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Redhead, J. W.

Publications and source records attributed to Redhead, J. W..

2 recordsLinked to original sources

Quantifying relative sponginess: a high-resolution model of landscape water retention as an ecosystem service

Unprecedented climate and land use changes are having major impacts on water-based ecosystem services (ES). It is crucial, therefore, to get an in-depth understanding of current levels of such ES provision, and how they could be impacted by changed environments or management. However, applying existing models for water-related ES pose substantial challenges, which include the need for in-depth specialist hydrological knowledge, the requirement for numerous datasets and parameters that may not be consistently available, and high computational costs. Additionally, there is often a mismatch between the resolution of the model output and parcel-level land management, at which ES information is often most valuable for supporting decision-making. Here we detail a rapid method for assessing water retention potential by estimating an areas sponginess - its capacity to absorb and retain precipitation. Our approach builds upon a topography-adjusted Curve Number methodology, a widely recognised and straightforward tool for estimating water run-off. We calculated run-off across 1 km grid cells (representing field- to farm-scale land parcels) in Great Britain based on the lands sponginess under storm conditions. The primary objective was to investigate the applicability of the method in estimating point-values -- i.e., the independent contribution of each grid square -- within the context of known limitations. The results enable the identification of areas with higher potential for the ES of water retention. Our model output illustrates the spectrum of sponginess across Great Britain, ranging from less than 30% of precipitation retained in city regions to as high as 99% in some rural, agricultural areas. Importantly, we demonstrate that the model is easy to run, can be used with freely-available data, and produces outputs compatible with grid-based models for other ES. Overall, the model provides an accessible approach to estimating the ES of water retention to researchers worldwide, even in data-scarce areas. Highlights- To inform land management decisions it is important to determine parcel-level ecosystem services, but there is a gap for services related to water. - Parcel-level assessments of a lands sponginess - the capacity to retain precipitation - are described. - Values are calculated using a topography-adjusted Curve Number methodology, which requires little data input or hydrological expertise. - Sponginess and run-off estimates are provided for Great Britain at 1 km resolution.

ecology↗

Sensitivities in associating land-system archetypes with sustainability metrics: Insights from simulations

Archetypes of land- and socio-ecological systems, generated using unsupervised classification methods, enable the assimilation of complex environmental and socio-economic information. Such simplification has considerable potential to feed into decision support systems for sustainability planning. But, the usefulness of archetypes depends on how well they relate to sustainability criteria, such as ecosystem service (ES) delivery, that are external to the input datasets employed for archetype generation. Sensitivities in such post-hoc association analyses, and the associated utility of the archetype framework in a decision support context, remain unexplored. Here we emulated post-hoc association analysis procedures using simulated socio-ecological datasets and ES response variables. Our simulations revealed a substantial influence on analysis performance from (1) the number of variables used as inputs in archetype generation, (2) the correlation structure of input datasets, (3) the type and distribution of input variables, and (4) the functional form (linear or non-linear) characterising the relationship between ES variables and their predictors. We observed near-identical performance when archetypes were generated using K-means clustering and Self-Organising Maps (SOMs) - two commonly used archetype classification methods. Further, better archetype classifier performance did not guarantee better discrimination of ES value distributions between archetypes. Our results suggest that designing a framework to generate archetypes for sustainability planning, and the selected methodological choices therein, should place greater emphasis on what the archetypes will be used for in downstream analyses, and not focus solely on archetype classifier performance. This would better ensure the identification of archetypes adaptable to a diverse array of sustainability indicators and sufficiently robust for monitoring decision outcomes over time.

ecology↗