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<p>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.<br>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.<br>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. </p>

<p>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.</p><p>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.</p><p>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. </p><p>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.</p><p>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.</p>

<p>The erratic drought nature and its great spatiotemporal variability make conventional forecasting systems or stochastic forecasting systems very limited in terms of the monitoring of its dynamic characteristics. Therefore, this study proposes a dynamic forecasting methodology based on machine learning models by tracking the spatial and temporal characteristics of drought events.</p><p>This methodology consists of four main phases. 1) the drought spatiotemporal characteristics calculation and extraction such as spatial aggregations or extensions, geospatial properties (area, perimeter), centroid location and trajectory from their connectivity, which are generated following the contiguous drought area analysis (CDA) proposed by Corzo--- 2) feature engineering and dataset preparation, which is consolidated according to the hierarchy and relative importance of the associated predictor and predictor variables 3) implementation of an intelligent analysis method based on deep neural network architecture (CNN, LSTM) techniques, which combines spatial observation mediated by convolution integrated with temporal analysis for prediction. Thus generating primary results against the future propagation pattern or trajectory of a spatial unit. 4) Analyzing the various model performances based on statistical metrics, validation of the generated trajectories using the area under the curve (AUC) and receiver operating characteristic (ROC) and error approach as Root Mean Square Error (RMSE).</p><p>This methodology is presented using indexes derived from the ERA 5 reanalysis dataset as SPEI and SPI on the Central America dry corridor (1979-2020), where the performance of the intelligent system will be evaluated not only taking into account the statistical performance, but also in the identification and forecasting of those regions with major drought generation tendencies.</p>

<p>Many regions in the world are threaten by Climate change, and there is a large global concern on the dependency of water contributions from neighbouring countries. In order to understand more how the water contributions from other region affect a river basin, global spatiotemporal information could be used to obtain budgets balance. This study proposes a methodology to analyse the atmospheric moisture balance around hydrological units (watersheds) using ERA 5 reanalysis data sets, allowing the evaluation of the role of spatiotemporal patterns associated with the transport of regional moisture fluxes and understanding how these components modulate regional water heterogeneity, sources and sinks. This study consists of 3 phases: 1) collection and validation of the required hydrometeorological sets and variables and two-dimensional discretization of the hydrographic domain or unit establishing the boundaries for computational analysis; then, estimation and evaluation of the contribution patterns of transported moisture fluxes based on the Eulerian model developed by Brubaker,1993. Finally, for each region, we proceed to estimate the spatiotemporal variations of the atmospheric water balance by establishing the calculation of the precipitation recycling rate as well as the fractions of horizontal moisture flux contributions from each direction or boundary, as well as their seasonality and interannual variability, magnitudes and concentration rates associated with flux divergence. As a case study, the Pamplonita river basin in Colombia was selected. Here we present these results, that have provided valuable information related to the identification of biases in the estimation of atmospheric water supplies, monitoring strategies and hydrological balance.</p>