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Natural and man-made lakes store the largest amount of available freshwater on Earth’s surface. Human activities and a changing climate increasingly threaten lakes, as evidenced by record-low water levels in some of the world’s largest lakes. Yet, studies that document lake volume changes are limited to a small subset of lakes or neglect to attribute the drivers of changes due to a lack of consistent long-term observations. Here, we merge three decades of high-resolution satellite observations, climate, and hydrologic models, to provide an unprecedented assessment of long-term global lake volume trends (1992-2020). Storage trends and their attributions are quantified for 1,978 large lakes that represent 95% of the total global lake volume. In support of reproducing our findings, the attached codes include constructing water body areas from Landsat satellites and deriving water body levels from the CryoSat-2 altimeter.&nbsp

Timely projections of seasonal streamflow extremes can be useful for the early implementation of annual flood risk adaptation strategies. However, predicting seasonal extremes is challenging particularly under non-stationary conditions and if extremes are connected in space. The goal of this study is to implement a space-time model for projection of seasonal streamflow extremes that considers the nonstationarity and spatio-temporal dependence of high flows. We develop a space-time model to project seasonal streamflow extremes for several lead times up to 2 months using a Bayesian Hierarchical Modelling (BHM) framework. This model is based on the assumption that streamflow extremes (3-day maxima) at a set of gauge locations are realizations of a Gaussian elliptical copula and generalized extreme value (GEV) margins with nonstationary parameters. These parameters are modeled as a linear function of suitable covariates from the previous season selected using the deviance information criterion (DIC). Finally, the copula is used to generate streamflow ensembles, which capture spatio-temporal variability and uncertainty. We apply this modelling framework to predict 3-day maximum flow in spring (May-June) at seven gauges in the Upper Colorado River Basin (UCRB) with 0 to 2 months lead time. In this basin, almost all extremes that cause severe flooding occur in spring as a result of snowmelt and precipitation. Therefore, we use regional mean snow water equivalent and temperature from the preceding winter season as well as indices of large-scale climate teleconnections – ENSO, AMO, and PDO – as potential covariates for 3-day maximum flow. Our model evaluation, which is based on the comparison of different model versions and the energy skill score, indicates that the model can capture the space-time variability of extreme flow well and that model skill increases with decreasing lead time. We also find that the use of climate variables slightly enhances skill relative to using only snow information. Median projections and their uncertainties are consistent with observations thanks to the representation of spatial dependencies through covariates in the margins and a Gaussian copula. This spatio-temporal modeling framework helps to plan seasonal adaptation and preparedness measures as predictions of extreme spring flows become available 2 months before actual flood occurrence.

In flat environments, groundwater is relatively shallow, tightly associated with surface water and climate, and can have either positive and negative impacts on natural and human systems depending on its depth. A linked modelling and analysis framework that seeks to capture linkages across multiple scales at the climate/water/ crop nexus in the Argentine Pampas is presented. This region shows a strong coupling between climate, soil water, and land use due to its extremely flat topography and poorly developed drainage networks. The work describes the components of the framework and, subsequently, presents results from simulations performed with the twin goals of (i) validating the framework as a whole and (ii) demonstrating its usefulness to explore interesting contexts such as unexperienced climate scenarios (wet/dry periods), hypothetical policies (e.g., differential grains export taxes), and adoption of non-structural technologies (e.g., cover crops) to manage wáter table depth. Fil: García, Guillermo A.. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Luis. Instituto de Matemática Aplicada de San Luis "Prof. Ezio Marchi". Universidad Nacional de San Luis. Facultad de Ciencias Físico, Matemáticas y Naturales. Instituto de Matemática Aplicada de San Luis "Prof. Ezio Marchi"; Argentina Fil: García, Pablo Enrique. Ministerio del Interior, Obras Públicas y Vivienda. Secretaría de Obras Públicas. Subsecretaría de Recursos Hídricos. Instituto Nacional del Agua y del Ambiente (Ezeiza); Argentina Fil: Rovere, Santiago Luis. No especifíca; Fil: Bert, Federico Esteban. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de Buenos Aires. Facultad de Agronomía. Departamento de Producción Vegetal. Cátedra de Cerealicultura; Argentina Fil: Schmidt, Federico Martín. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina Fil: Menendez, Angel Nicolas. Ministerio del Interior, Obras Públicas y Vivienda. Secretaría de Obras Públicas. Subsecretaría de Recursos Hídricos. Instituto Nacional del Agua y del Ambiente (Ezeiza); Argentina Fil: Nosetto, Marcelo Daniel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Luis. Instituto de Matemática Aplicada de San Luis "Prof. Ezio Marchi". Universidad Nacional de San Luis. Facultad de Ciencias Físico, Matemáticas y Naturales. Instituto de Matemática Aplicada de San Luis "Prof. Ezio Marchi"; Argentina Fil: Verdin, Andrew. State University of Colorado at Boulder; Estados Unidos Fil: Rajagopalan, Balaji. State University of Colorado at Boulder; Estados Unidos Fil: Arora, Poonam. Manhattan College; Estados Unidos Fil: Podestá, Guillermo P.. University Of Miami. Rosenstiel School Of Marine Atmospheric Science; Estados Unidos

A recent period of increased precipitation over the Argentinian Pampas expanded the boundary of rain-fed agriculture. However, such changes may not be sustainable if they arose from transient climate regime shifts. Considerable research exists on trends and cycles in sub-daily to annual precipitation metrics including the frequency and intensity of extreme precipitation. However, efforts to identify wetter and drier phases (or regimes) in this region are scant. This article aims to bridge that gap and advance our understanding of the multi-annual behavior of regional precipitation extremes, which can have the greatest impacts. It is unlikely that all extreme events are drawn from a single probability distribution or generated by the same physical processes. Hence, hidden mixtures of Poisson distributions are fitted to several precipitation frequency metrics to explore whether the annual to decadal variations in extreme precipitation frequency are greater than anticipated from a single system, and representative of regime shifts. Statistically significant improvements in the fit over single distributions were found for statistical mixture models of the frequency of very wet days, and the frequency of wet spells. This supports the hypothesis that multiple weather regimes exist giving rise to wetter or drier epochs. Posterior probabilities of hidden states from the fitted mixture distributions were used to identify wetter and drier years for comparison with sea surface temperature anomalies. This confirmed the presence of two distinct regimes, supporting other research, into the dynamical influences of precipitation behavior in the Argentine Pampas.

Purpose The purpose of this paper is to investigate the value of information in consumer safety complaints for organizational learning. Design/methodology/approach Empirical analysis of this study uses a novel secondary data set, which is formed by combining complaints data filed with the National Highway Traffic Safety Administration (NHTSA) for potential safety defects, and design change information from 2003 to 2011 model-year vehicles in the USA. Findings First, the paper demonstrates the value of information embedded in complaints. Second, in the case of radical product redesigns, owing to the lack of direct applicability of consumer feedback based learning, the impact of learning on product safety is found to be muted, third, the results suggest that the safety complaint rates vary by vehicle classes/categories and, fourth, the findings differ from prior research conclusions on vehicle quality. Prior research finds the debuting car models have the lowest repair rates among all car models produced in a given year, but the current study finds the debuting models to have the highest rates of safety complaints. Originality/value Quality management literature rarely examines the safety complaints data (which, unlike other consumer feedbacks, focuses exclusively on the safety hazards due to flaws that result in accidents). This paper fills the gap in literature by linking safety complaints with future product quality and organizational learning.

We developed a novel Bayesian Hierarchical Network Model (BHNM) for daily streamflow, which uses the spatial dependence induced by the river network topology, and average daily precipitation from t...

This study introduces a Bayesian logistic regression framework that is capable of providing skillful probabilistic forecasts of Arctic sea ice cover, along with quantifying the attendant uncertainties. The presence or absence of ice (absence defined as ice concentration below 15%) is modeled using a categorical regression model, with atmospheric, oceanic, and sea ice covariates at 1‐ to 7‐month lead times. The model parameters are estimated in a Bayesian framework, thus enabling the posterior predictive probabilities of the minimum sea ice cover and parametric uncertainty quantification. The model is fitted and validated to September minimum sea ice cover data from 1980 through 2018. Results show overall skillful forecasts of the minimum sea ice cover at all lead times, with higher skills at shorter lead times, along with a direct measure of forecast uncertainty to aide in assessing the reliability.

In September 2013 an extreme (1000-year) storm event in north central Colorado produced over 40 cm rainfall in four days, resulting in widespread flooding. Along with damage to roads, bridg...

Streamflow extremes, especially, summer seasonal streamflow in monsoon climate makes a significant contribution to the reliability of water resources and the health of ecology. The summer extreme precipitation and streamflow also cause severe floods resulting in loss of life and property. Large scale climate drivers impart strong spatial and temporal variability in the flow extremes, which needs to be modeled for use in efficient management of resources. To this end, we developed a space-time model to capture the variability of –summer season 3-day maximum streamflow. In this, the extremes at each station are assumed to be distributed as Generalized Extreme Value (GEV) distribution with non-stationary parameters. Thus, the parameters are modeled as a linear function of suitable covariates – typically, large scale climate variables and regional mean precipitation. In addition, the spatial dependence of the extremes is modeled via a Gaussian copula. The parameters of the nonstationary GEV at each location are estimated via maximum likelihood, whereas those of the Copula are estimated via the Inversion of Kendall’s tau estimator method. Ensembles of streamflow in time are based on the temporal varying covariates and from the Copula are generated, consequently, capturing the spatial and temporal variability and the attendant uncertainty. Furthermore, various return level can also be obtained from these simulations. The model is demonstrated by application to 3-day maximum summer streamflow in a representative basin from two different monsoonal climate – India and Southwest U.S. In addition to comparing the performance of the median of the simulations with the historic observations, we also compare the number of stations that exceed a specific level- say, 75th percentile which indicates the spatial performance. The model validation indicates that the model is able to capture the space-time variability, furthermore, it captures the variability in wet and dry years, consistent with observations. This framework can be applied to generate ensembles of at several lead times – week to seasonal, to provide risks of various levels of streamflow. This will be of immense use in water resources, agriculture and flood management and planning.

La Niña years tend to provide increased Indian summer monsoon (ISM) rainfall. However, observations show 6–8% reduction in ISM rainfall during post-1980 La Niñas relative to pre-1980. Using a suite of atmospheric general circulation model experiments, we replicate this observed phenomenon and attribute it to a combination of weakening La Niña events themselves plus strongly warming tropical Indian Ocean. We demonstrate that half of the ISM rainfall reduction during post-1980 La Niñas can be attributed to changes in the spatial pattern and intensity of La Niña within the tropical Pacific Ocean. Warmer eastern-equatorial Pacific Ocean temperatures during post-1980 La Niñas weaken the Walker circulation, resulting in large-scale anomalous subsidence over the Indian subcontinent, thereby inhibiting the deep convection that drives ISM rainfall. Further, we demonstrate the declining central ISM rainfall during La Niña years with increasing tropical Indian Ocean warming, which has several serious concerns for regional water resources and stability. MOE (Min. of Education, S’pore) Published version

The seismic response of gravity dams is typically derived under a deterministic finite element model for the dam-reservoir-foundation system. In the case where uncertainty in material properties should be incorporated into overall dam performance, the sensitive parameters can be treated as random variables. This paper presents the results of a study that considers the spatial distribution of random variables in the context of random field theory. Koyna Gravity Dam is used as a setting for numerical simulations. The concrete modulus of elasticity, mass density and tensile strength are all assumed to be random fields and generated based on the covariance matrix decomposition and midpoint discretization techniques. The anatomy of the random field seismic responses are presented first, followed by a set of parametric analyses. The impact of correlation length, a single- vs. double-random field, one- or two-dimensional material distributions, ground motion intensity and record-to-record variability and, lastly, dam class are all investigated herein. The uncertainty and dispersion of the seismic responses are quantified in each model; it is found that concrete heterogeneity affects the seismic performance evaluation and should be considered in a structural assessment and risk analysis.

La región pampeana de la República Argentina, una de las mayores llanuras del mundo, ha registrado en los últimos 50 años un fuerte ascenso en los niveles freáticos, con el consecuente aumento en la frecuencia de inundaciones. Esta dinámica tiene origen en dos procesos que se desarrollaron en ese período. En primer lugar, la zona presentó una tendencia hacia del aumento en las precipitaciones anuales. En segundo lugar se produjo un fuerte aumento del área dedicada a la agricultura, desplazando zonas con pasturas y pastizales, es decir, hubo un cambio en el uso del suelo. A través de ensayos numéricos con un modelo hidrológico (distribuido en el espacio y continuo en el tiempo, debidamente calibrado y verificado), se muestra en este trabajo que el aumento de las precipitaciones es el fenómeno que explica en mayor medida el incremento observado en los niveles freáticos, pero que la vegetación también juega un rol altamente significativo. Más aún, se pone de manifiesto la no linealidad de la respuesta del sistema hidrológico a los cambios en la precipitación y el uso del suelo, ya que la combinación de ambos efectos produce un resultado bastante inferior a la suma de cada uno de los efectos por separado. Adicionalmente, el modelo indica que existe una relación exponencial entre la profundidad de la napa y las áreas inundadas, estableciéndose una profundidad freática de 2 metros como el valor umbral a partir de la cual las áreas inundadas crecen significativamente. The Argentine Pampas, one of the largest plains in the world, has experienced during the last 50 years a strong rise in its water table level, with a consequent increase in the frequency of floods. This dynamics is associated with two processes that took place in this zone during this period. First, the annual rainfall has shown a positive trend; and secondly, change over to field crops has expanded throughout the Pampas, displacing grasslands and pastures, so there has been a land use change. Based on numerical simulations with a properly calibrated and verified hydrological model, distributed in space and continuous in time, this paper shows that the increase in rainfall is the prime phenomenon explaining the increase in groundwater levels, but that vegetation has also played a very significant role. Moreover, the non-linear response of this hydrological system to changes in precipitation and land use was put into evidence, as the combination of both effects produces a result that is much less intense than the sum of each of the individual effects. In addition, the model indicates that there is an exponential relationship between water table depth and the flooded areas, identifying a value of 2 meters for the water table depth as a threshold below which the flooded area grows significantly. Fil: Garcia, Pablo Ezequiel. Instituto Nacional del Agua. Laboratorio de Hidráulica; Argentina. Universidad de Buenos Aires. Facultad de Ingeniería; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina Fil: Badano, Nicolás Diego. Instituto Nacional del Agua. Laboratorio de Hidráulica; Argentina. Universidad de Buenos Aires. Facultad de Ingeniería; Argentina Fil: Menendez, Angel Nicolas. Universidad de Buenos Aires. Facultad de Ingeniería; Argentina. Instituto Nacional del Agua. Laboratorio de Hidráulica; Argentina Fil: Bert, Federico. Universidad de Buenos Aires. Facultad de Agronomía; Argentina. Asociación Argentina de Consorcios Regionales de Experimentación Agrícola; Argentina Fil: García, Guillermo Ariel. Asociación Argentina de Consorcios Regionales de Experimentación Agrícola; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Luis. Instituto de Matemática Aplicada de San Luis "Prof. Ezio Marchi". Universidad Nacional de San Luis. Facultad de Ciencias Físico, Matemáticas y Naturales. Instituto de Matemática Aplicada de San Luis "Prof. Ezio Marchi"; Argentina Fil: Podestá, Guillermo. University of Miami; Estados Unidos Fil: Rovere, Santiago. Universidad de Buenos Aires. Facultad de Ingeniería; Argentina Fil: Verdin, Andrew. State University of Colorado at Boulder; Estados Unidos Fil: Rajagopalan, Balaji. State University of Colorado at Boulder; Estados Unidos Fil: Arora, Poonam. Manhattan College; Estados Unidos

Recent studies have linked interannual sea level variability and extreme events along the U.S. northeast coast (NEC) to the North Atlantic Oscillation (NAO), a natural internal climate mode that prevails in the North Atlantic Ocean. The correlation between the NAO index and coastal sea level north of Cape Hatteras was weak from the 1960s to the mid-1980s, but it has markedly increased since around 1987. The causes for the decadal shift remain unknown. Yet understanding the abrupt change is vital for decadal sea level prediction and is essential for risk management. Here we use a robust method, the Bayesian dynamic linear model (DLM), to explore the nonstationary NAO impact on NEC sea level. The results show that a spatial pattern change of NAO-related winds near the NEC is a major cause of the NAO–sea level relationship shift. A new index using regional sea level pressure is developed that is a significantly better predictor of NEC sea level than is the NAO and is strongly linked to the intensity of westerly winds near the NEC. These results point to the vital importance of monitoring regional changes of wind and sea level pressure patterns, rather than the NAO index alone, to achieve more accurate predictions of sea level change along the NEC.

Online reviews provide information about products and services valuable for consumers in the context of purchase decision making. Online reviews also provide additional value to online retailers, as they attract consumers. Therefore, identifying the most-helpful reviews is an important task for online retailers. This research addresses the problem of predicting the helpfulness of online product reviews by developing a comprehensive research model guided by the theoretical foundations of signaling theory. Thereby, our research model posits that the reviewer of a product sends signals to potential buyers. Using a sample of Amazon.com product reviews, we test our model and observe that review content-related signals (i.e., specific review content and writing styles) and reviewer-related signals (i.e., reviewer expertise and non-anonymity) both influence review helpfulness. Furthermore, we find that the signaling environment affects the signal impact and that incentives provided to reviewers influence the signals sent. To demonstrate the practical relevance of our results, we illustrate by means of a problem-specific evaluation scenario that our model provides superior predictions of review helpfulness compared to earlier approaches. Furthermore, we provide evidence that the proposed evaluation scenario provides deeper insights than classical performance metrics. Our findings are highly relevant for online retailers seeking to reduce information overload and consumers' search costs as well as for reviewers contributing online product reviews.

Summary We present the application of a parametric stochastic weather generator within a nonstationary context, enabling simulations of weather sequences conditioned on interannual and multi-decadal trends. The generalized linear model framework of the weather generator allows any number of covariates to be included, such as large-scale climate indices, local climate information, seasonal precipitation and temperature, among others. Here we focus on the Salado A basin of the Argentine Pampas as a case study, but the methodology is portable to any region. We include domain-averaged (e.g., areal) seasonal total precipitation and mean maximum and minimum temperatures as covariates for conditional simulation. Areal covariates are motivated by a principal component analysis that indicates the seasonal spatial average is the dominant mode of variability across the domain. We find this modification to be effective in capturing the nonstationarity prevalent in interseasonal precipitation and temperature data. We further illustrate the ability of this weather generator to act as a spatiotemporal downscaler of seasonal forecasts and multidecadal projections, both of which are generally of coarse resolution.

A new paradigm of simulating suspended sediment load (SSL) with a Land Surface Model (LSM) is presented here. Five erosion and SSL algorithms were applied within a common LSM framework to quantify uncertainties and evaluate predictability in two steep, forested catchments (>1,000 km2). The algorithms were chosen from among widely used sediment models, including empirically based: monovariate rating curve (MRC) and the Modified Universal Soil Loss Equation (MUSLE); stochastically based: the Load Estimator (LOADEST); conceptually based: the Hydrologic Simulation Program—Fortran (HSPF); and physically based: the Distributed Hydrology Soil Vegetation Model (DHSVM). The algorithms were driven by the hydrologic fluxes and meteorological inputs generated from the Variable Infiltration Capacity (VIC) LSM. A multiobjective calibration was applied to each algorithm and optimized parameter sets were validated over an excluded period, as well as in a transfer experiment to a nearby catchment to explore parameter robustness. Algorithm performance showed consistent decreases when parameter sets were applied to periods with greatly differing SSL variability relative to the calibration period. Of interest was a joint calibration of all sediment algorithm and streamflow parameters simultaneously, from which trade‐offs between streamflow performance and partitioning of runoff and base flow to optimize SSL timing were noted, decreasing the flexibility and robustness of the streamflow to adapt to different time periods. Parameter transferability to another catchment was most successful in more process‐oriented algorithms, the HSPF and the DHSVM. This first‐of‐its‐kind multialgorithm sediment scheme offers a unique capability to portray acute episodic loading while quantifying trade‐offs and uncertainties across a range of algorithm structures., Key Points Multialgorithm sediment modeling is a novel way to incorporate uncertainty into land surface model estimates of sedimentThere are trade‐offs when finding an equilibrium between suspended sediment and the partitioning of streamflow into runoff and base flowTransferability of calibrated suspended sediment parameters is sensitive to the variability of the observed data

A regression tree-based diagnostic approach is developed to evaluate factors affecting US wastewater treatment plant compliance with ammonia discharge permit limits using Discharge Monthly Report (DMR) data from a sample of 106 municipal treatment plants for the period of 2004-2008. Predictor variables used to fit the regression tree are selected using random forests, and consist of the previous month's effluent ammonia, influent flow rates and plant capacity utilization. The tree models are first used to evaluate compliance with existing ammonia discharge standards at each facility and then applied assuming more stringent discharge limits, under consideration in many states. The model predicts that the ability to meet both current and future limits depends primarily on the previous month's treatment performance. With more stringent discharge limits predicted ammonia concentration relative to the discharge limit, increases. In-sample validation shows that the regression trees can provide a median classification accuracy of >70%. The regression tree model is validated using ammonia discharge data from an operating wastewater treatment plant and is able to accurately predict the observed ammonia discharge category approximately 80% of the time, indicating that the regression tree model can be applied to predict compliance for individual treatment plants providing practical guidance for utilities and regulators with an interest in controlling ammonia discharges. The proposed methodology is also used to demonstrate how to delineate reliable sources of demand and supply in a point source-to-point source nutrient credit trading scheme, as well as how planners and decision makers can set reasonable discharge limits in future.

In addition to providing water for nearly 2 billion people, snow drives resource selection by wildlife and influences the behavior and demography of many species. Because snow cover is highly spatially and temporally variable, mapping its extent using currently available satellite data remains a challenge. At present, there are no sensors acquiring daily data of Earth's entire surface at fine spatial resolutions (

We present a semi-Bayesian hierarchical modeling framework for conducting space–time frequency analysis of precipitation extremes over a large domain. In this framework, the data layer, the precipitation extreme – i.e., seasonal maximum precipitation, at each station in each year is modeled using a generalized extreme value (GEV) distribution with temporally varying parameters, which are decomposed as linear functions of covariates. The coefficients of the covariates are estimated via maximum likelihood (ML). In the process layer, the estimated ML coefficients of each of the covariates across the stations are spatially modeled with a Gaussian multivariate process which enables capturing the spatial structure and correlation between the spatial model parameters. Suitable priors are used for the spatial model hyperparameters to complete the Bayesian formulation. Since the Bayesian formulation is only at the second level, our model is semi-Bayesian and thus, the posteriors are conditional posterior distributions. With the conditional posterior distribution of spatial fields of the GEV parameters for each time, conditional posterior distribution of the nonstationary space–time return levels of the precipitation extremes are obtained. We demonstrate this framework by application to summer precipitation extreme at 73 stations covering a large domain of Southwest US consisting of Arizona, New Mexico, Colorado, and Utah. The results from fitting and cross-validation indicate that our model captures the historical variability at the stations very well. Conditional posterior distributions of return levels are simulated on a grid over the domain, which will be of immense utility in management of natural resources and infrastructure.