German Ricardo Santos Granados

Extreme urban floods increasingly threaten pedestrians, yet hazard assessments often emphasise peak inundation maps and overlook the duration and overlap of instability conditions that drive real-life exposure and operational decisions. We evaluate the spatiotemporal evolution of pedestrian hazard during a 3-h, 100-year design storm simulated with a coupled rainfall–flood (1D–2D) model for Cúcuta, Colombia (130.5 ha). Every 5 min, gridded flow velocity (V) and water depth (h) were extracted and translated into four hazard levels using widely adopted pedestrian stability indicators (V, h, and V·h). We quantify (i) the fraction of wet area in each hazard class through time (normalised by the instantaneous wet area and by the event’s maximum wet footprint) and (ii) persistence (time above thresholds per cell/sector) and simultaneity (co-occurrence of medium-high/high classes among the three indicators).The wet footprint expands rapidly and peaks at ~60 min before draining incompletely. Velocity shows an impulsive response, with high-V corridors emerging near the rising limb and collapsing shortly after the peak, while hazardous depths persist longer and concentrate in low-drainage sectors. The combined indicator V·h delineates a critical hazard window (~40–120 min), when threshold exceedance and indicator overlap are maximised, identifying recurrent hotspots and the time intervals most relevant for pedestrian management. The proposed curve-plus-persistence framework complements peak hazard mapping by providing quantitative criteria to prioritise interventions and define operational time windows for closures and warning measures.

In recent years, there has been growing concern about deforestation in the Amazon River basin, particularly in relation to its impact on regional water resources. This study performs a spatial and temporal analysis of deforestation variations between 2001 and 2020, using MODIS and Sentinel data. Using supervised classification techniques, we classified land changes into afforestation, deforestation and reforestation and analyzed the transitions between different land uses including forest, pasture, shrubland, crops and urbanization. Our findings show an annual forest loss of about 5,726 square kilometers, much of this deforestation is localized to specific subregions, although the overall size of the watershed suggests a lack of sensitivity.These results show a significant correlation with variations in evapotranspiration, estimated through a model calibrated with data from the ERA5 reanalysis. This dataset was used to analyze the standardized precipitation and evapotranspiration indexes (SPEI), revealing that, during the last 20 years under study, the region experienced an increase in both the magnitude and intensity of drought compared to the previous 20 years.In particular, a direct relationship is observed between aggressive agricultural policies in Brazil and Bolivia and increased deforestation rates. In addition, this study serves as the basis for complementary research work assessing the implications of these land use changes on river discharge and estimated groundwater recharge in the Amazon basin.

The atmospheric dynamics of the Amazon, critical for global environmental stability, have faced increasing influence from numerous El Niño and La Niña events in recent decades. While reanalysis data has incorporated these events through models and measurements, the intricate mechanics of spatial and temporal water vapor transport remain unclear. In this study, we present a preliminary analysis of these dynamics, utilizing over twenty years of ERA5 monthly data over atmospheric layer. Our investigation was constructed on two primary scales, each offering unique insights. The first scale aims to replicate and validate the system's seasonality concerning the Intertropical Convergence Zone (ITCZ), those patterns allow evaluating some patterns and its effects in land hydrological process observed along the basin integrating specific methods and models to clarify how the seasonality was replicated and validated. On the second scale, we delve into smaller hydrological sub-units of the Amazon, identifying their contribution to water recycling, and net fluxes across the basin boundaries. We provide an innovative estimation of transport paths using a 4 cardinal directional approach (brubaker box scheme modified) that makes possible identificate, analyse and understanding, water sources and sinks and its relevance in the normal hydrological production and synergically systems The findings indicate the system's relatively stable dynamics in terms of water vapor sources and altitudinal variation across atmospheric layers. Our methodology introduces a novel framework for calculating comprehensive trajectories of water vapor transport from a hybrid lagrangian-eulerian approach, significantly enhancing our understanding of the Amazon's hydrological cycle from an atmospheric perspective. To provide more precision, we specify that the stability observed in the system pertains to water vapor sources and altitudinal variation. These stable dynamics contribute valuable insights into the intricate water vapor transport mechanisms in the Amazon. Additionally, we highlight the implications of our findings for future research in understanding how to create alternatives to mitigate some impacts of El Niño and La Niña events on the Amazon's atmospheric dynamics.

Initial works on forecasting focused their efforts on one-dimensional precipitation time-series analysis. However, rainfall phenomena are sometimes quite heterogeneous and spatially variable in space and time, especially in extreme events. To address this issue, an integrated approach might be needed, where not only the spatio-temporal variability of rainfall is considered, but also the uncertainty that is present in forecasting. The objective of this research is to analyse the relationship between spatio-temporal rainfall objects estimated from numerical weather prediction models and their hydrological response in a river basin. It is assumed that a better understanding of this relation could help to characterize and forecast extreme phenomena. For this study, the Dapoling-Wangjiba catchment is evaluated, where observed precipitation and discharge data from 2006 to 2009 were available. The analysis is based on four main components:  first, the rainfall perturbed members data are obtained through the TIGGE dataset from ECMWF. Second, the object-based methodology ST-CORA is used in order to characterize the possible rainfall events via its spatio-temporal characteristics such as centroid, spatial coverage and duration. Third, a fully distributed and calibrated HAPI model is used for obtaining a simulated discharge in the catchment outlet and finally, an analysis between the statistics of the object’s characteristics and the hydrological response is carried out. The results of this research are expected to be used in future improvements on how forecasting and early warning and nowadays emitted and understood.

Magdalena's river basin represents a quarter of Colombia's surface, yet neighbouring ecosystems remain ignored while enduring unacceptable environmental conditions. At its outlet in the Caribbean Sea, several channels link it to Cienaga Grande de Santa Marta (CGSM), a deltaic coastal-lagoon ecosystem (4200 km2) from which around 15 to 20 % are water bodies. According to several studies, Magdalena River's overflows represent its primary freshwater source. However, the recorded discharge has gradually reduced, though the basin's rainfall shows a rising tendency. Additional discharge measurements close to the outlet evidenced that it was even lesser than records upstream counterintuitively. Consequently, the energy gradient from the river to the sea through the ecosystem is reversing more frequently. That has resulted in a continuous salinisation process of the lagoons, diminishment of the mangrove forest and lagoons extension, fauna migration and low water quality. This research aims to elaborate on the Magdalena River's outlet discharges vulnerabilities in Colombia, thus providing better insight into impact-based decision-making. Results suggest that the discharge regime responds to the El Niño Southern Oscillation (ENSO) phenomenon as it controls the country's dry/rain season. Further analysis indicates that a) low flows relate to El Niño periods and high flows to La Niña; b) the flow duration curve's slope is getting milder, meaning that high flows are decreasing whereas low flows are increasing; c) extreme discharges are getting smoother, and less disperse so that high and low flow peaks are within a smaller range; d) the dispersion diminishes radically during severe El Niño events, and e) although a priori the assumption is that the more severe El Niño events might bring lower discharge values, the minimum values recorded are more significant than in neutral ENSO conditions. Moreover, extreme discharge values during ENSO events, despite their severity, tended to have a horizontal asymptote that suggests human-driven control upstream, especially during El Niño periods. The Magdalena basin holds Colombia's hydropower network representing more than 70% of its electricity supply distributed in 33 operating plants. On the one hand, it is clear that during El Niño, the plants guarantee a minimum discharge downstream, as it is when the National Hydrometeorological Agency only considers drought protocols. However, during neutral ENSO conditions, the flows are not controlled and thus, impacts downstream arise. On the other hand, reservoirs have increased evaporation due to a large accumulated open water surface (611 km2 in total). Results show that water loss represents 40% to 80% of the current average discharge at the outlet (7000 m3/s), adding to the ecosystem depletion. The results urge decision-makers to reconsider the drought protocols applying an impact-based approach.

Bogotá’s River Basin, it’s an important basin in Cundinamarca, Colombia’s central region. Due to the complexity of the dynamical climatic system in tropical regions, can be difficult to predict and use the information of GCMs at the basin scale. This region is especially influenced by ENSO and non-linear climatic oscillation phenomena. Furthermore, considering that climatic processes are essentially non-linear and possibly chaotic, it may reduce the effectiveness of downscaling techniques in this region. In this study, we try to apply chaotic downscaling to see if we could identify synchronicity that will allow us to better predict. It was possible to identify clearly the best time aggregation that can capture at the best the maximum relations between the variables at different spatial scales. Aside this research proposes a new combination of multiple attractors. Few analyses have been made to evaluate the existence of synchronicity between two or more attractors. And less analysis has considered the chaotic behaviour in attractors derived from climatic time series at different spatial scales. Thus, we evaluate general synchronization between multiple attractors of various climate time series. The Mutual False Nearest Neighbours parameter (MFNN) is used to test the “Synchronicity Level” (existence of any type of synchronization) between two different attractors. Two climatic variables were selected for the analysis: Precipitation and Temperature. Likewise, two information sources are used: At the basin scale, local climatic-gauge stations with daily data and at global scale, the output of the MPI-ESM-MR model with a spatial resolution of 1.875°x1.875° for both climatic variables (1850-2005). In the downscaling process, two RCP (Representative Concentration Pathways)  scenarios are used, RCP 4.5 and RCP 8.5.For the attractor’s reconstruction, the time-delay is obtained through the  Autocorrelation and the Mutual Information functions. The False Nearest Neighbors method (FNN) allowed finding the embedding dimension to unfold the attractor. This information was used to identify deterministic chaos at different times (e.g. 1, 2, 3 and 5 days) and spatial scales using the Lyapunov exponents. These results were used to test the synchronicity between the various chaotic attractor’s sets using the MFNN method and time-delay relations. An optimization function was used to find the attractor’s distance relation that increases the synchronicity between the attractors.  These results provided the potential of synchronicity in chaotic attractors to improve rainfall and temperature downscaling results at aggregated daily-time steps. Knowledge of loss information related to multiple reconstructed attractors can provide a better construction of downscaling models. This is new information for the downscaling process. Furthermore, synchronicity can improve the selection of neighbours for nearest-neighbours methods looking at the behaviour of synchronized attractors. This analysis can also allow the classification of unique patterns and relationships between climatic variables at different temporal and spatial scales.

The importance of knowing and representing rural and urban development in water management is vital for its sustainability.  An essential part of the management required that stakeholders are more aware of the consequences of decisions and in some way, can link decisions towards sustainability.  For this, a mobile app serious game called Water Citizens has been proposed as knowledge dissemination and to provide a better understanding of the way decisions affect Sustainable Development Goals (SDGs). A complex model of a pilot region (Combeima in Ibague, Colombia) has been developed, and the model results are few into equations to estimate fluctuations of SDGs in the region. Running this complex model in real-time, for a mobile application, requires an extensive high-performance computing system linked to large and complex network setup. To solve this problem, a fast yet accurate surrogate model is proposed.Therefore, this study contemplates an analysis of methods to forecast sustainable development indicators evaluated through climate change scenarios for a period between 1989-2039. The proposed scenarios associated the public health, livestock, agriculture, engineering, education and environment sectors with climate variables, climate change projections, land cover and land use, water demands (domestic, agricultural and livestock) and water quality (BOD and TSS). Generating the possibility that each player can make decisions that represent the actions that affect or contribute to the demand, availability and quality of water in the region.Consequently, a set of indicators were selected to recreate the dimensions of each sector and reflect its relationship with the Sustainable Development Objectives, as opposed to the decisions made by each player. In addition, three categories were considered for the levels of sustainability: low (0.0 - 0.33), medium (0.34 - 0.66) and high (0.67 - 1.0) for the calculated SDG values. Self-learning techniques have been employed in the analysis of decision-making problems. In this study, the nearest K neighbours (k-NN) and a multilayer perceptron network (MLP) were used. Through an analysis based on the responses of the players and sustainability indexes, a multiple correlation analysis was developed in order to consolidate the learning dataset, which was randomly partitioned in proportions 0.7 and 0.3 for the training and test subsets respectively. Subsequently, the model fit and performance was carried out, analysing the MSE error metric and confusion matrix.Finally, the results of this study will allow to determine the potential of supervised learning models as a decision-making tool for the evaluation of sustainable development, as well as to obtain a better abstraction and representation of the water resource to the challenges related to climate adaptation and water sustainability measures of citizen action, besides generating new approaches for the use of artificial intelligence in land use planning and climate adaptation processes.

Drought is often conceptualised as an extreme weather event generated by anomalies in water resources availabilities. Understanding the behaviour and spatiotemporal distribution of drought events has become very important due to the possible teleconnections of drought propagation patterns. This understanding and if is possible representation of teleconnections between patterns could lead to better prediction and management of extreme events.This study develops a methodology to monitor spatiotemporal drought events in the dry corridor of Central America using the drought index SPI and SPEI for the period 1981 to 2020.This methodology consists of five stages. 1) collection and quality validation of the data sets used. 2) ERA5 and Observation datasets allow calibrating the precipitation and temperature values from historical gauge measurements. 3) Then, by the estimation and trend analysis of the drought index in different time scales (3, 6, 12 months) an initial baseline is defined. 4) Spatiotemporal association algorithms (based on computer vision) are used to characterise and monitoring the most extensive drought events. For this, the extreme and severe events (DI values below -1) threshold is estimated. 5)  Synchronic Integration between temporal patterns and spatial propagation is carried out to evaluate possible interactions or connections of drought events along the dry corridor of Central America. These results provide valuable information to evaluate the impacts on different sectors threatened by drought throughout the territory. This work presents preliminary results of an extended project looking at the dry corridor in Central America.