A generic parameter estimation algorithm was developed for the simultaneous extraction of multiple parameters from experimental or field observations. The basis of the method lies in the minimization of the variation between model predictions and observations via the iterative numerical determination of the functional minima of the model with respect to the parameters being determined. A multivariable Newton technique, and derivatives thereof, are used for this purpose. By relying on numerical differentiation in the iterative process, the technique is not limited by the model complexity, solution methodology, or scale of its domain. Model parameters have been estimated successfully for analytical and numerical solutions to dispersive and advective‐dispersive particle transport model equations. The effect of sampling or measurement error and initial parameter estimates on algorithm performance has been evaluated. This approach has been successfully applied to determine data requirements and constraints (qu...

Stormwater runoff introduces several pollutants to the receiving water bodies that may cause degradation of the water quality. Stormwater management systems such as detention facilities and wetland can improve the water quality by removing various pollutants associated with the runoff. The objective of this research project is to determine the performance and efficiency of two major regional detention facilities (RDFs) with different designs and structures in reducing pollutants based on various storm events in McAllen, Texas. The two sites are the McAuliffe RDF and the Morris RDF; each site was incorporated with a constructed wetland with a different design and structure to enhance the pollutant removal process. The McAuliffe RDF reduced the concentration and load of many stormwater constituents in comparison to the Morris RDF. The observed concentrations and pollutant loads of suspended solids were much lower in the runoff of the inlet compared to the outlet for both sites. The McAuliffe RDF showed better concentration and load reduction for nutrients, such as nitrogen and phosphorus, of different species. However, both sites did not show a significant improvement of organic material. In addition, the indicator bacteria concentration represented a fluctuation between the inlet and outlet at each site.

To address regional flooding in the United States, federal and state agencies are adopting strict drainage policies in any large-scale commercial development within the watershed boundary. The conventional approach of implementing a wet detention pond (WP) reduces the land cover and causes operation and maintenance challenges eventually. The present study developed a decision-support system (DSS) in the Lower Rio Grande Valley region of South Texas for optimal selection of Best Management Practices (BMPs) by substituting a portion of the WP footprint with three regionally promising low-impact development practices, namely, porous concrete pavement (PCP), bioretention (BR), and bioswale (BS). Source Load Assessment and Management Model for Windows (WinSLAMM) was used as the foundation for the DSS database and algorithm development. This tool suggested that the implementation of bioswale alone can considerably reduce the footprint and construction cost. Less than 0.95 ha of installation of BR and BS can mitigate 79–91% of runoff from a maximum of 5 ha of commercial development. A combination of BR, BS, and WP was found to reduce runoff significantly (~100%), which suggests that the successful adoption of DSS might support better planning of the urban stormwater management in the Lower Rio Grande Valley (LRGV).

Lower Laguna Madre (LLM) is designated as an impaired waterway for high concentrations of bacteria and low dissolved oxygen. The main freshwater sources to the LLM flow from the North and Central waterways which are composed of three main waterways: Hidalgo/Willacy Main Drain (HWMD), Raymondville Drain (RVD), and International Boundary & Water Commission North Floodway (IBWCNF) that are not fully characterized. The objective of this study is to perform a watershed characterization to determine the potential pollution sources of each watershed. The watershed characterization was achieved by developing a cyberinfrastructure, and it collects a wide inventory of data to identify which one of the three waterways has a major contribution to the LLM. Cyberinfrastructure development using the Geographic Information System (GIS) database helped to comprehend the major characteristics of each area contributing to the watershed supported by the analysis of the data collected. The watershed characterization process started with delineating the boundaries of each watershed. Then, geospatial and non-geospatial data were added to the cyberinfrastructure from numerous sources including point and nonpoint sources of pollution. Results showed that HWMD and IBWCNF watersheds were found to have a higher contribution to the water impairments to the LLM. HWMD and IBWCNF comprise the potential major sources of water quality impairments such as cultivated crops, urbanized areas, on-site sewage facilities, colonias, and wastewater effluents.