Simple hydrological models, such as the Seasonal Water Yield Model developed by the Natural Capital Project (InVEST SWYM), are attractive as data requirements are relatively easy to satisfy. However, simple models may produce unrealistic results when the underlying hydrological processes are inadequately described. We used the variation in performance of the InVEST SWYM across watersheds to identify correlates of poorly modeled outcomes of InVEST SWYM. We grouped 749 watersheds from across North America into five bioclimatic regions using nine environmental variables. For each region, we compared the predicted flow patterns to actual flow conditions over a 15-year period. The correlation between the modeled and actual flows was highly dispersed and relatively poor, with 92% of r² values less than 0.5 and 42% less than 0.1. We linked cryospheric variables to model performance in the bioclimatic region with the poorest model performance (the Low elevation Boreal Sub-humid region—LeBSh). After incorporating cryospheric conditions into the InVEST SWYM, predictions improved significantly in 30% of the LeBSh watersheds. We provide a relatively straightforward approach for identifying processes that simple hydrological models may not consider or which need further attention or refinement.

Detailed knowledge of nearshore topography and bathymetry is required for a wide variety of purposes, including ecosystem protection, coastal management, and flood and erosion monitoring and research, among others. Both topography and bathymetry are usually studied separately; however, many scientific questions and challenges require an integrated approach. LiDAR technology is often the preferred data source for the generation of topobathymetric models, but because of its high cost, it is necessary to exploit other data sources. In this regard, the main goal of this study was to present a methodological proposal to generate a topobathymetric model, using low-cost unmanned platforms (unmanned aerial vehicle and unmanned surface vessel) in a very shallow/shallow and turbid tidal environment (Bahía Blanca estuary, Argentina). Moreover, a cross-analysis of the topobathymetric and the tide level data was conducted, to provide a classification of hydrogeomorphic zones. As a main result, a continuous terrain model was built, with a spatial resolution of approximately 0.08 m (topography) and 0.50 m (bathymetry). Concerning the structure from motion-derived topography, the accuracy gave a root mean square error of 0.09 m for the vertical plane. The best interpolated bathymetry (inverse distance weighting method), which was aligned to the topography (as reference), showed a root mean square error of 0.18 m (in average) and a mean absolute error of 0.05 m. The final topobathymetric model showed an adequate representation of the terrain, making it well suited for examining many landforms. This study helps to confirm the potential for remote sensing of shallow tidal environments by demonstrating how the data source heterogeneity can be exploited.

The impacts of wildfire smoke on lake habitats remains unclear. We determined the metabolic response to smoke in the epi‐pelagic and two littoral habitats in Castle Lake, California. We compared light regime, gross primary production, ecosystem respiration, and net ecosystem production in years with and without smoke. During the smoke period incident ultraviolet‐B (UV‐B) radiation and photosynthetically active radiation (PAR) decreased by 53% and 28%, respectively, while the water column extinction coefficient of UV‐B and PAR increased by 20% and 18% respectively. Epi‐pelagic productivity increased during smoke cover because of decreased solar inputs. PAR values remained sufficient to saturate productivity, suggesting observed differences were primarily the result of changes in UV‐B. Littoral‐benthic productivity did not change, possibly reflecting adaptation to high‐intensity UV‐B light in these habitats. Our results highlight the importance of understanding how prolonged wildfire smoke alters the amount of energy produced from specific habitats in lakes.

Understanding the drivers and how they affect ecosystem metabolism is essential for developing effective management policy and plans. In this study, net ecosystem production (NEP), ecosystem respiration (R), and gross primary production (GPP) rates were estimated in relation to physicochemical, hydrological, and meteorological variables in La Salada (LS) and Sauce Grande (SG), two shallow lakes located in an important agricultural region with water management. LS is a mesosaline, mesotrophic-eutrophic lake, whereas SG is a hyposaline and eutrophic lake. GPP and R showed daily and seasonal variations, with R exceeding GPP during most of the study period in both lakes. Net heterotrophic conditions prevailed during the study period (NEP LS: −1.1 mmol O₂ m⁻² day⁻¹ and NEP SG: −1.25 mmol O₂ m⁻² day⁻¹). From data analysis, the temperature, wind speed, and lake volume are the main drivers of ecosystem metabolism for both lakes. Despite the significant differences between the two lakes, the NEP values were similar. The different hydrological characteristics (endorheic vs. flushing lake) were crucial in explaining why the two different systems presented similar ecosystem metabolic rates, emphasizing the importance of water management.