Development of a high-resolution land cover product of the Rocky Mountains with application to carbon concentrations in its streams: assessing anthropogenic, climatological, and morphological contributions

Abstract

In recent decades, the Rocky Mountain (RM) region has undergone significant changes associated with anthropogenic activities, such as urbanization and forest logging for agriculture, and natural disturbances, such as wildfires and bark beetle infestations. These changes have the potential to alter primary productivity and biomass carbon storage. Specifically, changes in dissolved organic carbon (DOC) in the RM streams are relevant because dissolved organic matter affects heterotrophic processes, acts as a source for the nutrient cycle, absorbs sunlight radiation, alters the transport of metals, and can promote the appearance of carcinogenic byproducts during water treatment. Specifically, recent studies have focused on the relationship between bark beetle infestations and stream organic matter, but have reached conflicting conclusions, possibly due to the small areas analyzed or an incomplete understanding of the processes that influence organic matter concentrations. Consequently, here we compile and process multiple datasets representing changes and features of the RM region for the period 1983-2012 with the purpose of assessing their relative influence on stream DOC concentrations. Land cover (LC) is especially important for modeling DOC. LC drives many environmental processes, so its assessment, monitoring, and characterization are essential. However, existing LC products each have different temporal and spatial resolutions and different LC classes and cannot be used for our goal of studying the large scale spatial-temporal variability of DOC. Here, we review the complexities of LC identification and propose a method for fusing multiple existing LC products to produce a single LC record for a large spatial-temporal grid, referred to as spatiotemporal categorical map fusion (SCaMF). We first reconcile the LC classes of different LC products and then present a probabilistic weighted nearest neighbor estimator of LC class. This estimator depends on three unknown parameters that are estimated using numerical optimization to maximize a user-defined agreement criterion. We illustrate the method using six LC products over the Rocky Mountains and show the improvement gained by supplying the optimization with data-driven information describing the spatial-temporal behavior of each LC class. Given the massive size of the LC products, we show how the optimal parameters for a given year are often optimal for other years, leading to shorter computing times. We implement the SCaMF methodology over a large region of the RM, encompassing sections of six states, to create a new LC product, SCaMF-RM. To do this, we adapt SCaMF to address the prediction of LC in large space-time regions that present nonstationarities, and we add more flexibility in the LC classifications of the predicted product. SCaMF-RM is produced at two high spatial resolutions, 30 and 50 m, and a yearly frequency for the 30-year period 1983-2012. When multiple products are available in time, we illustrate how SCaMF-RM captures relevant information from the different LC products and improves upon flaws in other products. Future work needed includes an exhaustive validation not only of SCaMF-RM but also of all input LC products. The features of the RM region for the period 1983-2012 representing possible influences on stream DOC concentrations are grouped in four categories: anthropogenic activities, forest disturbances, climate change, and the region’s morphology, including LC. We analyze the significance of each of these features when predicting base-flow DOC concentrations in the RM streams, because base-flow concentrations are more representative of the longer-term (annual to decadal) impacts. To do this, we use a statistical model to account for the possible correlation among features and for the intrinsic connectivity and hydrologic directionality of a stream network. While natural forest disturbances are positively correlated with increased DOC concentrations, the effect of urbanization is far greater. Similarly, higher maximum temperatures, which can be exacerbated by climate change, are also associated with elevated DOC concentrations. Overall, DOC concentrations present an increasing trend over time in the RM region.