CHARACTERIZATION OF UNCERTAINTY IN MODEL PARAMETER AND PRECIPITATION DATA ACROSS SEVERAL HEADWATER CATCHMENTS IN THE CANADIAN ROCKIES: A LARGE-SAMPLE HYDROLOGY APPROACH

Hydrologic modelling and prediction in the Canadian Rookies are hampered by the sparsity of hydro-climatic data, limited accessibility, and the complexity of the cold regions hydrologic processes. Previous studies in this region have mainly focused on very few heavily instrumented catchments, typically with limited generalizability to other catchments in the region. In this thesis, I adopt a “large-sample hydrology” approach to address some of the outstanding issues pertaining to data uncertainty, model parameter identifiability, and predictive power of hydrologic modelling in this region. My analyses cover 25 catchments with a range of physiographic and hydrologic properties located across the Canadian Rockies. To address forcing data uncertainty, which is commonly considered as the most dominant source of uncertainty in the hydrology of this region, I processed and utilized three different gridded-data products, namely ANUSPLIN, CaPA, and WFDEI. To make the problem tractable, I applied an efficient-to-run conceptual hydrologic model to simulate the hydrologic processes in this region under a variety of parameter and input data configurations. My analyses showed significant discrepancies in precipitation amounts between the different climate data products with varying degrees across the different catchments. Runoff ratios were quite variable under the different products and across the catchments, ranging from 0.25 to 2, highlighting the significant uncertainty in precipitation amounts. To handle precipitation uncertainty in hydrologic modelling, I developed and tested two strategies: (1) implementing a correction parameter for each data product separately, and (2) developing and parameterizing a linear combination of the different data products to have a unified, presumably more accurate data product. These new precipitation-correcting parameters along with a selected set of the hydrologic model parameters were analyzed and identified via Monte-Carlo simulation, considering three model performance criteria on streamflow simulation, namely Nash-Sutcliffe Efficiency (NSE), NSE on log-transformed streamflow (NSE-Log), and Percent Bias (PBias). Overall, the hydrologic model showed adequate performance in reproducing observed streamflows in most of the catchments, with NSE, NSE-Log, and PBIAS ranging in 0.36-0.87, 0.43-88, and 0.001%-34%, respectively. However, most of the model parameters showed limited identifiability, limiting the power of the model for the assessment of climate and land cover changes. Overall, WFDEI climate data provided the best performance in parameter identification, while demonstrating a superior performance in reproducing observed streamflows

A MODIS Imagery Toolkit for ArcGIS Explorer

NASA’s medium spatial resolution MODIS sensor provides near-global, daily remote sensing coverage of the Earth in 36 spectral bands that are optimized for monitoring a wide variety of environmental parameters. MODIS data is provided by NASA at no cost and is easily accessible via the Internet. As such, MODIS provides a rich source of remotely sensed data that can provide timely environmental information to military operations, disaster monitoring, and relief efforts. However, current workflows for downloading MODIS and identifying environmental features of interest require the use of sophisticated software operated by experienced analysts. These software packages have the added limitations of being expensive and not readily available in combat and/or disaster relief environments. This paper discusses the development of a set of software tools using existing geographic information system technology. These tools can enable analysts with limited experience and operating in difficult environments to easily access MODIS data and develop environmental spatial data from it for further analysis. Two different system architectures were developed as solutions—one that exists as a set of standalone tools in a desktop environment using ArcGIS Explorer, and one that exists as a client-server framework using ArcGIS Server with ArcGIS Explorer as the client

Development and Evaluation of a Multi-Year Fractional Surface Water Data Set Derived from Active/Passive Microwave Remote Sensing Data

abstract: The sensitivity of Earth’s wetlands to observed shifts in global precipitation and temperature patterns and their ability to produce large quantities of methane gas are key global change questions. We present a microwave satellite-based approach for mapping fractional surface water (FW) globally at 25-km resolution. The approach employs a land cover-supported, atmospherically-corrected dynamic mixture model applied to 20+ years (1992–2013) of combined, daily, passive/active microwave remote sensing data. The resulting product, known as Surface WAter Microwave Product Series (SWAMPS), shows strong microwave sensitivity to sub-grid scale open water and inundated wetlands comprising open plant canopies. SWAMPS’ FW compares favorably (R[superscript 2] = 91%–94%) with higher-resolution, global-scale maps of open water from MODIS and SRTM-MOD44W. Correspondence of SWAMPS with open water and wetland products from satellite SAR in Alaska and the Amazon deteriorates when exposed wetlands or inundated forests captured by the SAR products were added to the open water fraction reflecting SWAMPS’ inability to detect water underneath the soil surface or beneath closed forest canopies. Except for a brief period of drying during the first 4 years of observation, the inundation extent for the global domain excluding the coast was largely stable. Regionally, inundation in North America is advancing while inundation is on the retreat in Tropical Africa and North Eurasia. SWAMPS provides a consistent and long-term global record of daily FW dynamics, with documented accuracies suitable for hydrologic assessment and global change-related investigations.The final version of this article, as published in Remote Sensing, can be viewed online at: http://www.mdpi.com/2072-4292/7/12/1584

Improving Flood Inundation and Streamflow Forecasts in Snowmelt Dominated Regions

Much effort has been dedicated to expanding hydrological forecasting capabilities and improving understanding of the continental-scale hydrological modeling used to predict future hydrologic conditions and quantify consequences of climate change. In 2016, the National Oceanic and Atmospheric Administration’s (NOAA) Office of Water Prediction implemented the National Water Model (NWM) to provide nationally consistent, operational hydrologic forecasting capability across the continental U.S. The primary goal of this research was to develop hydrological tools that include modeling of flood inundation mapping and snowmelt contributions to river flow in snowmelt-dominated regions across the Western U.S. This dissertation first presents terrain analysis enhancements developed to reduce the overestimation of flooded areas, observed where barriers such as roads cross rivers, from the continental-scale flood inundation mapping method that uses NWM streamflow forecasts. Then, it reports on a systematic evaluation of the NWM snow outputs against observed snow water equivalent (SWE) and snow-covered area fraction (SCAF) at point locations across the Western U.S. This evaluation identified the potential causes responsible for discrepancies in the model snow outputs and suggests opportunities for future research directed towards model improvements. Then, it presents improvements to SWE modeling by quantifying the improvements when using better model inputs and implementing humidity information in separating precipitation into rain and snow. These results inform understanding of continental-scale hydrologic processes and how they should be modeled

Potential and Limitations of Open Satellite Data for Flood Mapping

Satellite remote sensing is a powerful tool to map flooded areas. In recent years, the availability of free satellite data significantly increased in terms of type and frequency, allowing the production of flood maps at low cost around the world. In this work, we propose a semi-automatic method for flood mapping, based only on free satellite images and open-source software. The proposed methods are suitable to be applied by the community involved in flood hazard management, not necessarily experts in remote sensing processing. As case studies, we selected three flood events that recently occurred in Spain and Italy. Multispectral satellite data acquired by MODIS, Proba-V, Landsat, and Sentinel-2 and synthetic aperture radar (SAR) data collected by Sentinel-1 were used to detect flooded areas using different methodologies (e.g., Modified Normalized Difference Water Index, SAR backscattering variation, and supervised classification). Then, we improved and manually refined the automatic mapping using free ancillary data such as the digital elevation model-based water depth model and available ground truth data. We calculated flood detection performance (flood ratio) for the different datasets by comparing with flood maps made by official river authorities. The results show that it is necessary to consider different factors when selecting the best satellite data. Among these factors, the time of the satellite pass with respect to the flood peak is the most important. With co-flood multispectral images, more than 90% of the flooded area was detected in the 2015 Ebro flood (Spain) case study. With post-flood multispectral data, the flood ratio showed values under 50% a few weeks after the 2016 flood in Po and Tanaro plains (Italy), but it remained useful to map the inundated pattern. The SAR could detect flooding only at the co-flood stage, and the flood ratio showed values below 5% only a few days after the 2016 Po River inundation. Another result of the research was the creation of geomorphology-based inundation maps that matched up to 95% with official flood maps

Managing Water Resources in Large River Basins

Management of water resources in large rivers basins typically differs in important ways from management in smaller basins. While in smaller basins the focus of water resources management may be on project implementation, irrigation and drainage management, water use efficiency and flood operations; in larger basins, because of the greater complexity and competing interests, there is often a greater need for long-term strategic river basin planning across sectors and jurisdictions, and considering social, environmental, and economic outcomes. This puts a focus on sustainable development, including consumptive water use and non-consumptive water uses, such as inland navigation and hydropower. It also requires the consideration of hard or technical issues—data, modeling, infrastructure—as well as soft issues of governance, including legal frameworks, policies, institutions, and political economy. Rapidly evolving technologies could play a significant role in managing large basins. This Special Issue of Water traverses these hard and soft aspects of managing water resources in large river basins through a series of diverse case studies from across the globe that demonstrate recent advances in both technical and governance innovations in river basin management

Uncertainties in the Hydrological Modelling Using Remote Sensing Data over the Himalayan Region

Himalayas the “roof of the world” are the source of water supply for major South Asian Rivers and fulfill the demand of almost one sixth of world’s humanity. Hydrological modeling poses a big challenge for Himalayan River Basins due to complex topography, climatology and lack of quality input data. In this study, hydrological uncertainties arising due to remotely sensed inputs, input resolution and model structure has been highlighted for a Himalayan Gandak River Basin. Firstly, spatial input DEM (Digital Elevation Model) from two sources SRTM (Shuttle Radar Topography Mission) and ASTER (Advanced Space borne Thermal Emission and Reflection Radiometer) with resolutions 30m, 90m and 30m respectively has been evaluated for their delineation accuracy. The result reveals that SRTM 90m has best performance in terms of least area delineation error (13239.28 km2) and least stream network delineation error. The daily satellite precipitation estimates TRMM 3B42 V7 (Tropical Rainfall Monitoring Mission) and CMORPH (Climate Prediction Center MORPHing Technique) are evaluated for their feasibly over these terrains. Evaluation based on various scores related to visual verification method, Yes/no dichotomous, and continuous variable verification method reveal that TRMM 3B42 V7 has better scores than CMORPH. The effect of DEM resolution on the SWAT (Soil Water Assessment Tool) model outputs has been demonstrated using sixteen DEM grid sizes (40m-1000m). The analysis reveals that sediment and flow are greatly affected by the DEM resolutions (for DEMs>300m). The amount of total nitrogen (TN) and total phosphorous (TP) are found affected via slope and volume of flow for DEM grid size ≥150m. The T-test results are significant for SWAT outputs for grid size >500m at a yearly time step. The SWAT model is accessed for uncertainty during various hydrological processes modeling with different setups/structure. The results reflects that the use of elevation band modeling routine (with six to eight elevation bands) improves the streamflow statistics and water budgets from upstream to downstream gauging sites. Also, the SWAT model represents a consistent pattern of spatiotemporal snow cover dynamics when compared with MODIS data. At the end, the uncertainty in the stream flow simulation for TRMM 3B42 V7 for various rainfall intensity has been accessed with the statistics Percentage Bias (PBIAS) and RSR (RMSE-observations Standard Deviation Ratio). The results found that TRMM simulated streamflow is suitable for moderate (7.5 to 35.4 mm/day) to heavy rainfall intensities (35.5 to 124.4 mm/day). The finding of the present work can be useful for TRMM based studies for water resources management over the similar parts of the world

FACILITATING AQUATIC INVASIVE SPECIES MANAGEMENT USING SATELLITE REMOTE SENSING AND MACHINE LEARNING FRAMEWORKS

The urgent decision-making needs of invasive species managers can be better met by the integration of biodiversity big data with large-domain models and environmental data products in the form of new workflows and tools that facilitate data utilization across platforms. Timely risk assessments allow for the spatial prioritization of monitoring that could streamline invasive species management paradigms and invasive species’ ability to prevent irreversible damage, such that decision makers can focus surveillance and intervention efforts where they are likely to be most effective under budgetary and resource constraints. I present a workflow that generates rapid spatial risk assessments on aquatic invasive species by combining occurrence data, spatially explicit environmental data, and an ensemble approach to species distribution modeling using five machine learning algorithms. For proof of concept and validation, I tested this workflow using extensive spatial and temporal occurrence data from Rainbow Trout (RBT; Oncorhynchus mykiss) invasion in the upper Flathead River system in northwestern Montana, USA. Due to this workflow’s high performance against cross-validated datasets (87% accuracy) and congruence with known drivers of RBT invasion, I developed a tool that generates agile risk assessments based on the above workflow and suggest that it can be generalized to broader spatial and taxonomic scales in order to provide data-driven management information for early detection of potential invaders. I then use this tool as technical input for a management framework that provides guidance for users to incorporate and synthesize the component features of the workflow and toolkit to derive actionable insight in an efficient manner