Spatio-Temporal Variation in Pluvial Flash Flood Risk in the Lhasa River Basin, 1991–2020

Abstract

The analysis of temporal and spatial variability in risk has garnered significant research attention, particularly regarding flash flood disasters in the context of warming and wetting conditions on the Qinghai–Tibetan Plateau. Focusing on the Lhasa River basin, this study develops a framework that integrates geographic information systems and a combined subjective–objective weighting approach to comprehensively assess flash flood risk despite limited observations. This paper investigates the distribution patterns of hazard, vulnerability, and the integrated risk of pluvial flash floods; demonstrates the reliability of the assessment results; and provides mitigation recommendations for disaster risk management at the county level. The results showed a trend in increasing flash flood risk in recent decades compared to the 1990s. Moreover, very-high- and high-risk areas were concentrated in downstream regions with frequent precipitation extremes and anthropogenic activity. From 1991 to 2020, the high to very high-risk areas gradually expanded from central Lhasa to neighbouring counties. This study contributes valuable insights into flash flood risk assessment cand mapping, which are crucial in terms of the protection of life and property in the plateau basin.

1. Introduction

Over the past several decades, flood disasters have constituted major global natural hazards due to their capacity to inflict enormous damage on human life, society, and the economy [1,2]. In 2022, floods accounted for over 50% of all natural disaster occurrences and caused 26% of the related fatalities worldwide [3]. The increasing unpredictability of precipitation patterns may lead to more severe flash floods in the future. Most studies on flash flood disasters have emphasized the variation in rainfall-driven risk for mountain regions and downstream areas [4]. The Qinghai–Tibetan Plateau, an essential ecological buffer for the Earth, is experiencing intensified flash flood impacts due to a warming and increasingly wet climate, compounded by underlying societal vulnerabilities [5,6,7]. Investigating flash flood risk changes from a historical perspective has been a hot topic in mountain hazard research due to the substantial and increasing adverse impacts.

The concept of flash flood risk has been investigated somewhat intensively in recent years. However, the components of integrated risk and its influencing factors are controversial [8]. The hazard of climate, human, and natural systems, and systemic vulnerability should be considered in terms of flash flood risk based on the definition given by the UN Department of Humanitarian Affairs [9], Shi et al. [10], the Intergovernmental Panel on Climate Change [11], and Collenteur et al. [12]. Quantifying and assessing flash flood risk is particularly necessary in terms of understanding changes in natural disasters. Studies and field observations confirmed that, on the Qinghai–Tibetan Plateau from 1945 to 2020, the annual distribution of pluvial flash floods mainly occurred during the main rainy season (June–September) and accounted for more than 90% of flood disaster events [13,14,15,16]. In Tibet, human activities and socio-economic factors are concentrated mainly in the Lhasa River basin. A major concern for flash flood prevention and management today involves continuing to obtain the spatio-temporal distributions of risk. However, due to limited observations, the temporal variabilities and spatial patterns of flash flood risk in the Lhasa River basin over recent decades remain inconclusive.

The combined effects of combined extreme precipitation [17,18], continuous expansion of urban areas [19,20], and population and infrastructure growth with the impacts of human activities [21,22] are the main drivers of increasing losses caused by flash floods. Pluvial flash floods have long been recognized as the natural hazards that have the most destructive influence on human beings around the world [23,24]. Studies on the generating mechanism of pluvial flash floods have indicated that the intensity, amount, and frequency of rainstorms can directly lead to surface runoff response [25,26]. Therefore, it is essential to understand the spatial and temporal variabilities in flash flood hazards and investigate their mitigation strategy [27]. Synergetic effects of the geographic factor, river system, road density, and land cover at the basin scale contribute to flash flood risk [19,28,29,30]. Simultaneously, a reasonable spatialization of the socio-economic vulnerability aids in identifying and quantifying flash flood risk in a mountainous area [31]. Socio-economic vulnerability is fundamentally based on the time series of population mobility and a comprehensive property inventory [32].

Geographical information systems (GISs) based on an analytic hierarchy process (AHP) are the most widely used and effective analysis method of flood risk evaluation [33,34]. Although some evaluation methods using AHP-entropy [35], hydrologic and hydraulic models [36], and machine learning algorithms [37] improve the objectivity of the evaluation results, they are limited by data availability and resolution. Most studies of flash flood risk have emphasized the impacts of anthropogenic factors [38,39]. Therefore, potential higher risk caused by human impacts, such as injuries, land development, and property damage, should be incorporated when applying AHP within a comprehensive flash flood risk framework [40,41].

Further studies on the dynamic variation in pluvial flash flood risk are necessary, particularly for mountainous areas with scarce observations and records of flood disasters, such as the Tibetan Plateau [42,43]. This study comprehensively considers hazards with precipitation characteristics and vulnerability to human impacts and analyzes the spatio-temporal distribution and evolution of flash flood risk in the Lhasa River basin from 1991 to 2020. The research objective is to determine the spatial distribution of pluvial flash flood risk in the Lhasa River basin over the past three decades. This paper contributes to a better understanding of the internal relationship between spatio-temporal patterns and driving factors of flash flood risk and is expected to help local governments adopt effective emergency prevention strategies for areas with high flash flood risks.

2. Materials and Methods

2.1. Study Area and Data

2.1.1. Study Area

The Lhasa River basin, located in the south-central Tibetan Plateau (29°20′–31°15′ N, 90°05′–93°20′ E), covers an area of 32,083 km² (Figure 1). The Lhasa River originates in the central Nyenchen Tanglha Mountains and flows through Naqu, Shannan, and Lhasa city to the Yarlung Tsangpo River. This basin is highly susceptible to pluvial flash floods due to its interannual and seasonal variations in rainfall characteristics, with an average annual precipitation of 400–500 mm. The rainy season extends from May to September, which is the time period that features the highest occurrence of pluvial flash floods. The average elevation and slope of the basin are over 4500 m and 21.4 degrees, respectively. The region’s diverse topography, including cropland, forests, grass, shrubs, glaciers, bare land, and man-made land interlaced within the mountains and valleys, further exacerbates the flash flood risk.

With the implementation of national rural revitalization and poverty alleviation policies, the population and gross domestic product (GDP) of Lhasa city have increased by 15% and 205%, respectively, over the past decade. However, the scarcity of statistical and observational data on flash flood disasters necessitates the thorough assessment and mapping of flood risks to maintain regional sustainability.

2.1.2. Data Collection

Basic data sets were collected from multiple sources, but with limited observation periods, for risk assessment of pluvial flash flooding (Table 1). We obtained high-resolution precipitation data from the National Tibetan Plateau Data Center “http://data.tpdc.ac.cn/en/ (accessed on 6 September 2022)” [44] and the China Meteorological Data Service Center “http://data.cma.cn/en (accessed on 6 September 2022)”. Figure 1b shows the locations of the seven meteorologic stations, including the Baingoin, Nagqu, Damxung, Lhari, Nyêmo, Lhasa, and Tsedang stations. The road and river networks were obtained from OpenStreetMap “https://openmaptiles.org/ (accessed on 9 March 2023)”. Land cover data products with a 30 m resolution were obtained from GlobeLand30 “http://www.globallandcover.com/ (accessed on 14 September 2022)”, and the gridded population with a 30 m × 30 m resolution was gathered from the Center for International Earth Science Information Network “http://www.ciesin.columbia.edu/ (accessed on 9 July 2024)” and LandScan Global “https://landscan.ornl.gov/ (accessed on 8 August 2024)”. The Resource and Environment Science and Data Center “https://www.resdc.cn/ (accessed on 9 July 2024)” provided population and GDP data with a 1 × 1 km resolution.

2.2. Indicators Selection

A GIS-based spatial multi-index model including the hazard and vulnerability factors was applied to assess flash flood risk in the Lhasa River basin. The Intergovernmental Panel on Climate Change definition of risk was used and the indicators were adjusted to the availability and resolution of the data. Thus, the indicators in the conceptual model were composed of three hierarchies: a target layer representing pluvial flash flood risk; index layers used for the hazard index and vulnerability index; and an indicator layer including 14 flash flood-inducing indicators used for constructing the multi-index system, including ten hazard indicators and four vulnerability indicators [45]. The indicators applied for flash flood risk assessment are listed in Table 2.

2.2.1. Hazard Indicators

The risk assessment of the flash flood index can be divided into two categories: disaster-causing factors and environmental factors. Hazard indicators related to rainfall were selected based on their typicality, universality, and easy spatialization and adopted from other studies [27,46,47]. Maximum 3 h precipitation (M3HP), maximum 1-day precipitation (M1DP), average precipitation (AP), and average days of precipitation extremes (ADs) during the rainy season were determined using inverse distance weighting from rainfall records provided by the seven meteorologic stations. The precipitation extremes associated with the AD indicator refers to the top 5% of days in terms of annual accumulated precipitation [48]. Here, the AD values were calculated based on the number of days when daily precipitation exceeded 30 mm in the Lhasa River basin [49]. The M3HP and M1DP were derived from the annual extreme values averaged over a 30-year period. However, to avoid interferences from outburst and snowmelt events, hazard indicators within non-flood seasons (October to April next year) were excluded from the risk assessment. The four disaster-causing indicators representing flash flood hazards in the Lhasa River basin during 1991–2020 are shown in Figure 2.

Disaster-causing environmental indicators included elevation (DEM), slope (SL), the topographic wetness index (TWI), distance to river (DTR), river density (RID), and curve number (CN). Figure 3 shows the spatial distribution of the other six indicators involving the environment in the Lhasa River basin. These factors are directly related to rainfall-runoff and are easy to obtain and spatialize [50,51]. The values of these indicators were derived directly or indirectly from basic geographical and hydrological data.

2.2.2. Vulnerability Indicators

Vulnerability indicators included population density (PD), GDP density (GD), road density (ROD), and artificial surface density (ASD) in this study, as these factors related to flood losses can extensively reflect human activities and property distribution [52,53]. It is crucial to note that, the higher the density of people and property during a flash flood event, the more injuries and deaths that occur in mountainous areas. PD, GD, ROD, and ASD were determined and mapped from 1 km × 1 km gridded data regarding population, GDP, road network, and land cover (Figure 4).

2.3. Indicators of Normalization and Weighting

2.3.1. Normalization

Indicator normalization is an essential step in the evaluation of flash flood risk. Given the different units and dimensions of the multi-factors, the data can be recomputed over the range from zero to unity using the Fuzzy Membership tool in the GIS environment. In general, the positive indicators, including M3HP, M1DP, AP, AD, TWI, RID, CN, PD, GD, ROD, and ASD were normalized using Equation (1) and negative indicators, such as DEM, SL, and DTR, were normalized using Equation (2).

(1)$y_{ij}={\displaystyle \frac{x_i-x_{min}}{x_{max}-x_{min}}}$

(2)$y_{ij}={\displaystyle \frac{x_{max}-x_i}{x_{max}-x_{min}}}$

where $y_{ij}$ is the normalized value of the indicator and $i=\mathrm{1,2},\cdots ,m$; $j=\mathrm{1,2},\cdots ,n$; m and n are evaluation indexes and indicators, respectively; $x_i$ is the original value of the $j\mathrm{t}\mathrm{h}$ indicator, $x_{max}$ and $x_{min}$ are the maximum and minimum values of the $j\mathrm{t}\mathrm{h}$ indicator, respectively.

2.3.2. Subjective Weighting

The complexity of factor weighting is further compounded by the interplay of the indicators and their contribution to flash flooding. Therefore, in this study, the AHP method based on the knowledgeability of the experts was used to determine the weights of each indicator. The first stage of the subjective weighting method involves conducting a pairwise assessment of hazard or vulnerability indicators using a comparison matrix R, constructed with Satty’s scale weights [54], which encompasses nine numbers ranging from 1 to 9, indicating increasing levels of significance (Table 3).

(3)$R=\left[\begin{array}{cc}\begin{array}{cc}1& a_{12}\\ a_{21}& 1\end{array}& \begin{array}{cc}\cdots & a_{1n}\\ \cdots & a_{2n}\end{array}\\ \begin{array}{cc}\cdots & \cdots \\ a_{n1}& a_{n2}\end{array}& \begin{array}{cc}1& \cdots \\ \cdots & 1\end{array}\end{array}\right]$

where $R_{ij}$ is the relative importance of factor i to factor j.

Experts then compared the hazard and vulnerability indicators pairwise based on their experience, with higher scores indicating greater importance, in order to determine the impact of each indicator on the risk of flash floods. To assign weights to index layer and each indicator to reduce the complexity of decision-making, five experts in the fields of hydrology, hydraulics, and emergency management were consulted to score the selected 14 indicators. The pairwise comparison of hazard and vulnerability factors is shown in Table 4.

In the scoring step, the final weights of the indicators and indicator layers were based on the feature importance of each indicator in relation to the extent of historical flash floods as well as on consultations with experts in the relevant field. Subsequently, each indicator score underwent normalization processing and a consistency test. The consistency index (CI) can be calculated through Equation (4).

(4)$CI={\displaystyle \frac{{\lambda }_{max}-n}{\left(n-1\right)RI}}$

where ${\lambda }_{max}$ is the maximum eigenvalue of matrix R, n is the number of criteria, and RI is the random index values. If the CI is less than 0.1, then the pairwise comparison can be considered to have acceptable consistency. The indicator weights determined through AHP are presented in Table 2.

2.3.3. Objective Weighting

The entropy method can determine the objective weights through a judgment matrix composed of evaluation indicators [55]. A smaller information entropy of an indicator indicates a greater degree of variation in its value, which can provide more information and play a more significant role in the evaluation, thus necessitating a greater weight value [56]. By processing the data of each indicator, the entropy $e_j$ weight of the $j\mathrm{t}\mathrm{h}$ indicator can be calculated using Equations (5) and (6).

(5)$e_j={\displaystyle \frac{-{\sum }_{i=1}^m{p}_{ij}\ln p_{ij}}{\ln m}}$

(6)$p_{ij}={\displaystyle \frac{y_{ij}}{{\sum }_{i=1}^m{y}_{ij}}}$

Then, the judgment matrix can be constructed and normalized using Equation (1) or Equation (2) when $p_{ij}=0$, $\underset{p_{\mathit{ij}}\to 0}{\lim }{p}_{ij}\ln p_{ij}=0$. The entropy weight $w_j$ of the $j\mathrm{t}\mathrm{h}$ factor was calculated using Equation (7).

(7)$w_j={\displaystyle \frac{1-e_j}{{\sum }_{j=1}^n\left(1-e_j\right)}}$

2.3.4. Combined Weighting and Integrated Risk Calculation

The AHP-entropy approach represents an amalgamation of subjective and objective method, rooted in the principle of minimizing relative entropy [57]. To reduce the uncertainty caused by subjective scoring in weight calculation, the combined method of AHP-entropy was used to calculate the new weight for each factor. The formula for calculating the combined weight $W_j$ is as follows:

(8)$W_j={\displaystyle \frac{{\left(w_{sj}{w}_{oj}\right)}^{1/2}}{{\sum }_{j=1}^n{\left(w_{sj}{w}_{oj}\right)}^{1/2}}}$

where $w_{sj}$ is the subjective weight calculated by AHP and $w_{oj}$ is the objective weight calculated by the entropy weight method.

The 14 indicator values for the two flood index assessments were calculated using ArcGIS Pro 3.0. The calculation formula for Flash Flood Risk assessment is given in Equation (9):

(9)$Flash Flood Risk=f\left(H, V\right)=H+V$

where H is the hazard index and V is the vulnerability index.

In addition, the values of H and V in Equation (9) were calculated using Equations (10) and (11), respectively:

(10)$H=f\left(X_{h,i}\right)={\sum }_{i=1}^n{X}_{h,i}{W}_{h,i}$

where $X_{h,i}$ is the normalized value of index i of classification h for hazard factors and $W_{h,i}$ is the weight of index i of classification h for hazard factors.

(11)$V=f\left(X_{v,j}\right)={\sum }_{j=1}^n{X}_{v,j}{W}_{v,j}$

where $X_{v,j}$ is the normalized value of index j of classification v for vulnerability factors and $W_{v,j}$ is the weight of index j of classification v for vulnerability factors.

In summary, to investigate the spatial distribution and decadal variation in flash flood risk at basin scales from 1991 to 2020, 14 flash flood-induced indicators with hazard and vulnerability indices were first considered. The weights of both the index and indicator layers were then computed as the relative contribution of each factor to the total importance of the integrated flash flood risk through the combined weighting method. Subsequently, the gridded values of all indicators were normalized and spatialized in ArcGIS Pro 3.0. Finally, the grided values were calculated to obtain the classification levels of hazard, vulnerability, and integrated risk through the natural breaks (Jenks) method corresponding to different temporal scales [58]. The classification of flash flood risk was based on inherent patterns, grouping similar values, and maximizing differences between classes where significant data value shifts occur. The risk levels were divided into very high, high, medium, low, and very low in descending order. Moreover, the areas and their proportions, categorized by different periods and risk levels, were statistically analyzed to investigate the dynamic variation in flash flood disaster risk in the Lhasa River basin from 1991 to 2020.

3. Results

3.1. Flash Flood Hazard

Figure 5 shows the spatio-temporal distribution of flash flood hazards in the Lhasa River basin from 1991 to 2020. The areas classified as having very high and high flash flood hazard levels evaluated using the AHP–entropy combined method were 9.1% (2926 km²), 11.2% (3606 km²), and 13.7% (4389 km²) in 1991–2000, 2001–2010, and 2011–2020, respectively (Figure 5). The area with very high flash flood hazard levels was significantly higher in 2001–2010 and 2011–2020 than in 1991–2000. At lower latitudes (below 30° N), including parts of Chengguan, Qushui, Duilongdeqing, Lhünzhub, and Dagzê counties, the hazard level was very high near the outlet in the downstream basin due to the spatial distribution of M3HP, M1DP, AP, and AD.

3.2. Flash Flood Vulnerability

Figure 6 shows the temporal and spatial changes in the vulnerability map of flash floods since the 1990s during rainy seasons. High-vulnerability areas were mainly distributed along the main road network and downstream of the basin around Lhasa city. Furthermore, the general temporal variation in very-high- and high-vulnerability areas was similar to the hazard assessment, as shown in Figure 6. The area rate of high to very high-vulnerability increased by 0.10% (33 km²) in 2001–2010 and 0.36% (115 km²) in 2011–2020, relative to 1991–2000. Very high- and high-vulnerability areas in the Lhasa River basin are mainly located in and around Chengguan county, where population density and local revenue are high.

3.3. Flash Flood Risk

3.3.1. Spatio-Temporal Variations in Flash Flood Risk

The spatio-temporal distribution maps of flash flood risk in the Lhasa River basin from 1991 to 2020 are shown in Figure 7a–c and the area rates of flash flood risk are summarized in Figure 8. From 1991 to 2000, more than 70% of the area had very low to low flash flood risk in the Lhasa River basin. However, the area’s high- to very-high-risk status increased over the study period by 2.83% (909 km²) in 2001–2010 and 1.86% (598 km²) in 2011–2020, relative to 1991–2000. These trends closely align with the temporal variation observed in flash flood hazard and vulnerability within the basin. The spatial distribution of flash flood areas with a high- and very-high-risk status showed a trend in increased flooding in the southwest and decreased flooding in the northeast. The areas under high to very high risk gradually expanded from Chengguan county in the center of Lhasa to the surrounding Qushui, Duilongdeqing, Lhünzhub, and Dagzê counties from 1991 to 2020. The contribution of flash flood risk in the Lhasa River basin is closely related to the magnitude of vulnerability due to its diverse population and GDP distribution.

3.3.2. Integrated Assessment and Verification

Regarding the integrated flash flood risk from 1991 to 2020, the spatial distribution of the five risk levels in the Lhasa River basin is shown in Figure 9. Combined, very-high and high-flash-flood-risk areas covered 5071 km² (15.8% of the basin), concentrated in urban centers and valley regions in the south of the river basin. Medium-flash-flood-risk areas were mainly distributed in the western, eastern, and northeastern part of the river basin, while low- and very-low-flash-flood risk areas were concentrated in the northern part of the basin, which is covered by bare land at high altitudes.

Historical observation and records of flash flood disasters during the rainy season in recent decades are limited. In this study, using data from field investigations and local emergency management departments of natural disasters, 80 historical flash flood events from 1993 to 2014 in the Lhasa River basin for location were used to verify the accuracy of the integrated assessment results (Figure 9). Overall, the flash flood risk mapping of the Lhasa River basin was reliable, even with insufficient flood disaster information. Of the 80 historical flash flood events, 22.5%, 26.3%, and 47.5% occurred in medium-, high-, and very-high-risk areas, respectively (Table 5).

4. Discussion

This study identified a distinct spatio-temporal distribution of flash flood risk during the rainy season, with higher risk levels being observed in downstream basins than at higher elevations and a trend in increasing rates of high- and very-high-risk areas from 1991 to 2020. Previous studies have systematically analyzed the impacts of precipitation variability and anthropogenic activities on flash flood risk changes [46,59]. These variations are largely attributable to the combined effects of the increasingly warmer and wetter climate of the Qinghai–Tibetan Plateau and the increasing population in downstream areas. Overall, the flash flood risk assessment framework used in this study, which considers hazard and vulnerability factors, provides a practical foundation for natural hazard analysis in basins with limited observations.

The evaluated distribution of very high flash flood risk in the Lhasa River basin was consistent with the locations of historical flood disaster sites (Figure 9). These results indicate that the mapping method developed in this study is suitable for application in the prevention and mitigation of rainfall-driven flood disasters. On a county scale, all of Chengguan, approximately 98.7% of Qushui, and 86.4% of Dagzê are at high or very high risk of flash flooding (Figure 10), while more than 30% of Duilongdeqing and Lhünzhub are at high risk. In these counties with higher risk, severe flash flood hazards in urban centers may be compounded by the valley topography and human activity. Sustainable prevention measures are therefore recommended for Chengguan and surrounding counties, along with optimized quick response strategies for settlements about emergency flood management [60,61].

The spatio-temporal patterns of flash flood risk validated using available historical records in this study enrich the basic information for flood disaster management and planning. While there is strong evidence that high risk flash flood areas are located around rainy and densely populated valleys in China [62], additional field survey data on a multi-scale level are needed. This is particularly important given the inherent complexity of the plateau, where diversities in exposed areas and vulnerable populations remain uncertain.

It should be noted that several dynamic socio-economic factors, such as population mobility due to travel seasons or epidemic prevention policies, were not considered in this work. Therefore, future studies should focus on the detailed representation of human activities for integrated flood risk assessment. In addition, it is necessary to quantify the integrated flood risk associated with multiple hazards as well as their interdependencies and interactions.

5. Conclusions

This study provides a thorough assessment of pluvial flash flood risk and its distribution pattern in the Lhasa River basin over the past 30 years in both temporal and spatial terms. We systematically compare the decadal variability of hazard and vulnerability indexes as well as the integrated flash flood risk during the rainy season. The evaluation results indicate that the GIS- and AHP-entropy-weighting-based evaluation methods are still reliable in this typical basin of the Qinghai–Tibetan Plateau, as most historical flood disaster sites fall within higher risk areas. These results reveal an increasing trend in higher risk areas from 1991 to 2020, while downstream regions with frequent precipitation extremes and anthropogenic activity are generally more prone to higher flash flood risk, with accompanying decadal and spatial variability. Risk maps for flash floods can inform the identification of potential hazards promptly for use by policymakers to mitigate disaster losses. Moreover, they will allow local emergency responses for flash floods to be updated dynamically rather than following a “one size fits all” approach.

Author Contributions

Conceptualization, Xiaoran Fu, Zhonggen Wang and Hongquan Sun; methodology, Xiaoran Fu, Dong Wang and Xin Su; software, Dong Wang and Jiayu Tian; validation, Pingping Sun, Jiayu Tian and Xiaoran Fu; formal analysis, Pingping Sun; investigation, Xiaoran Fu, Jiayu Tian and Liaofeng Liang; resources, Zhonggen Wang and Hongquan Sun; data curation, Dong Wang and Jiayu Tian; writing—original draft preparation, Xiaoran Fu; writing—review and editing, Xiaoran Fu, Zhonggen Wang and Hongquan Sun; visualization, Pingping Sun and Liaofeng Liang; supervision, Zhonggen Wang; project administration, Zhonggen Wang; funding acquisition, Zhonggen Wang and Hongquan Sun. All authors have read and agreed to the published version of the manuscript.

Data Availability Statement

Data are contained within the article.

Acknowledgments

We would like to express our sincere thanks to the reviewers and the journal editors for their constructive comments on this manuscript. The authors thank Yuhan Guo, Xiangyu Ye, and Zhen Huang for their help with the data survey.

Conflicts of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Footnotes

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Figures and Tables

Figure 1. Geographical location and outline of the Lhasa River basin: (a) location of the Tibetan Plateau and Yarlung Tsangpo River basin and (b) distribution of river networks and meteorologic stations.

Figure 2. Distribution of four disaster-causing indicators of flash flood hazards in the Lhasa River basin: (a) M3HP; (b) M1DP; (c) AP; and (d) AD.

Figure 3. Distribution of the six indicator-inducing environments of flash flood hazards in the Lhasa River basin: (a) DEM; (b) SL; (c) TWI; (d) DTR; (e) RID; and (f) CN.

Figure 4. Distribution of four indicators of flash flood exposure in the Lhasa River basin: (a) PD; (b) GD; (c) ROD; and (d) ASD.

Figure 5. Spatio-temporal distribution of flash flood hazard during (a) 1991–2000, (b) 2001–2010, and (c) 2011–2020.

Figure 6. Spatio-temporal distribution of flash flood vulnerability during (a) 1991–2000, (b) 2001–2010, and (c) 2011–2020.

Figure 7. Spatio-temporal distribution of flash flood risk during (a) 1991–2000, (b) 2001–2010, and (c) 2011–2020.

Figure 8. Area proportion variations in (a–e) hazard, (f–j) vulnerability, and risk (k–o) levels of flash floods in the Lhasa River basin during 1991–2000, 2001–2010, and 2011–2020.

Figure 9. Spatial distribution of flash flood risk in the Lhasa River basin from 1991 to 2020 (a) and investigation sites of flash flood disasters (b).

Figure 10. Area proportion of flash flood risk in the Lhasa River basin from 1991 to 2020 on a county scale.

Table 1

Data used for flash flood risk assessment in this study.

Type	Source	Period
Precipitation	National Tibetan Plateau Data Center	1991–2020
	China Meteorological Data Service Center
Elevation	Geospatial Data Cloud	2020
Road network	OpenStreetMap	2020
River network	OpenStreetMap	2020
Land cover	GlobeLand30 2020	2000, 2010, and 2020
Population	Center for International Earth Science Information Network	2000, 2010, and 2020
	Resource and Environment Science and Data Center
	LandScan Global
GDP	Resource and Environment Science and Data Center	2000, 2010, and 2020

Table 2

Flash flood-inducing indicators and weights *.

Index	Indicator	Meaning	Weight
			AHP	Entropy	Combined
Hazard	M3HP	Maximum 3 h precipitation in rainy season	0.17634	0.12965	0.18444
	M1DP	Maximum 1-day precipitation in rainy season	0.13811	0.12201	0.15834
	AP	Average precipitation in rainy season	0.10466	0.12369	0.13879
	AD	Average days of precipitation extremes in rainy season	0.06614	0.12793	0.11221
	DEM	Elevation	0.05888	0.11952	0.10233
	SL	Slope	0.03965	0.10199	0.07758
	TWI	Topographic wetness index	0.02661	0.10017	0.06298
	DTR	Distance to river	0.02068	0.06539	0.04486
	RID	River density	0.01602	0.02078	0.02226
	CN	Curve number	0.01154	0.06925	0.03449
Vulnerability	PD	Population density	0.15936	0.00189	0.02118
	GD	Gross domestic product density	0.09476	0.00002	0.00150
	ROD	Road density	0.03253	0.01622	0.02802
	ASD	Artificial surface density	0.05471	0.00150	0.01103

* Abbreviations: AHP, Analytic Hierarchy Process; M3HP, Maximum 3 h Precipitation; M1DP, Maximum 1-Day Precipitation; AP, Average Precipitation; AD, Average Days of Precipitation Extremes; DEM, Digital Elevation Model; SL, Slope; TWI, Topographic Wetness Index; DTR, Distance to River; RID, River Density; CN, Curve Number; PD, Population Density; GD, Gross Domestic Product Density; ROD, Road Density; ASD, Artificial Surface Density.

Table 3

Importance score in AHP.

Score of Importance	Description
1	Equally important
3	Moderately important
5	Strongly important
7	Very strongly important
9	Extremely important
2, 4, 6, 8	Intermediate important between the above two adjacent scores
Reciprocals	Inverse comparison

Table 4

Comparison matrix for flash flood risk factors *.

Matrix	M3HP	M1DP	AP	AD	DEM	SL	TWI	DTR	RID	CN	PD	GD	ROD	ASD
M3HP	1	3	4	6	7	8	8	9	9	9	2	5	8	7
M1DP	1/3	1	3	4	5	6	6	7	7	7	1/2	3	6	5
AP	1/4	1/3	1	3	3	4	5	5	5	6	1/4	1	5	3
AD	1/6	1/4	1/3	1	1	2	3	3	3	4	1/6	1/2	3	2
DEM	1/7	1/5	1/3	1	1	2	3	3	3	3	1/6	1/3	2	1
SL	1/8	1/6	1/4	1/2	1/2	1	2	2	2	2	1/7	1/4	1	1/2
TWI	1/8	1/6	1/5	1/3	1/3	1/2	1	1	2	2	1/7	1/4	1	1/2
DTR	1/9	1/7	1/5	1/3	1/3	1/2	1	1	1	1	1/8	1/5	1/2	1/3
RID	1/9	1/7	1/5	1/3	1/3	1/2	1/2	1	1	1	1/8	1/5	1/2	1/3
CN	1/9	1/7	1/6	1/3	1/3	1/2	1/2	1	1	1	1/8	1/5	1/2	1/3
PD	1/2	2	4	6	6	7	7	8	8	8	1	4	7	6
GD	1/5	1/3	1	2	3	4	4	5	5	5	1/4	1	4	3
ROD	1/8	1/6	1/5	1/2	1/2	1	1	2	2	2	1/7	1/4	1	1/2
ASD	1/7	1/5	1/3	1	1	2	2	3	3	3	1/6	1/3	2	1

* Abbreviations: M3HP, Maximum 3 h Precipitation; M1DP, Maximum 1-Day Precipitation; AP, Average Precipitation; AD, Average Days of Precipitation Extremes; DEM, Digital Elevation Model; SL, Slope; TWI, Topographic Wetness Index; DTR, Distance to River; RID, River Density; CN, Curve Number; PD, Population Density; GD, Gross Domestic Product Density; ROD, Road Density; ASD, Artificial Surface Density.

Table 5

Percentages of integrated risk assessment and historical disaster sites of flash floods at each level.

Risk Level	Risk Assessment		Historical Disaster Site
	Area (km2)	Proportion (%)	Count	Proportion (%)
Very low	3997	12.5	2	2.5
Low	11,726	36.5	1	1.3
Medium	11,289	35.2	18	22.5
High	2599	8.1	21	26.3
Very high	2471	7.7	38	47.5

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