# Incorporating Temporal and Spatial Variations of Groundwater into the Construction of a Water-Based Ecological Network: A Case Study in Denko County

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## Abstract

**:**

## 1. Introduction

## 2. Materials

#### 2.1. Site Description and Data Sources

#### 2.2. Physiography and Hydrogeological Conditions

^{2}and the groundwater resource reserves are 0.5258 billion m

^{3}. From the southeast to the northwest (I-I’), as shown in Figure 2, the water yield property of the aquifer first transitions from 1000–3000 to 3000–5000 m

^{3}/day; with the gradual close of the Piedmont alluvial fan, the water yields are significantly reduced. In the Lang Mountain area, the water yield property of the aquifer was less than 100 m

^{3}/day. From the surface to the depth of 200 m underground, most of the soil types are fine sand, which is intermittently intertwined with clay, loam layers, and muddy soil layers. The silty clay layer is mainly distributed in the Yellow River region. Two distinct faults appear in the alluvial area in front of the Lang Mountain.

## 3. Methodology

#### 3.1. Empirical Modal Decomposition Model

- (1)
- Find the local maxima in the original sequence $x\left(t\right)$. To better preserve the characteristics of the original sequence, the local maximum was defined as the value of a point in the time series, which is not larger than the previous or subsequent moment.
- (2)
- Next, the cubic spline function was used to interpolate, and the upper envelope sequence ${e}_{max}\left(t\right)$ of the original sequence $x\left(t\right)$ was obtained.
- (3)
- Similarly, the lower envelope ${e}_{min}\left(t\right)$ was obtained.
- (4)
- The maximum envelope ${e}_{max}\left(t\right)$ and the minimum envelope ${e}_{min}\left(t\right)$ were averaged, and the instantaneous average value $m\left(t\right)$ obtained:$$m\left(t\right)=\left[{e}_{max}\left(t\right)+{e}_{min}\left(t\right)\right]/2$$
- (5)
- The class normal value sequence $h\left(t\right)$ was obtained by subtracting the instantaneous mean sequence $m\left(t\right)$ from the original sequence $x\left(t\right)$:$$h\left(t\right)=x\left(t\right)-m\left(t\right)$$

#### 3.2. Co-Kriging Interpolation Algorithm

_{n}), soil heat flux (G), and sensible heat flux (H) for each pixel. The latent heat flux (ET) was acquired as a residual in the energy balance equation. ET is the flux of heat from the earth’s surface to the atmosphere that is associated with evaporation or transpiration of water. NDVI, atmospheric emissivity (ε), albedo ($\alpha $), and surface radiation temperature (${T}_{s}$) were initially computed to obtain the regional surface radiation balance, R

_{n}, followed by the regional surface energy balance. Soil heat flux G was computed by an empirical relation function with R

_{n}, $\alpha $, ${T}_{s}$, and NDVI. Sensible heat flux H was obtained by the surface temperature gradient $dT$. The key variables and formulas of the SEBAL model are shown in Table 2 and the flow chart of the computation of the sub-models using the SEBAL model is shown in Figure 4. The detailed operation process of the SEBAL model was described in the study of Lihong et al. [49].

#### 3.3. Point Pattern Analysis Algorithm

^{2}); ${u}_{ij}$ is the distance between the $i$th (focal) node and the $j$th (neighboring) node, where the focal node is located within area A; ${I}_{i}\left({u}_{ij}\right)$ is an indicator function, equaling 1 if ${u}_{ij}\le r$ and 0 otherwise; and ${W}_{ij}$ is a circular edge-correction, defined as the inverse of the proportion of a circle of radius r, placed over each point within the total study area A. If the entire circumference of the circle lies within A, then $w=1$. If $g\left(r\right)>1$, there were more pairs of points at distance r from each other than expected under a random pattern, which indicated aggregation at scale r. If $g\left(r\right)<1$, there were fewer points at distance r than expected under a random pattern, showing segregation at scale r, and $g\left(r\right)=1$ expresses a homogeneous Poisson process (complete spatial randomness, CSR). Node distribution function $g\left(r\right)$ is calculated as:

#### 3.4. Minimum Cumulative Resistance Surface Model

## 4. Results and Discussions

#### 4.1. Water Table Dynamics

#### 4.2. Spatial Distribution of Groundwater

#### 4.3. Point Pattern Analysis of Surface Water

#### 4.4. Construction of a Water-Based Ecological Network

^{2}. Level 2 mainly included lakes with an area greater than 5 km

^{2}and less than 10 km

^{2}. Level 3 mainly included lakes with an area greater than 1 km

^{2}and less than 5 km

^{2}. Level 4 included lakes and ponds with an area of less than 1 km

^{2}.

#### 4.5. Discussions on the Reliability of the Main Results

## 5. Conclusions

- The trend lines of 17 wells could be categorized into five types. The curve of four wells monotonically decreased, and six wells monotonically rose. The curve of three wells showed a trend from decline to rise, and the other three wells showed a trend from rise to decline. Only one well showed a rise–decline–rise trend.
- There were three representative areas where the groundwater level changed dramatically. Among them, the landscape type changed from a desert landscape to an oasis landscape in the first and second representative areas. The changes in the landscape pattern of these two regions had a positive effect on the spatial distribution of groundwater. However, the urbanization of the third representative area had a negative impact on the spatial distribution of groundwater. In summary, appropriate human interventions (such as the construction of an artificial oasis) are of great significance to the stability of arid and semi-arid ecological environments. Furthermore, the precipitation trends were related to the spatial distribution of groundwater depth.
- The spatial pattern of water nodes was characterized by a small-scale highly aggregated distribution and a large-scale uniform distribution. This pattern stabilized the surface water bodies and the regional environment. A more stable ecological network was constructed based on surface water bodies.
- Lakes, ponds, and major rivers were identified as ecological sources, and the ecological source was divided into four grades. Spatial data of WT depth, multiple buffering data of surface water bodies, and spatial density data of surface water bodies were used to construct resistance surfaces. The 2016 water-based ecological network in Denko County consisted of 391 ecological sites and 7360 ecological corridors.

## Acknowledgments

## Author Contributions

## Conflicts of Interest

## Appendix A

// Load the CHIRPS data var CHIRPS = ee.ImageCollection(‘UCSB-CHG/CHIRPS/PENTAD’); //Identify Denko County var GLTP = ee.FeatureCollection(‘ft:14UUynJScUtra5vI9cZ-iPWwZgcZOmbnpgX7qLQl_’); Map.addLayer(GLTP); //Identify region of interest var ROI // Select dates: the CHIRPS data is from 1981-01-01 to 2016-02-27 var precip = CHIRPS.filterDate(‘1990-01-01’, ‘2016-02-27’); var TS5 = Chart.image.series(precip, GLTP, ee.Reducer.mean(),1000, ‘system:time_start’).setOptions({ title: ‘Precipitation Full Time Series’, vAxis: {title: ‘mm/pentad’}, }); print(TS5); // Charts One Year var precip1year = CHIRPS.filterDate(‘2016-01-01’, ‘2016-12-31’); var TS1 = Chart.image.series(precip1year, GLTP, ee.Reducer.mean(),1000, ‘system:time_start’).setOptions({ title: ‘Precipitation 1-Year Time Series’, vAxis: {title: ‘mm/pentad’}, }); print(TS1); var Precip1 = precip1year.mean().clip(GLTP); var Precip=precip.mean().clip(GLTP); Map.addLayer(Precip1, {‘min’: 0, ‘max’: 70, ‘palette’:"CCFFCC,00CC66,006600"},’Precip 2015’); Map.addLayer(Precip, {‘min’: 0, ‘max’: 70, ‘palette’:"CCFFCC,00CC66,006600"},’Precip 2000-2015’); var path = Precip.getDownloadURL({ ‘scale’:250, ‘crs’: ‘EPSG:4326’, ‘region’: ‘[[21.4453125,-18.1249706393865], [33.310546875,-18.35452552912664], [34.27734375,-8.015715997869059], [21.59912109375,-7.776308503776192], [21.4453125,-18.1249706393865]]’ }); print (path);

## Appendix B

## Appendix C

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**Figure 6.**Decomposition of groundwater time series data of well BG1 based on the Empirical Modal Decomposition model.

**Figure 12.**Precipitation of Denko County from 1990 to 2016 (

**a**) and the sum of precipitation from July to August (

**b**).

**Table 1.**Well numbers under different sub-administrative districts of Denko County used for the study.

No. | Sub-Administrative District | Well No. | Elevation (m) | Longitude (°) | Latitude (°) |
---|---|---|---|---|---|

1 | Bayan Gaole | BG13 | 1040 | 106.98 | 40.33 |

BG14 | 1047 | 106.96 | 40.28 | ||

2 | Bu Longnao | BG16 | 1030 | 107.05 | 40.55 |

BG17 | 1041 | 107.08 | 40.52 | ||

BG19 | 1045 | 107.08 | 40.48 | ||

BG23 | 1040 | 107.05 | 40.42 | ||

3 | Du Kou | BG24 | 1042 | 107.08 | 40.42 |

BG26 | 1038 | 107.07 | 40.37 | ||

4 | Long Shenghe | BG8 | 1027 | 106.92 | 40.63 |

5 | Shajin Taohai Sumu | BG1 | 1029 | 106.73 | 40.70 |

BG2 | 1019 | 106.53 | 40.52 | ||

BG3 | 1029 | 106.57 | 40.55 | ||

BG5 | 1036 | 106.67 | 40.50 | ||

BG6 | 1028 | 106.80 | 40.67 | ||

BG9 | 1034 | 106.98 | 40.62 | ||

BG11 | 1039 | 106.80 | 40.57 | ||

BG12 | 1032 | 106.85 | 40.47 |

Key Variables | Formulas | Basic Variables |
---|---|---|

Normalized difference vegetation index | NDVI = (${\rho}_{3}-{\rho}_{4}$)/(${\rho}_{3}+{\rho}_{4}$) | ${\rho}_{3}$ and ${\rho}_{4}$: reflectivities for bands 4 and band 3 |

Atmospheric emissivity (ε) | ε = 1.009 + 0.047 ln(NDVI) | – |

The surface radiation temperature (K) | T_{s} = K_{2}/ln (K_{1}·ε/L_{6} + 1) | ${K}_{1}$: constant for Landsat images, 607.76 mW/cm^{2}/sr/$\mathsf{\mu}$m |

${K}_{2}$: constant for Landsat images, 1260.56 mW/cm^{2}/sr/$\mathsf{\mu}$m | ||

${L}_{6}$: the spectral radiance of band 6 | ||

Albedo | α = R_{s-reflected}/R_{s} | ${R}_{s-reflected}$: the reflected radiation (W/m^{2}) |

${R}_{s}$: the total incident shortwave radiation (W/m^{2}) | ||

Net radiation flux (W/m^{2}) | R_{n} = K_{in} − α·K_{in} + L_{in} − L_{out} | ${K}_{in}$: incoming shortwave radiation (W/m^{2}) |

$\alpha {K}_{in}$: reflected shortwave radiation (W/m^{2}) | ||

${L}_{in}$: incoming longwave radiation (W/m^{2}) | ||

${L}_{out}$: outgoing longwave radiation (W/m^{2}) | ||

Soil heat flux (W/m^{2}) | G = T_{s}·R_{n}/[$\alpha $·(0.0038$\alpha $ + 0.0074${\alpha}^{2}$) (1 − 0.98NDVI^{4})] | – |

Sensible heat fluxes (W/m^{2}) | H = $dT$·$\rho $·C_{p}/r_{a} | $dT$: temperature difference between the two certain heights (K) |

$\rho $: air density, 1.293 × 10^{3} kg/m^{3} | ||

C_{p}: air specific heat, 1004 J/kg/K | ||

r_{a}: aerodynamic resistance to heat transport (s/m) | ||

Latent heat fluxes (W/m^{2}) | $\mathsf{\lambda}$ET = R_{n} − G − H | – |

Instantaneous ET (mm/h) | ET_{inst} = 3600(R_{n} − G − H)/$\lambda $ | $\lambda $: latent heat of vaporization (J/kg) |

Wind speed at the blending height (m/s) | u_{200} = u*·ln(z_{200}/z_{0})/k | u*: the friction velocity at the weather station (m/s) |

z_{200}: the blending height, 200 m | ||

z_{0}: the measure of the land surface friction, 0.36 m in this case study | ||

k: the von Karman’s constant, 0.41 | ||

Friction velocity at each pixel (m/s) | u* = k·u_{200}/ln(z_{200}/z_{0}) | – |

Aerodynamic resistance to heat transport (s/m) | r_{a} = ln(z_{2}/z_{1})/(u*·k) | z_{1} and z_{2}: the heights in meters above zero displacement of the vegetation |

Groundwater Depth | 1990 | 1995 | 2000 | 2005 | 2010 | 2016 |
---|---|---|---|---|---|---|

Max | 20.54 | 19.43 | 19.91 | 22.36 | 20.71 | 20.83 |

Min | 0.21 | 0.23 | 0.50 | 0.18 | 0.20 | 0.57 |

Grade | Weight | |
---|---|---|

WT depth (m) | <2 | 1 |

2–4 | 2 | |

4–6 | 3 | |

6–8 | 4 | |

8–10 | 5 | |

10–12 | 6 | |

12–14 | 7 | |

> 14 | 8 | |

Distance from water bodies (m) | <500 | 1 |

500–1000 | 2 | |

1000–1500 | 3 | |

1500–2000 | 4 | |

2000–2500 | 5 | |

2500–3000 | 6 | |

3500–4000 | 7 | |

>4000 | 8 | |

Density of surface water bodies | 0.9–1 | 1 |

0.75–0.9 | 2 | |

0.6–0.75 | 3 | |

0.45–0.6 | 4 | |

0.3–0.45 | 5 | |

0.15–0.3 | 6 | |

0–0.15 | 7 | |

0 | 8 |

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## Share and Cite

**MDPI and ACS Style**

Yu, Q.; Jiang, Q.; Yang, D.; Yue, D.; Ma, H.; Huang, Y.; Zhang, Q.; Fang, M.
Incorporating Temporal and Spatial Variations of Groundwater into the Construction of a Water-Based Ecological Network: A Case Study in Denko County. *Water* **2017**, *9*, 864.
https://doi.org/10.3390/w9110864

**AMA Style**

Yu Q, Jiang Q, Yang D, Yue D, Ma H, Huang Y, Zhang Q, Fang M.
Incorporating Temporal and Spatial Variations of Groundwater into the Construction of a Water-Based Ecological Network: A Case Study in Denko County. *Water*. 2017; 9(11):864.
https://doi.org/10.3390/w9110864

**Chicago/Turabian Style**

Yu, Qiang, Qun’ou Jiang, Di Yang, Depeng Yue, Huan Ma, Yuan Huang, Qibin Zhang, and Minzhe Fang.
2017. "Incorporating Temporal and Spatial Variations of Groundwater into the Construction of a Water-Based Ecological Network: A Case Study in Denko County" *Water* 9, no. 11: 864.
https://doi.org/10.3390/w9110864