Narrow Results

Usage of a deterministic fractal-multifractal (FM) procedure to model high-resolution rainfall time series, as derived distributions of multifractal measures via fractal interpolating functions, is reported. Four rainfall storm events having distinct geometries, one gathered in Boston and three others observed in Iowa City, are analyzed. Results show that the FM approach captures the main characteristics of these events, as the fitted storms preserve the records' general trends, their autocorrelations and spectra, and their multifractal character.

Rainfall is a highly intermittent field over a wide range of time and space scales. We study a high resolution rainfall time series exhibiting large intensity fluctuations and localized events. We consider the return times of a given intensity, and show that the time series composed of these return times is itself also very intermittent, obeying to a hyperbolic probability density, entailing that the mean return time diverges. This is an unexpected property since mean return times are often introduced in meteorology, especially for the study of risk associated to extreme events. It suggests that the intermittency of first return times of extreme events should be taken into account when making statistical predictions.

The time series data of the monthly rainfall records (for the time period 1871–2002) in All India and different regions of India are analyzed. It is found that the distributions of the rainfall intensity exhibit perfect power law behavior. The scaling analysis revealed two distinct scaling regions in the rainfall time series.

Natural data sets, such as precipitation records, often contain geometries that are too complex to model in their totality with classical stochastic methods. In the past years, we have developed a promising deterministic geometric procedure, the fractal-multifractal (FM) method, capable of generating patterns as projections that share textures and other fine details of individual data sets, in addition to the usual statistics of interest. In this paper, we formulate an extension of the FM method around the concept of "closing the loop" by linking ends of two fractal interpolating functions and then test it on four geometrically distinct rainfall data sets to show that this generalization can provide excellent results.

This paper presents an empirical analysis of the role of different climate change adaptation strategies in supporting food productivity in Ethiopia. The analysis relies on unique primary survey data on 1000 farms producing cereal crops in the Nile Basin, Ethiopia. Based on monthly collected meteorological station data, the Thin Plate Spline method of spatial interpolation is used to impute the household specific rainfall and temperature values of each household. The rainfall data is disaggregated at season level (Meher and Belg). Econometric results show that the implementation of adaptation strategies supports farm productivity. Changing crops is found to be the most successful strategy, followed by the implementation of soil conservation and tree planting. We complement the analysis with some evidence on the determinants of adaptation. We find that extension services (both formal and farmer-to-farmer) and information on future climate changes affect positively and significantly the probability of adaptation through changing crops and tree planting. This finding highlights the crucial role played by information dissemination in improving farmers' decision-making.

Many studies of climate change adaptation have relied on farmers’ perceptions of climate change to explain why farmers are adopting new farming methods, and to advise adaptation policy framework that justifies Climate Smart Agriculture (CSA) especially in Africa. These studies have rarely verified whether farmers’ perceptions are consistent with observed changes in meteorological conditions to establish sufficient premise. This study compares farmers’ perceptions of changes in precipitation and temperature in a rainfed agriculture region of Ghana against objective measurements made in nearby weather stations in the region. The study finds that farmers correctly perceived the increase in temperature over time but incorrectly perceived a reduction in precipitation, while objective data showed high fluctuations with no clear trend. It is possible that farmers mistakenly assumed reduction in soil moisture meant to support crop growth requirements was caused by less rainfall when in fact it was caused by higher temperature.

Climate change poses mounting risks to agricultural development and rural livelihoods in Nigeria. This study investigates the impacts of climate change on agricultural sector employment in Nigeria. Agriculture provides income and sustenance for much of Nigeria’s rural population. However, smallholder rain-fed farming predominates, with minimal resilience to climate shifts. Historical data reveal rising temperatures and declining, erratic rainfall across Nigeria’s agro-ecological zones since the 1970s. Crop modeling predicts further climate changes will reduce yields of key staple crops. This threatens the viability of smallholder agriculture and risks widespread job losses. The study adopts a nonlinear autoregressive distributed lag (NARDL) modeling approach to evaluate climate change effects on agricultural sector employment in Nigeria from 1990 to 2020. Findings reveal reduced rainfall initially raises employment, as farming requires more labor in dry conditions. However, protracted droughts significantly reduce agricultural jobs. Increased temperatures consistently lower farm employment through reduced yields and incomes. Based on these findings, the study recommends that adaptive strategies are urgently needed to build resilience, promote climate-smart agriculture, and safeguard rural livelihoods.

In this paper, I advance and empirically support the indigenous religious values hypothesis, which holds that religions espouse values indigenous to the countries in which they developed. To identify the indigenous values of a religion’s homeland, I rely on the negative relationship between individualism and rainfall variation. I find strong empirical support for the hypothesis that contemporary individualism depends on rainfall variation in the homelands of religions to which a country’s population adheres. Indeed, this relationship explains over a quarter of the international variation in individualism. This effect is robust to controls for the role of religion in institutional and technological transfers and the confluence of conversion and colonisation. In keeping with the explicitly religious nature of the mechanism proposed here, I also find that rainfall variation in religion homelands plays a greater role in explaining the values of countries with greater religious freedom and the values of individuals who are more religious or members of religious minorities.

Through warming of the oceans, human-induced climate change effects accumulate. The result is higher ocean heat content (OHC), sea levels and sea surface temperatures (SSTs). The resulting environment invigorates tropical cyclones to make them more intense, bigger and longer lasting, and greatly increases their flooding rains. Here, the focus is on Atlantic hurricanes in 2017, featuring Harvey, Irma and Maria, and the huge damage that occurred. Hurricanes keep tropical oceans cooler as a consequence of their strong winds that increase evaporation. Planning for such supercharged hurricanes (adaptation) by increasing resilience (e.g., better building codes, flood protection) and preparing for contingencies (such as evacuation routes, power cuts) is essential but not adequate in many areas, including Texas, Florida and Puerto Rico where Harvey, Irma and Maria took their toll.