A sub-field scale critical source area index for legacy phosphorus management using high resolution data

AbstractDiffuse phosphorus (P) mitigation in agricultural catchments should be targeted at critical source areas (CSAs) that consider source and transport factors. However, development of CSA identification needs to consider the mobilisation potential of legacy soil P sources at the field scale, and the control of (micro)topography on runoff generation and hydrological connectivity at the sub-field scale. To address these limitations, a ‘next generation’ sub-field scale CSA index is presented, which predicts the risk of dissolved P losses in runoff from legacy soil P. The GIS-based CSA Index integrates two factors; mobile soil P concentrations (water extractable P; WEP) and a hydrologically sensitive area (HSA) index. The HSA Index identifies runoff-generating-areas using high resolution LiDAR Digital Elevation Models (DEMs), a soil topographic index (STI) and information on flow sinks and effects on hydrological connectivity. The CSA Index was developed using four intensively monitored agricultural catchments (7.5–11km2) in Ireland with contrasting agri-environmental conditions. Field scale soil WEP concentrations were estimated using catchment and land use specific relationships with Morgan P concentrations. In-stream total reactive P (TRP) concentrations and discharge were measured sub-hourly at catchment outlet bankside analysers and gauging stations during winter closed periods for fertiliser spreading in 2009–14, and hydrograph/loadograph separation methods were used to estimate TRP loads and proportions from quickflow (surface runoff). A strong relationship between TRP concentrations in quickflow and soil WEP concentrations (r2=0.73) was used to predict dissolved P concentrations in runoff at the field scale, which were then multiplied by the HSA Index to generate sub-field scale CSA Index maps. Evaluation of the tool showed a very strong relationship between the total CSA Index value within the HSA and the total TRP load in quickflow (r2=0.86). Using a CSA Index threshold value of ≥0.5, the CSA approach identified 1.1–5.6% of catchment areas at highest risk of legacy soil P transfers, compared with 4.0–26.5% of catchment areas based on an existing approach that uses above agronomic optimum soil P status. The tool could be used to aid cost-effective targeting of sub-field scale mitigation measures and best management practices at delivery points of CSA pathways to reduce dissolved P losses from legacy P stores and support sustainable agricultural production

Improving the identification of hydrologically sensitive areas using LiDAR DEMs for the delineation and mitigation of critical source areas of diffuse pollution

AbstractIdentifying critical source areas (CSAs) of diffuse pollution in agricultural catchments requires the accurate identification of hydrologically sensitive areas (HSAs) at highest propensity for generating surface runoff and transporting pollutants. A new GIS-based HSA Index is presented that improves the identification of HSAs at the sub-field scale by accounting for microtopographic controls. The Index is based on high resolution LiDAR data and a soil topographic index (STI) and also considers the hydrological disconnection of overland flow via topographic impediment from flow sinks. The HSA Index was applied to four intensive agricultural catchments (~7.5–12km2) with contrasting topography and soil types, and validated using rainfall-quickflow measurements during saturated winter storm events in 2009–2014. Total flow sink volume capacities ranged from 8298 to 59,584m3 and caused 8.5–24.2% of overland-flow-generating-areas and 16.8–33.4% of catchment areas to become hydrologically disconnected from the open drainage channel network. HSA maps identified ‘breakthrough points’ and ‘delivery points’ along surface runoff pathways as vulnerable points where diffuse pollutants could be transported between fields or delivered to the open drainage network, respectively. Using these as proposed locations for targeting mitigation measures such as riparian buffer strips reduced potential costs compared to blanket implementation within an example agri-environment scheme by 66% and 91% over 1 and 5years respectively, which included LiDAR DEM acquisition costs. The HSA Index can be used as a hydrologically realistic transport component within a fully evolved sub-field scale CSA model, and can also be used to guide the implementation of ‘treatment-train’ mitigation strategies concurrent with sustainable agricultural intensification

Establishing nationally representative benchmarks of farm-gate nitrogen and phosphorus balances and use efficiencies on Irish farms to encourage improvements

peer-reviewedAgriculture faces considerable challenges of achieving more sustainable production that minimises nitrogen (N) and phosphorus (P) losses and meets international obligations for water quality and greenhouse gas emissions. This must involve reducing nutrient balance (NB) surpluses and increasing nutrient use efficiencies (NUEs), which could also improve farm profitability (a win-win). To set targets and motivate improvements in Ireland, nationally representative benchmarks were established for different farm categories (sector, soil group and production intensity). Annual farm-gate NBs (kg ha−1) and NUEs (%) for N and P were calculated for 1446 nationally representative farms from 2008 to 2015 using import and export data collected by the Teagasc National Farm Survey (part of the EU Farm Accountancy Data Network). Benchmarks for each category were established using quantile regression analysis and percentile rankings to identify farms with the lowest NB surplus per production intensity and highest gross margins (€ ha−1). Within all categories, large ranges in NBs and NUEs between benchmark farms and poorer performers show considerable room for nutrient management improvements. Results show that as agriculture intensifies, nutrient surpluses, use efficiencies and gross margins increase, but benchmark farms minimise surpluses to relatively low levels (i.e. are more sustainable). This is due to, per ha, lower fertiliser and feed imports, greater exports of agricultural products, and for dairy, sheep and suckler cattle, relatively high stocking rates. For the ambitious scenario of all non-benchmark farms reaching the optimal benchmark zone, moderate reductions in farm nutrient surpluses were found with great improvements in profitability, leading to a 31% and 9% decrease in N and P surplus nationally, predominantly from dairy and non-suckler cattle. The study also identifies excessive surpluses for each level of production intensity, which could be used by policy in setting upper limits to improve sustainability.Environmental Protection Agenc

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

Characterisation of dairy soiled water in a survey of 60 Irish dairy farms

Dairy farming in Ireland generates an effluent known as dairy soiled water (DSW), which consists of a relatively dilute mixture of cow faeces, urine, spilt milk and detergents that is typically applied to grassland. However, relatively little is known about the volumes generated, nutrient content and management factors that influence volume and concentration. Sixty dairy farms that had a separate storage tank for storing DSW were selected for this study. The spatial distribution of the farms reflected the spatial distribution of dairy cows across the 26 counties of the Republic of Ireland, with each farm representing between 10,000 and 20,000 dairy cows. Samples were analysed for biochemical oxygen demand (BOD), ammonium N (NH4-N), total nitrogen (TN), potassium (K), phosphorus (molybdate-reactive and total) (MRP and TP) and dry matter (DM) content. Management characteristics and parlour properties were quantified. Factors influencing volume and concentration of DSW were determined using mixed model multiple regression analysis. On average, 9784 l (standard error 209 l) of DSW, including rainfall, was produced cow−1 year−1 and this contained significant quantities of total N, P and K (587, 80 and 568 mg l−1, respectively). A typical Irish dairy farm stocked at 1.9 cows ha−1 could therefore supply approximately 13, 2 and 12 kg ha−1 of total N, P and K, respectively, across the farm, annually to meet some of the nutrient requirements for herbage production and potentially replace some of the synthetic fertilizer use. Seventy one percent of samples were within the regulated concentration limits of soiled water for BOD (<2500 mg l−1), rising to 87% during the closed period for slurry spreading (mid October to mid-late January), while 81% were within the concentration limits for DM (<1% DM), rising to 94% during the closed period. The efficiency of a milking parlour (cows per unit, time taken) plays a key role in determining the volume of DSW generated. This, in turn, also influences the concentration of nutrients and other chemicals. Large variability was found in nutrient concentrations and this presents a challenge for effective nutrient management to maximise the fertilizer replacement value of DSW

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

Defining 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 0.25 m LiDAR DEMs (40 bare-earth points m−2), resampled 1 m and 2 m LiDAR DEMs, and a radar generated 5 m DEM. Furthermore, the resampled 1 m and 2 m 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 10 km2 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 5 m 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 5 m DEM (75–100% and 0–100%, respectively). Optimal DEM resolutions of 1–2 m 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 1–2 m 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