Watershed Delineation in the Field: A New Approach for Mobile Applications Using LiDAR Elevation Data

With the advancement of mobile devices, opportunities to take watershed management tasks out of the office and into the field can be realized. In turn, field workers can utilize these technologies to expedite the decision-making process so that they may focus on meeting with clients and addressing agricultural watershed management issues. High-resolution (∼1.5 m postspacing) elevation data gathered by light detection and ranging (LiDAR) provides the topographic detail necessary to model hydrology at the field-scale (∼1 km2). Non-artifactual surface depressions lead to erroneous surface flow patterns when using existing algorithms. So a sequential depression-filling algorithm (SDFA) has been developed to address topographies that contain these types of features. Given a rainfall amount, water distributed across the landscape accumulates and fills only those depressions as necessary, halting the filling process when the only depressions that remain require additional rainfall. After the filling process is completed, the watershed contributing area draining to any particular point of interest may be identified and in the future this may be used as input to hydrologic models. Methods have also been developed to implement subsurface drainage features such as culverts and tile-inlets as well as soil infiltration such that the dynamics of how water is shed from a given landscape can be better represented. Tile inlets and drainage features may be identified via user input and assigned a drainage rate while infiltration may be implemented by assigning a drainage rate to each grid cell in the DEM based on their soil-type. The combination of the sequential depression-filling algorithm and this drainage feature implementation provides the tools to model localized drainage patterns that will match user\u27s field observations at the scale of hundreds of hectares. The flow routing, depression identification, and filling procedures of the SDFA were compared to similar functions in the ArcGIS Hydrology Toolset under conditions where all depressions were filled in order to validate that those components of the algorithm are identical as intended. Furthermore, several digital elevation models (DEMs) were analyzed to determine the variability in hydrologic connectivity across these landscapes as a function of rainfall and as a function of DEM size. In addition to depression storage, the impacts of infiltration on hydrologic connectivity over these landscapes were also analyzed using the SCS Curve Number Method. The assumptions made by existing algorithms that require complete hydrologic connectivity do not hold up in all landscapes, even more so when considering the effects of infiltration. In these landscapes, surface hydrologic connectivity varies noticeably with rainfall excess and it is inaccurate to assume that the watershed should be modeled as a monotonically descending 14 surface. In an applicability study of DEM size, depression features began to be captured around the 1 km 2 scale while it is recommended to use DEMs larger than 2 km 2 to ensure that the depressional features and their contributing areas are completely captured within the DEM extent so that the SDFA may account for those features correctly. The SDFA algorithm was ported from Matlab to an Android application for mobile phones and tablets. The Watershed Delineation app is free and publicly available through the Google Play Store. Users may view DEMs on a Google Map, use the sequential depression-filling algorithm to fill depressions, and delineate watersheds. It was found that the performance of this algorithm is a function of the number of depressions in the DEM which increases with DEM resolution (due to signal-noise effects). At a 3-meter resolution, the ideal DEM dimensions suitable for use of the SDFA on a Google Nexus 4 phone are about 500 x 500 (225 hectares), which took 68 seconds to run. At DEM sizes much greater than this, performance is drastically reduced. As DEM resolution increases, noise effects in the data (which vary based on the raw LiDAR data) result in a high amount of depression features causing an excessive number of iterations of the filling procedure within the algorithm

Analysis of the behavior of three digital elevation model correction methods on critical natural scenarios

Abstract Study region The methods explored in this study were tested in two study areas: Italy and Cuba. Study focus Virtually all Digital Elevation Models (DEM) contain flat areas or depression pixels that may be artifacts or actual landscape representations. These features must be removed before any further hydrological application can proceed. Diverse algorithms have been developed for the purpose of correcting these aspects, differing in how they handle the nature of the depressions, as well as the adopted mathematical procedures. In the present work, the behavior of a standard ( Fill ) and two advanced ( TOPAZ and PEM4PIT ) DEM correction methods on three critical natural scenarios is analyzed. Extensive flat areas, abrupt slope changes and large depressions − expressed in terms of: (1) geomorphological changes (elevation, affected area and slope); (2) flow velocity; (3) river network and width functions (WF) − are affected. New hydrological insights for the region Results confirm improved performance of the advanced methods over the standard method for each case study in Italy and Cuba. The analyzed parameters also show that correction processes are strongly influenced by the relief, the size of the predominating depressions and the neighbouring depressions. There is no one method among those compared which works optimally for every type of correction, and given that the majority of basins have diverse topographical conditions, a different approach to the corrections process and its computational procedures is likely needed

Enhancing Operational Flood Detection Solutions through an Integrated Use of Satellite Earth Observations and Numerical Models

Among natural disasters floods are the most common and widespread hazards worldwide (CRED and UNISDR, 2018). Thus, making communities more resilient to flood is a priority, particularly in large flood-prone areas located in emerging countries, because the effects of extreme events severely setback the development process (Wright, 2013). In this context, operational flood preparedness requires novel modeling approaches for a fast delineation of flooding in riverine environments. Starting from a review of advances in the flood modeling domain and a selection of the more suitable open toolsets available in the literature, a new method for the Rapid Estimation of FLood EXtent (REFLEX) at multiple scales (Arcorace et al., 2019) is proposed. The simplified hydraulic modeling adopted in this method consists of a hydro-geomorphological approach based on the Height Above the Nearest Drainage (HAND) model (Nobre et al., 2015). The hydraulic component of this method employs a simplified version of fluid mechanic equations for natural river channels. The input runoff volume is distributed from channel to hillslope cells of the DEM by using an iterative flood volume optimization based on Manning\u2019s equation. The model also includes a GIS-based method to expand HAND contours across neighbor watersheds in flat areas, particularly useful in flood modeling expansion over coastal zones. REFLEX\u2019s flood modeling has been applied in multiple case studies in both surveyed and ungauged river basins. The development and the implementation of the whole modeling chain have enabled a rapid estimation of flood extent over multiple basins at different scales. When possible, flood modeling results are compared with reference flood hazard maps or with detailed flood simulations. Despite the limitations of the method due to the employed simplified hydraulic modeling approach, obtained results are promising in terms of flood extent and water depth. Given the geomorphological nature of the method, it does not require initial and boundary conditions as it is in traditional 1D/2D hydraulic modeling. Therefore, its usage fits better in data-poor environments or large-scale flood modeling. An extensive employment of this slim method has been adopted by CIMA Research Foundation researchers for flood hazard mapping purposes over multiple African countries. As collateral research, multiple types of Earth observation (EO) data have been employed in the REFLEX modeling chain. Remotely sensed data from the satellites, in fact, are not only a source to obtain input digital terrain models but also to map flooded areas. Thus, in this work, different EO data exploitation methods are used for estimating water extent and surface height. Preliminary results by using Copernicus\u2019s Sentinel-1 SAR and Sentinel-3 radar altimetry data highlighted their potential mainly for model calibration and validation. In conclusion, REFLEX combines the advantages of geomorphological models with the ones of traditional hydraulic modeling to ensure a simplified steady flow computation of flooding in open channels. This work highlights the pros and cons of the method and indicates the way forward for future research in the hydro-geomorphological domain

The influence of contributing area on the hydrology of the prairie pothole region of North America

This thesis formulates a conceptual framework developed from field observations that describes the influence of surface depressions or potholes on runoff generation in the prairie pothole region of the North American prairies. The fill-and-spill of potholes results in intermittent surface water connectivity between potholes within the basin. The extent of connectivity between potholes is dependent on antecedent water levels. Dynamic connectivity between potholes results in dynamic contributing areas for runoff. The concept of connectivity is manifested in the conceptual curves presented in this thesis. These conceptual curves model the response of runoff events for landscape types found in the prairie pothole region, and capture the influence of the spatial distribution and extent of surface storage on contributing area. The conceptual curves differ due to variations in the spatial distribution and extent of surface storage volume. An algorithm based on the conceptual framework proposed is presented. The algorithm, which uses the the D-8 drainage direction method, automates a methodology for identifying and quantifying runoff contributing area. The algorithm is applied in prairie pothole basins both to demonstrate its efficacy and to test the potential for using conceptual curves to describe the relationship between decreasing potential surface storage in the landscape and contributing area. The algorithm was applied to two digital elevation models (DEM) representative of the prairie pothole region. The first DEM was created using LiDAR elevation points at a 1 m resolution for the St. Denis watershed, and the second was created from orthophotos for the Smith Creek watershed at a 25 m resolution. Fieldwork in the St. Denis watershed was carried out to both provide a basis for the conceptual framework proposed and to validate the results of the algorithm. The fieldwork involved gathering snow survey data, identifying and describing surface water conditions during a snow melt runoff event in 2006, and measuring pond levels from 2004 – 2007. Results indicate that the proposed conceptual curves represent the non-linear relationship between potential surface storage and contributing area generated by the algorithm in the test basins. To test whether the underlying concepts of the algorithm were valid, the algorithm was used to model pond level depths measured in the St. Denis drainage basin after spring runoff in 2006 and 2007. An r2 value over 0.9 was calculated for the relationship between measured and modeled pond levels in both years. Based on this work, it is clear that any hydrologic study or model applied in the prairie pothole region should consider the effect of dynamic contributing areas on runoff generation

Developing a spatial approach to support local flood-risk-based land use planning

Flood-risk-based land use planning is largely a local government responsibility in Australia. In my research I sought to develop a new spatial approach to improve the implementation of flood-risk-based land use planning that can be used by local governments. In Australia, strategies for floodplain management to reduce and control flooding are best implemented at the land use planning stage. Flood-risk-based land use planning results in sustainable land management activities, including floodplain management. Flood-risk-based land use planning is largely dependent on flood behaviour across the land development areas, which can be documented in the form of planning zones that warn land use planners about the flood-threatened area. However, these are often out-dated so do not reflect the influence of recent land use changes. This is particularly for peri-urban areas (largely focus on riverine floods) and expensive to update. Developing a new approach based on geospatial science facilitates the implementation of flood-risk-based land use planning process and can provide a better approach for local governments in terms of cost, accuracy and ease of updating. To develop a new approach, a clear understanding of responsibility for flood-risk-based land use planning, the workflow processes in the authority responsible for such planning, the quality of existing database and how new data sources and the technology (e.g., LiDAR data and crowd source data) that can be used for flood-risk-based land use planning, is essential. The research involved a case study to document the process of flood-risk-based land use planning processes used by a Victorian council, an assessment of flood-relevant database completeness and accuracy, determination of missing datasets and information in the council’s flood-relevant GIS database, and development of methods based on GIS-embedded hydrological models to generate those datasets

Delineation of Surface Water Features Using RADARSAT-2 Imagery and a TOPAZ Masking Approach over the Prairie Pothole Region in Canada

The Prairie Pothole Region (PPR) is one of the most rapidly changing environments in the world. In the PPR of North America, topographic depressions are common, and they are an essential water storage element in the regional hydrological system. The accurate delineation of surface water bodies is important for a variety of reasons, including conservation, environmental management, and better understanding of hydrological and climate modeling. There are numerous surface water bodies across the northern Prairie Region, making it challenging to provide near-real-time monitoring and in situ measurements of the spatial and temporal variation in the surface water area. Satellite remote sensing is the only practical approach to delineating the surface water area of Prairie potholes on an ongoing and cost-effective basis. Optical satellite imagery is able to detect surface water but only under cloud-free conditions, a substantial limitation for operational monitoring of surface water variability. However, as an active sensor, RADARSAT-2 (RS-2) has the ability to provide data for surface water detection that can overcome the limitation of optical sensors. In this research, a threshold-based procedure was developed using Fine Wide (F0W3), Wide (W2) and Standard (S3) modes to delineate the extent of surface water areas in the St. Denis and Smith Creek study basins, Saskatchewan, Canada. RS-2 thresholding results yielded a higher number of apparent water surfaces than were visible in high-resolution optical imagery (SPOT) of comparable resolution acquired at nearly the same time. TOPAZ software was used to determine the maximum possible extent of water ponding on the surface by analyzing high-resolution LiDAR-based DEM data. Removing water bodies outside the depressions mapped by TOPAZ improved the resulting images, which corresponded more closely to the SPOT surface water images. The results demonstrate the potential of TOPAZ masking for RS-2 surface water mapping used for operational purposes

Leveraging Crowdsourced Navigation Data In Roadway Pluvial Flash Flood Prediction

This dissertation develops and tests a new data-driven framework for short-term roadway pluvial flash flood (PFF) risk estimation at the scale of road segments using crowdsourced navigation data and a simplified physics-based PFF model. Pluvial flash flooding (PFF) is defined as localized floods caused by an overwhelmed natural or engineered drainage system. This study develops a data curation and computational framework for data collection, preprocessing, and modeling to estimate the risk of PFF at road-segment scales. A hybrid approach is also developed that couples a statistical model and a simplified physics-based simulation model in a machine learning (ML) model to rapidly predict the risk of roadway PFF using Waze alerts in real-time

GIS-based, python modeling of the spatial and temporal distribution of water on the landscape for wetlands decision making

Wetlands provide many benefits for humans and the natural environment, but land use changes have reduced their number and areal extent. Interest has grown in examining surface water distribution both spatially and temporally, which help to determine those locations for which there is the greatest priority for wetland preservation or mitigation. This research first proposes a methodology to support that examination through the application of open channel hydraulics principles to flow over a landscape. The methodology, implemented through a Python script, automatically extracts landscape characteristics from a DEM and calculates hydraulic parameters. The parameters are used to determine water surface profiles using the Modified Euler's method. Multiple tests show that the script accurately produces profiles of flow between wetlands over a landscape. Such determinations are the first step in understanding where water will exist on the surface and where there may be infiltration to support wetland functions. Furthermore, a water balance methodology (where water will exist, how much will be there and for what period of time) is developed and demonstrated that focuses on small depressions, as locations where conservation efforts to create or regenerate wetlands may be achievable. Integral to this analysis is a detailed treatment of depressions in the landscape. Utilizing a digital elevation model, the methodology incorporates a cell-by-cell analysis to appropriately capture small-scale processes. Instead of treating these vital depressions as errors or being insignificant to the water balance calculations, they are retained. Flow direction is dynamically determined by the land surface and water characteristics. With potentially shallow flow in depressions, the use of Manning's equation incorporates stratified flow where differing values of Manning's n describe flow through and above vegetation. This real-time overland runoff model based on a short time step is implemented through a Python code using ArcGIS. Exercises on an artificial DEM with simulated precipitation demonstrate the ability of the model to accurately represent hydraulics principles. Simulations of two field sites over a period of a year, and incorporating precipitation, infiltration and evapotranspiration, demonstrate the ability to track water surface locations and extents with an accuracy necessary for decision making. Additionally, this research optimizes the Green Ampt infiltration model which allows for the calculation of infiltration rates with unsteady rainfall and then couples this Modified Green Ampt (MGA) model with a previously developed Dynamic Flow Direction (DFD) model to simulate overland flow. To test the accuracy of the improvements, results show shorter times to ponding, smaller total infiltration at the time of ponding and larger total infiltration with this Modified Green Ampt (MGA) model as compared with the results with a Traditional Green Ampt (TGA) model. Additionally, coupled with the DFD model, the MGA model takes surface water movement into consideration. The total water volume on the landscape with MGA is less than predicted by the TGA. Additionally, the inundation area is deeper than 0.05 m with MGA and is also smaller than the result with the TGA.Includes bibliographical reference

New data structure and process model for automated watershed delineation

DEM analysis to delineate the stream network and its associated subwatersheds are the primary steps in the raster-based parameterization of watersheds. There are two widely used methods for delineating subwatersheds. One of these is the Upstream Catchment Area (UCA) method. The UCA method employs a user specified threshold value of upstream catchment area to delineate subwatersheds from the extracted network of streams. The other common technique is the nodal method. In this approach, subwatersheds are initiated at stream network nodes, where nodes occur at the upstream starting point of streams and at the point of intersection of streams in the network. The UCA approach and the Nodal approach do not permit watershed initiation at points of specific interests. They also fail to explicitly recognize drainage features other than single channel reaches. That is, they exclude water bodies, wetlands, braided channels and other important hydrologic features. TOPAZ (TOpographic PArameteriZation) [Garbrecht and Martz, 1992], is a typical program for raster based, automated drainage analysis. It initiates subwatersheds at source points and at points of intersection of drainage channels. TOPAZ treats lakes as spurious depressions arising out of errors in DEM, and removes them. This research addresses one important limitation of the currently used modeling techniques and tools. It adds the capability to initiate watershed delineation at points of specific interest other than junction and source points in the delineated networks from the Digital Elevation Models (DEMs). The research project evaluates qualitative and quantitative aspects of a new Object Oriented data structure and process model for raster format data analysis in spatial hydrology. The concept of incorporating a user-specified analysis in extraction and parameterization of watersheds is based on the need to have a tool to allow for studies specific to certain points in the stream network including gauging stations. It is also based on the need for an improved delineation of hydrologic features (water bodies) in hydrologic modeling. The research project developed an interface module for TOPAZ [Garbrecht and Martz, 1992] to offset the aforementioned disadvantages of the subwatershed delineation techniques. The research developed an internal hybrid, raster-based, Object Oriented data structure and processing model similar to that of vector data type. The new internal data structure permits further augmentation of the software tool. This internal data structure and algorithms provide an improved framework for discretization of the important hydrologic entities (water bodies) and the capability of extracting homogenous hydrological subwatersheds

Continental-scale high-resolution river geometry and real-time inundation mapping

Flooding is the most threatening natural disaster worldwide considering the fatalities and property damage it causes. Recent flood disasters have raised concerns for accurate and responsive inundation forecast due to the rapid spread and astonishing destructive power of these events. Although recent development in large scale hydrologic simulation has enabled the real-time streamflow simulation operating on millions of river reaches, a framework for converting the forecast discharge into corresponding water surface elevation and inundation maps at a continental-scale is absent to better support local flood response. To accurately map flood inundation extent, a comprehensive description of the geometry of the channel is indispensable. As such, this dissertation presents an innovative approach for estimating river geometry and conducting inundation mapping at a continental-scale with a high spatial resolution. This approach is based on the concept of Height Above Nearest Drainage (HAND). Advanced hydrologic terrain analysis workflows have been designed to derive channel hydraulic properties, stage-discharge rating curves, and inundation extents using HAND. After the mechanism being presented, the implementation of this approach across the contiguous United States has been demonstrated using the 10-meter National Elevation Dataset. The integrity of the outputs has been validated through the comparison with best available references at multiple test sites. Considering the increasingly availability of high-resolution topographic data derived from lidar technology, the dissertation further presents how advanced geomorphic feature extraction tools are integrated into the proposed approach to overcome the challenges associated with the enrichment of terrain details. At last, this dissertation presents how banklines, an essential piece of river geometry characteristic as the boundary differentiates channel zone from floodplain, is detected with enhanced geomorphic feature extraction tools for improving large-scale hydrologic simulation and inundation mapping accuracy.Civil, Architectural, and Environmental Engineerin