49255 Catchment Modelling System-Hydrological Processes

Every hydrological cycle has a system structure where the system gets inputs and yields the intended outputs providing a feedback channel to correct any impediments (Moore, et al., n.d.). The hydrological cycle is a representation of the flows of water, energy, and the suspended or dissolved materials. The catchment based models used in the modern age are the spatial representation and the process representation.

In the spatial representation, the models are either lumped or distributed, while in the process representation, the models are black-box, grey-box, and white-box. A catchment denotes any area or part of a landscape which provides water via a lateral flow over the surface or underground at the water table level (USEPA SWMM, 2007). These areas are usually determined on the basis of watersheds on the surface topography. The structure of the landscape determines the lag time for arrival of rainfall to a point in the network and the temporal concentration of the streamflow hydrograph which results to the drainage network. The hydrological model description has different sections such as,

• Surface runoff
• Infiltration
• Aquifers
• RDII curves

The storm water modelling software (SWWMM) is useful in thousands of waste-water and storm water research studies. It is applied in the design and control strategies for the drainage and sewer systems in the urban areas. There is an evaluation on the impact of inflow and infiltration on the sanitation of a given catchment area. The software seeks to evaluate the efficacy of the BMPs for the reduction of wet weather pollutant loadings. The model is widely used in America in the weather and drainage system departments (Fairfield & LeyMarie, n.d.). The software modeler is well maintained by the US EPA and as a result it is always up-to-date and upgraded with regard to the different sections required. The system has basic interface and fewer in-built data management capabilities compared to the rainfall runoff modelling software. The system displays results in the following formats,

• Plan view
• Hydraulic profile
• Time series plots
• Scatter graph and
• Tabular data

Unfortunately, the modeler does not have GIS linkages as it only relies on in-house GIS linkages. It runs on a hydraulic engine that tends to be a bit unstable unlike other hydraulic engines (Gupta, et al., n.d.). The engine is constantly undergoing upgrades that gradually improve the performance. The Stanford watershed model (SWM) stands out at the best conceptual catchment model. It does a daily stamp on the expected inputs such as the soil-moisture budget and storage or routing functions for water redistribution. The information is captured on a daily timestep (Engelen, n.d.).

• To determine the quantity and quality of storm water from the up-stream catchment
• To escalate the expediency of the model by calibrating it beside the recorded data from a gauging station.
• The stage discharge table data in the text files was imported to the excel sheet and saved.
• The storm water modelling software gives outputs of data. The discharge data is compared to the recorded level and the line of best fit is obtained using EXCEL regression.
• The regression equation obtained is used to convert the recorded levels to a discharge.
• The predicted discharges are obtained from the SWMM software ouput.

Performing the regression analysis, the following outputs were attained.

• Summary section

I obtained the state discharge information and converted it to discharge where the state discharge is in cubic-mm/sec to millimeter. I performed the ANOVA statistical tests for the discharge data as shown in the first sheet of the workbook.

• Performing regression for the new discharge values against time I obtained the residual values. Thereafter, I was able to obtain the difference between the residuals and the predicted values as shown in the Diff column. The results are as illustrated in the graphs below.

The residual is obtained as the difference between the predicted and recorded discharge. The peak flow rate is generated from the residual as the maximum value. The residual is obtained as shown below.

The key variables analyzed in designing the catchment variables are the soil types and associations, geological types and their derived attributes, the land use in terms of vegetation cover and the management practices on the land, and the artificial or man-made drainage systems such as the sewers (Zevenbergen & Thorne, n.d.). In our system, the inputs of the model were,

• Precipitation either rain or snow (storm water)
• Suspended or dissolved load
• Pollutant referring to the point or non-point source

The output of the system refers to the stream discharge, evapotranspiration, groundwater transfer, suspended load and the pollutant. The modelling done using the hydrology simulation software is usually dependent on engineering approximations. Approximations highly result in the introduction of errors which need to be minimized or countered to avoid affecting the predictions that are made as a result. These errors can be introduced to the system at the stage of input or when processing the output. The modelling of stochastic and deterministic aspects of a hydrological system in the design of the probability distribution of the input. The probability distribution of an input, in this case, the normal distribution statistical model is implemented. 

The quantity and quality of the storm water is evaluated on the basis of its own attributes and their ability to sustain environmental values. Quantity denotes the mass of discharge, the aspects of the flow regime in terms of timing, frequency, and duration. Good quantity and quality ensures sustainability of a healthy ecological system. Based on the data analysis performed, the calibration is performed physically and it comprises of the altering model input stricture values to yield simulated values that are within a certain range of measured data. The regulated model may be utilized to mimic the multiple process such as the streamflow volumes, peak flows, and the sediments and nutrient tons.

Conclusion

In a nutshell, the modelling is as compared to the SWM output extracted data. The regression formula used from the data analysis is used to show the prediction and the results yielded are obtained as required by the modeler. 

References

Beven, K. & Kirkby, M. J., n.d. A physically based variable contributing area model of basin hydrology. Hydrological Sciences Bulletin, 24(1), pp. 43-69.

Elsenebeer, H. & Vertessy, R. A., 2000. Stormflow generation and flowpath characteristics in an Amazxonian rainforest catchment. Hydrological Processes, Volume 14, pp. 2367-2381.

Engelen, G., n.d. Modulus: A spatial Modelling Tool for Integrated Environmental Decision Making (Final Project report to European Commission EVN4-CT97-0685). [Online]
Available at: https://www.riks.n;l/RiksGeo/proj_mod.htm

Ewen, J. & Parkin, G., n.d. Validation of catchment models for predicting land use and climate change impacts. Journal of Hydrology, Issue 175, pp. 583-594.

Fairfield, J. & LeyMarie, P., n.d. Drainange networks from grid digital elevation models. Water Resources Research, Volume 27, pp. 709-717.

Gupta, V. K., Castro, S. L. & Over, T. M., n.d. On scaling exponents of spatial peak flows from rainfall and river network geometry. Journal of Hydrology, 81-104(187).

Moore, I. D., GRayson, R. B. & Ladson, A. R., n.d. Digital terrain modelling: A review of hydrological, geomorphological and biological applications. Hydrological Processes, 5(1), pp. 3-30.

Nix, S., n.d. Urban Storm water Modelling and Simulation. Bota Raton: Lewis Publishers.

USEPA SWMM, 2007. Storm Watter Management Model User's Manual. EPA/600/R-05/040, Volume 5.

Zevenbergen, L. W. & Thorne, C. R., n.d. Quantitative analysis of land surface topography. Earth surface processes and landforms, Issue 12, pp. 47-56.