NetSTORM - A Computer Program for Rainfall-Runoff Simulation and Precipitation Analysis

Sadeghi

2021

Preprint

In this study, optimal designs with minimum costs are obtained for various storm return periods. Then the risk analysis is used to determine the return period in which the design cost plus the damage risk cost is minimum. SWMM software was used to handle the simulation and the Network optimization was performed by using the genetic algorithm. The non-linear reservoir model to convert the rainfall into runoff and the dynamic wave model to perform the network hydraulic simulation in this software are utilized as a complicated simulation model. The results showed that the 10-year return-period storm in which the summation of the design and the damage risk costs are minimum is the one that should be selected. Also, the rational method of the software was applied as the simplest method of rainfall-runoff and the hydraulic calculations were performed using a Manning equation without considering the flow travel time. The results show that the return period of the risk analysis is the same as the first one whereas the total design costs are greater by 16.6%. Afterward, the classical rational method in which the flow travel time is considered was used to design the same network. The peak flows of the pipes were remarkably reduced, causing the design costs to be only 4.7% greater than the complicated precise method. It can be concluded that the simple classic rational method considering the flow travel time may be used in the design of storm sewer networks due to its acceptable accuracy and costs.

Section: 3the Rational Methods In the Swmm Modelmentioning

confidence: 99%

Evaluation of the Complicated Storm Sewer Networks Design Method and the Rational Method Using a Risk-Based Simulation-Optimization Approach

Sadeghi

2021

Preprint

“…Maximum annual 24-hour precipitation data for each sampling period were used to calculate design storms with 2, 5, 10, 25, 50, and 100-year recurrence intervals. The water resources software NetStorm (Heineman 2004) was used for this analysis. In this program, return periods are calculated using ranked precipitation depths fitted to a generalized extreme value (GEV) distribution following the method of L moments, as described by Hosking (1990).…”

Section: Methodsmentioning

confidence: 99%

Forecasting extreme events: making sense of noisy climate data in support of water resources planning

et al. 2019

Global climate model (GCM) projections are generally considered the best source of information for predicting future climate and hydrologic conditions in the face of a changing climate. Understanding and interpreting GCM projections is therefore critical for water resources planning. Unfortunately, this can be a challenging task as climate model data, particularly precipitation data, are notoriously noisy with large scatter and lacking in apparent patterns or trends. There is also usually large projection variability between models and model scenarios. This paper demonstrates a simple, practical method for synthesizing climate model data into more informative metrics using case studies of Atlanta, Georgia and Austin, Texas. Monthly and daily GCM projections, as well as historical observations, were translated into commonly used summary metrics for extreme event planning: peak 24-hour storm events and the Palmer Drought Severity Index (PDSI). Statistical trend analyses on these two metrics were used as a simple means to better understand the data. As expected, results identified significant, increasing, trends in projected 21st century temperatures for most GCM projections. Less expectedly, significant trends were also identified for projected future monthly and 24-hour maximum precipitation and drought severity. Implications of this work for water resources planning are discussed.

“…The downscaled data assumes changes from the historical to the projection period, so we conducted bias-correction with the linear scaling method implemented in the CMhyd tool (Teutschbein and Seibert, 2012). We further disaggregated daily precipitations into hourly resolution by using NetSTORM (Heineman, 2004). This study focused on the warm season months in Salt Lake City (May to September), so the October to April period was not considered in the simulation.…”

Section: Future Scenariosmentioning

confidence: 99%

Evaluating real-time control of stormwater drainage network and green stormwater infrastructure for enhancing flooding resilience under future rainfall projections

Burian²

2023

Preprint

Traditional stormwater control measures are designed to handle system loadings induced by fixed-size storm events. However, climate change is predicted to alter the frequency and intensity of flooding events, stimulating the need to explore another more adaptive flooding solution like real-time control (RTC). This study assesses the performance of RTC to mitigate impacts of climate change on urban flooding resilience. A simulated, yet realistic, urban drainage system in Salt Lake City, Utah, USA, shows that RTC improves the flooding resilience by up to 17% under climatic rainfall changes. Compared with green stormwatrer infrastructure (GSI), RTC exhibits a lower resistibility, lower flooding failure level, and higher recovery rate in system performance curves. Results articulate that keeping RTC’s performance consistent under ‘back-to-back’ storms requires a tradeoff between upstream dynamical operation and downstream flooding functionality loss. This research suggests that RTC provides a new path towards smart and resilient stormwater management strategy.