Translating Monsoon Event Precipitation into Rainfall Estimates for Joshua Tree National Park

Due to the remote nature of Joshua Tree National Park, few direct measurements of precipitation exist. This is especially true of summer monsoon events, which are localized and discrete yet provide all of the summer rainfall for the region. These events have an impact on wildlife, vegetation, and infrastructure. This project incorporated NEXRAD WSR-88D Level II data into a GIS environment to process rainfall events in order to enhance the park’s monitoring capability. An empirical relationship was derived to produce rainfall estimates from radar reflectivity data more accurately for the region. A toolset was developed within ArcGIS to automatically reformat and process NEXRAD datasets to produce precipitation data for monsoon events, with rainfall locations and amounts. This toolset also included methods to provide information on the amount of runoff, infiltrated water, and accumulated water volume produced from a precipitation event. These products can be fully integrated with vegetation information, facilities, and infrastructure locations for vegetation habitat modeling and infrastructure management

Facilitating radar precipitation data processing, assessment and analysis: A GIS-compatible python approach

A review of existing tools for radar data processing revealed a lack of open source software for automated processing, assessment and analysis of weather radar composites. The ArcGIS-compatible Python package radproc attempts to reduce this gap. Radproc provides an automated raw data processing workflow for nationwide, freely available German weather radar climatology (RADKLIM) and operational (RADOLAN) composite products. Raw data are converted into a uniform HDF5 file structure used by radproc’s analysis and data quality assessment functions. This enables transferability of the developed analysis and export functionality to other gridded or point-scale precipitation data. Thus, radproc can be extended by additional import routines to support any other German or non-German precipitation dataset. Analysis methods include temporal aggregations, detection of heavy rainfall and an automated processing of rain gauge point data into the same HDF5 format for comparison to gridded radar data. A set of functions for data exchange with ArcGIS allows for visualisation and further geospatial analysis. The application on a 17-year time series of hourly RADKLIM data showed that radproc greatly facilitates radar data processing and analysis by avoiding manual programming work and helps to lower the barrier for non-specialists to work with these novel radar climatology datasets. © 2019 The Author

Using Digital Elevation Models Derived from Airborne LiDAR and Other Remote Sensing Data to Model Channel Networks and Estimate Fluvial Geomorphological Metrics

Recent advances in remote-sensing technologies and analysis methods, specifically airborne-LiDAR elevation data and corresponding geographical information system (GIS) tools, present new opportunities for automated and rapid fluvial geomorphic (FGM) assessments that can cover entire watersheds. In this thesis, semi-automated GIS tools are used to extract channel centerlines and bankfull width values from digital elevation models (DEM) for five New England watersheds. For each study site, four centerlines are mapped. LiDAR and NED lines are delineated using ArcGIS spatial analyst tools with high-resolution (1-m to 2-m) LiDAR DEMs or USGS National Elevation Dataset (NED) DEMs, respectively. Resampled LiDAR decreases LiDAR DEM resolution and then runs spatial analyst tools. National Hydrography Dataset (NHD) lines are mapped by the USGS. All mapped lines are compared to centerlines delineated from photography and LiDAR DEMs. Bankfull widths at each site are determined through three methods. Regional regression equations are applied using variables derived from LiDAR and NED DEMs separately, producing two sets of width results. Additionally, the Hydrogeomorphological Geoprocessing Toolset (HGM) is used to extract widths from LiDAR data. Widths are also estimated visually from aerial photos and LiDAR DEMs. Widths measured directly in the field or derived from field-data are used as a baseline for comparison. I find that with a minimal amount of preprocessing, specifically through DEM resampling, LiDAR data can be used to model a channel that is highly correlated with the shape and location of the mapped channel. NED-derived channels model the mapped channel shape with even greater accuracy, and model the channel location only minimally less accurately. No tool used in this study accurately extracted bankfull width values, but analysis of LiDAR data by the HGM toolset did capture details that could not be resolved using regression equations. Overall, I conclude that automated, computerized LiDAR interpretation needs to improve significantly for the expense of data collection to be cost-effective at a watershed scale

A GIS-based methodological framework to identify superficial water sources and their corresponding conduction paths for gravity-driven irrigation systems in developing countries

The limited availability of fresh water is a major constraint to agricultural productivity and livelihood security in many developing countries. Within the coming decades, smallholder farmers in drought-prone areas are expected to be increasingly confronted with local water scarcity problems, but their access to technological knowledge and financial resources to cope with these problems is often limited. In this article, we present a methodological framework that allows for identifying, in a short period of time, suitable and superficial water sources, and cost-effective water transportation routes for the provisioning of gravity-driven irrigation systems. As an implementation of the framework, we present the automated and extensible geospatial toolset named “AGRI’’, and elaborate a case study in Western Honduras, where the methodology and toolset were applied to provide assistance to field technicians in the process of identifying water intake sites and transportation routes. The case study results show that 28 % of the water intake sites previously identified by technicians (without the support of AGRI) were found to be not feasible for gravity-driven irrigation. On the other hand, for the feasible water intake sites, AGRI was able to provide viable and shorter water transportation routes to farms in 70 % of the cases. Furthermore, AGRI was able to provide alternative feasible water intake sites for all considered farms, with correspondingly viable water transportation routes for 74 % of them. These results demonstrate AGRI’s potential to reduce time, costs and risk of failure associated with the development of low-cost irrigation systems, which becomes increasingly needed to support the livelihoods of some of the world’s most vulnerable populations

Catchment parameter analysis in fl ood hydrology using GIS applications

Published ArticleThe use of Geographical Information Systems (GIS) has permeated almost every field in the engineering, natural and social sciences, offering accurate, efficient, reproducible methods for collecting, viewing and analysing spatial data. GIS do not inherently have all the hydrological simulation capabilities that complex hydrological models do, but are used to determine many of the catchment parameters that hydrological models or design flood estimation methods require. The purpose of this study was to perform catchment parameter analysis using GIS applications available in the ArcGISTM environment. The paper will focus on the deployment of special GIS spatial modelling tools versus conventional manual methods used in conjunction with standard GIS tools to estimate typical catchment parameters, e.g. area, average catchment and watercourse slopes, main watercourse lengths and the catchment centroid. The manual catchment parameter estimation methods with GIS-based input parameters demonstrated an acceptable degree of association with the special GIS spatial modelling tools, but proved to be sensitive to biased user-input at different scale resolutions. GIS applications in an ArcGISTM environment for the purpose of catchment parameter analyses are recommended to be used as the standard procedure in any proposed hydrological assessment

Catchment parameter analysis in flood hydrology using GIS applications

Published ArticleThe use of Geographical Information Systems (GIS) has permeated almost every field in the engineering, natural and social sciences, offering accurate, efficient, reproducible methods for collecting, viewing and analysing spatial data. GIS do not inherently have all the hydrological simulation capabilities that complex hydrological models do, but are used to determine many of the catchment parameters that hydrological models or design flood estimation methods require. The purpose of this study was to perform catchment parameter analysis using GIS applications available in the ArcGISTM environment. The paper will focus on the deployment of special GIS spatial modelling tools versus conventional manual methods used in conjunction with standard GIS tools to estimate typical catchment parameters, e.g. area, average catchment and watercourse slopes, main watercourse lengths and the catchment centroid. The manual catchment parameter estimation methods with GIS-based input parameters demonstrated an acceptable degree of association with the special GIS spatial modelling tools, but proved to be sensitive to biased user-input at different scale resolutions. GIS applications in an ArcGISTM environment for the purpose of catchment parameter analyses are recommended to be used as the standard procedure in any proposed hydrological assessment

High resolution global gridded data for use in population studies

Recent years have seen substantial growth in openly available satellite and other geospatial data layers, which represent a range of metrics relevant to global human population mapping at fine spatial scales. The specifications of such data differ widely and therefore the harmonisation of data layers is a prerequisite to constructing detailed and contemporary spatial datasets which accurately describe population distributions. Such datasets are vital to measure impacts of population growth, monitor change, and plan interventions. To this end the WorldPop Project has produced an open access archive of 3 and 30 arc-second resolution gridded data. Four tiled raster datasets form the basis of the archive: (i) Viewfinder Panoramas topography clipped to Global ADMinistrative area (GADM) coastlines; (ii) a matching ISO 3166 country identification grid; (iii) country area; (iv) and slope layer. Further layers include transport networks, landcover, nightlights, precipitation, travel time to major cities, and waterways. Datasets and production methodology are here described. The archive can be downloaded both from the WorldPop Dataverse Repository and the WorldPop Project websit

Advanced of Mathematics-Statistics Methods to Radar Calibration for Rainfall Estimation; A Review

Ground-based radar is known as one of the most important systems for precipitation measurement at high spatial and temporal resolutions. Radar data are recorded in digital manner and readily ingested to any statistical analyses. These measurements are subjected to specific calibration to eliminate systematic errors as well as minimizing the random errors, respectively. Since statistical methods are based on mathematics, they offer more precise results and easy interpretation with lower data detail. Although they have challenge to interpret due to their mathematical structure, but the accuracy of the conclusions and the interpretation of the output are appropriate. This article reviews the advanced methods in using the calibration of ground-based radar for forecasting meteorological events include two aspects: statistical techniques and data mining. Statistical techniques refer to empirical analyses such as regression, while data mining includes the Artificial Neural Network (ANN), data Kriging, Nearest Neighbour (NN), Decision Tree (DT) and fuzzy logic. The results show that Kriging is more applicable for interpolation. Regression methods are simple to use and data mining based on Artificial Intelligence is very precise. Thus, this review explores the characteristics of the statistical parameters in the field of radar applications and shows which parameters give the best results for undefined cases. DOI: 10.17762/ijritcc2321-8169.15012

Afromontane forest ecosystem studies with multi-source satellite data

The Afromontane Forest of north Eastern Nigeria is an important ecological ecosystem endowed with flora and fauna species. The main goals of this thesis were to explore the potential of multi-source satellite remote sensing for the assessment of the biodiversity-rich Afromontane Forest ecosystem using different methods and algorithms to retrieve two major remote sensing -essential biodiversity variables (RS-EBV) which are related and are also the major determinants of biological and ecosystem stability

Flood hazard hydrology: interdisciplinary geospatial preparedness and policy

Thesis (Ph.D.) University of Alaska Fairbanks, 2017Floods rank as the deadliest and most frequently occurring natural hazard worldwide, and in 2013 floods in the United States ranked second only to wind storms in accounting for loss of life and damage to property. While flood disasters remain difficult to accurately predict, more precise forecasts and better understanding of the frequency, magnitude and timing of floods can help reduce the loss of life and costs associated with the impact of flood events. There is a common perception that 1) local-to-national-level decision makers do not have accurate, reliable and actionable data and knowledge they need in order to make informed flood-related decisions, and 2) because of science--policy disconnects, critical flood and scientific analyses and insights are failing to influence policymakers in national water resource and flood-related decisions that have significant local impact. This dissertation explores these perceived information gaps and disconnects, and seeks to answer the question of whether flood data can be accurately generated, transformed into useful actionable knowledge for local flood event decision makers, and then effectively communicated to influence policy. Utilizing an interdisciplinary mixed-methods research design approach, this thesis develops a methodological framework and interpretative lens for each of three distinct stages of flood-related information interaction: 1) data generation—using machine learning to estimate streamflow flood data for forecasting and response; 2) knowledge development and sharing—creating a geoanalytic visualization decision support system for flood events; and 3) knowledge actualization—using heuristic toolsets for translating scientific knowledge into policy action. Each stage is elaborated on in three distinct research papers, incorporated as chapters in this dissertation, that focus on developing practical data and methodologies that are useful to scientists, local flood event decision makers, and policymakers. Data and analytical results of this research indicate that, if certain conditions are met, it is possible to provide local decision makers and policy makers with the useful actionable knowledge they need to make timely and informed decisions