Uncertainty In Trends Research Articles

A truncated nuclear norm regularization model for signal extraction from GNSS coordinate time series

Precise signal extraction of Global Navigation Satellite System (GNSS) coordinate time series is of great significance for explaining geophysical phenomena. In this study, the low-rank characteristic of the Hankel matrix induced by the noise-free signals is utilized to recover the trend and seasonal signals from the observed GNSS coordinate time series. To this end, we first investigated the low rank characteristic of the Hankel matrix induced by the GNSS coordinate time series, then a Truncated Nuclear Norm Regularization (TNNR) technique was proposed by truncating the largest few singular values and minimizing the sum of the remaining singular values, so that the rank of matrix can be well approximated from its noisy data. To solve the minimization problem, an iterative technique called Accelerated Proximal Gradient Line-search (APGL) was employed by dividing the objective function into a closed non-smooth function and a convex smooth function. Further, an improved iterative procedure called TNNR-APGLM was proposed to boost the reconstruction performance of TNNR-APGL. Extensive experiments were carried out on simulated as well as real GNSS datasets to evaluate the performance of the TNNR-based methods. Experimental results indicate that the proposed scheme offers competitive performance with respect to the indicators such as misfit, trend uncertainty and residual power spectral density when compared with several State-Of-The-Art (SOTA) methods.

Effects of Anthropogenic Aerosol and Greenhouse Gas Emissions on Northern Hemisphere Monsoon Precipitation: Mechanisms and Uncertainty

Abstract Northern Hemisphere land monsoon (NHLM) precipitation exhibits multidecadal variability, decreasing over the second half of the twentieth century and increasing after the 1980s. We use a novel combination of CMIP6 simulations and several large ensembles to assess the relative roles of drivers of monsoon precipitation trends, analyzing the effects of anthropogenic aerosol (AA), greenhouse gas (GHG) emissions, and natural forcing. We decomposed summer global monsoon precipitation anomalies into dynamic and thermodynamic terms to assess the drivers of precipitation trends. We show that the drying trends are likely to be mainly due to increased AA emissions, which cause shifts of the atmospheric circulation and a decrease in moisture advection. Increases in GHG emissions cause monsoon precipitation to increase due to strengthened moisture advection. The uncertainty in summer monsoon precipitation trends is explored using three initial-condition large ensembles. AA emissions have strong controls on monsoon precipitation trends, exceeding the effects of internal climate variability. However, uncertainties in the effects of external forcings on monsoon precipitation are high for specific periods and monsoon domains, resulting from differences in how models simulate shifts in atmospheric circulation. The effect of AA emissions is uncertain over the northern African monsoon domain due to differences among climate models in simulating the effects of AA emissions on net shortwave radiation over the North Atlantic Ocean.

A Multidataset Assessment of Climatic Drivers and Uncertainties of Recent Trends in Evaporative Demand across the Continental United States

AbstractIncreased atmospheric evaporative demand has important implications for humans and ecosystems in water-scarce lands. While temperature plays a significant role in driving evaporative demand and its trend, other climate variables are also influential and their contributions to recent trends in evaporative demand are unknown. We address this gap with an assessment of recent (1980–2020) trends in annual reference evapotranspiration (ETo) and its drivers across the continental United States based on five gridded datasets. In doing so, we characterize the structural uncertainty of ETo trends and decompose the relative influences of temperature, wind speed, solar radiation, and humidity. Results highlight large and robust changes in ETo across much of the western United States, centered on the Rio Grande region where ETo increased 135–235 mm during 1980–2020. The largest uncertainties in ETo trends are in the central and eastern United States and surrounding the Upper Colorado River. Trend decomposition highlights the strong and widespread influence of temperature, which contributes to 57% of observed ETo trends, on average. ETo increases are mitigated by increases in specific humidity in non-water-limited regions, while small decreases in specific humidity and increases in wind speed and solar radiation magnify ETo increases across the West. Our results show increases in ETo across the West that are already emerging outside the range of variability observed 20–40 years ago. Our results suggest that twenty-first-century land and water managers need to plan for an already increasing influence of evaporative demand on water availability and wildfire risks.Significance StatementIncreased atmospheric thirst due to climate warming has the potential to decrease water availability and increase wildfire risks in water-scarce regions. Here, we identified how much atmospheric thirst has changed across the continental United States over the past 40 years, what climate variables are driving the change, and how consistent these changes are among five data sources. We found that atmospheric thirst is consistently emerging outside the range experienced in the late twentieth century in some western regions with 57% of the change driven by temperature. Importantly, we demonstrate that increased atmospheric thirst has already become a persistent forcing of western landscapes and water supplies toward drought and will be an essential consideration for land and water management planning going forward.

Urban and rural patterns of typhoon mortality in the Philippines

Storms continue to be the deadliest type of weather-related disasters globally. The Philippines is one of the most at risk countries to disasters, yet there continues to be gaps in understanding where and why people are killed in typhoons – the country's most prominent natural hazard. This research sought to understand how typhoon mortality varies across the Philippines at the municipal level, focusing on differences in rural and urban municipalities between 2005 and 2015. Generalised linear regression models (GLMs), including Poisson and negative binomial (NB), were used to analyse the relationship between typhoon mortality and level of urbanisation while controlling for social vulnerability and typhoon exposure. Findings indicate that typhoon mortality is disproportionality concentrated in emerging, rather than established, urban centres. Deaths from typhoons were significantly higher per capita in older age groups and amongst men, with drowning accounting for 71% of deaths, although there is uncertainty in these later trends which show the need for investment in national disaster databases. Our results make contributions to understanding of urban-rural patterns of disaster risk and the determinants of typhoon mortality in the Philippines.

Large discrepancies of global greening: Indication of multi-source remote sensing data

Global warming has a great impact on the activities of terrestrial vegetation. A consensus has been reached that the global vegetation is greening from the 1980–2010s. However, the trends of global vegetation are highly uncertain after 2000. Therefore, we used multi-source remote sensing vegetation index (VI), climate data, and Mann–Kendall trend analysis to explore the global vegetation trend and its uncertainty from 2001 to 2016. The effects of climate on the changes in vegetation were also investigated. We found that GIMMS-based VIs exhibited decreasing trends. By contrast, MODIS-based VIs and GLOBMAP LAI tended to increase. Evergreen broad-leaf forest contributed the most to the uncertainty of global vegetation trends, and the uncertainty of December–January–February and September–October–November was higher than that in the other seasons. The correlation of forest VI and temperature was the highest in March–April–May, whereas the correlation of non-forest VI and precipitation was higher than that of the forest. The anomalies of GIMMS-based VIs and mean annual precipitation were more consistent in the evergreen broad-leaf forest, woody savannas, mixed forest, evergreen needle-leaf forest, and deciduous needle-leaf forest than those in biomes under the impact of 2015–2016 El Niño.

Trends in Downwelling Longwave Radiance Over the Southern Great Plains

AbstractDownwelling longwave radiation is an important part of the surface energy budget. Spectral trends in the downwelling longwave radiance (DLR) provide insight into the radiative drivers of climate change. In this research, we process and analyze a 23‐year DLR record measured by the Atmospheric Emitted Radiance Interferometer (AERI) at the U.S. Department of Energy Atmospheric Radiation Program Southern Great Plains (SGP) site. Two AERIs were deployed at SGP with an overlapping observation period of about 10 years, which allows us to examine the consistency and accuracy of the measurements and to account for discrepancies between them due to errors associated with the instruments themselves. We then analyzed the all‐sky radiance trends in DLR, which are associated with the surface warming trend at SGP during this same period and also the complex changes in meteorological conditions. For instance, the observed radiance in the CO2 absorption band follows closely the near‐surface air temperature variations. The significant positive radiance trends in weak absorption channels, such as in the wings of the CO2 band and in the weak absorption channels in the H2O vibration‐rotational band, show earlier detectability of climate change. The magnitude of the radiance trend uncertainty in the DLR record mainly results from internal climate variability rather than from measurement error, which highlights the importance of continuing the DLR spectral measurements to unambiguously detect and attribute climate change.

Australian Coastal Sea Level Trends Over 16 yr of Reprocessed Jason Altimeter 20‐Hz Data Sets

AbstractThis article analyzes the variation of sea level trends along the Australian coast zone (0–100 km) using 16 yr (2002–2018) of tide gauge records, reprocessed Jason data and European Space Agency (ESA) Climate Change Initiative (CCI) sea level product. The reprocessed Jason data are first compared with tide gauge and 3‐yr Sentinel‐3A data. Then the empirical orthogonal function analysis demonstrates that local sea levels are not only significantly affected by the El Niño Southern Oscillation‐related signals, but also moderately correlated with the Indian Ocean Dipole. A multivariate linear regression model with optimized noise model is thus built to reduce the impact of natural variability on the trend estimation. The results show that the geographical pattern of sea level rise from reprocessed Jason data and ESA CCI sea level product are highly consistent, with the rates decreasing from the northeast coast (6–8 mm yr−1) anticlockwise to the southeast coast (2–4 mm yr−1). The sea level trends from reprocessed Jason data are in good agreement with those from tide gauge records, though they are ∼1.5 mm yr−1 higher than those from the ESA CCI data product. The discrepancy between two altimeter datasets is acceptable considering the trend uncertainty (i.e., one standard deviation) from 20‐Hz along‐track points is at the level of 1–2 mm yr−1. It is also found that sea level trends decrease significantly from 5.8 ± 1.5 mm yr−1 offshore to 3.4 ± 1.2 mm yr−1 toward the southeast coasts. This may be because the continental slope precludes the propagation of sea levels from the East Australian Current dominated oceans to the coast.

Correcting Coarse‐Grid Weather and Climate Models by Machine Learning From Global Storm‐Resolving Simulations

AbstractGlobal atmospheric “storm‐resolving” models with horizontal grid spacing of less than 5 km resolve deep cumulus convection and flow in complex terrain. They promise to be reference models that could be used to improve computationally affordable coarse‐grid global climate models across a range of climates, reducing uncertainties in regional precipitation and temperature trends. Here, machine learning of nudging tendencies as functions of column state is used to correct the physical parameterization tendencies of temperature, humidity, and optionally winds, in a real‐geography coarse‐grid model (FV3GFS with a 200 km grid) to be closer to those of a 40‐day reference simulation using X‐SHiELD, a modified version of FV3GFS with a 3 km grid. Both simulations specify the same historical sea‐surface temperature fields. This methodology builds on a prior study using a global observational analysis as the reference. The coarse‐grid model without machine learning corrections has too few clouds, causing too much daytime heating of land surfaces that creates excessive surface latent heat flux and rainfall. This bias is avoided by learning downwelling radiative flux from the fine‐grid model. The best configuration uses learned nudging tendencies for temperature and humidity but not winds. Neural nets slightly outperform random forests. Forecasts of 850 hPa temperature gain 18 hr of skill at 3–7 days leads and time‐mean precipitation patterns are improved 30% by applying the ML correction. Adding machine‐learned wind tendencies improves 500 hPa height skill for the first five days of forecasts but degrades time‐mean upper tropospheric temperature and zonal wind patterns thereafter.

Long-Term Changes in Ionospheric Climate in Terms of foF2

There is not only space weather; there is also space climate. Space climate includes the ionospheric climate, which is affected by long-term trends in the ionosphere. One of the most important ionospheric parameters is the critical frequency of the ionospheric F2 layer, foF2, which corresponds to the maximum ionospheric electron density, NmF2. Observational data series of foF2 have been collected at some stations for as long as over 60 years and continents are relatively well covered by a network of ionosondes, instruments that measure, among others, foF2. Trends in foF2 are relatively weak. The main global driver of long-term trends in foF2 is the increasing concentration of greenhouse gases, namely CO2, in the atmosphere. The impact of the other important trend driver, the secular change in the Earth’s main magnetic field, is very regional, being positive in some regions, negative in others, and neither in the rest. There are various sources of uncertainty in foF2 trends. One is the inhomogeneity of long foF2 data series. The main driver of year-to-year changes in foF2 is the quasi-eleven-year solar cycle. The removal of its effect is another source of uncertainty. Different methods might provide somewhat different strengths among trends in foF2. All this is briefly reviewed in the paper.

Process-based analysis of terrestrial carbon flux predictability

Abstract. Despite efforts to decrease the discrepancy between simulated and observed terrestrial carbon fluxes, the uncertainty in trends and patterns of the land carbon fluxes remains high. This difficulty raises the question of the extent to which the terrestrial carbon cycle is predictable and which processes explain the predictability. Here, the perfect model approach is used to assess the potential predictability of net primary production (NPPpred) and heterotrophic respiration (Rhpred) by using ensemble simulations conducted with the Max Planck Institute Earth system model. In order to assess the role of local carbon flux predictability (CFpred) in the predictability of the global carbon cycle, we suggest a new predictability metric weighted by the amplitude of the flux anomalies. Regression analysis is used to determine the contribution of the predictability of different environmental drivers to NPPpred and Rhpred (soil moisture, air temperature, and radiation for NPP, and soil organic carbon, air temperature, and precipitation for Rh). Global NPPpred is driven to 62 % and 30 % by the predictability of soil moisture and temperature, respectively. Global Rhpred is driven to 52 % and 27 % by the predictability of soil organic carbon and temperature, respectively. The decomposition of predictability shows that the relatively high Rhpred compared to NPPpred is due to the generally high predictability of soil organic carbon. The seasonality in NPPpred and Rhpred patterns can be explained by the change in limiting factors over the wet and dry months. Consequently, CFpred is controlled by the predictability of the currently limiting environmental factor. Differences in CFpred between ensemble simulations can be attributed to the occurrence of wet and dry years, which influences the predictability of soil moisture and temperature. This variability of predictability is caused by the state dependency of ecosystem processes. Our results reveal the crucial regions and ecosystem processes to be considered when initializing a carbon prediction system.

Functional disability with systematic trends and uncertainty: a comparison between China and the US

AbstractChina and the US are two contrasting countries in terms of functional disability and long-term care. China is experiencing declining family support for long-term care and developing private long-term care insurance. The US has a more developed public aged care system and private long-term care insurance market than China. Changes in the demand for long-term care are driven by the levels, trends and uncertainty in mortality and functional disability. To understand the future potential demand for long-term care, we compare mortality and functional disability experiences in China and the US, using a multi-state latent factor intensity model with time trends and systematic uncertainty in transition rates. We estimate the model with the Chinese Longitudinal Healthy Longevity Survey (CLHLS) and the US Health and Retirement Study (HRS) data. The estimation results show that if trends continue, both countries will experience longevity improvement with morbidity compression and a declining proportion of the older population with functional disability. Although the elderly Chinese have a shorter estimated life expectancy, they are expected to spend a smaller proportion of their future lifetime functionally disabled than the elderly Americans. Systematic uncertainty is shown to be significant in future trends in disability rates and our model estimates higher uncertainty in trends for the Chinese elderly, especially for urban residents.

The Regional Importance of Oxygen Demand and Supply for Historical Ocean Oxygen Trends

AbstractOcean deoxygenation is an emerging hazard for marine ecosystems and a fingerprint of anthropogenic change. Interior ocean oxygen concentrations respond to changes in ventilation that supply oxygen and the demand of biogeochemical processes that consume oxygen. A better understanding of their regional importance would improve confidence in Earth System Model projections, which underpin ecosystem risk and vulnerability assessments. Using a hindcast reanalysis simulation, we find that oxygen trends between 1975 and 2014 in low‐oxygen zones along eastern margins are strongly affected by biogeochemical processes that alter oxygen demand, while oxygen‐rich regions of the open ocean are driven by ventilation changes. A similar regional distinction emerges among CMIP6 Earth System Models. Therefore, while biogeochemical functioning is an important source of uncertainty in low‐latitude, low‐oxygen regions, uncertainty in global trends is due to insufficient physical supply changes in Earth System Models, which is confirmed using repeat hydrographic section data in the southern mid‐latitudes.

The inherent uncertainty of precipitation variability, trends, and extremes due to internal variability, with implications for Western US water resources

AbstractThe approximately century-long instrumental record of precipitation over land reflects a single sampling of internal variability. Thus, the spatiotemporal evolution of the observations is only one realization of `what could have occurred' given the same climate system and boundary conditions, but different initial conditions. While climate models can be used to produce initial-condition large ensembles that explicitly sample different sequences of internal variability, an analogous approach is not possible for the real world. Here, we explore the use of a statistical model for monthly precipitation to generate synthetic ensembles based on a single record. When tested within the context of the NCAR Community Earth System Model version 1 Large Ensemble (CESM1-LE), we find that the synthetic ensemble can closely reproduce the spatiotemporal statistics of variability and trends in winter precipitation over the extended contiguous United States, and that it is difficult to infer the climate change signal in a single record given the magnitude of the variability. We additionally create a synthetic ensemble based on the Global Precipitation Climatology Centre (GPCC) dataset, termed the GPCC-synth-LE; comparison of the GPCC-synth-LE with the CESM1-based ensembles reveals differences in the spatial structures and magnitudes of variability, highlighting the advantages of an observationally-based ensemble. We finally use the GPCC-synth-LE to analyze three water resource metrics in the Upper Colorado River Basin: frequency of dry, wet, and whiplash years. Thirty-one year ‘climatologies’ in the GPCC-synth-LE can differ by over 20% in these key water resource metrics due to sampling of internal variability, and individual ensemble members in the GPCC-synth-LE can exhibit large near-monotonic trends over the course of the last century due to sampling of variability alone.

Identifying the Drivers of Modeling Uncertainties in Isoprene Emissions: Schemes Versus Meteorological Forcings

AbstractIsoprene dominates global biogenic volatile organic compounds and has significant impacts on atmospheric chemistry. Global simulations of isoprene emissions show large uncertainties in both the trend and interannual variability. Here, we explored the uncertainties of simulated isoprene emissions during 1982–2015 using the Yale Interactive terrestrial Biosphere model, which is implemented with two emission schemes (PS_BVOC and Model of Emissions of Gases and Aerosols from Nature [MEGAN]) and driven by two long‐term reanalysis meteorology (WFDEI and Modern‐Era Retrospective Analysis for Research and Applications [MERRA]). Model evaluations show that both PS_BVOC and MEGAN schemes capture reasonable spatial patterns of the global isoprene emissions. The averaged fluxes from simulations using two meteorological forcings show isoprene trends of 0.69 Tg C m2 a−1 with PS_BVOC scheme but −0.22 Tg C m2 a−1 with MEGAN scheme. Such opposite trend is mainly because PS_BVOC considers both CO2 fertilization and inhibition effects to isoprene while the MEGAN scheme implements CO2 inhibition effect alone. Meanwhile, the averaged fluxes from simulations using two schemes yield interannual variability of 1.59% with WFDEI and 2.06% with MERRA reanalyses. Such discrepancies are mainly attributed to the larger variability in MERRA data. On the global scale, differences in schemes are the main driver of uncertainties in isoprene trends. In contrast, differences in meteorological forcings dominate the uncertainties in the interannual variability of isoprene emissions. Our sensitivity experiments reveal the key sources of modeling uncertainties, which are vital for the improvement of parameterizations and future projections of isoprene emissions.

Large uncertainties in trends of energy demand for heating and cooling under climate change

The energy demand for heating and cooling buildings is changing with global warming. Using proxies of climate-driven energy demand based on the heating and cooling Degree-Days methodology applied to thirty global climate model simulations, we show that, over all continental areas, the climate-driven energy demand trends for heating and cooling were weak, changing by less than 10% from 1950 to 1990, but become stronger from 1990 to 2030, changing by more than 10%. With the multi-model mean, the increasing trends in cooling energy demand are more pronounced than the decreasing trends in heating. The changes in cooling, however, are highly variable depending on individual simulations, ranging from a few to several hundred percent in most of the densely populated mid-latitude areas. This work presents an example of the challenges that accompany future energy demand quantification as a result of the uncertainty in the projected climate.

Assessing conservation status with extensive but low-resolution data: Application of frequentist and Bayesian models to endangered Athabasca River rainbow trout.

Use of extensive but low-resolution abundance data is common in the assessment of species at-risk status based on quantitative decline criteria under International Union for Conservation of Nature (IUCN) and national endangered species legislation. Such data can be problematic for 3 reasons. First, statistical power to reject the null hypothesis of no change is often low because of small sample size and high sampling uncertainty leading to a high frequency of type II errors. Second, range-wide assessments composed of multiple site-specific observations do not effectively weight site-specific trends into global trends. Third, uncertainty in site-specific temporal trends and relative abundance are not propagated at the appropriate spatial scale. A common result is the propensity to underestimate the magnitude of declines and therefore fail to identify the appropriate at-risk status for a species. We used 3 statistical approaches, from simple to more complex, to estimate temporal decline rates for a designatable unit (DU) of rainbow trout in the Athabasca River watershed in western Canada. This DU is considered a native species for purposes of listing because of its genetic composition characterized as >0.95 indigenous origin in the face of continuing introgressive hybridization with introduced populations in the watershed. Analysis of abundance trends from 57 time series with a fixed-effects model identified 33 sites with negative trends, but only 2 were statistically significant. By contrast, a hierarchical linear mixed model weighted by site-specific abundance provided a DU-wide decline estimate of 16.4% per year and a 3-generation decline of 93.2%. A hierarchical Bayesian mixed model yielded a similar 3-generation decline trend of 91.3% and the posterior distribution showed that the estimate had a >99% probability of exceeding thresholds for an endangered listing. We conclude that the Bayesian approach was the most useful because it provided a probabilistic statement of threshold exceedance in support of an at-risk status recommendation.

Going with the Trend: Forecasting Seasonal Climate Conditions under Climate Change

AbstractFor managing climate variability and adapting to climate change, seasonal forecasts are widely produced to inform decision-making. However, seasonal forecasts from global climate models are found to poorly reproduce temperature trends in observations. Furthermore, this problem is not addressed by existing forecast postprocessing methods that are needed to remedy biases and uncertainties in model forecasts. The inability of the forecasts to reproduce the trends severely undermines user confidence in the forecasts. In our previous work, we proposed a new statistical postprocessing model that counteracted departures in trends of model forecasts from observations. Here, we further extend this trend-aware forecast postprocessing methodology to carefully treat the trend uncertainty associated with the sampling variability due to limited data records. This new methodology is validated on forecasting seasonal averages of daily maximum and minimum temperatures for Australia based on the SEAS5 climate model of the European Centre for Medium-Range Weather Forecasts. The resulting postprocessed forecasts are shown to have proper trends embedded, leading to greater accuracy in regions with significant trends. The application of this new forecast postprocessing is expected to boost user confidence in seasonal climate forecasts.

Dealing with trend uncertainty in empirical estimates of European rainfall climate for insurance risk management

AbstractThe insurance industry uses mathematical models to estimate the risks due to future natural catastrophes. For climate‐related risks, historical climate data are a key ingredient used in making the models. Historical data for temperature and sea level often show clear and readily quantified climate change driven trends, and these trends would typically be accounted for when building risk models by adjusting earlier values to render the earlier data relevant to the future climate. For other climate variables, such as rainfall in many parts of the world, the questions of whether there are climate change driven trends in the historical data, and how to quantify them if there are, are less simple to answer. We investigate these questions in the context of European rainfall with a specific focus on how to deal with the uncertainty around trend estimates. We compare 10 empirical methodologies that one might use to model and predict trends, including traditional statistical testing and alternatives to statistical testing based on standard methods from model selection and model averaging. We emphasize prediction and risk assessment, rather than detection of trends, as our goal. Viewed in terms of this goal, the methods we consider each have qualitative and quantitative advantages and disadvantages. Understanding these advantages and disadvantages can help risk modellers make a choice as to which method to use, and based on the results we present, we believe that in many common situations model averaging methods, as opposed to statistical testing or model selection, are the most appropriate.

АНАЛИЗ КОНКУРЕНТОСПОСОБНОСТИ ПРОЕКТА ЭНЕРГОБЛОКА БН НОВОГО ПОКОЛЕНИЯ С УЧЁТОМ СИСТЕМНЫХ ТРЕБОВАНИЙ

The development of an energy system, including nuclear, is a long and multi-stage process. The complexity of the process is determined by the deep degree of workforce decomposition at nuclear power facilities, high capital and science intensity of the industry. When assessing the prospects, it is necessary to take into account many significant external factors and uncertainty of the future macroeconomics development trends. In the process of analysis and decision-making on the development of the country's energy system, there are many participants of technological concepts are involved, often with divergent interests. The main task of justifying decisions on the development of the energy industry and, in particular, nuclear power are coordination of interests of all subjects of relations and methodology the formation of mechanisms to ensure the development process. In such an assessment, the ultimate goal is sustainable development. To determine the technological development of the energy system with nuclear and non-nuclear power technologies for the next 5-10 years, it is necessary to assess the long-term prospects of such scenarios. This task requires a multi-criteria approach to analyze the competitiveness of technology using a list of criteria of competitive advantages, combining economic indicators and indicators that characterize safety, environmental impact, risks of implementation, development prospects. This paper discusses the results of multi-criteria system analysis using international assessment tools. The transfer of Russian nuclear power to the two-component nuclear energy system with VVER and BN reactors and the expansion of the international fuel business by providing a full range of NFC services was evaluated using a set of key criteria.

Data Construction and Spatiotemporal Trend Attribution of Runoff over the African Continent (1981–2016)

AbstractThe availability of streamflow records in Africa has been declining since the 1980s due to malfunctioning gauging stations and data collection failures. Africa also has insufficient hydrological information owing to the allocation of few resources to research efforts. Unreliable runoff datasets and large uncertainties in runoff trends due to climate change patterns and human activities are major challenges to water resource management in Africa. Therefore, this study aimed to improve runoff estimates and to assess runoff trend responses to climate change and human activities in Africa during 1981–2016. Using statistical methods, monthly gridded runoff datasets were generated for the period of 1981–2016 from a modified runoff curve number method calibrated with river discharge data from 535 gauging stations. According to the cross-validation results, the constructed runoff datasets comprised the Nash and Sutcliffe coefficients ranging from 0.5 to 1, coefficients of determination ranging from 0.5 to 1 and percent biases between ±25% for a large number of stations up to 73%, 80% and 91% of the 535 gauged catchments used as references. Analysis of runoff trend responses to climate change and human activities revealed that land cover change contributed more (72%) to the observed net runoff change (0.30%•a−1) than continental climate changes (28%). These contributions were results of cropland expansion rate of 0.46%•a−1 and a precipitation increase of 0.07%•a−1. The performance and simplicity of the statistical methods used in this study could be useful for improving runoff estimations in other regions with limited streamflow data data. The results of the current study could be important to natural resource managers and decision makers in terms of raising awareness of climate change adaptation strategies and agricultural land-use policies in Africa.