Regional intensity–duration–frequency analysis in the Eastern Black Sea Basin,Turkey, by using L-moments and regression analysis

The analysis of rainfall frequency is an important step in hydrology and water resources engineering. However, a lack of measuring stations, short duration of statistical periods, and unreliable outliers are among the most important problems when designing hydrology projects. In this study, regional rainfall analysis based on L-moments was used to overcome these problems in the Eastern Black Sea Basin (EBSB) of Turkey. The L-moments technique was applied at all stages of the regional analysis, including determining homogeneous regions, in addition to fitting and estimating parameters from appropriate distribution functions in each homogeneous region. We studied annual maximum rainfall height values of various durations (5 min to 24 h) from seven rain gauge stations located in the EBSB in Turkey, which have gauging periods of 39 to 70 years. Homogeneity of the region was evaluated by using L-moments. The goodness-of-fit criterion for each distribution was defined as the Z^DIST statistics, depending on various distributions, including generalized logistic (GLO), generalized extreme value (GEV), generalized normal (GNO), Pearson type 3 (PE3), and generalized Pareto (GPA). GLO and GEV determined the best distributions for short (5 to 30 min) and long (1 to 24 h) period data, respectively. Based on the distribution functions, the governing equations were extracted for calculation of intensities of 2, 5, 25, 50, 100, 250, and 500 years return periods (T). Subsequently, the T values for different rainfall intensities were estimated using data quantifying maximum amount of rainfall at different times. Using these T values, duration, altitude, latitude, and longitude values were used as independent variables in a regression model of the data. The determination coefficient (R ²) value indicated that the model yields suitable results for the regional relationship of intensity–duration–frequency (IDF), which is necessary for the design of hydraulic structures in small and medium sized catchments.     

Climate change research and analysis in India: an integrated assessment of a South–North divide

For more than a decade climate change has been the focus of much research and analysis. Despite the global implications of the problem, the overwhelming majority of the researchers involved worldwide in studying the problem and its possible solutions are from industrialized countries, and participation of lesser-industrialized countries has been limited. While the wide-ranging implications of this South–North divide are sometimes recognized, there is little analysis on the reasons for this divide, why it continues to exist, and what steps might be required to narrow it. Towards this end, this paper analyzes how climate change research and analysis is performed in India, a major lesser-industrialized country. Based on detailed interviews, it explores the factors that play a role in shaping the capability of India to perform, and respond to, climate-change analyses. Drawing on the Indian case study, the paper examines developing-country participation in the international climate science and assessment enterprise. This allows some reflection on the potential pitfalls for international discussions on climate change and what the international community and countries of the South can do to overcome them in order to address this conspicuous South–North divide.     

Geo-spatial analysis of temporal trends of temperature and its extremes over India using daily gridded (1°×1°) temperature data of 1969–2005

Daily gridded (1°×1°) temperature data (1969–2005) were used to detect spatial patterns of temporal trends of maximum and minimum temperature (monthly and seasonal), growing degree days (GDDs) over the crop-growing season (kharif, rabi, and zaid) and annual frequencies of temperature extremes over India. The direction and magnitude of trends, at each grid level, were estimated using the Mann–Kendall statistics (α = 0.05) and further assessed at the homogeneous temperature regions using a field significance test (α=0.05). General warming trends were observed over India with considerable variations in direction and magnitude over space and time. The spatial extent and the magnitude of the increasing trends of minimum temperature (0.02–0.04 °C year^?1) were found to be higher than that of maximum temperature (0.01–0.02 °C year^?1) during winter and pre-monsoon seasons. Significant negative trends of minimum temperature were found over eastern India during the monsoon months. Such trends were also observed for the maximum temperature over northern and eastern parts, particularly in the winter month of January. The general warming patterns also changed the thermal environment of the crop-growing season causing significant increase in GDDs during kharif and rabi seasons across India. The warming climate has also caused significant increase in occurrences of hot extremes such as hot days and hot nights, and significant decrease in cold extremes such as cold days and cold nights.     

Landslide susceptibility assesssment in the Uttarakhand area (India) using GIS: a comparison study of prediction capability of naïve bayes,multilayer perceptron neural networks,and functional trees methods

The objective of this study is to make a comparison of the prediction performance of three techniques, Functional Trees (FT), Multilayer Perceptron Neural Networks (MLP Neural Nets), and Naïve Bayes (NB) for landslide susceptibility assessment at the Uttarakhand Area (India). Firstly, a landslide inventory map with 430 landslide locations in the study area was constructed from various sources. Landslide locations were then randomly split into two parts (i) 70 % landslide locations being used for training models (ii) 30 % landslide locations being employed for validation process. Secondly, a total of eleven landslide conditioning factors including slope angle, slope aspect, elevation, curvature, lithology, soil, land cover, distance to roads, distance to lineaments, distance to rivers, and rainfall were used in the analysis to elucidate the spatial relationship between these factors and landslide occurrences. Feature selection of Linear Support Vector Machine (LSVM) algorithm was employed to assess the prediction capability of these conditioning factors on landslide models. Subsequently, the NB, MLP Neural Nets, and FT models were constructed using training dataset. Finally, success rate and predictive rate curves were employed to validate and compare the predictive capability of three used models. Overall, all the three models performed very well for landslide susceptibility assessment. Out of these models, the MLP Neural Nets and the FT models had almost the same predictive capability whereas the MLP Neural Nets (AUC = 0.850) was slightly better than the FT model (AUC = 0.849). The NB model (AUC = 0.838) had the lowest predictive capability compared to other models. Landslide susceptibility maps were final developed using these three models. These maps would be helpful to planners and engineers for the development activities and land-use planning.     

Trend analysis of air temperature time series in Greece and their relationship with circulation using surface and satellite data: 1955–2001

Summary In this study, trends of annual and seasonal surface air temperature time series were examined for 20 stations in Greece for the period 1955–2001, and satellite data for the period 1980–2001. Two statistical tests based on the least square method and one based on the Mann-Kendall test, which is also capable of detecting the starting year of possible climatic discontinuities or changes, were used for the analysis. Greece, in general, shows a cooling trend in winter for the period 1955–2001, whereas, summer shows an overall warming trend, however, neither is statistically significant. As a result, the overall trend of the annual values is nearly zero. Comparison with corresponding trends in the Northern Hemisphere (NH) shows that temperatures in Greece do not follow the intense warming trends. Satellite data indicate a remarkable warming trend in mean annual, winter and summer in Greece for the period 1980–2001, and a slight warming trend in annual, spring and autumn for the NH. Comparison with the respective trends detected in the surface air temperature for the same period (1980–2001) shows they match each other quite well in both Greece and the NH. The relationship between temperature variability in Greece and atmospheric circulation was also examined using correlation analysis with three circulation indices: the well-known North Atlantic Oscillation Index (NAOI), a Mediterranean Oscillation Index (MOI) and a new Mediterranean Circulation Index (MCI). The MOI and MCI indices show the most interesting correlation with winter temperatures in Greece. The behaviour of pressure and the height of the 500hPa surface over the Mediterranean region supports these results.     

Monthly and annual statistical distributions of daily rainfall at the Fabra Observatory (Barcelona, NE Spain) for the years 1917–1999

Summary Three empirical distributions of the daily rainfall collected at the Fabra Observatory from 1917 to 1999 are fitted to different statistical models. The first two are designated as the distributions of cumulative amounts and cumulative times. The third distribution accounts for the time interval between two consecutive rainy days with rain amounts equalling or exceeding a threshold amount. Whereas the distribution of cumulative amounts follows an exponential model at monthly and annual scale, except for a few cases, the distribution of the cumulative times is well modelled by a Weibull function, whether monthly or annual scales are considered. The distribution of time intervals also follows a Weibull distribution for the different thresholds considered. In addition, the combination of the two first distributions leads to the normalised rainfall curve, NRC, which is also reproduced satisfactorily by a beta (type 1) distribution. It is worth mentioning that the NRCs follow the expected behaviour with respect to the coefficient of variation of daily rain amounts at monthly and annual scales. In addition, a better understanding of fluctuations and time trends affecting the daily pluviometric regime is achieved by analysing the annual NRCs. The impact of some features of this rain regime, developed for Barcelona, a crowded metropolitan area, on many human activities, may provides the focus of future interdisciplinary analyses.     

Trend analysis of maximum,minimum, and average temperatures in Bangladesh: 1961–2008

The present study is about the analysis of mean maximum and mean minimum temperatures carried out on annual, seasonal, and monthly timescales examining the data from 15 meteorological stations in Bangladesh for the period 1961–2008. Various spatial and statistical tools were used to display and analyze trends in temperature data. ArcGIS was used to produce the spatially distributed temperature data by using Thiessen polygon method. The nonparametric Mann–Kendall test was used to determine whether there is a positive or negative trend in data with their statistical significance. Sen’s method was also used to determine the magnitude of the trends. The results reveal positive trends in annual mean and mean maximum temperatures with 95 % significance. Trend test reveals that the significant positive trend is found in June to November in case of mean maximum temperature, but according to the mean minimum temperature, the situation is different and a significant positive trend was found from November to February. The analysis of the whole record reveals a tendency toward warmer years, with significantly warmer summer periods and slightly colder winters. These warming patterns may have important impacts on energy consumption, water supply, human health, and natural environment in Bangladesh.     

Seizing policy windows: Policy Influence of climate advocacy coalitions in Brazil,China, and India, 2000–2015

What drives the development of climate policy? Brazil, China, and India have all changed their climate policies since 2000, and single-case analyses of climate policymaking have found that all three countries have had climate coalitions working to promote climate policies. To what extent have such advocacy coalitions been able to influence national policies for climate-change mitigation, and what can explain this? Employing a new approach that combines the advocacy coalition framework (ACF) with insights from comparative environmental politics and the literature on policy windows, this paper identifies why external parameters like political economy and institutional structures are crucial for explaining the climate advocacy coalitions’ ability to seize policy windows and influence policy development. We find that the coalitions adjust their policy strategies to the influence-opportunity structures in each political context—resulting in confrontation in Brazil, cooperation in China, and a complementary role in India.     

“A sky of brass and burning winds”: documentary evidence of rainfall variability in the Kingdom of Lesotho, Southern Africa, 1824–1900

Forecasting austral summer rainfall in southern Africa is hampered by a lack of long-term instrumental data. This paper extends the historical record for the subcontinent by presenting the first extensive 19th century climate history for Lesotho derived from documentary evidence. The data sources included unpublished English-, French- and Sesotho-language materials archived in Lesotho, South Africa and the UK. These included letters, journals and reports written by missionaries and colonial authorities, which were supplemented by newspapers, diaries, travelogues and other historical sources. Each source was read in chronological order, with any climate information recorded verbatim. Observations were classified into five categories (Very Wet, Relatively Wet, ‘Normal’, Relatively Dry, and Very Dry) based upon the predominant documented climate during each ‘rain-year’ (July to June). The latter portion of the chronology was then compared for accuracy against available instrumental precipitation records from Maseru (1886–1900). The results yield a semi-continuous record of climate information from 1824 to 1900. Data are restricted to lowland areas, but reveal drought episodes in 1833–34, 1841–42, 1845–47, 1848–51, 1858–63, 1865–69, 1876–80, 1882–85 and 1895–99 (the most severe drought years being 1850–51 and 1862–63) and wet periods or floods in 1835–36, 1838–41, 1847–48, 1854–56, 1863–65, 1873–75, 1880–81, 1885–86 and 1890–94. The rainfall chronology is compared with similar records for South Africa, Botswana and Zimbabwe. Linkages to possible forcing mechanisms, including ENSO teleconnections and historical coral-derived southwest Indian Ocean sea surface temperature variations are also explored.     

Simulations of Boreal Summer Intraseasonal Oscillations with the Climate Forecast System,version 2, over India and the Western Pacific: Role of Air–Sea Coupling

In this study, we examine the characteristics of the boreal summer monsoon intraseasonal oscillation (BSISO) using the second version of the Climate Forecast System (CFSv2) and revisit the role of air–sea coupling in BSISO simulations. In particular, simulations of the BSISO in two carefully designed model experiments are compared: a fully coupled run and an uncoupled atmospheric general circulation model (AGCM) run with prescribed sea surface temperatures (SSTs). In these experiments an identical AGCM is used, and the daily mean SSTs from the coupled run are prescribed as a boundary condition in the AGCM run. Comparisons indicate that air–sea coupling plays an important role in realistically simulating the BSISO in CFSv2. Compared with the AGCM run, the coupled run not only simulates the spatial distributions of intraseasonal rainfall variations better but also shows more realistic spectral peaks and northward and eastward propagation features of the BSISO over India and the western Pacific. This study indicates that including an air–sea feedback mechanism may have the potential to improve the realism of the mean flow and intraseasonal variability in the Indian and western Pacific monsoon region.     

“Reporting on climate change: A computational analysis of U.S. newspapers and sources of bias, 1997–2017”

News organizations constitute key sites of science communication between experts and lay audiences, giving many individuals their basic worldview of complex topics like climate change. Previous researchers have studied climate change news coverage to assess accuracy in reporting and potential sources of bias. These studies typically rely on manually coding articles from a handful of prestigious outlets, not allowing comparisons with smaller newspapers or providing enough diversity to assess the influence of partisan orientation or localized climate vulnerability on content production. Making these comparisons, this study indicates that partisan orientation, scale of circulation, and vulnerability to climate change correlate with several topics present in U.S. newspaper coverage of climate change. After assembling a corpus of over 78,000 articles covering two decades from 52 U.S. newspapers that are diverse in terms of geography, partisan orientation, scale of circulation, and objectively measured climate risk, a coherent set of latent topics were identified via an automated content analysis of climate change news coverage. Topic model results indicate that while outlet bias does not appear to impact the prevalence of coverage for most topics surrounding climate change, differences were evident for some topics based on partisan orientation, scale, or vulnerability status, particularly those relating to climate change denial, impacts, mitigation, or resource use. Overall, this paper provides a comprehensive study of U.S. newspaper coverage of climate change and identifies specific topics where outlet bias constitutes an important contextual factor.