# Analysis of Long-Term Trend of Stream Flow and Interaction Effect of Land Use and Land Cover on Water Yield by SWAT Model and Statistical Learning in Part of Urmia Lake Basin, Northwest of Iran

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## Abstract

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## 1. Introduction

## 2. Materials and Methods

#### 2.1. Study Area

^{2}(Figure 1). In particular, it covers the western part of Urmia Lake, the largest lake in Iran, which is on the threshold of complete desiccation due to decreases in its water level originating from a combination of human interference (overexploitation of water resources, dam construction, LULC change) and climatic factors (drought, climate change). There is not a consensus among researchers about the intensity of contributing factors for this phenomenon, while many studies have been conducted to address the causes of this ecosystem degradation. For instance, Fathian et al. [18] indicated that streamflow in the ULB is primarily associated with changes in temperature rather than being caused by precipitation.

^{2}[18]. There are 14 permanent rivers and several seasonal rivers in the West Azerbaijan Province, although the most important rivers that lie in the study area are Nazloo-Chai, Barandooz-Chai, Rozeh-Chai, Balanj-Chai, Shahrchay, and Gedarchay. The majority of the study area is mountainous, with elevations fluctuating between less than 1400 m and more than 2600 m. Therefore, despite its Mediterranean climate, it is quite cold at higher altitudes that contain vast areas of snow cover in the winter [20]. The average annual precipitation varies from 200 to 300 mm, the majority of which originates from snow fall, and the temperature fluctuates between −20 °C in the winter and 40 °C in the summer. In terms of groundwater resources, due to a higher level of groundwater in the western part of Lake Urmia compared to the east, north, and south, a groundwater gradient is created that leads to a recharge of the lake by an aquifer in the western part [21].

#### 2.2. Trend and Change Point Analysis

_{i}) (e.g., flowrate), can be decomposed as follows:

_{i}denotes the time. The change points are implicitly embedded in the seasonal and trend components represented by the parameters ${\mathrm{\Theta}}_{S}$ and ${\mathrm{\Theta}}_{T}$, which imply the locations and numbers of change points in the seasonal and trend part of the time series, and ${\epsilon}_{i}$ is the error (noise) term with the respective mean and unknown variance of 0 and ${\delta}^{2}$($N=(0,{\delta}^{2})$). The Bayes’ theorem is employed to find the unknown parameters $M=\left\{{\mathrm{\Theta}}_{T},{\mathrm{\Theta}}_{S},{\delta}^{2}\right\}$ as a posterior probability distribution simulated by Markov Chain Monte Carlo (MCMC) sampling as follows [5]:

#### 2.3. Hydrologic Simulation by SWAT Model

^{2}) and Nash–Sutcliffe (NS).

^{2}varies between 0 and 1, with values close to 1 implying perfect model prediction. Following each calibration run, the sensitivity of the involved parameters was inspected in terms of the t-statistic and p-values using a global sensitivity analysis. Additionally, the uncertainty of the SWAT model’s simulations can be assessed by the values of the p-factor and r-factor. The p-factor indicates the percentage of measured data bracketed by the 95% prediction boundary (e.g., 95 PPU, which is obtained by applying a Latin hypercube sampling method in order to produce the final cumulative distribution of the model outputs). In contrast, the r-factor denotes the average width of the 95 PPU band divided by the standard deviation of the observed variable [40]. Both of the latest performance metrics vary between 0 and 1, with values closer to 1 implying higher model performance and efficiency [41]. Once the SWAT’s performance reached a certain acceptable level, the model was validated on data from the validation period (2010–2013) by the same range of parameters obtained in the calibration period. Following successful calibration and validation of the SWAT model, the amount of WYLD for each HRU and sub-basin were extracted in order to attribute the levels of WYLD to each LULC class. These results were then aggregated for subsequent analysis.

#### 2.4. Multiple Linear Regression and Johnson–Neyman Interaction Analysis

^{2}) and root mean square error (RMSE).

_{1}given the values of x

_{2}), the two roots of the moderator that satisfy this equality can be solved by a quadratic formula as follows:

_{2}that satisfy the following equations can be obtained as:

## 3. Results and Discussion

#### 3.1. Trend Analysis of Stream Flow

^{3}/s, whereas the maximum recorded values of discharge were 60 m

^{3}/s in Babaroud and Bighaleh. Tepik resides in an upstream part of agricultural fields, while the latest stations are in the downstream part of agricultural lands, which implies the high rate of water consumption in agricultural sector. Another point worth noting is the significant decrease in water inflow since 2010, with peak values hardly ever approaching 40 m

^{3}/s for all the considered stations. Due to the fact that the Tepik and Babaroud stations had the longest and most complete discharge records, they were further processed by the BEAST’s time series decomposition for change point detection.

_{s}implies the order of a seasonal component if any periodic variation is being detected in the flow rate. The trend component and its associated probabilities were depicted in the fifth and sixth panel, whereas the last panel demonstrates the error term (${\epsilon}_{i}$) as explained in Equation (1). In this context, the most probable seasonal and trend change points have been illustrated by vertical dashed lines in the respective panels.

^{2}) and root mean square error (RMSE) of the Bayesian’s model were 0.82 and 6.34 for Tepik compared to 0.85 and 3.57 for Babaroud, which indicates the outperformance of the model fitted to Babaroud station. The probability distribution of having a change point in the trend at each respective point for Tepik demonstrated that there were two most probable change points in 1968 and 1991, with associated probabilities of 0.99 and 0.65. The other detected change points in 2000, 1987, and 1965 are less likely to be significant change points, as their respective probabilities were 0.43, 0.41 and 0.35 (Table 2).

_{T}) is expected to have taken place between 1991 and 2000 (e.g., location of probable change points highlighted with vertical dashed lines).

_{s}) in the Tepik station.

#### 3.2. Water Yield Estimation by the SWAT Model

_{2}(runoff curve number) and SOL_AWC (available water capacity of the soil layer) were the most sensitive and influential parameters on fluctuations in streamflow. The former parameter is a function of soil permeability, land use, and antecedent soil water conditions, thereby making it very important for the accurate estimation of surface runoff. This finding is consistent with the results of many other studies around the world [46,47,48]. For instance, Nossent et al. [49] performed research investigating the sensitivity of the SWAT’s major parameters using Sobel’s sensitivity analysis. It was found that the curve number (CN

_{2}) was by far the most important parameter in that about 65% of the variations in the simulated stream flow were caused by fluctuations in CN

_{2}, either directly by the variation of the parameter itself (25%) or by interactions with other parameters. Similarly, in research conducted by Kushwaha and Jain [50] in India, the CN

_{2}turned out to be the most sensitive parameter influencing water yield. More specifically, in the latest study, the CN

_{2}received the highest rank among the parameters affecting stream flow, whereas the SOL_AWC was identified as the most sensitive parameter influencing the base flow. This is also consistent with the results in our current research. Xueman et al. [51] further indicated that the CN

_{2}and SOL_AWC were not only the most sensitive parameters controlling surface runoff, but also their interaction had a major effect on the average annual runoff.

^{2}and NS are rendered in Table 3. Considering the calibration data, all of the hydrometric stations produced R

^{2}values larger than 0.5, which indicates satisfactory results. In particular, other than the Gedarchay station in the downstream part of the watershed yielding an R

^{2}of 0.57, the amounts of R

^{2}for all of the other stations were larger than 0.6. The best results were obtained for the Bighaleh and Abajalo stations, with R

^{2}values of 0.75 and 0.68, respectively. The spatial locations of these stations can be found in Figure 1. The latest performance metric denotes the proportion of variation explained by the model. For instance, regarding the Bighaleh and Abajalo stations, 75% and 68% of the variation in calibration time series could be explained by the SWAT model. The validation time series implies the generalization ability of the model for other hydrometeorological conditions. Therefore, a model with high performance for both calibration and validation data can be applied for the prediction of future conditions. In this case, other than the Tepik station yielding a lower R

^{2}(0.49), the amount of R

^{2}for all of the stations during the validation period were higher than 0.6, and the best results were obtained for Bighaleh, Hashem Abad, and Dizaj, with R

^{2}values of 0.73, 0.73 and 0.72, respectively.

#### 3.3. Main and Interaction Effects of LULCs on Water Yield

^{2}and RMSE, both RR and ENR produced very similar results for the training and validation data set. In particular, the R

^{2}values associated with the latest methods were 0.20 and 0.27 for the training and validation part. In other words, these models were able to explain 20 and 27 percent of the variance of WYLD within the extent of the study area by only accounting for the dominant land use and land covers (LULCs), along with the elevation and index of land use intensification (La). It should be noted that we have not considered some more important parameters, including climatic variables such as precipitation and temperature, in our calculations. In spite of the fact that fluctuations in climate variables and the LULC are the primary drivers of water yield, the intensity of effect for climatic parameters (especially rainfall and evapotranspiration) on WYLD seems to be more significant than that of the LULC [57], which can justify the low values of R

^{2}produced in this part of the research.

^{2}values of 0.18 and 0.22 compared to the RMSE of 22.05 and 21.69, which indicated that the higher accuracy of model for validation data in comparison with the training set was still the case. In order to further investigate the influence of each LULC class, together with the elevation and La index, on WYLD at sub-basin level, the trend of the lambda parameter (in log scale) was investigated against the coefficients of the ridge regression as one of the best performed models (Figure 5). It should be noted that, for better interpretation, only the main effect of the predictors was investigated in these regression models.

## 4. Conclusions

^{2}values of larger than 0.8 for both stations. The date of identified change points implied that at least part of the Urmia Lake water level decrease can be attributed to declines in surface water inflow rates respective to rivers located in the ULB. In addition to the uncertainty of predictions reflected in the 95% credible intervals, the probability of each change point was also specified, which is regarded as an additional advantage of BEAST algorithm. In another part of the study, water yield, as one of the primary hydrologic aspects of ecosystem services, was estimated in each sub-basin in the study area by the SWAT model. For this purpose, the model parameters were calibrated (1981–2009) and validated (2010–2013) for the stream flow records in six hydrometric stations. The results of global sensitivity analysis indicated that CN

_{2}and SOL_AWC were the most influential parameters on fluctuations in stream flow within the study area.

^{2}and NS values of larger than 0.5 and 0.36, respectively. Therefore, it was concluded that the SWAT model succeeded in simulating the dominant hydrological processes within the extent of the study area. The main statistical effects of LULC, elevation, and index of land use intensification (La) on water yield were inspected by three linear regression models against the problem of collinearity, which was dominant between explanatory variables based on the results of the variance inflation factor (VIF). The respective linear regression models consisted of ridge regression (RR), lasso regression (LR), and elastic net regression (ENR). The RR and ENR models exhibited very similar results and slightly better performance than that of the LR, with R

^{2}values of 0.20 and more than 0.25 versus RMSE values of 20 and 19 for the training and validation data, respectively. It was found that elevation and rangeland had a strong predictive power for WYLD, whereas UTRN and URML exhibited a strong negative effect on WYLD. The intensity of the main effects of the other explanatory variables on WYLD was lower.

## Supplementary Materials

## Author Contributions

## Funding

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

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**Figure 4.**Simulation of stream flow by SWAT model for calibration and validation periods, including the observed, upper 95 PPU (U95 PPU), and lower 95 PPU (L95 PPU).

**Figure 5.**Trend of ridge coefficients against log lambda for different predictors. SWRN (soil), RNGE (rangeland), AGRR (agricultural lands), UTRN (urban areas), URML (roads), Elev (elevations), La (index of land-use intensification).

Parameter Types | Description | Unit | Min | Max | Fitted Value |
---|---|---|---|---|---|

Surface flow parameters | |||||

r__SOL_K | Saturated hydraulic conductivity | mm/h | −0.8 | 0.8 | varied over watershed |

r__SOL_BD | Moist bulk density | g/cm^{3} | −0.3 | 0.3 | varied over watershed |

r__SOL_AWC | Available water capacity of soil top layer | mm H_{2}O/mm soil | 0 | 3 | varied over watershed |

r__HRU_SLP | Average slope steepness | m/m | −0.5 | 3 | varied over watershed |

r__OV_N | Manning’s “n” value for overland flow | - | −0.5 | 3 | varied over watershed |

r__SLSUBBSN | Average slope length | m | −0.2 | 0.2 | varied over watershed |

r__CN2 | Initial SCS runoff curve number for moisture condition II | - | −0.3 | 0.3 | varied over watershed |

v__ESCO | Soil evaporation compensation factor | - | 0 | 1 | varied over watershed |

Groundwater flow parameters | |||||

v__GWQMN | Threshold depth of water in shallow aquifer required for return flow to occur | mm | 500 | 5000 | varied over watershed |

v__GW_REVAP | Groundwater “revap” coefficient | - | 0.02 | 0.2 | varied over watershed |

v__REVAPMN | Threshold depth of water in shallow aquifer required for percolation to deep aquifer to occur | mm | 0 | 500 | varied over watershed |

v__GW_DELAY | Groundwater delay time | days | 0 | 100 | varied over watershed |

v__RCHRG_DP | Deep aquifer percolation fraction | - | 0 | 0.5 | varied over watershed |

v__ALPHA_BF | Baseflow recession constant | 1/days | 0 | 0.2 | varied over watershed |

Snowmelt parameters | |||||

v__SFTMP | Snowfall temperature | °C | −5 | 5 | −0.46 |

v__SMTMP | Snowmelt base temperature | °C | −5 | 5 | 2.5 |

v__SMFMX | Maximum melt rate for snow during year | mm H_{2}O/°C-day | 0 | 5 | 3.13 |

v__SMFMN | Minimum melt rate for snow during the year | mm H_{2}O/°C-day | 0 | 5 | 0.35 |

v__TIMP | Snow pack temperature lag factor | - | 0 | 1 | 0.79 |

Elevation band parameters | |||||

v__TLAPS | Temperature lapse rate | °C/km | −8 | −4 | −5.15 |

v__PLAPS | Precipitation lapse rate | mm H_{2}O/km | 0 | 100 | 7.03 |

**Table 2.**Probable trend and seasonal change points identified by BEAST in Tepik and Babaroud stations.

Babaroud Station | Tepik Station | ||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|

trend change points | seasonal change points | trend change points | seasonal change points | ||||||||

prob(cpPr) | time(year) (cp) | #scp | prob(cpPr) | time(year) (cp) | #scp | prob(cpPr) | time(year) (cp) | #scp | prob(cpPr) | time (cp) | #scp |

0.978 | 1991.25 | 1 | 0.923 | 1987.66 | 1 | 0.99 | 1968.58 | 1 | 0.724 | 1993.3 | 1 |

0.753 | 2000.50 | 2 | 0.847 | 1950.50 | 2 | 0.65 | 1991.58 | 2 | 0.501 | 1991.2 | 2 |

0.653 | 1969.25 | 3 | 0.695 | 1990.75 | 3 | 0.43 | 2000.50 | 3 | 0.438 | 1987.8 | 3 |

0.611 | 1958.25 | 4 | 0.562 | 1966.33 | 4 | 0.41 | 1987.25 | 4 | 0.435 | 1987.1 | 4 |

0.550 | 1964.25 | 5 | 0.493 | 1969.16 | 5 | 0.35 | 1965.25 | 5 | 0.355 | 1954.5 | 5 |

0.453 | 1992.58 | 6 | 0.331 | 1966.3 | 6 | ||||||

0.440 | 2002.75 | 7 | 0.247 | 1968.8 | 7 | ||||||

0.375 | 2002.16 | 8 | 0.236 | 1990.0 | 8 | ||||||

0.341 | 1996.50 | 9 | 0.101 | 1996.6 | 9 | ||||||

0.318 | 1997.41 | 10 | 0.093 | 1961.5 | 10 |

**Table 3.**Performance metrics of SWAT model for calibration and validation data in six hydrometric stations.

Validation | Calibration | Gauging Stations | ||||||
---|---|---|---|---|---|---|---|---|

r-Factor | p-Factor | NS | R^{2} | r-Factor | p-Factor | NS | R^{2} | |

0.46 | 0.49 | 0.55 | 0.62 | 0.63 | 0.64 | 0.68 | 0.68 | Abajalo |

0.61 | 0.27 | 0.37 | 0.49 | 0.76 | 0.30 | 0.49 | 0.65 | Tepik |

0.47 | 0.29 | 0.61 | 0.72 | 0.51 | 0.41 | 0.66 | 0.67 | Dizaj |

0.34 | 0.40 | 0.70 | 0.73 | 0.44 | 0.51 | 0.53 | 0.64 | Hashem Abad |

1.13 | 0.39 | 0.52 | 0.66 | 1.02 | 0.46 | 0.47 | 0.57 | Gedarchay |

0.72 | 0.50 | 0.71 | 0.73 | 0.81 | 0.57 | 0.74 | 0.75 | Bighaleh |

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## Share and Cite

**MDPI and ACS Style**

Sakizadeh, M.; Milewski, A.; Sattari, M.T.
Analysis of Long-Term Trend of Stream Flow and Interaction Effect of Land Use and Land Cover on Water Yield by SWAT Model and Statistical Learning in Part of Urmia Lake Basin, Northwest of Iran. *Water* **2023**, *15*, 690.
https://doi.org/10.3390/w15040690

**AMA Style**

Sakizadeh M, Milewski A, Sattari MT.
Analysis of Long-Term Trend of Stream Flow and Interaction Effect of Land Use and Land Cover on Water Yield by SWAT Model and Statistical Learning in Part of Urmia Lake Basin, Northwest of Iran. *Water*. 2023; 15(4):690.
https://doi.org/10.3390/w15040690

**Chicago/Turabian Style**

Sakizadeh, Mohamad, Adam Milewski, and Mohammad Taghi Sattari.
2023. "Analysis of Long-Term Trend of Stream Flow and Interaction Effect of Land Use and Land Cover on Water Yield by SWAT Model and Statistical Learning in Part of Urmia Lake Basin, Northwest of Iran" *Water* 15, no. 4: 690.
https://doi.org/10.3390/w15040690