The Relationships between Tropical Pacific and Atlantic SST and Northeast Brazil Monthly Precipitation

ABSTRACT The monthly patterns of northeast Brazil (NEB) precipitation are analyzed in relation to sea surface temperature (SST) in the tropical Pacific and Atlantic Oceans, using singular value decomposition. It is found that the relationships between precipitation and SST in both basins vary considerably throughout the rainy season (February-May). In January, equatorial Pacific SST is weakly correlated with precipitation in small areas of southern NEB, but Atlantic SST shows no significant correlation with regional precipitation. In February, Pacific SST is not well related to precipitation, but south equatorial Atlantic SST is positively correlated with precipitation over the northern Nordeste, the latter most likely reflecting an anomalously early (or late) southward migration of the ITCZ precipitation zone. During March, equatorial Pacific SST is negatively correlated with Nordeste precipitation, but no consistent relationship between precipitation and Atlantic SST is found. Time-lagged analyses show the potential for forecasting either seasonal mean or monthly precipitation patterns with some degree of skill. In some instances, individual monthly mean SST versus seasonal mean (FebruaryMay) precipitation relationships differ considerably from the corresponding monthly SST versus monthly precipitation relationships. It is argued that the seasonal mean relationships result from the relatively strong monthly relationships toward the end of the season, combined with the considerable persistence of SST in both oceans

Environmental risk assessment of wetland ecosystems using Bayesian belief networks

Wetlands are valuable natural capital and sensitive ecosystems facing significant risks from anthropogenic and climatic stressors. An assessment of the environmental risk levels for wetlands’ dynamic ecosystems can provide a better understanding of their current ecosystem health and functions. Different levels of environmental risk are defined by considering the categories of risk and the probability and severity of each in the environment. Determining environmental risk levels provides a general overview of ecosystem function. This mechanism increases the visibility of risk levels and their values in three distinct states (i.e., low, moderate, and high) associated with ecosystem function. The Bayesian belief network (BBN) is a novel tool for determining environmental risk levels and monitoring the effectiveness of environmental planning and management measures in reducing the levels of risk. This study develops a robust methodological framework for determining the overall level of risks based on a combination of varied environmental risk factors using the BBN model. The proposed model is adopted for a case study of Shadegan International Wetlands (SIWs), which consist of a series of Ramsar wetlands in the southwest of Iran with international ecological significance. A comprehensive list of parameters and variables contributing to the environmental risk for the wetlands and their relationships were identified through a review of literature and expert judgment to develop an influence diagram. The BBN model is adopted for the case study location by determining the states of variables in the network and filling the probability distribution tables. The environmental risk levels for the SIWs are determined based on the results obtained at the output node of the BBN. A sensitivity analysis is performed for the BBN model. We proposed model-informed management strategies for wetland risk control. According to the BBN model results, the SIWs ecosystems are under threat from a high level of environmental risk. Prolonged drought has been identified as the primary contributor to the SIWs’ environmental risk levels

Climate Change Impacts on Groundwater and Dependent Ecosystems - in press

[EN] Aquifers and groundwater-dependent ecosystems (GDEs) are facing increasing pressure from water consumption, irrigation and climate change. These pressures modify groundwater levels and their temporal patterns and threaten vital ecosystem services such as arable land irrigation and ecosystem water requirements, especially during droughts. This review examines climate change effects on groundwater and dependent ecosystems. The mechanisms affecting natural variability in the global climate and the consequences of climate and land use changes due to anthropogenic influences are summarised based on studies from different hydrogeological strata and climate zones. The impacts on ecosystems are discussed based on current findings on factors influencing the biodiversity and functioning of aquatic and terrestrial ecosystems. The influence of changes to groundwater on GDE biodiversity and future threats posed by climate change is reviewed, using information mainly from surface water studies and knowledge of aquifer and groundwater ecosystems. Several gaps in research are identified. Due to lack of understanding of several key processes, the uncertainty associated with management techniques such as numerical modelling is high. The possibilities and roles of new methodologies such as indicators and modelling methods are discussed in the context of integrated groundwater resources management. Examples are provided of management impacts on groundwater, with recommendations on sustainable management of groundwaterThe preparation of this review was partly funded by EC 7th framework Project GENESIS (Contract Number 226536).Klove, B.; Ala-Aho, P.; Bertrand, G.; Gurdak, JJ.; Kupfersberger, H.; Kværner, J.; Muotka, T.... (2014). Climate Change Impacts on Groundwater and Dependent Ecosystems - in press. Journal of Hydrology. 518(Part B):250-266. https://doi.org/10.1016/j.jhydrol.2013.06.037S250266518Part

Analysis and regionalization of northern european winter precipitation based on its relationship with the North Atlantic oscillation

The relationship between the North Atlantic oscillation (NAO) and winter temperature and precipitation over northern Europe has long been known. However, its strength is variable within this region. In this paper, an analysis of the regional variability of the influence of the NAO on winter precipitation in northern Europe is developed using empirical orthogonal function analysis, cluster analysis and simple correlation. Results show that precipitation in most of the region studied is affected by the NAO, although with varying intensity. The NAO strongly influences winter precipitation along the Norwegian coast, in northern Sweden and in southern Finland. It is evident that the region on the lee side of the Scandes (the mountain chain between Norway and Sweden) is protected from the effects of the moist western winds from the Atlantic that, in turn, are strongly related to the NAO. As a result, precipitation in this shielded area is mainly related to southeasterly winds. Copyright (C) 2003 Royal Meteorological Society

model based global assessment of hydrological pressure

The overarching objective of GlobalHydroPressure is to provide global model-based support for assessing and quantifying the fundamental hydrological pressure in basins worldwide. A consistent and reliable estimation of this pressure is a prerequisite for assessment of vulnerability and resilience to the total, multiple environmental pressure, including both natural and human-driven components. [More](http://www.waterjpi.eu/joint-calls/joint-call-2017-ic4water/booklet/globalhydropressure-1/globalhydropressure

Influence of Sea Surface Temperature on Rainfall and Runof in Northeastern South America: Analysis and Modeling

This work represents an amalgam of a group of studies with the purpose of understanding the influence of the Pacific and Atlantic sea surface temperature on precipitation and river discharge in Northeastern South America. Sea surface temperature is a good representative of phenomena such as ENSO that, in turn, cause worldwide climate variability. The patterns of the Atlantic Ocean sea surface temperature anomaly also plays a very important role in the precipitation over the neighboring regions and a special investigation was also carried out to better understanding this influence. The influence of these oceans' sea surface temperature on the intraseasonal variability of precipitation in Northeast Brazil was also a particular subject of study. Statistical methods were largely used both during these investigations and in the development of models for forecasting discharge long term in advance at some sites in the Amazon, Orinoco and Tocantins River Basins. Sea surface temperature anomalies in both oceans significantly influence precipitation over northeastern South America. The Atlantic Ocean, however, plays a more important role in the case of precipitation over Northeast Brazil while the Pacific Ocean seems to have stronger influence over eastern and northern Amazonia. As a result of changes in precipitation, the river discharge in the Amazon Region is also influenced by changes in sea surface temperature patterns. The discharge of rivers located to the north of the Amazon River is mainly influenced by the Pacific sea surface temperature while the Atlantic influences the rivers to the south of the Amazon River. This influence could be clearly observed using the forecast models. Two different methodologies were used to develop forecast models: Canonical Correlation Analysis and Artificial Neural Network. The first is a linear technique and the second a non-linear one. In both cases, the models developed using Pacific sea surface temperature were better at forecasting discharge at sites to the north of the Amazon River and those developed from Atlantic sea surface temperature at forecasting discharges at sites to the south of the Amazon River. Even though the use of a non-linear technique improved the accuracy of the models in general, it considerably improved the capacity of Atlantic sea surface temperature to forecast discharge. This general improvement was to some degree expected considering the very complex and non-linear mechanisms that transform precipitation into discharge