Blue Nile Runoff Sensitivity to Climate Change

This study describes implementation of hydrological climate change impact assessment tool utilising a combination of statistical spatiotemporal downscaling and an operational hydrological model known as the Nile Forecasting System. A spatial rainfall generator was used to produce high-resolution (daily, 20km) gridded rainfall data required by the distributed hydrological model from monthly GCM outputs. The combined system was used to assess the sensitivity of upper Blue Nile flows at Diem flow gauging station to changes in future rainfall during the June-September rainy season based on output from three GCMs. The assessment also incorporated future evapotranspiration changes over the basin. The climate change scenarios derived in this study were broadly in line with other studies, with the majority of scenarios indicating wetter conditions in the future. Translating the impacts into runoff in the basin showed increased future mean flows, although these would be offset to some degree by rising evapotranspiration. Impacts on extreme runoff indicated the possibility of more severe floods in future. These are likely to be exacerbated by land-use changes including overgrazing, deforestation, and improper farming practices. Blue Nile basin flood managers therefore need to continue to prepare for the possibility of more frequent floods by adopting a range of measures to minimise loss of life and guard against other flood damage

Analysis of rainfall variability in the Logone catchment, Lake Chad basin

The socio-economic consequences posed by climate change in Africa are giving increasing emphasis to the need for trend analysis and detection of changes in hydro-climatic variables in data deficient areas. This study analyzes rainfall data from seventeen rain gauges unevenly distributed across the Logone catchment in the Lake Chad basin (LCB) over a fifty-year period (1951-2000). After quality control of the rainfall data using homogeneity tests, non-parametric MannKendall (MK) and Spearman rho tests were applied to detect the presence of trends. Trend magnitude was calculated using Sen’s Slope Estimator. Results of the homogeneity test showed that rainfall was homogeneous across the catchment. Trend analysis revealed the presence of negative trends for annual rainfall at all the stations. Results of long term trend analysis at a monthly time scale revealed the presence of statistically insignificant positive trends at 32% of the stations. Spatially, the analysis showed a clear distinction in rainfall magnitude between the semi-arid and Sudano zones. The slope of the trend lines for annual rainfall averaged over the respective zones was higher in the semi-arid zone (-4.37) compared to the Sudano zone (-4.02). However, the station with the greatest reduction in annual rainfall (-8.06 mm) was located in the Sudano zone

Evaluating global reanalysis precipitation datasets with rain gauge measurements in the Sudano-Sahel region: case study of the Logone catchment, Lake Chad Basin

Africa has a paucity of long(term reliable meteorological ground station data and reanalysis products are used to provide the climate estimations that are important for climate change projections. This paper uses monthly observed precipitation records in the Logone catchment of the Lake Chad Basin (LCB) to evaluate the performance of two global reanalysis products: the Climate Forecasting System Reanalysis (CFSR) and ERA Interim datasets. The two reanalysis products reproduced the monthly, annual and decadal cycle of precipitation and variability relatively accurately albeit with some discrepancies. The catchment rainfall gradient was also well captured by the two products. There are good correlations between the reanalysis and rain gauge datasets though significant deviations exist, especially for CFSR. Both reanalysis products overestimated rainfall in 68% of the rain gauge stations. ERA Interim produced the lowest bias and mean absolute error (MAE) with average values of 2% and 6.5mm/month respectively compared to 15% and 34mm/month for the CFSR. However, both reanalysis products systematically underestimated annual rainfall in the catchment during the period 1997(2002 for ERA(Interim and 1998(2000 for CFSR. This research demonstrates that evaluating reanalysis products in remote areas like the Logone catchment enables users to identify artefacts inherent in reanalysis datasets. This will facilitate improvements in certain aspects of the reanalysis forecast model physics and parametrisation to improve reanalysis dataset quality. Our study concludes that the application of each reanalysis product in the catchment will depend on the purpose for which it is to be used and the spatial scale required

Evaluating global reanalysis datasets as input for hydrological modelling in the Sudano-Sahel region

This paper investigates the potential of using global reanalysis datasets as input for hydrological modelling in the data-scarce Sudano-Sahel region. To achieve this, we used two global atmospheric reanalyses (Climate Forecasting System Reanalysis and European Center for Medium-Range Weather Forecasts (ECMWF) ERA-Interim) datasets and one global meteorological forcing dataset WATCH Forcing Data methodology applied to ERA-Interim (WFDEI). These datasets were used to drive the Soil and Water Assessment Tool (SWAT) in the Logone catchment in the Lake Chad basin. Model performance indicators after calibration showed that, at daily and monthly time steps, only WFDEI produced Nash Sutcliff Efficiency (NSE) and Coefficient of Determination (R2) values above 0.50. Despite a general underperformance compared to WFDEI, CFSR performed better than the ERA-Interim. Model uncertainty analysis after calibration showed that more than 60% of all daily and monthly observed streamflow values at all hydrometric stations were bracketed within the 95 percent prediction uncertainty (95PPU) range for all datasets. Results from this study also show significant differences in simulated actual evapotranspiration estimates from the datasets. Overall results showed that biased corrected WFDEI outperformed the two reanalysis datasets; meanwhile CFSR performed better than the ERA-Interim. We conclude that, in the absence of gauged hydro-meteorological data, WFDEI and CFSR could be used for hydrological modelling in data-scarce areas such as the Sudano-Sahel region

Estimating the amount of cold water wastage in UK households

Current and future water demand pressures arising from inadequate supplies and forecasts of increased population in the UK have prompted the government and other stakeholders to set up strategies that will ensure sustainable water supplies. Demand-side management strategies are one of the key priorities being pursued and the water regulator, Ofwat, has set a target of 13% reduction from 2010 levels of per capita household water consumption in England and Wales by 2030. This paper reports on estimated water wastage in UK households resulting from users waiting for water to reach a sufficiently warm temperature when using a hot water outlet (including kitchen sinks, showers and hand wash basins). It is estimated that, on average, 10% of the daily average per capita water consumption in UK households is wasted in this way. Possible means of reducing this waste are identified and a recommendation is made that boilers should carry a water efficiency rating in addition to the standard energy efficiency rating

Mining network-level properties of Twitter altmetrics data

© 2019, Akadémiai Kiadó, Budapest, Hungary. Social networking sites play a significant role in altmetrics. While 90% of all altmetric mentions come from Twitter, the known microscopic and macroscopic properties of Twitter altmetrics data are limited. In this study, we present a large-scale analysis of Twitter altmetrics data using social network analysis techniques on the ‘mention’ network of Twitter users. Exploiting the network-level properties of over 1.4 million tweets, corresponding to 77,757 scholarly articles, this study focuses on the following aspects of Twitter altmetrics data: (a) the influence of organizational accounts; (b) the formation of disciplinary communities; (c) the cross-disciplinary interaction among Twitter users; (d) the network motifs of influential Twitter users; and (e) testing the small-world property. The results show that Twitter-based social media communities have unique characteristics, which may affect social media usage counts either directly or indirectly. Therefore, instead of treating altmetrics data as a black box, the underlying social media networks, which may either inflate or deflate social media usage counts, need further scrutiny

Extracting scientific trends by mining topics from Call for Papers

© 2019, Emerald Publishing Limited. Purpose: The purpose of this paper is to present a novel approach for mining scientific trends using topics from Call for Papers (CFP). The work contributes a valuable input for researchers, academics, funding institutes and research administration departments by sharing the trends to set directions of research path. Design/methodology/approach: The authors procure an innovative CFP data set to analyse scientific evolution and prestige of conferences that set scientific trends using scientific publications indexed in DBLP. Using the Field of Research code 804 from Australian Research Council, the authors identify 146 conferences (from 2006 to 2015) into different thematic areas by matching the terms extracted from publication titles with the Association for Computing Machinery Computing Classification System. Furthermore, the authors enrich the vocabulary of terms from the WordNet dictionary and Growbag data set. To measure the significance of terms, the authors adopt the following weighting schemas: probabilistic, gram, relative, accumulative and hierarchal. Findings: The results indicate the rise of “big data analytics” from CFP topics in the last few years. Whereas the topics related to “privacy and security” show an exponential increase, the topics related to “semantic web” show a downfall in recent years. While analysing publication output in DBLP that matches CFP indexed in ERA Core A* to C rank conference, the authors identified that A* and A tier conferences not merely set publication trends, since B or C tier conferences target similar CFP. Originality/value: Overall, the analyses presented in this research are prolific for the scientific community and research administrators to study research trends and better data management of digital libraries pertaining to the scientific literature

A methodology for the risk assessment of climate variability and change under uncertainty

Existing methods for the assessment of the potential impacts of climate change in productive activities and sectors are usually limited to point estimates that do not consider the inherent variability and uncertainty of climatic and socioeconomic variables. This is a major drawback given that only a limited and potentially misleading estimation of risk can be expected when ignoring such determinant factors. In this paper, a new methodology is introduced that is capable of integrating the agent's beliefs and expert judgment into the assessment of the potential impacts of climate change in a quantitative manner by means of an objective procedure. The goal is to produce tailor-made information to assist decision-making under uncertainty in a way that is consistent with the current state of knowledge and the available subjective "expert" information. Time-charts of the evolution of different risk measures, that can be relevant for assisting decision-making and planning, can be constructed using this new methodology. This methodology is illustrated with a case study of coffee production in Mexico. Time-dependent probabilistic scenarios for coffee production and income, conditional on the agent's beliefs and expert judgment, are developed for the average producer under uncertain future conditions. It is shown that variability in production and income, generated by introducing climate variability and uncertainty are important factors affecting decision-making and the assessment of economic viability that are frequently ignored. The concept of Value at Risk, commonly applied in financial risk management, is introduced as a means for estimating the maximum expected loss for a previously chosen confidence level. Results are tailor-made for agents that have incomplete information and different beliefs. In this case study, the costs of climate change for coffee production in Veracruz are estimated to have a present value representing from 3 to 14 times the current annual value of coffee production in the state. © 2011 The Author(s)