ABSTRACT:

Flooding is one of the most destructive natural hazards in tropical mountain basins, yet detailed vulnerability assessments remain scarce where observational data are limited. In this study, we compiled and harmonized high‐resolution geomorphological, hydroclimatic, land‐cover, soil, and population datasets for the 1,777 km² Guatiquía River watershed in the Colombian Andes, covering the period 1991–2022 (DEM at 12.5 m, CHIRPS precipitation at 5.5 km, ERA5 reanalysis at 25 km, MapBiomas land cover at 30 m, and IGAC soil maps) ArticleGuatiquiaRiverWa…. We derived key conditioning factors: slope, Topographic Wetness Index (TWI), Curve Number (CN3), population density, and an Extreme Precipitation Susceptibility Index (EPSI) composed of six ETCCDI climate extremes, and applied a Frequency Ratio (FR) model to quantify their spatial correlation with historical flood occurrences. The resulting vulnerability map highlights the middle‐lower basin, particularly around Villavicencio, as the most susceptible zone, driven by flat terrain, high moisture accumulation, low infiltration (CN3 > 70), and recurrent intense rainfall. Model validation via Receiver Operating Characteristic (ROC) analysis yielded an Area Under the Curve of 0.82, demonstrating robust predictive performance. This work provides the first comprehensive, data‐driven flood vulnerability assessment for the Guatiquía watershed and offers a transferable methodology for other data‐scarce Andean basins. All processed datasets and derived layers are publicly available to support regional water‐resources management and climate‐adaptation planning.

ABSTRACT:

This resource includes eight temperature indices used to assess the accuracy of spatiotemporal variability and trends in temperature extremes in Colombia. T
These indices were calculated monthly for the 1997–2016 period.

* Hottest Day (TXx)
* Coldest Night (TNn)
* Diurnal Temperature Range (DTR)
* Mean Temperature (2TM)
* Percentile-Based Threshold Indices, which measure:
Number of days below the 10th percentile (Cold Nights—TN10p and Cold Days—TX10p)
Number of days above the 90th percentile (Warm Nights—TN90p and Warm Days—TX90p)

To estimate these indices, six high-resolution gridded datasets with varying spatial and temporal resolutions were used:

* ERA5 (0.25°)
* ERA5-Land (0.10°)
* AgERA5 (0.10°)
* MSWX (0.10°)
* CHELSA (0.01°)
* CHIRTS (0.05°)

The performance of these temperature-gridded products in calculating climate extremes was compared with observed data from selected ground weather stations for the period 1997–2016.
Initially, long-term climate data for daily minimum (TN) and maximum temperatures (TX) were assessed from 664 and 629 weather stations, respectively. These records, provided by the Colombian Institute of Hydrology, Meteorology, and Environmental Studies (IDEAM), spanned from 1997 to 2016. After filtering out stations with more than 20% missing data, 153 stations remained, which are located at elevations ranging from 0 to 3500 meters above sea level. Most of these stations are situated in the country's interior, particularly in the Andean region (74%), whereas the peripheral areas have limited data availability due to fewer in-situ stations.
To estimate the missing data in the observed monthly temperature indices, the nonlinear principal component analysis (NLPCA) method was employed. This technique has been previously applied to estimate missing data in various contexts, including precipitation series, extreme precipitation indices, and streamflow data on daily, monthly, and seasonal scales. NLPCA is a nonlinear extension of principal component analysis that utilizes Artificial Neural Networks (ANN), specifically employing the inverse NLPCA method for its calculations.