Browsing by Author "Campozano Parra, Lenin Vladimir"

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A causal flow approach for the evaluation of global climate models
(2020) Vázquez Patiño, Angel Oswaldo; Campozano Parra, Lenin Vladimir; Mendoza Sigüenza, Daniel Emilio; Samaniego Alvarado, Esteban Patricio
© 2020 Royal Meteorological Society Global climate models (GCMs) are generally used to forecast weather, understand the present climate, and project climate change. Their reliability usually rests on their capability to represent climatic processes, and most evaluations directly measure the spatiotemporal agreement of scalar climate variables. However, climate naturally involves complex interactions that are hard to infer and, therefore, difficult to evaluate. Climate networks (CNs) have been used to infer flows of mass and energy in the complex climate system. Here, an Evaluation of Models by Causal Flows (EMCaF) is proposed. EMCaF focuses on the assessment of properties about mass and energy flows in the CNs derived from GCMs. First, causal CNs are inferred from GCMs, and then the capabilities to reproduce characteristic transfer flows are assessed with reference models. A more in-depth feature is the possibility to assess how climate change disturbs CNs properties. In addition to the quantitative difference between modelled and observed values taken into account in standard evaluations, the EMCaF approach aims to assess the weaknesses and strengths of GCMs to represent climate mechanisms and processes that couple different components of the climate system. The comparison of models through this approach allows having complimentary feedback on model evaluations to understand possible causes of errors and enable a judgement based on processes. The approach is illustrated by evaluating one GCM and subsequently assessing changes of its CNs under future climate projections. Results show that known climatic patterns are assimilated and that causal strength patterns are likely to agree with the wind magnitude as a transfer factor. Significative issues are then explored, showing the capabilities of the approach and allowing understand fundamental structures in transport flows, compare their properties, and assess changes in the future. Different alternatives and considerations in each step of the approach are discussed to expand its applicability.
A Variational merging approach to the spatial description of environmental variables
(2018) Ulloa, Jacinto Israel; Samaniego Alvarado, Esteban Patricio; Campozano Parra, Lenin Vladimir; Ballari, Daniela Elisabet
High resolution images of environmental variables are highly valuable sources of information in research and environmental management. Obtaining spatially continuous information from ground observations is challenging due to the wide variety of factors that affect classical interpolation methods. While geostatistical methods have produced noteworthy results, they generally rely on important assumptions and strongly depend on the availability of observed data. In turn, satellite‐based or model‐based gridded images generally represent the global spatial structure of environmental variables, but are subject to bias. With the objective of exploiting the benefits of both sources of information, we propose a new mathematical formulation to merge observed data with gridded images of environmental variables using partial differential equations in a variational setting. With a …
Aplicación de ‘aprendizaje profundo’ para el pronóstico de precipitación a partir de datos de reflectividad de radar meteorológico
(2019-04-26) Godoy Mendía, Alberto Steven; Vázquez Patiño, Angel Oswaldo; Campozano Parra, Lenin Vladimir
The studies published on rain forecasting in the South American region using Deep Learning techniques are very scarce. The available hydrometeorological monitoring networks, usually rain gauge networks, have not provided sufficient data to achieve satisfactory results in the prediction of these patterns (Bendix et al., 2017). In this work, Deep Learning techniques are applied to face this problem. Using information collected from a network of meteorological radars that exist in Ecuador, RadarNet-Sur, this work applies deep learning techniques to the mentioned information and proposes a methodology for rain forecasting. The methodology presented consists of three steps, radar image prediction with Deep Learning techniques, a transformation of the former’s output to precipitation terms, and, interpretation of the results obtained. The results lend themselves to a discussion of how to improve the quality of the prediction obtained. This, despite working with a limited data set, so that there is a possibility of improving the model if working with a representative set. However, the model is not immediately applicable because it does not learn all the existing relationships and patterns for the test set. This is why some solutions to be carried out in future works are discussed that could significantly improve the performance of the model.
Assessment of the Impact of Higher Temperatures Due to Climate Change on the Mortality Risk Indexes in Ecuador Until 2070
(2022) Campozano Parra, Lenin Vladimir
Extreme weather conditions, including intense heat stress due to higher temperatures, could trigger an increase in mortality risk. One way to evaluate the increase in mortality risk due to higher temperatures is the high risk warming (HRW) index, which evaluates the difference between the future and base period of a given percentile of daily maximum temperature (Tmax). Another is to calculate the future increase in the number of days over the temperature of such percentile, named high risk days (HRD) index. Previous studies point to the 84th percentile as the optimum temperature. Thus, this study aims to evaluate HRW and HRD indexes in Ecuador from 2011 to 2070 over the three natural climate zones, e.g., Coast, Andes, and Amazon. This climate analysis is based on historical data from meteorological stations and projections from CSIRO-MK36, GISS-E2, and IPSL-CM5A-MR, CMIP5 global climate models with dynamical scale reduction through weather research forecasting (WRF). The representative concentration pathways (RCPs), 8.5, were considered, which are related to the highest increases in future temperature. The results indicate that HRW and HRD will experience a larger increase in the period 2041–2070 compared with the period 1980–2005; in particular, these two indices will have a progressively increasing trend from 2011 onward. Specifically, the HRW calculated from the CMIP5 models for all stations is expected to grow from 0.6°C to 1.4°C and 1.8°C to 4.6°C for 2010–2040 and 2041–2070, respectively. Also, it is expected that the HRD for all stations will increase from 42 to 74 and 120 to 227 warming days for 2011–2040 and 2041–2070, respectively. The trends derived using Sen’s slope test show an increase in the HRW between 0.5°C and 0.9°C/decade and of the HRD between 2.88 and 4.9 days/decade since 1985. These results imply a high increase in heat-related mortality risks related to climate change in Ecuador. In terms of spatial distribution, three Ecuadorian regions experienced more critical temperature conditions with higher values of HRW and HRD for 2070. As a response to the increased frequency trends of warming periods in tropical areas, urgent measures should be taken to review public policies and legislation to mitigate the impacts of heat as a risk for human health in Ecuador.
Causality and climate networks approaches for evaluating climate models, tracing flows, and selecting physically meaningful predictors
(Universidad de Cuenca, 2022-04-14) Vázquez Patiño, Angel Oswaldo; Samaniego Alvarado, Esteban Patricio; Campozano Parra, Lenin Vladimir
Climate consists of many components, for example, atmosphere, hydrosphere, cryosphere, and biosphere. All the components act under mechanisms that relate them in a highly non-linear way, making the climate a complex system. This complexity is a challenge to study the climate and its implications at various spatiotemporal scales. However, the dependence of anthropogenic activities on the climate has encouraged its study in order, for example, to anticipate its periodic changes and, as far as possible, extreme events that may have adverse effects. As climate study is an intricate task, several approaches have been used to unravel the underlying processes that dominate its behavior. Those approaches range from linear correlation analysis to complex machine learning-based knowledge discovery analysis. This last approach has become more relevant after the introduction of sophisticated climate simulation models and high-tech equipment (e.g., satellite) that allow a climate record of greater coverage (spatial and temporal) and that, together, have generated ubiquitous large databases. One of the knowledge discovery approaches based on this big data is based on climate networks. Nevertheless, causal reasoning methods have also been used recently to infer and characterize these networks, which are called causal climate networks. Several studies have been carried out with climate networks; however, the recent introduction of causality methods makes the study of climate with causal climate networks an opportunity to explore and exploit them more widely. In addition, the particularities of the climate make it necessary to understand specific operational issues that must be taken into account when applying networks. This thesis aims to propose new methodologies and applications of causal climate networks following as a common thread the characterization of physical phenomena that manifest themselves at different spatial scales. For this, different case studies have been taken. They are the climate in South America and a large part of the Pacific and Atlantic oceans, then, reducing the scale, the surrounding factors that influence the rainfall of Ecuador, and, finally, the selection of predictors for downscaling models in an Andean basin. Among the main results are the following three. First, a methodology for evaluating global climate models based on what is called here as causal flows. Second, an approach that studies causal flows and helps trace influence paths in flow fields. Third, the presentation of evidence that shows the effectiveness of methods based on causality in selecting predictors for downscaling models. The thesis contributes to efforts to bridge the gap between the climate science and causal inference communities. This through the study and application of causal reasoning and taking advantage of the enormous amounts of climate data available today
Climate change influences of temporal and spatial drought variation in the andean high mountain basin
(2019) Zhiña Villa, Dario Xavier; Montenegro Ambrosi, Martin Patricio; Montalván Pérez, Lisseth Mariela; Mendoza Sigüenza, Daniel Emilio; Contreras Silva, Juan José; Campozano Parra, Lenin Vladimir; Avilés Añazco, Alex Manuel
Climate change threatens the hydrological equilibrium with severe consequences for living beings. In that respect, considerable differences in drought features are expected, especially for mountain-Andean regions, which seem to be prone to climate change. Therefore, an urgent need for evaluation of such climate conditions arises; especially the effects at catchment scales, due to its implications over the hydrological services. However, to study future climate impacts at the catchment scale, the use of dynamically downscaled data in developing countries is a luxury due to the computational constraints. This study performed spatiotemporal future long-term projections of droughts in the upper part of the Paute River basin, located in the southern Andes of Ecuador. Using 10 km dynamically downscaled data from four global climate models, the standardized precipitation and evapotranspiration index (SPEI) index was used for drought characterization in the base period (1981−2005) and future period (2011−2070) for RCP 4.5 and RCP 8.5 of CMIP5 project. Fitting a generalized-extreme-value (GEV) distribution, the change ratio of the magnitude, duration, and severity between the future and present was evaluated for return periods 10, 50, and 100 years. The results show that magnitude and duration dramatically decrease in the near future for the climate scenarios under analysis; these features presented a declining effect from the near to the far future. Additionally, the severity shows a general increment with respect to the base period, which is intensified with longer return periods; however, the severity shows a decrement for specific areas in the far future of RCP 4.5 and near future of RCP 8.5. This research adds knowledge to the evaluation of droughts in complex terrain in tropical regions, where the representation of convection is the main limitation of global climate models (GCMs). The results provide useful information for decision-makers supporting mitigating measures in future decades.
Climate changes of hydrometeorological and hydrological extremes in the Paute basin, Ecuadorean Andes
(2014-02-19) Mora Serrano, Diego Esteban; Campozano Parra, Lenin Vladimir; Cisneros Espinoza, Felipe Eduardo
Investigation was made on the climate change signal for hydrometeorological and hydrological variables along the Paute River basin, in the southern Ecuador Andes. An adjusted quantile perturbation approach was used for climate downscaling, and the impact of climate change on runoff was studied 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 homogeneous for almost the entire region with an average annual increase of approximately +2.0 °C. However, the warmest periods of the year show lower changes than the colder periods. For rainfall, downscaled results project increases in the mean annual rainfall depth and the extreme daily rainfall intensities along the basin for all sites and all scenarios. Higher changes in extreme rainfall intensities are for the wetter region. These lead to changes in catchment runoff flows, with increasing high peak flows and decreasing low peak flows. The changes in high peak flows are related to the changes in rainfall extremes, whereas the decreases in the low peak flows are due to the increase in temperature and potential evapotranspiration together with the reduction in the number of wet days. ©Author(s) 2014.
Climate changes of hydrometeorological and hydrological extremes in the Paute basin, Ecuadorean Andes
(2014) Mora Serrano, Diego Esteban; Campozano Parra, Lenin Vladimir; Cisneros Espinoza, Felipe Eduardo; Wyseure, Guido; Willems, Patrick
Investigation was made on the climate change signal for hydrometeorological and hydrological variables along the Paute River basin, in the southern Ecuador Andes. An adjusted quantile perturbation approach was used for climate downscaling, and the impact of climate change on runoff was studied 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 homogeneous for almost the entire region with an average annual increase of approximately +2.0 °C. However, the warmest periods of the year show lower changes than the colder periods. For rainfall, downscaled results project increases in the mean annual rainfall depth and the extreme daily rainfall intensities along the basin for all sites and all scenarios. Higher changes in extreme rainfall intensities are for the wetter region. These lead to changes in catchment runoff flows, with increasing high peak flows and decreasing low peak flows. The changes in high peak flows are related to the changes in rainfall extremes, whereas the decreases in the low peak flows are due to the increase in temperature and potential evapotranspiration together with the reduction in the number of wet days.
Climatology and teleconnections of mesoscale convective systems in an andean basin in southern Ecuador: the case of the Paute basin
(2018) Samaniego Alvarado, Esteban Patricio; Campozano Parra, Lenin Vladimir; Célleri Alvear, Rolando Enrique; Albuja Silva, Edgar Cristóbal
Mesoscale convective systems (MCSs) climatology, the thermodynamic and dynamical variables, and teleconnections influencing MCSs development are assessed for the Paute basin (PB) in the Ecuadorian Andes from 2000 to 2009. The seasonality of MCSs occurrence shows a bimodal pattern, with higher occurrence during March-April (MA) and October-November (ON), analogous to the regional rainfall seasonality. The diurnal cycle of MCSs shows a clear nocturnal occurrence, especially during the MA and ON periods. Interestingly, despite the higher occurrence of MCSs during the rainy seasons, the monthly size relative frequency remains fairly constant throughout the year. On the east of the PB, the persistent high convective available potential and low convective inhibition values from midday to nighttime are likely related to the nocturnal development of the MCSs. A significant positive correlation between the MCSs occurrence to the west of the PB and the Trans-Niño index was found, suggesting that ENSO is an important source of interannual variability of MCSs frequency with increasing development of MCSs during warm ENSO phases. On the east of the PB, the variability of MCSs is positively correlated to the tropical Atlantic sea surface temperature anomalies south of the equator, due to the variability of the Atlantic subtropical anticyclone, showing main departures from this relation when anomalous conditions occur in the tropical Pacific due to ENSO.
Comparison of Statistical Downscaling Methods for Monthly Total Precipitation: Case Study for the Paute River Basin in Southern Ecuador
(2016) Campozano Parra, Lenin Vladimir; Tenelanda Patiño, Daniel Orlando; Sánchez Cordero, Esteban Remigio; Samaniego Alvarado, Esteban Patricio; Feyen, Jan
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.
Effectiveness of causality-based predictor selection for statistical downscaling: a case study of rainfall in an Ecuadorian Andes basin
(2022) Campozano Parra, Lenin Vladimir; Vázquez Patiño, Angel Oswaldo; Avilés Añazco, Alex Manuel; Samaniego Alvarado, Esteban Patricio
Downscaling aims to take large-scale information and map it to smaller scales to reproduce local climate signals. An essential step in implementing a parsimonious downscaling model is the selection of a subset of relevant predictors that increase the simulation accuracy. According to relevant literature, predictive models that use predictors chosen through causality methods, not widely used in downscaling, are more interpretable and robust. Characteristics that do not necessarily pursue the selection methods are commonly used in machine learning (ML), where the primary objective is to maximize accuracy. This study then explores whether the improvement in interpretability and robustness of models that use causally selected predictors is not achieved at the expense of poor downscaling performance in terms of simulation accuracy. To this end, one set of three methods based on causality and one set of four standard predictor selection methods used in ML are compared, centered on the downscaling performance of rainfall in an Ecuadorian Andes basin. One causality-based selection method is based on Granger causality (GC), and two are based on a constraint-based algorithm for structure learning, namely the Peter and Clark algorithm (PCalg) and the based on PCalg and the momentary conditional independence (PCMCI). Regarding the methods used in ML, one is based on the variance of the predictors (variance threshold), two are based on univariate statistical tests (F test and mutual information), and one is based on the recursive elimination of predictors (RFE) according to their contribution to the prediction model. The number of methods in each set results from those that are considered standard in many ML libraries or have been used for regression or climatology (causality-based). The selected predictors are used to train statistical downscaling models based on the random forest and support vector machine learning algorithms to assess their performance and rank the selection methods. Among the most remarkable findings, the methods based on causality show a robust selection. For instance, the causality-based methods selected 53 different predictors in one of the considered stations, while the ML-based methods selected 73 for downscaling models with similar performance. Furthermore, when the predictors used in the best performing downscaling models are analyzed, it is shown that the different selection methods based on causality tend to choose a subset of predictors that do not differ greatly, contrary to the methods based on machine learning. This evidences a more concise selection of the causality-based methods. Finally, the evaluation metrics of the downscaling models showed that the best selection method is RFE, but the following best were consistently those based on causality (mainly PCMCI). The study evidences no significant performance detriment when using the causally selected predictors.
Evaluación de la precipitación mensual en el Ecuador según los modelos globales de clima del CMIP5 y de modelos regionales de clima de alta resolución
(2019-04-25) Espinoza Brito, Luz Gabriela; Campozano Parra, Lenin Vladimir
The change in precipitation patterns affects society economically and environmentally. For that reason, having rainfall projections will allow better planning for water use and thus avoid damages caused by droughts and floods. The present thesis is oriented to evaluate in Ecuador in the present, the precipitation generated by three global climate models of the IPCC AR5 and their respective dynamically downscale results by means of a regional climate model (WRF). Several statistical metrics were evaluated, e.i, correlation, root mean square error RMSE and standard deviation to characterize the performance in the Ecuadorian region. The interpretation of the results showed that the global and regional models have low representatives in eastern zones and in valleys of the inter-Andean valley. Models downscaled results showed relative superiority in all metrics used, but where are most reliable is on the coast and the western part of the Western Cordillera. In conclusion, the global models evaluated do not allow having an adequate representation of precipitation given the climatic and orographic variability of Ecuador. On the other hand, the results of regional modeling increase the quality of the data but in the same areas that the global models present an adequate resolution. A larger scale reduction, increase the monitoring with comparable series of precipitation and analysis by climatic regions, would help us to better understand the behavior of rain in the regions of the Sierra and the Orient where the representativeness of the expected climate.
Evaluación de métodos de relleno para series temporales de precipitación y temperatura diarias: el caso de los Andes ecuatorianos
(2014) Samaniego Alvarado, Esteban Patricio; Campozano Parra, Lenin Vladimir; Sánchez Cordero, Esteban Remigio; Avilés Añazco, Alex Manuel
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 coefficientto 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.
Evaluating extreme climate indices from CMIP3&5 global climate models and reanalysis data sets: a case study for present climate in the Andes of Ecuador
(2017) Campozano Parra, Lenin Vladimir; Vázquez Patiño, Angel Oswaldo; Tenelanda Patiño, Daniel Orlando; Feyen, Jan; Samaniego Alvarado, Esteban Patricio; Sánchez Cordero, Esteban Remigio
The reliability of climate models depends ultimately on their adequacy in relevant real situations. However, climate in mountains, a very sensitive system, is scarcely monitored, making the assessment of global climate models (GCMs) projections problematic. This is even more critical for tropical mountain regions, where complex atmospheric processes acting across scales are specially challenging for GCMs. To help bridge this gap, we evaluated the representation of extreme climate indices by GCMs and reanalysis data in the Andes of Ecuador. This work presents an intercomparison of 11 climate precipitation indices (Climate Change Detection and Indices, ETCCDIs) reconstructed for the period 1 January 1981–31 December 2000 using the data of six climate stations situated in a medium-sized Andean catchment in southern Ecuador, reanalysis data sets (RAD) ERA40, ERA-Interim, NCEP/NCAR Reanalysis 1 (NCEP/NCAR-R1) and NCEP/DOE Reanalysis 2 (NCEP/DOE-R2), and the data sets of 19 and 29 models of the Coupled Model Intercomparison Project, Phases 3 and 5 (CMIP3&5). Temporal and spatial analysis highlights that the values and the variability of ETCCDIs based on reanalysis and CMIP3&5 data overestimate observations, especially in ENSO years. However, frequency-type indices are in general better captured than amount-related indices in RAD. In general, reanalysis data displayed a similar uncertainty as the CMIP model data sets and some indices present lower uncertainty. The uncertainty of ETCCDIs based on CMIP5 remains similar to CMIP3 GCMs, with small variations. The indices using NCEP/NCAR-R1, NCEP/DOE-R2, and ERA-Interim data performed better than those obtained with the ERA40 data sets, with no discernible improvement between both NCEP products. It can be concluded that for the given study region CMIP3&5 models and reanalysis products with respectively good and poor performance, exist, however data should be carefully screened before use. Furthermore, these results confirm that the specificity of the studied region is a key to identify limiting aspects on the GCMs and reanalysis extreme climate representation.
Evaluation of downscaled estimates of monthly temperature and precipitation for a Southern Ecuador case study
(2016) Ochoa Sánchez, Ana Elizabeth; Campozano Parra, Lenin Vladimir; Sánchez Cordero, Esteban Remigio; Gualan Saavedra, Ronald Marcelo; Samaniego Alvarado, Esteban Patricio
The downscaling of global climate models (GCMs) aims at incorporating finer scale information to their horizontal resolution in order to represent regional and local processes better. There are two main approaches to downscaling: statistical (based on data relationships between synoptic atmospheric variables and observations of local variables) and dynamical (based on the modelling of regional atmospheric processes and land-surface interactions). In this study, some predictive capabilities regarding the generation of station-scale mean monthly temperature and rainfall of both a statistical artificial neural network (ANN-based) and a dynamical weather research and forecasting (WRF-based) downscaling approach are assessed. We have devised two versions of the statistical downscaling approach. One of them includes regional orographic variables as predictors to allow for spatial extrapolation; the other is purely local. Historical observational data, from the period 1990 to 1999, of two major watersheds in the Ecuadorian Southern Andes, the Jubones and Paute river basins, were used. Since, to a certain extent, the value added by downscaling techniques can be attributed to terrain information, it is worth noting that some characteristics of the selected catchments (as notorious altitude differences and the presence of qualitatively different precipitation regimes) provide a scientifically interesting location for exploring how finer scale effects are captured. For this reason, we concentrate on the ability of downscaling techniques to reproduce seasonality. A decade of evaluation proved that both approaches were able to qualitatively describe precipitation and temperature seasonal variations for different regimes at representative weather stations. Furthermore, the seasonality of precipitation represented by both downscaling approaches surpassed the seasonality representation of reanalysis data. However, shortcomings on the estimates were found. Specifically, dynamical downscaled precipitation estimates were prone to overestimation. Despite the fact that the considered downscaling approaches are different in nature, their ability to represent the high spatio-temporal variability in this region highlights the importance of evaluating their strengths and limitations. © 2016 Royal Meteorological Society.
Finding teleconnections from decomposed rainfall signals using dynamic harmonic regressions: a tropical andean case study
(2019) Mendoza Sigüenza, Daniel Emilio; Samaniego Alvarado, Esteban Patricio; Mora Serrano, Diego Esteban; Espinoza Mejía, Jorge Mauricio; Campozano Parra, Lenin Vladimir
Global climate is a multi-scale system whose subsystems interact complexly. Notably, the Tropical-Andean region has a strong rainfall variability because of the confluence of many global climate processes altered by morphological features. An approach for a synthetical climate description is the use of global indicators and their regional teleconnections. However, typically this is carried out using filters and correlations, which results in seasonal and inter-annual teleconnections information, which are difficult to integrate into a modeling framework. A new methodology, based on rainfall signal extraction using dynamic-harmonic-regressions (DHR) and stochastic-multiple-linear-regressions (SMLR) between rainfall components and global signals for searching intra-annual and inter-annual teleconnections, is proposed. DHR gives non-stationary inter-annual trends and intra-annual quasi-periodic oscillations for monthly rainfall measurements. Time-variable amplitudes of quasi-periodical oscillations are crucial for finding intra-annual teleconnections using SMLR, while trends are better suited for the case of inter-annual ones. The methodology is tested over a Tropical-Andean region in southern Ecuador. The following results were obtained: (1) trans-NiÃ±o-Index (TNI) and Tropical-South-Atlantic signals are strongly connected to inter-annual and intra-annual time-scales. (2) However, TNI progressively weakens its relation with intra-annual components; meanwhile, El-NiÃ±o-Southern-Oscillation 3 gains ground for such time-scales. (3) Finally, an inter-annual connection with the North-Atlantic-Oscillation (NAO) is revealed. These results are consistent with previous literature, although the TNI and NAO connections are interesting findings, taking into account the differences in the connected scales. These results show the methodologyâ€™s capability of unraveling global teleconnections in different space and time scales using attributes embedded in an integral mathematical framework, which could be interesting for other purposesâ€”such as the analysis of climate mechanisms or climate modeling. Â© 2018, Springer-Verlag GmbH Germany, part of Springer Nature.
Future meteorological droughts in Ecuador: decreasing trends and associated spatio temporal features derived from CMIP5 models
(2020) Campozano Parra, Lenin Vladimir; Ballari, Daniela Elisabet; Montenegro Ambrosi, Martin Patricio; Avilés Añazco, Alex Manuel
Droughts are one of the most spatially extensive disasters that are faced by societies. Despite the urgency to define mitigation strategies, little research has been done regarding droughts related to climate change. The challenges are due to the complexity of droughts and to future precipitation uncertainty from Global Climate Models (GCMs). It is well-known that climate change will have more impact on developing countries. This is the case for Ecuador, which also has the additional challenges of lacking meteorological drought studies covering its three main regions: Coast, Highlands, and Amazon, and of having an intricate orography. Thus, this study assesses the spatio-temporal characteristics of present and future droughts in Ecuador under Representative Concentrations Pathways (RCP) 4.5 and 8.5. The 10 km dynamically downscaled products (DGCMs) from Coupled Model Intercomparison Project 5 (CMIP5) was used. The Standardized Precipitation Index (SPI) for droughts was calculated pixel-wise for present time 1981–2005 and for future time 2041-2070. The results showed a slightly decreasing trend for future droughts for the whole country, with a larger reduction for moderate droughts, followed by severe and extreme drought events. In the Coast and Highland regions, the intra-annual analysis showed a reduction of moderate and severe future droughts for RCP 4.5 and for RCP 8.5 throughout the year. Extreme droughts showed small and statistically non-significant decreases. In the Amazon region, moderate droughts showed increases from May to October, and decreases for the rest of the year. Additionally, severe drought increases are expected from May to December, and decreases from January to April. Finally, extreme drought increases are expected from January to April, with larger increases in October and November. Thus, in the Amazon, the rainy period showed a decreasing trend of droughts, following the wetter in wet- and drier in dry paradigm. Climate change causes decision-making process and calls for adaptation strategies being more challenging. In this context, our study has contributed to better mapping the space-time evolution of future drought risk in Ecuador, thus providing valuable information for water management and decision making as Ecuador faces climate change. © Copyright © 2020 Campozano, Ballari, Montenegro and Avilés.
Imágenes TRMM para identificar patrones de precipitación e índices ENSO en Ecuador
(Universidad de Cuenca, 2014) Campozano Parra, Lenin Vladimir; Ballari, Daniela Elisabet; Célleri Alvear, Rolando Enrique; Universidad de Cuenca; Dirección de Investigación de la Universidad de Cuenca
Understanding spatio-temporal precipitation patterns and how they are related to ENSO are important to predict the spatial impact of ENSO events and develop early warning systems. While ENSO has been previously studied in Ecuador using rain gauge data, its impact on the spatial rainfall pattern is largely unknown. Thus, the purpose of this study was to use the Tropical Rainfall Measuring Mission satellite imagery (TRMM) to identify spatio-temporal patterns of precipitation from January to April, the season more affected by ENSO in Ecuador, and their relation with ENSO indexes. Principal component analysis was applied over the 16-year TRMM imagery, and score time series were correlated with the tropical Pacific surface sea temperature (tP SST) of Niño 1+2 region as well as with the Trans Niño Index (TNI). Results suggest that TNI and Niño 1+2 indicators are both needed to estimate the impact of tP SST on the precipitation during JA season. Strong positive anomalies on Niño 1+2 are positively correlated with higher precipitation in coastal plain of Ecuador and below average precipitation in the Amazon region. High values of TNI are directly related to an enhancement of precipitation all over the country.
Investigating the relationships between precipitable water vapor estimations and heavy rainfall over the Eastern Pacific Ocean and Ecuadorian regions
(Universidad de Cuenca, 2023-03-20) Serrano Vincenti, María Sheila Fabiola; Villacís Erazo, Marcos Joshua; Condom, Thomas; Campozano Parra, Lenin Vladimir
Among the weather phenomena, rainfall is difficult to forecast, despite the theoretical and technical challenges inherently related to its prediction, its impact in economic and everyday activities, clearly justify its study. Numerical Weather Prediction Models are widely used to predict rainfall, such as the Weather Research & Forecasting Model (WRF), However, they underperform when is set to predict intense events and when working with complex and steep topographies. Recent studies have proposed the estimation of Precipitable Water Vapor PWV, as a tool that can help predict and understand the mechanisms that trigger intense rainfall. PWV is mainly sourced from satellite products and from indirectly measurements which derive it through the delay of the Global Navigation Positioning System (GNSS) signals quite accurately. Thus, the present work studies the relationship between intense rain and satellite sourced PWV over the ocean, the relationship of PWV-GNSS over the Coast, Sierra and Amazon of Ecuador, and the comparison of the PWV-GNSS with the data modeled in WRF. As main results, we point an empirical model between the satellite PWV and the maximum values of rainfall over the ocean. In addition, PWV-GNSS loading and unloading periods related to the diurnal cycle of rainfall over the land, and relationships with intense rain events were identified; and finally, the main discrepancies between the observed PWV-GNSS data and rainfall with WRF modeled data over areas of the Equatorial Andes.
Un modelo híbrido de probabilidad de elección para la estimación de la demanda de Quitocable
(2018) Lojano Gutiérrez, Juan Pablo; Rojas Alvarado, Alex Heriberto; Rojas Alvarado, Vilma Elizabeth; Solano Quinde, Lizandro Damián; Campozano Parra, Lenin Vladimir
The traditional models for the calculation of passenger demand based on prediction through quantifiable variables such as time, operational costs (Diesel, Spare Parts, etc.), fare, gender, among the main ones, have been extensively used for modeling processes of the final user choice. However, a new trend of research developed in recent years includes aspects of relevance such as human behavior measured by latent variables, i.e. variables not directly quantifiable, within the analysis and demand models. This article aims to estimate through a hybrid model the demand for the Quitocables system. The results show a robustness of these models in relation to traditional discrete choice models. , as indicated in the conclusions, the comfort and safety parameters that define in a better way the choice of a mode of transport