RESUMO
The knowledge of rainfall regimes is a relevant requirement for many activities such as water resources planning, risk management, agriculture activities management, and other hydrologic applications. The present study has consisted of validating four techniques (one linear, one non-linear, and two hybrids) that allow identifying homogenous regions. We take the monthly rainfall in the Southwestern Colombia (Nariño), an area of 33,268 km2 characterized by complex topography and local factors that can influence the rainfall behavior, to test all techniques. The results showed overall the best performance for the approach related to non-linear principal component analysis and self-organizing map. However, in all mainly prevail two regions: the Andean Region and Pacific Region with a bimodal and unimodal regime, respectively. The bimodal one dominates over the Andes mountains range and the unimodal one the coastal zone. The application of non-linear approaches provided a better understanding of the seasonality of rainfall, and the results may be useful for water resource management.
Assuntos
Agricultura , Chuva , ColômbiaRESUMO
Changes observed in the current climate and projected for the future significantly concern researchers, decision-makers, and the general public. Climate indices of extreme rainfall events are a trend assessment tool to detect climate variability and change signals, which have an average reliability at least in the short term and given climatic inertia. This paper shows 12 climate indices of extreme rainfall events for annual and seasonal scales for 12 climate stations between 1969 to 2019 in the Metropolitan area of Cali (southwestern Colombia). The construction of the indices starts from daily rainfall time series, which although have between 0.5% and 5.4% of missing data, can affect the estimation of the indices. Here, we propose a methodology to complete missing data of the extreme event indices that model the peaks in the time series. This methodology uses an artificial neural network approach known as Non-Linear Principal Component Analysis (NLPCA). The approach reconstructs the time series by modulating the extreme values of the indices, a fundamental feature when evaluating extreme rainfall events in a region. The accuracy in the indices estimation shows values close to 1 in the Pearson's Correlation Coefficient and in the Bi-weighting Correlation. Moreover, values close to 0 in the percent bias and RMSE-observations standard deviation ratio. The database provided here is an essential input in future evaluation studies of extreme rainfall events in the Metropolitan area of Cali, the third most crucial urban conglomerate in Colombia with more than 3.9 million inhabitants.
RESUMO
The Colombian Biogeographic Choco (CBC) and the La Plata Basin (LPB) are regions with high biodiversity. However, these areas are characterized by scarce climatological information, complex orography, and rain-gauge network unevenly distributed. Interpolated data from the ground station might overcome these aspects. For this reason, is necessary to identify the best technique for the spatial interpolation of rainfall. Hence, the spatial interpolation techniques were applied to annual and seasonal rainfall in the CBC and LPB. Geostatistical results and deterministic approaches were compared by cross-validation. Cokriging with spherical (gaussian) model is the best interpolator in the CBC (LPB), as indicated by the lowest root mean square error (RMSE) and a standardized RMSE close to one. The CBC shows three rainfall cores: the northern, 9,000 mm/year; the central-southern, 10,000 mm/year; and the southern, 7,000 mm/year. The LPB shows a west-east rainfall gradient, with a minimum to the west (450 mm/year) and a maximum in the mid-west (2,000 mm/year). To the north of the LPB, rainfall reaches 1,500 mm/year, while in the south it reaches only 900 mm/year. The results in our study may be useful for scientists and decision-makers for use in environmental and hydrological models for the CBC and the LPB.
