# Exploring Empirical Linkage of Water Level–Climate–Vegetation across the Three Georges Dam Areas

^{*}

## Abstract

**:**

## 1. Introduction

**X**cause time series

**Y**, the inclusion of the past of both

**X**and

**Y**should boost the prediction of the presence of

**Y**compared to exclusively using the past of

**Y**. The non-linear GC framework overcomes the lagged correlations that arise from the autocorrelation, seasonality and inter-annual variability, and are applicable to large datasets with excellent computational scalability. There also emerged deep learning-based casual inference methods in recent years [10].

^{2}at the water level of 175 m [14]. After two tentative impounding operations to the elevations of 135 m and 156 m above the sea level at 10 June 2003, and 27 October 2006, respectively, the TGR started impounding water after the flood season with the water level of 175 m above mean sea level reached 26 October 2010. The water level would change regularly for water supply during the dry season, shipping navigation between late March to middle April, discharging water to 145 m for flood preparation, flood peak smoothing during floods and impounding water during the storage season. Although, few works have reported the influence of climate variability and TGR operations on streamflow in the Yangtze River [15], and the linkage of water level with climate and vegetation feedback is also poorly understood. To this end, we answer the questions as follows: (1) Does the vegetation of the TGR area (TGRA) undergo furious change after the regular TGR operation? (2) To what extent do anthropogenic activities such as water level regulation causes the vegetation change? (3) How many days of the accumulated water level cause the vegetation changes? (4) Does the causality still hold in the drying stage, impounding stage and falling stage of the TGRA?

## 2. Study Area

^{3}/s, water is completely released. Otherwise, flood control operations are conducted considering the downstream safety and release capacity of hydro turbines. The storage stage begins at the end of the flood season, with the water level gradually rising to no more than 162 m at the end of September, and to 175 m no earlier than the end of October. From November to December, the reservoir water level would be kept as high as possible if the storage level has already reached the final stage of 175 m, otherwise the water level should continue to rise up to 175 m.

## 3. Data and Method

- Satellite-based NDVI for investigating vegetation changes.
- Ground-measured air temperature and precipitation for understanding climate variations.
- Hydrological elements including daily water level and streamflow for mining the causality linkages.

#### 3.1. Vegetation Dynamics Considering Climate Impacts

#### 3.2. Constructing Causality Linkages from Multivariate Time Series Data

#### 3.3. Quantified Explained Variances

_{t}uses X

_{t}

_{-1}, X

_{t}

_{-2}, …, X

_{t-p}with Y

_{t-}

_{1}, Y

_{t-}

_{2}, …, Y

_{t-p}and W

_{t-}

_{1}, W

_{t-}

_{2}… W

_{t-p}. While the base model means that the prediction of Y

_{t}use Y

_{t}

_{-1}, Y

_{t-}

_{2}, …, Y

_{t-p}with W

_{t-1}, W

_{t-2}… W

_{t-p}. Second, the determined Granger causality of GC(X->Y) is obtained with Equation (3):

## 4. Results and Analysis

#### 4.1. Causality Linkages among Water Level–Climate–Vegetation in the TGRA

#### 4.2. Explainable Variances of NDVI with Respect to the Water Level

**k**days. We derived the cumulative variable to incorporate the potential impact of the TGR water resources scheduling on the vegetation dynamics in the study area. Based on the indirect linkages between 10 days CCOWL and the daily NDVI obtained, we conducted the following experiments considering the different cumulative periods ($k\in [10,20,30]$). According to the non-linear GC, the full model represents the cumulated sum of the last

**k**day’s water level, and the lagged climate variables are predictors. The baseline model represents only past values of NDVI and the lagged climate variables are included as predictors. Note that the lagged time windows were the same as the cumulative periods. The response variable was the daily NDVI, which is the same for both the full model and the baseline model. The non-linear GC represents the improvement of the coefficient of determination by subtracting the predication accuracy from the full model with respect to that of the baseline model. Figure 3 shows the results of the feedbacks of vegetation with respect to the cumulative sum of the water level (CSWL) in the study area. The GCs of the five regions range from 0.127 to 0.205. The results showed that the 10 days CSWL of the TGR could explain the vegetation variance up to 20.5% in these regions. With the increase in distance between the metrological stations and TGR, GCs increase from 0.1278 to 0.2057. GCs decrease from 0.2057 to 0.1461 when the distance from the stations to the TGR increase above 75 km. The GC of stations in the TGRA is the weakest, which is highly correlated with the complex and rugged topography of the region that could easily affect the vegetation dynamic [33]. The implementation of forestry ecological projects led to afforestation as reported in [34]. The result also suggested that the water resources scheduling would influence on the regional scale, which correspond with Wu’s research. The causality between the CSWL and the daily NDVI is at the regional scale rather than the local scale.

#### 4.3. Spatial–Temporal Granger Causality across TGRA

## 5. Discussion and Conclusions

## Author Contributions

## Funding

## Acknowledgments

## Conflicts of Interest

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**Figure 1.**The study area across the Three George Reservoir area (TGRA). The inner most is the TGRA containing 21 meteorological stations. The other four buffering regions are depicted using different colors. The thick blue line depicts the Yangtze River and the thin blue lines depict the branches of the Yangtze River.

**Figure 2.**A deep-learning-based causal network inference method applied on the time series data of the Yichang and Beipei stations (preserving prominent causality linkages only). (

**a**) Observed data causal network of the Yichang station (

**b**) Observed data causal network of the Beipei station.

**Figure 3.**Feedbacks of vegetation dynamics with respect to the cumulative sum of 10 days’ water level.

**Figure 4.**Feedbacks of vegetation dynamics with respect to the cumulative sum of 20 days’ water level.

**Figure 5.**Feedbacks of vegetation dynamics with respect to the cumulative sum of 30 days’ water level.

**Figure 6.**The spatial–temporal causality for the vegetation dynamics due to the water level variations across the TGRA.

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**MDPI and ACS Style**

Huang, W.; Zhou, J.; Zhang, D.
Exploring Empirical Linkage of Water Level–Climate–Vegetation across the Three Georges Dam Areas. *Water* **2020**, *12*, 965.
https://doi.org/10.3390/w12040965

**AMA Style**

Huang W, Zhou J, Zhang D.
Exploring Empirical Linkage of Water Level–Climate–Vegetation across the Three Georges Dam Areas. *Water*. 2020; 12(4):965.
https://doi.org/10.3390/w12040965

**Chicago/Turabian Style**

Huang, Wei, Jianzhong Zhou, and Dongying Zhang.
2020. "Exploring Empirical Linkage of Water Level–Climate–Vegetation across the Three Georges Dam Areas" *Water* 12, no. 4: 965.
https://doi.org/10.3390/w12040965