Experimental daily ensemble streamflow forecasting system using physical model output in a Bayesian hierarchical framework

Cardiovascular diseases (CVDs) are the leading cause of death globally and the number one cause of death globally. Over 75% of CVD deaths take place in low- and middle-income countries. Hence, comprehensive information about the spatio-temporal distribution of mortality due to cardio vascular disease is of interest. We fitted different spatio-temporal models within Bayesian hierarchical framework allowing different space-time interaction for mortality mapping with integrated nested Laplace approximations to analyze mortality data extracted from the health and demographic surveillance system in Kersa District in Hararege, Oromia Region, Ethiopia. The result indicates that non-parametric time trends models perform better than linear models. Among proposed models, one with non-parametric trend, type II interaction and second order random walk but without unstructured time effect was found to perform best according to our experience and. simulation study. An application based on real data revealed that, mortality due to CVD increased during the study period, while administrative regions in northern and south-eastern part of the study area showed a significantly elevated risk. The study highlighted distinct spatiotemporal clusters of mortality due to CVD within the study area. The study is a preliminary assessment step in prioritizing areas for further and more comprehensive research raising questions to be addressed by detailed investigation. Underlying contributing factors need to be identified and accurately quantified.

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Improving statistical forecasts of seasonal streamflows using hydrological model output

Hydrology and Earth System Sciences Discussions ◽

10.5194/hessd-9-8701-2012 ◽

2012 ◽

Vol 9 (7) ◽

pp. 8701-8736 ◽

Cited By ~ 3

Author(s):

D. E. Robertson ◽

P. Pokhrel ◽

Q. J. Wang

Keyword(s):

Hydrological Model ◽

Historical Record ◽

Base Flow ◽

Seasonal Forecasting ◽

Streamflow Forecasting ◽

Eastern Australia ◽

True Source ◽

Hybrid Forecasting ◽

Forecasting System ◽

Seasonal Streamflow

Abstract. Statistical methods traditionally applied for seasonal streamflow forecasting use predictors that represent the initial catchment condition and future climate influences on future streamflows. Observations of antecedent streamflows or rainfall commonly used to represent the initial catchment conditions are surrogates for the true source of predictability and can potentially have limitations. This study investigates a hybrid seasonal forecasting system that uses the simulations from a dynamic hydrological model as a predictor to represent the initial catchment condition in a statistical seasonal forecasting method. We compare the skill and reliability of forecasts made using the hybrid forecasting approach to those made using the existing operational practice of the Australian Bureau of Meteorology for 21 catchments in eastern Australia. We investigate the reasons for differences. In general, the hybrid forecasting system produces forecasts that are more skilful than the existing operational practice and as reliable. The greatest increases in forecast skill tend to be (1) when the catchment is wetting up but antecedent streamflows have not responded to antecedent rainfall, (2) when the catchment is drying and the dominant source of antecedent streamflow is in transition between surface runoff and base flow, and (3) when the initial catchment condition is near saturation intermittently throughout the historical record.

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A Bayesian hierarchical approach to improve model parameter estimates and predictions of silage maize phenology in Germany

10.5194/egusphere-egu21-7962 ◽

2021 ◽

Author(s):

Michelle Viswanathan ◽

Tobias KD Weber ◽

Andreas Scheidegger ◽

Thilo Streck

Keyword(s):

Environmental Conditions ◽

Plant Phenology ◽

Parameter Estimates ◽

Crop Species ◽

Bayesian Hierarchical ◽

Crop Models ◽

Hierarchical Framework ◽

Silage Maize ◽

Parameterized Model ◽

Species Specific

Crop models are used to evaluate the impact of climate change on food security by simulating plant phenology, yield, biomass and leaf area index. Plant phenology defines the timing of crucial growth stages and physiological processes that influence organ appearance and assimilate partitioning. It is governed by environmental factors such as solar radiation, temperature and water availability. Plant phenology is not only specific for the crop species, but also depends on the cultivar. Additionally, growth of a cultivar could vary depending on the environment. Common crop models cannot fully capture the influence of the environment on phenology, resulting in cultivar-specific parameters that are environment-dependent. These parameter estimates may be unreliable in case of limited data. Moreover, crucial species-specific information is ignored. On the other hand, in large regional-scale models covering multiple cultivars and environments, information about the cultivars grown is generally not available. In this case, a shared set of parameters for the crop species would suppress within-species differences leading to unreliable predictions.A Bayesian hierarchical framework enables us to alleviate these problems by honouring the multi-level data structure. Additionally, we can reflect the uncertainty from different sources, for example, model inputs and measurements. In this study we implement a Bayesian hierarchical framework to estimate parameters of the Soil-Plant-Atmosphere System Simulation (SPASS) model for simulating phenological development of different cultivars of silage maize grown over all the contrasting climatological regions of Germany.We used data from the German weather service on the phenological development stages of silage maize grown across Germany between 2009 and 2019. During this period, silage maize was grown in over 3000 unique location-year combinations. Maize crops were differentiated into early, mid-early, mid-late and late ripening groups and were further classified into cultivars within each ripening group. Within the hierarchical framework, we estimate maize species-specific parameters as well as parameters per ripening group and cultivar, through Bayesian model calibration. We analyse the influence of environmental conditions on parameter estimates, to further develop the hierarchical structure. We perform cross-validation to assess the prediction quality of the parameterized model.With this approach, we show that robust parameter estimates account for differences between cultivars, ripening groups as well as different environmental conditions. The parameterized model can be used for large-scale phenology predictions of silage maize grown across Germany. These parameter estimates may perform better than independent species- or cultivar-specific estimates, in predicting phenology of future cultivars where specific cultivar characteristics are not known.

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An Evaluation of Model Output Statistics for Subseasonal Streamflow Forecasting in European Catchments

Journal of Hydrometeorology ◽

10.1175/jhm-d-18-0195.1 ◽

2019 ◽

Vol 20 (7) ◽

pp. 1399-1416

Author(s):

Simon Schick ◽

Ole Rössler ◽

Rolf Weingartner

Keyword(s):

Air Temperature ◽

Land Surface ◽

Linear Models ◽

Surface Air Temperature ◽

Grid Cell ◽

Model Output ◽

Streamflow Forecasting ◽

Seasonal Forecasts ◽

Total Runoff ◽

Model Output Statistics

AbstractSubseasonal and seasonal forecasts of the atmosphere, oceans, sea ice, or land surfaces often rely on Earth system model (ESM) simulations. While the most recent generation of ESMs simulates runoff per land surface grid cell operationally, it does not typically simulate river streamflow directly. Here, we apply the model output statistics (MOS) method to the hindcast archive of the European Centre for Medium-Range Weather Forecasts (ECMWF). Linear models are tested that regress observed river streamflow on surface runoff, subsurface runoff, total runoff, precipitation, and surface air temperature simulated by ECMWF’s forecast systems S4 and SEAS5. In addition, the pool of candidate predictors contains observed precipitation and surface air temperature preceding the date of prediction. The experiment is conducted for 16 European catchments in the period 1981–2006 and focuses on monthly average streamflow at lead times of 0 and 20 days. The results show that skill against the streamflow climatology is frequently absent and varies considerably between predictor combinations, catchments, and seasons. Using streamflow persistence as a benchmark model further deteriorates skill. This is most pronounced for a catchment that features lakes, which extend to about 14% of the catchment area. On average, however, the predictor combinations using the ESM runoff simulations tend to perform best.

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Exploring the relationship between the skill of hydrological ensemble predictions and catchment descriptors

10.5194/egusphere-egu2020-22659 ◽

2020 ◽

Author(s):

Emixi Valdez ◽

Francois Anctil ◽

Maria-Helena Ramos

Keyword(s):

Ensemble Forecasting ◽

Ensemble Prediction ◽

Hydrological Models ◽

Streamflow Forecasting ◽

Forecast Performance ◽

Ensemble Prediction System ◽

Sources Of Uncertainty ◽

Skill Scores ◽

Forecasting System ◽

The Relationship

Skillful hydrological forecasts are essential for decision-making in many areas such as preparedness against natural disasters, water resources management, and hydropower operations. Despite the great technological advances, obtaining skillful predictions from a forecasting system, under a range of conditions and geographic locations, remain a difficult task. It is still unclear why some systems perform better than others at different temporal and spatial scales. Much work has been devoted to investigate the quality of forecasts and the relative contributions of meteorological forcing, catchment&#8217;s initial conditions, and hydrological model structure in a streamflow forecasting system. These sources of uncertainty are rarely considered fully and simultaneously in operational systems, and there are still gaps in understanding their relationship with the dominant processes and mechanisms that operate in a given river basin. In this study, we use a multi-model hydrological ensemble prediction system (H-EPS) named HOOPLA (HydrOlOgical Prediction Laboratory), which allows to account separately for these three main sources of uncertainty in hydrological ensemble forecasting. Through the use of EnKF data assimilation, of 20 lumped hydrological models, and of the 50-member ECMWF medium-range weather forecasts, we explore the relationship between the skill of ensemble predictions and the many descriptors (e.g. catchment surface, climatology, morphology, flow threshold and hydrological regime) that influence hydrological predictability.&#160;We analyze streamflow forecasts at 50 stations spread across Quebec, France and Colombia, over the period from 2011 to 2015 and for lead times up to 9 days. The forecast performance is assessed using common metrics for forecast quality verification, such as CRPS, Brier skill score, and reliability diagrams.&#160;Skill scores are computed using a probabilistic climatology benchmark, which was generated with the hydrological models forced by resampled historical meteorological data. Our results contribute to relevant literature on the topic and bring additional insight into the role of each descriptor in the skill of a hydrometeorological ensemble forecasting chain, serving as a possible guide for potential users to identify the circumstances or conditions in which it is more efficient to implement a given system.&#160;

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Severe Weather Prediction Using Storm Surrogates from an Ensemble Forecasting System

Weather and Forecasting ◽

10.1175/waf-d-15-0138.1 ◽

2016 ◽

Vol 31 (1) ◽

pp. 255-271 ◽

Cited By ~ 41

Author(s):

Ryan A. Sobash ◽

Craig S. Schwartz ◽

Glen S. Romine ◽

Kathryn R. Fossell ◽

Morris L. Weisman

Keyword(s):

Extreme Values ◽

Weather Prediction ◽

Severe Weather ◽

Model Output ◽

Ensemble Forecasts ◽

Weather Forecasts ◽

Smoothing Kernel ◽

Risk Areas ◽

Forecasting System ◽

Computational Resources

Abstract Probabilistic severe weather forecasts for days 1 and 2 were produced using 30-member convection-allowing ensemble forecasts initialized by an ensemble Kalman filter data assimilation system during a 32-day period coinciding with the Mesoscale Predictability Experiment. The forecasts were generated by smoothing the locations where model output indicated extreme values of updraft helicity, a surrogate for rotating thunderstorms in model output. The day 1 surrogate severe probability forecasts (SSPFs) produced skillful and reliable predictions of severe weather during this period, after an appropriate calibration of the smoothing kernel. The ensemble SSPFs exceeded the skill of SSPFs derived from two benchmark deterministic forecasts, with the largest differences occurring on the mesoscale, while all SSPFs produced similar forecasts on synoptic scales. While the deterministic SSPFs often overforecasted high probabilities, the ensemble improved the reliability of these probabilities, at the expense of producing fewer high-probability values. For the day 2 period, the SSPFs provided competitive guidance compared to the day 1 forecasts, although additional smoothing was needed to produce the same level of skill, reducing the forecast sharpness. Results were similar using 10 ensemble members, suggesting value exists when running a smaller ensemble if computational resources are limited. Finally, the SSPFs were compared to severe weather risk areas identified in Storm Prediction Center (SPC) convective outlooks. The SSPF skill was comparable to the SPC outlook skill in identifying regions where severe weather would occur, although performance varied on a day-to-day basis.

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Bayesian Hierarchical Framework for Occupational Hygiene Decision Making

The Annals of Occupational Hygiene ◽

10.1093/annhyg/meu060 ◽

2014 ◽

Cited By ~ 4

Keyword(s):

Decision Making ◽

Occupational Hygiene ◽

Bayesian Hierarchical ◽

Hierarchical Framework

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The benefit of using an ensemble of seasonal streamflow forecasts in water allocation decisions

10.5194/hess-2020-60 ◽

2020 ◽

Author(s):

Alexander Kaune ◽

Faysal Chowdhury ◽

Micha Werner ◽

James Bennett

Keyword(s):

Water Allocation ◽

Irrigation Season ◽

Streamflow Forecasting ◽

Additional Time ◽

Initial Allocation ◽

Available Water ◽

Streamflow Forecasts ◽

Forecasting System ◽

Reservoir Inflow ◽

Allocation Decisions

Abstract. The area to be cropped in irrigation districts needs to be planned according to the allocated water, which in turn is a function of the available water resource. Initially conservative estimates of future (in) flows in rivers and reservoirs may lead to unnecessary reduction of the water allocated. Though water allocations may be revised as the season progresses, inconsistency in allocation is undesirable to farmers as they may then not be able to use that water, leading to an opportunity cost in agricultural production. We assess the benefit of using reservoir inflow estimates derived from seasonal forecast datasets to improve water allocation decisions. A decision model is developed to emulate the feedback loop between simulated reservoir storage and water allocations to irrigated crops, and is evaluated using inflow forecasts generated with the Forecast Guided Stochastic Scenarios (FoGSS) model, a 12-month ensemble streamflow forecasting system. Two forcings are used to generate the forecasts: ESP (historical rainfall) and POAMA (calibrated rainfall forecasts from the POAMA climate prediction system). We evaluate the approach in the Murrumbidgee basin in Australia, comparing water allocations obtained with an expected reservoir inflow from FoGSS against the allocations obtained with the currently used conservative estimate based on climatology, as well as against allocations obtained using observed inflows (perfect information). The inconsistency in allocated water is evaluated by determining the total changes in allocated water made every 15 days from the initial allocation at the start of the water year to the end of the irrigation season, including both downward and upward revisions of allocations. Results show that the inconsistency due to upward revisions in allocated water is lower when using the forecast datasets (POAMA and ESP) compared to the conservative inflow estimates (reference) which is beneficial to the planning of cropping areas by farmers. Overconfidence can, however, lead to an increase in undesirable downward revisions. This is more evident for dry years than for wet years. Over the 28 years for which allocation decisions are evaluated, we find that the accuracy of the available water estimates using the forecast ensemble improves progressively during the water year; especially one and a half months before the start of the cropping season in November. This is significant as it provides farmers additional time to make key decision on planting.

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Russian Rivers Streamflow Forecasting Using Hydrograph Extrapolation Method

Hydrology ◽

10.3390/hydrology9010001 ◽

2021 ◽

Vol 9 (1) ◽

pp. 1

Author(s):

Sergei Borsch ◽

Yuri Simonov ◽

Andrei Khristoforov ◽

Natalia Semenova ◽

Valeria Koliy ◽

...

Keyword(s):

Real Time ◽

Lead Time ◽

River Flow ◽

Water Levels ◽

Streamflow Forecasting ◽

Forecast Verification ◽

First Approximation ◽

Average Slope ◽

Linear Formula ◽

Forecasting System

This paper presents a method of hydrograph extrapolation, intended for simple and efficient streamflow forecasting with up to 10 days lead time. The forecast of discharges or water levels is expressed by a linear formula depending on their values on the date of the forecast release and the five previous days. Such forecast techniques were developed for more than 2700 stream gauging stations across Russia. Forecast verification has shown that this method can be successfully applied to large rivers with a smooth shape of hydrographs, while for small mountain catchments, the accuracy of the method tends to be lower. The method has been implemented into real-time continuous operations in the Hydrometcentre of Russia. In the territory of Russia, 18 regions have been identified with a single dependency of the maximum lead time of good forecasts on the area and average slope of the catchment surface for different catchments of each region; the possibilities of forecasting river streamflow by the method of hydrograph extrapolation are approximately estimated. The proposed method can be considered as a first approximation while solving the problem of forecasting river flow in conditions of a lack of meteorological information or when it is necessary to quickly develop a forecasting system for a large number of catchments.

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Aggregating Distributional Treatment Effects: A Bayesian Hierarchical Analysis of the Microcredit Literature

10.31222/osf.io/7tkvm ◽

2017 ◽

Cited By ~ 7

Author(s):

Rachael Meager

Keyword(s):

Treatment Effects ◽

Randomized Trials ◽

Economic Knowledge ◽

Hierarchical Analysis ◽

Discrete Variables ◽

Unknown Parameters ◽

Distributional Effects ◽

Continuous Outcome ◽

Bayesian Hierarchical ◽

Hierarchical Framework

This paper develops methods to aggregate evidence on distributional treatment effects from multiple studies conducted in different settings, and applies them to the microcredit literature. Several randomized trials of expanding access to microcredit found substantial effects on the tails of household outcome distributions, but the extent to which these findings generalize to future settings was not known. Aggregating the evidence on sets of quantile effects poses additional challenges relative to average effects because distributional effects must imply monotonic quantiles and pass information across quantiles. Using a Bayesian hierarchical framework, I develop new models to aggregate distributional effects and assess their generalizability. For continuous outcome variables, the methodological challenges are addressed by applying transforms to the unknown parameters. For partially discrete variables such as business profits, I use contextual economic knowledge to build tailored parametric aggregation models. I find generalizable evidence that microcredit has negligible impact on the distribution of various household outcomes below the 75th percentile, but above this point there is no generalizable prediction.

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