# Impact of Spatial Rainfall Scenarios on River Basin Runoff Simulation a Nan River Basin Study Using the Rainfall-Runoff-Inundation Model

## Abstract

**:**

^{2}watershed, namely the Nan River Basin in Thailand. This study utilized data from the 2014 storm event, incorporating temporal information from 28 rainfall stations to estimate rainfall in the spatial distribution scenarios. The six statistics, Volume Bias, Peak Bias, Root Mean Square Error, Correlation, and Mean Bias, were used to determine the accuracy of the estimated rainfall and runoff. Overall, the Simple kriging (SKG) method outperformed the other scenarios based on the statistical values to validate with measured rainfall data. Similarly, SKG demonstrated the closest match between simulated and observed runoff, achieving the highest correlation (0.803), the lowest Root Mean Square Error (164.48 cms), and high Nash-Sutcliffe Efficiency coefficient (0.499) values. This research underscores the practical significance of spatial interpolation methods, such as SKG, in combination with digital elevation models (DEMs) and landuse/soil type datasets, in delivering reliable runoff simulations considering the RRI model on the river basin scale.

## 1. Introduction

^{2}and evaluates the data with three hydrological models. Drawing from the work of Bell and Moore [11], they place particular emphasis on high spatial resolution rainfall data, especially in the context of convective rainfall events.

^{2}in northern Thailand and utilizes rainfall data from 2014 to interpolate spatial and temporal rainfall patterns. This analysis spans wet and dry seasons, offering insights into river basin catchment behavior. We conducted a sensitivity study, comparing different rainfall datasets as the input data for the RRI model and evaluating the simulated runoff with observed discharge hydrographs. It is significant to reveal that this study points to sensitivity analysis without calibration and assesses the effect of varying rainfall data inputs on the RRI model’s performance.

## 2. Data Sets and Methods

#### 2.1. Nan River Basin

^{2}. The main river originates in the Bor-Klua District, Nan Province, at the north of the area, between latitude 17d 42′12″ N and latitude 19d 37′48″ N and longitude 100d 06′30″ E to longitude 101d 21′48″ E. Approximately 88% of the region consists of mountainous terrain, while the remaining 12% is inhabited by residents within the watershed. Downstream from the SIRIKIT dam, which serves as the modeling river outlet, the river bed features a steep slope of approximately 1/1500. Moving upstream, the slope becomes flat (1/10,000), followed by a steep slope (1/600). The elevation in this area ranges from 70 to 1200 m above mean sea level and has a mean annual rainfall of approximately 1380 mm. The Wa River, Nam Pua River, and Nam Yao River are essential tributaries contributing to the complex hydrological system. Fluvial has been observed in the Tawang Pha, Muang Nan, and Wiang Sa areas as vulnerable areas.

#### 2.2. Rainfall-Runoff-Inundation Model

#### 2.3. Storm Events and Rainfall Observation Data

#### 2.4. Topography Data

^{2}, as shown in Figure 2a. The specific SRTM data for the study area was obtained under index No. srtm_57_09.

#### 2.5. Landuse and Soil Types

## 3. Methodology

#### 3.1. Rainfall Spatial Scenario

#### 3.1.1. Inverse Distance Weight (IDW)

#### 3.1.2. Thiessen Polygon (TSP)

#### 3.1.3. Surface Polynomial (SPL)

#### 3.1.4. Simple Kriging (SKG)

#### 3.1.5. Ordinary Kriging (OKG)

#### 3.1.6. Semi-Variogram Model

#### 3.2. Simulation Model Setup

^{2}, and $D$: the depth of river channel in m.

#### 3.3. Performance Statistics

## 4. Results and Discussion

#### 4.1. Rainfall Spatial Distribution Interpolation

#### 4.2. Runoff Data Resulted from Rainfall Spatial Distribution Interpolation

## 5. Conclusions

## Funding

## Acknowledgments

## Conflicts of Interest

## References

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**Figure 1.**Location of this study area. (

**a**) The Nan River Basin location in Thailand. (

**b**) The Nan River Basin in detail: the red dot represents the observation station for rainfall, and the yellow triangle represents the runoff observation station.

**Figure 2.**Topographical characteristics of the Nan River Basin. (

**a**) Topography; (

**b**) Flow direction; and (

**c**) Flow accumulation.

**Figure 5.**Scatter plot of rainfall data for observed and estimated distribution scenarios: (

**a**) IDW, (

**b**) TSP, (

**c**) SPL, (

**d**) SKG, and (

**e**) OKG.

**Figure 7.**Root Mean Square Error (RMSE) at observed points for each rainfall distribution scenario on rainfall estimation.

**Figure 8.**Runoff hydrograph at the runoff station for observed runoff and simulated runoff in each rainfall distribution scenario.

Landuse Type | n’s Manning |
---|---|

Forest | 0.50 |

Deforestation | 0.40 |

Grasslands | 0.30 |

Cropland | 0.35 |

Urban and Build-up | 0.05 |

Water bodies | 0.04 |

Soil Type | Soil Depth, m | Saturated Hydraulic Conductivity (ka), cm/h | Beta, ka/kc | Green-Ampt Parameter | ||
---|---|---|---|---|---|---|

Ksv, cm/h | Porosity | Capillary Head, cm | ||||

Clay | 1.0 | 0.462 | 0.06 | 0.475 | 31.63 | |

Clay loam | 1.0 | 0.882 | 0.20 | 0.464 | 20.88 | |

Loam | 1.0 | 2.500 | 1.32 | 0.463 | 8.89 | |

Sandy clay | 2.0 | 0.781 | 0.12 | 0.430 | 23.90 | |

Sandy clay loam | 1.5 | 2.272 | 0.30 | 0.398 | 21.85 | |

Sandy loam | 1.5 | 12.443 | 2.18 | 0.453 | 11.01 | |

Silty clay | 1.0 | 0.366 | 0.10 | 0.430 | 29.22 | |

Silty loam | 1.0 | 2.591 | 0.68 | 0.501 | 16.68 | |

Stone | 1.5 | - | - | - | - |

Rainfall Distribution Scenario | Rainfall Volume, MCM |
---|---|

OBS | 7900.01 |

IDW | 7045.41 |

TSP | 3012.01 |

SPL | 7578.28 |

SKG | 7787.04 |

OKG | 10,653.01 |

Distribution Scenario | Volume Bias, % | Peak Bias, % | RMSE, mm | Correlation | Mean Bias, mm | Nash-Sutcliffe |
---|---|---|---|---|---|---|

IDW | −1.84 | −18.35 | 7.66 | 0.85 | −0.24 | −0.053 |

TSP | −59.47 | −60.23 | 11.22 | 0.80 | −5.51 | −0.073 |

SPL | 0.38 | −23.66 | 7.11 | 0.88 | 0.05 | −0.047 |

SKG | −0.18 | −20.12 | 5.08 | 0.92 | −0.02 | −0.049 |

OKG | 4.39 | −12.94 | 6.86 | 0.90 | 0.46 | −0.048 |

Distribution Scenario | Volume Bias, % | Peak Bias, % | RMSE, cms | Correlation | Mean Bias, cms | Nash-Sutcliffe |
---|---|---|---|---|---|---|

IDW | 16.88 | −29.20 | 184.12 | 0.755 | 64.19 | 0.437 |

TSP | −40.59 | −59.46 | 275.05 | 0.691 | −168.02 | 0.427 |

SPL | 33.25 | −16.73 | 239.53 | 0.704 | 135.89 | 0.223 |

SKG | 6.80 | −35.80 | 164.48 | 0.803 | 27.99 | 0.499 |

OKG | 43.64 | 1.92 | 275.37 | 0.702 | 185.38 | −0.557 |

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**MDPI and ACS Style**

Pakoksung, K.
Impact of Spatial Rainfall Scenarios on River Basin Runoff Simulation a Nan River Basin Study Using the Rainfall-Runoff-Inundation Model. *Eng* **2024**, *5*, 51-69.
https://doi.org/10.3390/eng5010004

**AMA Style**

Pakoksung K.
Impact of Spatial Rainfall Scenarios on River Basin Runoff Simulation a Nan River Basin Study Using the Rainfall-Runoff-Inundation Model. *Eng*. 2024; 5(1):51-69.
https://doi.org/10.3390/eng5010004

**Chicago/Turabian Style**

Pakoksung, Kwanchai.
2024. "Impact of Spatial Rainfall Scenarios on River Basin Runoff Simulation a Nan River Basin Study Using the Rainfall-Runoff-Inundation Model" *Eng* 5, no. 1: 51-69.
https://doi.org/10.3390/eng5010004