Copernicus’ Role in the Scientific Revolution: Philosophical Merits and Influence on Later Scientists

Nicolaus Copernicus\u27 publication of De Revolutionibus Orbium Coesltium marks the beginning of a revolution in the field of astronomy and physics. Within 150 years, a heliocentric system became almost universally accepted in the scientific community. Copernicus’ model was significant not because it of its scientific merit, but because of its ideological appeal to scientists during the 16th through 18th century. This paper explores the philosophical foundations of Copernicus\u27 model, and examines his influence in later work of four significant astronomers and physicists, Brahe, Kepler, Galileo, and Newton

VALIDATION OF SATELLITE PRECIPITATION (TRMM 3B43) IN ECUADORIAN COASTAL PLAINS, ANDEAN HIGHLANDS AND AMAZONIAN RAINFOREST

Precipitation monitoring is of utmost importance for water resource management. However, in regions of complex terrain such as Ecuador, the high spatio-temporal precipitation variability and the scarcity of rain gauges, make difficult to obtain accurate estimations of precipitation. Remotely sensed estimated precipitation, such as the Multi-satellite Precipitation Analysis TRMM, can cope with this problem after a validation process, which must be representative in space and time. In this work we validate monthly estimates from TRMM 3B43 satellite precipitation (0.25° x 0.25° resolution), by using ground data from 14 rain gauges in Ecuador. The stations are located in the 3 most differentiated regions of the country: the Pacific coastal plains, the Andean highlands, and the Amazon rainforest. Time series, between 1998 – 2010, of imagery and rain gauges were compared using statistical error metrics such as bias, root mean square error, and Pearson correlation; and with detection indexes such as probability of detection, equitable threat score, false alarm rate and frequency bias index. The results showed that precipitation seasonality is well represented and TRMM 3B43 acceptably estimates the monthly precipitation in the three regions of the country. According to both, statistical error metrics and detection indexes, the coastal and Amazon regions are better estimated quantitatively than the Andean highlands. Additionally, it was found that there are better estimations for light precipitation rates. The present validation of TRMM 3B43 provides important results to support further studies on calibration and bias correction of precipitation in ungagged watershed basins

VALIDATION OF SATELLITE PRECIPITATION (TRMM 3B43) IN ECUADORIAN COASTAL PLAINS, ANDEAN HIGHLANDS AND AMAZONIAN RAINFOREST

Abstract. Precipitation monitoring is of utmost importance for water resource management. However, in regions of complex terrain such as Ecuador, the high spatio-temporal precipitation variability and the scarcity of rain gauges, make difficult to obtain accurate estimations of precipitation. Remotely sensed estimated precipitation, such as the Multi-satellite Precipitation Analysis TRMM, can cope with this problem after a validation process, which must be representative in space and time. In this work we validate monthly estimates from TRMM 3B43 satellite precipitation (0.25° x 0.25° resolution), by using ground data from 14 rain gauges in Ecuador. The stations are located in the 3 most differentiated regions of the country: the Pacific coastal plains, the Andean highlands, and the Amazon rainforest. Time series, between 1998 – 2010, of imagery and rain gauges were compared using statistical error metrics such as bias, root mean square error, and Pearson correlation; and with detection indexes such as probability of detection, equitable threat score, false alarm rate and frequency bias index. The results showed that precipitation seasonality is well represented and TRMM 3B43 acceptably estimates the monthly precipitation in the three regions of the country. According to both, statistical error metrics and detection indexes, the coastal and Amazon regions are better estimated quantitatively than the Andean highlands. Additionally, it was found that there are better estimations for light precipitation rates. The present validation of TRMM 3B43 provides important results to support further studies on calibration and bias correction of precipitation in ungagged watershed basins. </jats:p

Climate changes of hydrometeorological and hydrological extremes in the Paute basin, Ecuadorean Andes

Investigation was made on the climate change signal for hydrometeorological and hydrological variables for the Paute River basin, in southern Ecuador Andes, making use of an adjusted quantile perturbation approach for climate downscaling, and the impact of climate change on runoff for two nested catchments within the basin. The analysis was done making use of long daily series of seven representative rainfall and temperature sites along the study area and considering climate change signals of global and regional climate models for IPCC SRES scenarios A1B, A2 and B1. The determination of runoff was carried out using a lumped conceptual rainfall-runoff model. The study found that the range of changes in temperature is despicably lower that the range of changes in rainfall. However, changes differ from site to site, showing that more significant changes in temperature are observed at higher elevation sites. For rainfall, high differences in rainfall change are found and strongly related to the rainfall regime. Higher changes are detected for sites located in regions with bimodal rainfall regime. In addition, higher changes are observed on higher temporal resolutions. The runoff changes are strongly related to the changes in rainfall peaks, more than with the changes in temperature; also showing strong spatial differences over the Andean region considered.status: publishe

Comparison of Statistical Downscaling Methods for Monthly Total Precipitation: Case Study for the Paute River Basin in Southern Ecuador

Downscaling improves considerably the results of General Circulation Models (GCMs). However, little information is available on the performance of downscaling methods in the Andean mountain region. The paper presents the downscaling of monthly precipitation estimates of the NCEP/NCAR reanalysis 1 applying the statistical downscaling model (SDSM), artificial neural networks (ANNs), and the least squares support vector machines (LS-SVM) approach. Downscaled monthly precipitation estimates after bias and variance correction were compared to the median and variance of the 30-year observations of 5 climate stations in the Paute River basin in southern Ecuador, one of Ecuador’s main river basins. A preliminary comparison revealed that both artificial intelligence methods, ANN and LS-SVM, performed equally. Results disclosed that ANN and LS-SVM methods depict, in general, better skills in comparison to SDSM. However, in some months, SDSM estimates matched the median and variance of the observed monthly precipitation depths better. Since synoptic variables do not always present local conditions, particularly in the period going from September to December, it is recommended for future studies to refine estimates of downscaling, for example, by combining dynamic and statistical methods, or to select sets of synoptic predictors for specific months or seasons

An empirical model for rainfall maximums conditioned to tropospheric water vapor over the Eastern Pacific Ocean

One of the most difficult weather variables to predict is rain, particularly intense rain. The main limitation is the complexity of the fluid dynamic equations used by predictive models with increasing uncertainties over time, especially in the description of brief, local, and high intensity precipitation events. Although computational, instrumental and theoretical improvements have been developed for models, it is still a challenge to estimate high intensity rainfall events, especially in terms of determining the maximum rainfall rates and the location of the event. Within this context, this research presents a statistical and relationship analysis of rainfall intensity rates, total precipitable water (TPW), and sea surface temperature (SST) over the ocean. An empirical model to estimate the maximum rainfall rates conditioned to TPW values is developed. The performance of the maximum rainfall rate model is spatially evaluated for a case study. High-resolution TRMM 2A12 satellite data with a resolution of 5.1 x 5.1 km and 1.67 s was used from January 2009 to December 2012, over the Eastern Pacific Nino area in the tropical Pacific Ocean (0-5 degrees S; 90-81 degrees W), comprising 326,092 rain pixels. After applying the model selection methodology, i.e., the Akaike Information Criterion (AIC) and the Bayesian Information Criterion (BIC), an empirical exponential model between the maximum possible rain rates conditioned to TPW was found with R-2 = 0.96, indicating that the amount of TPW determines the maximum amount of rain that the atmosphere can precipitate exponentially. Spatially, this model unequivocally locates the rain event; however, the rainfall intensity is underestimated in the convective nucleus of the cloud. Thus, these results provide an additional constraint for maximum rain intensity values that should be adopted in dynamic models, improving the quantification of heavy rainfall event intensities and the correct location of these events

Flow resistance of vegetated oblique weir-like obstacles during high water stages

At high water stages, obstacles (submerged and particularly emerged vegetation) in the flood plains of a river contribute to the flow resistance and hamper the conveyance capacity. In particular the elevated vegetated parts are expected to play an important role. The objective of this research work is to determine the form drag due to vegetated oblique weir-like obstacles. Experiments have been performed to measure the energy head losses for a range of subcritical flow conditions, varying discharges and downstream water levels. The energy head loss caused by the submerged vegetated weir-like obstacle has been modeled using an expansion loss form drag model that has been derived from the one-dimensional momentum conservation equation and accounts for the energy loss associated with a deceleration of the flow downstream of a sudden expansion. The results have been compared with the experimental data and showed an overall good agreement.Hydraulic EngineeringCivil Engineering and Geoscience

Maskana. Revista científica

Series continuas de precipitación y temperatura facilitan y mejoran considerablemente la calibración y validación de modelos hidrológicos y climáticos, utilizados entre otras cosas, para la planificación y manejo de recursos hídricos y el pronóstico de los posibles efectos del cambio climático en el regimen lluvia-escorrentia de las cuencas hidrográficas. La bondad de ajuste de los modelos está entre los factores que dependen de la continuidad de las series temporales. En países en vías de desarrollo los vacíos en las series temporales de variables climáticas es común. Ya que los vacíos en las series temporales pueden comprometer severamente la utilidad de los datos, este estudio aplicado en la cuenca del río Paute en los Andes Ecuatorianos, examina el desempeño de 17 métodos determinísticos de relleno de datos diarios de las variables precipitación y temperatura media. A pesar de la existencia de métodos de relleno más sofisticados como métodos estocásticos o métodos de inteligencia artificial, en este estudio se dio preferencia a métodos determinísticos por su robustez, facilidad de MASKANA, Vol. 5, No. 1, 2014 Revista semestral de la DIUC 100 implementación, y eficiencia computacional. Los resultados revelan que para rellenar series temporales de precipitación diaria, el método de regresión lineal múltiple ponderada es el mejor, debido a la consideración de la razón entre el coeficiente de correlación de Pearson y la distancia con respecto a otras estaciones como factor de ponderación, dando mayor importancia a las estaciones más cercanas altamente correlacionadas. Para temperatura, la media climatológica del día fue claramente el mejor método, posiblemente debido a la escacez de datos de estaciones cercanas localizadas también en elevaciones diferentes, sugiriendo la necesidad de considerar en futuros estudios el impacto de la elevación en la interpolación de datos.Continuous time series of precipitation and temperature considerably facilitate and improve the calibration and validation of climate and hydrologic models, used inter alia for the planning and management of earth’s water resources and for the prognosis of the possible effects of climate change on the rainfall-runoff regime of basins. The goodness-of-fit of models is among other factors dependent from the completeness of the time series data. Particular in developing countries gaps in time series data are very common. Since gaps can severely compromise data utility this research with application to the Andean Paute river basin examines the performance of 17 deterministic infill methods for completing time series of daily precipitation and mean temperature. Although sophisticated approaches for infilling gaps, such as stochastic or artificial intelligence methods exist, preference in this study was given to deterministic approaches for their robustness, easiness of implementation and computational efficiency. Results reveal that for the infilling of daily precipitation time series the weighted multiple linear regression method outperforms due to considering the ratio of the Pearson correlation coefficient to the distance, giving more weight to both, highly correlated and nearby stations. For mean temperature, the climatological mean of the day was clearly the best method, most likely due to the scarcity of weather stations measuring temperature, and because the few available stations are located at different elevations in the landscape, suggesting the need to address in future studies the impact of elevation on the interpolation.Cuencavolumen 5, número 1 (junio 2014

Maskana. Revista científica

Series continuas de precipitación y temperatura facilitan y mejoran considerablemente la calibración y validación de modelos hidrológicos y climáticos, utilizados entre otras cosas, para la planificación y manejo de recursos hídricos y el pronóstico de los posibles efectos del cambio climático en el regimen lluvia-escorrentia de las cuencas hidrográficas. La bondad de ajuste de los modelos está entre los factores que dependen de la continuidad de las series temporales. En países en vías de desarrollo los vacíos en las series temporales de variables climáticas es común. Ya que los vacíos en las series temporales pueden comprometer severamente la utilidad de los datos, este estudio aplicado en la cuenca del río Paute en los Andes Ecuatorianos, examina el desempeño de 17 métodos determinísticos de relleno de datos diarios de las variables precipitación y temperatura media. A pesar de la existencia de métodos de relleno más sofisticados como métodos estocásticos o métodos de inteligencia artificial, en este estudio se dio preferencia a métodos determinísticos por su robustez, facilidad de MASKANA, Vol. 5, No. 1, 2014 Revista semestral de la DIUC 100 implementación, y eficiencia computacional. Los resultados revelan que para rellenar series temporales de precipitación diaria, el método de regresión lineal múltiple ponderada es el mejor, debido a la consideración de la razón entre el coeficiente de correlación de Pearson y la distancia con respecto a otras estaciones como factor de ponderación, dando mayor importancia a las estaciones más cercanas altamente correlacionadas. Para temperatura, la media climatológica del día fue claramente el mejor método, posiblemente debido a la escacez de datos de estaciones cercanas localizadas también en elevaciones diferentes, sugiriendo la necesidad de considerar en futuros estudios el impacto de la elevación en la interpolación de datos.Continuous time series of precipitation and temperature considerably facilitate and improve the calibration and validation of climate and hydrologic models, used inter alia for the planning and management of earth’s water resources and for the prognosis of the possible effects of climate change on the rainfall-runoff regime of basins. The goodness-of-fit of models is among other factors dependent from the completeness of the time series data. Particular in developing countries gaps in time series data are very common. Since gaps can severely compromise data utility this research with application to the Andean Paute river basin examines the performance of 17 deterministic infill methods for completing time series of daily precipitation and mean temperature. Although sophisticated approaches for infilling gaps, such as stochastic or artificial intelligence methods exist, preference in this study was given to deterministic approaches for their robustness, easiness of implementation and computational efficiency. Results reveal that for the infilling of daily precipitation time series the weighted multiple linear regression method outperforms due to considering the ratio of the Pearson correlation coefficient to the distance, giving more weight to both, highly correlated and nearby stations. For mean temperature, the climatological mean of the day was clearly the best method, most likely due to the scarcity of weather stations measuring temperature, and because the few available stations are located at different elevations in the landscape, suggesting the need to address in future studies the impact of elevation on the interpolation.CuencaVol. 5, no. 1 (junio 2014