The watershed determined by Aburrá Valley system, located in northwestern Colombia, has significant urban development and steep hills. These features, together with the typical intense storms of the region, make the watershed prone to the occurrence of flash floods during the rainy seasons, affecting vulnerable communities. We propose a hybrid observational-modeling strategy to generate 30-minute discharge forecasts in different locations of the watershed, using an operational distributed hydrological model, information from stream gauges, and weather radar-derived precipitation using a quantitative precipitation estimation (QPE) technique. The forecast methodology is triggered when any stream gauge of interest reports levels over a predefined threshold. As a first step, the model uses different rainfall scenarios for the following 30 minutes. Every 5 minutes, the model forecast is executed after updating the observed rainfall and the rainfall scenarios. The scenarios correspond to (i) a lagrangian extrapolation of the precipitation fields, (ii) to a cellular automata-based extrapolation and to (iii) the last observed rain field multiplied by a time-varying ad-hoc factor based on historical event analysis. To parametrize the hydrological model and to validate the prediction methodology, we use 173 storm events from 2013 to 2018. The methodology is evaluated using the Nash coefficient, the Klin-Gupta index, differences in time-to-peak discharge, peak-discharge differences, and total storm-event volume differences. Operationally, the forecasted streamflow corresponds to the scenario with the best historical performance, given the total amount of observed rainfall. The overall results suggest that the described approach is promising. However, there are still some cases in which the method leads to discharge underestimation. Considering the forecast uncertainty, the results show that it is possible to design flash floods alerts using this simple but robust methodology.

Numerical Weather Prediction models (NWP) have been used extensively since the ’40-’50s. Despite the advances in the field, the representation and forecast of the magnitude and variability of tropical processes in models is still a challenge. One of the steps to improve the precipitation forecasts using limited-area models is to evaluate which set of physical schemes and model domain configurations represent in a better way the actual behavior observed in the tropics. We implemented, as a part of a regional risk management strategy, two different operational weather forecast strategies for a complex terrain region in the Andes mountain range in northern South America. Both strategies, together, generate a total of eleven different forecasts every day, using the Weather Research and Forecasting model (WRF) with initial and boundary conditions from the Global Forecast System (GFS). The first configuration, implemented over five years ago and referred to as SYNAPSIS, includes three nested domains (18, 6 and 2 km) and is carried out every day using the 12 UTC GFS run and three different microphysics parametrizations: Eta Ferrier scheme, Purdue Lin Scheme and Thompson Scheme. The forecast lead-time of the latter strategy is 120 hours, and it does not use data assimilation. Since December of 2017, we implemented a second configuration termed RDFS, with two nested domains (12 and 2.4 Km), which carried out four times a day using the 00, 06, 12 and 18 UTC GFS runs. This configuration has a 30-hours lead time with the Thompson microphysics scheme. In RDFS, two WRF forecast runs are performed for each start hour, one assimilating weather radar reflectivity and the other without assimilation as control run, for a total of eight forecast runs daily. In this study, we assess the rainfall and temperature forecasts for all the different configurations using precipitation derived from reflectivity from weather radar, and air temperature at 2m from a network of automatic weather stations. We use 6 hourly and monthly skill scores (RMSE, BIAS, and Correlation coefficient) to quantify the precipitation differences between the SYNAPSIS and the RDFS configurations. To evaluate the impact of data assimilation in the precipitation forecast, we aggregate the results in a region within the inner domain, and then we calculate the average precipitation forecast between 0 and 36 predicted hours for RDFS with and without data assimilation. The results suggest a strong relationship between the forecast start time and the improve of precipitation forecast accuracy using data assimilation. The diurnal cycle of precipitation in the study region has a minimum in the morning (12 UTC) and a maximum in the afternoon (00 UTC) and during the night (09 UTC). The correspondence between the forecast improvement using data assimilation and the diurnal cycle of precipitation is likely due to the amount of assimilated data. In order to quantify the precipitation differences between the diffe

The Aburrá Valley is a narrow highly complex mountainous terrain located in the Colombian Andes. Due to topographical features of the region, and the tropical setting, the meteorological variability is very high and in specific periods of the year limit the atmospheric pollutant vertical dispersion, resulting in high concentrations within the valley. The presence of prevalent low-level clouds in these periods reduce incoming solar radiation to the surface thus diminishing surface sensible heat flux to the lower atmosphere. Therefore, the spatial distribution and temporal variability of cloud coverage play a crucial role in the surface energy balance in the region. Cloud variability is also relevant to study the local hydrological cycle and long-term climate variability and change. The most widespread techniques for cloud observations are human observations, which strongly depends on the objectivity and commitment of the observer, and satellite observations, that has the disadvantage of having a relatively low temporal and spatial resolution for some applications. This research focuses on the implementation of an operational system for in-situ clouds detection based on a ground-based network of whole-sky visible cameras. The operational system uses computer vision techniques and image classification algorithms such as Support Vector Machine (SVM), K-Nearest Neighbors (KNN) among others to obtain. The algorithm starts with a group of training images with set attribute tags such as cloudy and cloud-free skies, to identify and calculate the percentage of clouds coverage in untagged images. The methodology then projects the images from the natural polar coordinates system of whole-sky cameras to 2D cartesian coordinates. Following the reprojection, overlapping images from each camera are combined using a panorama stitching technique to generate a single regional cloud fraction map. Clear-sky combined direct and indirect incoming solar radiation are adjusted using the regional cloud map to generate an estimate of the spatial distribution of cloud-forced incoming solar radiation. Cloud height from ceilometers and in-situ pyranometer measurements of incoming solar radiation provide the additional required information to generate the radiation maps.

One of the challenges in the numerical weather models is the adequate representation of soil-vegetation-atmosphere interaction at different spatial scales, including scenarios with heterogeneous land cover and complex mountainous terrain. The interaction determines the energy, mass and momentum exchange at the surface and could affect different variables including precipitation, temperature, and wind. In order to quantify the long-term climate impact of changes in local land use and to assess the role of topography, two numerical experiments were examined. The first experiment allows assessing the continuous growth of urban areas within the Aburrá Valley, a complex terrain region located in Colombian Andes. The Weather Research Forecast model (WRF) is used as the basis of the experiment. The basic setup involves two nested domains, one representing the continental scale (18 km) and the other the regional scale (2 km). The second experiment allows drastic topography modification, including changing the valley configuration to a plateau. The control run for both experiments corresponds to a climatological scenario. In both experiments, the boundary conditions correspond to the climatological continental domain output. Surface temperature, surface winds, and precipitation are used as the main variables to compare both experiments relative to the control run. The results of the first experiment show a strong relationship between land cover and the variables, especially for surface temperature and wind speed, due to the strong forcing land cover imposes on the albedo, heat capacity and surface roughness, changing temperature and wind speed magnitudes. The second experiment removes the winds spatial variability related to hill slopes, the direction and magnitude is modulated only by the trade winds and roughness of land cover.

Terrestrial water storage (TWS) plays a key role in land-surface interaction and the hydrological cycle. Changes in the variability of its components, including surface storage, soil moisture and groundwater can lead to significant changes in local climate, water supply sources and agricultural production. In this study, we assess the impacts of climate variability and change in surface and subsurface hydrological variables over the Magdalena-Cauca basin (Colombia) using a distributed conceptual hydrological model (WMF) driven by precipitation and temperature from four GCMs (general circulation models). WMF is calibrated using daily precipitation products from the Tropical Rainfall Measuring Mission (TRMM). Monthly mean anomalies of surface and groundwater storage outputs show consistency with Gravity Recovery and Climate Experiment (GRACE) data. Past (1970-2001) and future (2021-2050 and 2071-2100) precipitation from GCMs are statistically downscaled using a quantile mapping method to a spatial resolution of 0.25 by 0.25 degrees. Mean precipitation projections over the country are highly dependent on the selected GCMs, but the evidence shows agreement in a decrease towards the lower part of the basin; these projections are also present in surface runoff simulations. Annual mean streamflow follows the sign of the mean rainfall change over the basin. On the other hand, soil properties, topography, and geomorphological characteristics condition the patterns of subsurface and groundwater storage change (magnitude and localization), with the upper part of the Colombian Andes and the river mouth presenting the greatest changes. Results also show that mean soil moisture decrease in all scenarios, associated with changes in precipitation, but also due to the influence of temperature and evapotranspiration. The latter could lead to changes in the soil-atmosphere interaction, energetic conditions of the ecosystems and the need for agricultural mitigation strategies.

Cities are the most sensitive and vulnerable places to climate variability and change and weather-related extreme events given high population density, with the aggravating factor that urban climate also suffers modifications due to the widespread replacement of the natural surface altering the local thermal conditions. The Aburrá Valley is a narrow valley located at the tropical Andes in northern South America with urban areas between 1300 and 2000 m.a.s.l, a population of approximate 3.9 million people, and a comfortable climate relative to standard indoor conditions. In this work, we examine observed weather patterns in the local scale and the urban canopy layer (UCL) using data from weather stations at sites with different surface features regarding vegetation/non-impervious fractions and urban structure (Sky View Factor SVF). UCL data is available from two data sources, the first one from a field campaign using all-in-one weather sensors at the valley´s bottom, and the second one from a low-cost sensor network with robust temperature and humidity data as part of a local citizen science project with measurements in a diverse altitude range. Results suggest that at the local scale there exist different climate mean conditions due to altitude, with significant weather variability depending on radiation levels and rainfall occurrence, but at the same time, the urban effects are evident since the lowest altitude stations do not necessarily register the highest temperatures depending on the local characteristics. UCL measurements show that, while the altitude defines a background state, there are notable differences between places mainly influenced by insolation changes due to vegetation around and above sensors. Currently, the local population does not perceive thermal stress as a risk factor because it is not difficult to find places with appropriate thermal conditions when thermal discomfort arises. However, this research is relevant considering the projected local surface temperature increase due to climate change and the inexistence of baseline studies assessing the thermal comfort in outdoors to support local adaptation actions. The results of this study are useful for urban planning and building design to improve thermal conditions, especially in open spaces.