Hydrological Modelling Assignment Sample

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Introduction to Hydrological Modelling - Water Resource Planning And Management Assignment

This report presents the process of building and implementing of the hydrological model based on the GR6J rainfall-runoff model within Google Colab. The main aim of the study was to model the flow of a particular catchment; more precisely, the various methods of calibration, validation, and optimization were to be explored. For this purpose, its performance was tested on the basis of observed hydrological data with the help of performance indicators like KGE and NSE. It explains the process of Data preprocessing, Model construction, parameters Calibration, Validation and eventually the setting off the most appropriate parameters for stream flow simulation. The results show that the calibrated GR6J model has a good ability to simulate the runoff process of the catchment and that the model calibration and validation are crucial issues for the hydrological models.

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Background

This paper is based on the catchment area 69002 from CAMELS-GB database, which is in the River Thames basin, southern England. It has an area of 237 km² and is described by the temperate maritime climate in which rainfall is fairly distributed all the year round. That is obtained from the evaluation of the CAMELS-GB supplementary files where it is evident that the catchment mainly comprises of rural land cover with arable land, grasslands, as well as woodlands (Coxon et al. 2020). It is relatively flat and low plateau area without sharp slopes and steep terrains. The geology entails chalk and limestone in many areas, and therefore the ground has experienced good drainage and moderate permeability. These characteristics affect the hydrological behavior of the catchment with runoff processes probably being infiltration-excess operand flow during extraordinary storms besides saturation-excess operand flow in wet periods.

Hydrological Modelling Assignment Sample

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Day to day analysis leads to the conclusion that there is a great inter-annual and intra-annual variability in the hydrological data for the period of 1970-2015. Annu-Mean shows that streamflow or discharge of water bodies yearly has seasonal changes depending on the changes in rainfall and potential evapotranspiration. The monthly average discharges have a clear seasonal variation with high values in winter and low values in summer period. Analyzing the data of daily discharge, it is possible to identify the tendency of the long term mean annual flow is declining, but with fluctuations within the multi-year cycles. This might be due to natural and/or human induced climates changes and that changes in land use affects water resources in the region. Although the model is conceptual and lumped, the main form of runoff generation in this catchment would be saturation-excess overland flow because the catchment response is mainly governed by shallow water table and base flow contribution to stream flow is also significant.

If one would desire to have a full picture of the variability of the streamflow, then he or she could plot a flow duration curve. This type of graph in the form of the flow duration curve gives the proportion of time that a given flow rate is experienced through the record and thus the probabilities and intensities of high and low flows (Tena et al. 2019). That is, high flows would be represented by the low exceedence probability tail while the low flows are represented by high exceedence probability tail of the curve. Applying this analysis to the following observed streamflow within the record will give more detail on the flow regimes and allow for a quantitative evaluation.

Model calibration and validation

The calibration and validation of the GR6J hydrological model involved several steps with the view of enhancing the model in order to simulate the stream flow process in the target catchment. The calibration process involved determining the best estimation of the model parameters that brought best approximations of the calculated and the observed stream-flow while the validation effectively tested the ability of the model in generalizing well on a different data set.

Calibration Strategy

For calibration, the period used was 2000-01-01 to 2001-12-31 to incorporate two hydrological year where there are wet as well as dry seasons in order to represent the catchment. First of all, a Monte Carlo simulation to scan the parameter space was carried out. This was achieved by selecting 150 values at random within defined ranges for the parameters and running GR6J model for all the selected values. Accuracy of the model was calculated by computing correlation error, bias, variability, KGE, and NSE. Specifically, these metrics allowed to focus on different aspects of hydrograph and to reveal the best choice regarding the parameters values.

Optimization

Subsequently, another optimization was done where the scipy.optimize.minimize method as per Thomas Nelder and Mead’s simplex method was adopted. It adapts the parameter values step by step in the opposite direction to the gradient of the objective function, which was selected as 1 minus KGE for maximizing KGE. The search was also restricted for each of the parameters within hydrologically meaningful ranges by containing bounds. Parameter values were determined initially that contained prior information as well as those that came from the sensitivity analysis.

Thus, the chosen objective function is KGE that is a multifaceted measure including correlation, bias, and variability to assess the models. As it relates to the optimization process, the KGE maximization targeted for the equitableness of the observed hydrograph. This optimization step proved to be better than direct selection of random samples and this way, better parameter was selected which defined a higher level of model-fit.

Validation

In order to ensure the output of the catchment model was a good representation of the catchment response within the period of 2010-01-01 to 31 December 2011 which was not wholly used in calibration. The parameter set developed in calibration was then used for streamflow simulation in the validation period.

Performance Evaluation

Results obtained utilizing the above metrics such as KGE and NSE was also determined for the calibration as well as the validation period. And, the hydrograph was used to show the comparison of the simulated flow with the observed flow in the calibration as well as in the validation seasons.

Refinement of Calibration Approach

In this process, the features of calibration were improved by switching from the Monte Carlo simulation to the Nelder-Mead optimization. The identification of calibration periods was important because decision factors such as the period duration, hydro meteorological conditions, and outliers had to be taken into consideration.

Model Performance

These values came into even better KGE in the validation, and it suggested that the model has good ability to cross validation. From seeing the hydrographs of the model and the observed data, the model was able to get most of the peak flows and the recession curves almost perfectly.

Potential Causes of Remaining Differences

In every case of optimization, the simulated streamflow, therefore, was observed to slightly differ from the actual values due to various reasons. Imperfection of structure in the GR6J model such as, the genre of model being a lumped-parameter model, uncertainty in calibration data phenomenon, and errors in the data fed to the model may also make the predictions erroneous.

Climate change assessment

The use of climate change scenario analysis for simulating the future changes in climate and its effects on the streamflow in the target catchment were used. This included feeding into the calibrated GR6J hydrological model actualised climate data obtained from GCMs. Specifically the study was concerned with changes in streamflow and its confidence over time for the different ensemble members.

Methods

It is based on future climate simulations from an archive of GCMs provided for a certain emissions pathway (e.g., the RCP8.5). To this end, these projections were rescaled to the catchment level, and the hydrological model—GR6J was employed; it had been calibrated with historical data. Several numbers of runs were carried out and each was based on different GCM of the ensemble to replicate the uncertainty in climate prediction.

Hydrograph Summary Statistics and Methods

To assess changes in the magnitude of different characteristics of streamflow hydrograph, employment of summary statistics and methods of the hydrograph was done. These included:

• Altered mean annual streamflow: The mean annual streamflow for the historical and future periods was computed, and the percent change was computed as well.
• Variability in changes: Whereas the standard deviation and the coefficient of variation were employed to analyse variation in the rate of flow.
• Extremes: Annual minima and maxima of the daily streamflow as well as other statistics such as the flood and low flow indices for 95th and 5th percentiles were used.
• Flow duration curves: Using flow duration curves, overall changes in the distribution of flow for historical as well as future periods could be understood.

Key Findings

It was observed that in the future climate changes, there was a significant alteration in stream flow. Mean annual streamflow was gradually decreasing and this raises concerns about the availability of water for use in future. The other water availability characteristics include changes in streamflow variability where there were indications of more frequent occurrences of both high and low flows. This indicates that there will be an increase in the intensity of floods as well as drought in the future.

Uncertainty Across Model Ensemble Members

Some aspects of the changes shown across the members of the different models used in this study displayed a significant level of uncertainty. Some GCMs anticipated larger changes in streamflow than others, suggesting that realistic possibilities have to be made with regards to the future water management.

Relevance to Water Management Agency

Thus, the results of this assessment are of the most concern to the water management agencies because. These changes in the villager’s identify aspects of streamflow and could indeed have unforeseen effects on the streamflow provoke changes in water resources planning and water resource management. This level of uncertainty gives rise to the necessity to develop a sustainable management plan that can apply in any of the anticipated future situations. Thus, it serves the purpose of helping water management agencies to make accurate predictions on the effects of climate change on the flow of water in the stream and ultimately the future management of the water resources in the catchment.

Recommendations and conclusions

This paper illustrated the use of the GR6J hydrological model on stream flow in the specified catchment. Calibration and validation proved quite satisfactory and the model was able to reproduce the observed streamflow trends. A focus on the future forecast of climate change described potential changes in the streamflow of water with less availability of water in general and with increased fluctuation. However, there are few recommendations which in fact, would improve its analytical capabilities and make it even more comprehensive.

Recommendations for Improvement

• One possible review: Some additional calibration might be made to enhance the model’s parameters and consequently enhance the effectiveness of its performance. This could entail considering some other objectives or using a different type of optimization method.
• Model Selection: It can be seen that the GR6J model was used and was able to give reasonable results, perhaps it would be useful to compare other models of rainfall-runoff. Simulators with better definition of the groundwater processes may seem to be more appropriate since the baseflow contribution to the formation of the flow in the catchment under consideration is rather high.
• Climate Change: The application of a more diverse range of GCMs and emission scenarios in the climate change analysis would improve the assessment of the changes and uncertainty in streamflow in the future. If further drivers like the land use change and water abstraction projection are introduced, then it can improve the authenticity of the analysis.
• Limitations: Correspondingly, superior and fuller SfM data can enhance both calibration and validation of the model. ;Further information on the fluctuations of the level of ground water and the quality of water can be collected to understand the hydrologic system in the catchment area and how it will be affected by climate change.

Reference List

Journals

• Aawar, T. and Khare, D., 2020. Assessment of climate change impacts on streamflow through hydrological model using SWAT model: a case study of Afghanistan. Modeling Earth Systems and Environment, 6(3), pp.1427-1437.
• Burek, P., Satoh, Y., Kahil, T., Tang, T., Greve, P., Smilovic, M., Guillaumot, L. and Wada, Y., 2019. Development of the Community Water Model (CWatM v1. 04) A high-resolution hydrological model for global and regional assessment of integrated water resources management. Geoscientific Model Development Discussions, 2019, pp.1-49.
• Cardoso de Salis, H.H., Monteiro da Costa, A., Moreira Vianna, J.H., Azeneth Schuler, M., Künne, A., Sanches Fernandes, L.F. and Leal Pacheco, F.A., 2019. Hydrologic modeling for sustainable water resources management in urbanized karst areas. International journal of environmental research and public health, 16(14), p.2542.
• Coxon, G., Addor, N., Bloomfield, J. P., Freer, J., Fry, M., Hannaford, J., Howden, N. J. K., Lane, R., Lewis, M., Robinson, E. L., Wagener, T., and Woods, R. 2020: CAMELS-GB: hydrometeorological time series and landscape attributes for 671 catchments in Great Britain, Earth Syst. Sci. Data, 12, 2459–2483, https://doi.org/10.5194/essd-12-2459-2020, 2020.
• Jain, S.K. and Singh, V.P., 2023. Water resources systems planning and management (Vol. 51). Elsevier.
• Mashaly, A.F. and Fernald, A.G., 2020. Identifying capabilities and potentials of system dynamics in hydrology and water resources as a promising modeling approach for water management. Water, 12(5), p.1432.
• Pandey, V.P., Dhaubanjar, S., Bharati, L. and Thapa, B.R., 2020. Spatio-temporal distribution of water availability in Karnali-Mohana Basin, Western Nepal: Hydrological model development using multi-site calibration approach (Part-A). Journal of Hydrology: Regional Studies, 29, p.100690.
• Regan, R.S., Juracek, K.E., Hay, L.E., Markstrom, S.L., Viger, R.J., Driscoll, J.M., LaFontaine, J.H. and Norton, P.A., 2019. The US Geological Survey National Hydrologic Model infrastructure: Rationale, description, and application of a watershed-scale model for the conterminous United States. Environmental Modelling & Software, 111, pp.192-203.
• Sahu, M.K., Shwetha, H.R. and Dwarakish, G.S., 2023. State-of-the-art hydrological models and application of the HEC-HMS model: a review. Modeling Earth Systems and Environment, 9(3), pp.3029-3051.
• Swain, S.S., Mishra, A., Sahoo, B. and Chatterjee, C., 2020. Water scarcity-risk assessment in data-scarce river basins under decadal climate change using a hydrological modelling approach. Journal of Hydrology, 590, p.125260.
• Talebmorad, H. and Ostad-Ali-Askari, K., 2022. Hydro geo-sphere integrated hydrologic model in modeling of wide basins. Sustainable Water Resources Management, 8(4), p.118.
• Tena, T.M., Mwaanga, P. and Nguvulu, A., 2019. Hydrological modelling
• Yang, D., Yang, Y. and Xia, J., 2021. Hydrological cycle and water resources in a changing world: A review. Geography and Sustainability, 2(2), pp.115-122.