Hydrological Modeling of Large River Basin using Soil Moisture Accounting Model and Monte Carlo Simulation
DOI:
https://doi.org/10.48048/tis.2024.7696Keywords:
Keywords:SMA model ,MC Simulation, Optimization,Uncertanity&,Sensitivity Analysis.Abstract
This description outlines a Geographic Information System (GIS)-based rainfall-runoff model that simulates the flow of water in a river basin. The model operates on a daily time step and consists of 4 non-linear storage components: Interception, soil moisture, channel, and groundwater. It employs (SCS) Unit Hydrograph model to determine unit hydrograph ordinates. The model replicates the movement and storage of water in various parts of the basin, including vegetation, the soil surface, the soil profile, and groundwater layers. To address uncertainty, a Monte Carlo simulation feature is integrated into the model. Monte Carlo Simulation involves predicting outcomes by generating numerous iterations using estimated ranges of values for variables with inherent uncertainty. This feature generates required number of sample sets with random parameter values. The model is run for all these realizations during a calibration period, and performance metrics like NSE are calculated for each calibration year to assess prediction uncertainty, model parameter weights are computed by normalizing the corresponding likelihood values. These weights sum up to one and represent the probabilistic distribution of predicted variables, illustrating the impact of structural and parameter errors on model predictions. A sensitivity analysis reveals that the Muskingum constants K and X have the greatest influence on model performance, while parameters Фgw, Фsw, Фfc, and Фpc have a minimal effect on the model’s performance. The outcomes presented in the findings indicate that the Soil moisture accounting Model successfully forecasted peak discharge by leveraging the existing historical data. The accuracy of both volume and timing in the predictions suggests the model’s appropriateness for the examined catchments.
HIGHLIGHTS
- The hydrological model is built in python indicating precipitation distributed into infiltration, runoff, storage in the root zone, and percolation into deeper groundwater storage
- The model’s behavior under uncertain conditions, is studied using Monte Carlo Simulation. 10,000 simulation is run within 120 s
- SMA model is linked with the Muskingum method, calculates the discharge at the outlet of the basin (Suitable to Indian scenario)
- Sensitivity analysis identify pivotal parameters, and concentrating efforts on minimizing uncertainty in those critical variables
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