Root behavior of savanna species in Brazil's Pantanal wetland.

The objective of this study was to determine the maximum depth, structure, diameter and biomass of the roots of common woody species in two savanna physiognomies (savanna woodland and open woody savanna) in Brazil's Pantanal wetland. The root systems of 37 trees and 34 shrubs of 15 savanna species were excavated to measure their length and depth and estimate the total root biomass through allometric relationships with stem diameter at ground level. In general, statistical regression models between root weight and stem diameter at ground level showed a significance of P < 0.05 and R2 values close to or above 0.8. The average depths of the root system in wetland savanna woodland and open woody savanna are 0.8 ± 0.3 m and 0.7 ± 0.2 m, respectively, and differ from the root systems of savanna woody species in non-flooding areas, whose depth usually ranges from 3 to 19 m.Weattribute this difference to the adaptation of woody plant to the shallow water table, particularly during the wet season. This singularity of woody species in wetland savannas is important when considering biomass and carbon stocks for national and global carbon inventories.

Probabilistic methods for seasonal forecasting in a changing climate: Cox-type regression models.

For climate risk management, cumulative distribution functions (CDFs) are an important source of information. They are ideally suited to compare probabilistic forecasts of primary (e.g. rainfall) or secondary data (e.g. crop yields). Summarised as CDFs, such forecasts allow an easy quantitative assessment of possible, alternative actions. Although the degree of uncertainty associated with CDF estimation could influence decisions, such information is rarely provided. Hence, we propose Cox-type regression models (CRMs) as a statistical framework for making inferences on CDFs in climate science. CRMs were designed for modelling probability distributions rather than just mean or median values. This makes the approach appealing for risk assessments where probabilities of extremes are often more informative than central tendency measures. CRMs are semi-parametric approaches originally designed for modelling risks arising from time-to-event data. Here we extend this original concept beyond time-dependent measures to other variables of interest. We also provide tools for estimating CDFs and surrounding uncertainty envelopes from empirical data. These statistical techniques intrinsically account for non-stationarities in time series that might be the result of climate change. This feature makes CRMs attractive candidates to investigate the feasibility of developing rigorous global circulation model (GCM)-CRM interfaces for provision of user-relevant forecasts. To demonstrate the applicability of CRMs, we present two examples for El Ni ? no/Southern Oscillation (ENSO)-based forecasts: the onset date of the wet season (Cairns, Australia) and total wet season rainfall (Quixeramobim, Brazil). This study emphasises the methodological aspects of CRMs rather than discussing merits or limitations of the ENSO-based predictors.