Browsing by Author "Ballari, Daniela Elisabet"

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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 …
Altitudinal and temporal evapotranspiration dynamics via remote sensing and vegetation index-based modelling over a scarce-monitored, high-altitudinal Andean páramo ecosystem of Southern Ecuador
(2019) Ramón Reinozo, Mayra; Ballari, Daniela Elisabet; Cabrera Cabrera, Juan Geovanny; Crespo Sánchez, Patricio Javier; Carrillo Rojas, Galo José
In the tropical Andes, the paramo ecosystem is known as water towers and the main water supplier for the cities of the Andean region. Nevertheless, considering that evapotranspiration (ET) is the major water loss and the lack of in situ evapotranspiration measurements in high altitudinal paramo ecosystems, ET dynamics on the hydrological regulation remains largely unexplored. Therefore, to close this gap, we focused on a remote sensing approach. This study addressed the altitudinal and temporal dynamics of actual evapotranspiration using a crop coefficient based on a Vegetation Index (VI) model. Enhanced Vegetation Index (EVI), Normalized Difference Vegetation Index (NDVI) and Soil-Adjusted Vegetation Index (SAVI) retrieved from Landsat imagery were evaluated. Four remote sensing images and ground-level meteorological data for a 10-month period were used to create ET maps from each VI. A cubic spline interpolation was used to obtain daily ET time series between two satellite overpass dates. Aggregated monthly values were used to validate against ET calculated from water balance. Results revealed that EVI-based ET outperformed the other VI-based ET. The results showed 30% of subestimation (Pbias%) in relation to the water balance. For upgraded results, an extended satellite images time series and a fine calibration are needed. Regarding the altitudinal variability, ET exhibited a strong dependence on land cover characteristics. Our work provides a plausible method to estimate ET in paramo ecosystems in the absence of ET measurements and with a scarcity of clear sky images, further evaluation is necessary to improve ET estimations using remote sensing in the future.
Comparación de métodos de interpolación geoestadísticos con y sin varibles auxiliares para la precipitación anual (2003-2008) en Ecuador
(2016) Pérez Paredes, Daniel Fernando; Ballari, Daniela Elisabet
The study compares two geostatistical methods for interpolation, ordinary kriging (without secondary variables) and universal kriging (with secondary variables), in the analysis of the distribution of the annual precipitation of Ecuador in the period 2003-2008. The secondary variables explored are satellite images, being selected for the study images of precipitation TRMM, cloud fraction, cloud water content, cloud top temperature, cloud top pressure, soil temperature and digital elevation model. The method of study consist in three stages. First, the interpolation without secondary variables of annual precipitation is made (ordinary kriging). Second, the interpolation with secondary variables (universal kriging). To select the right secondary variables, multiple linear regression is applied. Third, cross validation is used in order to evaluate the goodness of the interpolations. The cross validation results showed that the best method for interpolation was universal kriging with TRMM, followed nearly by the combination of TRMM and cloud water content. With these secondary variables, also, the standard deviation of the interpolation is minimized, generating a homogenous deviation all across the country. This deviation is inferior at the one generated by the interpolation without secondary variables. Finally, the secondary variables allowed to represent the variability of the precipitation in the Amazon, region with a lack of stations.
Comparación de métodos de interpolación para la evaluación de datos de precipitación espacial y temporal provenientes de pluviómetros y un radar meteorológico en la cuenca alta del río Paute
(2017) Salinas Orrego, Luis Angel; Avilés Añazco, Alex Manuel; Ballari, Daniela Elisabet
The quantitative estimate temporal space of precipitation in a given area is of great importance as a tool for modelling and water resources management such as water for human consumption, agriculture and energy, mitigation of drought, control floods, road designs, the crops forecast and monitoring of adverse weather events as well as for many other applications. This project is focused on the evaluation of the effectiveness of interpolation method IDW (Inverse Distance Weight), using in a first case, only information of rain gauges and a second case integrating images of weather radar with rain gauge data applied to three strong events of precipitation in three different resolutions of pixel (100, 250 y 500 m). The results show that the second case as the optimal for estimating the average rainfall in places devoid of information from data collected by rain gauges and radar in the upper basin of the river Paute. This study establishes the implementation of a programming code, which allows to evaluate the above - mentioned interpolation techniques, so that you get to become guide for the future analyses or investigation.
Evaluación de Imágenes Satelitales de Precipitaciones (TRMM 3B43 mensual) en región Costa, Sierra y Oriente del Ecuador
(2014) Castro Loayza, Edisson Santiago; Ballari, Daniela Elisabet
Accurate precipitation data is of utmost importance for water resources management. However, it is difficult to estimate, especially in regions of high spatial variability and scarcity of rain gauge networks, such as Ecuador. Algorithms that use satellite outputs to estimate precipitation spatially can cope with this problem. Nevertheless, they have a limited accuracy and resolution; therefore, their evaluation is necessary in order to find additional datasets that support characterization of precipitation patterns in a region.The present research evaluates the TRMM 3B43 algorithm precipitation estimates on a monthly scale with fourteen rain gauge data series from the National Institute of Meteorology and Hydrology of Ecuador (INAMHI). Stations are distributed in the three regions of Ecuador (Costa, Sierra and Oriente) and the time series are from 1998 to 2010.In general, TRMM 3B43 estimates capture monthly precipitation qualitatively in the study region. According to the statistical errors and satellite scores calculated, the Coastal and Oriental regions are better estimated quantitatively than the Andean region.Evaluation of a newly launched algorithm that is widely used and consistent results in a complex region provide methodology and conclusions that can be extrapolated to more detailed studies in the future such as calibration and bias correction of precipitation estimates.
Evaluación de imágenes satelitales de precipitaciones GPM (Global Precipitation Measurement) a escala mensual para el Azuay
(2016) Cabrera Atiencia, Eduardo Benigno; Ballari, Daniela Elisabet
Precipitation measurements are essential for extreme weather events prediction and a broad range of applications such as agriculture, climate change and energy generation. In the Ecuadorian Andes, precipitation is highly variable, mainly because of the topographical conditions. In addition, low density of monitoring networks makes difficult spatial representation. In this context, satellite images are an important tool to estimate precipitation. However, before their use is needed to evaluate them. The objective of this study is to evaluate precipitations estimates from the GPM IMERG V03 algorithm on a monthly basis. It uses data sets of 10 rain gauges from the ecohydrological observatories of the Department of Water Resources and Environmental Sciences (iDRHiCA). The study area is Azuay province and the analysis is performed for the period April 2014 to January 2015. Results showed that IMERG V03 qualitatively estimates the monthly precipitation in Azuay. According to statistical errors and detection rates, it was found that quantitatively, IMERG properly ’ estimate precipitation, except for Susudel and San Fernando stations. IMERG indicates a promising prospect of hydrological utility. It anticipates reasonably products as an improvement over its TMPA predecessor, given its ability to detect light rain and solid precipitation. As more IMERG data with new versions are released, more studies about the water and climate are going to be needed to explore future IMERG potentials.
Evaluación de imágenes satelitales de precipítaciones GPM (Global Precipitation Measurement) a escala subdiaria para la provincia del Azuay
(2016) Andrade Pillaga, Omar Fabricio; Ballari, Daniela Elisabet
Precipitation is an important meteorological variable in the hydrologic water cycle. Therefore, it becomes essential to measure and analyze precipitation behavior. In order to improve precipitation estimations, it is that satellite products have become a valuable tool. They provide spatially continuous data and at regular time intervals such as monthly, daily and sub-daily. One of the latest released products of remote sensing for precipitation are the GPM mission (Global Precipitation Measurement). The aim of this work is to evaluate precipitation estimates for the 3IMERGHH V03 algorithm at sub-daily scale (30 minutes) using series of continuous data from 7 meteorological stations belonging to the Department of Water Resources and Environmental Sciences at the University of Cuenca (IDRHICA). The study area is the Province of Azuay and the analysis period is from March 12th to December 31st, 2014. Results show that sub-daily satellites images qualitatively represented precipitation compared to rain gauges. However, the results of statistical errors and detection rates were not satisfactory for the sub-daily scale. The difference is attributed to the complex topography and climate and the small number of rain gauges in the study region used by GMP for image correction. In particular, satellite images overestimated precipitation for PiscícolaChirimachay, Virgin Cajas, Toreadora and Susudel rain gauges (Quinuas basins and Jubones micro-basins). While they underestimated for Principal, Base and San Fernando rain gauges located in the micro-basin of Zhurucay. In addition, it was shown that satellite images have better performance as the time scale increased, i.e. they are more reliable at daily than at sub-daily scale. These results allow us to recommend the use of these images for applications requiring temporal scales superior to daily.
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.
Incertidumbre en la precipitación espacial diaria causada por redes pluviográficas dispersas en una microcuenca de páramo densamente monitoreada
(2016) Seminario Guallpa, Paulo Sebastián; Ballari, Daniela Elisabet
Uncertainty in areal rainfallestimation has direct implications for water resource management. Especially in mountain catchments, where variability is high and monitoring is often scarce. This is particularly true for the Páramo, a mountain ecosystem that provides water to millions of people in the Andean region. In this study, the uncertainty in daily precipitation estimation of a páramomicrocatchmentis quantified. Recordsfrom a monitoring network with an unprecedentednumber of sensors in the region were used (13 sensors, approximately 1 per 0.6km2). Two sources of uncertainty were analyzed: 1) the effect of interpolation method; and 2) the impact of reducing sensordensity. Through Ordinary Kriging and IDW methods, best daily estimates were obtained and compared with all possible estimates obtained by discarding tipping buckets. For 25% of the days, none of the methods reflected the variability inside the microcatchment. In the remaining days, both methods performed well. For all sensor densities, between 20% and 30% of all gauge networks estimated the mean daily areal precipitation without bias. When the sensor density decreased,the mean daily absolute error increased exponentially. All instances of the study revealed that the uncertainty in estimating rainfall insidethe microcatchment could be critical for further purposes.
Integración de imágenes satelitales de precipitación TRMM con información de pluviómetros para los años 2001 y 2002 en Ecuador
(2006) Peña Saltos, Pablo Gabriel; Ballari, Daniela Elisabet
An accurate spatiotemporal characterization of precipitation becomes an important tool for management and modeling water resources, such as agriculture and energy, mitigation of drought, flood control, road design, and forecasting of crops and extreme events. This paper focuses on the evaluation of the effectiveness that can present geostatistical interpolation methods to mergeTRMM satellite images with rain gauges data in a monthly time scale for continental Ecuador. Kriging interpolation techniques were evaluated: Ordinary, Universal and Regression, the latest one results as the optimal; however, the other methods showdependable predictions when we analyze spatial rainfall. This research provides a code as implementation that allows the evaluationof geostatistical methods, so it is set to be a guide for future analysis of Kriging interpolation techniques.
Modos de movilidad de los niños y niñas en edad escolar: exploración de la incidencia de factores socioeconómicos, de percepción y de mesoescala urbana utilizando Random Forest
(2021) Ballari, Daniela Elisabet; Hermida Palacios, María Augusta; Orellana Vintimilla, Daniel Augusto; Hermida Palacios, Carla Marcela; Salgado Castillo, Francisco
Analyzing and understanding the incidence of socioeconomic, perception and urban mesoscale factors on mobility modes of school-age children is essential to motivate a more sustainable mobility. This study explores the relationship between socioeconomic, perception and urban mesoscale factors on the daily mobility modes of school-age boys and girls (6 to 12 years old) in the intermediatesized city of Cuenca (Ecuador, Latin America). Random Forest, as a classifcation machine learning method, was used to classify the mobility modes into walk, bus and car, and to identify factor importance in each mode. The data were obtained from a mobility survey carried out on Cuenca households in 2019. Even if school bus is a usual mobility mode for schoolchildren, it could not be accounted in this study because it was not contained in the original survey. The results showed that the best model for walk and bus mobility modes was with all the factor groups, while for Car, as expected, was the socioeconomic model. Even if the most important factors were cars´ number per family and socioeconomic level, we also found that, in order to encourage walking as the mobility mode, the perception factors are relevant. Similarly, in order to encourage bus mobility mode, the urban mesoscale factors should be accounted for. This study contributes with data and a methodological approach that could infuence public policy regarding scholar-aged active mobility.
Rainfall monitoring network design using conditioned latin hypercube sampling and satellite precipitation estimates: an application in the Ecuadorian Amazon
(2018-06-07) Contreras Silva, Juan José; Ballari, Daniela Elisabet
Rain gauge networks are crucial for enhancing the spatio-temporal characterization of precipitation. In tropical regions, scarcity of rain gauge data and climatic variability, make conventional approaches to design rain gauge networks inadequate and impractical. In this study, we propose the use of conditioned Latin Hypercube Sampling (cLHS) method with multi-temporal layers of remotely sensed precipitation measurements for capturing the spatio-temporal precipitation patterns in ungauged areas. The study was conducted in the Amazon Region of Ecuador, for which monthly precipitation averages were derived based on a 16-year period of Tropical Rainfall Measuring Mission (TRMM 3B43 V7) data which were used as prior information to select representative sampling points through cLHS. Two scenarios for the sampling design were considered and evaluated, one without and one with restrictions on accessible sites according to the proximity to roads and settlements. Results showed that both optimized networks captured the variability of precipitation according to the TRMM climatology. Furthermore, evaluation against an independent satellite precipitation dataset showed that the optimized networks support mapping precipitation based on ordinary kriging (OK). Comparison with regular and random sampling methods showed that, particularly when a practical scenario is considered, the optimized network provided more reliable results over time, highlighting the suitability of the network to capture temporal changes and map precipitation with high accuracy. The proposed approach could be easily adopted in other ungauged and poorly accessible regions for rain gauge network design as well as to the design of multi-objective monitoring networks.
Rainfall monitoring network design using conditioned latin hypercube sampling and satellite precipitation estimates: an application in the ungauged Ecuadorian Amazon
(2019) Moreno Contreras, Juan Mario vinicio; Ballari, Daniela Elisabet; De bruin, Sytze; Samaniego Alvarado, Esteban Patricio
Rain gauge networks are crucial for enhancing the spatio‐temporal characterization of precipitation. In tropical regions, scarcity of rain gauge data, climatic variability, and variable spatial accessibility make conventional approaches to design rain gauge networks inadequate and impractical. In this study, we propose the use of conditioned Latin hypercube sampling (cLHS) method with multi‐temporal layers of remotely sensed precipitation measurements for capturing the spatio‐temporal precipitation patterns in ungauged areas. The study was conducted in the Amazon region of Ecuador, for which monthly precipitation averages were derived based on a 16‐year period of Tropical Rainfall Measuring Mission (TRMM 3B43 V7) data which were used as prior information to select representative sampling points through cLHS. Two scenarios for the sampling design were considered and evaluated, one without and one …
Spatial association to characterize the climate teleconnection patterns in Ecuador based on satellite precipitation estimates
(IEEE, 2020) Ballari, Daniela Elisabet; Campozano Parra, Lenin Vladimir; Samaniego Alvarado, Esteban Patricio; Orellana Vintimilla, Daniel Augusto
Climate teleconnections show remote and large-scale relationships between distant points on Earth. Their relations to precipitation are important to monitor and anticipate the anomalies that they can produce in the local climate, such as flood and drought events impacting agriculture, health, and hydropower generation. Climate teleconnections in relation to precipitation have been widely studied. Nevertheless, the spatial association of the teleconnection patterns (i.e., the spatial delineation of regions with teleconnections) has been unattended. Such spatial association allows to characterize how stable (heterogeneity/dependent and statistically significant) is the underlying spatial phenomena for a given pattern. Thus our objective was to characterize the spatial association of climate teleconnection patterns related to precipitation using an exploratory spatial data analysis approach. Global and local indicators of spatial association (Moran's I and LISA) were used to detect spatial patterns of teleconnections based on TRMM satellite images and climate indices. Moran's I depicted high positive spatial association for different climate indices, and LISA depicted two types of teleconnections patterns. The homogenous patterns were localized in the Coast and Amazonian regions, meanwhile the disperse patterns had a major presence in the Highlands. The results also showed some areas that, although with moderate to high teleconnection influences, had a random spatial patterns (i.e., non-significant spatial association). Other areas showed both teleconnections and significant spatial association, but with dispersed patterns. This pointed out the need to explore the local underlying features (topography, orientation, wind and micro-climates) that restrict (non-significant spatial association) or reaffirm (disperse patterns) the teleconnection patterns.
Tendencias en infraestructuras de datos espaciales en el contexto latinoamericano
(Universidad de Cuenca, 2014) Ballari, Daniela Elisabet; Vilches, Luis; Randolf Perez, Diego; Pacheco, Diego; Fernández, Virginia; Universidad de Cuenca; Dirección de Investigación de la Universidad de Cuenca
Because of technological trends such as mobile devices, sensors and web2.0, Spatial Data Infrastructures (SDI) are converging to mobile systems in real-time and with active citizen participation. While these trends have been already analyzed and integrated as part of the SDI in Europe and North America, in the Latin American context, they have not been deeply investigated. Therefore, in order to contribute to its progress in Latin America, this article presents the state of the art of SDI’s trends in Latin America from a literature review, complemented with a regional survey. The results show that despite major initiatives implementing SDI’s trends; their progress in Latin America is still incipient, being still in what is call the second stage of evolution.
Uav monitoring for enviromental management in galapagos islands
(INTERNATIONAL SOCIETY FOR PHOTOGRAMMETRY AND REMOTE SENSING, 2016-07-12) Ballari, Daniela Elisabet; Acosta Arias, Enrique Alfredo; Espinoza, A; Morocho Zurita, Carlos Villie; Orellana, D
In the Galapagos Islands, where 97% of the territory is protected and ecosystem dynamics are highly vulnerable, timely and accurate information is key for decision making. An appropriate monitoring system must meet two key features: on one hand, being able to capture information in a systematic and regular basis, and on the other hand, to quickly gather information on demand for specific purposes. The lack of such a system for geographic information limits the ability of Galapagos Islands' institutions to evaluate and act upon environmental threats such as invasive species spread and vegetation degradation. In this context, the use of UAVs (unmanned aerial vehicles) for capturing georeferenced images is a promising technology for environmental monitoring and management. This paper explores the potential of UAV images for monitoring degradation of littoral vegetation in Puerto Villamil (Isabela Island, Galapagos, Ecuador). Imagery was captured using two camera types: Red Green Blue (RGB) and Infrarred Red Green (NIR). First, vegetation presence was identified through NDVI. Second, object-based classification was carried out for characterization of vegetation vigor. Results demonstrates the feasibility of UAV technology for base-line studies and monitoring on the amount and vigorousness of littoral vegetation in the Galapagos Islands. It is also showed that UAV images are not only useful for visual interpretation and object delineation, but also to timely produce useful thematic information for environmental management.
Validation of satellite precipitation (TRMM 3B43) in Ecuadorian coastal plains, Andean highlands and Amazonian rainforest
(INTERNATIONAL SOCIETY FOR PHOTOGRAMMETRY AND REMOTE SENSING, 2016-07-12) Ballari, Daniela Elisabet; Campozano Parra, Lenin Vladimir; Castro, E
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.
Virtual control volume approach to the study of climate causal flows: identification of humidity and wind pathways of influence on rainfall in Ecuador
(2020) Vázquez Patiño, Angel Oswaldo; Campozano Parra, Lenin Vladimir; Ballari, Daniela Elisabet; Córdova Mora, Mario Andrés; Samaniego Alvarado, Esteban Patricio
Unraveling the relationship between humidity, wind, and rainfall is vitally important to understand the dynamics of water vapor transport. In recent years, the use of causal networks to identify causal flows has gained much ground in the field of climatology to provide new insights about physical processes and hypothesize previously unknown ones. In this paper, the concept of a virtual control volume is proposed, which resembles the Eulerian description of a vector field, but is based on causal flows instead. A virtual control surface is used to identify the influence of surrounding climatic processes on the control volume (i.e., the study region). Such an influence is characterized by using a causal inference method that gives information about its direction and strength. The proposed approach was evaluated by inferring and spatially delineating areas of influence of humidity and wind on the rainfall of Ecuador. It was possible to confirm known patterns of influence, such as the influence of the Pacific Ocean on the coast and the influence of the Atlantic Ocean on the Amazon. Moreover, the approach was able to identify plausible new hypotheses, such as the influence of humidity on rainfall in the northern part of the boundary between the Andes and the Amazon, as well as the origin (the Amazon or the tropical Atlantic) and the altitude at which surrounding humidity and wind influence rainfall within the control volume. These hypotheses highlight the ability of the approach to exploit a large amount of scalar data and identify pathways of influence between climatic variables.
Watershed services in the Humid tropics: opportunities from recent advances in ecohydrology
(FAPESP, 2018) Hamel, Perrine; Riveros Iregui, Diego; Ballari, Daniela Elisabet; Browning, Trevor N.; Célleri Alvear, Rolando Enrique; Chandler, David G.; Chun Sun, Kwak Pan; Destouni, Georgia; Jacobs, Suzanne R.; Scott, Jasechko; Johnson, Mark S.; Krishnaswamy, Jagdish; Poca, María; Pompeu, Patricia Vieira; Rocha, Humberto
In response to increasing pressures on water resources, watershed services management programs are implemented throughout the tropics. These programs aim to promote land management activities that enhance the quantity and quality of water available to local communities. The success of these programs hinges on our ability to (a) understand the impacts of watershed interventions on ecohydrology; (b) model these impacts and design efficient management programs; and (c) develop strategies to overcome barriers to practical policy development, including resource limitations or the absence of baseline data. In this paper, we review opportunities in ecohydrological science that will help address these three challenges. The opportunities are grouped into measurement techniques, modelling approaches, and access to resources in our hyperconnected world. We then assess management implications of both the knowledge gaps and the new research developments related to the effect of land management. Overall, we stress the importance of policy-relevant knowledge for implementing efficient and equitable watershed services programs in the tropics.