Global Optimization-Based Calibration Algorithm for a 2D Distributed Hydrologic-Hydrodynamic and Water Quality Model

Hydrodynamic models with rain-on-the-grid capabilities are usually computationally expensive. This makes the use of automatic calibration algorithms hard to apply due to the large number of model runs. However, with the recent advances in parallel processing, computational resources, and increasing high-resolution climatologic and GIS data, high-resolution hydrodynamic models can be used for optimization-based calibration. This paper presents a global optimization-based algorithm to calibrate a fully distributed hydrologic-hydrodynamic and water quality model (HydroPol2D) using observed data (i.e., discharge, or pollutant concentration) as input. The algorithm can find a near-optimal set of parameters to explain observed gauged data. The modeling framework presented here, although applied in a poorly-gauged catchment, can be adapted for catchments with more detailed observations. We applied the algorithm in different cases of the V-Tilted Catchment, the Wooden-Board catchment, and in an existing urban catchment with heterogeneous data. The results of automatic calibration indicate $\mathrm{NSE} = 0.99$ for the V-Tilted catchment, $\mathrm{RMSE} = 830~\mathrm{mgL^{-1}}$ for salt concentration pollutographs (i.e., 8.3% of the event mean concentration), and $\mathrm{NSE} = 0.89$ for the urban catchment case study. This paper also explores the issue of equifinality in modeling calibration (EqMC). Equifinality is defined as the set of different parameter combinations that can provide equally good or accepted results, within the physical parameter ranges. EqMC decreases with the number of events and increases with the choice of partially or nonproducing runoff ones. Furthermore, results indicate that providing more accurate parameter ranges based on a priori knowledge of the catchment is fundamental to reduce the chances of finding a set of parameters with equifinality.Comment: Preprint submitted to Journal of Hydrolog

Low Impact Development practices in the context of United Nations Sustainable Development Goals: A new concept, lessons learned and challenges

The increase in urbanization and climate change brings new challenges to the cities’ sustainability and resilience, mainly related to flood and drought events. Among these challenges, it can be highlighted the physical and health damage to the population, interruption of water, energy and food supply services, damage to basic infrastructure, economic losses and contamination of urban rivers. To contribute to the increase of resilience in urban centers, LID practices have been used as a new approach of mitigation and adaptation within urban drainage systems, aiming at runoff retention, peak flow attenuation, pollutant removal and ecosystem services restoration (e.g., resources recycling, carbon sequestration, thermal comfort and landscape integration). These different mitigation purposes and complementary benefits provided by LID practices can be related to the different Sustainable Development Goals (SDG) presented by the United Nations (UN), to achieve countries’ systemic sustainability. The identification of local techniques that contribute to the different SDG helps to achieve their territorialization and application as public policy. Therefore, this paper presents a literature review, categorizing the studies into different generations based on their main application purpose and presents a linkage of the LID benefits to different SDG. Some challenges were identified requiring further investigation, such as the need to identify and quantify the energy demands for LID practices maintenance and their incorporation in the system final energy balance, identification of processes that contribute to carbon sequestration and emission, and risks of emerging pollutants for human health from water reuse and nutrient cycling for sustainable agriculture

Flood Risk Mitigation and Valve Control in Stormwater Systems: State-Space Modeling, Control Algorithms, and Case Studies

The increasing access to non-expensive 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 develop a novel state-space model of hydrologic and hydrodynamic processes in reservoirs and one-dimensional channels. The model is tested under different types of reservoir control strategies based on real-time measurements (reactive control), and based on predictions of the future behavior of the system (predictive control) using rainfall forecastings. We apply the modeling approach in a system composed by a single watershed, reservoir, and a channel connected in series, respectively, for the San Antonio observed rainfall data. 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 model predictive control strategy to control stormwater systems due to 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

Simulation-Optimization Approach for the Logistics Network Design of Biomass Co-Firing with Coal at Power Plants

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

Generalizing rapid flood predictions to unseen urban catchments with conditional generative adversarial networks

Two-dimensional hydrodynamic models are computationally expensive. This drawback can limit their application to solving problems requiring real-time predictions or several simulation runs. Although the literature presented improvements in using Deep Learning as an alternative to hydrodynamic models, Artificial Neural Networks applications for flood prediction cannot satisfactorily predict floods for areas outside the training datasets with different boundary conditions. In this paper, we used a conditional generative adversarial network (cGAN) aiming to generalize flood predictions in catchments not included in the training process. The proposed method, called cGAN-Flood, uses two cGAN models to solve a rain-on-grid problem by first identifying wet cells and then estimating the water depths. The cGANs were trained using HEC-RAS outputs as ground truth. cGAN-Flood distributes a target flood volume (vt) in a given catchment, which can be calculated via water balance from hydrological simulations. Our approach was trained on ten and tested on five urban catchments with distinct characteristics. The cGAN-Flood was compared to HEC-RAS for different rainfall magnitudes and surface roughness. We also compared our approach to the Weighted Cellular Automata 2D (WCA2D), a rapid flood model (RFM) used for rain-on-grid simulations. Our method successfully predicted water depths in the testing areas, showing that cGAN-Flood could generalize to different locations. However, cGAN-Flood tended to underestimate depths in channels in some areas for events with a small peak of precipitation intensity. cGAN-Flood was 50 and 250 times faster than WCA2D and HEC-RAS, respectively. Due to its computational efficiency and accuracy, we suggest that cGAN-Flood can be applied when fast simulations are necessary, and it can be a viable modeling solution for flood forecasts in large-scale watersheds.Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Sanitary Engineerin

Assessing the Impacts of Super Storm Flooding in the Transportation Infrastructure \u2013 Case Study: San Antonio, Texas [Supporting Dataset]

69A3551747106National Transportation Library (NTL) Curation Note: As this dataset is preserved in a repository outside U.S. DOT control, as allowed by the U.S. DOT's Public Access Plan (https://doi.org/10.21949/1503647) Section 7.4.2 Data, the NTL staff has performed NO additional curation actions on this dataset. The current level of dataset documentation is the responsibility of the dataset creator. NTL staff last accessed this dataset at its repository URL on 2022-11-11. If, in the future, you have trouble accessing this dataset at the host repository, please email [email protected] describing your problem. NTL staff will do its best to assist you at that time.Flooding are likely to increase worldwide due to climate change. Large storms, referred here as superstorms, defined as events with return period equal or larger than 100 years, can lead to an increase of property damages and loss of life. The ability to predict and plan for the impacts of superstorms on transportation infrastructure is key to mitigate future damages and losses. This study analyzed 51 combinations of future projections for representative concentration pathways (RCP) 4.5 and 8.5 scenarios, which were used to calculate future 1st and 3rd quartiles, median, minimum and maximum intensity-duration-frequency curves (IDF). A HEC-HMS and GSSHA models were built for Leon Creek and Upper San Antonio watersheds. HEC-RAS 1D and 2D were used to evaluate flooding in 20 bridges and the extent of flooded area and roads in both watersheds and to test flood control scenarios. Land use modification with 5, 10 and 15% of LID areas in the watersheds were simulated. The use of levees and altering channels were evaluated. In addition, we evaluated how an increasing the storage capacity of the Olmos Dam would contribute to reduce flood impacts downstream. Results show that the 3rd quartile of projected IDF is closest to the one originated with observed precipitation, which is likely to increase in the future. The near future (2025-2049) under RCP 4.5 scenario presented the greatest increase in intensity. HEC-HMS models showed that discharge peak will increase for all future periods under both scenarios, for the 100- and 500-years storms. Flood projections generated by GSSHA for 100- and 500-years and future precipitation showed that flooded area can increase significantly. For instance, the increase in flooded roads can be more than 80% in near future for 500-year storm in Leon Creek watershed. HEC-RAS analysis showed that all 20 analyzed bridges can be flooded with 500-years storm with climate change and 15 with the 100-year storm. Simulation showed that LID implementation and the elevation of the Olmos Dam\u2019s crest were ineffective to protect transportation infrastructure. Enhancing cross-sections of the main channels and the use of levees can mitigate the impact in some bridges. This study illustrates the need for updates in the design criteria of current and future transportation infrastructure. The total size of the described zip file is 24.8 MB. Files with the .xlsx extension are Microsoft Excel spreadsheet files. These can be opened in Excel or open-source spreadsheet programs. Docx files are document files created in Microsoft Word. These files can be opened using Microsoft Word or with an open source text viewer such as Apache OpenOffice