Defining optimal DEM resolutions and point densities for modelling hydrologically sensitive areas in agricultural catchments dominated by microtopography

AbstractDefining critical source areas (CSAs) of diffuse pollution in agricultural catchments depends upon the accurate delineation of hydrologically sensitive areas (HSAs) at highest risk of generating surface runoff pathways. In topographically complex landscapes, this delineation is constrained by digital elevation model (DEM) resolution and the influence of microtopographic features. To address this, optimal DEM resolutions and point densities for spatially modelling HSAs were investigated, for onward use in delineating CSAs. The surface runoff framework was modelled using the Topographic Wetness Index (TWI) and maps were derived from 0.25m LiDAR DEMs (40 bare-earth points m−2), resampled 1m and 2m LiDAR DEMs, and a radar generated 5m DEM. Furthermore, the resampled 1m and 2m LiDAR DEMs were regenerated with reduced bare-earth point densities (5, 2, 1, 0.5, 0.25 and 0.125 points m−2) to analyse effects on elevation accuracy and important microtopographic features. Results were compared to surface runoff field observations in two 10km2 agricultural catchments for evaluation. Analysis showed that the accuracy of modelled HSAs using different thresholds (5%, 10% and 15% of the catchment area with the highest TWI values) was much higher using LiDAR data compared to the 5m DEM (70–100% and 10–84%, respectively). This was attributed to the DEM capturing microtopographic features such as hedgerow banks, roads, tramlines and open agricultural drains, which acted as topographic barriers or channels that diverted runoff away from the hillslope scale flow direction. Furthermore, the identification of ‘breakthrough’ and ‘delivery’ points along runoff pathways where runoff and mobilised pollutants could be potentially transported between fields or delivered to the drainage channel network was much higher using LiDAR data compared to the 5m DEM (75–100% and 0–100%, respectively). Optimal DEM resolutions of 1–2m were identified for modelling HSAs, which balanced the need for microtopographic detail as well as surface generalisations required to model the natural hillslope scale movement of flow. Little loss of vertical accuracy was observed in 1–2m LiDAR DEMs with reduced bare-earth point densities of 2–5 points m−2, even at hedgerows. Further improvements in HSA models could be achieved if soil hydrological properties and the effects of flow sinks (filtered out in TWI models) on hydrological connectivity are also considered

Weighted risk assessment of critical source areas for soil phosphorus losses through surface runoff mechanisms

This work was supported by the NERC QUADRAT DTP [grant number 2280708].Peer reviewedPublisher PD

An Assessment Of The Impact Of Dem Interpolation Technique, Resolution, And Terrain Type On The Extraction Of Drainage Network

This research used points extracted from high-resolution DEMs (1m) to investigate the impact of resolution, interpolation method and topography on the accuracy of drainage network extraction. The investigation was conducted by evaluating the accuracy of the estimations of streams length, streams number, drainage density, and the Longitudinal Root Mean Square Error (LRMSE) of the extracted drainage networks from different DEMs interpolated using Topo to raster, Natural Neighbor (NN), kriging and IDW interpolation methods at 5, 10, 15 and 20m resolutions over moderate, steep, and gentle slope terrain. Each evaluation conducted yielded a different result, but the accuracy of the streams length estimation for most of the DEMs at all the sites increases with an increase in streams order. The total lengths of all the streams of each of the extracted networks at gentle and steep slope sites are shorter than those of the corresponding reference networks though, 15 and 20m kriging and IDW DEMs created longer streams at the moderate slope site. IDW DEMs have proven reliable for streams length estimation while Topo to raster 5, 10, and 15m for streams number estimation. In general, N.N. extracted networks are the only networks that show consistency in the streams length and number estimations, drainage density estimation as well as in LRMSE and DEM RMSE computation at all the resolutions and for all the sites. Therefore, the accuracy of N.N. DEMs and their derivatives do not rapidly change with change in resolution, especially between 5 and 20m at all (steep, gentle and moderate) terrain types

Evaluating the effects of generalisation approaches and DEM resolution on the extraction of terrain indices in KwaZulu Natal, South Africa

Digital elevation model (DEM) data are elemental in deriving primary topographic attributes which serve as input variables to a variety of hydrologic and geomorphologic studies. There is however still varied consensus on the effect of DEM source and resolution on the application of these topographic attributes to landscape characterisation. While elevation data for South Africa are available from several major sources and resolutions: Shuttle Radar Topographic Mission (SRTM), Earth ENV and Stellenbosch University DEM (SUDEM). Limited research has been conducted in a local context comparing the extraction of terrain attributes to high resolution Digital Terrain Data (DTM) such as LiDAR (Light Detection and Ranging) that are becoming increasing available. However, the utility of LiDAR to topographic analyses presents its own challenges in terms of operational-relevant resolution, processing demands and limited spatial coverage. There is a need to quantify the impact that generalisation approaches have on simplifying detailed DEMs and to compare the accuracy and reliability of results between high resolution and coarse resolution data on the extraction of localized topographic variables. In this regional study, we analyse the accuracy on selected local terrain attributes: elevation, slope and topographic wetness index derived from DEMs from varying sources, at different spatial resolutions and using three generalisation algorithms, namely: mean cell aggregation, nearest neighbour and hydrological corrected topo-to-raster. We show that topographic variable extraction is highly dependent on DEM source and generalisation approach and while higher resolution DEMs may represent the “true“ surface more accurately, they do not necessarily offer the best results for all extracted variables. Our results highlight the caveats of selecting DEMs not “fit-for-purpose” for topographic analysis and offer a simple yet effective solution for reconciling the selection of DEMs based on neighbourhood size resolution prior to terrain analyses and topographic feature characterization

The Beast from the East: impact of an atypical cold weather event on hydrology and nutrient dynamics in two Irish catchments

peer-reviewedA historic lack of continuous stream nutrient monitoring at the catchment scale limits understanding of the effects of snowstorms. The most significant snowstorm since 1985, nicknamed “the Beast from the East”, occurred in February–March 2018. High-frequency stream outlet monitoring in two close but hydrologically and agriculturally contrasting catchments (<1,200 ha) captured phosphorus (total and reactive), total oxygenated nitrogen (TON), temperature and discharge dynamics during and after the event. The grassland catchment consists of poorly drained gley soils and exhibits overland flow pathways, while the arable catchment consists of well-drained brown earths and is dominated by subsurface pathways. Nitrate (NO3-N) concentrations were initially elevated (3.50 and 7.89 mg/L for poorly drained grassland and well-drained arable catchments, respectively) before becoming diluted by meltwater. Total reactive phosphorus (TRP) displayed a distal (anti-clockwise) concentration-discharge hysteresis in the poorly drained grassland catchment suggesting low mobilisation from the soil. Conversely, the well-drained arable catchment displayed proximal (clockwise) hysteresis, indicative of the mobilisation from stream and bank sediment. These relatively infrequent snow events behave similarly to heavy rainfall as regards nutrient losses, albeit subject to a time-lag induced by the speed of snowmelt and the soil moisture deficit (SMD) prior to snowfall. Antecedent land management is crucial to mitigate risk. The current absence of records and analyses of catchment response, particularly nutrient dynamics, to atypical cold weather events in Ireland limits understanding of their effects on water quality. The present study provides the first such baseline information from which land management strategies and the implications for attaining environmental targets can be explored

An assessment of the critical source areas and transport pathways of diffuse pollution in the Umngeni Catchment, South Africa.

Masters Degree. University of KwaZulu-Natal, Pietemaritzburg.The difficulty in locating and managing diffuse pollution sources and their transport pathways is one of the reasons for the continued degradation of surface water in South Africa. Dealing with this problem is complex, as the sources and transport pathways of the pollutants are often not known because of the diffuse nature of the pollution. This study demonstrates the constraints of conventional diffuse pollution assessment approaches in identifying the Critical Source Areas (CSAs) and transport pathways of diffuse pollution, as applied in the uMngeni Catchment, South Africa. The use of various risk-based modelling approaches are reviewed for identifying the risk of diffuse pollution generation and transportation across a catchment landscape. The Sensitive Catchment Integrated Modelling and Analysis Platform (SCIMAP) Model is a risk-based tool that was developed to give a spatial representation of diffuse pollution sources. In this study, the SCIMAP Model was applied to identify and prioritise the protection and control of nutrient CSAs and transport pathways within the uMngeni Catchment. The results of the study were displayed in a catchment scale web map. The hydrological connectivity risk in the catchment was higher in the high-lying western areas and lower in the middle-eastern areas. The upper and middle parts of the catchment that are dominated by commercial agriculture and built-up urban areas were identified as the most impactful CSAs for intervention. The results are immediately applicable to water managers in the catchment and are strongly linked to the investment efforts in ecological infrastructure. A walkover survey revealed that the SCIMAP Model was able to direct the CSA investigations to the nutrient sources at four out of five locations. The survey also revealed that the accuracy of the modelled transport pathways increased with an increase in the elevation difference. The sensitivity of the SCIMAP Model to input land cover weightings was assessed, using an objective function. A high sensitivity of the modelled high-risk areas was observed on the intermediate diffuse pollution risk map, and a slight sensitivity of the modelled high-risk areas on the final diffuse pollution risk map, when the input landcover weightings were increased and decreased by 5%, 10% and 15%. This implies that caution should be practised in the formulation of the input land cover weightings, as they are a potential source of error in the model outputs. It is concluded that SCIMAP is a valuable tool for identifying the CSAs and transport pathways of diffuse pollution in a catchment. The results of the model can better inform the management of diffuse pollution and guide investments in the protection of the ecological infrastructure in the uMngeni Catchment. However, the establishment of input land cover weightings is very important and should receive priority in similar studies in the future

Influence of Land Management on Soil Erosion, Connectivity and Sediment Delivery in Agricultural Catchments:Closing the Sediment Budget

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Using high-resolution topography for spatial prioritisation of gully erosion management across catchments of the Great Barrier Reef, Australia

The Great Barrier Reef (GBR) running along ~2 000km of the north-eastern coast of Australia is a UNESCO World Heritage site and is the largest living structure on Earth. The GBR is at the forefront of environmental issues currently faced by Australia, with significant economic, environmental, social, and cultural value. Terrigenous fine sediment affects water quality in the GBR and contributes to the degradation of significant marine environments. Gully erosion is believed to be an important contributor of this fine sediment, and this has garnered recent attention from the Australian Government. A key challenge to managing gully erosion across catchments of the GBR is the large scale of the combined area (>400 000 km2). Recent advances in Light Detection and Ranging (LiDAR) have enabled generation of high-resolution (~1 m) digital elevation models (DEMs) over large areas. Over recent years airborne LiDAR data captures have covered many areas of the GBR catchments, with ~50 000 km2 of topography data with a spatial resolution of 1 m or finer. This newly available source of high-resolution data presents an opportunity to map and predict locations of gully erosion across large areas, reducing the need for time-consuming fieldwork. However, there is a need for further development of suitable methods to exploit this data. The core aim of this PhD has been to develop a set of tools and algorithms for using high-resolution topography data to map gullies and areas susceptible to future gully erosion. Novel analysis methods were developed into open-source computer programs with a general focus on creating resources to assist researchers and practitioners managing and assessing gully erosion over large areas. The overall approach is split into to two broad categories of analysis. The first focuses on gully management at small scales (tens of square kilometres), and the second focuses on large scales (hundreds to thousands of square kilometres). The algorithms developed from each of the two halves are designed to work in unison to prioritise gully erosion management first at large scales and subsequently at small scales. This PhD has developed and assessed novel methods for using high-resolution topography data to map and predict gully erosion across catchments of the GBR. A core focus has been on proposing 'standard' methods for computing the required inputs for topographic models of gully occurrence in the landscape. The broader goal of this was to help move the field closer to a set of tools that allow researchers to readily compare model results between landscapes and regions free of bias introduced by variations in sampling procedures. This work has highlighted the potential benefit of using high-resolution topography, particularly airborne LiDAR, but that consistency with methodologies is key to enabling comparisons across landscapes. The methods developed also have applications to other environments, particularly semi-arid regions, and have all been developed in open-source programming languages to help facilitate adoption. Results from applying two different topographic models of gullies showed that land clearing and a transition from natural forests to agricultural landscapes has likely led to increased gullying across catchments of the GBR. This finding is consistent with other studies globally and provides important context for gully management priorities in this region

A Global Perspective on Phosphorus Management Decision Support in Agriculture: Lessons Learned and Future Directions

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