Stochastic Environmental Research and Risk Assessment 

About: Stochastic Environmental Research and Risk Assessment is an academic journal published by Springer Science+Business Media. The journal publishes majorly in the area(s): Computer science & Environmental science. It has an ISSN identifier of 1436-3240. Over the lifetime, 2646 publications have been published receiving 59212 citations.

Equifinality of formal (DREAM) and informal (GLUE) Bayesian approaches in hydrologic modeling

Abstract: In recent years, a strong debate has emerged in the hydrologic literature regarding what constitutes an appropriate framework for uncertainty estimation. Particularly, there is strong disagreement whether an uncertainty framework should have its roots within a proper statistical (Bayesian) context, or whether such a framework should be based on a different philosophy and implement informal measures and weaker inference to summarize parameter and predictive distributions. In this paper, we compare a formal Bayesian approach using Markov Chain Monte Carlo (MCMC) with generalized likelihood uncertainty estimation (GLUE) for assessing uncertainty in conceptual watershed modeling. Our formal Bayesian approach is implemented using the recently developed differential evolution adaptive metropolis (DREAM) MCMC scheme with a likelihood function that explicitly considers model structural, input and parameter uncertainty. Our results demonstrate that DREAM and GLUE can generate very similar estimates of total streamflow uncertainty. This suggests that formal and informal Bayesian approaches have more common ground than the hydrologic literature and ongoing debate might suggest. The main advantage of formal approaches is, however, that they attempt to disentangle the effect of forcing, parameter and model structural error on total predictive uncertainty. This is key to improving hydrologic theory and to better understand and predict the flow of water through catchments.

Maximum likelihood Bayesian averaging of uncertain model predictions

Abstract: Hydrologic analyses typically rely on a single conceptual-mathematical model. Yet hydrologic environments are open and complex, rendering them prone to multiple interpretations and mathematical descriptions. Adopting only one of these may lead to statistical bias and underestimation of uncertainty. A comprehensive strategy for constructing alternative conceptual-mathematical models of subsurface flow and transport, selecting the best among them, and using them jointly to render optimum predictions under uncertainty has recently been developed by Neuman and Wierenga (2003). This paper describes a key formal element of this much broader and less formal strategy that concerns rendering optimum hydrologic predictions by means of several competing deterministic or stochastic models and assessing their joint predictive uncertainty. The paper proposes a Maximum Likelihood Bayesian Model Averaging (MLBMA) method to accomplish this goal. MLBMA incorporates both site characterization and site monitoring data so as to base the outcome on an optimum combination of prior information (scientific knowledge plus data) and model predictions. A preliminary example based on real data is included in the paper.

Drought forecasting using stochastic models

Abstract: Drought is a global phenomenon that occurs virtually in all landscapes causing significant damage both in natural environment and in human lives. Due to the random nature of contributing factors, occurrence and severity of droughts can be treated as stochastic in nature. Early indication of possible drought can help to set out drought mitigation strategies and measures in advance. Therefore drought forecasting plays an important role in the planning and management of water resource systems. In this study, linear stochastic models known as ARIMA and multiplicative Seasonal Autoregressive Integrated Moving Average (SARIMA) models were used to forecast droughts based on the procedure of model development. The models were applied to forecast droughts using standardized precipitation index (SPI) series in the Kansabati river basin in India, which lies in the Purulia district of West Bengal state in eastern India. The predicted results using the best models were compared with the observed data. The predicted results show reasonably good agreement with the actual data, 1–2 months ahead. The predicted value decreases with increase in lead-time. So the models can be used to forecast droughts up to 2 months of lead-time with reasonably accuracy.

The North Atlantic Oscillation

Abstract: The North Atlantic Oscillation (NAO) is the most important mode of variability in the northern hemisphere (NH) atmospheric circulation. Put simply, the NAO measures the strength of the westerly winds blowing across the North Atlantic Ocean between 40°N and 60°N. The NAO is not a regional, North Atlantic phenomenon, however, but rather is hemispheric in extent. Based on 60 years of data from 1935 to 1995, Hurrell (1996) estimates that the NAO accounts for 31% of the variance in hemispheric winter surface air temperature north of 20°N. The present article provides an overview of the NAO, its role in the atmospheric circulation, its close relationship to the Arctic Oscillation of Thompson and Wallace (1998), and its influence on the underlying North Atlantic Ocean. Some discussion is also given on the dynamics of the NAO, the possible role of ocean surface temperature, and recent evidence that the stratosphere plays an important role in modulating the NAO.

Geometric approach to statistical analysis on the simplex

Abstract: The geometric interpretation of the expected value and the variance in real Euclidean space is used as a starting point to introduce metric counterparts on an arbitrary finite dimensional Hilbert space. This approach allows us to define general reasonable properties for estimators of parameters, like metric unbiasedness and minimum metric variance, resulting in a useful tool to better understand the logratio approach to the statistical analysis of compositional data, who's natural sample space is the simplex.