# Assessment of Streamflow Simulation for a Tropical Forested Catchment Using Dynamic TOPMODEL—Dynamic fluxEs and ConnectIvity for Predictions of HydRology (DECIPHeR) Framework and Generalized Likelihood Uncertainty Estimation (GLUE)

^{1}

^{2}

^{3}

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^{†}

## Abstract

**:**

## 1. Introduction

^{2}) Peninsular Malaysia, is a large catchment with a tendency to experience extreme flooding [34] and significant land use change. Recent flood events were in 2014 and 2017 [35,36]. The highest water level recorded in the Kelantan catchment was in 2014, which highlights the important work of hydrological research [34]. A land use classification study for 20 years, from 1994 to 2014, revealed a 13.7% decrease in forest land and a 6.2% increase in oil palm plantations [37] in the catchment. These spurred many hydrological and modeling studies to be conducted in the area [38,39]. Some modeling studies on the Kelantan catchment adopted semidistributed approaches like HEC-HMS in modeling runoff using the Soil Conservation Service Curve Number (SCS-CN) method [39,40,41]. There have been studies that reported good model performance using the method [37,39]. Other methods such as the Integrated Flood Analysis System (IFAS) [38] have also been applied. However, the CN method, which imposes an empirical approach, has been developed based on humid rain-fed agricultural areas and is noted to performing poorly for a forested catchment [42]. Other model applications in the Kelantan catchment are either lumped conceptual models [43,44] where the performance varied with poor validation (R2 < 0.5), or based on machine learning approaches [45], where the performance is better (NSE > 0.9). Unlike in the lumped model where the processes were not made explicit, the machine learning is able to “learn” the surface-subsurface complexity from the data but is not spatially identified.

## 2. Materials and Methods

#### 2.1. Study Area

^{2}. The population of Kelantan based on 2015 data is 1.718 million [47] with approximately one third of the population living in the downstream area of the catchment [34], which is the most flood-prone.

#### 2.2. Input Data

#### 2.3. The Concept and the Framework of the Rainfall Runoff Model

#### 2.4. GLUE Analysis

## 3. Results and Discussion

^{2}catchment based on the given output station at Jam Guillemard Bridge. It returns 2415 HRUs which is an area ranging from 0.0009 Km

^{2}to 56.50 Km

^{2}, classified based on:

- Topographic classifier: three slope classes, three area classes, and five elevation classes
- 10 Rainfall grid classes—gauges data gridded after Thiessen polygon applied
- 29 Potential evapotranspiration grid classes

#### 3.1. Streamflow Simulation and Model Performance

^{3}/s. A study of flood risk in the Kelantan catchment between 1992 to 2014 showed that the maximum discharge at the Guillemard station is 12,900 m

^{3}/s [70]. That is, should the missing record of the peak value approximate 12,900 m

^{3}/s the simulation does indeed come close to representing the peak. Referring to the segmented simulation presented in Figure 4, the model prediction returning $NSE$ below 0.5 are for peak events 5 and 7, Figure 4c,e, while the better simulations returning $NSE$ above 0.5 are Peak 1, 3, 6, 8 as presented in Figure 4a,b,d,f. Simulation (c) fails to predict the magnitude of the two highest peaks. The deviation before the peak is also high but the recession does fit the observed. Meanwhile, simulation (e) overpredicts the first highest peak and fails to capture the second one completely. The deviation before the peak is small but the recession is simulated more quickly than the observed. In both events, the performance measures are similar but the indications of model underperformance are different. Hence, the reason why the model is able to predict some peaks but not others could not be inferred.

#### 3.2. Analysis of Model Parameters

## 4. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## Abbreviations

DECIPHeR | Dynamic fluxEs and ConnectIvity for Predictions of Hydrology |

DEM | Digital Eelevation Model |

DID | The Department of Irrigation and Drainage |

DTA | Digital Terrain Analysis |

FDC | Flow Duration Curve |

GLEAM | Global Land Evaporation Amsterdam Model |

GLUE | Generalized Likelihood Uncertainty Estimation |

HRU | Hydrological Response Unit |

IFAS | Integrated Flood Analysis System |

NSE | Nash Sutcliffe Efficiency |

SCS-CN | Soil Conservation Service Curve Number |

SRTM | Shuttle Radar Topography Mission |

USGS | United State Geological Survey |

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**Figure 1.**(

**a**) Kelantan River network and catchment with rainfall and streamflow stations, (

**b**) The Kelantan catchment location in Peninsular Malaysia.

**Figure 2.**Flow Duration Curve plot of the 32 years observed data, simulation result of the highest rank parameter, and the GLUE uncertainty boundary.

**Figure 3.**(

**a**) The mean areal precipitation, (

**b**) The observed and the simulated streamflow of the highest rank parameter set.

**Figure 4.**The observed and the GLUE uncertainty boundary. (

**a**) Peak 1, (

**b**) Peak 3, (

**c**) Peak 5, (

**d**) Peak 6, (

**e**) Peak 7, (

**f**) Peak 8.

**Figure 5.**Scatter plot of efficiency versus behavioral parameters values (

**a**) Form of exponential decline in conductivity, (

**b**) Effective lateral saturated transmissivity, (

**c**) Maximum root zone storage, (

**d**) Channel routing velocity, (

**e**) Unsaturated zone time delay, (

**f**) Maximum effective deficit of subsurface saturated zone, for 32 years’ simulation.

**Figure 6.**Scatter plot of efficiency versus behavioral parameters values for Peak 1 (

**a**) 5000 simulations, (

**b**) 10,000 simulations.

Parameter | Description | Lower Limit | Upper Limit |
---|---|---|---|

$SZM$ [m] | Form of exponential decline in conductivity | 0.001 | 0.07 |

$ln\left({T}_{0}\right)ln$[m${}^{2}$ h${}^{-1}$] | Effective lateral saturated transmissivity | −7 | 5 |

$S{r}_{max}$ [m] | Maximum root zone storage | 0.005 | 0.15 |

$S{R}_{init}$ [m] | Initial root zone deficit | 0 | 0.01 |

${T}_{d}$ [m h${}^{-1}$] | Unsaturated zone time delay | 0.1 | 40 |

$CHV$ [m h${}^{-1}$] | Channel routing velocity | 250 | 4000 |

${S}_{max}$ [m] | Maximum effective deficit of subsurface saturated zone | 0.2 | 3 |

Peak | Simulation Range (Year-Month) | Rainfall-Runoff Ratio (Q/P) | Highest Recorded Peak (m ^{3}/s) | Numerical Goodness of Fit for the Highest Rank of Parameter Set | Measurement to Fall inside the GLUE Uncertainty Limits (%) | |||
---|---|---|---|---|---|---|---|---|

$\mathit{N}\mathit{S}\mathit{E}$ | ${\mathit{R}}^{\mathbf{2}}$ | $\mathit{R}\mathit{M}\mathit{S}\mathit{E}$ | $\mathit{P}\mathit{B}\mathit{I}\mathit{A}\mathit{S}$ | |||||

1 | 2014-06/2015-05 | 0.40 | 7613.5 | 0.68 | 0.74 | 448.18 | −21.0 | 14.52 |

2 | 2012-06/2013-03 | 0.36 | 6215.5 | 0.17 | 0.34 | 826.43 | −49.6 | NA |

3 | 2009-06/2010-05 | 0.50 | 7786.0 | 0.70 | 0.75 | 423.43 | −18.5 | 28.76 |

4 | 2007-06/2008-05 | 0.67 | 8028.4 | 0.25 | 0.62 | 638.94 | 6.4 | NA |

5 | 2001-06/2002-05 | 0.50 | 6111.8 | 0.40 | 0.64 | 392.20 | −14.1 | 13.97 |

6 | 1993-08/1994-03 | 0.57 | 8533.7 | 0.72 | 0.75 | 478.00 | −12.8 | 38.68 |

7 | 1988-07/1989-04 | 0.64 | 9775.1 | 0.32 | 0.48 | 772.83 | 30.5 | 17.10 |

8 | 1986-06/1987-05 | 0.45 | 6680.5 | 0.78 | 0.81 | 434.26 | −16.0 | 23.83 |

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**MDPI and ACS Style**

Fadhliani; Zulkafli, Z.; Yusuf, B.; Nurhidayu, S.
Assessment of Streamflow Simulation for a Tropical Forested Catchment Using Dynamic TOPMODEL—Dynamic fluxEs and ConnectIvity for Predictions of HydRology (DECIPHeR) Framework and Generalized Likelihood Uncertainty Estimation (GLUE). *Water* **2021**, *13*, 317.
https://doi.org/10.3390/w13030317

**AMA Style**

Fadhliani, Zulkafli Z, Yusuf B, Nurhidayu S.
Assessment of Streamflow Simulation for a Tropical Forested Catchment Using Dynamic TOPMODEL—Dynamic fluxEs and ConnectIvity for Predictions of HydRology (DECIPHeR) Framework and Generalized Likelihood Uncertainty Estimation (GLUE). *Water*. 2021; 13(3):317.
https://doi.org/10.3390/w13030317

**Chicago/Turabian Style**

Fadhliani, Zed Zulkafli, Badronnisa Yusuf, and Siti Nurhidayu.
2021. "Assessment of Streamflow Simulation for a Tropical Forested Catchment Using Dynamic TOPMODEL—Dynamic fluxEs and ConnectIvity for Predictions of HydRology (DECIPHeR) Framework and Generalized Likelihood Uncertainty Estimation (GLUE)" *Water* 13, no. 3: 317.
https://doi.org/10.3390/w13030317