Throughout the years, wildfires have negatively impacted ecological systems and urban areas. Hence, reinforcing territorial risk management strategies against wildfires is essential. In this study, we built an early alert system (EAS) with two different Machine Learning (ML) techniques to calculate the meteorological conditions of two Colombian areas: (i) A 3D convolutional neural net capable of learning from satellite data and (ii) a convolutional network to bias-correct the Weather Research and Forecasting (WRF) model output. The results were used to quantify the daily Fire Weather Index and were coupled with the outcomes from a land cover analysis conducted through a Naïve-Bayes classifier to estimate the probability of wildfire occurrence. These results, combined with an assessment of global vulnerability in both locations, allow the construction of daily risk maps in both areas. On the other hand, a set of short-term preventive and corrective measures were suggested to public authorities to implement, after an early alert prediction of a possible future wildfire. Finally, Soil Management Practices are proposed to tackle the medium- and long-term causes of wildfire development, with the aim of reducing vulnerability and promoting soil protection. In conclusion, this paper creates an EAS for wildfires, based on novel ML techniques and risk maps.

Seasonal variability, understood as intra-annual patterns in the rise and fall of river levels, has implications for ecosystems and the populations that inhabit them. Despite the growing hydropower generation in tropical countries, there is little research on how dams alter these patterns. Most conclusions have been drawn from rivers in other latitudes with different seasonal characteristics (Chong et al., 2021). Recognising that seasonal variability downstream of dams not only responds to seasonal climatic patterns and long-standing socio-natural interactions (Jackson et al., 2022) but also reflects fluctuations in energy market (Krause, 2017), the aim of this paper is to describe the complexity of the new conditions imposed by a hydropower dam on the seasonality of stream flow in a tropical river. To advance in this, we analyzed seasonal patterns of daily, weekly and monthly flows in three Colombian rainfall dominated rivers before and after damming and explored their relationship with the energy market. A composite methodology was used for the statistical analysis of the time series, their entropies and signal frequencies. The results suggest that in tropical rivers, hydropower dams decouple stream flow from precipitation, reduce stream flow harmonics, amplitude and intensity at monthly scale, and increase peaks frequency and number at weekly and daily scales. Dams, also reduce stream flow standard deviation and transition gradualness at sub monthly scales. We show how the use of a single index masks changes in seasonal diversity and tropical river variability and illustrate how the dominant frequencies of the energy market regroup stream flow variability on different time scales.