Temporal and Spatial Changes in Evapotranspiration and Its Potential Driving Factors in Mongolia over the Past 20 Years

Cha, Ersi; Tubuxin, Bayaer; Bao, Yuhai; Yong, Mei; Zhang, Xiang

doi:10.3390/rs14081856

Cited by 9 publications

(6 citation statements)

References 57 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Previous studies primarily focused on investigating the temporal attribution of ET variations, with limited research examining the spatial distribution patterns of ET. The analysis of ET spatial distribution patterns gradually gained traction following the introduction of geographic detectors [26]. The results obtained from the geographic detector herein demonstrate that sunshine duration exerted the greatest influence on the spatial distribution pattern of ET in Southwest China, which aligned with our expectations.…”

Section: Key Factors Affecting Spatial Distribution Pattern Of Et In ...supporting

confidence: 84%

“…This study utilized the Geographic Detector to assess the impact of different environmental factors on the spatial distribution pattern of ET. The Geographic Detector had been widely employed in detecting the spatial heterogeneity of ET and similar research elements (e.g., soil moisture) [26,27]. The principle of this method is straightforward: it determines whether the independent variable has an influence on the dependent variable and the magnitude of the impact by detecting whether the spatial distribution of the dependent variable and the independent variable is consistent [28].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Spatiotemporal Variations in Evapotranspiration and Their Driving Factors in Southwest China between 2003 and 2020

Zhang,

Zhou,

Yang

et al. 2023

Remote Sensing

View full text Add to dashboard Cite

The widespread distribution of karst landforms has led to a shortage of water resources in Southwest China. Understanding the spatiotemporal variations in and driving factors of evapotranspiration (ET) in this area is crucial for understanding and predicting severe water resource shortage. This study conducted trend analysis using meteorological data from 2003 to 2020 as well as remote sensing products such as Penman–Monteith–Leuning equation version 2 (PML-V2) ET. The factors influencing the spatial distribution pattern of average ET were identified using a geographical detector. Partial correlation analysis was performed to characterize the relationships between ET and the factors governing its variations, determined using the random forest model. The results demonstrated the following: (1) The average ET decreased with increasing latitude and altitude, primarily affected by the landform type in terms of longitude and displaying “W”-shaped fluctuations. Overall, the annual ET exhibited a significant (p < 0.05) increasing trend, with 72.63% of its area under the increasing trend. (2) The results of the geographic detector indicated sunshine duration as the strongest explanatory factor of the spatial distribution of ET, followed by enhanced vegetation index (EVI), landform type, precipitation, elevation, slope, and aspect. Instead of an individual factor, the interplay between multiple factors more considerably influenced the spatial distribution pattern of ET. (3) The EVI exhibited an overall increasing trend, with a significant increase over 73.59% of the study area and a positive correlation with ET. Thus, the increase in EVI had the strongest impact on ET in the study area, which was further confirmed by the results of the random forest model for 42.92% of the study area. Thus, the present findings clarify the spatiotemporal variations in and driving factors of ET in Southwest China and can serve as a benchmark for policies aiming to develop and manage water resources in this region.

show abstract

Section: Key Factors Affecting Spatial Distribution Pattern Of Et In ...supporting

confidence: 84%

Section: Introductionmentioning

confidence: 99%

Spatiotemporal Variations in Evapotranspiration and Their Driving Factors in Southwest China between 2003 and 2020

Zhang,

Zhou,

Yang

et al. 2023

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…The Theil-Sen median trend analysis is a robust nonparametric statistical method, which did not require the data to obey a certain distribution pattern and reduced the influence of noise [8]. This method has been widely used in the trend analysis of ET time-series data at the pixel scale [8,12,16,26,27]. The Theil-Sen estimator (S ET ) indicated the median of the slope of ET data combinations and was calculated as follows:…”

Section: Theil-sen Median Trend Analysismentioning

confidence: 99%

“…The Mann-Kendall test was insensitive to missing data, independent of data distribution and required fewer assumptions. This method is recommended by the World Meteorological Organization as a nonparametric statistical test and has been widely used in trend testing [8,27,[30][31][32][33][34][35][36].…”

Section: Mann-kendall Testmentioning

confidence: 99%

“…Cheng et al investigated the spatiotemporal ET patterns in China [5]. The MOD16 ET product has also been used to analyze the trend of ET variation in northwestern Mexico [6], Bulgaria [7], Mongolia [8], China [5,[9][10][11], Henan Province [12], Guizhou Province [13], Shanxi Province [14], Xinjiang [15], Yinchuan Plain [16] and Sanjiang Plain [17].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Spatiotemporal Variation in Actual Evapotranspiration and the Influencing Factors in Ningxia from 2001 to 2020

Liu

Song

Kong

et al. 2022

IJERPH

View full text Add to dashboard Cite

Surface evapotranspiration (ET) is an important part of the hydrological cycle. Based on the MOD16 ET product and the data collected by meteorological stations, this study investigated, for the first time, the characteristics, variation trend and influencing factors of actual ET in Ningxia from 2001 to 2020 along temporal and spatial scales using the Theil–Sen median trend analysis, Mann–Kendall test and Hurst index, and predicted the future trend of ET. The results revealed a strong correlation between the MOD16 ET product and ET data collected at meteorological stations (r = 0.837, R2 = 0.701). Over the past 20 years, the annual ET in Ningxia showed an overall increasing trend, and the proportion of the increasing area was 96.58%. Quarterly ET varied over time, with the highest value in the third quarter and the lowest value in the second quarter. Annual ET showed a positive correlation with normalized difference vegetation index (NDVI), surface temperature and precipitation but no correlation with relative humidity. Additionally, the Hurst index revealed areas showing a persistent increase in ET, accounting for 84.91% of the total area, indicating that the future trend of ET in Ningxia is consistent with the past trend.

show abstract

Effects of landscape attributes and climate variables on catchment hydrology

et al. 2023

View full text Add to dashboard Cite

Catchments characteristics, such as geomorphology, geology, soil, land use, and climatic variables, play an important role in total stream flow responses, a critical resource for people and the environment. Most of the previous literatures were applied a conventional statistical regression model to assess the relationship between landscape-climate descriptors, and streamflow and PET. However, a conventional statistical regression model didn’t consider dependence of explanatory variables that were collected or extracted across both space and time. This paper investigated the impacts of landscape attributes and climate variables on catchment scale temporal variation of total streamflow and spatio-temporal variation of potential evapotranspiration (PET) in the Mille catchment using multiple linear regression techniques, and the importance of this study was to test spatial autocorrelation in the spatial regression model which is required to properly assess and quantify the relationship between hydrological regime response components and Landscape-climate descriptors in a catchment with topographically complex, and high spatio-temporal climatic variation like in our case study area, the Mille catchment. Statistical regression analysis revealed significant relationships between streamflow and climate variables, especially with rainfall. Mean maximum temperature is the most dominant factor controlling temporal variation of potential evapotranspiration at a monthly scale, whereas NDVI is the most significant factor that controls the spatial variability of PET. The multiple regression model shows that 91.1% of temporal variation in streamflow was accounted for rainfall, whereas, 96.6% and 78.4% of temporal and spatial variation in potential evapotranspiration was accounted for in maximum temperature and NDVI, respectively. Methods also can be applied to catchments with similar landscape attributes and climate variables.

show abstract

Temporal and Spatial Changes in Evapotranspiration and Its Potential Driving Factors in Mongolia over the Past 20 Years

Cited by 9 publications

References 57 publications

Spatiotemporal Variations in Evapotranspiration and Their Driving Factors in Southwest China between 2003 and 2020

Spatiotemporal Variations in Evapotranspiration and Their Driving Factors in Southwest China between 2003 and 2020

Spatiotemporal Variation in Actual Evapotranspiration and the Influencing Factors in Ningxia from 2001 to 2020

Effects of landscape attributes and climate variables on catchment hydrology

Contact Info

Product

Resources

About