A model for Shell Creek in Nebraska has been developed in HEC-HMS to simulate its hydrological behavior.
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A model for Shell Creek in Nebraska has been developed in HEC-HMS to simulate its hydrological behavior. The model consists of six subbasins, as shown in Figure 1. The upstream portion of the model comprises three subbasins, each representing distinct areas contributing to the flow of Shell Creek. These subbasins have unique characteristics, such as land use, soil type, and slope.
The three upstream subbasins are interconnected at Junction 1, where the flow from each subbasin converges before moving downstream. In the downstream portion of the model, three subbasins receive flow from Junction 1, representing areas influenced by Shell Creek’s flow as it progresses downstream. These three downstream subbasins are connected at Junction 2, where the flow from the subbasins merges before reaching the final outlet.
Both Junction 1 and Junction 2 are connected to Sink-1, which represents the ultimate outlet of the model. Sink-1 could be a gauge or monitoring point allowing the observation of the outflow from the entire system.
The model includes a meteorologic model called Met1, responsible for providing essential meteorological inputs, such as rainfall, temperature, and other weather-related data, to the HEC-HMS simulation. These inputs drive the hydrological processes within the model, affecting the flow and behavior of Shell Creek.
In addition to the subbasins, junctions, and sink, the model incorporates six reaches to connect the subbasins with the respective junctions. The routing method employed for these reaches is Muskingum, a popular and widely used method for simulating channel flow and routing.
To efficiently monitor the outflow from all subbasins, the model employs a single gauge instead of individual gauges for each subbasin. This approach simplifies the model setup and data collection process, making it easier to manage and calibrate the model accurately. The precipitation details of the gage are shown in Figure 2
Overall, this HEC-HMS model for Shell Creek in Nebraska provides a comprehensive representation of the watershed’s hydrological behavior, considering the subbasins, junctions, reaches, meteorological inputs, and the use of a single gauge for monitoring the outflow. The Muskingum routing method is used to simulate channel flow and routing within the model, enabling detailed analysis and water resource management in the Shell Creek watershed.
Having a single gauge for all subbasins in hydrological modeling offers several advantages in terms of simplicity, data efficiency, and representation of the overall system behavior. One of the primary benefits is the simplified model setup, as managing a single gauge reduces the complexity of the model and data management process (Yazdi and Zeinivand, 2018). Instead of setting up multiple gauges for each subbasin, researchers can focus on a single location, streamlining the model structure and calibration process (Yazdi and Zeinivand, 2018). This leads to reduced data requirements, as only one location needs to be monitored and processed, saving time and effort in data collection (Gupta et al., 2012).
The integration of flow from all subbasins at a single gauge allows for a holistic view of the watershed’s behavior (Yazdi and Zeinivand, 2018). The integrated hydrograph generated by the single gauge represents the combined flow response of the entire system to rainfall events (Gupta et al., 2012). This representation provides a more comprehensive understanding of the watershed dynamics.
Calibration of the model becomes more straightforward with a single gauge since the focus is on matching the observed hydrograph at one location (Yazdi and Zeinivand, 2018). Researchers can fine-tune the model parameters to achieve better agreement between the simulated and observed data.
Using a single gauge also ensures data consistency throughout the model simulation (Gupta et al., 2012). All subbasins contribute to the flow at the same location, eliminating the possibility of conflicting or mismatched data from multiple gauges.
Furthermore, resource efficiency is a significant advantage of employing a single gauge. Running simulations with fewer gauges requires fewer computational resources, making it more feasible for large and complex models or limited computational power (Yazdi and Zeinivand, 2018).
While a single gauge provides valuable insights into the overall watershed behavior, it is essential to acknowledge that in some cases, multiple gauges may be necessary for more detailed analysis or specific research objectives.
In conclusion, using a single gauge in hydrological modeling simplifies the model setup, reduces data requirements, and ensures data consistency, leading to better efficiency and understanding of the overall watershed behavior.
Starting the manual calibration process, the first adjustment was made to the SCS curve number, which was reduced by 10%. This modification resulted in a lower peak of the model hydrograph, decreasing from 25,000 cfs to 22,000 cfs.
Next, the lag time was considered as it could influence both the timing and magnitude of the peak. To increase the lag time by 30%, the parameters in the SCS unit hydrograph were multiplied by 1.3. However, this change did not have the desired effect on the hydrograph, so the focus shifted to another parameter – the Muskingum K for routing. Initially set at 0.5 HR for all reaches, the Muskingum K values were increased to 1.2 HR by adding 1.5. This adjustment resulted in a smoother curve (Figure 3). For further improvements, the Muskingum K parameter was increased again by adding 0.5, leading to a more refined and smoother curve.
Subsequently, the Muskingum K values were tested repeatedly, but no significant changes were observed in the hydrograph. At this point, a decision was made to alter the number of reaches. By doubling the number of reaches, the peak was further lowered to 17,000 cfs, creating a flatter appearance for the hydrograph.
In summary, the manual calibration process involved multiple adjustments to various parameters, such as the SCS curve number, lag time, and Muskingum K values. These modifications resulted in changes to the peak’s magnitude, timing, and curve smoothness. Doubling the number of reaches had a notable impact on the peak flow and the overall shape of the hydrograph, providing valuable insights into the calibration process.
Methodology:
The manual calibration process involved iteratively adjusting various model parameters and evaluating their impact on the model hydrograph. The adjustments were made using expert judgment and knowledge of the watershed’s characteristics. The key parameters that were modified during the calibration process include:
SCS Curve Number: The initial abstraction in the SCS method was controlled by the curve number. It was reduced by 10% to increase the available rainfall for direct runoff and subsequently lower the peak flow of the model hydrograph.
Lag Time: The lag time, which influences both the timing and magnitude of the peak, was increased by 30%. This adjustment was made by modifying the SCS unit hydrograph parameters.
Muskingum K for Routing: The Muskingum K values, initially set at 0.5 HR for all reaches, were increased to 1.2 HR. Further increases by adding 0.5 were performed to refine the curve and better match the observed hydrograph.
Number of Reaches: To improve the representation of flow propagation and routing complexity, the number of reaches was doubled, leading to a flatter appearance for the hydrograph.
Results and Insights:
The reduction in the SCS curve number resulted in a decrease in the peak flow of the model hydrograph. This change aligned the initial abstraction with observed data and improved the peak flow representation.
Despite increasing the lag time by 30%, the desired peak delay was not achieved. The influence of lag time alone might have been limited due to the dominance of other parameters, such as routing and travel times.
Adjustments to the Muskingum K parameter led to a smoother curve in the model hydrograph. The increased values affected the peak timing and overall shape, contributing to better agreement with the observed data.
Doubling the number of reaches significantly impacted the peak flow and the hydrograph’s shape. The increased complexity in routing improved the model’s representation of flow propagation.
Discussion and Limitations:
While the manual calibration process resulted in significant improvements, achieving an exact match between the model and observed hydrograph might not always be possible. The hydrologic response of the watershed is influenced by various interacting factors, and the calibration process involves striking a balance between multiple parameters.
The inability to delay the peak despite adjusting the lag time might be attributed to the dominance of other parameters or spatial variability in the watershed’s characteristics. Additionally, the model structure, including the representation of hydrologic processes, might have influenced the peak timing.
Conclusion:
The manual calibration process for the HEC-HMS model involved adjustments to the SCS curve number, lag time, Muskingum K values, and the number of reaches. These modifications resulted in changes to the peak magnitude, timing, and curve smoothness, ultimately leading to a better match between the model and observed hydrograph. The calibration process is an iterative and challenging task, and expert judgment and sensitivity analysis play crucial roles in achieving an improved model performance.
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