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Peatlands are recognised as an important but vulnerable ecological resource. Understanding the effects of existing damage, in this case erosion, enables more informed land management decisions to be made. Over the growing seasons of 2013 and 2014 photosynthesis and ecosystem respiration were measured using closed chamber techniques within vegetated haggs and erosional peat pans in Dartmoor National Park, southwest England. Below-ground total and heterotrophic respiration were measured and autotrophic respiration estimated from the vegetated haggs. The mean water table was significantly higher in the peat pans than in the vegetated haggs; because of this, and the switching from submerged to dry peat, there were differences in vegetation composition, photosynthesis and ecosystem respiration. In the peat pans photosynthetic CO2 uptake and ecosystem respiration were greater than in the vegetated haggs and strongly dependent on the depth to water table (r2 > 0.78, p < 0.001). Whilst in the vegetated haggs, photosynthesis and ecosystem respiration had the strongest relationships with normalised difference vegetation index (NDVI) (r2 = 0.82, p < 0.001) and soil temperature at 15 cm depth (r2 = 0.77, p = 0.001). Autotrophic and total below-ground respiration in the vegetated haggs varied with soil temperature; heterotrophic respiration increased as water tables fell. An empirically derived net ecosystem model estimated that over the two growing seasons both the vegetated haggs (29 and 20 gC m−2; 95% confidence intervals of − 570 to 762 and − 873 to 1105 gC m−2) and the peat pans (7 and 8 gC m−2; 95% confidence intervals of − 147 to 465 and − 136 to 436 gC m−2) were most likely net CO2 sources. This study suggests that not only the visibly degraded bare peat pans but also the surrounding vegetated haggs are losing carbon to the atmosphere, particularly during warmer and drier conditions, highlighting a need for ecohydrological restoration. [ABSTRACT FROM AUTHOR]

Peatlands are recognized as important carbon stores; despite this, many have been drained for agricultural improvement. Drainage has been shown to lower water tables and alter vegetation composition, modifying primary productivity and decomposition, potentially initiating peat loss. To quantify CO2 fluxes across whole landscapes, it is vital to understand how vegetation composition and CO2 fluxes vary spatially in response to the pattern of drainage features. However, Molinia caerulea-dominated peatlands are poorly understood despite their widespread extent. Photosynthesis (PG600) and ecosystem respiration (REco) were modelled (12 °C, 600 µmol photons m−2 s−1, greenness excess index of 60) using empirically derived parameters based on closed-chamber measurements collected over a growing season. Partitioned below-ground fluxes were also collected. Plots were arranged ⅛, ¼ and ½ the distance between adjacent ditches in two catchments located in Exmoor National Park, southwest England. Water table depths were deepest closest to the ditch and non-significantly ( p = 0·197) shallower further away. Non- Molinia species coverage and the Simpson diversity index significantly decreased with water table depth ( p < 0·024) and increased non-significantly ( p < 0·083) away from the ditch. No CO2 fluxes showed significant spatial distribution in response to drainage ditches, arguably due to insignificant spatial distribution of water tables and vegetation composition. Whilst REco showed no significant spatial variation, PG600 varied significantly between sites ( p = 0·012), thereby controlling the spatial distribution of net ecosystem exchange between sites. As PG600 significantly co-varied with water table depths ( p = 0·034), determining the spatial distribution of water table depths may enable CO2 fluxes to be estimated across M. caerulea-dominated landscapes. © 2015 The Authors. Ecohydrology published by John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Upland ecosystems are recognized for their importance in providing valuable ecosystem services including water storage, water supply and flood attenuation alongside carbon storage and biodiversity. The UK contains 10-15% of the global resource of upland blanket peatlands, the hydrology and ecology of which are highly sensitive to external anthropogenic and climatic forcing. In particular, drainage of these landscapes for agricultural intensification and peat extraction has resulted in often unquantified damage to the peatland hydrology, and little is understood about the spatially distributed impacts of these practices on near-surface wetness. This paper develops new techniques to extract spatial data describing the near-surface wetness and hydrological behaviour of drained blanket peatlands using airborne thermal imaging data and airborne Light Detection and Ranging (LiDAR) data. The relative thermal emissivity (Ɛr) of the ground surface is mapped and used as a proxy for near-surface wetness. The results show how moorland drainage and land surface structure have an impact on airborne measurements of thermal emissivity. Specifically, we show that information on land surface structure derived from LiDAR can help normalize signals in thermal emissivity data to improve description of hydrological condition across a test catchment in Exmoor, UK. An in situ field hydrological survey was used to validate these findings. We discuss how such data could be used to describe the spatially distributed nature of near-surface water resources, to optimize catchment management schemes and to deliver improved understanding of the drivers of hydrological change in analogous ecosystems. Copyright © 2014 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

The increased use of high temporal resolution in-stream sensors, deployed operationally in industry or for research, produces a large volume of high frequency data. Records generated may contain information value additional to the original purpose, which is not generally explored; and so the full value of these large datasets is rarely realised. Records are available at a frequency and volume that increases the visibility of previously hidden patterns. Alongside targeted research, these data may provide additional insight into hydrochemical behaviour in rivers, and therefore support an improved understanding of both landscape and in-stream processes which can affect drinking water resources.This study investigates the relationships between high frequency water quality parameters and rainfall-runoff driven water quality events in rivers within predominantly rural catchments in the UK. The research improves understanding of the pattern-processes information content of routine data collected from high frequency in-situ sensors in the context of the potential impact on drinking water treatment. The catchments investigated were selected as part of a wider programme of interventions and monitoring for drinking water protection, including farm advise and landscape restoration. Archives for in-situ sensors operated as part of drinking water source monitoring and treatment over the period of the study (2012 - ongoing) contain records for water quality indicators (including; ammonia, colour, conductivity, dissolved oxygen, pH, turbidity) at 5 minute frequency. Additional data (flow, stage, rainfall) from the environmental regulator and from targeted research-based monitoring by the authors were also obtained. The time series datasets were quality controlled using a series of filters written and adapted in R. This was followed by the identification and extraction of rainfall-runoff driven water quality events, which was automated using a base flow separation recursive digital filter and threshold based rainfall-runoff pairing script. A range of visualisation and multivariate techniques were used to identify and investigate patterns, with a focus on hysteresis and both long term and abrupt changes in recorded colour and turbidity. [ABSTRACT FROM AUTHOR]

Catchment management has increasingly become the chosen approach to reduce both point source and diffuse pollution from agriculture in freshwater. In the UK, water companies are, to some extent, driving this movement: investments in pollution reduction upstream are thought to provide a sustainable alternative to increased capital spending for water treatment downstream. However, evidence of both water quality improvement at catchment scale and of the potential financial benefits to water companies and their customers from diffuse pollution reduction are lacking. This knowledge gap is being addressed using the catchment management programme set up by South West Water, the water utility company in southwest England, as a case study. Our work focuses on 9 catchments where the treatment and production of drinking water is being affected by water quality issues from a combination of parameters (i.e. dissolved organic carbon and colour, nutrients, sediments, pesticides, algae, geosmin and 2-MIB) and which are subject to a range of on-farm intervention measures. Overall, the monitoring programme aims to both quantify the extent of the catchment management measures (i.e. farm intervention mapping) and water quality change in associated watercourses. For each catchment, a bespoke, tiered, monitoring programme was set up to quantify water quality change at several spatial and temporal scales. Data collection included: (1) local, intervention-based, monitoring of water quality (i.e. before/after or upstream/downstream), (2) sub-catchment, in-situ and event-based, monitoring of reservoir feeder streams and upstream of abstraction in rivers, and (3) continuous monitoring using data that are routinely collected by the water utility company at catchment scale. Our work shows that, as a first step, mapping of the type, cost, geographical location and extent of catchment interventions is required in order to link interventions and water quality change. However, it only provided a partial understanding of the impact of catchment management on water quality. We show that data at multiple scales (water treatment works and contributing catchments) are needed to evidence the effect of catchment management measures on water quality and inform water utility decision making. Therefore, an appropriate, tiered, monitoring strategy to capture change at these scales is needed. We propose that such strategy is tailored for each catchment and parameters of interest, using techniques that are appropriate depending on the change to be detected, and the type of data required.Process-based modelling techniques of water movements through the catchment may help evaluate a catchment scale understanding of change. However, with sporadic or low-temporal and spatial resolution data, some catchment processes are poorly represented. In addition, change may take longer than expected. Therefore, results remain indicative in the short-term, and are potentially unrepresentative of long-term effects. Overall, the tiered monitoring approach built around operational and regulatory monitoring by the water company and in-situ sampling provided a breadth of data at different spatial and temporal scales. Such a monitoring approach that combines different types and sources of datasets and monitoring strategies at different scales was found to be the most cost-effective way to provide a holistic understanding of change without duplicating efforts. [ABSTRACT FROM AUTHOR]