Mohamed M. Morsy

HydroShare: Sharing Diverse Environmental Data Types and Models as Social Objects with Application to the Hydrology Domain

The types of data and models used within the hydrologic science community are diverse. New repositories have succeeded in making data and models more accessible, but are, in most cases, limited to particular types or classes of data or models and also lack the type of collaborative and iterative functionality needed to enable shared data collection and modeling workflows. File sharing systems currently used within many scientific communities for private sharing of preliminary and intermediate data and modeling products do not support collaborative data capture, description, visualization, and annotation. In this article, we cast hydrologic datasets and models as “social objects” that can be published, collaborated around, annotated, discovered, and accessed. This article describes the generic data model and content packaging scheme for diverse hydrologic datasets and models used by a new hydrologic collaborative environment called HydroShare to enable storage, management, sharing, publication, and annotation of the diverse types of data and models used by hydrologic scientists. The flexibility of HydroShares data model and packaging scheme is demonstrated using multiple hydrologic data and model use cases that highlight its features.

Distributed Stormwater Controls for Flood Mitigation within Urbanized Watersheds: Case Study of Rocky Branch Watershed in Columbia, South Carolina

AbstractFor highly urbanized watersheds where space is limited, distributed low impact development (LID) stormwater controls could offer an effective retrofit to address flooding problems. The goal of this study is to determine the feasibility of using distributed LID controls within an urbanized watershed for flood mitigation. The Rocky Branch Watershed in Columbia, South Carolina, is an excellent case study because it experiences flash floods almost annually and has limited space for traditional, centralized stormwater controls to mitigate these floods. The Storm Water Management Model (SWMM) was used to model flooding with rain gardens as the LID approach for flood mitigation owing to their storage potential. The results of the study suggest that rain gardens with 30-cm berm heights and a total area equal to 20% of the impervious surfaces within the watershed should provide sufficient storage to mitigate flooding for rain events up to and including a 10-year storm event. Once sufficient storage is avai...

Effect of Rain Gauge Proximity on Rainfall Estimation for Problematic Urban Coastal Watersheds in Virginia Beach, Virginia

AbstractIn urban areas, it is important to have spatially and temporally dense rainfall measurements for flood modeling, monitoring, and prediction. The objective of this paper is to quantify the e...

Leveraging Open Source Software and Parallel Computing for Model Predictive Control Simulation of Urban Drainage Systems Using EPA-SWMM5 and Python

The active control of stormwater systems is a potential solution to increased street flooding in low-lying, low-relief coastal cities due to climate change and accompanying sea level rise. Model predictive control (MPC) has been shown to be a successful control strategy generally and as well as for managing urban drainage specifically. This research describes and demonstrates the implementation of MPC for urban drainage systems using open source software (Python and The United States Environmental Protection Agency (EPA) Storm Water Management Model (SWMM5). The system was demonstrated using a simplified use case in which an actively-controlled outlet of a detention pond is simulated. The control of the pond’s outlet influences the flood risk of a downstream node. For each step in the SWMM5 model, a series of policies for controlling the outlet are evaluated. The best policy is then selected using an evolutionary algorithm. The policies are evaluated against an objective function that penalizes primarily flooding and secondarily deviation of the detention pond level from a target level. Freely available Python libraries provide the key functionality for the MPC workflow: step-by-step running of the SWMM5 simulation, evolutionary algorithm implementation, and leveraging parallel computing. For perspective, the MPC results were compared to results from a rule-based approach and a scenario with no active control. The MPC approach produced a control policy that largely eliminated flooding (unlike the scenario with no active control) and maintained the detention pond’s water level closer to a target level (unlike the rule-based approach).

Integrating scientific cyberinfrastructures to improve reproducibility in computational hydrology: Example for HydroShare and GeoTrust

Abstract The reproducibility of computational environmental models is an important challenge that calls for open and reusable code and data, well-documented workflows, and controlled environments that allow others to verify published findings. This requires an ability to document and share raw datasets, data preprocessing scripts, model inputs, outputs, and the specific model code with all associated dependencies. HydroShare and GeoTrust, two scientific cyberinfrastructures under development, can be used to improve reproducibility in computational hydrology. HydroShare is a web-based system for sharing hydrologic data and models as digital resources including detailed, hydrologic-specific resource metadata. GeoTrust provides tools for scientists to efficiently reproduce and share geoscience applications. This paper outlines a use case example, which focuses on a workflow that uses the MODFLOW model, to demonstrate how the cyberinfrastructures HydroShare and GeoTrust can be integrated in a way that easily and efficiently reproduces computational workflows.

A cloud-based flood warning system for forecasting impacts to transportation infrastructure systems

Abstract The ability to quickly and accurately forecast flooding is increasingly important as extreme weather events become more common. This work focuses on designing a cloud-based real-time modeling system for supporting decision makers in assessing flood risk. The system, built using Amazon Web Services (AWS), automates access and pre-processing of forecast data, execution of a computationally expensive and high-resolution 2D hydrodynamic model, Two-dimensional Unsteady Flow (TUFLOW), and map-based visualization of model outputs. A graphical processing unit (GPU) version of TUFLOW was used, resulting in an 80x execution time speed-up compared to the central processing unit (CPU) version. The system is designed to run automatically to produce near real-time results and consume minimal computational resources until triggered by an extreme weather event. The system is demonstrated for a case study in the coastal plain of Virginia to forecast flooding vulnerability of transportation infrastructure during extreme weather events.