72 research outputs found
Large-scale Process-based Urban Hydrological Modeling Framework
No full textUrban flooding presents a significant global challenge, exacerbated by increasingly frequent extreme climate events and rapid urbanization. Mitigation efforts typically involve implementing various strategies to enhance stormwater storage and infiltration. However, the complexity of these strategies in large metropolitan areas, coupled with the often inadequate representation of below-ground urban stormwater networks (BUSNs) in current hydrologic models, highlights the necessity of a comprehensive and scalable framework for planning and assessing potential vulnerabilities. This dissertation addresses these challenges by introducing a novel, physically-based urban modeling framework designed to simulate urban runoff generation and routing across natural and urban surface areas and through BUSNs. Our framework's innovative design ensures a balanced representation of natural and urban components and effectively overcomes data scarcity and computational limitations common in large-scale urban modeling. It utilizes graph theory and various land datasets to derive BUSNs, providing a practical solution to the BUSN data scarcity issue. The algorithm demonstrated its effectiveness in four U.S. metropolitan areas with partially available BUSN data. Applicable at both local, e.g., watershed, and larger, e.g., Conterminous United States (CONUS), scales, first, we evaluated our framework at nine representative urban watersheds in Houston, Texas. We gained crucial insights into the capacity and limitations of BUSNs during flood events, underlining the importance of diverse flood mitigation strategies. Particularly, we demonstrated the nonlinear relationship between the reduction impact of peak flows and the magnitude of the peak, stressing the role of increased storage capacity for impervious areas in flood mitigation. Second, at the CONUS scale, we integrated urban water management practices and lake routing, presenting a comprehensive tool for assessing mitigation strategies. Moreover, we devised a multiscale approach for efficient derivation of BUSNs at CONUS scale, by drawing on concepts from graph theory and hydrologic conditioning of elevation data. An in-depth evaluation revealed complex interactions between natural and anthropogenic factors in influencing hydrological responses, highlighting the varied impacts of BUSNs and reservoir operations on flooding. Our proposed modeling framework is an effective tool for simulating urban hydrological responses under different hydroclimate conditions, assisting in urban planning, water management strategies, and mitigating urban flooding risks. It addresses existing models' limitations related to modeling BUSNs, providing a comprehensive and scalable solution to climate change-induced urban flooding.Civil and Environmental Engineering, Department o
Accessing Hydrology and Climatology Database Using Web Services Through Python
No full textPreparing and processing input data is one the most time consuming parts of hydrological and climate modeling, both physical and data-driven. The required input data are scattered through the web offered in various formats and through several web service protocols. A major barrier for streamlining the use of these web services in the scientific community, is the required technical skills and knowledge. HyRiver is a stack of Python libraries that bridges this gap by providing high-level APIs to these web services. This showcase provides a workflow for carrying out a data analysis across US East, West, and Gulf coasts. We get various datasets such as daily streamflow, river network, and climate data from different sources. Presentation materials are available here
An agriprecision decision support system for weed management in pastures
No full textPastures are a vital source of dairy products and cattle nutrition, and as such, play a significant role in New Zealand’s agricultural economy. However, weeds can be a major problem for pastures, making it a challenge for dairy farmers to monitor and control them. Currently, most of the tasks for weed management are done manually, and farmers lack persistent technology for weed control. This motivated us to design, implement, and evaluate a Decision Support System (DSS) to detect weeds in pastures and provide decisions for the cleanup of weeds. Our proposed system uses two primary inputs: weeds and bare patches. We created a synthetic dataset to train a weed detection model and designed a fuzzy inference system to assess a pasture. We also used a neuro-fuzzy system in our DSS to evaluate our fuzzy model and tune its parameters for better functioning and accuracy. Our work aims to assist dairy farmers in better weed monitoring, as well as to provide 2D maps of weed density and yield score, which can be of significant value when no digital and meaningful images of pastures exist. The system can also support farmers in scheduling, recommending prohibitive tasks, and storing historical data for pasture analysis, collaborated by stakeholders.fuzzy systemsobject detectionpasture managementdecision makingdecision support systemsfuzzy neural networksPeer reviewe
cheginit/hydrodata: v0.7.1
No full textThis is a bug fix that addresses the nlcd function issue where not all None layers are dropped. Also, a new flag called title_ypos was added to the plot.signatures function for adjusting the vertical position of the supertitle which is useful for multi-line titles
Barriers and facilitators to patient engagement in patient safety from patients and healthcare professionals' perspectives: A systematic review and meta-synthesis
No full textAIMS: To explore patients' and healthcare professionals' (HCPs) perceived barriers and facilitators to patient engagement in patient safety. METHODS: We conducted a systematic review and meta-synthesis from five computerized databases, including PubMed/MEDLINE, Embase, Web of Science, Scopus and PsycINFO, as well as grey literature and reference lists of included studies. Data were last searched in December 2019 with no limitation on the year of publication. Qualitative and Mix-methods studies that explored HCPs' and patients' perceptions of barriers and facilitators to patient engagement in patient safety were included. Two authors independently screened the titles and the abstracts of studies. Next, the full texts of the screened studies were reviewed by two authors. Potential discrepancies were resolved by consensus with a third author. The Mixed Methods Appraisal Tool was used for quality appraisal. Thematic analysis was used to synthesize results. RESULTS: Nineteen studies out of 2616 were included in this systematic review. Themes related to barriers included: patient unwillingness, HCPs' unwillingness, and inadequate infrastructures. Themes related to facilitators were: encouraging patients, sharing information with patients, establishing trustful relationship, establishing patient-centred care and improving organizational resources. CONCLUSION: Patients have an active role in improving their safety. Strategies are required to address barriers that hinder or prevent patient engagement and create capacity and facilitate action.Full Tex
pyro: a framework for hydrodynamics explorations and prototyping
No full text<p>`pyro` is a Python-based simulation framework designed for ease of<br>
implementation and exploration of hydrodynamics methods. It is<br>
built in a object-oriented fashion, allowing for the reuse of<br>
the core components and fast prototyping of new methods.</p>
cheginit/hydrodata: Release v0.9.1
No full textPlease check HISTORY.rst file for a detailed list
of changes
Effects of high-quality elevation data and explanatory variables on the accuracy of flood inundation mapping via Height Above Nearest Drainage
No full text<jats:p>Abstract. Given the availability of high-quality and high-spatial-resolution digital elevation maps (DEMs) from the United States Geological Survey's 3D Elevation Program (3DEP), derived mostly from light detection and ranging (lidar) sensors, we examined the effects of these DEMs at various spatial resolutions on the quality of flood inundation map (FIM) extents derived from a terrain index known as Height Above Nearest Drainage (HAND). We found that using these DEMs improved the quality of resulting FIM extents at around 80 % of the catchments analyzed when compared to using DEMs from the National Hydrography Dataset Plus High Resolution (NHDPlusHR) program. Additionally, we varied the spatial resolution of the 3DEP DEMs at 3, 5, 10, 15, and 20 m (meters), and the results showed no significant overall effect on FIM extent quality across resolutions. However, further analysis at coarser resolutions of 60 and 90 m revealed a significant degradation in FIM skill, highlighting the limitations of using extremely coarse-resolution DEMs. Our experiments demonstrated a significant burden in terms of the computational time required to produce HAND and related data at finer resolutions. We fit a multiple linear regression model to help explain catchment-scale variations in the four metrics employed and found that the lack of reservoir flooding or inundation upstream of river retention systems was a significant factor in our analysis. For validation, we used Interagency Flood Risk Management (InFRM) Base Level Engineering (BLE)-produced FIM extents and streamflows at the 100- and 500-year event magnitudes in a sub-region in eastern Texas.
</jats:p>
cheginit/pygeohydro: Release v0.9.2
No full textPlease check HISTORY.rst file for a detailed list
of changes
- …
