Browsing by Author "Orellana Alvear, Johanna Marlene"

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Análisis de la influencia de las propiedades microfísicas de la lluvia en el índice de erosividad para eventos de precipitación en la ciudad de Cuenca
(2019-05-15) Pesántez Vallejo, Diana Valeria; Romero Añazco, María Augusta; Célleri Alvear, Rolando Enrique; Orellana Alvear, Johanna Marlene
Rainfall erosivity is one of the main factors used to predict soil erosion. This factor depends on the microphysical properties of the rainfall, i.e. the diameter and the velocity of the raindrops. Nowadays, in our study site (city of Cuenca) there is a lack of information about the parameters that affect soil degradation processes and the type of rainfall that causes the greatest hydric erosion in its soils. Hence, our aim is to analyze the influence of the rainfall microphysical properties in the erosivity index. Our data corresponds to the period February 2017 - February 2018, measured with a Thies laser disdrometer and a Micro Rain Radar, both located at University of Cuenca's Balzay campus. Within this time period, five erosive events were found, for which the erosivity index (EI) was determined. The results showed that the microphysical properties of the rainfall have a direct influence in the EI index. Furthermore, raindrops with larger diameter and faster velocity had the greatest impact in the EI index. Finally, the analysis of the vertical profile showed that the behavior of the microphysical properties of the rainfall is different in each erosion event.
Analysis of drop size distribution and rain characteristics of extreme events at different locations in the Tropical Andes
(Universidad de Cuenca, 2024-01-02) Suárez Montenegro, Miguel Andrés; Orellana Alvear, Johanna Marlene; Urgilés Calle, María Gabriela
Extreme rainfall events in the tropical Andes have been the cause of economic and social disturbances throughout the years. Furthermore, climate change increases the frequency of these kinds of events. A few spatiotemporal studies have been performed on the behaviour of extreme rainfall events, but never went into depth about their hydrometeors' microphysics. Even the definition of what can be considered an extreme rainfall event has yet to be concisely defined. Thus, we have aimed to analyse, observe and compare how the drops of extreme and non- extreme events differentiate. This was done using data from three different sites in the tropical Andes where hydrometeor data and drop size distribution (DSD) were acquired using laser disdrometers. The results show that the percentage of rainfall events that are extreme is similar across the tropical Andes (≈8%) and that if an event has an accumulation higher than 15 mm or lasts more than 3 hours, it’s likely an extreme event. Additionally, it was observed that the DSD of all extreme events shows that drops for all diameters during these events are bigger than during non-extreme rainfall events.
Analysis of rainfall types in the tropical Andes based on a clustering approach using observations of vertically pointing micro-rain radar and laser disdrometer
(Universidad de Cuenca, 2021-03-08) Urgilés Calle, María Gabriela; Orellana Alvear, Johanna Marlene
Lack of rainfall information at high temporal resolution in areas with a complex topography as the Tropical Andes is one of the main obstacles to study its rainfall dynamics. Furthermore, rainfall types (e.g., stratiform, convective) are usually defined by using thresholds of some rainfall characteristics such as intensity and velocity. However, these thresholds highly depend on the local climate and the study area. In consequence, these thresholds are a constraining factor for the rainfall class definitions because they cannot be generalized. Thus, this study aims to analyze rainfall-event types by using a data-driven clustering approach based on the k-means algorithm that allows accounting for the similarities of rainfall characteristics of each rainfall type. It was carried out using three years of data retrieved from a vertically pointing Micro Rain Radar (MRR) and a laser disdrometer. The results show two main rainfall types (convective and stratiform) in the area which highly differ in their rainfall features. Also, a mixed type was found as a subgroup of the stratiform type. The stratiform type was found more frequently throughout the year. Furthermore, rainfall events of short duration (less than 70 mins) were prevalent in the study area. This study will contribute to analyze the rainfall formation processes and the vertical profile.
Assessment of native radar reflectivity and radar rainfall estimates for discharge forecasting in mountain catchments with a random forest model
(2020) Orellana Alvear, Johanna Marlene; Célleri Alvear, Rolando Enrique; Rütger, Rollenbeck; Muñoz Pauta, Paul Andres; Contreras Andrade, Pablo Andrés; Bendix, Jorg
Discharge forecasting is a key component for early warning systems and extremely useful for decision makers. Forecasting models require accurate rainfall estimations of high spatial resolution and other geomorphological characteristics of the catchment, which are rarely available in remote mountain regions such as the Andean highlands. While radar data is available in some mountain areas, the absence of a well distributed rain gauge network makes it hard to obtain accurate rainfall maps. Thus, this study explored a Random Forest model and its ability to leverage native radar data (i.e., reflectivity) by providing a simplified but efficient discharge forecasting model for a representative mountain catchment in the southern Andes of Ecuador. This model was compared with another that used as input derived radar rainfall (i.e., rainfall depth), obtained after the transformation from reflectivity to rainfall rate by using a local Z-R relation and a rain gauge-based bias adjustment. In addition, the influence of a soil moisture proxy was evaluated. Radar and runoff data from April 2015 to June 2017 were used. Results showed that (i) model performance was similar by using either native or derived radar data as inputs (0.66 < NSE < 0.75; 0.72 < KGE < 0.78). Thus, exhaustive pre-processing for obtaining radar rainfall estimates can be avoided for discharge forecasting. (ii) Soil moisture representation as input of the model did not significantly improve model performance (i.e., NSE increased from 0.66 to 0.68). Finally, this native radar data-based model constitutes a promising alternative for discharge forecasting in remote mountain regions where ground monitoring is scarce and hardly available.
Atmosphere-surface fluxes modeling for the high Andes: the case of páramo catchments of Ecuador
(2020) Carrillo Rojas, Galo José; Schulz, Hans Martin; Orellana Alvear, Johanna Marlene; Ochoa Sánchez, Ana Elizabeth; Trachte, Katja; Célleri Alvear, Rolando Enrique; Bendix, Jorg
© 2019 Elsevier B.V. Interest in atmosphere-surface flux modeling over the mountainous regions of the globe has increased recently, with a major focus on the prediction of water, carbon and other functional indicators in natural and disturbed conditions. However, less research has been centered on exploring energy fluxes (net radiation; sensible, latent and soil heat) and actual evapotranspiration (ETa) over the Neotropical Andean biome of the páramo. The present study assesses the implementation and parameterization of a state-of-art Land-Surface Model (LSM) for simulation of these fluxes over two representative páramo catchments of southern Ecuador. We evaluated the outputs of the LSM Community Land Model (CLM ver. 4.0) with (i) ground-level flux observations from the first (and highest) Eddy Covariance (EC) tower of the Northern Andean páramos; (ii) spatial ETa estimates from the energy balance-based model METRIC (based on Landsat imagery); and (iii) derived ETa from the closure of the water balance (WB). CLM's energy predictions revealed a significant underestimation on net radiation, which impacts the sensible and soil heat fluxes (underestimation), and delivers a slight overestimation on latent heat flux. Modeled CLM ETa showed acceptable goodness-of-fit (Pearson R = 0.82) comparable to ETa from METRIC (R = 0.83). Contrarily, a poor performance of ETa WB was observed (R = 0.46). These findings provide solid evidence on the CLM's accuracy for the ETa modeling, and give insights in the selection of other ETa methods. The study contributes to a better understanding of ecosystem functioning in terms of water loss through evaporative processes, and might help in the development of future LSMs’ implementations focused on climate / land use change scenarios for the páramo.
Calibration of X-band radar for extreme events in a spatially complex precipitation region in north peru: machine learning vs. empirical approach
(2021) Orellana Alvear, Johanna Marlene
Cost-efficient single-polarized X-band radars are a feasible alternative due to their highsensitivity and resolution, which makes them well suited for complex precipitation patterns. Thefirst horizontal scanning weather radar in Peru was installed in Piura in 2019, after the devastatingimpact of the 2017 coastal El Niño. To obtain a calibrated rain rate from radar reflectivity, we employa modified empirical approach and draw a direct comparison to a well-established machine learningtechnique used for radar QPE. For both methods, preprocessing steps are required, such as clutterand noise elimination, atmospheric, geometric, and precipitation-induced attenuation correction,and hardware variations. For the new empirical approach, the corrected reflectivity is related to raingauge observations, and a spatially and temporally variable parameter set is iteratively determined.The machine learning approach uses a set of features mainly derived from the radar data. Therandom forest (RF) algorithm employed here learns from the features and builds decision trees toobtain quantitative precipitation estimates for each bin of detected reflectivity. Both methods capturethe spatial variability of rainfall quite well. Validating the empirical approach, it performed betterwith an overall linear regression slope of 0.65 and r of 0.82. The RF approach had limitations with thequantitative representation (slope = 0.44 and r = 0.65), but it more closely matches the reflectivitydistribution, and it is independent of real-time rain-gauge data. Possibly, a weighted mean of bothapproaches can be used operationally on a daily basis
Caracterización de la precipitación espacial en las cuencas hidrográficas de los ríos Tomebamba y Yanuncay
(2018) Vélez Brito, Lisseth Cristina; Célleri Alvear, Rolando Enrique; Orellana Alvear, Johanna Marlene
Knowledge of environmental variables is important because of their influence on human activities. A factor of interest is precipitation. The water resource is essential for the development of the populations, being vital for activities such as livestock, agriculture, industry, domestic activities, generation of electricity and for the development of life itself. Throughout the years, measurement techniques and equipment have been implemented to collect information on precipitation in an area of interest. The objective of this work was to characterize precipitation in the Tomebamba and Yanuncay hydrographic basins through the use of precipitation images from the CAXX meteorological radar. The study was carried out at the level of sub-basins, altitudinal zones and land use, reporting values of average monthly precipitation, standard deviation, cell with maximum value of precipitation and cell with minimum value of precipitation. Map statistics were applied using Python 2.7.12 software and cartographic files were processed using ArcGis 10.5 software. The characteristics of the precipitation in each sub-basin during wet and dry months of 2016-2017 were defined. In addition, the distribution of precipitation between the sub-basins was compared and the correlation between precipitation and altitude was calculated using a simple linear regression. The results showed that there is a high correlation between the precipitations of the sub-basins, being in Yanuncay always lower than in Tomebamba, but there is a low correlation between precipitation and altitude. By other hand, at the level of land use, rainfall did not present any pattern.
Clustering of rainfall types using micro rain radar and laser disdrometer observations in the tropical andes
(2021) Célleri Alvear, Rolando Enrique; Orellana Alvear, Johanna Marlene; Urgilés Calle, María Gabriela; Bendix, Jorg; Trachte, Katja
Lack of rainfall information at high temporal resolution in areas with a complex topography as the Tropical Andes is one of the main obstacles to study its rainfall dynamics. Furthermore, rainfall types (e.g., stratiform, convective) are usually defined by using thresholds of some rainfall characteristics such as intensity and velocity. However, these thresholds highly depend on the local climate and the study area. In consequence, these thresholds are a constraining factor for the rainfall class definitions because they cannot be generalized. Thus, this study aims to analyze rainfall-event types by using a data-driven clustering approach based on the k-means algorithm that allows accounting for the similarities of rainfall characteristics of each rainfall type. It was carried out using three years of data retrieved from a vertically pointing Micro Rain Radar (MRR) and a laser disdrometer. The results show two main rainfall types (convective and stratiform) in the area which highly differ in their rainfall features. In addition, a mixed type was found as a subgroup of the stratiform type. The stratiform type was found more frequently throughout the year. Furthermore, rainfall events of short duration (less than 70 min) were prevalent in the study area. This study will contribute to analyze the rainfall formation processes and the vertical profile.
Determination of climatic conditions related to precipitation anomalies in the tropical andes by means of the random forest algorithm and novel climate indices
(2021) Célleri Alvear, Rolando Enrique; Córdova Mora, Mario Andrés; Orellana Alvear, Johanna Marlene; Rollenbeck, Rütger T.
Understanding precipitation and its relation with atmospheric and oceanic conditions is vital in the face of climate change. This is crucial in the Tropical Andes (TA) because millions of people depend on water originated in the cordillera. Unfortunately, the paucity of meteorological monitoring that exists in mountainous regions is accentuated in the tropics. In this context, climate indices, remotely sensed, and gridded datasets, are useful tools to study climate and precipitation in the TA, and additional climate indices can be calculated from reanalysis datasets. The combination of this information with traditional indices has the potential to improve our understanding of precipitation. Our objective was to use the k-means algorithm to regionalize precipitation in the TA (different regions have different climate), and then use the random forest algorithm to study the variables related to precipitation in each of these regions in seasonal timescales. Here, we show the suitability of the random forest algorithm to reveal climate processes and the high potential of the novel climate indices to improve the regressions. We found that convective available potential energy was the most important variable for precipitation in the northern TA, except for the Chocó, where v at 850 hPa was the most important one. Meanwhile, vertical integral of divergence of moisture flux was the most important one in the southern TA. Interestingly, in the DJF season when the South American low-level jet (SALLJ) is more active, u and v at 850 hPa showed their lowest relative importance and the total column of water vapour showed its maximum, this could indicate that precipitation anomalies are controlled by atmospheric moisture availability rather than by the speed of the SALLJ during DJF. These results deepen our understanding of precipitation anomalies in the TA and the related oceanic and atmospheric conditions. The proposed methodology was proven to be suitable and it could be used in the future to test and formulate new hypotheses, and to forecast seasonal precipitation
Difference in the amount and meteorological patterns in the highlands and lowlands in the Galápagos Archipelago
(Universidad de Cuenca, 2024-10-01) Tenelanda Patiño, Pablo Andrés; Célleri Alvear, Rolando Enrique; Orellana Alvear, Johanna Marlene
Precipitation variability in the Galápagos Islands is crucial for climate change adaptation, biodiversity conservation and water resource management. This study aims to compare the event-scale precipitation characteristics (ESPC), such as intensity, duration and rainfall accumulation at different altitudes during ENSO from 2022 to 2024 on Santa Cruz Island. Precipitation data were used from a new network of automatic weather stations (AWS) installed at five sites: 2, 422 and 849 m a.s.l. on the windward side, and 22 and 619 m a.s.l. on the leeward side. The analysis included two phases: La Niña (April 2022 - January 2023) and El Niño (June 2023 - April 2024). Comparisons was made in two ways: first, the different altitudes within each phase were compared, and second, the two phases were compared at each altitude. For comparison, the distribution of each ESPC and the Mann-Whitney U-test were used to determine whether the differences were significant. For each ENSO phase, less intense and longer duration events occurred at the highest altitude of 849 m a.s.l. Outliers in each ESPC were significantly higher and more frequent during El Niño, especially on the windward side. This study provides a detailed analysis of precipitation characteristics and their variation according to ENSO phases, providing valuable insights for biodiversity conservation and water resource management on the island.
Efecto de advección sobre la acumulación de lluvia espacialmente distribuida usando imágenes de un radar meteorológico
(2019-04-04) Guallpa Guallpa, Mario Xavier; Orellana Alvear, Johanna Marlene; Célleri Alvear, Rolando Enrique
Weather radar networks are an excellent tool for Quantitative Precipitation Estimation (QPE), due to their high resolution in space and time, particularly in remote mountain areas such as the Tropical Andes. Nevertheless, the reduction of the temporal and spatial resolution might severely reduce the quality of QPE. Thus, the main objective of this study was to analyze the impact of spatial and temporal resolution of radar data on the cumulative QPE. For this, data from the world's highest X-band weather radar (4450 m a.s.l.) located in the Andes of Ecuador (Paute river basin) and from a rain gauge network were used. Different time resolution (1-, 5-, 10-, 15-, 20-, 30- and 60-min) and spatial resolutions (0.5, 0.25 and 0.1 km) were evaluated. An optical flow method was validated for eleven rainfall events (with different features) and applied to enhance the temporal resolution of radar data to 1-min intervals. The results show that 1-min temporal resolution images are able to capture in detail rain event features. The radar-rain gauge correlation decreases considerably when the time resolution increases (r from 0.69 to 0.31 for time resolution from 1-min to 60-min). No significant difference was found in the total water volume (3%) calculated with the three spatial resolutions data. Spatial resolution of 0.5 km on radar imagery is suitable to quantify rainfall in the mountain Andes.
Efecto de la elevación en la distribución espacio-temporal de la precipitación a escala mensual en las cuencas de los ríos Tomebamba y Yanuncay usando datos de radar
(2019-05-17) Salinas Orrego, Rocío Esperanza; Sarmiento Vásquez, Pedro sebastián; Orellana Alvear, Johanna Marlene; Célleri Alvear, Rolando Enrique
Rainfall depth and its spatial variation affects water resources availability, which is relevant in watersheds management. In mountain areas, one of the factors that influences rainfall characteristics is elevation. The present study determines the effect of elevation in space time rainfall distribution in the Tomebamba and Yanuncay river basins. Data from a radar located on the Paragüillas hill at the northern limit of the Cajas National Park was used, regarding to the months April 2015, 2016, 2017, May 2015, 2016, November 2016, January and February 2017. It was found that between 2800 m s.n.m. and 3300 m s.n.m. the highest rainfall is recorded in six of the eight months of study, mainly in the rainiest months. Also, between 3300 m s.n.m. and 3900 m s.n.m. the greatest rainfall spatial variability is seen; additionally, it was determined that the Tomebamba basin is more humid than the Yanuncay basin. This study is pioneer in the identification of precipitation-altitude relationship in mountain areas in Ecuador with radar data. It is appreciated that there is no unique linear relationship between elevation and rainfall distribution. Moreover, there is no relationship between elevation and rainfall variability. Future investigations could be focused on determining the influence of the slope in the space-time orographic rainfall variability.
Flash-flood forecasting in an andean mountain catchment-development of a step-wise methodology based on the random forest algorithm
(2018) Muñoz Pauta, Paul Andrés; Orellana Alvear, Johanna Marlene; Willems, Patrick; Célleri Alvear, Rolando Enrique
Flash-flood forecasting has emerged worldwide due to the catastrophic socio-economic impacts this hazard might cause and the expected increase of its frequency in the future. In mountain catchments, precipitation-runoff forecasts are limited by the intrinsic complexity of the processes involved, particularly its high rainfall variability. While process-based models are hard to implement, there is a potential to use the random forest algorithm due to its simplicity, robustness and capacity to deal with complex data structures. Here a step-wise methodology is proposed to derive parsimonious models accounting for both hydrological functioning of the catchment (eg, input data, representation of antecedent moisture conditions) and random forest procedures (eg, sensitivity analyses, dimension reduction, optimal input composition). The methodology was applied to develop short-term prediction models of varying time duration (4, 8, 12, 18 and 24 h) for a catchment representative of the Ecuadorian Andes. Results show that the derived parsimonious models can reach validation efficiencies (Nash-Sutcliffe coefficient) from 0.761 (4-h) to 0.384 (24-h) for optimal inputs composed only by features accounting for 80% of the model’s outcome variance. Improvement in the prediction of extreme peak flows was demonstrated (extreme value analysis) by including precipitation information in contrast to the use of pure autoregressive models. View Full-Text
Flood early warning systems using machine learning techniques: the case of the Tomebamba catchment at the southern Andes of Ecuador
(2021) Bendix, Jor; Muñoz Pauta, Paúl Andrés; Orellana Alvear, Johanna Marlene; Célleri Alvear, Rolando Enrique; Feyen, Jan
Worldwide, machine learning (ML) is increasingly being used for developing flood early warning systems (FEWSs). However, previous studies have not focused on establishing a methodology for determining the most efficient ML technique. We assessed FEWSs with three river states, No-alert, Pre-alert and Alert for flooding, for lead times between 1 to 12 h using the most common ML techniques, such as multi-layer perceptron (MLP), logistic regression (LR), K-nearest neighbors (KNN), naive Bayes (NB), and random forest (RF). The Tomebamba catchment in the tropical Andes of Ecuador was selected as a case study. For all lead times, MLP models achieve the highest performance followed by LR, with f1-macro (log-loss) scores of 0.82 (0.09) and 0.46 (0.20) for the 1 h and 12 h cases, respectively. The ranking was highly variable for the remaining ML techniques. According to the g-mean, LR models correctly forecast and show more stability at all states, while the MLP models perform better in the Pre-alert and Alert states. The proposed methodology for selecting the optimal ML technique for a FEWS can be extrapolated to other case studies. Future efforts are recommended to enhance the input data representation and develop communication applications to boost the awareness of society of floods.
Hydrometeorological factors controlling the stable isotopic composition of precipitation in the highlands of south Ecuador
(2022) Zhiña Villa, Dario Xavier; Mosquera Rojas, Giovanny Mauricio; Esquivel Hernández, Germain; Córdova Mora, Mario Andrés; Sánchez Murillo, Ricardo; Orellana Alvear, Johanna Marlene; Crespo Sánchez, Patricio Javier
Knowledge about precipitation generation remains limited in the tropical Andes due to the lack of water stable isotope (WSI) data. Therefore, we investigated the key factors controlling the isotopic composition of precipitation in the Páramo highlands of southern Ecuador using event-based (high frequency) WSI data collected between November 2017 and October 2018. Our results show that air masses reach the study site preferentially from the eastern flank of the Andes through the Amazon basin (73.2%), the Orinoco plains (11.2%), and the Mato Grosso Massif (2.7%), whereas only a small proportion stems from the Pacific Ocean (12.9%). A combination of local and regional factors influences the δ18O isotopic composition of precipitation. Regional atmospheric features (Atlantic moisture, evapotranspiration over the Amazon Forest, continental rain-out, and altitudinal lapse rates) are what largely control the meteoric δ18O composition. Local precipitation, temperature, and the fraction of precipitation corresponding to moderate to heavy rainfalls are also key features influencing isotopic ratios, highlighting the importance of localized convective precipitation at the study site. Contrary to δ18O, d-excess values showed little temporal variation and could not be statistically linked to regional or local hydrometeorological features. The latter reveals that large amounts of recycled moisture from the Amazon basin contributes to local precipitation regardless of season and predominant trajectories from the east. Our findings will help to improve the isotope-based climatic models and enhance paleoclimate reconstructions in the southern Ecuador highlands. © 2022 American Meteorological Society.
Identificación del comportamiento temporal de la precipitación para la distribución de tamaño de gotas (DSD) con la relación reflectividad (z) - rango de lluvia (r), en el páramo de la microcuenca del Zhurucay.
(2017) Pauta Luna, Olmedo Xavier; Célleri Alvear, Rolando Enrique; Orellana Alvear, Johanna Marlene
Raindrop distribution respondss to the precipitation process into a microphysical level, taking into account variables like N(Di) and the mean (Dm); which classify the rain, through Dm variability on a case of rain. Afterwards, raindrop distribution N(Di) was obtained based on the classification. By this way, it was possible to observe the relationship and differences that exists between each other. In association with DSD, rain characterization is necessary, with relation Z-R, in order to obtain constants (a – b) that fits precisely to each type of obtained rain. Data from precipitation from the disdrometer (Thies Clima), located in Zhurucay micro-basin, were collected. The project included a database of 170 cases corresponding to a two-year period (2012-2014). The DSD worked with 4 types of rain: First Stratiform(FS), Second Stratiform(SS), Initial Convective(IC) and Continue Convective(CC), which were classified by monthly periods. DSD results showed that the raindrop distribution for each type of rain was different, demonstrating the different inclination and forms of raining in specific months. Moreover, April and July generated two types of intence rain, with drop of 8mm of diameter, knowing possible impacts of erosivity in this period. The characterization of Z-R obtained through a regression showed that the constants for each type of rain had different values. The study proved the existence of different types of rain at high mountain areas and the difference among each other through their distribution and in the value of constants that can be useful for future research and conservation studies.
Influence of random forest hyperparameterization on short-term runoff forecasting in an andean mountain catchment
(2021) Contreras Andrade, Pablo Andrés; Orellana Alvear, Johanna Marlene; Muñoz, Paul; Bendix, Jorg; Célleri Alvear, Rolando Enrique
The Random Forest (RF) algorithm, a decision-tree-based technique, has become a promising approach for applications addressing runoff forecasting in remote areas. This machine learning approach can overcome the limitations of scarce spatio-temporal data and physical parameters needed for process-based hydrological models. However, the influence of RF hyperparameters is still uncertain and needs to be explored. Therefore, the aim of this study is to analyze the sensitivity of RF runoff forecasting models of varying lead time to the hyperparameters of the algorithm. For this, models were trained by using (a) default and (b) extensive hyperparameter combinations through a grid-search approach that allow reaching the optimal set. Model performances were assessed based on the R2, %Bias, and RMSE metrics. We found that: (i) The most influencing hyperparameter is the number of trees in the forest, however the combination of the depth of the tree and the number of features hyperparameters produced the highest variability-instability on the models. (ii) Hyperparameter optimization significantly improved model performance for higher lead times (12- and 24-h). For instance, the performance of the 12-h forecasting model under default RF hyperparameters improved to R2 = 0.41 after optimization (gain of 0.17). However, for short lead times (4-h) there was no significant model improvement (0.69 < R2 < 0.70). (iii) There is a range of values for each hyperparameter in which the performance of the model is not significantly affected but remains close to the optimal. Thus, a compromise between hyperparameter interactions (i.e., their values) can produce similar high model performances. Model improvements after optimization can be explained from a hydrological point of view, the generalization ability for lead times larger than the concentration time of the catchment tend to rely more on hyperparameterization than in what they can learn from the input data. This insight can help in the development of operational early warning systems.
Optimization of X-Band radar rainfall retrieval in the southern Andes of Ecuador using a random forest model
(2019) Orellana Alvear, Johanna Marlene; Bendix, Jorg; Rollenbeck,, Rütger T; Célleri Alvear, Rolando Enrique
Despite many efforts of the radar community, quantitative precipitation estimation (QPE) from weather radar data remains a challenging topic. The high resolution of X-band radar imagery in space and time comes with an intricate correction process of reflectivity. The steep and high mountain topography of the Andes enhances its complexity. This study aims to optimize the rainfall derivation of the highest X-band radar in the world (4450 m a.s.l.) by using a random forest (RF) model and single Plan Position Indicator (PPI) scans. The performance of the RF model was evaluated in comparison with the traditional step-wise approach by using both, the Marshall-Palmer and a site-specific Z−R relationship. Since rain gauge networks are frequently unevenly distributed and hardly available at real time in mountain regions, bias adjustment was neglected. Results showed an improvement in the step-wise approach by using the site-specific (instead of the Marshall-Palmer) Z−R relationship. However, both models highly underestimate the rainfall rate (correlation coefficient < 0.69; slope up to 12). Contrary, the RF model greatly outperformed the step-wise approach in all testing locations and on different rainfall events (correlation coefficient up to 0.83; slope = 1.04). The results are promising and unveil a different approach to overcome the high attenuation issues inherent to X-band radars.
Precipitation characteristics at two locations in the tropical andes by means of vertically pointing micro-rain radar observations
(2019) Seidel, Jochen; Huggel, Christian; Célleri Alvear, Rolando Enrique; Bendix, Jorg; Fernández Rosales, Ciro Walter; Figueroa Tauquino, Rafael; Orellana Alvear, Johanna Marlene; Trachte, Katja
© 2019 by the authors. In remote areas with steep topography, such as the Tropical Andes, reliable precipitation data with a high temporal resolution are scarce. Therefore, studies focusing on the diurnal properties of precipitation are hampered. In this paper, we investigated two years of data from Micro-Rain Radars (MRR) in Cuenca, Ecuador, and Huaraz, Peru, from February 2017 to January 2019. This data allowed for a detailed study on the temporal precipitation characteristics, such as event occurrences and durations at these two locations. Our results showed that the majority of precipitation events had durations of less than 3 h. In Huaraz, precipitation has a distinct annual and diurnal cycle where precipitation in the rainy season occurred predominantly in the afternoon. These annual and diurnal cycles were less pronounced at the site in Cuenca, especially due to increased nocturnal precipitation events compared to Huaraz. Furthermore, we used a fuzzy logic classification of fall velocities and rainfall intensities to distinguish different precipitation types. This classification showed that nightly precipitation at both locations was predominantly stratiform, whereas (thermally induced) convection occurred almost exclusively during the daytime hours.
The coastal El Niño event of 2017 in Ecuador and Peru: a weather radar analysis.
(2022) Célleri Alvear, Rolando Enrique
The coastal regions of South Ecuador and Peru belong to the areas experiencing the strongest impact of the El Niño Southern Oscillation phenomenon. However, the impact and dynamic development of weather patterns during those events are not well understood, due to the sparse observational networks. In spite of neutral to cold conditions after the decaying 2015/16 El Niño in the central Pacific, the coastal region was hit by torrential rainfall in 2017 causing floods, erosion and landslides with many fatalities and significant damages to infrastructure. A new network of X-band weather radar systems in South Ecuador and North Peru allowed, for the first time, the spatio-temporally high-resolution monitoring of rainfall dynamics, also covering the 2017 event. Here, we compare this episode to the period 2014–2018 to point out the specific atmospheric process dynamics of this event. We found that isolated warming of the Niño 1 and 2 region sea surface temperature was the initial driver of the strong rainfall, but local weather patterns were modified by topography interacting with the synoptic situation. The high resolution radar data, for the first time, allowed to monitor previously unknown local spots of heavy rainfall during ENSO-related extreme events, associated with dynamic flow convergence initiated by low-level thermal breezes. Altogether, the coastal El Niño of 2017, at the same time, caused positive rainfall anomalies in the coastal plain and on the eastern slopes of the Andes, the latter normally associated only with La Niña events. Thus, the 2017 event must be attributed to the La Niña Modoki type.