Validation of AnnAGNPS at the field and farm-scale using an integrated AGNPS/GIS system

Non-Point Source (NPS) pollution models are effective watershed-scale predictors of NPS loadings and useful evaluators of agricultural Best Management Practices (BMPs) and water quality Total Maximum Daily Loads (TMDLs). The work reported in this thesis examined two applications of the AGricultural Non-Point-Source (AGNPS) pollution model: 1) predicting surface runoff, nutrient loading, and sediment yield predictions for an artificially delineated farm-scale watershed; and 2) evaluating relative benefits of different BMPs on reducing sediment accumulation in a lake surrounded by agricultural land. A procedure using identification, extraction, and processing of critical area data using an ArcView Geographic Information System (GIS) was used in both applications. In the first, 30 years of synthetic climate data were used to generate event and source accounting predictions for a multi-use 600-acre research farm in South Louisiana. Runoff water quality predictions for hydrologic cells in standard and artificially delineated watershed simulations were compared. Estimates for sediment, N and P loading in paired watershed cells agreed well, indicating that an integrated AGNPS/GIS system can reliably simulate runoff and NPS loadings for artificially delineated watersheds. Thus, successful implementation of AGNPS for an extracted small-scale region eliminated processing extraneous data, hence reducing simulation time and work required. This approach could allow land operators to initiate and/or evaluate nutrient and site management plans. The second application used AGNPS to evaluate benefits of different BMPs on reducing sedimentation in a small lake. Extensive land clearing in the 1970s for row crop production in Avoyelles Parish accelerated sediment deposition in local waterbodies. Data for depth of the original bottom of an approximately 2 ha lake below recent (\u3c 30 years) sediment estimated from 137Cs, Pb, clay and organic matter profiles), and sediment bulk density and texture were used to calibrate the AGNPS water quality model for representative hydrologic cells discharging into this lake. Upland erosion and sediment discharge rates predicted under alternative, conservation management practices indicate that sediment accumulation in this lake could have been substantially reduced

HydroSHEDS

HydroSHEDS (Hydrological data and maps based on SHuttle Elevation Derivatives at multiple Scales) is a mapping product that provides hydrographic information for regional and global-scale applications in a consistent format. Derived from elevation data of the Shuttle Radar Topography Mission (SRTM), the application offers users a suite of geo-referenced data sets (vector and raster), including stream networks, watershed boundaries, drainage directions, and ancillary data layers such as flow accumulations, distances, and river topology information. Educational levels: Middle school, High school, Undergraduate lower division, Undergraduate upper division, Graduate or professional

Digital Elevation Model Error in Terrain Analysis

Digital elevation models (DEMs) have been an important topic in geography and surveying sciences for decades due to their geomorphological importance as the reference surface for gravita-tion-driven material flow, as well as the wide range of uses and applications. When DEM is used in terrain analysis, for example in automatic drainage basin delineation, errors of the model collect in the analysis results. Investigation of this phenomenon is known as error propagation analysis, which has a direct influence on the decision-making process based on interpretations and applications of terrain analysis. Additionally, it may have an indirect influence on data acquisition and the DEM generation. The focus of the thesis was on the fine toposcale DEMs, which are typically represented in a 5-50m grid and used in the application scale 1:10 000-1:50 000. The thesis presents a three-step framework for investigating error propagation in DEM-based terrain analysis. The framework includes methods for visualising the morphological gross errors of DEMs, exploring the statistical and spatial characteristics of the DEM error, making analytical and simulation-based error propagation analysis and interpreting the error propagation analysis results. The DEM error model was built using geostatistical methods. The results show that appropriate and exhaustive reporting of various aspects of fine toposcale DEM error is a complex task. This is due to the high number of outliers in the error distribution and morphological gross errors, which are detectable with presented visualisation methods. In ad-dition, the use of global characterisation of DEM error is a gross generalisation of reality due to the small extent of the areas in which the decision of stationarity is not violated. This was shown using exhaustive high-quality reference DEM based on airborne laser scanning and local semivariogram analysis. The error propagation analysis revealed that, as expected, an increase in the DEM vertical error will increase the error in surface derivatives. However, contrary to expectations, the spatial au-tocorrelation of the model appears to have varying effects on the error propagation analysis depend-ing on the application. The use of a spatially uncorrelated DEM error model has been considered as a 'worst-case scenario', but this opinion is now challenged because none of the DEM derivatives investigated in the study had maximum variation with spatially uncorrelated random error. Sig-nificant performance improvement was achieved in simulation-based error propagation analysis by applying process convolution in generating realisations of the DEM error model. In addition, typology of uncertainty in drainage basin delineations is presented.Lukuisten käyttötarkoitusten ja sovellusmahdollisuuksien ansioista digitaaliset korkeusmallit, eli maan pinnanmuotoja esittävät numeeriset mallit, ovat olleet tutkimuksen kohteena maantieteen ja maanmittaustieteiden aloilla vuosikymmeniä. Kun korkeusmallia käytetään maastoanalyysissä, esimerkiksi automaattisessa valuma-aluerajauksessa tai tulvavaarakartoituksessa, mallissa olevat virheet kasautuvat analyysin tulokseen. Virheenkasautumisanalyysillä on suora vaikutus maastoanalyysipohjaiseen päätöksentekoon ja lisäksi sen avulla voidaan vaikuttaa epäsuorasti korkeusmallin luontiin tähtäävään tiedonkeruuseen sekä mallin tuottavien laskentamenetelmien käyttöön. Väitöskirja esittää kolmevaiheisen prosessin korkeusmallipohjaisten maastoanalyysien virheenkasautumisen tutkimiseksi. Työssä käytetyissä korkeusmalleissa maanpinnan korkeudet esitettiin 10-30m hilassa tyypillisen sovellusmittakaavan ollessa 1:10 000-1:50 000. Prosessiin kuuluu menetelmiä korkeusmallien karkeiden virheiden visuaaliseen havaitsemiseen, virheen tilastolliseen karakterisointiin ja virhemallin luontiin, analyyttiseen ja simulaatio-pohjaiseen virheenkasautumisanalyysiin sekä virheenkasautumisanalyysin tulosten tulkintaan. Virhemallin luonnissa käytettiin spatiaalisen tilastotieteen menetelmiä. Tulokset osoittivat, että tutkittujen korkeusmallien virheiden kuvaaminen ja mallintaminen kattavasti oli haastavaa. Tämä johtui mallien virhejakauman poikkeavien havaintojen suuresta määrästä sekä morfologisista karkeista virheistä, joiden visuaaliseksi havaitsemiseksi esitetään jakojäännöskarttojen käyttöä. Lisäksi korkeusmallien virheiden globaali karakterisointi osoittautui karkeaksi yleistykseksi todellisuudesta johtuen virheen paikkariippuvuudesta. Tämä osoitettiin empiirisesti käyttäen vertausaineistona koko tutkimusalueen kattavaa topografiseen laserkeilaukseen perustuvaa korkeusmallia. Virheenkasautumisanalyysit osoittivat, että korkeusmallin virheen kasvaessa myös maastoanalyysien virheet kasvoivat ja joissain tapauksissa virheiden kasvu oli huomattavasti ennalta arvioitua suurempaa. Virheen spatiaalisen autokorrelaation vaikutus analyysituloksiin oli sovellusriippuvaa. Spatiaalisesti autokorreloimatonta virhemallia on yleisesti pidetty pahimman tapauksen huomioonottavana mallina, mutta millään työssä käsitellyistä maastoanalyyseistä suurin epävarmuus ei liittynyt autokorreloimattomaan virhemalliin. Simulaatiopohjaisen virheenkasautumisanalyysin toteutuksessa sovellettu prosessikonvoluutiomenetelmä mahdollistaa korkeustiedon epävarmuuden huomioonottavien vuorovaikutteisten paikkatietopalvelujen luomisen. Esimerkkeinä mainittakoon jääpatojen aiheuttama tulvavaarakartoitus ja vaarallisten aineiden kuljetuksiin liittyvät onnettomuudet, joissa nestemäisten kemikaalien leviämisestä ympäristöön on saatava nopeasti luotettava kuva mikrotason valuma-alueanalyysillä

Predictive Modeling of Envelope Flood Extents Using Geomorphic and Climatic-Hydrologic Catchment Characteristics

A topographic index (flood descriptor) that combines the scaling of bankfull depth with morphology was shown to describe the tendency of an area to be flooded. However, this approach depends on the quality and availability of flood maps and assumes that outcomes can be directly extrapolated and downscaled. This work attempts to relax these problems and answer two questions: (1) Can functional relationships be established between a flood descriptor and geomorphic and climatic-hydrologic catchment characteristics? (2) If so, can they be used for low-complexity predictive modeling of envelope flood extents? Linear stepwise and random forest regressions are developed based on classification outcomes of a flood descriptor, using high-resolution flood modeling results as training benchmarks, and on catchment characteristics. Elementary catchments of four river basins in Europe (Thames, Weser, Rhine, and Danube) serve as training data set, while those of the Rh\uf4ne river basin in Europe serve as testing data set. Two return periods are considered, the 10- and 10,000-year. Prediction of envelope flood extents and flood-prone areas show that both models achieve high hit rates with respect to testing benchmarks. Average values were found to be above 60% and 80% for the 10- and the 10,000-year return periods, respectively. In spite of a moderate to high false discovery rate, the critical success index value was also found to be moderate to high. It is shown that by relating classification outcomes to catchment characteristics, the prediction of envelope flood extents may be achieved for a given region, including ungauged basins

Evaluation of Scale Issues in SWAT

In Soil and Water Assessment Tool (SWAT), oftentimes, Critical Source Area (CSA), the minimum upstream drainage area that is required to initiate a stream, is used to subdivide a watershed. In the current literature, CSA has been used as a trial and error process to define the subwatershed levels. On the other hand, the ongoing collaboration of the United States Environmental Protection Agency Office of Water and the United States Geological Survey has promoted a national level predefined catchments and flowlines called National Hydrography Dataset (NHD) Plus to ease watershed modeling in the United States. The introduction of NHDPlus can eliminate the uncertain nature in defining the number of subwatersheds required to model the hydrologic system. This study demonstrates an integrated modeling environment with SWAT and NHDPlus spatial datasets. A spatial tool that was developed in a Geographical Information System (GIS) environment to by-pass the default watershed delineation in ArcSWAT, the GIS interface to SWAT, with the introduction of NHDPlus catchments and flowlines, was used in this study. This study investigates the effect of the spatial size (catchment area) of the NHDPlus and the input data resolution (cell/pixel size) within NHDPlus catchments on SWAT streamflow and sediment prediction. In addition, an entropy based watershed subdivision scheme is presented by using the landuse and soil spatial datasets with the conventional CSA approach to investigate if one of the CSAs can be considered to produce the best SWAT prediction on streamflow. Two watersheds (Kings Creek, Texas and Sugar Creek, Indiana) were used in this study. The study shows that there exists a subwatershed map that does not belong to one of the subwatershed maps produced through conventional CSA approach, to produce a better result on uncalibrated monthly SWAT streamflow prediction. Beyond the critical threshold, the CSA threshold which gives the best uncalibrated monthly streamflow prediction among a given set of CSAs, the SWAT performance can be improved further by subdividing some of the subwatersheds at this critical threshold. The study also shows that the input data resolution (within each NHDPlus catchments) does not have an influence on SWAT streamflow prediction for the selected watersheds. However, there is a change on streamflow prediction as the area of the NHDPlus catchment changes. Beyond a certain catchment size (8-9% of the watershed area), as the input data resolution becomes finer, the total sediment increases whereas the sediment prediction in high flow regime decreases. As the NHDPlus catchment size changes, the stream power has an influence on total sediment prediction. However, as the input data resolution changes, but keeping the NHDPlus catchment size constant, the Modified Universal Soil Loss Equation topographic factor has an influence on total sediment prediction

Analysis and visualisation of digital elevation data for catchment management

River catchments are an obvious scale for soil and water resources management, since their shape and characteristics control the pathways and fluxes of water and sediment. Digital Elevation Models (DEMs) are widely used to simulate overland water paths in hydrological models. However, all DEMs are approximations to some degree and it is widely recognised that their characteristics can vary according to attributes such as spatial resolution and data sources (e.g. contours, optical or radar imagery). As a consequence, it is important to assess the ‘fitness for purpose’ of different DEMs and evaluate how uncertainty in the terrain representation may propagate into hydrological derivatives. The overall aim of this research was to assess accuracies and uncertainties associated with seven different DEMs (ASTER GDEM1, SRTM, Landform Panorama (OS 50), Landform Profile (OS 10), LandMap, NEXTMap and Bluesky DTMs) and to explore the implications of their use in hydrological analysis and catchment management applications. The research focused on the Wensum catchment in Norfolk, UK. The research initially examined the accuracy of the seven DEMs and, subsequently, a subset of these (SRTM, OS 50, OS10, NEXTMap and Bluesky) were used to evaluate different techniques for determining an appropriate flow accumulation threshold to delineate channel networks in the study catchment. These results were then used to quantitatively compare the positional accuracy of drainage networks derived from different DEMs. The final part of the thesis conducted an assessment of soil erosion and diffuse pollution risk in the study catchment using NEXTMap and OS 50 data with SCIMAP and RUSLE modelling techniques. Findings from the research demonstrate that a number of nationally available DEMs in the UK are simply not ‘fit for purpose’ as far as local catchment management is concerned. Results indicate that DEM source and resolution have considerable influence on modelling of hydrological processes, suggesting that for a lowland catchment the availability of a high resolution DEM (5m or better) is a prerequisite for any reliable assessment of the consequences of implementing particular land management measures. Several conclusions can be made from the research. (1) From the collection of DEMs used in this study the NEXTMap 5m DTM was found to be the best for representing catchment topography and is likely to prove a superior product for similar applications in other lowland catchments across the UK. (2) It is important that error modelling techniques are more routinely employed by GIS users, particularly where the fitness for purpose of a data source is not well-established. (3) GIS modelling tools that can be used to test and trial alternative management options (e.g. for reducing soil erosion) are particularly helpful in simulating the effect of possible environmental improvement measures