Enhanced watershed modeling and data analysis with a fully coupled hydrologic model and cloud-based flow analysis

2014 Summer.Includes bibliographical references.In today's world of increased water demand in the face of population growth and climate change, there are no simple answers. For this reason many municipalities, water resource engineers, and federal analyses turn to modeling watersheds for a better understanding of the possible outcomes of their water management actions. The physical processes that govern movement and transport of water and constituents are typically highly nonlinear. Therefore, improper characterization of a complex, integrated, processes like surface-subsurface water interaction can substantially impact water management decisions that are made based on existing models. Historically there have been numerous tools and watershed models developed to analyze watersheds or their constituent components of rainfall, run-off, irrigation, nutrients, and stream flow. However, due to the complexity of real watershed systems, many models have specialized at analyzing only a portion of watershed processes like surface flow, subsurface flow, or simply analyzing local monitoring data rather than modeling the system. As a result many models are unable to accurately represent complex systems in which surface and subsurface processes are both important. Two popular watershed models have been used extensively to represent surface processes, SWAT (Arnold et al, 1998), and subsurface processes, MODFLOW (Harbaugh, 2005). The lack of comprehensive watershed simulation has led to a rise in uncertainty for managing water resources in complex surface-subsurface driven watersheds. For this reason, there have been multiple attempts to couple the SWAT and MODFLOW models for a more comprehensive watershed simulation (Perkins and Sophocleous, 1999; Menking, 2003; Galbiati et al., 2006; Kim et al., 2008); however, the previous couplings are typically monthly couplings with spatial restrictions for the two models. Additionally, most of these coupled SWAT-MODFLOW models are unavailable to the general public, unlike the constituent SWAT and MODFLOW models which are available. Furthermore, many of these couplings depend on a forced equal spatial discretization for computational units. This requires that one MODFLOW grid cell is the same size and location of one SWAT hydrologic response unit (HRU). Additionally, many of the previous couplings are based on a loose monthly average coupling which might be insufficient in natural spring and irrigated agricultural driven groundwater systems which can fluctuate on a sub-monthly time scale. The primary goal of this work is to enhance the capacity for modeling watershed processes by fully coupling surface and subsurface hydrologic processes at a daily time step. The specific objectives of this work are 1) to examine and create a general spatial linkage between SWAT and MODFLOW allowing the use of spatially-different existing models for coupling; 2) to examine existing practices and address current weaknesses for coupling of the SWAT and MODFLOW models to develop an integrated modeling system; 3) to demonstrate the capacity of the enhanced model compared to the original SWAT and MODFLOW models on the North Fork of the Sprague River in the Upper Klamath Basin in Oregon. The resulting generalized daily coupling between a spatially dis-similar SWAT and MODFLOW model on the North Fork of the Sprague River has resulted in a slightly more lower representation of monthly stream flow (monthly R2 = 0.66, NS = 0.38) than the original SWAT model (monthly R2 = 0.60, NS = 0.57) with no additional calibration. The Log10 results of stream flow illustrate an even greater improvement between SWAT-MODFLOW correlation (R2) but not the overall simulation (NS) (monthly R2 = 0.74, NS = -0.29) compared to the original SWAT (monthly R2 = 0.63, NS = 0.63) correlation (R2). With an improved water table representation, these SWAT-MODFLOW simulation results illustrate a more in depth representation of overall stream flows on a groundwater influenced tributary of the Sprague River than the original SWAT model. Additionally, with the increased complexity of environmental models there is a need to design and implement tools that are more accessible and computationally scalable; otherwise their use will remain limited to those that developed them. In light of advancements in cloud-computing technology a better implementation of modern desktop software packages would be the use of scalable cloud-based cyberinfrastructure, or cloud-based environmental modeling services. Cloud-based deployment of water data and modeling tools assist in a scalable as well as platform independent analysis; meaning a desktop, laptop, tablet, or smart phone can perform the same analyses. To utilize recent advancements in computer technology, a further focus of this work is to develop and demonstrate a scalable cloud-computing web-tool that facilitates access and analysis of stream flow data. The specific objectives are to 1) unify the various stream flow analysis topics into a single tool; 2) to assist in the access to data and inputs for current flow analysis methods; 3) to examine the scalability benefits of a cloud-based flow analysis tool. Furthermore, the new Comprehensive Flow Analysis tool successfully combined time-series statistics, flood analysis, base-flow separation, drought analysis, duration curve analysis, and load estimation into a single web-based tool. Preliminary and secondary scalability testing has revealed that the CFA analyses are scalable in a cloud-based cyberinfrastructure environment to a request rate that is likely unrealistic for web tools

Regionalising a soil-plant model ensemble to simulate future yields under changing climatic conditions

Models are supportive in depicting complex processes and in predicting their effects. Climate models are applied in many areas to assess the possible consequences of climate change. Even though Global Climate Models (GCM) have now been regionalised to the national level, their resolution of down to 5x5 km2 is still rather coarse from the perspective of a plant modeller. Plant models were developed for the field scale and work spatially explicitly. This requires to make adjustments if they are applied at coarser scales. The regionalisation of plant models is reasonable and advantageous against the background of climate change and policy advice, both gaining in importance. The higher the spatial and temporal heterogeneity of a region, the greater the computational need. The (dis)aggregation of data, frequently available in differing resolutions or quality, is often unavoidable and fraught with high uncertainties. In this dissertation, we regionalised a spatially-explicit crop model ensemble to improve yield projections for winter wheat under a changing climate. This involved upscaling a crop model ensemble consisting of three crop models to the Stuttgart region, which has an area of 3,654 km2. After a thorough parameter estimation performed with a varying number of Agricultural Response Units on a high-performance computing cluster, yield projections up to the year 2100 were computed. The representative concentration pathways of the Intergovernmental Panel on Climate Change (IPCC) RCP2.6 (large reduction of CO2 emissions) and RCP8.5 (worst case scenario) served as a framework for this effort. Under both IPCC scenarios, the model ensemble predicts stable winter wheat yields up to 2100, with a moderate decrease of 5 dt/ha for RCP2.6 and a small increase of 1 dt/ha for RCP8.5. The variability within the model ensemble is particularly high for RCP8.5. Results were obtained without accounting for a potential progress in wheat breeding.Modelle helfen uns dabei, komplexe Prozesse abzubilden um Vorhersagen über deren Wirkung treffen zu können. Klimamodelle werden in vielen Bereichen eingesetzt, um die möglichen Konsequenzen des Klimawandels abzuschätzen. Auch wenn globale Klimamodelle (GCM) inzwischen bis hinunter auf die nationale Ebene regionalisiert wurden, ist ihre Auflösung mit bis zu 5x5 km2 aus der Sicht der Pflanzenmodellierung noch immer recht gering. Da Pflanzenmodelle für die Feldskala entwickelt wurden und deshalb räumlich explizit sind, muss eine Anpassung erfolgen, um sie auf größeren Skalen als der Feldskala anwenden zu können. Die Regionalisierung von Pflanzenmodellen ist nicht nur in Verbindung mit Klimasimulationen sinnvoll, sondern generell in der Politikberatung. Hier wie dort gewinnen regionale Anwendungen an Bedeutung. Je höher die räumliche und zeitliche Heterogenität einer Region, desto größer ist die benötigte Rechenkapazität. Die (Dis-)Aggregierung von Datensätzen, die oftmals in unterschiedlicher Auflösung oder Qualität vorliegen, ist meist nicht zu vermeiden und mit hohen Unsicherheiten behaftet. Das Ziel dieser Dissertation ist die Regionalisierung von räumlich-expliziten Simulationen des Pflanzenwachstums, um Ertragsprojektionen für Winterweizen unter einem sich wandelnden Klima zu erhalten. Dafür wurde ein Pflanzenmodellensemble, bestehend aus drei Pflanzenmodellen, auf die Ebene der Region Stuttgart, mit einer Fläche von 3.654 km2, skaliert. Nach einer sorgfältigen Parameterschätzung basierend auf drei verschiedenen Sets von Landwirtschaftlichen Response Units auf einem High Performance Rechencluster, wurden Ertragsprojektionen bis zum Jahr 2100 berechnet. Die repräsentativen Konzentrationspfade des Intergovernmental Panel on Climate Change (IPCC) RCP2.6 (drastische Reduzierung der CO2-Emissionen) und RCP8.5 (Worst-Case-Szenario) dienten als Rahmen für die Simulationen. Das Modellensemble zeigt im Ergebnis stabile Winterweizen-Erträge bis 2100 für beide Szenarien, mit einem Rückgang von 5 dt/ha bei RCP2.6 und einem geringen Anstieg von 1 dt/ha bei RCP8.5. Insbesondere bei RCP8.5 ist die Variabilität innerhalb des Ensembles sehr hoch. Zu berücksichtigen ist, dass der Züchtungsfortschritt in den Ergebnissen nicht abgebildet wurde

Broad-scale flood modelling in the cloud : validation and sensitivities from hazard to impact

Broad-scale flood modelling is a growing research area with applications in insurance, adaption and response. This has been fuelled by increasing availability of continental-global datasets providing inputs to a mounting array of models. However, outputs vary greatly and validation is challenging. This research developed a novel, consistent methodology for assigning performance scores to models using a range of gridded datasets and an accurate numerical 2D hydrodynamic modelling system. Validation using both extent and discharge was conducted for Storm Desmond in Northern England and the global applicability of the methodology demonstrated across Europe and in Indonesia. To meet computational demands, a cloud computing framework was implemented using a PostgreSQL database. Visualisation of results was achieved using a newly designed web interface. Finally OpenStreetMap data was overlaid to demonstrate the sensitivity of impacts to flood model inputs. The main findings are that relative importance of precipitation and topographic data changes depending on the metrics used for validation. More variability in peak discharge error was found between models using different rainfall inputs (22-70%) than different DEMs (9-37%). Conversely, flood extent critical success index (CSI) was more sensitive to the choice of topography (25-32%) than rainfall (27-30%), though overall variability in CSI was low. This was echoed in the impacts analysis with higher sensitivity of feature inundation to topography than rainfall. Importantly, there was far more overall variability in discharge accuracy than extent which indicates that reproduction of peak discharge is a more powerful measure for assessing model performance. Models driven by globalcontinental precipitation products underestimated peaks more than those using Met Office rain gauge data, though better performance was demonstrated by replacing ERA-Interim with the updated ERA5 dataset. The research highlights a growing need for more robust validation of broad scale flood simulations, and the difficulties this presents. Strong influence of dataset choice on infrastructure inundation has consequences for insurance premiums, development planning and adaptation to climate change risks which should not be ignored.NERC for funding the research through the Data, Risk and Environmental Analytical Methods (DREAM) training centre

Earth Observation Open Science and Innovation

geospatial analytics; social observatory; big earth data; open data; citizen science; open innovation; earth system science; crowdsourced geospatial data; citizen science; science in society; data scienc

Machine Learning Research Trends in Africa: A 30 Years Overview with Bibliometric Analysis Review

In this paper, a critical bibliometric analysis study is conducted, coupled with an extensive literature survey on recent developments and associated applications in machine learning research with a perspective on Africa. The presented bibliometric analysis study consists of 2761 machine learning-related documents, of which 98% were articles with at least 482 citations published in 903 journals during the past 30 years. Furthermore, the collated documents were retrieved from the Science Citation Index EXPANDED, comprising research publications from 54 African countries between 1993 and 2021. The bibliometric study shows the visualization of the current landscape and future trends in machine learning research and its application to facilitate future collaborative research and knowledge exchange among authors from different research institutions scattered across the African continent

CIRA annual report FY 2016/2017

Reporting period April 1, 2016-March 31, 2017

NASA Tech Briefs, September 2011

Topics covered include: Fused Reality for Enhanced Flight Test Capabilities; Thermography to Inspect Insulation of Large Cryogenic Tanks; Crush Test Abuse Stand; Test Generator for MATLAB Simulations; Dynamic Monitoring of Cleanroom Fallout Using an Air Particle Counter; Enhancement to Non-Contacting Stress Measurement of Blade Vibration Frequency; Positively Verifying Mating of Previously Unverifiable Flight Connectors; Radiation-Tolerant Intelligent Memory Stack - RTIMS; Ultra-Low-Dropout Linear Regulator; Excitation of a Parallel Plate Waveguide by an Array of Rectangular Waveguides; FPGA for Power Control of MSL Avionics; UAVSAR Active Electronically Scanned Array; Lockout/Tagout (LOTO) Simulator; Silicon Carbide Mounts for Fabry-Perot Interferometers; Measuring the In-Process Figure, Final Prescription, and System Alignment of Large; Optics and Segmented Mirrors Using Lidar Metrology; Fiber-Reinforced Reactive Nano-Epoxy Composites; Polymerization Initiated at the Sidewalls of Carbon Nanotubes; Metal-Matrix/Hollow-Ceramic-Sphere Composites; Piezoelectrically Enhanced Photocathodes; Iridium-Doped Ruthenium Oxide Catalyst for Oxygen Evolution; Improved Mo-Re VPS Alloys for High-Temperature Uses; Data Service Provider Cost Estimation Tool; Hybrid Power Management-Based Vehicle Architecture; Force Limit System; Levitated Duct Fan (LDF) Aircraft Auxiliary Generator; Compact, Two-Sided Structural Cold Plate Configuration; AN Fitting Reconditioning Tool; Active Response Gravity Offload System; Method and Apparatus for Forming Nanodroplets; Rapid Detection of the Varicella Zoster Virus in Saliva; Improved Devices for Collecting Sweat for Chemical Analysis; Phase-Controlled Magnetic Mirror for Wavefront Correction; and Frame-Transfer Gating Raman Spectroscopy for Time-Resolved Multiscalar Combustion Diagnostics