Spatial distribution of precipitation and evapotranspiration estimates from Worldclim and Chelsa datasets: Improving long-term water balance at the watershed-scale in the Urabá region of Colombia

Abstract

In this paper, we have evaluated high-resolution spatial gridded climate data from two long-term global datasets, WorldClim V.2.0 and Chelsa V.1.2, in representing variables like precipitation and temperature for the urabá region of Colombia. additionally, climate variables from these datasets have been used to estimate evapotranspiration using traditional methods such as the Turc, hargreaves and Thornthwaite equations. finally, the results of long-term spatial climate characterization are used to apply the water balance equation in the surface at the watershed scale, to obtain the long-term average streamflow of the main streams of the urabá region; these streamflows are compared with the observations of hydrological stations. We find that the WorldClim and Chelsa rainfall estimates show average differences between 20% and 23% compared to the average annual rainfall in the area from in situ measurements. both datasets are able to reproduce the rainfall average annual cycle, although Chelsa shows a slightly better performance. regarding near surface air temperature we find that WorldClim shows a good performance, while Chelsa significantly underestimates the average temperature. finally, we found that the hargreaves and Thornthwaite methods lead to the best performance in estimating streamflow from the water balance, probably because details of the seasonal behavior of variables like temperature and radiation are explicitly included in these methods. On the other hand, the Turc method yields larger estimates of evapotranspiration and therefore the corresponding derived streamflows are lower than those observed. The good performance of the WorldClim and Chelsa datasets in representing variables like precipitation, temperature, and the derived watershed-scale streamflow, suggest that these long-term global climate datasets can be used to study the spatial distribution of important hydrological variables in the urabá region of Colombia, and consequently the estimation of average streamflows through the method of the long-term water balance.

Spatiotemporal climate variability in the Andes of northern Peru: Evaluation of gridded datasets to describe cloud forest microclimate and local rainfall

“…CHIRPS provided the best estimate of rainfall magnitude both inside and outside forest, increasing linearly with observed values and explaining around half the variation for in situ rain gauges. Other studies have found CHIRPS to be the best option in tropical regions (Beck et al ., 2017), although WorldClim and Chelsa explained >70% of the variation for higher density stations in Brazil and Colombia (Bastidas Osejo et al ., 2019; de Oliveira‐Júnior et al ., 2021). In northern Peru, CHIRPS predicted rainfall greater than 150 mm·month −1 and 1,000 mm·year −1 poorly.…”

“…Using different interpolation versus statistical downscaling approaches, on average WorldClim was within 1°C outside forest whereas Chelsa was within 1°C inside forest, an improvement for forest biodiversity studies. WorldClim also overestimated maximum temperatures in tropical rainforest (Jucker et al ., 2018) whereas Chelsa underestimated mean temperatures at Colombian weather stations (Bastidas Osejo et al ., 2019). Our results inform choice of gridded datasets for different applications.…”

Projected Changes in the Water Budget for Eastern Colombia Due to Climate Change

“…Information regarding vegetation comes mostly from Governmental institutes, as well as the detailed land use information that was also obtained from regional, territorial development plans. In the same way, a review of the results of the model and a comparison with results of other studies nearby, in tropical or similar areas was made [25][26][27][28][29][30][31], to verify the veracity of the results and ensure that they are within a correct range of magnitudes.…”

A Comprehensive Step-by-Step Guide to Using Data Science Tools in the Gestion of Epidemiological and Climatological Data in Rice Production Systems

“…Meanwhile, WC corresponds to a set of average monthly interpolated climate data from meteorological stations on a multi-resolution grid [15]. These datasets have been validated and used in studies for purposes of flow estimation, evapotranspiration, the estimation of precipitation and its spatio-temporal variability, and for modeling the distribution of pathogen populations in plants as a function of climate [16][17][18][19].…”