During the last decade, wildfires in the Aburrá Valley watershed, located in northwestern Colombia, have caused significant forest and ecosystem losses, health issues in nearby communities associated with aerosols from biomass burning, and increases in the CO2 emissions. Human activities, along with weather variability, modulate the occurrence of forest fires during the dry seasons, and the efforts to reduce them have shown limited success, highlighting the need for the development of holistic prevention strategies. We implemented a general strategy involving real-time monitoring, modeling, and warning based on a distributed Bayesian model coupled with a distributed hydrological model and a regional weather model (WRF) to estimate wildfire susceptibility in the basin. The model operates with a spatial resolution of 60m and an hourly temporal resolution. The model uses static and time-dependent (dynamic) information. Static variables include land use, urban-rural fringe area, historical fire occurrence, and are updated occasionally. The dynamic variables change at each time step, and they depend on meteorological conditions and include soil moisture, cumulative rainfall during the last ten days, and an estimation of the surface temperature. These variables are obtained from in-situ rain gauges and quantitative precipitation estimation (QPE) techniques using C-band weather radar reflectivity, in-situ pyranometers and automatic weather stations, and output from a distributed hydrological model and WRF-based weather forecasts. The Bayesian model allows the generation of fire susceptibility predictions that help optimize prevention strategies implemented by the fire departments in the region. The model has been evaluated using the location of historical wildfires showing high skill. Along with the model, there are efforts in the region implemented for early-detection, and quantification of forest fires using in-situ and drone-borne thermal and high-definition cameras, a continuous monitoring strategy is established.

We use numerical solutions of the Richard’s Equations for 3D porous media to investigate the influence of agricultural subsurface drainage as a hydrologic process and its effect on the hydrologic regime of a watershed. Specifically, we determine the relation between subsurface seepage and subsurface storage in hillslopes with (drained) and without (undrained) subsurface drainage. Simulations are performed in Hydrus3D and the output is analyzed with MATLAB’s curve fitting tools, to create simple ordinary differential equations that represent the relationship between subsurface flow and subsurface storage for hillslopes of varying topographical gradients and shapes. We have determined an ‘activation point’ below which the seepage/storage relationship is roughly linear, and above which the drained and undrained simulations behave according to different nonlinear functional forms. Although the seepage/storage relationship of flat hillslopes have parametric consistencies independent of the hillslope gradient, the addition of curvature increases the complexity. In this work, we describe approximations to account for curved hillslopes. From our formulation, subsurface flow for varying hillslopes can be approximated using only the water storage and the topography of the hillslope. Reducing the system from partial differential equations (Hydrus) to ordinary differential equations improves scalability of the model. Simplified equations are used to study the consequences of large-scale changes in agricultural landscapes due to subsurface drainage.