff of 291 mm. The land types are diverse in the region, including farmland/grassland mosaic, grassland, shrub, evergreen coniferous forest, water body, herb wetland, sparse vegetation, and snow. The annual average snow-cover area in the Xiying River Basin is 796.8 km2, accounting for 74.46% of the total basin area. In the high-altitude areas in the southern part of the basin, snowfall days exceed 180. Runoff is mainly from rainfall and snowmelt in the mountains, which can easily form floods.

2.3. Data and Parameters

The data required for the GBEHM mainly include digital elevation mode data (DEM), meteorological forcing data, soil data, land cover data, and observed runoff. DEM is the Shuttle Radar Topography Mission (SRTM) DEM product, with a spatial resolution of 90 m, provided by the Geospatial Data Cloud site, Computer Network Information Center, Chinese Academy of Sciences (http://www.gscloud.cn, accessed on 25 March 2022). DEM constitutes the main data of the GBEHM to divide watersheds and extract channel parameters. The long–short wave radiation, wind speed, air temperature, air pressure, and specific humidity in the meteorological forcing data are from the China meteorological facing dataset (1979–2018) [52]. Precipitation data are from ERA5-Land hourly data from 1950 to present [53]. Soil data were obtained from the Chinese soil dataset based on the world soil database (hwsd) (v1.1) [54], mainly including soil bulk density, porosity, soil profile depth, saturated water conductivity, saturated water content, and organic carbon. The land cover data are from the Land Cover Map of China in 2000 [55]. The above three types of data are the primary input data for the GBEHM. Daily runoff data (from 1980 to 1987 and 2012 to 2018) observed at the Jiutiaoling gauge were obtained from the Hydrology Station of Gansu Province. The simulation results of the GBEHM were validated with daily runoff observations. All of the above data located within the study area were selected. Meteorological forcing data with a spatial resolution of 1 km and a temporal resolution of 1 h were resampled, and soil data with a spatial resolution of 1 km were also resampled.
The simulated snow distribution is validated by the China MODIS Daily Cloudless 500 m Snow Area Product Dataset [56] and a dataset of snow phenology in China based on MODIS from 2000 to 2020 [57]. The two datasets were provided by the National Cryosphere Desert Data Center. (http://www.ncdc.ac.cn, accessed on 7 April 2023). We extracted the study area from the two datasets.
The parameters to be calibrated in the GBEHM include the differentiation temperature of rain and snow, snow surface roughness, groundwater porosity, groundwater water hydraulic conductivity, lateral flow distribution coefficient, and evapotranspiration adjustment coefficient. Based on the observed runoff of the hydrological station, we applied the Shuffled Complex Evolution Algorithm (SCE-UA) [58] with RMSE as the objective function to calibrate the above parameters automatically. The values of each parameter are shown in Table 1.

3. Results

3.1. Performance of the GBEHM

3.1.1. Validation by Daily Discharge

In this study, the simulation period is from 1980 to 2018, the warm-up period is from 2000 to 2011, the calibration period is from 2012 to 2018, and the validation period is from 1980 to 1987 (Figure 2). The simulation accuracy of daily discharge at the Jiutiaoling gauge was verified by comparing the observed average daily discharges with the simulation results. In the calibration period, the NSE, PBIAS, RMSE, R2, and MAE were 0.66, −20.29%, 7.68 m3/s, 0.44, and 4.71 m3/s, respectively. In the validation period, the NSE, PBIAS, RMSE, R2, and MAE were 0.60, −11.18%, 7.53 m3/s, 0.55, and 4.62 m3/s, respectively. The results show that the model has reliable accuracy in discharge simulation.

3.1.2. Validation by Peak Discharge

At the Jiutiaoling gauge, we compared the data of daily observed discharge greater than or equal to 48 m3/s with the simulated volumes at a corresponding time to verify the simulation accuracy of this model for peak discharge (Figure 3). The MAE between the simulated values and the observations is 11.41 m3/s and the RMSE is 14.42 m3/s. Although there are differences between the observed and simulated values in some years, the simulation results are satisfactory on the whole.

3.1.3. Validation by Remotely Sensed Snow Data

To verify the performance of the GBEHM in simulating the time distribution of snow cover, we compared the simulated monthly SCA (snow cover area) data from 2001 to 2018 with remotely sensed SCA data in the basin (Figure 4). The RMSE is 188.98 km2 and R2 is 0.57, between the simulated and remotely sensed monthly SCA. We also compared the simulated yearly SCD (average snow cover days) data from 2001 to 2018 with remotely sensed SCD data to verify the simulation effect of the model on the spatial distribution of snow cover (Figure 5). The simulated snow distributions show good agreement with the remotely sensed data both temporally and spatially.

3.2. Flood Characteristics and Variation Trends

3.2.1. Flood Characteristics

In the basin, floods mainly occurred in July and August (Figure 6a), accounting for 32.7% and 39.1%, respectively. Over the past 40 years, floods have mainly occurred 0, 1, 2, 3, 4, or 5 times per year (Figure 6b). The maximum annual frequency of floods is twice or three times, accounting for 23.83% and 22.26%, respectively. Spring flooding is less frequent and summer flooding predominates in the basin (Figure 6c).

3.2.2. Flood Trends

We extracted 110 flood events during the study period. The maximum peak discharge is 153.64 m3/s, the minimum is 48.20 m3/s, and the average is 65.98 m3/s.
Out of the 110 floods in the basin, four floods occurred from March to May. These four floods occurred on 24 May 1982; 10 May 1983; 30 April 1984; and 25 March 2005. The spring floods are fewer and earlier.
The other 106 floods occurred from June to September. We analyzed the changes in summer flood events and applied the MK and the PR to test these trends at the 0.05 confidence level. Peak discharge decreased slightly at a rate of 0.50 m3/s/10a, and the frequency of floods decreased slightly at a rate of 0.3 times/10a. These decreasing trends were not significant (Figure 7).

3.3. Climate Change Trends

According to the annual variation in air temperature and precipitation in the basin (Figure 8), the annual average air temperature exhibited a significant increase of 0.76 °C/10a. The annual precipitation (precipitation is the sum of rainfall and snowfall) decreased at a rate of 5.59 mm/10a, and the annual rainfall increased at a rate of 11.01 mm/10a. These trends were inapparent. The annual snowfall showed a significant decreasing trend of 14.41 mm/10a.
The annual average air temperature of the basin is −1.47 °C, the minimum is −3.15 °C, and the maximum is 0.18 °C. The annual average precipitation is 787.76 mm, of which 551.41 mm is rainfall and 236.35 mm is snowfall, accounting for 30.0% of precipitation.
The highest air temperature is 10.41 °C in July and the lowest is −15.54 °C in January. The average air temperature for each month exhibited a significant increasing trend, with the greatest increase from January to March, at 1.02 °C/10a, 1.10 °C/10a, and 0.98 °C/10a, respectively.
The monthly precipitation is not evenly distributed. The precipitation in August is the largest, 157.12 mm, and the precipitation in December is the smallest, 7.21 mm. The rainfall is mainly concentrated from May to September, accounting for 79.70% of the total precipitation. The rainfall from July to August is the highest, 144.17 mm and 147.52 mm, respectively. Snowfall is distributed throughout the year but is mainly concentrated in March–May and September–October, which account for 68.35% of the total snowfall. The maximum snowfall, which occurred in April and May, was 36.75 mm and 36.05 mm, respectively. Rainfall in March exhibited a significant increasing trend of 0.23 mm/10a and in April it showed a pronounced increasing trend of 2.44 mm/10a. The rainfall decreased from May to July, and increased slightly from August to October, while these changes were inapparent. From June to September, the snowfall decreased significantly, at a rate of 2.75 mm/10a, 2.10 mm/10a, 2.48 mm/10a, and 2.58 mm/10a, respectively. The snowfall in other months decreased slightly, but not significantly.

3.4. Snowmelt Contribution

3.4.1. Snowmelt Runoff Contribution

We analyzed the annual variation trends of runoff, snowmelt runoff, and snowmelt runoff contribution in the watershed (Figure 9a). The total runoff depth exhibited a slight increase of 8.86 mm/10a. However, snowmelt runoff depth and snowmelt runoff contribution showed a significant decreasing trend of 5.86 mm/10a and 2.12%/10a, respectively.
The annual average runoff depth is 382.77 mm in the basin, of which 78.95 mm is snowmelt runoff depth, and the snowmelt runoff contribution is 20.63%.
The total runoff depth is mainly concentrated from June to October. The maximum total runoff depth occurred in August at 78.74 mm. Snowmelt runoff depth appeared mainly from May to August, but rarely from December to February. The snowmelt runoff contribution was more from March to May, with the largest contribution of 45.43% in March.
The total runoff depth decreased slightly but not significantly in March, April, and July. There was a significant downward trend in June, at 3.94 mm/10a, and a significant upward trend in the rest of the month, with the largest increase in September, at 6.40 mm/10a. Snowmelt runoff depth increased slightly in February. It decreased in the rest of the months, particularly in June and July, which exhibited a significant decreasing trend of 1.43 mm/10a and 0.58 mm/10a, respectively. Snowmelt runoff contribution decreased slightly in February, June, July, and December. It exhibited a significant decrease in the rest of the months, with the largest decrease of 7.50%/10a in March.
In the last 40 years, the monthly and annual averages of air temperature, precipitation, rainfall, snowfall, total runoff depth, snowmelt runoff depth, and snowmelt runoff contribution in the Xiying River basin are shown in Table 2.

3.4.2. Snowmelt’s Contributions to Floods

By comparing the daily rainfall, total runoff depth, and snowmelt runoff depth (Figure 10), it can be seen that the spring runoff of the Xiying River basin mainly comes from snowmelt, and the summer runoff mainly comes from rainfall. Snowmelt events rapidly increase spring runoff, while rainfall events lead to peak summer and fall runoff.
In the basin, four floods out of the 110 flood events occurred from March to May, and the other 106 floods occurred from June to September. The average snowmelt contribution to spring floods is 85.78%, which are snowmelt-driven floods. The average snowmelt contribution to summer and autumn floods is 10.70%.
We analyzed the annual variation characteristics of peak discharge, snowmelt discharge, and snowmelt contribution for the remaining 106 floods. The peak discharge, snowmelt in floods, and snowmelt contribution to floods decreased slightly in summer and autumn (Figure 9b), at a rate of 0.50 m3/s/10a, 0.08 m3/s/10a and 0.04%/10a, respectively.

4. Discussion

4.1. Changed Snowmelt Contribution to Floods in the Xiying River Basin

In the Xiying River basin, we find that annual air temperature increases significantly, annual rainfall increases slightly, annual snowfall decreases significantly, and annual precipitation decreases slightly. The slight decrease in annual precipitation is due to a noticeable decrease in annual snowfall. Our results also confirm previous studies that rising air temperature may lead to a shift from solid to liquid precipitation [59,60].
The simulation results based on the GBEHM, which tracks the snowmelt path, show that 45.03% of the runoff comes from rainfall, 20.63% from snowmelt, and 34.34% from groundwater. Snowmelt is the main source of spring runoff and continues to contribute to summer and autumn runoff. Rainfall is the main source of summer and autumn runoff, and groundwater primarily supplements winter runoff. The calculated annual snowmelt runoff contribution in this basin is close to that of the surrounding watersheds. For example, in the upper reaches of the Heihe River basin, the contribution of snowmelt runoff is 19.8% from August 2000 to August 2001 [61] and 15.6% from 2004 to 2015 [42].
There are fewer floods in spring and more floods in summer. The average snowmelt contribution to spring floods is 85.78%; these are snowmelt-driven floods. The average contribution of snowmelt to summer floods is 10.70%, and the peak discharge mainly comes from rainfall; these are rainfall-driven floods.
Global warming affects the rain–snow ratios, snow periods, snowpack characteristics, and snowmelt processes in high-latitude and alpine mountain basins [62]. With the change of climate, the total runoff increased slightly, but the snowmelt runoff and the contribution of snowmelt runoff both decreased significantly, indicating that the rainfall runoff and the contribution of rainfall runoff both increased noticeably. The contribution of snowmelt runoff from June to September is 21.30%. Therefore, rainfall events mainly trigger summer floods, and snowpack and snowmelt play an important role in the formation and change of floods in this basin.
The air temperature increased significantly, and the snowfall and snowmelt runoff depth decreased noticeably, decreasing the frequency of snowmelt-driven floods. With the obvious rise in air temperature, the rainfall increased slightly, the snowfall decreased significantly (the total precipitation decreased slightly), and the snowmelt and the contribution of snowmelt to rainfall-driven floods showed a slowly declining trend. As a result, both the amount of rainfall and the contribution of rainfall to rainfall-driven floods have shown a gradually increasing trend. The contribution of snowmelt to rainfall-driven floods slowly weakened, resulting in a slight decrease in the intensity and frequency of rainfall-driven floods.
The contributions of snowmelt and rainfall to floods have changed under the influence of climate change, which is the main cause of flood variability. Other factors, such as changes in soil moisture and evapotranspiration resulting from climate change, may also affect floods [10,63]. The results could help us to further understand the contribution of snowmelt to flooding and assess the possible flood risk on the Tibetan Plateau under the influence of climate change.

4.2. Comparison of Snowmelt’s Contribution to Floods Between the Xiying River and the Other Regions of the World

Firstly, our study confirms that rising air temperature could decrease snowmelt-driven floods, and not only increase the floods as previous studies reported. For example, flood frequency increased in the central United States [15], peak discharge exhibited a significantly increasing trend in the European Alps [64], flood intensity increased in Sweden [65], and peak discharge showed a decreasing trend in western Russia [66]. The frequency of floods decreased in northern Norway [29]. Some studies considering flood-generation mechanisms have also found that regional or global floods do not necessarily increase under the influence of climate warming [21,22,23,67]. These studies have suggested that climate change does not always lead to a corresponding increase in flooding due to reduced snowmelt and lower soil moisture in the early stages. This is consistent with our analysis of the trends in spring snowmelt floods. In the Xiying River basin, the average snowmelt contribution to spring floods is 85.78%, and snowmelt accounts for most peak discharge. As the climate warms, annual snowfall, snowmelt runoff depth, and snowmelt runoff contribution decrease, resulting in a decrease in frequency of spring floods.
Secondly, previous studies suggest that increased rainfall leads to increased floods, but our study further indicates that the changed snowmelt would add to the uncertainties and even decrease the size and frequency of flood in snow-packed high mountain areas. Bloschl et al. [8] noted that increased rainfall intensity in autumn and winter led to increased flood intensity in northwestern Europe. Vormoor et al. [26] found that increased rainfall frequency in southern and western Norway resulted in increased flood frequency. Related studies have also shown that climate warming leads to a general increase in heavy rainfall [10,68,69,70], exacerbating the increase in floods [71,72,73]. However, they are not entirely consistent with our findings. The conclusion is that increased rainfall leads to increased rainfall-driven floods because it assumes that these floods come only from rainfall. In fact, in most mountainous areas, rainfall-driven floods are caused by a combination of rainfall and snowmelt. While rainfall dominates peak discharge, snowpack and snowmelt play a significant role in the formation and variability of rainfall-driven floods.

4.3. The Advantages and Limitations of the Evaluation Method

We propose a more reasonable method for evaluating snowmelt’s contributions to floods. By tracing the flow paths of snowmelt water in different media, such as snowpack, soil, and groundwater, the GBEHM was applied to evaluate the contribution of snowmelt to peak discharge on a timescale of days. Li et al. [42] used this method to accurately evaluate the snowmelt runoff contribution of the upper reaches of the Heihe River basin in China, but without fully considering snowmelt’s contribution to peak discharge. Kampf and Lefsky [39] and Agnihotri and Coulibaly [37], respectively, used temperature index models to evaluate snowmelt’s contribution to annual peak discharge in the France Mountains in Colorado, United States, and snowmelt’s contribution to spring floods in the Lagrange River Basin and the upper Assiniboine River in Canada. Although temperature index models can easily evaluate snowmelt’s contributions to floods, there is much uncertainty because it is highly dependent on regional temperature index and other parameters.
There are some limitations to the current method of evaluating snowmelt’s contributions to floods that should not be neglected. Firstly, uncertainties in the forcing data, especially precipitation, can affect the simulation accuracy of flood peaks. Due to the complex terrain, scarce meteorological and hydrological stations of the basin, and the lack of detailed ground observation data, the forcing data we used have not been fully verified. Secondly, the simulation results need to be further verified. In this study, we verify the simulation results of the GBEHM through observed runoff at the Jiutiaoling gauge station. However, some variables or related processes, such as rainfall, snowmelt, and snowmelt paths, have not been directly tested [42]. The snowmelt contributions to floods are estimated using the snowmelt water paths tracking method of the GBEHM, needing further verification with the isotope tracer method. Thirdly, we only analyzed flood changes over the past 40 years due to the limitations of available data, while the suggested length of flood trend detection is generally 50 years [74]. We believe that more observational and meteorological forcing data will further improve the accuracy of the results.

5. Conclusions

In this paper, we propose a more reasonable method for evaluating snowmelt contributions to floods. We apply the GBEHM to evaluate snowmelt contributions to peak discharge on a time scale in days by tracing snowmelt water flow paths in different media, such as snowpack, soil, and groundwater. The impact of snowmelt on floods under the influence of climate change is analyzed by studying the changes in the contributions of snowmelt and rainfall to floods. The conclusions are as follows.
(1)
In the Xiying River basin, the annual average air temperature exhibited a significant increase of 0.76 °C/10a in the past 40 years. The annual precipitation (precipitation is the sum of rainfall and snowfall) decreased at a rate of 5.59 mm/10a, and the annual rainfall increased at a rate of 11.01 mm/10a. These trends were inapparent. The annual snowfall showed a significant decreasing trend of 14.41 mm/10a.
(2)
In the study area, under the influence of climate change, the frequency of snowmelt-driven floods decreased significantly, and flood time advanced notably, while the intensity and frequency of rainfall-driven floods slowly decreased. The causes of snowmelt-driven flood change are the significant increase in air temperature and the noticeable decrease in snowfall and snowmelt runoff depth. The contribution of snowmelt to rainfall-driven floods slowly weakened, resulting in a slight decrease in the intensity and frequency of rainfall-driven floods.
(3)
Rising air temperature can decrease snowmelt-driven floods. In most mountainous areas, rainfall and snowmelt together promote the formation of and change in floods. While rainfall dominates peak discharge, snowpack and snowmelt play a significant role in the formation and variability of rainfall-driven floods.
(4)
The contributions of snowmelt and rainfall to floods have changed under the influence of climate change, which is the main cause of flood variability. The changed snowmelt would add to the uncertainties and even decrease the size and frequency of floods in snow-packed high mountain areas.
It is projected that rainfall will increase significantly while snow cover will decrease notably on the Tibetan Plateau in the future [75]. In this case, rainfall’s contributions to floods will become more significant, while snowmelt’s contributions to floods will gradually weaken. Although there are currently some limitations to assessing the contribution of snowmelt to flooding using this method, our study can help to understand the contribution of snowmelt to floods in the Tibetan Plateau under the influence of climate change. In addition, the method requires more observational data and more precise model-driven data to better assess regional flood risk.

Author Contributions

L.N., investigation, methodology, data analysis, writing—original draft preparation; J.W., investigation, writing—review and editing; H.L., writing—review and editing; X.H., data acquisition. All authors have read and agreed to the published version of the manuscript.

Funding

The financial support for this work was received from the National Natural Science Foundation of China (No. 42171391) and the Hydraulic Science Experimental Research and Technology Extension Project of Gansu Province (No. 23GSLK002).

Data Availability Statement

The data presented in this study are available on request from the corresponding author.

Acknowledgments

We thank Donghang Shao, Yaru Yang, Wenzheng Ji, and Qin Zhao of the Northwest Institute of Eco-Environment and Resources for data processing assistance.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. The Xiying River Basin. Elevations, rivers, and hydrological station. Daily discharges from the basin are observed at the Jiutiaoling gauge.
Figure 1. The Xiying River Basin. Elevations, rivers, and hydrological station. Daily discharges from the basin are observed at the Jiutiaoling gauge.
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Figure 2. Comparison between observed and simulated average daily discharge at the Jiutiaoling gauge. The red line represents the observed average daily discharge, and the blue represents the simulation results. The validation period is from 1980 to 1987, and the calibration period is from 2012 to 2018.
Figure 2. Comparison between observed and simulated average daily discharge at the Jiutiaoling gauge. The red line represents the observed average daily discharge, and the blue represents the simulation results. The validation period is from 1980 to 1987, and the calibration period is from 2012 to 2018.
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Figure 3. Comparison between simulated and observed peak discharge in the basin. The blue dots represent simulated and observed peak discharge; the dashed black line is the 1:1 line.
Figure 3. Comparison between simulated and observed peak discharge in the basin. The blue dots represent simulated and observed peak discharge; the dashed black line is the 1:1 line.
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Figure 4. Comparison of remotely sensed and simulated monthly SCA (snow cover area) from 2001 to 2018 in the basin. Monthly remotely sensed SCA data are marked as orange dots and lines. Monthly simulated SCA data are marked as blue dots and lines.
Figure 4. Comparison of remotely sensed and simulated monthly SCA (snow cover area) from 2001 to 2018 in the basin. Monthly remotely sensed SCA data are marked as orange dots and lines. Monthly simulated SCA data are marked as blue dots and lines.
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Figure 5. Comparison of remotely sensed and simulated annual average SCD (snow cover days) from 2001 to 2018 in the basin.
Figure 5. Comparison of remotely sensed and simulated annual average SCD (snow cover days) from 2001 to 2018 in the basin.
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Figure 6. Flood characteristics in the basin: (a) represents the time of flood distribution; (b) represents the annual frequency of floods; (c) represents the frequency of floods for each month of every year from 1980 to 2018; the frequency of floods for the yellow zone is 0, for the green zone 1, and for the dark blue zone 3.
Figure 6. Flood characteristics in the basin: (a) represents the time of flood distribution; (b) represents the annual frequency of floods; (c) represents the frequency of floods for each month of every year from 1980 to 2018; the frequency of floods for the yellow zone is 0, for the green zone 1, and for the dark blue zone 3.
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Figure 7. Variation trends of summer floods in the basin: (a) represents peak discharges and trends; (b) represents the annual frequency of floods and trends.
Figure 7. Variation trends of summer floods in the basin: (a) represents peak discharges and trends; (b) represents the annual frequency of floods and trends.
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Figure 8. Annual climate change trends in the basin. The green solid lines represent the trends in air temperature and precipitation, respectively. The yellow solid lines represent the trends in rainfall and snowfall, respectively. The solid gray lines are trend lines.
Figure 8. Annual climate change trends in the basin. The green solid lines represent the trends in air temperature and precipitation, respectively. The yellow solid lines represent the trends in rainfall and snowfall, respectively. The solid gray lines are trend lines.
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Water 17 00507 g009
Figure 9. Annual variation trends in runoff depth and peak discharge. (a) represents the trends in total runoff depth, snowmelt runoff depth, and snowmelt runoff contribution; (b) represents the trends in peak discharge, snowmelt in floods, and snowmelt’s contribution to floods.
Figure 9. Annual variation trends in runoff depth and peak discharge. (a) represents the trends in total runoff depth, snowmelt runoff depth, and snowmelt runoff contribution; (b) represents the trends in peak discharge, snowmelt in floods, and snowmelt’s contribution to floods.
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Water 17 00507 g010
Figure 10. Daily rainfall, snowmelt runoff depth, and total runoff depth from 1980 to 2018. The blue line represents daily rainfall, the orange line represents snowmelt runoff depth, and the green line represents total runoff depth.
Figure 10. Daily rainfall, snowmelt runoff depth, and total runoff depth from 1980 to 2018. The blue line represents daily rainfall, the orange line represents snowmelt runoff depth, and the green line represents total runoff depth.
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Table 1. Key parameters of the GBEHM and their values.
Table 1. Key parameters of the GBEHM and their values.
Parameter DescriptionUnitValueMethod
differentiation temperature of rain and snow°C1.745SCE-UA
snow surface roughnessm0.012SCE-UA
groundwater porosity-0.118SCE-UA
groundwater water hydraulic conductivitym/s34.22SCE-UA
lateral flow distribution coefficient-0.063SCE-UA
evapotranspiration adjustment coefficient-1.078SCE-UA
Table 2. Monthly and annual averages of each variable.
Table 2. Monthly and annual averages of each variable.
VariablesMonthly ValuesAnnual
Values
JanFebMarAprMayJunJulAugSepOctNovDec
Temperature (°C)−15.54−11.59−6.060.184.838.4410.419.415.15−1.00−8.25−13.61−1.47
Precipitation (mm)9.3016.3129.1046.2478.67119.81149.89157.12112.0849.8412.207.21787.76
Rainfall (mm)0.000.030.959.4842.62105.14144.17147.5285.7415.580.150.00551.41
Snowfall (mm)9.3016.2828.1536.7536.0514.675.719.5926.3334.2612.047.21236.35
TR (mm)5.215.5612.0119.0925.9842.8471.6578.7467.8135.7911.966.13382.77
SR (mm)0.010.865.468.6610.4912.6012.6511.339.456.091.150.2178.95
CSR (%)0.2715.4445.4345.3740.3929.4017.6514.3913.9317.029.613.3720.63
Notes: In the table, TR is the total runoff depth, SR is the snowmelt runoff depth, and CSR is the contribution of snowmelt to runoff.
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Niu, L.; Wang, J.; Li, H.; Hao, X. Weakened Snowmelt Contribution to Floods in a Climate-Changed Tibetan Basin. Water 2025, 17, 507. https://doi.org/10.3390/w17040507

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Niu L, Wang J, Li H, Hao X. Weakened Snowmelt Contribution to Floods in a Climate-Changed Tibetan Basin. Water. 2025; 17(4):507. https://doi.org/10.3390/w17040507

Chicago/Turabian Style

Niu, Liting, Jian Wang, Hongyi Li, and Xiaohua Hao. 2025. "Weakened Snowmelt Contribution to Floods in a Climate-Changed Tibetan Basin" Water 17, no. 4: 507. https://doi.org/10.3390/w17040507

APA Style

Niu, L., Wang, J., Li, H., & Hao, X. (2025). Weakened Snowmelt Contribution to Floods in a Climate-Changed Tibetan Basin. Water, 17(4), 507. https://doi.org/10.3390/w17040507

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