# Analysis on the Spatio-Temporal Changes of LST and Its Influencing Factors Based on VIC Model in the Arid Region from 1960 to 2017: An Example of the Ebinur Lake Watershed, Xinjiang, China

^{1}

^{2}

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## Abstract

**:**

## 1. Introduction

_{2}, in the atmosphere [29]. Although the urbanization process in some areas is not fast, the effect of LUCC on LST is worth studying, especially for arid areas.

## 2. Materials and Methods

#### 2.1. Study Area

#### 2.2. Data Sources

#### 2.3. VIC Model

_{n}is net radiation (W/m

^{2}), H is sensible heat flux (W/m

^{2}), ${\rho}_{w}{L}_{e}E$ is latent heat flux (W/m

^{2}), and G is surface heat flux (W/m

^{2}). The net radiation is calculated by the following formula:

_{S}is the downward short-wave radiation ($\mathsf{\mu}\mathrm{m}$), $\epsilon $ is the specific emissivity(%), R

_{L}is the downward long-wave radiation ($\mathsf{\mu}\mathrm{m}$), and $\sigma $ is the Stefan-Boltzmann constant ($\sigma $ = 5.67 × 10

^{−8}W/(m

^{2}·°C

^{4})). The calculation formula of sensible heat is as follows:

_{h}is the aerodynamic impedance (s/m). The surface heat flux is estimated by the heat of two layers of soil. For the upper layer (the first layer + the second layer), the total depth is assumed to be D

_{1}(m), then its calculation formula is:

_{1}is the temperature of the upper soil (°C). Assuming that the depth of the underlying soil is D

_{2}(m) and the temperature at the bottom of the soil is constant, then:

_{S}is the coefficient of soil heat conductivity (J/m

^{3}·°C), ${T}_{1}^{+}$ and ${T}_{1}^{-}$ is the soil temperature (°C) at the beginning and the end of the period in the upper soil respectively, and T

_{2}is the constant temperature (°C) of the lower soil. Currently, it is believed that $\kappa $ and C

_{S}do not change with the change of soil moisture content. According to Equations (4) and (5), the calculation formula of surface heat flux can be deduced as follows:

#### 2.4. Wavelet Analysis

#### 2.4.1. Morlet Continuous Wavelet

#### 2.4.2. Cross-Wavelet Transform (XWT)

#### 2.4.3. Wavelet Coherence (WTC)

## 3. Results

#### 3.1. Accuracy Verification

_{m}(m

^{3}/s); the nonlinear base flow velocity D

_{s}(m

^{3}/s); the soil moisture content W

_{s}(%); and the three layers of soil thickness d

_{1,}d

_{2}and d

_{3}(m) during the nonlinear base flow. The selection of model parameters was based on traditional parameter calibration methods [46]. The above parameters were adjusted according to the empirical interval of the hydrological parameters to make the peak discharge value match the measured data. The above steps were repeated several times until a satisfactory result was obtained. The parameters are calibrated by repeatedly calculating the NSE and R

^{2}of the simulated and the measured values of runoff. The NSE and R

^{2}of the monthly runoff of Wenquan hydrological station during the verification period (2002–2017) are 0.44 and 0.47, respectively. The Final selected parameters are described as follows (Table 1):

#### 3.1.1. Accuracy Verification in Terms of Time

^{2}, the Root Mean Squared Error RMSE, the Mean Absolute Error MAE, the Nash-Sutcliffe efficiency coefficient NSE were selected to evaluate the simulation accuracy of the model. The R

^{2}, RMSE, MAE, NSE values of annual simulated LSTs are 0.51 2.21, 1.85, and 0.17, respectively (Figure 3b). The R

^{2}, RMSE, MAE, NSE values of monthly simulated LSTs are 0.98, 3.93, 3.31, and 0.93, respectively (Figure 3d). It can be seen from the respective boxplots (Figure 3e,f) that the difference between the simulated and measured monthly data is not very large. The medians are 11.11 °C and 11.65 °C respectively. The measured values are slightly higher. The simulated and measured annual values have a large gap. They are 8.56 °C and 10.07 °C, the difference is 1.5 °C, our simulated values are underestimated.

#### 3.1.2. Accuracy Verification in Terms of Space

#### 3.2. Temporal and Spatial Variation Characteristics of the LST

#### 3.3. Delayed Correlation Analysis of LST and Meteorological Elements

#### 3.4. The Impact of LUCC on LST

^{2}, accounting for 35.1% of the total area, followed by grassland, with an area of 18,476.3 km

^{2}, accounting for 34.9% of the total area. Bare ground accounted for 13.9% of the total area, and cropland accounted for 3.8% of the total area. Deciduous broadleaf forest occupied the smallest area, accounting for only 0.14% of the total area.

^{2}and accounting for 65.6% of the total area, followed by non-vegetation land with an area of 11,904.9 km

^{2}, accounting for 22.5% of the total area. The two accounted for 88.1% of the total area. The area of cropland decreased to 4184.4 km

^{2}, accounting for only 7.9% of the total area. The area of open shrubland dropped sharply, most of them were transformed into grassland or non-vegetation land.

## 4. Discussion

#### 4.1. Harmful Effects Caused by Changes in LST in Arid Regions

#### 4.2. Evaluation of VIC Model for LST Simulation

## 5. Conclusions

- Although the model simulates the LST well in time and the space verification results are relatively good, the LST simulation in the high-altitude area of the cold month is seriously overestimated, which may be related to the occurrence of snowfall or to the altitude. Further research is needed.
- The LST of the Ebinur Lake Watershed shows an overall increasing trend, and the annual average LST is higher in the central and eastern parts of the basin. On the temporal scale, the daily and monthly average LSTs showed unimodal trends. The interdecadal monthly changes are not obvious, and the monthly average LST from 2010 to 2017 fluctuates more than in other periods.
- It is worth mentioning that there is a sudden change affected by the mean LST on a time scale of 1~2a (1980–1996); that is, there is a “strong-weak” transition in the LST.
- From 1960 to 2017, the LUCC of the Ebinur Lake Watershed underwent major changes, and the reduction of open shrubs may have caused the LST increase in this area.

## Author Contributions

## Funding

## Acknowledgments

## Conflicts of Interest

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**Figure 3.**(

**a**). Comparison of LST changes between measured and simulated annual values. (

**b**). Verification accuracy of measured and simulated annual value LST. (

**c**). Comparison of LST changes between measured and simulated monthly values. (

**d**). Verification accuracy of measured and simulated monthly value LST. (

**e**,

**f**). Boxplot of monthly and annual simulated and measured value, respectively.

**Figure 6.**Interdecadal changes and annual mean LST spatial distribution in the Ebinur Lake Watershed.

**Figure 7.**XWT analysis of mean wind speed and LST (

**a**), XWT analysis of mean temperature and LST (

**b**), XWT analysis of annual precipitation and LST (

**c**), XWT analysis of relative humidity and LST (

**d**), XWT analysis of duration of sunshine and LST (

**e**). (Note: The horizontal axis of the wavelet period diagram corresponds to the time of the original time series, the vertical axis represents the period of change, and the color represents the intensity of the change period. In this figure, yellow represents the high intensity of the change cycle. The thin solid line is the Cone of Influence (COI), and only the energy spectrum within this solid line needs to be considered. In order to eliminate the interference of the boundary effect, the thick solid line represents the key value that passes the significance test with 95% confidence; the arrow indicates the relative phase difference: → indicates that the two changes in phase are consistent, ← indicates that the two changes in phase are opposite, ↑ indicates that the phase change of the meteorological element is 90° ahead of the LST phase, and ↓ indicates that the phase change of the meteorological element is 90° behind the LST phase [47,48]). (The same below).

**Figure 8.**WTC analysis of mean wind speed and LST (

**a**), WTC analysis of mean temperature and LST (

**b**), WTC analysis of annual precipitation and LST (

**c**), WTC analysis of relative humidity and LST (

**d**), WTC analysis of duration of sunshine and LST (

**e**). (Note: The right axis generally reflects the consistency of periodic "change trends" between sequences, similar to the correlation coefficient. The larger the coefficient, the higher the correlation and the more consistent the change.).

B | D_{m} | Ds | Ws | d_{1} | d_{2} | d_{3} |
---|---|---|---|---|---|---|

0.25 | 3.5 | 0.05 | 0.1 | 0.1 | 0.1 | 1.5 |

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Amantai, N.; Ding, J.
Analysis on the Spatio-Temporal Changes of LST and Its Influencing Factors Based on VIC Model in the Arid Region from 1960 to 2017: An Example of the Ebinur Lake Watershed, Xinjiang, China. *Remote Sens.* **2021**, *13*, 4867.
https://doi.org/10.3390/rs13234867

**AMA Style**

Amantai N, Ding J.
Analysis on the Spatio-Temporal Changes of LST and Its Influencing Factors Based on VIC Model in the Arid Region from 1960 to 2017: An Example of the Ebinur Lake Watershed, Xinjiang, China. *Remote Sensing*. 2021; 13(23):4867.
https://doi.org/10.3390/rs13234867

**Chicago/Turabian Style**

Amantai, Nigenare, and Jianli Ding.
2021. "Analysis on the Spatio-Temporal Changes of LST and Its Influencing Factors Based on VIC Model in the Arid Region from 1960 to 2017: An Example of the Ebinur Lake Watershed, Xinjiang, China" *Remote Sensing* 13, no. 23: 4867.
https://doi.org/10.3390/rs13234867