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In certain countries where fresh water is in short supply, the effluents from wastewater treatment plants are being recycled for other uses. For quality assurance, tertiary disinfection treatments are required. This study aims to evaluate the inactivating efficacy with an ultraviolet (UV) system on fecal bacteria from effluents of urban wastewater treatment facilities and the post-treatment influence of the environmental illumination. The effect from different UV doses was determined for native and standardized lyophilized strains of Escherichia coli right after the irradiation as well as after 24 h of incubation under light or dark conditions. To achieve 3 log-reductions of the initial bacterial concentration, a UV dose of approximately 12 mJ cm−2 is needed for E. coli ATCC 8739 and native E. coli. However, there is a risk of the reactivation of 0.19% and 1.54% of the inactivated organisms, respectively, if the treated organisms are stored in an illuminated environment. This suggests that the post-treatment circumstances affect the treatment success; storing the treated water under an illuminated environment may pose a risk even if an effective inactivation was achieved during the irradiation. [ABSTRACT FROM AUTHOR]

Nonlinear static and dynamic analyses are increasingly being used to evaluate the seismic performance of structures. The level of detail that mathematical models should include depends on the required analysis. In a collapse performance evaluation of special steel moment frames, models must include material and element deterioration due to local geometrical instabilities that will allow triggering structural instability. However, capturing these effects in distributed plasticity models is challenging for two reasons: (1) the selection of a constitutive law that incorporates the geometrical phenomenon, and (2) the localization issues related to the material softening. This paper proposes a simple approach to implement phenomenological calibrated constitutive law that includes compression softening applied on a distributed plasticity model. Additionally, a regularization method is proposed to reduce mesh-sensitivity, ensuring an objective response. The constitutive law and the regularization method are tested under different modeling levels: material, element, and structure. Furthermore, the model is used to perform collapse analyses of 2-, 4-, 8-, 12-, and 20-story special steel moment frame buildings. The proposed model can incorporate critical features, such as flexure-axial force interaction, postpeak deterioration, and an objective global response regardless of the element discretization. [ABSTRACT FROM AUTHOR]

Worldwide and likewise in Ecuador, the 1982–1983 and 1997–1998 El Niño Southern Oscillation (ENSO) events had devastating effects in the economic and human dimension. Thus, scientists and decision markers look for a deeper knowledge about ENSO and its phases El Niño (EN)/La Niña (LN). Recent research highlights the changing nature of ENSO under opposite conditions of the Pacific Decadal Oscillation (PDO), making the assessment of the ENSO–PDO relation in Ecuador urgent. This study explores the time‐frequency characteristics of rainfall in the coast of Ecuador from January to April (PC‐JA) and evaluates the influence of PDO in the relation of ENSO with PC‐JA. For this, wavelet analysis was used to asses this nonstationary problem, Five long‐term (1964–2014) ground stations were used. The main results indicate that during the warm PDO period 1980–2000, the high wavelet coherence (ca. 0.9) implies a strong coupling between ENSO and PC‐JA. For cold PDO periods, prior to 1980 and after 2005, such coupling weakens with coherence about 0.5. This might indicate that PDO influence the relation between ENSO and rainfall in the coast of Ecuador. This coupling, during warm PDO, enhances high rainfall when in phase with EN, and drought conditions in LN events. The weak coupling of ENSO‐PC‐JA during cold PDO produces high rainfall amounts in Niño Neutral conditions and droughts during Neutral and LN. To account for ENSO flavours variability, the wavelet coherence between PC‐JA and the two ENSO uncorrelated indices E and C from Takahashi et al. was studied. Interestingly, we show that PDO warm phase influences the relation of Eastern Pacific related E index with PC‐JA from 2 to 8 years periods, and that the orthogonal Central Pacific related C index is not affected. These results raise questions about the validity of ENSO indices for contrasting PDO phases. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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 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 data set 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. A sampling scheme to monitor precipitation distribution along the year is proposed thought the use of conditioned Latin hypercube sampling and satellite precipitation images. It was applied on monthly TRMM images in the relatively ungauged region of the Ecuadorian Amazon, in which accessibility restrictions were considered. The proposed method captured effectively the spatial and temporal changes of precipitation and supports mapping precipitation in the region. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The identification of area‐wise homogeneous precipitation regions helps to unveil similar precipitation patterns and amounts, where similar atmospheric processes at diverse temporal scales are likely to occur. However, although scientifically and socially relevant, the regionalization of precipitation is challenging, specially in areas of complex orography and with sparse monitoring. This limits our understanding of complex spatio‐temporal dependencies and hinders any information‐based resource management decision‐making. Gridded satellite precipitation products are useful in this context, even though they contain bias errors. Spatial functional data analysis (sFDA) is a novel technique that considers time as well as space dependencies by means of spatial autocorrelation and complete time functions, one for each spatial point. Therefore, the aim of this study is to evaluate sFDA as a tool to regionalize seasonality and intensity precipitation patterns, having Ecuador as a case study. The Tropical Rainfall Measuring Mission (TRMM 3B43) satellite precipitation is used to create an exhaustive spatial delineation. To the best of our knowledge, this is the first time that a sFDA regionalization approach is performed on gridded satellite precipitation. The complex orography and heat‐driven atmospheric processes in Ecuador's latitude make it a highly non‐trivial case to test the aforementioned technique. As a result, five relevant regions of precipitation seasonality were spatially delineated and temporally characterized. Three of them were zonally oriented, and two meridional‐wise in the coast. In addition, 20 relevant intensity regions across Ecuador were identified specially in regions with sparse monitoring. The regions were related to regional climate processes. However, limitations were found in regions with important orographic precipitation and locally variability patterns, probably due to the shortcomings of TRMM precipitation quantification. After the successful application of hierarchical regionalization using sFDA in a tropical region with sparse monitoring, it is reasonable to conclude that sFDA is a robust method to detect compact and meaningful homogeneous areas. [ABSTRACT FROM AUTHOR]

Abstract: 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 convenient functional, formed as the sum of two competing terms, two simultaneous goals are achieved: to improve the description of the spatial structure in maps generated by simple deterministic interpolation methods and to increase the reliability of satellite‐based or model‐based images. Either satellite‐based or model‐based information is included in a regularity term to provide the global spatial structure, while in situ data are included in a fidelity term. The resulting maps can be considered a merging of interpolated in situ data with satellite or model imagery. The method is first evaluated using simulated images generated by geostatistical simulation and then applied to actual temperature and precipitation data in selected regions in the Tropical Andes. The results indicate that the method is capable of generating realistic maps while performing well in terms of validation. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Spatial prediction of precipitation with high resolution is a challenging task in regions with strong climate variability and scarce monitoring. For this purpose, the quasi-continuous supply of information from satellite imagery is commonly used to complement in situ data. However, satellite images of precipitation are available at coarse resolutions, and require adequate methods for spatial downscaling and calibration. The objective of this paper is to introduce and evaluate a 2-step spatial downscaling approach for monthly precipitation applied to TRMM 3B43 (from 0.25° 27 km to 5 km resolution), resulting in 5 downscaled products for the period 01-2001/12-2011. The methodology was evaluated in 3 contrasting climatic regions of Ecuador. In step 1, bilinear resampling was applied over TRMM, and used as a reference product. The second step introduces further variability, and consists of four alternative gauge-satellite merging methods: (1) regression with in situ stations, (2) regression kriging with in situ stations, (3) regression with in situ stations and auxiliary variables, and (4) regression kriging with in situ stations and auxiliary variables. The first 2 methods only use the resampled TRMM data set as an independent variable. The last 2 methods enrich these models with auxiliary environmental factors, incorporating atmospheric and land variables. The results showed that no product outperforms the others in every region. In general, the methods with residual kriging correction outperformed the regression models. Regression kriging with situ data provided the best representation in the Coast, while regression kriging with in situ and auxiliary data generated the best results in the Andes. In the Amazon, no product outperformed the resampled TRMM images, probably due to the low density of in situ stations. These results are relevant to enhance satellite precipitation, depending on the availability of in situ data, auxiliary satellite variables and the particularities of the climatic regions. [ABSTRACT FROM AUTHOR]

Mountain regions worldwide present a pronounced spatiotemporal precipitation variability, which added to scarce monitoring networks limits our understanding of the generation processes involved. To improve our understanding of clouds and precipitation dynamics and cross-scale generation processes in mountain regions, we analyzed spatiotemporal rainfall patterns using satellite cloud products (SCP) in the Paute basin (900–4200 m a.s.l. and 6481 km2) in the Andes of Ecuador. Precipitation models, using SCP and GIS data, reveal the spatial extension of three regimes: a three-modal (TM) regime present across the basin, a bimodal (BM) regime, along sheltered valleys, and a unimodal (UM) regime at windward slopes of the eastern cordillera. Subsequently, the spatiotemporal analysis using synoptic information shows that the dry season of the BM regime during boreal summer is caused by strong subsidence inhibiting convective clouds formation. Meanwhile, in UM regions, low advective shallow cap clouds mainly cause precipitation, influenced by water vapor from the Amazon and enhanced easterlies during boreal summer. TM regions are transition zones from UM to BM and zones on the windward slopes of the western cordillera. These results highlight the suitability of satellite and GIS data-driven statistical models to study spatiotemporal rainfall seasonality and generation processes in complex terrain, as the Andes. [ABSTRACT FROM AUTHOR]

A simple multiscale analysis framework for heterogeneous solids based on a computational homogenization technique is presented. The macroscopic strain is linked kinematically to the boundary displacement of a circular or spherical representative volume which contains the microscopic information of the material. The macroscopic stress is obtained from the energy principle between the macroscopic scale and the microscopic scale. This new method is applied to several standard examples to show its accuracy and consistency of the method proposed. [ABSTRACT FROM AUTHOR]

Due to the presence of boulders and different morphologies, mountain rivers contain various resistance sources. To correctly simulate river flow using 1-D hydrodynamic models, an accurate estimation of the flow resistance is required. In this article, a comparison between the physical roughness parameter (PRP) and effective roughness coefficient (ERC) is presented for three of the most typical morphological configurations in mountain rivers: cascade, step-pool, and plane-bed. The PRP and its variation were obtained through multiple measurements of field variables and an uncertainty analysis, while the ERC range was derived with a GLUE procedure implemented in HEC-RAS, a 1-D hydrodynamic model. In the GLUE experiments, two modes of the Representative Friction Slope Method (RFSM) between two cross-sections were tested, including the variation in the roughness parameter. The results revealed that the RFSM effect was limited to low flows in cascade and step-pool. Moreover, when HEC-RAS selected the RSFM, only acceptable results were presented for plane-bed. The difference between ERC and PRP depended on the flow magnitude and the morphology, and as shown in this study, when the flow increased, the ERC and PRP ranges approached each other and even overlapped in cascade and step-pool. This research aimed to improve the roughness value selection process in a 1-D model given the importance of this parameter in the predictability of the results. In addition, a comparison was presented between the results obtained with the numerical model and the values calculated with the field measurements [ABSTRACT FROM AUTHOR]

River flow velocity is determined by the energy available for flow motion and the energy fraction lost by flow resistance. We compared the performance of different equations for the Darcy-Weisbach resistance coefficient (f) and empirical equations to predict flow velocity. The set of equations was tested using data from the Quinuas headwater mountain river in the Andean region. The data was collected in three Cascades, two Step-pools, and one Plane-bed covering a wide range of velocity magnitudes. The results reveal that nondimensional hydraulic geometry equations (NDHG) with a Nash-Sutcliffe efficiency index (EF) varying from 0.6–0.85 provide the most accurate velocity prediction. Furthermore, the study proposes a methodology applicable to all morphologies for defining the NDHG parameters using easily measured field data. The results show an improvement in predictability with EF values in the range of 0.81–0.86. Moreover, the methodology was tested against data from the literature, which was not divided into morphologies providing EF values of around 0.9. The authors encourage the application of the presented methodology to other reaches to obtain additional data about the NDHG parameters. Our findings suggest that those parameters could be related to reach characteristics (e.g., certain characteristic grain size), and in that case, the methodology could be useful in ungauged streams. [ABSTRACT FROM AUTHOR]

Modelling climate is complex due to multi-scale interactions and strong nonlinearities. However, climate signals are typically quasi-periodical and are likely to depend on exogenous - variables. Motivated by this insight, we propose a strategy to circumvent modelling complexity based on the following ideas. 1) The observed signals can be decomposed into non-stationary trends and quasi-periodicities through Dynamic-Harmonic-Regressions (DHR). 2) The main-frequencies and decomposed signals can be used for constructing a harmonic model with varying parameters depending on exogenous-variables. 3) The State-Dependent-Parameter (SDP) technique allows for the dynamical estimation of these parameters. The resulting DHR-SDP combined approach is applied to rainfall-monthly modelling, using global-climate signals as exogenous-variables. As a result, 1) the model yields better predictions than standard alternative techniques; 2) the model is robust regarding data limitations and useful for several-steps-ahead forecasting; 3) interesting relations between global-climate states and the local rainfall's seasonality are obtained from the SDP estimated functions. • Global climate information drives a non-linear local climate model based on DHR and SDP techniques. • The model is useful for predictive and gap-filling purposes, outperforming other related stochastic alternatives. • The model has the potential to establish climate indicators and can be a starting point for the identification of non-linear climate mechanisms through different scales. • The model could be a complementary tool when combined with more complex deterministic options. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]