Water Resources Systems Analysis - University of Texas San Antonio

Systems Analysis methods use algorithmic and mathematical approaches for problem-solving. These are powerful methods that can be applied to solve complex design and management problems for water resources systems and other engineering areas. This class will focus on optimization methods, such as linear programming, integer programming, nonlinear programming, genetic algorithms, and dynamic programming, and their application to water resources systems. Advanced Systems Analysis methods, including sensitivity analysis, alternatives generation, and multi-objective optimization will be introduced to address the complexities associated with public sector decision-making. Course taught at University of Texas San Antonio

Water Resources Systems Analysis Reading Assignments

Read these articles: https://ascelibrary.org/doi/10.1061/%28ASCE%290733-9496%282000%29126%3A3%28118%29 https://ascelibrary.org/doi/10.1061/%28ASCE%290733-9372%281990%29116%3A1%28182%29 https://ascelibrary.org/doi/10.1061/%28ASCE%290733-9496%281986%29112%3A3%28339%29 https://link.springer.com/article/10.1007/s11269-015-0970-6 https://ascelibrary.org/doi/10.1061/%28ASCE%290733-9496%282005%29131%3A6%28441%29 Answer the following questions: 1) Describe in your own words what is the problem that is being addressed? Why is it important? 2) What system is being modeled? Identify the system boundaries, processes, inputs and outputs. 3) How the system is being modeled? Identify the main state variables, parameters, initial conditions and boundary conditions. 4) What is the optimization method used to solve the problem. Describe the method. 5) What are the objective functions, constraints and decision variables? 6) What are the main conclusions and insights generated by the use of optimization? Analyze critically the methodology, identifying limitations and weaknesses and provide suggestions to better address the problem

Complex Adaptive Systems Simulation-Optimization Framework for Adaptive Urban Water Resources Management

Population growth, urbanization and climate change threaten urban water systems. The rise of demands caused by growing urban areas and the potential decrease of water availability caused by the increase of frequency and severity of droughts challenge the continued well-being of society. Due to increasing environmental and financial constraints, water management paradigms have shifted from supply augmentation to demand management, and water conservation initiatives may efficiently decrease water demands to more sustainable levels. To provide reliable assessment of the efficiencies of different demand management strategies, new modeling techniques are needed that can simulate decentralized decisions of consumers and their interactions with the water system. An integrated simulation-optimization framework, based on the paradigm of Complex Adaptive Systems, is developed here to model dynamic interactions and adaptations within social, built, and natural components of urban water systems. The framework goes beyond tradition engineering simulations by incorporating decentralized, heterogeneous and autonomous agents, and by simulating dynamic feedback loops among modeling components. The framework uses modeling techniques including System Dynamics, Cellular Automata, and Agent-based Modeling to simulate housing and population growth, a land use change, residential water consumption, the hydrologic cycle, reservoir operation, and a policy/decision maker. This research demonstrates the applicability of the proposed framework through a series of studies applied to a water supply system of a large metropolitan region that is located in a semi-arid region and suffers recurrently from severe droughts. A set of adaptive demand management strategies, that apply contingency restrictions, land use planning, and water conservation technologies, such as rainwater harvesting systems, are evaluated. A multi-objective Evolutionary Algorithm is coupled with the CAS simulation framework to identify optimal strategies and explore conflicting objectives within a water system. The results demonstrate the benefits of adaptive management by updating management decisions to changing conditions. This research develops a new hydrologic sustainability metric, developed to quantify the stormwater impacts of urbanization. The Hydrologic Footprint Residence captures temporal and spatial hydrologic characteristics of a flood wave passing through a stream segment and is used to assess stormwater management scenarios, including Best Management Practices and Low Impact Development

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