Spatial modelling of wetness for the Antarctic Dry Valleys

This paper describes a method used to model relative wetness for part of the Antarctic Dry Valleys using Geographic Information Systems (GIS) and remote sensing. The model produces a relative index of liquid water availability using variables that influence the volume and distribution of water. Remote sensing using Moderate Resolution Imaging Spectroradiometer (MODIS) images collected over four years is used to calculate an average index of snow cover and this is combined with other water sources such as glaciers and lakes. This water source model is then used to weight a hydrological flow accumulation model that uses slope derived from Light Detection and Ranging (LIDAR) elevation data. The resulting wetness index is validated using three-dimensional visualization and a comparison with a high-resolution Advanced Land Observing Satellite image that shows drainage channels. This research demonstrates that it is possible to produce a wetness model of Antarctica using data that are becoming widely available

Hydrological response to ~30 years of agricultural surface water management

Amongst human practices, agricultural surface-water management systems represent some of the largest integrated engineering works that shaped floodplains during history, directly or indirectly affecting the landscape. As a result of changes in agricultural practices and land use, many drainage networks have changed producing a greater exposure to flooding with a broad range of impacts on society, also because of climate inputs coupling with the human drivers. This research focuses on three main questions: which kind of land use changes related to the agricultural practices have been observed in the most recent years (~30 years)? How does the influence on the watershed response to land use and land cover changes depend on the rainfall event characteristics and soil conditions, and what is their related significance? The investigation presented in this work includes modelling the water infiltration due to the soil properties and analysing the distributed water storage offered by the agricultural drainage system in a study area in Veneto (north-eastern Italy). The results show that economic changes control the development of agro-industrial landscapes, with effects on the hydrological response. Key elements that can enhance or reduce differences are the antecedent soil conditions and the climate characteristics. Criticalities should be expected for intense and irregular rainfall events, and for events that recurrently happen. Agricultural areas might be perceived to be of low priority when it comes to public funding of flood protection, compared to the priority given to urban ones. These outcomes highlight the importance of understanding how agricultural practices can be the driver of or can be used to avoid, or at least mitigate, flooding. The proposed methods can be valuable tools in evaluating the costs and benefits of the management of water in agriculture to inform better policy decision-making

Large scale characterization of artificial drainage networks: new opportinities from high resolution topography .

openTES-81

Using high-resolution topography for advancing the understanding of mass and energy transfer across landscapes

textChannel networks are a critical means of mass and energy transfer across the landscape. High resolution topographic imagery derived from lidar scans, provides new opportunities in the observation and analysis of these processes, especially as the resolution of these data is proportionate to channel and hillslope process scales. Channel feature extraction algorithms supply a method with which to analyze hydrologic and geomorphic processes; automatic, open-source frameworks such as GeoNet aim to provide a reliable platform for this task to the academic, public service, and professional communities. In this thesis, the GeoNet algorithm is tested across different types of landscapes and data resolutions. Innovative analysis methods are also assessed within the framework in order to advance the method in parallel with advancing understanding of channel processes and improving technologies. The goal of this research is to assess GeoNet performance across landscape type in terms of relief, vegetation, and anthropogenic influence and make recommendations for future development of the algorithm and the GeoNet user community. An alternate spectral analysis-based filtering method is tested, as well as curvature-based filtering of erroneously identified channel heads. Results from alternate filtering testing indicate that the nonlinear Perona-Malik filter is superior to a spectral-based filtering approach. The use of a contour curvature threshold is not wholly successful at removing spuriously identified channel heads in natural landscapes. This research also introduces the analysis of urban landscapes into the GeoNet repertoire, and examines the effects of data resolution on feature extraction therein. An urban dataset from the Austin, TX area is tested and the optimal settings for GeoNet are identified; field work at the site is used to validate the results. The recommendations resulting from this work aim to improve in the functionality and versatility of GeoNet, and enhance accessibility for the user-community.Civil, Architectural, and Environmental Engineerin

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

Terrain surfaces and 3-D landcover classification from small footprint full-waveform lidar data: application to badlands

This article presents the use of new remote sensing data acquired from airborne fullwaveform lidar systems. They are active sensors which record altimeter profiles. This paper introduces a set of methodologies for processing these data. These techniques 5 are then applied to a particular landscape, the badlands, but the methodologies are designed to be applied to any other landscape. Indeed, the knowledge of an accurate topography and a landcover classification is a prior knowledge for any hydrological and erosion model. Badlands tend to be the most significant areas of erosion in the world with the highest erosion rate values. Monitoring and predicting erosion within 10 badland mountainous catchments is highly strategic due to the arising downstream consequences and the need for natural hazard mitigation engineering. Additionaly, beyond the altimeter information, full-waveform lidar data are processed to extract intensity and width of echoes. They are related to the target reflectance and geometry. Wa will investigate the relevancy of using lidar-derived Digital Terrain Models (DTMs) and 15 to investigate the potentiality of the intensity and width information for 3-D landcover classification. Considering the novelty and the complexity of such data, they are presented in details as well as guidelines to process them. DTMs are then validated with field measurements. The morphological validation of DTMs is then performed via the computation of hydrological indexes and photo-interpretation. Finally, a 3-D landcover classification is performed using a Support Vector Machine classifier. The introduction of an ortho-rectified optical image in the classification process as well as full-waveform lidar data for hydrological purposes is then discussed

Classification of Drainage Crossings on high-resolution digital elevation models: A deep learning approach

High-Resolution Digital Elevation Models (HRDEMs) have been used to delineate fine-scale hydrographic features in landscapes with relatively level topography. However, artificial flow barriers associated with roads are known to cause incorrect modeled flowlines, because these barriers substantially increase the terrain elevation and often terminate flowlines. A common practice is to breach the elevation of roads near drainage crossing locations, which, however, are often unavailable. Thus, developing a reliable drainage crossing dataset is essential to improve the HRDEMs for hydrographic delineation. The purpose of this research is to develop deep learning models for classifying the images that contain the locations of flow barriers. Based on HRDEMs and aerial orthophotos, different Convolutional Neural Network (CNN) models were trained and compared to assess their effectiveness in image classification in four different watersheds across the U.S. Midwest. Our results show that most deep learning models can consistently achieve over 90% accuracies. The CNN model with HRDEMs as the sole input feature was found to be the best-fit one. The addition of aerial orthophotos and their derived spectral indices is insignificant to or even worsens the model’s accuracy. The selected best-fit model exhibits excellent transferability over different geographic contexts. This work can be applied to improve elevation-derived hydrography mapping at fine spatial scales

Modeling the spatial and temporal trends of water quality in boreal managed watersheds

Land use changes have altered natural hydrological pathways and biogeochemical cycling of carbon, nitrogen and phosphorus, among other elements, affecting the quality of aquatic ecosystems such as rivers, lakes and coastal areas. In this dissertation, the spatial and temporal trends of water quality variation in Finnish managed watersheds was studied by applying methods of multivariate statistics, time-series analysis, ecohydrological modeling and high-resolution geospatial data. The results show the complex effects of current land use, particularly agriculture, on stream water quality. New emerging trends of nutrient concentrations and loads were detected in the time-series analysis, such as an increase in the concentrations and loads of dissolved reactive phosphorus and total nitrogen, and a decrease in suspended sediment concentration in streams. This might be linked to the current erosion reduction strategy of land management for water protection. An ecohydrological modeling assessment showed an increasing downstream nutrient export from agricultural watershed under climate change scenarios. The modeling results also showed a potential nutrient export reduction by restoring potential biogeochemical hotspot areas - wet areas or areas prone to water saturation. These areas can function as nutrient sinks and enhance the watershed resiliency. High-resolution geospatial data allowed easier and more accurate mapping of wet areas as well as the extracting of their hydraulic characteristics. However, the ecohydrological models involved several sources of uncertainties, which need to be carefully addressed with extensive observational data, expert knowledge of model parameter definitions, proper modeling unit selection and empirical knowledge of the functioning of the studied watershed system. The results of this dissertation highlight the importance of combined methods for watershed management research, and the proper identification of the biophysical processes in the modeling of non-point pollutant sources; this can in turn lead to an efficient water protection measure, and restoring biogeochemical hotspot areas within the watershed.Vedenlaadun alueellisten ja ajallisten vaihteluiden mallintaminen viileän vyöhykkeen valuma-alueilla. Maankäytön muutokset ovat vaikuttaneet luonnollisiin hydrologisiin prosesseihin sekä hiilen, typen ja fosforin biogeokemiallisiin kiertoihin. Nämä puolestaan vaikuttavat vesiekosysteemien tilaan joissa, järvissä ja rannikkoalueella. Väitöstutkimuksessa tutkittiin vedenlaadun alueellisia ja ajallisia muutoksia suomalaisessa maaseutumaisemassa käyttäen monimuuttujamenetelmiä, aikasarja-analyysejä, ekohydrologista mallinnusta ja erotuskyvyltään tarkkoja paikkatietoaineistoja. Tulokset todentavat maatalouteen kytkeytyvien maankäytön piirteiden kompleksisia vaikutuksia jokivesien laatuun. Aikasarja-analyysit osoittivat myös aiemmin tuntemattomia trendejä jokivesien ravinteiden määrissä ja pitoisuuksissa, esimerkkeinä liuenneen reaktiivisen fosforin määrän ja pitoisuuden lisääntyminen sekä sedimenttisuspension väheneminen; molemmat eroosion vähentämiseen tähtäävien vesiensuojelutoimien seurauksena. Ekohydrologinen mallinnus osoitti myös sen, että ravinteiden huuhtoutuminen maatalousvaltaisilla valuma-alueilla lisääntyy ilmastonmuutoksen seurauksena. Tulokset kannustavat biogeokemiallisten avainalueiden, kuten kosteikkojen ja vettä keräävien painanteiden kunnostamiseen, jolloin ravinteiden huuhtoutuminen vähenee. Ravinnenieluina toimiessaan ne voivat myös parantaa valumaalueen ekologista kestävyyttä ja palautumiskykyä. Tutkimuksessa osoitettiin myös erotuskyvyltään tarkkojen paikkatietoaineistojen hyödyllisyys avainalueiden kartoituksessa ja alueiden hydrologisten ominaisuuksien tunnistamisessa. Ekohydrologiseen mallinnukseen sisältyy toisaalta myös epävarmuustekijöitä, joihin tulisi paneutua vielä kattavammin hyödyntäen asiantuntijatietoa parametrien täsmentämisessä, määrittämällä tarkennettuja mallinnusyksiköitä tai hyödyntäen empiirisiä tutkimustietoja valuma-alueen toiminnasta. Väitöstutkimus osoittaa myös sen, miten erilaisten tutkimusmenetelmien yhdistely vahvistaa valuma-aluetarkastelua ja siihen liittyen erilaisten biofysikaalisten prosessien ymmärtämistä ja keskeisten päästölähteiden mallintamista. Näin muodoin yhdistelmämenetelmien käyttö tukee entistä tehokkaampien vesiensuojelutoimien kehittämistä ja valumaalueiden biogeokemiallisten avainalueiden kunnostamistaModelado de las tendencias temporales y espaciales de la calidad del agua en cuencas hidrográficas boreales manejados. El cambio del uso del suelo ha alterado los procesos hidrológicos naturales y los ciclos biogeoquímicos del carbono, el nitrógeno y el fósforo, entre otros elementos, afectando directamente la calidad de los ecosistemas acuáticos como los ríos, lagos y zonas costeras. En esta disertación, las tendencias espaciales y temporales de la variación de la calidad del agua en cuencas hidrográficas finlandesas se estudiaron mediante la aplicación de métodos de estadística multivariante, análisis de series de tiempo, modelos ecohidrológicos y datos geoespaciales de alta resolución. Los resultados muestran los efectos complejos del uso actual del suelo, particularmente la agricultura, en la calidad del agua de los ríos y corrientes. Se detectaron nuevas tendencias emergentes de concentraciones y cargas de nutrientes en el análisis de series temporales, como un aumento en la concentración y carga del fósforo disuelto reactive y nitrógeno total, y una disminución en la concentración de sedimentos en suspensión en los ríos y corrientes. Esto podría estar vinculado a la estrategia actual de manejo del suelo, orientado a la reducción de la erosión para la protección del agua. Una evaluación a través de modelización ecohidrológica mostró un aumento de la exportación de nutrientes aguas abajo de la cuenca agrícola bajo escenarios de cambio climático. Los resultados de la modelización también mostraron una posible reducción de la exportación de nutrientes mediante la restauración de posibles zonas críticas biogeoquímicas: áreas húmedas o áreas propensas a la saturación de agua. Estas áreas pueden funcionar como sumideros de nutrientes y mejorar la resiliencia de la cuenca. Los datos geoespaciales de alta resolución permitieron un fácil y más preciso cartografiado de las áreas húmedas, así como la extracción de sus características hidráulicas. Sin embargo, los modelos ecohidrológicos involucraron varias fuentes de incertidumbre, que deben abordarse cuidadosamente con bastantes datos de observación, conocimiento experto de las definiciones de los parámetros del modelo, selección adecuada de la unidad de modelado y conocimiento empírico del funcionamiento del sistema de la cuenca estudiada. Los resultados de esta disertación destacan la importancia de los métodos combinados para la investigación de gestión de cuencas hidrográficas y la identificación adecuada de los procesos biofísicos en la modelización de fuentes contaminantes difusas; esto a su vez puede conducir a una medidaeficiente de protección del agua, y restauración de áreas claves de alta función biogeoquímica dentro de la cuenca

An Automated Framework to Identify Lost and Restorable Wetlands in the Prairie Pothole Region

Abstract While progress has been made in automating wetland identification, identifying lost and restorable wetlands remains a challenge. A suite of automated methods was developed and applied to the Nose Creek watershed near Calgary, Alberta to establish a historical wetland inventory and the proportion of permanently versus temporarily lost wetlands. A power-law function of wetland area vs. wetland frequency using wetlands derived from the fusion of a high resolution digital elevation model and near-infrared data identified permanent loss of 11.0% by number and 0.6% by area. The difference between historical and existing wetlands was used to estimate a further temporary loss of 61.1% by number and 78.3% by area. Historical wetlands lost to ditch drainage are easily restored by ditch plugging. Therefore, an algorithm was created using digital terrain analysis that distinguished drainage ditches intersecting wetlands using surface curvature. The 1,588 ditch-drained wetlands identified represent a potential recovery of 11.7% of the temporary loss by number and 12.5% by area. Automated techniques to estimate wetland loss and identify priority wetlands for restoration provide powerful tools for wetland management