Modelling gully erosion in urban areas is challenging due to difficulties with equifinality and parameter identification, which complicates quantification 0of management impacts on runoff and sediment production. We calibrated a model (AnnAGNPS) of an ephemeral gully network that formed on unpaved roads following a storm event in an urban watershed (0.2 km²) in Tijuana, Mexico. Latin hypercube sampling was used to create 500 parameter ensembles. Modelled sediment load was most sensitive to the Soil Conservation Service (SCS) curve number, tillage depth (T_D), and critical shear stress (τ_c). Twenty-one parameter ensembles gave acceptable error (behavioural models), though changes in parameters governing runoff generation (SCS curve number, Manning’s n) were compensated by changes in parameters describing soil properties (T_D, τ_c), resulting in uncertainty in the optimal parameter values. The most suitable parameter combinations or “behavioural models” were used to evaluate uncertainty under management scenarios. Paving the roads increased runoff by 146–227%, increased peak discharge by 178–575%, and decreased sediment load by 90–94% depending on the ensemble. The method can be used in other watersheds to simulate runoff and gully erosion, to quantify the uncertainty of model-estimated impacts of management activities on runoff and erosion, and to suggest critical field measurements to reduce uncertainties in complex urban environments.

Urbanization can increase sheet, rill, gully, and channel erosion. We quantified the sediment budget of the Los Laureles Canyon watershed (LLCW), which is a mixed rural-urbanizing catchment in Northwestern Mexico, using the AnnAGNPS model and field measurements of channel geometry. The model was calibrated with five years of observed runoff and sediment loads and used to evaluate sediment reduction under a mitigation scenario involving paving roads in hotspots of erosion. Calibrated runoff and sediment load had a mean-percent-bias of 28.4 and − 8.1, and root-mean-square errors of 85% and 41% of the mean, respectively. Suspended sediment concentration (SSC) collected at different locations during one storm-event correlated with modeled SSC at those locations, which suggests that the model represented spatial variation in sediment production. Simulated gully erosion represents 16%–37% of hillslope sediment production, and 50% of the hillslope sediment load is produced by only 23% of the watershed area. The model identifies priority locations for sediment control measures, and can be used to identify tradeoffs between sediment control and runoff production. Paving roads in priority areas would reduce total sediment yield by 30%, but may increase peak discharge moderately (1.6%–21%) at the outlet.

Context: Recently, Unoccupied Aerial Systems (UAS) with photographic or Light Detection and Ranging (LIDAR) sensors have incorporated on-board survey-grade Global Navigation Satellite Systems that allow the direct georeferencing of the resulting datasets without Ground Control Points either in Real-Time (RTK) or Post-Processing Kinematic (PPK) modes. These approaches can be useful in hard-to-reach or hazardous areas. However, the resulting 3D models have not been widely tested, as previous studies tend to evaluate only a few points and conclude that systematic errors can be found. Objectives: We test the absolute positional accuracy of point clouds produced using UAS with direct-georeferencing systems. Methods: We test the accuracy and characteristics of point clouds produced using a UAS-LIDAR (with PPK) and a UAS-RGB (Structure-from-Motion or SfM photogrammetry with RTK and PPK) in a challenging environment: a coastline with a composite beach and cliff. The resulting models of each processing were tested using as a benchmark a point cloud surveyed simultaneously by a Terrestrial Laser Scanner. Results: The UAS-LIDAR produced the most accurate point cloud, with homogeneous cover and no noise. The systematic bias previously observed in the UAS-RGB RTK approaches are minimized using oblique images. The accuracy observed across the different surveyed landforms varied significantly. Conclusions: The UAS-LIDAR and UAS-RGB with PPK produced unbiased point clouds, being the latter the most cost-effective method. For the other direct georeferencing systems/approaches, the acquisition of GCP or the co-registration of the resulting point cloud is still necessary.

Soil erosion, poor water quality, and degraded ecosystems impose major cost burdens and challenges for stormwater managers. We present a stochastic hydro‐financial watershed modeling framework for designing an Environmental Impact Bond (EIB)—a new form of financing for comprehensive, watershed scale interventions. EIBs provide capital for interventions that is repaid over time with interest by stakeholders who experience reduced costs (savings). The EIB is also structured so stakeholders and investors share the reward of cost savings. The framework estimates cost savings from interventions and accounts for aleatory and epistemic uncertainty in costs, which in turn impacts the financial terms of the EIB. In particular, we show a method to reward investors for taking on the risk that interventions fail. The framework is applied to a transnational pollution and sedimentation problem on the U.S.‐Mexico border and has broad applicability for a wide range of environmental problems.

The coexistence of marine sensitive areas with the oil industry requires robust preparedness and rapid response capabilities for monitoring and mitigating oil spill events. Scientifically proven satellite-based methods for the visual detection of oil spills are widely recognized as effective, low-cost, transferable, scalable, and operational solutions, particularly in developing economies. Following meticulous design and implementation, we adopted and executed a relatively low-cost operational monitoring and alert system for oil spill detection over the ocean surface and alert issuance. We analyzed over 1500 satellite images, issuing over 70 warning reports on oil slicks and spills in the southern Gulf of Mexico. To assess the system’s efficiency and performance, we leveraged data from three major oil spill incidents in the study region during June and July of 2023 in the study region, covering a maximum area of 669 km² and tracked for 12 to 24 days. We documented the evolution of these oil spills by integrating satellite sensing data with on-site Lagrangian drifting buoys, a network of high-frequency radars, and citizen reports to validate the outcomes of this system. We generated timely technical information on the spill’s evolution, informing decision-makers and local community leaders to strengthen their mitigation response capabilities. Additionally, we developed a robust database with spectral and spatiotemporal features of satellite-detected oil, thereby contributing to advancing the scientific understanding of sea surface dynamics related to natural and anthropogenic oil sources. This study also highlights immediate-, medium-, and long-term research agendas and establishes a reference for a sustained, transferable, and operational oil spill monitoring system.