1. Evaluation of GR4J Rainfall-Runoff Model for part of Baitarani Sub-Basin
Sandeep Bisht
Assistant Director
Basin planning
Central Water Commission
Deep Shikha
Assistant Director
Basin planning
Central Water Commission
Neeraj Kumar Sharma
Deputy Director
Basin planning
Central Water Commission

2. Hydrological models:
Hydrological models are conceptual representation of part of hydrological cycles.
These can be used for various applications in water resources management and engineering, such as flood estimation, flood forecasting, long term low flow forecasting, trend detection or design and management of reservoirs, etc.
Rainfall-runoff model is used to derive runoff for a particular area from inputs of rainfall and potential evapotranspiration.
Here, GR4J model (using Source software developed by e-water, Australia) has been used to analyse rainfall-runoff modelling for a part of Baitarani sub-basin.
GR4J model is the last modified version of the GR3J model originally proposed by Edijatno and Michel and then successively improved by Nascimento and Edijatno.

3. GR4J model
GR4J model is a catchment water balance model that relates runoff to rainfall evpotransipiration using daily data. The model contains two stores and has four parameters :
X1 : Maximum capacity of production store (mm),
X2 : groundwater exchange coefficient (mm),
X3 : the one-day maximal capacity of the routing reservoir (mm),
X4 : time peak ordinate of hydrograph unit UH1 (days).

4. GR4J model parameters- physical interpretation
Production Store (X1) :
represents storage in the surface of soil which can store rainfall.
evapotranspiration and percolation occurs in this storage.
capacity of this storage depends on the types of soil in that river basin. Few porosity of soil can make production store bigger.
Groundwater exchange coefficient (X2) :
a function of groundwater exchange which influence routing store.
When it has a negative value, then water enter to depth aquifer, when it has a positive value, then water exit from aquifer to storage(routing storage).
Routing storage (X3) :
represents amount of water which that can be stored in soil porous.
value of this routing store depends to the type and the humidity of soil.
Time Peak (X4) :
represents time when the ordinate peak of flood hydrograph is created on GR4J modelling. X3 X2

5. Baitarani sub-basin Sub-basin of Brahmani-Biatarani basin
River Baitarani originates from Gonasika/Guptaganga hills in odisha and drains in Bay of Bengal
Total area of the sub-basin= sq. km
Majority of the basin is in Odisha (>90%) and a small portion in Jharkhand

6. Brahmani- Baitarani basin
Baitarani sub-basin

7. The model is calibrated with the observed flow data at Anandpur Gauge and Discharge site (G&D)
Area of study :
Anandpur G&D site
Champua G&D site
Kanjhari Dam
Baitarani sub catchment map (Ascii format)

8. Data used: Baitarani sub catchment map in Ascii format
Baitarani Evapotranspiration in .csv format for the period
Baitarani Rainfall in .csv format for the period
Anandpur G&D site discharge in .csv format for the period
Warm-up period: to
Calibration period: to
Validation period: to

9. Rainfall and PET data:
Rainfall data
PET data

10. Review of data: Data should be reliable.
Sources of error: Manual, observation techniques, missing data, etc.
Missing data.
Analysis of flow data with respect to rainfall data at the different locations. e.g. flow data at Anandpur G&D site was analysed with respect to rainfall in anandpur, flow data at Champua G&D site and outflow data for Kanjhari.

11. Steps involved in calibration:
Calibration targets -- load observed data, associate data with the corresponding node or link in the model and define the objective function(s) that we want the optimiser to use to assess how well it fits.
Period definition -- define the time period(s) for calibration. A warm up period is also defined here, where the overall model simulation starts at some point in time before the first calibration period commences.
Metaparameter definition -- define parameters that will be modified during the calibration to improve the model fit to the observed data.
Select optimisation function -- choose and configure the optimisation strategy.

12. Period selection:
Calibration of the objective function for the calibration commences after the simulation reaches the warm up end date.
Warm up period should be sufficiently long so that the volume of the water in each of the stores will not be influenced by the volume that was set within those stores at the start of the warm up period.
Start of warm up
End of warm up
End of calibration

13. Objective function used:
Nash-Sutcliffe Efficiency (NSE) has been used as objective function given by
where Qo is the mean of observed discharges, and Qm is modelled discharge. 
is observed discharge at time t.
Nash–Sutcliffe efficiency can range from −∞ to 1.
E = 1 means a perfect match of modelled discharge to the observed data.
E = 0 indicates that the model predictions are as accurate as the mean of the observed data,
E < 0 means the observed mean is a better predictor than the model.

14. Optimisation:
The optimisation method used is shuffled complex evolution (SCE).
The SCE algorithm is designed to optimise the parameters that are assigned for the model within the source.
It is a global optimiser that learns the parameter set for calibrationfrom previous run.

15. Performance indicators/statistics:
Pearson’s Product Moment Correlation Coefficient (r) - a measure of the linear correlation between two variables X and Y. This value is calculated by performing a linear regression between the selected data sets. Values of r are in the range
Coefficient of Determination (R2) also called efficiency is obtained by squaring the value of r and is a measure of model strength.
Relative Volume Error (V) which is a measure of the relative difference between two data sets. Values for volume can range from where 0.0 indicates that there is no difference in the totals of the data sets

16. Calibration run:

17. Results: simulated vs observed flow at Anandpur G&D site
Calibration
Validation
Simulated flow
Observed flow

18. Results cont… S. No. r Volume efficiency 1 Calibration 0.901 10.374
0.806 2
Validation
0.905
0.811

19. Conclusion:
Although most of the new models (distributed) show more and more complex structures, parsimonious rainfall-runoff models are still useful. Despite some intrinsic limitations, lumped conceptual models like GR4J, have the advantage to be lowly parameterised and limit the problems of uncertainty arising due to poorly defined parameters and too complex model structures.
The model used in the present case study is performing well against the performance criterias given the uncertainity in both the input and the observed dataset. It can be concluded from the obtained r value that a strong positive linear relationship appears to exist between simulated and observed flows.
There is a scope further for improving the accuracy of the simulated flows and other results by having a data with greater degree of confidence as input and for calibration. This further signifies the role of data in modeling.

20. Thank you