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The 4.2 ka event was a period of global drought and climate cooling linked to the widespread disruption of Neolithic culture. However, the detailed climatic changes during this period are not completely understood. In this paper, we report a high-resolution pollen record for the Dayanghu wetland in the Zhejiang region of southeastern China, which we use to infer mean annual precipitation (MAP) fluctuations during the period spanning 4600–4000 calendar years before present (cal. yr BP). There was a long-term trend of decreasing MAP during this interval, consistent with weakening of the East Asian Summer Monsoon since the mid-Holocene. This trend was punctuated by episodes of relatively high MAP at 4350–4320, ca. 4260, and 4220–4200 cal. yr BP, and extreme droughts at 4200 and 4150 cal. yr BP. We propose that solar activity promoted El Niño events and contributed to dramatic MAP fluctuations in the Dayanghu wetland. La Niña events and negative-phase intervals of the Pacific Decadal Oscillation may have been responsible for the extreme drought at 4200–4150 cal. yr BP. In summary, we propose a close relationship between MAP and solar forcing, with short-term enhanced solar activity increasing the intensity of the East Asian Summer Monsoon and contributing to increasing decadal variability in the inner tropical Pacific, which ultimately leads to increased precipitation variability in southeast China. The results improve our understanding of the detailed climatic features of the 4.2 ka event. • High-resolution pollen record for ca. 4.2 ka for the Zhejiang region, China. • Pollen-based mean annual precipitation was quantitatively reconstructed. • Long-term droughts tended to occur during 4600–4000 cal. yr BP. • La Niña-dominated ENSO contributed to a catastrophic drought at 4200–4150 cal. yr BP. [ABSTRACT FROM AUTHOR]

This paper aims at assess district-wise vulnerability index of the state of Karnataka State, which is predominantly is rainfed and is highly susceptible to climatic variability. Secondary data on relevant indicators were collected to prepare indices viz., crop production losses, exposure, sensitivity and adaptive capacity. Following normalization and using appropriate weights for indicators, these four indices were used for constructing vulnerability index, which can be used a rapid assessment method for prioritizing districts that need measures to moderate the detrimental impact of climate change. It has been observed that Climatic variability caused higher production losses in cereals, pulses and oilseeds in Davangere, Gulbarga and Raichur districts, respectively. Districts like Koppal, Raichur, Bijapur Gulbarga, Gadag, Bagalkote and Bellary were placed under extreme degree of exposure. As per the sensitivity index scores, Kolar district is the most sensitive. Further, Bengaluru (Urban), Dakshin Kannada and Kodagu are ranked first, second and third in terms of adaptive capacity in the state. Overall, vulnerability index scores indicate that Gulbarga, Koppal, Raichur, Bellary, Bagalkote, Bijapur and Belgaum are extremely vulnerable districts in the state. It was also estimated that around 70% of the cultivated area, which supports 60% and 67% of livestock and rural population of the state, respectively are facing ‘extreme to high’ level of vulnerability. The ranking based prioritization of the vulnerable areas calls for a holistic approach for each district or a group of districts to reduce their sensitivity, minimize exposure to rainfall variability through implementation of site-specific and leverage adaptive capacity through better health and education facilities, expansion of employment opportunities in other sectors or reducing over dependence on agriculture. [ABSTRACT FROM AUTHOR]

The boundary between the humid eastern and the arid western regions of the Great Plains of North America is of great economic interest and historic intrigue, yet its location is controversial. Areas to the east of this boundary have historically enjoyed the benefits of fertile soil coupled with more favorable rainfall and reliable surface water, permitting conventional agriculture to flourish over a remarkably large percentage of the eastern Great Plains. The expansion of population and agriculture during the nineteenth century across the western Great Plains tested the extent that non-irrigated, row crop agriculture could be successful in areas where year-to-year rainfall was unreliable. In this paper, we quantify the historic annual variability of soil moisture and hydrologic conditioning in the Great Plains resulting from climatic variability, show the regions that historically demonstrate unreliable precipitation, and identify the extent of arid regions of the central United States based on modeled annual soil moisture variability. We asked how arid climates have influenced soil formation patterns at small cartographic scales, and how soil properties buffer or enhance soil moisture regimes (at the udic–ustic boundary) to climate variability at larger cartographic scales. At small cartographic scales, a climate-only model worked nearly as well as a climate-and-soils model in mapping the region's soil moisture boundary; however, a climate-only model missed important local soil influences. Finally, we demonstrate that long-term climate and climate variability are reflected in the depth and concentration of the calcic soil properties. From a practical standpoint, our work highlights that soils with higher water holding capacity dampen periodic short-term rainfall deficits, while soils with lower water holding capacity can exhibit edaphic drought during otherwise normal climate years. [ABSTRACT FROM AUTHOR]

Agricultural production in “open-sky systems” such as extensive cattle ranching on natural grasslands is subject to inter-annual climatic variability and other market fluctuations. To tackle the dependency of livestock breeding on these factors, we conducted participatory modeling with cattle producers in Uruguay. The methodology consists of simulating possible scenarios to collectively evaluate the different herd management practice alternatives. In this paper, we present an Agent-Based Model built with stakeholders and designed to represent a breeding system on a typical extensive grazing area in the basaltic soils region (BR) of Uruguay. This model has three main modules: environment, biophysical and decisional sub-models. This modularity allows the conducting of virtual experiments to reveal how some herd management decisions (such as seasonal stocking rate adjustments) combined with a climatic series can result in resilience against drought periods and market movements. Long-term simulations were implemented to analyze the sensitivity of the model to key management parameters with varying climate conditions. The inter-annual climatic variability can seriously affect cattle production, even with conservative stocking rates. Rigid strategies are bound to fail and cause systems to break. Adaptive management emerged as a critical option for the sustainability of livestock breeding. The inter-annual climatic variability can seriously affect cattle production, even with conservative stocking rates. This result highlights the importance of adaptive management, one that can react to a changing environment, for the sustainability of livestock breeding. [ABSTRACT FROM AUTHOR]

Summary: The distribution-free Mann–Kendall test is widely used for the assessment of significance of trends in many hydrologic and climatic time series. Previous studies have suggested both exact and approximate formulas for the calculation of the variance of the test statistic when the data are serially correlated. This paper outlines a procedure for the calculation of the exact distribution of the Mann–Kendall trend test statistic for persistent data with an arbitrary correlation structure. The particular cases of the AR(1) (first order autoregressive) model and the Fractional Gaussian Noise (FGN) model are presented for sample sizes between 3 and 9. While it has been previously shown that the Normal distribution gives a reasonable approximation to the exact distribution for large values of sample size n, a more accurate approximation based on the Beta distribution is proposed for moderate values of n. The application of the test to small samples is illustrated by testing the significance of recent trends starting in 1990 in 58 world river flow time series. The results confirm the effect of scaling in small samples and the benefits of using the Beta distribution as an approximation. [Copyright &y& Elsevier]

Abstract: The perception of a changing climate, which impacts also hydrological processes, is now generally admitted. However, the way of handling the changing nature of climate in hydrologic practice and especially in hydrological statistics has not become clear so far. The most common modelling approach is to assume that long-term trends, which have been found to be omnipresent in long hydrological time series, are ‘deterministic’ components of the time series and the processes represented by the time series are nonstationary. In this paper, it is maintained that this approach is contradictory in its rationale and even in the terminology it uses. As a result, it may imply misleading perception of phenomena and estimate of uncertainty. Besides, it is maintained that a stochastic approach hypothesizing stationarity and simultaneously admitting a scaling behaviour reproduces climatic trends (considering them as large-scale fluctuations) in a manner that is logically consistent, easy to apply and free of paradoxical results about uncertainty. [Copyright &y& Elsevier]

In the present paper, the climatic variability over several time scales, from the year up to millennia is discussed. After reviewing some modelling efforts of the latter, it will be shown evidence that periods ranging from the year up to the century contain a great deal of variability that differs from that produced by a red noise process. The analysis is based on 50 years of precipitation data derived from the National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) reanalysis. After transforming the data into a Gaussian index, known as the standardized precipitation index (SPI), the time evolution of this index has been studied, first over the globe and then zooming over Europe. It has been found that significative trends can be detected in the data. The technique here employed is a standard principal component analysis (PCA), which, for the case of the Gaussian field under study, exhausts the knowledge of the multivariate probability density function field. On these grounds some speculations on the interaction between drought occurrence and the North Atlantic Ocean circulation will be offered. [Copyright &y& Elsevier]