Back

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

Evans, P. M.; Dussaillant, A.; Varma, V. M.; Redhead, J. W.; Pywell, R. F.; Storkey, J.; Mead, A.; Bullock, J. M.

2024-10-13 ecology
10.1101/2024.10.11.617771 bioRxiv
Show abstract

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.

Matching journals

The top 4 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.