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Gauged and ungauged catchments: strategies for producing the relevant hydrographs with sufficient accuracy WaterEurope - Workgroup 05 Yesenia Pereyra Mario Lpez Muoz Anis Boukra Leqi Tian Arthur Henriet Gyembo Tenzin Edwar Forero

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Gauged and ungauged catchments: strategies for producing the relevant hydrographs with sufficient accuracy

WaterEurope - Workgroup 05

Yesenia Pereyra Anis Boukra Arthur Henriet Edwar Forero

Supervisor: Mr.Gourbesville

Date: 15/02/2019

Mario López Muñoz Leqi Tian Gyembo Tenzin

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Outline

1. Introduction 2. What is a model? 3. How can we select the proper model for this case? 4. Calibration of Models applied on Gauged Catchment 5. Calibration of Models applied on Ungauged Catchment 6. Comparison of the results 7. Recommendations 8. References

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Outline - Introduction - Model - Classification - Selection - Results - Comparison - Recommendations - References

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  • 1. Introduction

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We must design two new bridges, which will be affected by rivers within a gauged and an ungauged catchment. We are required to create a hydrograph to know the catchments behaviour

Main objective

Outline - Introduction - Model - Selection - Results - Comparison - Recommendations - References

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Var Catchment: Location

  • f the new bridges
  • 1. Introduction

Main objective

Outline - Introduction - Model - Selection - Results - Comparison - Recommendations - References

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Gauged catchment

Ungauged catchment

Discharge measurements data about the catchment are available No data of discharge on the catchment is available

  • 1. Introduction

Key concepts: Gauged? Ungauged?

Outline - Introduction - Model - Selection - Results - Comparison - Recommendations - References

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  • 1. Introduction

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Why do we need discharge measurements data?

➔ To calibrate the model accurately to adjust it as possible to the real conditions. How can we obtain accurate hydrographs for ungauged catchments?

Outline - Introduction - Model - Selection - Results - Comparison - Recommendations - References

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  • 2. Through a Model…. What is a model?

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Representation of input, system, and output for a mathematical model

➔ A mathematical model is needed to simulate the catchment behaviour using mathematical equations, logical statements, initial and boundary conditions and expressing relationships between input and output.

Outline - Introduction - Model - Selection - Results - Comparison - Recommendations - References

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  • 3. How can we select the proper model for this case?

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Model

Data available Level accuracy Hydrological problem Computing power

➔ There is no model defined for each case, it depends of a wide numbers of variables ➔ We will analyse what kind of model is recommended to create a suitable hydrograph in ungauged catchments

Outline - Introduction - Model - Selection - Results - Comparison - Recommendations - References

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Models selected to analysis

  • MIKE SHE model
  • HEC-HMS
  • ANN (Artificial Neural Network)
  • NAM

Gauged

Var catchment

4th 17:00 to 6th 00:00 November 94

Hydrological Event studied

  • 3. How can we select the proper model for this case?

Outline - Introduction - Model - Selection - Results - Comparison - Recommendations - References

  • MIKE SHE model
  • HEC-HMS
  • ANN (Artificial Neural Network)
  • NAM

Ungauged

Vésubie

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Hydrological Models Stochastic Deterministic Lumped Semi-distributed Distributed

HEC-HMS

  • 3. How can we select the proper model for this case?

Outline - Introduction - Model - Selection - Results - Comparison - Recommendations - References

ANN NAM SHE

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Main characteristics of models selected: MIKE SHE

  • Hydrological model : Spatially distributed catchment parameters and rainfall to calculate

the discharge

  • Rectangular mesh and finite difference method for the calculation
  • Overland flow thanks to diffusive wave approximation of Saint Venant equations
  • 2D model with a 75 m resolution DEM

Parameters used :

  • Rainfall (Thiessen)
  • DEM 75m
  • Strickler number
  • Net Rainfall Fraction
  • 3. How can we select the proper model for this case?

Outline - Introduction - Model - Selection - Results - Comparison - Recommendations - References

SHE

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  • Lumped Model - spatial variations and characteristics & processes are averaged
  • Fast simulation & easy to understand
  • Requires less parameters
  • Warning: simplified assumptions about some parameters

Parameters used :

  • Gage Weight - Thiessen
  • SCS Curve Number with poor

soil conditions

  • SCS UH - Transformed Method
  • Lag Time
  • Constant monthlyflow at

Lower Var

  • 3. How can we select the proper model for this case?

Main characteristics of models selected: HEC-HMS

HEC-HMS

Outline - Introduction - Model - Selection - Results - Comparison - Recommendations - References

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  • 3. How can we select the proper model for this case?

Artificial neural networks as approximators of stochastic processes.

Outline - Introduction - Model - Selection - Results - Comparison - Recommendations - References

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NAM (Nedbør-Afstrømnings-Model)

  • precipitation-runoff-model
  • continuously accounting for the water content in four

different and mutually interrelated storages.

  • Each storage represents different physical elements of

the catchment.

  • Basic inputs

Catchment area（DEM 75m） Precipitation Evapotranspiration

  • Extend components

Temperature

  • 3. How can we select the proper model for this case?

Main characteristics of models selected: NAM

Outline - Introduction - Model - Selection - Results - Comparison - Recommendations - References

NAM is a set of linked mathematical statements describing, in a simplified quantitative form, the behavior of the land phase of the hydrological cycle.

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Results obtained with MIKE SHE on the Var Catchment

For the studied event (04/11/1994 18:00 - 06/11/1994 00:00), we calibrated the Var catchment changing some parameters (Strickler, Net Rainfall Fraction). Lack of accuracy from cross-section or rainfall data

  • 4. Calibration of Models applied on Gauged Catchment

Outline - Introduction - Model - Selection - Results - Comparison - Recommendations - References

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Results obtained with MIKE SHE on the Vésubie

We assume that Vesubie and Lower Var subcatchments presents the same physical parameters. For the studied event (04/11/1994 18:00 - 06/11/1994 00:00), and in order to have more accurate results, we applied a scale effect (0.14) on the Vésubie, using the Var values to obtain the following Hydrograph.

  • 5. Calibration of Models applied on Ungauged Catchment

Outline - Introduction - Model - Selection - Results - Comparison - Recommendations - References

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Results obtained with ANN on the Var Catchment

  • 4. Calibration of Models applied on Gauged Catchment

Outline - Introduction - Model - Selection - Results - Comparison - Recommendations - References

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Results obtained with ANN on the Vésubie

  • 4. Calibration of Models applied on Gauged Catchment

Outline - Introduction - Model - Selection - Results - Comparison - Recommendations - References

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  • 6. Comparison of the results

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Results obtained on the Vésubie (ungauged)

Outline - Introduction - Model - Selection - Results - Comparison - Recommendations - References

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  • 6. Comparison of the results

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Results obtained on the Lower Var (gauged)

Outline - Introduction - Model - Selection - Results - Comparison - Recommendations - References

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  • 7. Recommendations

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For this project, to determine the behaviour of the rivers discharges, we will work with MIKE SHE, a deterministic model. Notwithstanding, it could demand more computational power, but we will produce more accurate results. It’s the more solid model, which take into account several hydrological and physical parameters. MIKE SHE HEC-HMS ANN NAM

Advantages

High detail Flexibility on the watershed configuration Quick Easy implementation when is adopted Quick

Disadvantages

Long simulation Accuracy depending on the data quality Simplified model Non hydrological parameters - Less accuracy Low detail Applying it is not feasible

Outline - Introduction - Model - Selection - Results - Comparison - Recommendations - References

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  • 8. References

22 [1]

  • M. R. Belli, M. Conti, P. Crippa, and C. Turchetti, "Artificial neural networks as approximators of stochastic processes," Neural

Networks, vol. 12, no. 4, pp. 647-658, 1999/06/01/ 1999. [2]

  • W. H. Farmer and R. M. Vogel, "On the deterministic and stochastic use of hydrologic models," Water Resources Research, vol. 52,
  • no. 7, pp. 5619-5633, 2016.

[3]

  • R. M. Vogel, "Stochastic watershed models for hydrologic risk management," Water Security, vol. 1, pp. 28-35, 2017/07/01/ 2017.

[4]

  • Y. Ermoliev, T. Ermolieva, T. Kahil, M. Obersteiner, V. Gorbachuk, and P. Knopov, "Stochastic Optimization Models for Risk-Based

Reservoir Management," Cybernetics Systems Analysis, pp. 1-10, 2019. [5]

  • J. Grundmann, S. Hörning, and A. Bárdossy, "Stochastic reconstruction of spatio-temporal rainfall patterns by inverse hydrologic

modelling," Hydrology Earth System Sciences, vol. 23, no. 1, pp. 225-237, 2019. [6]

  • D. Koutsoyiannis, "Knowable moments for high-order stochastic characterization and modelling of hydrological processes,"

Hydrological Sciences Journal, pp. 1-15, 2019. [7] C.-Y. Xu, L. Xiong, and V. P. Singh, "Black-Box hydrological models," Handbook of Hydrometeorological Ensemble Forecasting, pp. 341-387, 2019. [8]

  • C. Beaumont, "Stochastic models in hydrology," Progress in Physical Geography, vol. 3, no. 3, pp. 363-391, 1979.

[9]

  • H. Jonsdottir, Stochastic modelling of hydrologic systems. Technical University of Denmark, 2006.

[10] Jain, Sharad K. , and V. P. Singh . Hydrological Cycles, Models and Applications to Forecasting. Springer Berlin Heidelberg, 2017. [11] Xu CY., Xiong L., Singh V.P. (2019) Black-Box Hydrological Models. In: Duan Q., Pappenberger F., Wood A., Cloke H., Schaake J. (eds) [12] DHI, 2017. MIKE SHE Volume 1: User Guide [13] DHI, 2017. MIKE SHE Volume 2: Reference Guide [14] Hydroeurope Project Page [online] Available at: sites.google.com/a/aquacloud.net/19we/ [Accessed 8 Feb. 2019]

Outline - Introduction - Model - Selection - Results - Comparison - Recommendations - References

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Thank you for your attention!