# Research on the Features of Rainfall Regime and Its Influence on Surface Runoff and Soil Erosion in the Small Watershed, the Lower Yellow River

^{1}

^{2}

^{3}

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^{†}

## Abstract

**:**

_{30}) to analyze the degree of influence of rainfall features on surface runoff and erosion, and the results show that precipitation is the main influencing factor affecting the variation in surface runoff, and the maximum 30 min rainfall intensity is the main factor impacting the variation in sediment yield. The results can provide a theoretical basis for soil conservation, hydrological forecasting, and non-point source pollution management.

## 1. Introduction

_{30}) defined by the Universal Soil Loss Equation is commonly used to calculate rainfall erosivity for an individual rainfall event, which is equal to the product of the total kinetic energy of a rainfall (E, MJ·h m

^{−2}) and maximum 30 min rainfall intensity (I

_{30}, mm·h

^{−1}) during the rainfall. A study by Tu et al. [24] in Jiangxi province, China, found a power function relationship between the rainfall erosion indicator (EI

_{30}) and precipitation (P) [24] and a primary function relationship between the rainfall erosion indicator (EI

_{30}) and sediment yield (S) [25]. Liu et al. [26] found that soil erosion intensity and rainfall duration (D) were power functions on bare slopes and exponential functions on vegetated slopes in the Qinghai–Tibet Plateau region of China. Deng et al. [27] conducted a simulated rainfall experiment at the Agricultural Science Experiment Station of Zhejiang University; the results showed that the runoff (W) and sediment yield (S) increase with the increase in rainfall intensity (I) on different slopes, and the relationship can be expressed as a power function.

_{30}), and to analyze the effects of different rainfall regimes on the runoff and sediment yield. The results show that Rainfall Regime II, with high rain intensity, short duration, and high-frequency characteristics, is most likely to produce runoff and sediment in the watershed. Fang et al. [30] conducted the same study in the Three Gorges area of China and found that Rainfall Regime II (which has the largest precipitation (P) and rainfall duration (D)) has the largest mean runoff coefficient and mean sediment yield. Yan et al. [31], in the Molisol region of northeast China, found that Rainfall Regime I (characterized by maximum precipitation (P) and maximum 30 min rainfall intensity (I

_{30})) had the highest soil loss rate. The results of Peng and Wang [22], using the hierarchical clustering method for karst landscape areas in Guizhou Province, China, are consistent with Yan et al. [31]; they both found that rainfall regimes with high-intensity features caused the most surface runoff and soil erosion and were the most destructive rainfall regimes in the region. In the Iguatú watershed in Ceará State, Brazil, dos Santos et al. [32] used a hierarchical clustering method based on three indicators: precipitation (P), rainfall duration (D), and maximum 30 min rainfall intensity (I

_{30}), and it was found that the precipitation (P) and maximum 30 min rainfall intensity (I

_{30}) of Rainfall Regime I in this basin were the largest, the rainfall duration (D) was the longest, and its occurrence frequency was the smallest but most likely to cause runoff and sediment yield, so this rainfall regime is most concerning. In summary, the rainfall characteristics of different regions are different, and there are differences in the main rainfall characteristics that cause the production of runoff and sediment, while the analysis methods of rainfall regimes mostly adopt the K-mean clustering method or hierarchical clustering method but lack the rationality test of rainfall regimes’ classification; moreover, the relationship between several rainfall characteristics, such as precipitation (P), rainfall duration (D), rainfall intensity (I), maximum 30 min rainfall intensity (I

_{30}), and the response of runoff and sediment yield, is unclear.

_{30}) and runoff and sediment yield are established to quantify the relationship between rainfall characteristics indicators (P, D, I, and I

_{30}), and runoff and sediment yield are established using multiple linear regression and path analysis in order to provide a theoretical foundation for regional non-point source pollution control and soil and water conservation.

## 2. Materials and Methods

#### 2.1. Study Area

^{2}), which is located in the Low Foothill Area of South Central Shandong Province, is part of to the Dawen River basin, and the first-level basin is the Yellow River basin (Figure 1). The cumulative length of the dry ditch in the basin is 1.26 km, with an average ratio of 0.14; the cumulative length of main branch ditch is 4.09 km, with an average ratio of 0.18; and the average gully density is 5.82 km/km

^{2}. A runoff observatory has been set up at the outlet of the watershed. The annual runoff of the basin is 2.189 × 10

^{5}m

^{3}, and the annual sediment yield is 9.582 × 10

^{4}kg.

#### 2.2. Rainfall Data Monitoring

^{−1}), and maximum 30 min rainfall intensity (I

_{30}, mm·h

^{−1}) were collated for each rainfall event. The definitions of the four rainfall indicators are listed below:

^{−1}.

_{30}, mm), divided by 0.5 h; the value obtained is the maximum 30 min rainfall intensity (I

_{30}, mm·h

^{−1}).

#### 2.3. Runoff and Sediment Yield Data Monitoring

^{3}) and the sediment yield (S, kg) for each rainfall event is as follows:

^{3}), ${R}_{n-1}$ is streamflow (m

^{3}/s) recorded by the device at the beginning of the time period, ${R}_{n}$ is streamflow (m

^{3}/s) recorded by the device at the end of the time period, $d$ is the number of periods of this rainfall, and ${t}_{n}$ is the duration of the time period(s).

#### 2.4. Statistical Analysis

#### 2.4.1. Principle of the Elbow Rule

#### 2.4.2. Path Analysis

## 3. Results

#### 3.1. Rainfall Status Statistics of the Study Area Watersheds from 2021 to 2022

#### 3.2. Clustering Analysis of Precipitation Events in Culai Mountain Watershed from 2021 to 2022

#### 3.2.1. Precipitation–Runoff–Sediment Yield Correlation Analysis

^{−1}), maximum 30 min rainfall intensity (I

_{30}, mm·h

^{−1}), runoff (W, m

^{3}), and sediment yield (S, kg) for the 11 rainfall events with runoff and sediment production occurring in 2021–2022. The results showed that there was a significant positive correlation (p < 0.01) between P and W, S, and P was significantly positively correlated with D at the p < 0.05 level; D was significantly positively correlated with S at the p < 0.05 level; and I

_{30}was significantly positive correlated with S at the p < 0.01 level and with W at the p < 0.05 level (Table 2). Three indicators, P, D, and I

_{30}, were more closely related to W and S, while the correlation of I on W and S was slightly lower compared to the other three indicators.

_{30}were selected, and different clustering methods were used to cluster the 59 rainfall events that occurred in the Culai Mountain watershed from 2021 to 2022.

#### 3.2.2. Clustering Analysis of Rainfall Events

- Elbow rule

- 2.
- K-means clustering method

_{30}were used as classification indicators and classified by K-means clustering, and Table 3 displays the results of the classification.

- 3.
- Systematic clustering method

#### 3.2.3. Reasonableness Analysis of Clustering Results

_{30}was greater than 1.96 (p > 0.05). Accordingly, the D and I

_{30}of Rainfall Regime I can be judged as not obeying the normal distribution, while the rest of the indicators are in line with the normal distribution (Table 4).

_{30}obeyed both normal distribution and variance chi-square condition, and One-Way ANOVA and Bonferroni method were used for analysis.

_{30}(Table 5). However, whether P, D, and I

_{30}differ from each other under the three rainfall regimes needs to be determined via post hoc comparison.

_{30}of the three rain regimes were significantly different from each other (p < 0.05). There was a significant difference (p < 0.05) in the D between Rainfall Regime I, II, and Rainfall Regime III, but the difference in the rainfall duration (D) between Rainfall Regime II and III was not significant (p > 0.05).

#### 3.2.4. Analysis of the Rainfall Regime Features of the Culai Mountain Watershed

_{30}are Rainfall Regime III > Rainfall Regime II > Rainfall Regime I, from largest to smallest. Therefore, the features of Rainfall Regime I are small amounts of precipitation with a short duration and low intensity, the features of Rainfall Regime II are medium amounts of precipitation with medium duration and intensity, and the features of Rainfall Regime III are heavy precipitation with a long duration and high intensity.

#### 3.3. Investigation of the Relationship between Rainfall Indicators and the Response of Runoff and Sediment Yield

#### 3.3.1. Equation Fitting of Four Indicators to the Runoff and Sediment Yield

_{30}, and W were a linear function (Figure 7a,d); D was a quadratic square related to W (Figure 7b); I is related to W as a power function (Figure 7c). In the fitting equation of each rainfall characteristic indicator to S, P, D, I

_{30}, and S are all quadratic functions (Figure 7e,f,h); I is related to S as a power function (Figure 7g).

#### 3.3.2. Multiple Linear Regression Analysis and Path Analysis

_{1}), rainfall duration (X

_{2}), rainfall intensity (X

_{3}), and maximum 30 min rainfall intensity (X

_{4}).

_{1}, X

_{2}, X

_{3}, and X

_{4}, on Y

_{1}and Y

_{2}showed test values of 2.255 and 2.461, respectively, indicating that the data satisfy the independence condition. The residuals basically conformed to the normal distribution, and the distribution was unordered random distribution, which satisfied the requirement of variance chi-squared and met the requirement of regression analysis. Therefore, multiple linear regression analysis was used to explore the relationship between the independent variables X

_{1}, X

_{2}, X

_{3}, and X

_{4}and the dependent variables Y

_{1}and Y

_{2}. The results of multiple linear regression were subjected to F-test, and the F-statistic values were 35.122 (p < 0.01) and 50.951 (p < 0.01), which reached a highly significant level.

_{3}and X

_{4}on Y

_{1}and Y

_{2}according to the significance tests of the respective variables in the regression analysis; Based on the results of multiple linear regression analysis, the effects of X

_{1}and X

_{2}on Y

_{1}and Y

_{2}were not statistically significant (p > 0.05).

^{2}of linear regressions were 0.959 and 0.971, respectively, indicating that the four rainfall characteristic indicators could better explain the variation of Y

_{1}and Y

_{2}. The F-test results for the multiple linear regression equations of rainfall characteristics indicators and runoff and sediment yield all reached a highly significant level (p < 0.01), indicating that the regression relationships were statistically significant and allowed for a multi-factor path analysis. The results are shown in Table 7.

_{1}are shown in Table 8. The direct path coefficient of X

_{1}, X

_{2}, X

_{3}, and X

_{4}on Y

_{1}are all positive, and the sum of the indirect path coefficient of each indicator is also positive, indicating that the direct effects of the four indicators on Y

_{1}are positive, and at the same time, the effects of each indicator on the Y

_{1}through other indicators are also positive. Given the decision coefficients, it can be seen that the indicator with the greatest combined effect on Y

_{1}is X

_{1}, followed by X

_{4}, and again by X

_{3}, with the least being X

_{2}.

_{2}are shown in Table 9. The direct path coefficient of X

_{1}, X

_{2}, X

_{3}, and X

_{4}on Y

_{2}are all positive, indicating that the direct effects of the four indicators on Y

_{2}are positive. X

_{4}has the greatest direct effect on Y

_{2}, followed by X

_{3}, with X

_{2}in third place and X

_{1}in last. Given the total indirect path coefficient, it is clear that although X

_{1}has the least direct effect on Y

_{2}, its indirect effect on Y

_{2}is the greatest. Meanwhile, it is noteworthy that the sum of the indirect path coefficient of X

_{3}on Y

_{2}is −0.006, indicating that the effect of rainfall intensity X

_{3}on Y

_{2}through the remaining three indicators is weakly negative.

_{3}and X

_{4}is in the top two positions of each rainfall indicator, indicating that the direct effects of the two indicators on the runoff and sediment yield are greater. The direct effects of X

_{1}and X

_{2}on runoff and sediment yield are small, but the indirect path coefficients are generally large, and both are positive, indicating that although the direct effects of X

_{1}and X

_{2}on runoff and sediment yield are small, they still play an important role in runoff and sediment generation through other indicators.

_{1}on Y

_{1}and Y

_{2}are 0.454 (first place) and 0.407 (second place). The multiple regression analysis results showed that X

_{3}and X

_{4}had significant effects on Y

_{1}and Y

_{2}. Combining the results of multiple regression analysis and path analysis, the main rainfall indicators that affect the runoff and sediment generation in the Culai Mountain watershed are X

_{1}, X

_{3}, and X

_{4}.

#### 3.3.3. Construction of a Rainfall Characteristic Indicator–Runoff–Sediment Yield Fitting Model in Culai Mountain Watershed

_{1}), rainfall intensity (X

_{3}), and maximum 30 min rainfall intensity (X

_{4}). Using the above rainfall characteristic indicators, regression equations between precipitation, rainfall intensity, and maximum 30 min rainfall intensity and runoff and sediment yield were established as follows:

^{2}of the fitted equations were 0.945 and 0.956, respectively, indicating that the multiple linear regression model can better reflect the influence of rainfall characteristic indicators on the runoff and sediment generation in the Culai Mountain watershed.

## 4. Discussion

#### 4.1. Study on the Characteristics of Rainfall Regime in Culai Mountain Watershed

#### 4.2. The Effect of Rainfall Regime on the Production of Runoff and Sediment

_{30}) is the largest, which tends to make the soil form surface crusts and seals, reducing the infiltration of rainwater into the soil and increasing surface runoff, which in turn increases the transport of sediment [54,55]. This result was also observed by dos Santos et al. [32] in the state of Ceará, Brazil, whose study showed that the rainfall with the greatest precipitation, the longest rainfall duration, and a greater maximum 30 min rainfall intensity occurred with the lowest frequency, but produced larger amounts of runoff and sediment, and that this regime of rainfall is of concern.

_{30}of Rainfall Regime III is the largest. When Rainfall Regime III occurs, it generates a larger amount of runoff, and the runoff affects the sediment production process, thus generating a larger sediment yield.

#### 4.3. Response of Rainfall Characteristic Indicators to the Production of Runoff and Sediment

_{30}> I > D, and the order of the magnitude of the combined influence on the production of sediment is I

_{30}> P > D > I. P was the main influencing factor for the variation of runoff, and I

_{30}was the main influencing factor for the variation in sediment yield; this result is generally consistent with the research results in several regions of China [58].

_{30}) were selected in this paper based on the analysis results of the influence of the rainfall characteristic indicators on the runoff and sediment yield, and the response relationship models of the runoff and sediment yield in the Culai Mountain watershed with rainfall characteristic indicators were established, respectively. In a similar study, Kou et al. [59] modeled the link between runoff and rainfall characteristic indicators via P, D, and I

_{30}in the Pearl River Basin of southern China’s red soil region. The response relationship between the sediment yield modulus and rainfall characteristic indicators was modeled by P, I, I

_{30}, and R (rainfall erosion force). The similarity with that paper is that both selected P and I

_{30}for the construction of the runoff and sediment yield model. The difference is that in the construction of the runoff model, the influence of D on the runoff was considered to be greater than that of I. This paper finds that the influence of I on the runoff is greater, which is caused by the differences in climatic features between different study areas.

## 5. Conclusions

- There are three regimes of rainfall in the Culai Mountain watershed. Rainfall Regime I is small rainfall with a short duration and low intensity, Rainfall Regime II is medium rainfall with medium duration and medium intensity, and Rainfall Regime III is heavy rainfall with a long duration and high intensity. The frequency of Rainfall Regime I is the highest, and the frequency of Rainfall Regime III is the lowest, but Rainfall Regime III is the main power source for the runoff and sediment yield in the Culai Mountain watershed.
- Analysis of the influence of individual rainfall characteristic indicators on the runoff and sediment yield, in which precipitation, rainfall intensity, and maximum 30 min rainfall intensity have a bigger impact on runoff and sediment production in the watershed.
- Multiple linear regression models were constructed for watershed runoff, sediment yield and precipitation, rainfall intensity, and maximum 30 min rainfall intensity, respectively, with R
^{2}of 0.945 for the runoff equation and R^{2}of 0.956 for the sediment yield equation. Analyzed from the perspective of rainfall regime characteristics, precipitation was the main factor influencing the variation in the runoff, and the maximum 30 min rainfall intensity was the main factor influencing the variation in sediment yield.

## Author Contributions

## Funding

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

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**Figure 2.**Monthly precipitation and monthly average erosive precipitation map of the Culai Mountain watershed for 2021–2022.

**Figure 4.**Systematic clustering method spectrum chart. The rainfall serial number is the order of the occurrence of 59 rainfall events, the yellow part is rainfall regime I, the green part is rainfall regime II and the blue part is rainfall regime III.

**Figure 5.**Variability between P, D, and I

_{30}(post hoc comparison results). Bars of the same color with different letters (a, b, c) indicate that the regime of rainfall they represent is significantly different for this indicator.

**Figure 7.**Relationships between different rainfall characteristic and runoff, sediment yield. (

**a**,

**e**) Relationship between precipitation and runoff, sediment yield, (

**b**,

**f**) Relationship between rainfall duration and runoff, sediment yield, (

**c**,

**g**) Relationship between rainfall intensity and runoff, sediment yield, (

**d,h**) Relationship between maximum 30 min rainfall intensity and runoff, sediment.

**Figure 8.**Proportions of cumulative precipitation, cumulative runoff, and cumulative sediment yield under different rainfall regimes.

**Table 1.**Information on the 11 rainfalls with runoff and sediment production during the study period.

Serial Number | Start Time of Rainfall | End Time of Rainfall | P (mm) | D (h) | I (mm·h^{−1}) | I_{30} (mm·h^{−1}) | W (m^{3}) | S (kg) |
---|---|---|---|---|---|---|---|---|

16 | 2021/07/08 17:38 | 2021/07/08 21:09 | 17.50 | 3.52 | 4.97 | 20.20 | 34,675.90 | 12,800.00 |

17 | 2021/07/09 03:57 | 2021/07/09 10:28 | 17.50 | 6.50 | 2.69 | 30.00 | 25,468.81 | 9830.00 |

19 | 2021/07/12 19:59 | 2021/07/13 07:57 | 18.50 | 11.97 | 1.55 | 17.00 | 16,500.63 | 6470.00 |

25 | 2021/07/28 09:25 | 2021/07/29 08:45 | 155.00 | 23.33 | 6.64 | 26.00 | 60,525.33 | 25,900.00 |

34 | 2021/08/29 14:25 | 2021/08/31 21:38 | 97.50 | 55.22 | 1.77 | 61.00 | 52,463.56 | 25,890.00 |

36 | 2021/09/04 05:15 | 2021/09/05 16:48 | 47.50 | 35.56 | 1.34 | 11.00 | 23,129.28 | 9340.00 |

37 | 2021/09/18 10:58 | 2021/09/20 08:39 | 121.50 | 45.68 | 2.66 | 44.00 | 51,132.03 | 25,160.00 |

39 | 2021/09/25 21:41 | 2021/09/27 01:56 | 75.50 | 28.25 | 2.67 | 31.00 | 40,578.31 | 18,410.00 |

51 | 2022/06/26 13:23 | 2022/06/27 21:08 | 117.10 | 31.75 | 3.69 | 63.74 | 60,175.46 | 26,530.00 |

52 | 2022/07/05 12:01 | 2022/07/06 13:05 | 82.20 | 25.08 | 3.28 | 23.36 | 45,640.04 | 14,330.00 |

56 | 2022/07/28 04:05 | 2022/07/29 01:06 | 49.10 | 21.00 | 2.34 | 34.40 | 27,561.48 | 16,980.00 |

**Table 2.**Pearson correlation coefficient matrix of rainfall characteristic indicators, runoff, and sediment yield in Culai Mountain watershed.

P | D | I | I_{30} | W | S | |
---|---|---|---|---|---|---|

P | 1 | |||||

D | 0.631 * | 1 | ||||

I | 0.442 | −0.324 | 1 | |||

I_{30} | 0.523 | 0.591 | −0.039 | 1 | ||

W | 0.910 ** | 0.525 | 0.557 | 0.660 * | 1 | |

S | 0.894 ** | 0.647 * | 0.386 | 0.786 ** | 0.925 ** | 1 |

**Table 3.**Results of K-means clustering method. The rainfall serial number is the order of the occurrence of 59 rainfall events.

Rainfall Regime | Rainfall Serial Number |
---|---|

I | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 26 27 28 29 30 31 32 33 35 38 40 41 42 43 44 45 46 49 54 58 |

II | 36 39 47 48 50 52 53 55 56 57 59 |

III | 25 34 37 51 |

Rainfall Regime | Rainfall Indicator | Skewness Coefficient (S) | Kurtosis Coefficient (K) |
---|---|---|---|

I | P | 1.867 | 1.243 |

D | 5.801 | 6.752 | |

I_{30} | 3.734 | 1.307 | |

II | P | 1.852 | 0.202 |

D | 0.149 | 1.338 | |

I_{30} | 0.076 | 0.209 | |

III | P | 0.827 | 0.665 |

D | 1.064 | 0.050 | |

I_{30} | 0.802 | 0.506 |

P | D | I_{30} | |
---|---|---|---|

Test method | Welch’s ANOVA | One-Way ANOVA | |

Statistical value | 71.481 | 22.518 | 26.998 |

Significance | <0.001 | 0.001 | <0.001 |

Rainfall Regime | Rainfall Indicator | Frequency | The of Rainfall Event with Runoff and Sediment Production | |||
---|---|---|---|---|---|---|

P (mm) | D (h) | I_{30} (mm·h^{−1}) | ||||

I | Mean | 7.386 | 4.475 | 11.165 | 44 | 3 |

V_{25} | 2.000 | 0.500 | 3.188 | |||

V_{75} | 12.000 | 6.628 | 15.383 | |||

II | Mean | 49.136 | 18.053 | 29.745 | 11 | 4 |

V_{25} | 37.000 | 8.000 | 23.360 | |||

V_{75} | 60.600 | 26.000 | 35.000 | |||

III | Mean | 122.775 | 43.233 | 48.685 | 4 | 4 |

V_{25} | 102.400 | 31.435 | 30.500 | |||

V_{75} | 146.625 | 59.548 | 63.055 |

**Table 7.**Results of the multiple linear regression analysis of runoff and sediment yield in Culai Mountain watershed.

Rainfall Indicator | VIF | Runoff (Y_{1}) | Sediment Yield (Y_{2}) | ||||
---|---|---|---|---|---|---|---|

Regression Coefficient | Standardized Coefficients | Significance (p) | Regression Coefficient | Standardized Coefficients | Significance (p) | ||

Precipitation (X_{1}) | 7.566 | 97.557 | 0.298 | 0.238 | 42.713 | 0.268 | 0.208 |

Rainfall duration (X_{2}) | 6.921 | 301.554 | 0.309 | 0.205 | 156.681 | 0.330 | 0.119 |

Rainfall intensity (X_{3}) | 4.995 | 5253.313 | 0.539 | 0.027 | 1864.568 | 0.393 | 0.044 |

Maximum 30 min rainfall intensity (X_{4}) | 1.633 | 308.865 | 0.343 | 0.018 | 204.732 | 0.467 | 0.002 |

Constant value | −1378.781 | −2207.69 | |||||

R^{2} | 0.959 | 0.971 |

**Table 8.**Results of the path analysis of the influence of each rainfall characteristic indicator on the runoff (Y

_{1}) in Culai Mountain watershed.

Rainfall Indicator | Correlation Coefficients | Direct Path Coefficient | Indirect Path Coefficient | Decision Coefficient | ||||
---|---|---|---|---|---|---|---|---|

X_{1}-Y_{1} | X_{2}-Y_{1} | X_{3}-Y_{1} | X_{4}-Y_{1} | Total | ||||

X_{1} | 0.910 ** | 0.298 | 0.094 | −0.271 | 0.356 | 0.179 | 0.454 | |

X_{2} | 0.525 | 0.309 | 0.194 | 0.198 | 0.403 | 0.795 | 0.229 | |

X_{3} | 0.557 | 0.539 | 0.136 | −0.048 | −0.027 | 0.061 | 0.310 | |

X_{4} | 0.660 * | 0.343 | 0.161 | 0.088 | 0.024 | 0.273 | 0.335 |

**Table 9.**Results of the path analysis of the influence of each rainfall characteristic indicator on the sediment yield (Y

_{2}) in Culai Mountain watershed.

Rainfall Indicator | Correlation Coefficients | Direct Path Coefficient | Indirect Path Coefficient | Decision Coefficient | ||||
---|---|---|---|---|---|---|---|---|

X_{1}-Y_{2} | X_{2}-Y_{2} | X_{3}-Y_{2} | X_{4}-Y_{2} | Total | ||||

X_{1} | 0.894 ** | 0.268 | 0.208 | 0.174 | 0.244 | 0.626 | 0.407 | |

X_{2} | 0.647 * | 0.330 | 0.169 | −0.127 | 0.276 | 0.318 | 0.318 | |

X_{3} | 0.386 | 0.393 | 0.119 | −0.107 | −0.018 | −0.006 | 0.149 | |

X_{4} | 0.786 ** | 0.467 | 0.140 | 0.195 | −0.015 | 0.320 | 0.516 |

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## Share and Cite

**MDPI and ACS Style**

Zhao, L.; Zhang, Z.; Dong, F.; Fu, Y.; Hou, L.; Liu, J.; Wang, Y.
Research on the Features of Rainfall Regime and Its Influence on Surface Runoff and Soil Erosion in the Small Watershed, the Lower Yellow River. *Water* **2023**, *15*, 2651.
https://doi.org/10.3390/w15142651

**AMA Style**

Zhao L, Zhang Z, Dong F, Fu Y, Hou L, Liu J, Wang Y.
Research on the Features of Rainfall Regime and Its Influence on Surface Runoff and Soil Erosion in the Small Watershed, the Lower Yellow River. *Water*. 2023; 15(14):2651.
https://doi.org/10.3390/w15142651

**Chicago/Turabian Style**

Zhao, Long, Zhe Zhang, Fei Dong, Yicheng Fu, Lei Hou, Jingqiang Liu, and Yibing Wang.
2023. "Research on the Features of Rainfall Regime and Its Influence on Surface Runoff and Soil Erosion in the Small Watershed, the Lower Yellow River" *Water* 15, no. 14: 2651.
https://doi.org/10.3390/w15142651