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[1] This paper develops a Bayesian approach to analysis of a generalized least squares (GLS) regression model for regional analyses of hydrologic data. The new approach allows computation of the posterior distributions of the parameters and the model error variance using a quasi-analytic approach. Two regional skew estimation studies illustrate the value of the Bayesian GLS approach for regional statistical analysis of a shape parameter and demonstrate that regional skew models can be relatively precise with effective record lengths in excess of 60 years. With Bayesian GLS the marginal posterior distribution of the model error variance and the corresponding mean and variance of the parameters can be computed directly, thereby providing a simple but important extension of the regional GLS regression procedures popularized by Tasker and Stedinger (1989), which is sensitive to the likely values of the model error variance when it is small relative to the sampling error in the at-site estimator.Citation: Reis, D. S., Jr., J. R. Stedinger, and E. S. Martins (2005), Bayesian generalized least squares regression with application to log Pearson type 3 regional skew estimation, Water Resour. Res., 41, W10419,

The subseasonal-to-seasonal (S2S) predictive timescale, encompassing lead times ranging from 2 weeks to a season, is at the frontier of forecasting science. Forecasts on this timescale provide opportunities for enhanced application-focused capabilities to complement existing weather and climate services and products. There is, however, a ‘knowledge-value’ gap, where a lack of evidence and awareness of the potential socio-economic benefits of S2S forecasts limits their wider uptake. To address this gap, here we present the first global community effort at summarizing relevant applications of S2S forecasts to guide further decision-making and support the continued development of S2S forecasts and related services. Focusing on 12 sectoral case studies spanning public health, agriculture, water resource management, renewable energy and utilities, and emergency management and response, we draw on recent advancements to explore their application and utility. These case studies mark a significant step forward in moving from potential to actual S2S forecasting applications. We show that by placing user needs at the forefront of S2S forecast development – demonstrating both skill and utility across sectors – this dialogue can be used to help promote and accelerate the awareness, value and co-generation of S2S forecasts. We also highlight that while S2S forecasts are increasingly gaining interest among users, incorporating probabilistic S2S forecasts into existing decision-making operations is not trivial. Nevertheless, S2S forecasting represents a significant opportunity to generate useful, usable and actionable forecast applications for and with users that will increasingly unlock the potential of this forecasting timescale.

Abstract:Seasonal streamflow forecasts based on climate information can guide water managers toward superior reservoir operations, leading to improved water resources management efficiency. Uncertainty, however, is always present in seasonal streamflow forecasts, affecting the forecast value. Thus, a forecast should not be considered complete without a description of its uncertainty, which is critical for climate risk and water resources management. This study investigates the uncertainties of a seasonal streamflow forecast system for Northeastern Brazil based on climate precipitation forecasts and hydrologic modeling. These two sources of uncertainty are treated independently and then compared in order to guide future investments in the forecast system. Sea surface temperature is considered to be the primary source of uncertainty for the seasonal precipitation forecasts, based upon a 10-member climate model ensemble. Parameter uncertainty is considered to be the only source of uncertainty for the hydrologic model. Estimation of parameter uncertainty is estimated by the Shuffled Complex Evolution Metropolis algorithm, which employs a Markov Chain Monte Carlo scheme to provide the posterior distribution of the parameters and form uncertainty bounds on streamflow forecasts. Results indicate that uncertainties associated with the climate forecast are much larger than those from parameter estimation in the hydrologic model. Although model structure has not been considered in the evaluation of hydrologic uncertainties, this study indicates that future efforts to address the predominant source of uncertainty should focus on the climate prediction models.

The skew map developed by Hardison in 1974 was based on records of at least 25 years in lengths. It is still used today, over 30 years later. The first edition of Bulletin 17 states: "It is expected that Plate 1 [the skew map] will be revised as more data become available and more extensive studies are completed." Today, tremendous advances in computing power and spatial statistical methods allow for a much better analysis of the larger data set now available. This paper describes a Bayesian Generalized Least Squares (B-GLS) framework together with diagnostic statistics introduced by Reis et al. (2005) that can be used to develop regional skew relationships. An example using data from the Illinois River Basin illustrates useful diagnostic statistics including pseudo R 2 , Bayesian plausibility, leverage, influence and-influence. The B-GLS framework and diagnostic statistics developed in this analysis are being applied to an ongoing study in the southeast United States which will produce a regional skew estimator.

O artigo apresenta os resultados obtidos com a análise morfométrica fl uvial, em seus aspectos linear, espacial e hipsométrico, de algumas das bacias hidrográfi cas contribuintes ao reservatório da Usina Hidrelétrica (UHE) Corumbá IV, no Município de Luziânia (GO). O objetivo da pesquisa foi a realização de análise de parâmetros morfométricos fl uviais, como subsídio para verifi cação da propensão de ocorrência de processos erosivos e de aporte de sedimentos em bacias hidrográfi cas no entorno do reservatório da Usina Hidrelétrica (UHE) Corumbá IV. Foram utilizadas imagens dos sensores remotos orbital e suborbital, como imagem do satélite Quick-Bird e ortofotos obtidas antes do alagamento do reservatório da usina hidrelétrica (UHE) Corumbá IV, bem como modelos numéricos de terreno (MNT), com dados de mapa topográfi co e de interferometria de radar da missão Shutlle Radar Topografi c Mission (SRTM-NASA), além da base cartográfi ca digital e de mapa geológico na escala de 1:250.000. Foram utilizados algoritmos de geoprocessamento inseridos em sistemas de informações geográfi cas, o sistema ArcGis e o sistema Spring. A Revista Brasileira de Geomorfologia v. 14, nº 2 (2013) www.ugb.org.br

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