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BY 4.0 license Open Access Published by De Gruyter Open Access January 25, 2024

The research of common drought indexes for the application to the drought monitoring in the region of Jin Sha river

  • Xingyu Zhou and Xinhui Xu EMAIL logo
From the journal Open Geosciences

Abstract

Based on MODIS data from 2010 to 2020 and precipitation, air temperature, and soil moisture data of 33 meteorological stations in Jinsha River Basin from 1990 to 2020, the applicability of different remote sensing drought indexes in Jinsha River Basin was studied. These indexes include temperature condition index (TCI) and temperature vegetation drought index (TVDI), the results of vegetation condition index (VCI), vegetation supply water index (VSWI), standardized precipitation evapotranspiration index (SPEI), and standardized precipitation index (SPI) showed that TCI and TVDI, VSWI and TCI, VSWI and TVDI, VSWI and TVDI, VSWI and TVDI, VSWI and TVDI, VSWI and TVDI, SPEI and SPI, respectively. The correlation between VSWI and VCI was significant. VCI had the lowest correlation with SPEI and SPI. The average correlation coefficient between TCI and VSWI was similar. The correlation between VSWI, SPEI, and SPI was low in January, March, and October and reached significant or above levels in other months. TVDI had the highest correlation with SPEI and SPI. TVDI was significantly correlated with soil moisture every month of the year, indicating that TVDI can be effectively used for remote sensing drought monitoring in Jinsha River Basin and has strong adaptability. According to the temporal and spatial analysis of drought monitoring in the Jinsha River Basin by TVDI, the drought areas in December and January are mainly located in the middle reaches of the Jinsha River Basin, while the light drought areas are mainly located in the upper and lower reaches of the Jinsha River Basin. From March to June, the risk of severe drought increased in the middle reaches of the Jinsha River, and the moderate drought area in the Jinsha River Basin also increased. The drought from July to November was weaker than in the previous months. The moderate drought area is mainly located in the middle and lower reaches of the Jinsha River, and the mild drought area is mainly distributed in the upper reaches of the Jinsha River Basin.

Drought is a climate phenomenon that threatens human and social economic growth due to the scarcity of fresh water resources. At the same time, drought is also a global natural disaster. With the development of society and the increasing demand for water resources, the number of arid areas in the world has increased. In recent years, the Jinsha River Basin located in the upper reaches of the Yangtze River has had frequent droughts, and there are significant differences in time and space. Therefore, the Jinsha River Basin is more vulnerable to drought. With the development of remote sensing technology, the use of remote sensing technology for drought monitoring has gradually replaced the traditional means of drought monitoring. Therefore, it is very important to study the most suitable drought index for Jinsha River Basin, monitor the possible drought in the future, and minimize the corresponding loss as much as possible.

Ying et al. compared the monitoring results of seven remote sensing drought indexes in the Hedong area of Gansu Province in 2006 and evaluated each index according to relative soil humidity [1]. Xuebin et al. analyzed the applicability of different remote-sensing drought indexes in eastern Inner Mongolia [2]. Sanjay et al. used the vegetation condition index (VCI) and precipitation data to investigate and analyze the drought situation in Rajasthan, India. The results show that it is reliable to combine the ground observation data with remote sensing data for drought monitoring [3]. Sandholti et al. first proposed the temperature vegetation drought index (TVDI) [4]. TVDI is widely used in drought remote sensing monitoring [5] because of its simple calculation, only using remote sensing data, and significant correlation with the ground-measured data. Patel et al. used TVDI to monitor drought in semi-humid areas of India [6]. In 2014, Ezzine et al. analyzed the regional adaptability of three remote sensing drought indexes in monitoring agricultural drought in two land types (rain-fed and vegetation-covered areas) in the Mediterranean semi-arid region [7]. Currently, the drought monitoring methods in the Jinsha River Basin are still relatively traditional, and the remote sensing drought index has not been widely used. Moreover, the existing research on the applicability of the remote sensing drought index mainly focuses on the drought in agricultural areas, but there are few research results for reference. Therefore, according to the MODIS data of Jinsha River Basin from 2010 to 2020, temperature condition index (TCI), TVDI, VCI, and vegetation supply water index (VSWI) are calculated, and SPEI and SPI are calculated based on the meteorological data of temperature and precipitation from 1990 to 2020. Through the correlation analysis of four kinds of remote sensing drought indexes, the relationship between the remote sensing drought indexes and the consistent degree of drought description is reflected. SPEI, SPI, and four kinds of remote sensing drought indexes were used to evaluate the ability of different remote sensing indexes to monitor drought in the Jinsha River Basin. At the same time, the correlation analysis between drought index and soil moisture was carried out, and the spatial and temporal distribution of dry early in each month from 2010 to 2020 in Jinsha River Basin was analyzed in order to provide the basis for dynamic monitoring of dry early in Jinsha River Basin by remote sensing.

1 Overview of the study area

Jinsha River Basin is located in the upper reaches of the Yangtze River, 90°–105°E, 24–36 N, and flows through the Qinghai–Tibet Plateau, Hengduan Mountains, the northern Yunnan Plateau, and the southwestern Sichuan Mountains. The relative height difference in the basin is large, with an average elevation of 2,000 m [8]. The mainstream has a total length of 3,481 km and a total area of 474,600 km2, accounting for about 26% of the area of the Yangtze River Basin. The zonality of vegetation latitude in the basin is obvious, such as semi-desert valley, forest, alpine meadow, etc. The annual rainfall is about 710 mm, mainly from July to September. The annual average runoff is about 4,750 m3/s. Evapotranspiration in the basin is greater than precipitation, which is the main reason for water shortage and drought in the basin [9]. The geographical location of the study area is shown in Figure 1.

Figure 1 
               Schematic diagram of the study area.
Figure 1

Schematic diagram of the study area.

2 Data and preprocessing

MODIS is a new generation of remote sensing optical instruments mounted on Terra and Aqua satellites, which has the advantages of high spatial–temporal resolution, high spectral resolution, and convenient access. At the same time, it can provide various characteristic information such as atmosphere, ocean, and land [10]. In this article, MOD11A2 Land Surface Temperature (LST) and MOD13A3 vegetation index (NDVI) data downloaded from NASA's official website (https://www.nasa.gov/) from 2010 to 2020 are used. The MODIS Reprojection’ Tools software developed by NASA is used to convert the downloaded data into map projection and format, and the LST data are synthesized monthly by the maximum synthesis method, which effectively eliminates the influence of solar altitude angle, satellite angle of view, orbital drift, and cloud cover [11]. Monthly normalized difference vegetation index (NDVI) data and LST data are obtained in the required range.

The meteorological data come from the National Meteorological Science Data Center (http://data.cma.cn/), including monthly precipitation data and temperature data from 33 meteorological stations in the region from 1990 to 2020. The missing data and some abnormal data values are interpolated by arithmetic mean value [12].

The soil moisture data “CLDASV2.0” product is provided by the National Meteorological Information Center. Compared with similar products at home and abroad, this product has higher quality in China, and its spatial and temporal distribution characteristics are more reasonable and accurate. It adopts more than 2,400 national automatic station time observations, and the product format is .netCDF, which can be downloaded from the China Meteorological Data Service Network (http:/ldata.cma.cn/). Studies have proved that 0–10 cm of soil moisture can effectively represent the actual situation of ground drought [13,14]. Therefore, this article uses the soil humidity of “CLDASV2.0” products from 2010 to 2010 (0–10 cm) for research.

3 Research methods

3.1 Vegetation state index VCI

NDVI is a widely used vegetation index in drought research, which can accurately reflect the temporal and spatial variation characteristics of vegetation cover. However, because it can only reflect the influence of a single factor on vegetation, it is lacking in cross-regional research. Therefore, Kogan et al. proposed the vegetation state index VCI based on NDVI [15]. The calculation formula is

(1) VCI = NDVI i NDVI min NDVI max NDVI min ,

where NDVI is the normalized vegetation index, NDVI i is the value of the current NDVI, NDVI max is the maximum value of NDVI in the same period of many years, and NDVI min is the minimum value of NDVI in the same period of many years. The VCI value range is [0–1]. The larger the VCI value, the better the growth state of plants; on the contrary, the worse the growth state of plants, thus reflecting the severity of drought.

3.2 Temperature state index (TCI)

Kogan established the temperature state index based on the time series of surface temperature, which reflected the soil moisture status according to the principle that vegetation temperature or soil surface temperature increased with the decrease of water quantity and provided the basis for drought evaluation [16]. The calculation formula is

(2) TCI = LST max LST i LST max LST min ,

where TCI is the temperature state index, LST is the current land surface observation temperature, LST max is the maximum value of LST in the same period of many years, and LST min is the minimum value of LST in the same period of many years. The TCI value range is [0–1], and the more severe the drought, the closer the TCI value is to 0, and vice versa.

3.3 TVDI

Sandholt et al. proposed the TVDI by using the simplified NDVI-TS characteristic space. The wet edge is considered a straight line parallel to the NDVI axis, while the dry edge has a linear relationship with NDVI [4]. Based on this, TVDI is established, and its calculation formula is

(3) TVDI = T i ( l 1 + h 1 × NDVI ) ( l 2 + h 2 × NDVI ) ( l 1 + h 1 × NDVI ) ,

where l 1 and h 1 , l 2 and h 2 are the fitting coefficients of “dry edge” and “wet edge” equations, respectively; T i is the surface temperature, and NDVI is the normalized difference vegetation index. Both are obtained from remote sensing data. TVDI is in the range of [0–1]. The larger the value, the deeper the drought degree, and vice versa.

3.4 Vegetation water supply index VSWI

The principle of the vegetation index is that if there is enough water in the soil, vegetation will grow normally; if soil water shortage causes vegetation to be in a state of water shortage, first, plant growth is affected, and NDVI is reduced. Second, the canopy temperature of vegetation rises [17]. The calculation formula is

(4) VSWI = NDVI LST ,

where NDVI is the normalized vegetation index and LST is the surface temperature. The less the water in the soil, the smaller the VSWI value, and vice versa.

3.5 Standardized precipitation evapotranspiration index (SPEI)

The SPEI is an index that normalizes the cumulative probability distribution of the difference between precipitation and potential evapotranspiration. The Penman–Monteith equation is used to replace the Thornth–Waite equation to calculate potential evapotranspiration and unbiased probability weighted moment is used to fit [18].

3.6 Standardized precipitation index (SPI)

SPI is a multi-time scale drought index, as suggested by McKee et al. [18]. The gamma function is used to describe the change of precipitation, and the cumulative percentage distribution of standardized precipitation is used to divide the drought grade. See the literature [19] for the specific calculation method.

4 Results and analysis

4.1 Correlation analysis of remote sensing drought index

According to the difference in plant performance after water stress in different growth stages, four common remote sensing drought indexes (TCI, TVDI, VCI, and VSWI) were calculated from MODIS data of Jinsha River Basin from 2010 to 2020, and the correlation coefficients among the four remote sensing drought indexes were calculated. Table 1 shows that TCI and TVDI, VSWI and TCI, VSWI and TVDI, and VSWI and VCI are significantly correlated. The correlation between TCI and VCI was poor in winter and spring, which was due to the closure of stomata on the surface of vegetation leaves in winter, which led to the decrease of photosynthesis, photoinhibition of leaves, damage of chlorophyll ultrastructure, and decrease of chlorophyll content. In addition, in order to cope with the impact of lower temperatures in spring and winter, the vegetation morphology will also change, resulting in a greater impact on the NDVI value of VCI. The correlation between VCI and TVDI was low in March and April, which may be due to the low vegetation coverage in early spring, and the vegetation growth was not obvious, which led to a greater impact on the NDVI value. At present, there are more than 20 kinds of remote sensing drought indexes. According to Hao’s research, TCI, TVDI, and VSWI are classified as the classification system of canopy temperature change, and VCI is classified as the classification system of crop morphology and greenness. The consistency between different classes is good, but the consistency between different classes is poor [20].

Table 1

Correlation coefficient of drought index in each month in the study area

TCI and VCI VCI and TVDI TCI and TVDI VSWI and TCI VSWI and TVDI VSWI and VCI
January 0.42* −0.55** 0.92** 0.72** −0.76** 0.41*
February 0.28 −0.38* 0.93** 0.68** −0.46* 0.81**
March 0.16 −0.08 0.87** 0.69** −0.73** 0.69**
April 0.13 −0.05 0.93** 0.82** −0.63** 0.61**
May 0.62** −0.46** 0.67** 0.66** −0.44* 0.87**
June 0.61** −0.97** 0.59** 0.83** −0.45* 0.86**
July 0.38* −0.62** 0.81** 0.78** −0.62** 0.48*
August 0.59** −0.31* 0.83** 0.86** −0.61** 0.77**
September 0.46* −0.41* 0.86** 0.81** −0.68** 0.66**
October 0.49* −0.42* 0.73** 0.77** −0.56** 0.63**
November 0.30 −0.78** 0.42* 0.41* −0.44* 0.76**
December 0.33 −0.36* 0.85** 0.70** −0.60** 0.55**

**There was a significant correlation at 0.01 level (bilateral). *There was a significant correlation at 0.05 level (bilateral). TCI is the temperature state index; TVDI is the temperature vegetation drought index; VCI is the vegetation state index; VSWI is the vegetation water supply index.

4.2 Correlation analysis of remote sensing drought index, SPEI, and SPI

SPEI and SPI are widely used indicators in meteorological drought monitoring. Meteorological drought mainly refers to water shortage caused by water evaporation and precipitation imbalance over time, and drought remote sensing indicators mainly focus on the impact of drought on plants. Although these two views are different, meteorological drought is the basis of other types of droughts. A more mature meteorological drought index can be used to evaluate the ability of the remote sensing index to monitor drought. In order to avoid the noise in a single grid point of spatial data, correlation analysis was made between the average value of the remote sensing drought index corresponding to meteorological stations and the standardized evapotranspiration index and SPI of corresponding meteorological stations. As shown in Tables 2 and 3, TVDI has the highest and negative correlation with SPEI and SPI, and the average correlation coefficient reaches −0.61 and −0.64, respectively, reaching significant and above correlation levels throughout the year, indicating that TVDI can be effectively used for remote sensing drought monitoring in Jinsha River Basin, and has strong adaptability. VCI had the lowest correlation with SPEI and SPI, and the average correlation coefficients were 0.34 and 0.35, respectively. The correlation of VSWI with SPEI and SPI was low in January, March, and October and reached significant or above levels in other months. The average correlation coefficients were 0.43 and 0.48, respectively, which may be due to the decrease in the VSWI value and the aggravation of drought in January, March, and October due to the lack of precipitation. When the soil water supply was insufficient, the NDVI value decreased and the LST value increased, which led to the decrease of VSWI. TCI was similar to VSWI, with average correlation coefficients 0.45 and 0.48, respectively.

Table 2

Correlation between SPEI and remote sensing drought index in different months

SPEI and VCI SPEI and VSWI SPEI and TVDI SPEI and TCI
January 0.06 0.12 −0.58** 0.53**
February 0.13 0.42* −0.51** 0.61**
March 0.46* 0.32 −0.44* 0.29
April 0.36 0.41* −0.69** 0.59**
May 0.41* 0.51** −0.49* 0.45*
June 0.38 0.42* −0.68** 0.26
July 0.55** 0.44* −0.52** 0.37
August 0.35 0.51** −0.40* 0.68**
September 0.37 0.73** −0.77** 0.49**
October 0.42* 0.11 −0.73** 0.21
November 0.16 0.48* −0.81** 0.52**
December 0.41* 0.69** −0.71** 0.43*

**There was a significant correlation at 0.01 level (bilateral). *There was a significant correlation at 0.05 level (bilateral). VCI is the vegetation state index; TCI is the temperature state index; VSWI is the vegetation water supply index; TVDI is the temperature vegetation drought index; SPEI is the standardized precipitation evapotranspiration index.

Table 3

Correlation between SPI and remote sensing drought index in different months

SPI and VCI SPI and VSWI SPI and TVDI SPI and TCI
January 0.08 0.15 −0.47* 0.62**
February 0.22 0.56** −0.64** 0.67**
March 0.49* 0.20 −0.40* 0.25
April 0.29 0.38 −0.72** 0.61**
May 0.55** 0.68 ** −0.59** 0.41*
June 0.24 0.53** −0.76** 0.22
July 0.42* 0.43* −0.58** 0.40*
August 0.31 0.62** −0.43* 0.77**
September 0.43* 0.80** −0.85** 0.59**
October 0.49* 0.07 −0.89** 0.19
November 0.22 0.54** −0.72** 0.57**
December 0.40* 0.80** −0.63** 0.46*

**There was a significant correlation at 0.01 level (bilateral). *There was a significant correlation at 0.05 level (bilateral). VCI is the vegetation state index; TCI is the temperature state index; VSWI is the vegetation water supply index; TVDI is the temperature vegetation drought index; and SPEI is the standardized precipitation evapotranspiration index.

4.3 Correlation analysis of TVDI and soil moisture

The decrease of soil moisture caused by drought will be reflected in vegetation growth and coverage, so it is very important to study the relationship between soil moisture and TVDI. The correlation between soil moisture and temperature, vegetation drought index of each station in the study area, is shown in Table 4. As shown in the table, TVDI and soil moisture are significantly correlated in each month of the year. Its spatial distribution is shown in Figure 2, with extremely significant correlation areas mainly located in Derong, Lijiang, Yongsheng, Dayao, Yuanmou, Huidong, Ningnan, Qiaojia, and other places in the middle reaches of Jinsha River. There was a significant correlation in other places. It can be seen that TVDI can be effectively used for remote sensing drought monitoring in Jinsha River Basin and has strong adaptability.

Table 4

Correlation between TVDI and soil moisture in different months

Jan. Feb. March April May June July August Sept. October Nov. Dec.
−0.43* −0.56* −0.62** −0.66** −0.59** −0.69** −0.76** −0.72** −0.83* −0.68** −0.48* −0.51*

**There was a significant correlation at 0.01 level (bilateral). *There was a significant correlation at 0.05 level (bilateral). VCI is the vegetation state index; TCI is the temperature state index; VSWI is the vegetation water supply index; TVDI is the temperature vegetation drought index; SPEI is the standardized precipitation evapotranspiration index.

Figure 2 
                  Correlation between soil moisture and TVDI.
Figure 2

Correlation between soil moisture and TVDI.

4.4 Spatial and temporal characteristics of drought in the Jinsha River Basin

According to the definition of drought degree by TVDI, Table 5 shows the spatial characteristics of monthly drought from January to December, as shown in Figure 3. The results show that the drought areas in December and mid-January are mainly located in the middle reaches of Jinsha River Basin, and the main areas are located in Lijiang, Ninglang, Heqing, Yongsheng, Binchuan, Huaping, Panzhihua, Yongren, Dayao, Yuanmou, Wuding, Luquan, Dongchuan, Huili, Huidong, Ningnan Qiaojia, and other places show that the probability of moderate drought is high in December and January every year. Light drought areas are mainly located in the upper and lower reaches of the Jinsha River Basin. The main areas are located in Butuo, Jinyang, Zhaotong, Yibin, Leibo, Yongshan, Suijiang, Yibin, Batang, Deqin, Shangri La, dege, Baiyu, etc. From March to June, with the decrease of rainfall in the middle reaches of the Jinsha River Basin, the higher temperature leads to the increase of severe drought areas, which are mainly located in Huaping, Yuanmou, Binchuan, Yongren, Panzhihua, and other places in the middle reaches of Jinsha River. The drought areas in the upper and lower reaches of Jinsha River also increase, mainly located in Butuo, Jinyang, Zhaotong, Yibin, Leibo, Yongshan, Suijiang, Yibin, Batang, Deqin, Shangri La, etc. Deron. Mangkang, Batang, and other places. It shows that the dry early in March to June is increased compared with the previous months, and the drought is more serious, and the probability of re-occurrence is higher. From July to November, the middle drought area is mainly located in the middle and lower reaches of Jinsha River. While mild drought areas are mainly distributed in Batang, Deqin, Shangri La, dege, Baiyu, etc. It also shows that the rainfall and runoff increase gradually from July to November, and the drought situation is weakened compared with the previous months.

Table 5

TVDI drought intensity

Strength grade Drought type TVDI value range
1 No drought 0 < TVDI < 0.46
2 feebly arid* 0.46 ≤ TVDI < 0.57
3 Moderate drought 0.57 ≤ TVDI < 0.76
4 Severe drought 0.76 ≤ TVDI < 0.86
5 Extremely severe drought 0.86 ≤ TVDI < 1
Figure 3 
                  Spatial distribution of monthly drought in Jinsha River Basin in recent 10 years.
Figure 3 
                  Spatial distribution of monthly drought in Jinsha River Basin in recent 10 years.
Figure 3

Spatial distribution of monthly drought in Jinsha River Basin in recent 10 years.

5 Discussion

The meteorological index of drought is becoming more and more mature and plays an important role in drought monitoring. Due to the large temporal and spatial differences of climate in Jinsha River Basin, the understanding of drought in this basin is not enough. At the same time, drought is a very complex scientific problem, which is related to rainfall, temperature, evapotranspiration, altitude, soil type, underlying surface structure, and other factors. Its production, dissipation, and mutation mechanisms are complex. The same drought index with different time scales has different drought index values. At present, there is no accurate drought index that can adapt to all aspects. At present, there are many research studies on the remote sensing drought index, but there are few research studies on the effectiveness of remote sensing drought monitoring in the Jinsha River Basin. In order to test the regional validity of remote sensing drought index, different scholars adopt different methods according to the limitation of data, such as soil moisture data measured on the ground, precipitation data observed by meteorological stations, and meteorological drought index data derived from precipitation data. Drought monitoring technology developed from remote sensing technology is favored for its continuous and intuitive monitoring results in space. At present, a variety of remote sensing drought indexes have been established, but the algorithms and application scope of many remote sensing drought indexes still need to be improved. In the study of the applicability of the remote sensing drought index, only by comprehensively considering the various factors and time scales of remote sensing drought index and comprehensively using a variety of different remote sensing drought indexes, can we conduct more accurate and objective research and evaluate the advantages and disadvantages of remote sensing drought index monitoring results. In the absence of a widely accepted remote sensing drought monitoring index at present, it is of great practical significance to use a meteorological drought index to evaluate the monitoring results of the remote sensing drought index according to the transmission law of drought.

6 Conclusion

Based on the MODIS data of Jinsha River Basin from 2010 to 2020, TCI, TVDI, VCI, and VSWI are calculated, and SPEI and SPI are calculated based on the meteorological data of temperature and precipitation from 1990 to 2020. Through the correlation analysis of four kinds of remote sensing drought indexes, the relationship between the remote sensing drought indexes and the consistent degree of drought description is reflected. SPEI, SPI, and four kinds of remote sensing drought indexes were used to evaluate the ability of different remote sensing indexes to monitor drought in the Jinsha River Basin. At the same time, the correlation analysis between TVDI, SPEI, and SPI soil moisture showed that TVDI and SPEI, SPI soil moisture reached significant and above levels throughout the year. The results show that TVDI can be effectively used in drought monitoring of the Jinsha River Basin and has strong adaptability. The main conclusions are as follows:

  1. The correlation between TCI and TVDI, VSWI and TCI, VSWI and TVDI, and VSWI and VCI was significant which passed the significance test of P < 0.05. The correlation between TCI and VCI is poor in winter and spring, which may be due to the damage of chlorophyll ultrastructure and the decrease of chlorophyll content in winter vegetation. In addition, in order to cope with the impact of lower temperatures in spring and winter, the vegetation morphology will also change, resulting in a greater impact on the calculation of the NDVI value of VCI. The results showed that TCI, TVDI, and VSWI should be classified as the classification system of canopy temperature change, and VCI should be classified as the classification system of crop morphology and greenness.

  2. SPEI and SPI are widely used indicators in meteorological drought monitoring. A mature meteorological drought index can be used to evaluate the ability of the remote sensing index to monitor drought. VCI had the lowest correlation with SPEI and SPI. The average correlation coefficient between TCI and VSWI was similar. The correlation between VSWI, SPEI, and SPI was low in January, March, and October and reached significant or above levels in other months. TVDI had the highest correlation with SPEI and SPI in the whole year, showing a negative correlation, and the average correlation coefficients were −0.61 and −0.64, respectively. Soil moisture is an important index to reflect drought. TVDI is significantly correlated with soil moisture in every month of the year. The extremely significant correlation areas are mainly located in Derong, Lijiang, Yongsheng, Dayao, Yuanmou, Huidong, Ningnan, Qiaojia, and other places in the middle reaches of the Jinsha River. The results show that TVDI can be effectively used in drought monitoring of the Jinshajiang River Basin and has strong adaptability

  3. According to the temporal and spatial analysis of drought monitoring in the Jinsha River Basin by TVDI, the drought areas in December and January are mainly located in the middle reaches of the Jinsha River Basin, while the light drought areas are mainly located in the upper and lower reaches of the Jinsha River Basin. From March to June, with the gradual decrease of rainfall in the middle reaches of the Jinsha River Basin, the higher the temperature, the higher the risk of severe drought in the middle reaches of the Jinsha River, and the drought areas in the whole area of the Jinsha River basin also increased. From July to November, with the passage of time, the rainfall and runoff increased gradually, and the drought situation weakened compared with the previous months. The moderate drought area is mainly located in the middle and lower reaches of the Jinsha River, while the mild drought area is mainly distributed in the upper reaches of the Jinsha River Basin.

Acknowledgments

The authors are grateful for the Industry-University Cooperation Collaborative Education Project (No. 220701223223337, No. 220503776315455), 2021 Yunnan University School-Level Graduate Public Elective Course (XJGXK202105),the National Natural Science Foundation of China (No. 42061052), and Production and Education Research Project of the Ministry of Education for this study.

  1. Conflict of interest: The authors declare no competing interests.

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Received: 2022-06-28
Revised: 2022-11-25
Accepted: 2023-05-09
Published Online: 2024-01-25

© 2024 the author(s), published by De Gruyter

This work is licensed under the Creative Commons Attribution 4.0 International License.

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