An official website of the United States government
A combined sewer system (CSS) collects rainwater runoff, domestic sewage, and industrial wastewater in the same pipe. The volume of wastewater can sometimes exceed the system capacity during heavy rainfall events. When this occurs, untreated stormwater and wastewater discharge directly to nearby streams, rivers, and other water bodies. This would threaten public health and the environment, contributing to drinking water contamination and other concerns. Minimizing sewer overflows requires an optimization method that can provide an optimal sequence of decision variables at control gates. Conventional strategies use classical optimization algorithms, such as genetic algorithms and pattern search, to find the optimal sequence of decision variables. However, these conventional frameworks are very time-consuming, and it is almost impossible to achieve near real-time optimal control. This paper presents a faster optimization framework by using a new optimal control tool: reinforcement learning. The environment (flow modeler) used in this paper is the numerical model: Environmental Protection Agency’s Storm Water Management Model (EPA SWMM) to ensure the accuracy of environment response. The reward function is constructed based on the calculated water depth and overflow rate from SWMM. The process keeps minimizing the reward function to obtain the optimal flow release sequence at each controlled orifice gate. The combined sewer system (CSS) of the Puritan-Fenkell 7-mile facility in Detroit, MI, is chosen as the case study.
more »
« less
Regression modeling of combined sewer overflows to assess system performance
Abstract Combined sewer overflows (CSOs) occur when untreated raw sewage mixed with rainwater, runoff, or snowmelt is released during or after a storm in any community with a combined sewer system (CSS). Climate change makes CSOs worse in many locales; as the frequency and severity of wet weather events increases, so do the frequency and volume of CSO events. CSOs pose risks to humans and the environment, and as such, CSS communities are under regulatory pressure to reduce CSOs. Yet, CSS communities lack the tools needed, such as performance indicators, to assess CSS performance. Using the city of Cumberland, Maryland as a case study, we use public data on CSOs and precipitation over a span of 16 years to identify a new critical rainfall intensity threshold that triggers likely CSO incidence, and a multiple linear regression model to predict CSO volume using rainfall event characteristics. Together, this indicator and modeling approach can help CSS communities assess the performance of their CSS over time, especially to evaluate the effectiveness of efforts to reduce CSOs.
more »
« less
- PAR ID:
- 10378611
- Publisher / Repository:
- DOI PREFIX: 10.2166
- Date Published:
- Journal Name:
- Water Science and Technology
- Volume:
- 86
- Issue:
- 11
- ISSN:
- 0273-1223
- Page Range / eLocation ID:
- p. 2848-2860
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Abstract Urban development, topographic relief, and coastal boundaries can all exert influences on storm hydroclimatology, making rainfall and flood frequency analysis a major challenge. This study explores heterogeneity in extreme rainfall in the Baltimore Metropolitan region at small spatial scales using hydrometeorological analyses of major storm events in combination with hydroclimatological analyses based onstorm catalogsdeveloped using a 16‐year record of high‐resolution bias‐corrected radar rainfall fields. Our analyses demonstrate the potential for rainfall frequency methods using storm catalogs combined with stochastic storm transposition (SST); procedures are implemented for Dead Run, a small (14.3 km2) urban watershed located within the Baltimore Metropolitan area. The results point to the pronounced impact of complex terrain (including the Chesapeake Bay to the east, mountainous terrain to the west and urbanization in the region) on the regional rainfall climatology. Warm‐season thunderstorm systems are shown to be the dominant mechanism for generating extreme, short‐duration rainfall that leads to flash flooding. The SST approach is extended through the implementation of amultiplier fieldthat accounts for spatial heterogeneities in extreme rainfall magnitude. SST‐based analyses demonstrate the need to consider rainfall heterogeneity at multiple scales when estimating the rainfall intensity‐duration‐frequency relationships.more » « less
-
Abstract Future climates will alter the frequency and size of rain events in drylands, potentially affecting soil microbes that generate carbon feedbacks to climate, but field tests are rare. Topsoils in drylands are commonly colonized by biological soil crusts (biocrusts), photosynthesis‐based communities that provide services ranging from soil fertilization to stabilization against erosion. We quantified responses of biocrust microbial communities to 12 years of altered rainfall regimes, with 60 mm of additional rain per year delivered either as small (5 mm) weekly rains or large (20 mm) monthly rains during the summer monsoon season. Rain addition promoted microbial diversity, suppressed the dominant cyanobacterium,Microcoleus vaginatus, and enhanced nitrogen‐fixing taxa, but did not consistently increase microbial biomass. The addition of many small rain events increased microbial biomass, whereas few, large events did not. These results alter the physiological paradigm that biocrusts are most limited by the amount of rainfall and instead predict that regimes enriched in small rain events will boost cyanobacterial biocrusts and enhance their beneficial services to drylands.more » « less
-
Abstract Using observational rainfall datasets, we identify a positive correlation between precipitation over Central Equatorial Africa (CEA) and the Indian Ocean Dipole (IOD) during September‐December (SOND) for the period 1981–2019. Rainfall amount significantly increases during positive IOD events. The enhancement in precipitation is primarily attributed to increased rainfall frequency and reaches the maximum in October. IOD impacts rainfall via modifying the Walker circulation over the tropical Indian Ocean and moisture in the middle troposphere over CEA. The Madden‐Julian Oscillation (MJO) activity covaries with IOD to modulate the African Easterly Jet, which is critical to convection development over CEA. SOND rainfall has increased for the last two decades, which is concurrent with increases in both the IOD index and the correlation between IOD and rainfall. The IOD‐congruent rainfall changes potentially account for much of rainfall trends in southern and eastern CEA.more » « less
-
null (Ed.)Abstract. Extreme weather and climate events such as floods, droughts, and heat waves can cause extensive societal damages. While various statistical and climate models have been developed for the purpose of simulating extremes, a consistent definition of extreme events is still lacking. Furthermore, to better assess the performance of the climate models, a variety of spatial forecast verification measures have been developed. However, in most cases, the spatial verification measures that are widely used to compare mean states do not have sufficient theoretical justification to benchmark extreme events. In order to alleviate inconsistencies when defining extreme events within different scientific communities, we propose a new generalized Spatio-Temporal Threshold Clustering method for the identification of extreme event episodes, which uses machine learning techniques to couple existing pattern recognition indices with high or low threshold choices. The method consists of five main steps: (1) construction of essential field quantities; (2) dimension reduction; (3) spatial domain mapping; (4) time series clustering; and (5) threshold selection. We develop and apply this method using a gridded daily precipitation dataset derived from rain gauge stations over the contiguous United States. We observe changes in the distribution of conditional frequency of extreme precipitation from large-scale well-connected spatial patterns to smaller-scale more isolated rainfall clusters, possibly leading to more localized droughts and heat waves, especially during the summer months. The proposed method automates the threshold selection process through a clustering algorithm and can be directly applicable in conjunction with modeling and spatial forecast verification of extremes. Additionally, it allows for the identification of synoptic-scale spatial patterns that can be directly traced to the individual extreme episodes, and it offers users the flexibility to select an extreme threshold that is linked to the desired geometrical properties. The approach can be applied to broad scientific disciplines.more » « less
