Forested watersheds supply over two thirds of the world’s drinking water. The last decade has seen an increase in the frequency and intensity of wildfires that is threatening these source watersheds, and necessitating more expensive water treatment to address degrading water quality. Given increasing wildfire frequency in a changing climate, it is important to understand the magnitude of water quality impacts following fire. Here, we conducted a meta-analysis to explore post-fire changes in the concentrations of nitrogen (N) and phosphorus (P) species, dissolved organic carbon (DOC), and total suspended sediments (TSS) in 121 sites around the world. Changes were documented over each study’s respective duration, which for 90% of sites was 5 years or fewer. We find concurrent increases in C, N and P species, highlighting a tight coupling between biogeochemical cycles in post-fire landscapes. We find that fire alters N and P speciation, with median increases of 40-60% in the proportion of soluble inorganic N and P relative to total N and P. We also found that fire decreases C:N and C:P ratios, with median decreases ranging from 60-70%. Finally we observe a “hockey stick”-like response in changes to the concentration distribution, where increases in the highest concentration ranges are much greater than increases at lower concentrations. Our study documents strong heterogeneity in responses of water quality to wildfire that have been unreported so far in the literature.
Historic land alterations and agricultural intensification have resulted in legacy phosphorus (P) accumulations within lakes and reservoirs. Internal loading from such legacy stores can be a major driver of future water quality degradation. Yet, little is known about the magnitude and spatial patterns of legacy P accumulation in lentic systems, and how watershed disturbance trajectories drive these patterns. Here, we used a meta-analysis of 113 paleolimnological studies across 124 lakes and 4 reservoirs (referred here on as lakes) in 20 countries to quantify the linkages between the 100-year trajectories of P concentrations in lake sediments, watershed inputs, and lake morphology. We find five distinct clusters for lake sediment P trajectories, with lakes in the developing and developed world showing distinctly different patterns. Lakes in the developed world (Europe and North America) with early agricultural intensification had the highest sediment P concentrations (1,176-1,628 mg/kg), with a peak between the 1970-1980s and a decline since then, while lakes in the developing world, specifically China, documented monotonically increasing sediment P concentrations (857-1,603 mg/kg). Sediment P trajectories reflected watershed disturbance patterns and were driven by a combination of anthropogenic drivers (fertilizer input and population density) and lake morphology (watershed to lake area ratio). Specifically, we found the largest legacy accumulation rates to occur in shallow lakes experiencing long-term land-use disturbances. These links between land-use change and P accumulation in lentic systems can provide insights about inland water quality response and help to develop robust predictive models useful for resource managers and decision-makers.
Abstract. In recent decades, advances in the flexibility and complexity of hydrologic models have enhanced their utility in scientific studies and practice alike. However, the increasing complexity of these tools leads to a number of challenges, including steep learning curves for new users and issues regarding the reproducibility of modelling studies. Here, we present the RavenR package, an R package that leverages the power of scripting to both enhance the usability of the Raven hydrologic modelling framework and provide complementary analyses that are useful for modellers. The RavenR package contains functions that may be useful in each step of the model-building process, particularly for preparing input files and analyzing model outputs. The utility of the RavenR package is demonstrated with the presentation of six use cases for a model of the Liard River basin in Canada. These use cases provide examples of visually reviewing the model configuration, preparing input files for observation and forcing data, simplifying the model discretization, performing realism checks on the model output, and evaluating the performance of the model. All of the use cases are fully reproducible, with additional reproducible examples of RavenR functions included with the package distribution itself. It is anticipated that the RavenR package will continue to evolve with the Raven project and will provide a useful tool to new and experienced users of Raven alike.
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