A multifractal analysis based on the time series of temperature, pressure, relative humidity, wind speed, and wind direction was performed for 16 weather stations located in the hydrographic basin of the Guadalupe River in Baja California, Mexico. Our analysis included a 38-year dataset from MERRA-2 database, we investigated the multifractal nature of daily time series data for climatic variables associated with the Santa Ana Winds. We employed the Multifractal Detrended Fluctuation Analysis (MFDFA) method to extract multifractal complexity parameters (${\alpha }_0$, $∆\alpha$, and $r$). This was adequate to evaluate the multifractality of the time series that represented the conditions of the phenomenon’s occurrence. From the estimation of the generalized Hurst exponent (hq), it was possible to characterize the time series of the meteorological variables in terms of the characteristics of persistence, anti-persistence, or randomness. Finally, the values corresponding to the parameters and characteristics of the multifractal spectrum or singularities can be used as quantitative and qualitative indicators to describe the dynamics of meteorological processes during the occurrence of the Santa Ana winds in the Guadalupe basin.

A multifractal formalism relates multiscale quantities to the multifractal spectrum. The multifractal framework provides significant analytical advantages by incorporating a wide range of statistical moment orders $\left(q\right)$, thereby enabling a more comprehensive characterization of the intrinsic structural variability embedded in the dataset. The scaling properties of the analyzed rainfall time series was studied using Legendre transformation. This tool is effective for detecting multifractality in the time series of interest and for extracting information on scaling behavior. The obtained parameters may ultimately aid in performing multifractal modeling. The 50-year-long daily rainfall time series shows multifractal properties. The analysis of the generalized Hurst exponent $h(q$) enabled the classification of time series’ temporal dynamics, distinguishing between persistent, anti-persistent, and uncorrelated behavior. The multifractal analysis proves to be an effective and robust tool to characterize precipitation time series in the context of climate change research. Ultimately, the parameters and features derived from the multifractal spectrum—such as singularity strengths and spectrum width—serve as both quantitative and qualitative metrics for characterizing the spatiotemporal dynamics of rainfall in the semi-arid region of the Central Mexican Plateau.

A fractal analysis based on the time series of precipitation, temperature, pressure, relative humidity, and wind speed was performed for 16 weather stations located in the hydrographic basin of the Guadalupe River in Baja California, Mexico. Days on which the phenomenon known as Santa Ana winds occurs were identified based on the corresponding criteria of wind speed (≥4.5 m/s) and wind direction (between 0° and 90°). Subsequently, the time series was formed with data representing the days on which this phenomenon occurs in each of the analyzed weather stations. A time series was additionally formed from the days in which the Santa Ana winds condition does not occur. Hurst exponents and fractal dimension were estimated applying the rescaled range method to characterize the established time series in terms of characteristics of persistence, anti-persistence, or randomness along with the calculation of the climate predictability Index. This enabled the behavior and correlation analysis of the meteorological variables associated with Santa Ana winds occurrence. Finally, this type of research study is instrumental in understanding the regional dynamics of the climate in the basin, and allows us to establish a basis for developing models that can forecast the days of occurrence of the Santa Ana winds, in such a way that actions or measures can be taken to mitigate the negative consequences generated when said phenomenon occurs, such as fires and droughts.

Santa Ana winds are extreme meteorological events that strongly affect the U.S.–Mexico border region, often associated with droughts, high fire risk, and hydrological imbalance. Understanding the temporal behavior of key atmospheric variables during these events is crucial for integrated water resource management in semi-arid regions such as the Guadalupe Basin in northern Baja California. In this study, we explored the predictive capability of several hybrid deep learning architectures—Long Short-Term Memory (LSTM), Convolutional Neural Network combined with LSTM (CNN–LSTM), and Bidirectional LSTM with Attention (BiLSTM–Attention)—to model the temporal evolution of wind speed, wind direction, temperature, relative humidity, and atmospheric pressure using Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) reanalysis data from 1980 to 2020. Model performance was evaluated using RMSE, MAE, and R² metrics and compared against persistence and climatology baselines. The BiLSTM–Attention model achieved the best overall performance, showing particularly high accuracy for temperature (R² = 0.95) and relative humidity (R² = 0.76), while maintaining angular errors below 35° for wind direction. The results demonstrate the potential of hybrid deep learning models to capture nonlinear temporal dependencies in meteorological time series and provide a methodological framework to enhance hydrometeorological understanding and water resource management in the Guadalupe Basin under Santa Ana wind conditions.

This study evaluated the water supply and regulation of the San Pedro River basin, located in the municipality of Puerto Libertador (Córdoba, Colombia), under climate change scenarios, using the SWAT (Soil and Water Assessment Tool) hydrological model. The model was calibrated and validated in SWAT-CUP using the SUFI-2 algorithm, based on observed streamflow series and sensitive hydrological parameters. Observed and satellite climate data, CHIRPS for precipitation and ERA5-Land for temperature, radiation, humidity, and wind, were employed. Climatic data were integrated along with spatial information on soils, land use, and topography, allowing for an adequate representation of the basin’s heterogeneity. The model showed acceptable performance (NSE > 0.6; PBIAS < ±15%), reproducing the seasonal variability and the average flow behavior. Climate projections under RCP 4.5 and RCP 8.5 scenarios, derived from the MIROC5 model (CMIP5), indicated a slight decrease in mean streamflow and an increase in interannual variability for the period 2040–2070, suggesting a potential reduction in surface water availability and natural hydrological regulation by mid-century. The Water Regulation Index (WRI) exhibited a downward trend in most sub-basins, particularly in areas affected by forest loss and agricultural expansion. The WRI showed a downward trend in most sub-basins, especially those with loss of forest cover and a predominance of agricultural uses. These findings provide basin-specific evidence on how climate change and land-use pressures may jointly affect hydrological regulation in tropical Andean–Caribbean basins. These results highlight the usefulness of the SWAT model as a decision-support tool for integrated water resources management in the San Pedro River basin and similar tropical Andean–Caribbean catchments, supporting basin-scale climate adaptation planning. They also emphasize the importance of conserving headwater ecosystems and forest cover to sustain hydrological regulation, reduce vulnerability to flow extremes, and enhance long-term regional water security.