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Deep learning (DL) models are a promising complement to hydrodynamic models. However, the application of DL for detailed predictions in large domains has not yet been tested. We aim to narrow this gap by improving flood mapping resolution derived from large coarse flood models. We used cGAN-Flood, a conditional generative adversarial-based model (cGAN), that showed satisfactory generalization. We demonstrate the applicability of cGAN-Flood by coupling it with mesh- and raster-based coarse models. A Hydrologic Engineering Center (HEC) River Analysis System (RAS) model (cell size of 32 m), which is mesh-based, was created for a 350 km2 watershed. In contrast, a HydroPol2D model, raster-based, was created for a 150 km2 watershed with a 15 m pixel size. We evaluated our method's performance against a 3 m resolution HEC-RAS model in seven catchments across the cities of San Antonio and Sao Paulo. Results indicate the cGAN-Flood improved flood map accuracy, illustrating how DL can enhance flood mapping resolution. [ABSTRACT FROM AUTHOR]

Low impact developments (LIDs) are control measures to restore the hydrologic regime and enhance stormwater quality. Due to LID's expensive capital and maintenance cost, the placement of LID controls in a watershed is an important planning task and still an open question in the specialized literature. This study proposes a simulation-optimization approach to place bioretention systems within a watershed to optimize their effectiveness. The Stormwater Management Model (SWMM) and the Non-dominated Sorting Genetic Algorithm III (NSGAIII) were coupled to identify the near-optimal locations of bioretentions for near-optimal quality and quantity controls, considering runoff volume, peak flow, total suspended solids, total nitrogen, and cost. Trade-offs were identified between cost versus other objective functions. The results suggest no specific spatial preference in placement of bioretentions under different rainfall regimes in watershed scale. However, in subcatchment scale, the near-optimal placement under single storm events is either maximum or none, while distributed under continuous simulation. [ABSTRACT FROM AUTHOR]

The increasing access to inexpensive sensors, computing power, and more accurate forecasting of storm events provides unique opportunities to shift flood management practices from static approaches to an optimization-based real-time control (RTC) of urban drainage systems. Recent studies have addressed a plethora of strategies for flood control in stormwater reservoirs; however, advanced control theoretic techniques are not yet fully investigated and applied to these systems. In addition, there is an absence of a coupled integrated control model for systems composed of watersheds, reservoirs, and channels for flood mitigation. To this end, we developed a novel nonlinear state-space model of hydrologic and hydrodynamic processes in watersheds, reservoirs, and one-dimensional channels. The model was tested under different types of reservoir control strategies based on real-time measurements (reactive control) and predictions of the future behavior of the system (predictive control) using rainfall forecastings. We applied the modeling approach in a system composed of a single watershed, reservoir, and channel connected in series for the observed rainfall data in San Antonio. Results indicate that for flood mitigation, the predictive control strategy outperforms the reactive controls not only when applied for synthetic design storm events, but also for a continuous simulation. Moreover, the predictive control strategy requires smaller valve operations while still guaranteeing efficient hydrological performance. From the results, we recommend the use of the nonlinear model predictive control strategy to control stormwater systems because of the ability to handle different objective functions, which can be altered according to rainfall forecasting and shift the reservoir operation from flood-based control to strategies focused on increasing detention times, depending on the forecasting. [ABSTRACT FROM AUTHOR]

State estimation (SE) in water distribution networks (WDNs), the problem of estimating all unknown network heads and flows given select measurements, is challenging due to the nonconvexity of hydraulic models and significant uncertainty from water demands, network parameters, and measurements. To this end, a probabilistic modeling for SE in WDNs is proposed. After linearizing the nonlinear hydraulic WDN model, the proposed probabilistic SE (PSE) shows that the covariance matrix of unknown system states (unmeasured heads and flows) can be linearly expressed by the covariance matrix of three uncertainty sources (i.e., measurement noise, network parameters, and water demands). Instead of providing deterministic results for unknown states, the proposed PSE approach: 1) regards the system states and uncertainty sources as random variables and yields variances of individual unknown states; 2) considers thorough modeling of various types of valves and measurement scenarios in WDNs; and 3) is also useful for uncertainty quantification, extended period simulations, and confidence limit analysis. The effectiveness and scalability of the proposed approach are tested using several WDN case studies. [ABSTRACT FROM AUTHOR]

Addressing challenges in urban water infrastructure systems, including aging infrastructure, supply uncertainty, extreme events, and security threats, depends highly on water distribution networks modeling emphasizing the importance of realistic assumptions, modeling complexities, and scalable solutions. In this study, we propose a derivative-free, linear approximation for solving the network water flow problem. The proposed approach takes advantage of the special form of the nonlinear head loss equations, and, after the transformation of variables and constraints, the water flow problem reduces to a linear optimization problem that can be efficiently solved by modern linear solvers. Ultimately, the proposed approach amounts to solving a series of linear optimization problems. We demonstrate the proposed approach through several case studies and show that the approach can model arbitrary network topologies and various types of valves and pumps, thus providing modeling flexibility. Under mild conditions, we show that the proposed linear approximation converges. We provide sensitivity analysis and discuss in detail the current limitations of our approach and suggest solutions to overcome these. All the codes, tested networks, and results are freely available on Github for research reproducibility. [ABSTRACT FROM AUTHOR]

Abstract: Many engineering design problems must optimize multiple objectives. While many objectives are explicit and can be mathematically modeled, some goals are subjective and cannot be included in a mathematical model of the optimization problem. A set of alternative non-dominated fronts that represent multiple optima for problem solution can be identified to provide insight about the decision space and to provide options and alternatives for decision-making. This paper presents a new algorithm, the Multi-objective Niching Co-evolutionary Algorithm (MNCA) that identifies distinct sets of non-dominated solutions which are maximally different in their decision vectors and are located in the same non-inferior regions of a Pareto front. MNCA is demonstrated to identify a set of non-dominated fronts with maximum difference in decision vectors for a set of real-valued problems. [Copyright &y& Elsevier]

The natural hydrologic flow regime is altered by urbanization, which can be mitigated through best management practices (BMPs) or low impact development (LID). Typically, the effectiveness of different management scenarios is tested by comparing post- and predevelopment instantaneous peak flows. This approach, however, does not capture the extent of hydrologic change and the effect on downstream communities. A new hydrologic sustainability metric is presented here to quantify the impact of urbanization on downstream water bodies on the basis of the inundation dynamics of the flow regime. The hydrologic footprint residence (HFR) is designed to capture both temporal and spatial hydrological changes to an event-based flow regime by calculating the inundated areas and duration of a flood. The HFR is demonstrated for a hypothetical watershed and a watershed on the Texas A&M University Campus, located in College Station, Texas. For the campus watershed, three design storms (2-, 10-, and 100-year) and a set of historical events (during the period 1978-2009) are simulated for various management scenarios, representing predevelopment conditions, development on campus, BMP-based control, and LID-based control. The results indicate that the HFR can better capture alterations to the shape of the hydrograph compared with the use of the peak flow only. [ABSTRACT FROM AUTHOR]

Damodaram, Chandana, Marcio H. Giacomoni, C. Prakash Khedun, Hillary Holmes, Andrea Ryan, William Saour, and Emily M. Zechman, 2010. Simulation of Combined Best Management Practices and Low Impact Development for Sustainable Stormwater Management. Journal of the American Water Resources Association (JAWRA) 1-12. DOI: 10.1111/j.1752-1688.2010.00462.x Abstract: Urbanization causes increased stormwater runoff volumes, leading to erosion, flooding, and the degradation of instream ecosystem health. Although Best Management Practices (BMPs) are used widely as a means for controlling flood runoff events, Low Impact Development (LID) options have been proposed as an alternative approach to better mimic the natural flow regime by using decentralized designs to control stormwater runoff at the source, rather than at a centralized location in the watershed. For highly urbanized areas, LID practices such as rainwater harvesting, green roofs, and permeable pavements can be used to retrofit existing infrastructure and reduce runoff volumes and peak flows. This paper describes a modeling approach to incorporate these LID practices in an existing hydrologic model to estimate the effects of LID choices on streamflow. The modeling approach has been applied to a watershed located on the campus of Texas A&M University in College Station, Texas, to predict the stormwater reductions resulting from retrofitting existing infrastructure with LID technologies. Results demonstrate that use of these LID practices yield significant stormwater control for small events and less control for flood events. A combined BMP-LID approach is tested for runoff control for both flood and frequent rainfall events. [ABSTRACT FROM AUTHOR]

This work proposes a hybrid scheme that combines a simulation model and a mathematical programming model for designing logistic networks for co-firing biomass, specifically switchgrass, in conventional coal-fired power plants. The advantages of co-firing biomass include: (1) the creation of green jobs; (2) the efficient use of current power plant infrastructure; (3) fostering the penetration of renewable energy into power networks; and, (4) the reduction of greenhouse gas (GHG) emissions. The novelty of this work lies in the inclusion of (1) the inherent variability of biomass supply at the parcel level, and (2) the effects of climate change on future biomass supply when designing a feedstock logistic network. The design optimization is conducted at the farm/parcel level (most, if not all, previous works have used county level average data) and integrates the crop growth predictions employing United States Department of Agriculture's (USDA's) Agricultural Land Management with Numerical Assessment Criteria (ALMANAC) simulation model; the output of the simulations is input into the mixed integer linear programming (MILP) hub-and-spoke model to minimize the overall cost of the logistic network. Specifically, the MILP-based model selects the parcels and depot locations as well as biomass transportation flows by taking into consideration different types of soil, land cover characteristics, and predicted yields, which account for both historical and forecasted weather data. The hybrid methodology was tested by solving realistic situations, which considered varying weather conditions. The gross results indicate that the optimized logistic network enabled meeting a 20% biomass co-firing rate demand, which reduced 1,158,867 Mg per year in GHG emissions by co-firing with biomass. [ABSTRACT FROM AUTHOR]