Are we adapting to climate change?

Human systems will have to adapt to climate change. Understanding of the magnitude of the adaptation challenge at a global scale, however, is incomplete, constrained by a limited understanding of if and how adaptation is taking place. Here we develop and apply a methodology to track and characterize adaptation action; we apply these methods to the peer-reviewed, English-language literature. Our results challenge a number of common assumptions about adaptation while supporting others: (1) Considerable research on adaptation has been conducted yet the majority of studies report on vulnerability assessments and natural systems (or intentions to act), not adaptation actions. (2) Climate change is rarely the sole or primary motivator for adaptation action. (3) Extreme events are important adaptation stimuli across regions. (4) Proactive adaptation is the most commonly reported adaptive response, particularly in developed nations. (5) Adaptation action is more frequently reported in developed nations, with middle income countries underrepresented and low-income regions dominated by reports from a small number of countries. (6) There is limited reporting on adaptations being developed to take advantage of climate change or focusing on women, elderly, or children.     

SSBUV and NOAA-11 SBUV/2 Solar Variability Measurements

The Shuttle SBUV (SSBUV) and NOAA-11 SBUV/2 instruments measured solar spectral UV irradiance during the maximum and declining phase of solar cycle 22. The SSBUV data accurately represent the absolute solar UV irradiance between 200–405 nm, and also show the long-term variations during eight flights between October 1989 and January 1996. These data have been used to correct long-term sensitivity changes in the NOAA-11 SBUV/2 data, which provide a near-daily record of solar UV variations over the 170–400 nm region between December 1988 and October 1994. The NOAA-11 data demonstrate the evolution of short-term solar UV activity during solar cycle 22.     

A spatially and temporally adaptive solution of Richards’ equation

Efficient, robust simulation of groundwater flow in the unsaturated zone remains computationally expensive, especially for problems characterized by sharp fronts in both space and time. Standard approaches that employ uniform spatial and temporal discretizations for the numerical solution of these problems lead to inefficient and expensive simulations. In this work, we solve Richards’ equation using adaptive methods in both space and time. Spatial adaption is based upon a coarse grid solve and a gradient error indicator using a fixed-order approximation. Temporal adaption is accomplished using variable order, variable step size approximations based upon the backward difference formulas up to fifth order. Since the advantages of similar adaptive methods in time are now established, we evaluate our method by comparison with a uniform spatial discretization that is adaptive in time for four different one-dimensional test problems. The numerical results demonstrate that the proposed method provides a robust and efficient alternative to standard approaches for simulating variably saturated flow in one spatial dimension.     

A big-data spatial,temporal and network analysis of bovine tuberculosis between wildlife (badgers) and cattle

Bovine tuberculosis (TB) poses a serious threat for agricultural industry in several countries, it involves potential interactions between wildlife and cattle and creates societal problems in terms of human-wildlife conflict. This study addresses connectedness network analysis, the spatial, and temporal dynamics of TB between cattle in farms and the European badger (Meles meles) using a large dataset generated by a calibrated agent based model. Results showed that infected network connectedness was lower in badgers than in cattle. The contribution of an infected individual to the mean distance of disease spread over time was considerably lower for badger than cattle; badgers mainly spread the disease locally while cattle infected both locally and across longer distances. The majority of badger-induced infections occurred when individual badgers leave their home sett, and this was positively correlated with badger population growth rates. Point pattern analysis indicated aggregation in the spatial pattern of TB prevalence in badger setts across all scales. The spatial distribution of farms that were not TB free was aggregated at different scales than the spatial distribution of infected badgers and became random at larger scales. The spatial cross correlation between infected badger setts and infected farms revealed that generally infected setts and farms do not coexist except at few scales. Temporal autocorrelation detected a two year infection cycle for badgers, while there was both within the year and longer cycles for infected cattle. Temporal cross correlation indicated that infection cycles in badgers and cattle are negatively correlated. The implications of these results for understanding the dynamics of the disease are discussed.