The value of hydrologic information for watershed management programs: The case of Camboriú, Brazil

Article explores the value of hydrologic modeling and monitoring with respect to two dimensions: scientific credibility and use of generated knowledge in the design, implementation, and evaluation of the watershed management program in the Camboriú watershed, Brazil

Using near-term forecasts and uncertainty partitioning to improve predictions of low-frequency cyanobacterial events

Near-term ecological forecasts provide resource managers advance notice of changes in ecosystem services, such as fisheries stocks, timber yields, or water and air quality. Importantly, ecological forecasts can identify where uncertainty enters the forecasting system, which is necessary to refine and improve forecast skill and guide interpretation of forecast results. Uncertainty partitioning identifies the relative contributions to total forecast variance (uncertainty) introduced by different sources, including specification of the model structure, errors in driver data, and estimation of initial state conditions. Uncertainty partitioning could be particularly useful in improving forecasts of high-density cyanobacterial events, which are difficult to predict and present a persistent challenge for lake managers. Cyanobacteria can produce toxic or unsightly surface scums and advance warning of these events could help managers mitigate water quality issues. Here, we calibrate fourteen Bayesian state-space models to evaluate different hypotheses about cyanobacterial growth using data from eight summers of weekly cyanobacteria density samples in an oligotrophic (low nutrient) lake that experiences sporadic surface scums of the toxin-producing cyanobacterium, Gloeotrichia echinulata. We identify dominant sources of uncertainty for near-term (one-week to four-week) forecasts of G. echinulata densities over two years. Water temperature was an important predictor in calibration and at the four-week forecast horizon. However, no environmental covariates improved over a simple autoregressive (AR) model at the one-week horizon. Even the best fit models exhibited large variance in forecasted cyanobacterial densities and often did not capture rare peak density occurrences, indicating that significant explanatory variables in calibration are not always effective for near-term forecasting of low-frequency events. Uncertainty partitioning revealed that model process specification and initial conditions uncertainty dominated forecasts at both time horizons. These findings suggest that observed densities result from both growth and movement of G. echinulata, and that imperfect observations as well as spatial misalignment of environmental data and cyanobacteria observations affect forecast skill. Future research efforts should prioritize long-term studies to refine process understanding and increased sampling frequency and replication to better define initial conditions. Our results emphasize the importance of ecological forecasting principles and uncertainty partitioning to refine and understand predictive capacity across ecosystems.Accepted manuscrip

Biomass burning drives atmospheric nutrient redistribution within forested peatlands in Borneo

Biomass burning plays a critical role not only in atmospheric emissions, but also in the deposition and redistribution of biologically important nutrients within tropical landscapes. We quantified the influence of fire on biogeochemical fluxes of nitrogen (N), phosphorus (P), and sulfur (S) in a 12 ha forested peatland in West Kalimantan, Indonesia. Total (inorganic + organic) N, ${{{\rm{NO}}}_{3}}^{-}$ –N, ${{{\rm{NH}}}_{4}}^{+}$ –N, total P, ${{{\rm{PO}}}_{4}}^{3-}$ –P, and ${{{\rm{SO}}}_{4}}^{2-}$ –S fluxes were measured in throughfall and bulk rainfall weekly from July 2013 to September 2014. To identify fire events, we used concentrations of particulate matter (PM _10 ) and MODIS Active Fire Product counts within 20 and 100 km radius buffers surrounding the site. Dominant sources of throughfall nutrient deposition were explored using cluster and back-trajectory analysis. Our findings show that this Bornean peatland receives some of the highest P (7.9 kg ${{{\rm{PO}}}_{4}}^{3-}$ –P ha ^−1 yr ^−1 ) and S (42 kg ${{{\rm{SO}}}_{4}}^{2-}$ –S ha ^−1 yr ^−1 ) deposition reported globally, and that N deposition (8.7 kg inorganic N ha ^−1 yr ^−1 ) exceeds critical load limits suggested for tropical forests. Six major dry periods and associated fire events occurred during the study. Seventy-eight percent of fires within 20 km and 40% within 100 km of the site were detected within oil palm plantation leases (industrial agriculture) on peatlands. These fires had a disproportionate impact on below-canopy nutrient fluxes. Post-fire throughfall events contributed >30% of the total inorganic N ( ${{{\rm{NO}}}_{3}}^{-}$ –N + ${{{\rm{NH}}}_{4}}^{+}$ –N) and ${{{\rm{PO}}}_{4}}^{3-}$ –P flux to peatland soils during the study period. Our results indicate that biomass burning associated with agricultural peat fires is a major source of N, P, and S in throughfall and could rival industrial pollution as an input to these systems during major fire years. Given the sheer magnitude of fluxes reported here, fire-related redistribution of nutrients may have significant fertilizing or acidifying effects on a diversity of nutrient-limited ecosystems