During the last few years, the debate over the changes in the global average temperature has been one of the most important political issues. A better understanding of the effects of the temperature increase on other variables is one of the main challenges in climate. In the last IPCC report, due in part to the limited rainfall data in the tropics, there is no reliable conclusion about the observed and expected long-term precipitation changes in association with the global temperature increase. Since water resources are essential for energy generation and food production in the Department of Antioquia, Colombia, a better understanding of changes in rainfall in the long term becomes a vital tool for decision making. This research presents an assessment of rain variability at different temporal and spatial scales over Colombia, and more specifically over Antioquia. The data used was corresponds to long-term records of 86 rain gauges, in addition to 9 temperature stations, and TRMM precipitation products. The use of in-situ rain gauge information allows focusing on a spatial scale useful not only for a general understanding of precipitation changes but also for engineering and other practical applications. Analyses reveal that while there are no long-term trends in precipitation at the monthly or longer timescales, relatively short-lived extreme events show long-term changes in intensity and frequency. Results show that the shorter the duration of the intense events, the higher the magnitude of the increasing intensity trend. Similarly, for more intense events, the trends are also larger and more significant from a statistical point of view. Analysis of temperatures shows a clear relationship with extreme precipitation events with scaling features explained via the Clausius-Clapeyron relation, controlling the intensification of precipitation. The long-term rainfall trends are compared with modeling results from the different scenarios of a small set of CMIP runs given that most models do not adequately represent Colombia´s precipitation climatology. The results indicate a substantial reduction of return period of extreme events with implications in engineering: the current hydraulic designs would be obsolete in less than 50 years if the increment in the frequency of intense events is not considered in the design.

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.

Shortages of electricity in Colombia associated with droughts during El Niño-Southern Oscillation warm events lead government to seek diversification of the electricity matrix, which is mainly hydro (66%). Additionally, clean technologies like wind and solar power are alternatives to climate change. Potentially, solar radiation is high year-round in Colombia due to its low latitude, but clouds are the most critical limiting factor. In this paper, we measure and analyze the variations in efficiency resulting from different weather variables. Toward that end, we measure three photovoltaic systems located in three different zones of the valley. Simultaneously, we analyzed some of the limiting factors which influence solar radiation. For that, we consider radiation from 3 pyranometers, air temperature and relative humidity from meteorological sensors, water vapor density, liquid and solid water content from a microwave radiometer, reflectances as a proxy of clouds from GOES 16 satellite visible band 2. To identify clouds, we estimated radiance thresholds for the GOES information in the three locations of interest from changes in the surface radiation data. These thresholds provide identification of the most sensible hours for radiation based on the anomalies of the radiation under cloudiness conditions. Lastly, the efficiencies on each location were calculated considering the power data, as a proportion of the horizontal global radiation and the area of each solar panel.