# Rainfall-Runoff Modelling Considerations to Predict Streamflow Characteristics in Ungauged Catchments and under Climate Change

^{*}

## Abstract

**:**

## 1. Introduction

- Do we need a different calibration objective function or criteria to target each specific streamflow characteristic or signature? or
- Is there a general calibration criteria that can adequately reproduce most streamflow characteristics? (Hence allowing for a consistent simulation of streamflow time series and the different streamflow characteristics using one single set of parameter values); or
- Do we need a couple of calibration criteria for groups of similar types of streamflow characteristics? and
- What are the implications when the calibrated model is then used to predict changes in the different flow characteristics under climate change?

## 2. Data and Methods

^{2}to 3000 km

^{2}(10th and 90th percentile range). As can been seen in Figure 1, apart from the arid interior (where there is very little data), the catchments cover all parts of Australia particularly the populated and important agricultural region in the south-east.

^{2.5}

#### 2.1. Model Simulation in Single Catchments

#### 2.2. Prediction in Ungauged Catchments

#### 2.3. Modelling Climate Change Impact on Streamflow Characteristics

## 3. Results

#### 3.1. Model Simulation in Single Catchments

#### 3.2. Prediction in Ungauged Catchments

#### 3.3. Modelling Climate Change Impact on Streamflow Characteristics

## 4. Discussion

#### 4.1. Hydrological Prediction in Ungauged Catchments

#### 4.2. Hydrological Prediction under Climate Change

_{2}, and changed hydrologic regime [25]. In any case, from a rainfall-runoff modelling perspective, climate impact modelling should at the very least use parameter values from model calibration against an objective criteria that specifically targets the streamflow characteristic that is being assessed.

## 5. Summary and Conclusions

## Author Contributions

## Acknowledgments

## Conflicts of Interest

## References

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**Figure 2.**Calibration and validation results for the three rainfall-runoff models showing percentage of the 780 catchments with assessment results for the four streamflow characteristics exceeding the values on the y-axis.

**Figure 3.**Prediction in ungauged catchment results for far south-east Australia for the three rainfall-runoff models from regional calibration parameters and nearest neighbour parameters, showing percentage of catchments with assessment results for the four streamflow characteristics exceeding the values on the y-axis.

**Figure 4.**Prediction in ungauged catchment results for south-east Australia for the three rainfall-runoff models from regional calibration parameters and nearest neighbour parameters, showing percentage of catchments with assessment results for the four streamflow characteristics exceeding the values on the y-axis.

**Figure 5.**Prediction in ungauged catchment results for far south-west Australia for the three rainfall-runoff models from regional calibration parameters and nearest neighbour parameters, showing percentage of catchments with assessment results for the four streamflow characteristics exceeding the values on the y-axis.

**Figure 6.**Prediction in ungauged catchment results for north-east Australia for the three rainfall-runoff models from regional calibration parameters and nearest neighbour parameters, showing percentage of catchments with assessment results for the four streamflow characteristics exceeding the values on the y-axis.

**Figure 7.**Prediction in ungauged catchment results for northern Australia for the three rainfall-runoff models from regional calibration parameters and nearest neighbour parameters, showing percentage of catchments with assessment results for the four streamflow characteristics exceeding the values on the y-axis.

**Figure 8.**GR4J modelled change in mean annual streamflow, high flow days (number of days with streamflow above the 95% percentile daily streamflow) and low flow days (number of days with streamflow below the 5th percentile daily streamflow) for a 10% change in rainfall from modelling with Nash–Sutcliffe efficiency (NSE)-Daily-Bias, NSE-High and NSE-Low parameter values. The bars show the median, 25th and 75th percentiles, and 10th and 90th percentiles, percentage change from the catchments in each of the five regions.

**Figure 9.**SIMHYD modelled change in mean annual streamflow, high flow days (number of days with streamflow above the 95% percentile daily streamflow) and low flow days (number of days with streamflow below the 5th percentile daily streamflow) for a 10% change in rainfall from modelling with NSE-Daily-Bias, NSE-High and NSE-Low parameter values. The bars show the median, 25th and 75th percentiles, and 10th and 90th percentiles, percentage change from the catchments in each of the five regions.

**Figure 10.**Xinanjiang modelled change in mean annual streamflow, high flow days (number of days with streamflow above the 95% percentile daily streamflow) and low flow days (number of days with streamflow below the 5th percentile daily streamflow) for a 10% change in rainfall from modelling with NSE-Daily-Bias, NSE-High and NSE-Low parameter values. The bars show the median, 25th and 75th percentiles, and 10th and 90th percentiles, percentage change from the catchments in each of the five regions.

**Figure 11.**Projected percentage change in mean annual rainfall, potential evapotranspiration (PET) and runoff (median and the 10th and 90th percentile values from hydrological modelling informed by projections from the 42 Coupled Model Intercomparison Project Phase 5 (CMIP5) global climate models) for Representative greenhouse gas Concentration Pathway (RCP) 8.5 for 2046–2075 relative to 1976–2005 (adapted from [31]).

Entire Dataset | Far South-East Australia | South-East Australia | South-West Australia | North-East Australia | Northern Australia | |
---|---|---|---|---|---|---|

Number of Catchments | 780 | 286 | 161 | 125 | 82 | 105 |

Catchment Area (km^{2}) | 373 * (85–3415) * | 258 (83–909) | 387 (89–2532) | 474 (72–6442) | 469 (102–2303) | 1658 (265–11143) |

Mean Annual Rainfall (mm) | 867 (557–1420) | 913 (633–1382) | 847 (671–1350) | 698 (326–981) | 1040 (707–2020) | 1087 (644–1544) |

Mean Annual Streamflow (mm) | 124 (21–534) | 172 (38–566) | 99 (24–385) | 37 (5–160) | 216 (34–1021) | 192 (72–560) |

Mean Annual Streamflow (×10^{6} m^{3}) (GL) | 46 (7–437) | 39 (9–236) | 42 (10–211) | 20 (3–99) | 103 (248–544) | 380 (64–2054) |

Runoff Coefficient | 0.14 (0.04–0.38) | 0.18 (0.06–0.42) | 0.12 (0.04–0.29) | 0.06 (0.01–0.16) | 0.19 (0.05–0.55) | 0.21 (0.10–0.39) |

95th Percentile Daily Streamflow (mm) | 1.20 (0.14–5.50) | 1.59 (0.41–5.50) | 0.87 (0.16–3.32) | 0.43 (0.02–1.94) | 1.64 (0.20–10.49) | 2.74 (0.70–7.80) |

95th Percentile Daily Streamflow (×10^{3} m^{3}) (ML) | 419 (64–4313) | 394 (85–2115) | 347 (68–1816) | 169 (14–1189) | 697 (144–4511) | 4936 (637–30070) |

5th Percentile Daily Streamflow (×10^{3} m^{3}) (ML) ^{#} | 0.61 (0.04–35) | 2.70 (0.10–65) | 0.67 (0.04–18) | 0.16 (0.01–3.7) | 0.10 (0.04–26) | 0.63 (0.04–60) |

Number of Zero Days per Year | 10 (0–178) | 1 (0–98) | 11 (0–126) | 89 (0–286) | 19 (0–107) | 37 (0–177) |

^{#}Zero flow days are ignored in calculating the 5th percentile daily stramflow.

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**MDPI and ACS Style**

Chiew, F.H.S.; Zheng, H.; Potter, N.J.
Rainfall-Runoff Modelling Considerations to Predict Streamflow Characteristics in Ungauged Catchments and under Climate Change. *Water* **2018**, *10*, 1319.
https://doi.org/10.3390/w10101319

**AMA Style**

Chiew FHS, Zheng H, Potter NJ.
Rainfall-Runoff Modelling Considerations to Predict Streamflow Characteristics in Ungauged Catchments and under Climate Change. *Water*. 2018; 10(10):1319.
https://doi.org/10.3390/w10101319

**Chicago/Turabian Style**

Chiew, Francis H.S., Hongxing Zheng, and Nicholas J. Potter.
2018. "Rainfall-Runoff Modelling Considerations to Predict Streamflow Characteristics in Ungauged Catchments and under Climate Change" *Water* 10, no. 10: 1319.
https://doi.org/10.3390/w10101319