# Quantifying Landscape Pattern–Hydrological Process Linkage in Northwest Iran

^{1}

^{2}

^{3}

^{4}

^{5}

^{*}

## Abstract

**:**

^{2}to 0.28 in the Boran Watershed with 10,268.95 km

^{2}. The correlation coefficient (r > 0.42; p-value < 0.05) was obtained significantly between RLI and only five landscape metrics, including the largest patch index (LPI), landscape shape index (LSI), landscape division index (DIVISION), splitting index (SPLIT), and Shannon’s diversity index (SHDI). In addition, a regression model with R

^{2}of 0.97 and 0.67, respectively, in calibration and validation steps was established between river base flow as the dependent variable and main waterway length, LPI, LSI, SPLIT, modified Simpson’s diversity index (MSIDI), and λS as independent variables. The result confirms the significant interdependence of RLI and landscape characteristics, which can be used to interpret the landscape’s dynamic and its effects on hydrological processes.

## 1. Introduction

## 2. Materials and Methods

#### 2.1. Study Area

#### 2.2. Data Used

#### 2.3. Landscape Metrics Calculation

#### 2.4. RLI Characterization

^{2}and the whole area of the study region (A) in km

^{2}, the runoff landscape index (RLI) was calculated for each patch (Equation (2)) and the whole landscape of the study region (Equation (3)) [3].

_{j}; a

_{ij}is the area of patch ij in km

^{2}; A is the total area of the landscape in km

^{2}. All land cover (λC), soil (λK), and topography (λS) factors and RLI are dimensionless.

#### 2.5. Correlation and Regression Analysis

^{2}, and ME were respectively mean absolute error, root mean square error, coefficient of determination, root mean square error, and mean error. In addition, the ${O}_{i}$ and ${P}_{i}$, respectively, showed the observed and predicted values of mean base flow (mm) for the ith watershed, ${\overline{O}}_{m}$ and ${\overline{P}}_{m}$ were the mean values of observed and predicted base flow (mm), and $N$ was the total number of watersheds.

## 3. Results

#### 3.1. Spatial Changes of Landscape Metrics

#### 3.2. Spatial Changes in RLI and Its Factors

#### 3.3. Results of Correlation and Regression Analysis

^{2}of 0.98, the correlation coefficient of 0.94, and a significant level (α < 0.001), the assumption of a significant linear relationship between independent and dependent variables with a 95% confidence level was confirmed. Furthermore, based on the equation provided for base flow estimation (Equation (8)), the inverse relationship between the stream length as well as the direct relationship with LPI, LSI, SPLIT, MSIDI, and topography factor (λS) was detected with the base flow.

^{2}, and ME were obtained at 0.02, 0.60, 0.60, and −0.39, indicating the relatively appropriate performance of backward regression.

## 4. Discussion

^{2}of 0.88 and 0.91, as well as RMSE of 0.005 and 0.018, respectively, for calibration and validation steps. Partial least squares (PLS) regression results [16] showed a significant direct correlation between the base flow and LPI_forest and an inverse correlation between the base flow and AI_agriculture. The framework provided by the present results plays a base role in regional and provincial planning from landscape ecology and hydrology perspectives.

## 5. Conclusions

^{2}of 0.97 and 0.67, respectively, in the calibration and validation steps (α < 0.01). These findings can be applied to simply determine the relationships between landscape patterns and watershed hydrology, providing quantitative information to natural resource authorities in formulating practical and adaptive programs. Due to the simple and widely used characteristics used to develop RLI, implementing this research methodology in ungauged watersheds around the world is highly emphasized. As a concluding remark, the application of this approach in areas with different landscapes and various hydrological responses can reveal important aspects of the relationship between land use distribution patterns and processes related to erosion and sediment production. In addition, the relationship between different components of the landscape and other components of the water cycle could be a future research topic.

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

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**Figure 7.**Box-plot of land cover (λC), soil (λK), and topography (λS) factors and runoff landscape index (RLI).

**Figure 9.**Validation results for regression analysis of base flow and study variables for 25% of the study watersheds.

Landscape Metric | Symbol | Unit | Formula | Value |
---|---|---|---|---|

Patch density | PD | No. per 100 ha | $\mathrm{P}\mathrm{D}=\frac{{\mathrm{n}}_{\mathrm{i}}}{\mathrm{A}}\left(10000\right)\left(100\right)$ | PD > 0 |

Landscape shape index | LSI | Dimensionless | $\mathrm{L}\mathrm{S}\mathrm{I}=\frac{0/25{\mathrm{E}}^{\mathrm{*}}}{\sqrt{\mathrm{A}}}$ | LSI ≥ 1 |

Splitting index | SPLIT | Dimensionless | $\mathrm{S}\mathrm{P}\mathrm{L}\mathrm{I}\mathrm{T}=\frac{{\mathrm{A}}^{2}}{\sum _{\mathrm{j}=1}^{\mathrm{m}}\sum _{\mathrm{j}=1}^{\mathrm{n}}{\mathrm{a}}_{\mathrm{i}\mathrm{j}}^{2}}$ | 1 ≦ SPLIT ≦ number of cells in the landscape squared |

Shannon’s diversity index | SHDI | Dimensionless | $\mathrm{S}\mathrm{H}\mathrm{D}\mathrm{I}=-{\displaystyle \sum _{\mathrm{i}=1}^{\mathrm{m}}}{\mathrm{P}}_{\mathrm{i}}\ast \mathrm{ln}{\mathrm{P}}_{\mathrm{i}}$ | 0 ≦ SHDI ≦ 1 |

Modified Simpson’s diversity index | MSIDI | Dimensionless | $\mathrm{M}\mathrm{S}\mathrm{I}\mathrm{D}\mathrm{I}=-\mathrm{ln}{\displaystyle \sum _{\mathrm{i}=1}^{\mathrm{m}}}{\mathrm{p}}_{\mathrm{i}}^{2}$ | MSIDI ≥ 0, without limit |

Landscape division index | DIVISION | Dimensionless | $\mathrm{D}\mathrm{I}\mathrm{V}\mathrm{I}\mathrm{S}\mathrm{I}\mathrm{O}\mathrm{N}={\left[1-{\displaystyle \sum _{\mathrm{j}=1}^{\mathrm{n}}}{\left(\frac{{\mathrm{a}}_{\mathrm{i}\mathrm{j}}}{\mathrm{A}}\right)}^{2}\right]}^{}$ | 0 ≦ DIVISION ≦ 1 |

Contiguity index distribution | CONTIG_MN | Dimensionless | $\mathrm{C}\mathrm{O}\mathrm{N}\mathrm{T}\mathrm{I}\mathrm{G}=\frac{\left[\frac{\sum _{\mathrm{r}=1}^{\mathrm{*}}{\mathrm{c}}_{\mathrm{i}\mathrm{j}\mathrm{r}}}{{\mathrm{a}}_{\mathrm{i}\mathrm{j}\mathrm{r}}^{\mathrm{*}}}\right]-1}{\mathrm{v}-1}$ | 0 ≦ CONTIG_MN ≦ 1 |

Largest patch index | LPI | % | $\mathrm{L}\mathrm{P}\mathrm{I}=\frac{\mathrm{max}\left({\mathrm{a}}_{\mathrm{i}\mathrm{j}}\right)}{\mathrm{A}}(100)$ | 0 < LPI ≦ 100 |

Interspersion and juxtaposition index | IJI | % | $\mathrm{I}\mathrm{J}\mathrm{I}=\frac{-\sum _{\mathrm{i}=1}^{\mathrm{m}}\sum _{\mathrm{k}=\mathrm{i}+1}^{\mathrm{m}}\left[\left(\frac{{\mathrm{e}}_{\mathrm{i}\mathrm{k}}}{\mathrm{E}}\right)\ast \mathrm{ln}\left(\frac{{\mathrm{e}}_{\mathrm{i}\mathrm{k}}}{\mathrm{E}}\right)\right]}{\mathrm{ln}\left(0.5\left[\mathrm{m}\left(\mathrm{m}-1\right)\right]\right)}\left(100\right)$ | 0 < IJI ≦ 100 |

Aggregation index | AI | % | $\mathrm{A}\mathrm{I}=\left[\frac{{\mathrm{g}}_{\mathrm{i}\mathrm{i}}}{\mathrm{m}\mathrm{a}\mathrm{x}\underset{.}{\to}{\mathrm{g}}_{\mathrm{i}\mathrm{i}}}\right]\left(100\right)$ | 0 ≦ AI ≦ 100 |

Mean Euclidean nearest neighbor distance | ENN_MN | m | $\frac{{\displaystyle {\sum}_{j=1}^{n}{h}_{ij}}}{N}$ | ENN_MN > 0, without limit. |

Land Cover Classification | Reference | Study Area | Rainfall Interception Loss (%) | Land Cover Factor (λC) | |
---|---|---|---|---|---|

Forest | Pinus nigra | [32] | Reforest Campus of University Agriculture Shirvan | 34.77 | 0.46 |

Cupressus sempervirens | 44.97 | 0.45 | |||

Robinia pseudoacacia | 9.78 | 0.8 | |||

Platanus orientalis | 5.5 | 0.83 | |||

Natural stand (Fagus orientalis) and exotic plantation (Picea abies) | [33] | Siahkal Forests, Gilan | 11.7 | 0.77 | |

Fagus orientalis Lipsky | [34] | Kheyrud forest research station of University of Tehran | 33.7 | 0.47 | |

Quercus brantii | [35] | Zagros forests, Ilam | 58.26 | 0.41 | |

Pinus eldarica | [36] | Tehran Chitgar Forest Park | 59.25 | 0.41 | |

Cupressus | 62.28 | ||||

Fagus orientalis Lipsky | [37] | Educational-research Forest of Shast-Kalateh of Gorgan | 60.7 | 0.40 | |

Fagus Orientalis Lipsky | [38] | Siyahkal Shenrood Forests (Caspian Region) | 51.3 | 0.40 | |

Quercus castaneifolia | [39] | Kheyrud Forest Research Station of Tehran | 0.26 | 0.79 | |

Shrub lands | Rosa persica | [40] | Campus of the Ferdowsi University of Mashhad, Khorasan Razavi | 22 | 0.78 |

Peganum harmala | 39 | 0.45 | |||

Fagus orientalis and a Picea abies | [41] | Kelardasht Region, North of Iran | 48.6 26.5 | 0.43 0.75 | |

Bush | [42] | Haihe River Basin, China | 11.26 | 0.79 | |

Grassland | Belongs to grasslands with long grass | [43] | South Central Great Plains, USA | 44 | 0.55 |

Grass | [42] | Haihe River Basin, China | 3.78 | 0.85 | |

Wetlands | Appartient à un lagon natural | [44] | Upstream of the Biebrza watershed, Poland | 13 | 0.80 |

Agricultural land | Fermes corn | [45] | Agricultural land in Varmin, located southwest of Tehran (Iran) | 11.2–19.9 | 0.40 |

Urban land | Urban areas | [46] | Tianjin, Haihe Watershed, China | 48.2–64 | 0.50 |

Urban catchments | [47] | 20 urban watersheds from around the world | 32.82 | ||

Evergreen benjamin tree F. | [48] | Querétaro City in central Mexico | 2.4 | 0.78 | |

Rural land | Rural areas | [46] | Tianjin, Haihe Watershed, China | 85–66 | 0.40 |

Bare land | Bare land | [49] | Yangou Watershed, southern China | 21.28 | 0.80 |

Drainage Class | Very Poor | Poor | Imperfectly | Moderately Good | good | Somewhat Excessive | Excessive |
---|---|---|---|---|---|---|---|

Runoff coefficient | 0.8 | 0.7 | 0.6 | 0.5 | 0.4 | 0.3 | 0.2 |

**Table 4.**Results of the correlation coefficient between landscape metrics, base flow, runoff, and RLI.

PD | LPI | LSI | CONTIGMN | ENNMN | IJI | DIVISION | SPLIT | SHDI | MSIDI | AI | Base flow | Runoff | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|

PD | 1.00 | ||||||||||||

LPI | 0.99 | 1.00 | |||||||||||

LSI | 0.98 | 0.97 | 1.00 | ||||||||||

CONTIGMN | 0.99 | 0.99 | 0.98 | 1.00 | |||||||||

ENNMN | 0.99 | 0.99 | 0.99 | 0.99 | 1.00 | ||||||||

IJI | 0.99 | 1.00 | 0.98 | 1.00 | 1.00 | 1.00 | |||||||

DIVISION | 0.99 | 0.99 | 0.99 | 0.99 | 0.99 | 0.99 | 1.00 | ||||||

SPLIT | 0.98 | 0.97 | 1.00 | 0.98 | 0.99 | 0.98 | 0.99 | 1.00 | |||||

MSIDI | 0.99 | 0.98 | 0.99 | 0.99 | 0.99 | 0.99 | 1.00 | 0.99 | 1.00 | ||||

SHEI | 1.00 | 0.99 | 0.98 | 1.00 | 1.00 | 1.00 | 1.00 | 0.99 | 1.00 | 1.00 | |||

AI | 0.99 | 1.00 | 0.98 | 1.00 | 1.00 | 1.00 | 1.00 | 0.99 | 0.99 | 1.00 | 1.00 | ||

Base flow | 0.93 | 0.93 | 0.98 | 0.94 | 0.95 | 0.94 | 0.95 | 0.98 | 0.95 | 0.94 | 0.94 | 1.00 | |

Runoff | 0.93 | 0.92 | 0.98 | 0.93 | 0.94 | 0.93 | 0.95 | 0.97 | 0.94 | 0.94 | 0.93 | 1.00 | 1.00 |

RLI | −0.36 | −0.58 | 0.94 | −0.26 | 0.16 | −0.21 | 0.53 | 0.88 | 0.42 | −0.01 | 0.06 | 0.89 | 0.94 |

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## Share and Cite

**MDPI and ACS Style**

Rasoulzadeh, A.; Mostafazadeh, R.; Mobaser, J.A.; Alaei, N.; Hazbavi, Z.; Kisi, O.
Quantifying Landscape Pattern–Hydrological Process Linkage in Northwest Iran. *Atmosphere* **2023**, *14*, 1814.
https://doi.org/10.3390/atmos14121814

**AMA Style**

Rasoulzadeh A, Mostafazadeh R, Mobaser JA, Alaei N, Hazbavi Z, Kisi O.
Quantifying Landscape Pattern–Hydrological Process Linkage in Northwest Iran. *Atmosphere*. 2023; 14(12):1814.
https://doi.org/10.3390/atmos14121814

**Chicago/Turabian Style**

Rasoulzadeh, Ali, Raoof Mostafazadeh, Javanshir Azizi Mobaser, Nazila Alaei, Zeinab Hazbavi, and Ozgur Kisi.
2023. "Quantifying Landscape Pattern–Hydrological Process Linkage in Northwest Iran" *Atmosphere* 14, no. 12: 1814.
https://doi.org/10.3390/atmos14121814