LB 11-104 - Maximizing the value of large-scale data to measure ecological attributes in land to water restoration projects

Restoring terrestrial areas is urgently needed to build resilience and protect water ecosystems. However, despite billions of dollars spent in water quality monitoring programs, decision makers across the globe are confronted with inadequate information to know where to prioritize restoration projects that benefit the land to water interface. Here, we illustrate opportunities and limitations of using data mapped at large spatial scales ( > 1 km and global extents) in land to water restoration prioritisation at local scales (e.g., at the sub-basin, watershed and stream levels). We focused in three regions with different levels of hydrological and biological data availability: the Upper Midwest of the United States, New Zealand and North-western South America. For each region, we identified the areas with the highest sources of nutrient runoff using novel large-scale datasets (e.g. HydroATLAS, LakeATLAS, HydroWASTE, MapSPAM). Then, we used this information to prioritize the selection of sites where restoration could reduce the impact of sources associated with land cover change (e.g. atmospheric deposition, farm fertilizers, manure), while maximizing biodiversity metrics. Our results show that models based on large scale datasets had a high model estimation performance (R2 > 0.75) of total loads and yields sources. Models based on large scale data also prioritized similar sites for restoration at the sub-basin and watershed scales compared to models that used local datasets. However, spatial biases in the distribution of monitoring sites increased the uncertainty in prioritization at stream scales. We discuss how this framework could be used to improve decisions of where to restore to maximize benefits across the land to water interface, particularly in data poor regions. We also present avenues of research to determine at what point the use of large-scale datasets ceases to improve decision making for restoration projects at local scales.