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A energia solar é considerada um dos recursos de energia renovável mais importantes para diminuir a dependência de combustíveis fósseis. Portanto, este estudo tem como objetivo realizar uma revisão sistemática da literatura sobre pesquisas de alto impacto sobre os impactos ambientais evitados com o uso da energia solar fotovoltaica no setor industrial para investigar tendências neste corpo da literatura. Para tanto, foram aplicados filtros ao portfólio bruto entre 209 artigos e selecionados 22 para identificar os estudos mais relevantes e de alto impacto na área. Os resultados mostram as principais tendências do referido corpo da literatura, as possíveis conexões da energia solar fotovoltaica e os impactos ambientais na indústria, bem como os principais investigadores que realizam os estudos. Este estudo fornece uma visão geral dos impactos evitados com o uso da energia solar fotovoltaica nas possibilidades de pesquisas futuras.

Using climate scenarios from only 1 or a small number of global climate models (GCMs) in climate change impact studies may lead to biased assessment due to large uncertainty in climate projections. Ensemble means in impact projections derived from a multi-GCM ensemble are often used as best estimates to reduce bias. However, it is often time consuming to run process-based models (e.g. hydrological and crop models) in climate change impact studies using numerous climate scenarios. It would be interesting to investigate if using a reduced number of climate scenarios could lead to a reasonable estimate of the ensemble mean. In this study, we generated a single ensemble-mean climate scenario (En-WG scenario) using ensemble means of the change factors derived from 20 GCMs included in CMIP5 to perturb the parameters in a weather generator, LARS-WG, for selected locations across Canada. We used En-WG scenarios to drive crop growth models in DSSAT ver. 4.7 to simulate crop yields for canola and spring wheat under RCP4.5 and RCP8.5 emission scenarios. We evaluated the potential of using the En-WG scenarios to simulate crop yields by comparing them with crop yields simulated with the LARS-WG generated climate scenarios based on each of the 20 GCMs (WG scenarios). Our results showed that simulated crop yields using the En-WG scenarios were often close to the ensemble means of simulated crop yields using the 20 WG scenarios with a high probability of outperforming simulations based on a randomly selected GCM. Further studies are required, as the results of the proposed approach may be influenced by selected crop types, crop models, weather generators, and GCM ensembles.

The extraction of natural resources, such as minerals, oil, and gas, can have profound economic effects. The application of economic analysis methods in impact studies of resource extraction projects holds potential to inform decision-making in order to optimise gains and minimise negative externalities. This paper aims to identify and characterise peer-reviewed publications that report on economic studies implemented as part of impact assessments of resource extraction projects. We conducted a systematic scoping review in PubMed and Scopus of articles published between 1998 and 2020. Out of 1,579 raw hits, we identified 13 articles describing 15 economic analyses of resource extraction projects. Half of the identified papers presented economic analyses conducted in the context of mining and oil/gas projects. The majority of the identified studies dealt with the cost and/or benefits of environmental and/or social impacts. Only one study investigated economic aspects associated with potential health impacts. Given the small number of papers identified, economic analysis of impacts associated with natural resource extraction projects seems to be a small field of published research. Yet the inclusion of economic analysis in impact assessment of resource extraction projects holds promise to better harness benefits for local communities and governments while minimising negative externalities. © 2021 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

This study investigates the uncertainty in hydroclimatic modelling chain components on future changes in potential evapotranspiration, specially on low flows, at the watershed scale. A large domain...

The human impacts of changes in heat events depend on changes in the joint behavior of temperature and humidity. Little is currently known about these complex joint changes, either in observations or projections from general circulation models (GCMs). Further, GCMs do not fully reproduce the observed joint distribution, implying a need for simulation methods that combine information from GCMs with observations for use in impact studies. We present an observation-based, conditional quantile mapping approach for the simulation of future temperature and humidity. A temperature simulation is first produced by transforming historical temperature observations to include projected changes in the mean and temporal covariance structure from a GCM. Next, a humidity simulation is produced by transforming humidity observations to account for projected changes in the conditional humidity distribution given temperature, using a quantile regression model. We use the Community Earth System Model Large Ensemble (CESM1-LE) to estimate future changes in summertime (June–August) temperature and humidity over the continental United States (CONUS), and then use the proposed method to create future simulations of temperature and humidity at stations in the Global Summary of the Day dataset. We find that CESM1-LE projects decreases in summertime humidity across CONUS for a given deviation in temperature from the forced trend, but increases in the risk of high dewpoint on historically hot days. In comparison with raw CESM1-LE output, our observation-based simulation largely projects smaller changes in the future risk of either high or low humidity on days with historically warm temperatures.

In its current form, the Automated Surface Observing System (ASOS) provides automated precipitation type reports of rain, snow, and freezing rain. Unknown precipitation can also be reported when the system recognizes precipitation is occurring but cannot classify it. A new method has been developed that can reprocess the raw ASOS 1-min-observation (OMO) data to infer the presence of freezing drizzle. This freezing drizzle derivation algorithm (FDDA) was designed to identify past freezing drizzle events that could be used for aviation product development and evaluation (e.g., Doppler radar hydrometeor classification algorithms, and improved numerical modeling methods) and impact studies that utilize archived datasets [e.g., National Transportation Safety Board (NTSB) investigations of transportation accidents in which freezing drizzle may have played a role]. Ten years of archived OMO data (2005–14) from all ASOS sites across the conterminous United States were reprocessed using the FDDA. Aviation routine weather reports (METARs) from human-augmented ASOS observations were used to evaluate and quantify the FDDA’s ability to infer freezing drizzle conditions. Advantages and drawbacks to the method are discussed. This method is not intended to be used as a real-time situational awareness tool for detecting freezing drizzle conditions at the ASOS but rather to determine periods for which freezing drizzle may have impacted transportation, with an emphasis on aviation, and to highlight the need for improved observations from the ASOS.

Currently, there are a large number of diverse climate datasets in existence, which differ, sometimes greatly, in terms of their data sources, quality control schemes, estimation procedures, and spatial and temporal resolutions. Choosing an appropriate dataset for a given application is therefore not a simple task. This study compares nine global/near-global precipitation datasets and three global temperature datasets over 3138 North American catchments. The chosen datasets all meet the minimum requirement of having at least 30 years of available data, so they could all potentially be used as reference datasets for climate change impact studies. The precipitation datasets include two gauged-only products (GPCC and CPC-Unified), two satellite products corrected using ground-based observations (CHIRPS V2.0 and PERSIANN-CDR V1R1), four reanalysis products (NCEP CFSR, JRA55, ERA-Interim, and ERA5), and one merged product (MSWEP V1.2). The temperature datasets include one gauge-based (CPC-Unified) and two reanalysis (ERA-Interim and ERA5) products. High-resolution gauge-based gridded precipitation and temperature datasets were combined as the reference dataset for this intercomparison study. To assess dataset performance, all combinations were used as inputs to a lumped hydrological model. The results showed that all temperature datasets performed similarly, albeit with the CPC performance being systematically inferior to that of the other three. Significant differences in performance were, however, observed between the precipitation datasets. The MSWEP dataset performed best, followed by the gauge-based, reanalysis, and satellite datasets categories. Results also showed that gauge-based datasets should be preferred in regions with good weather network density, but CHIRPS and ERA5 would be good alternatives in data-sparse regions.

Several studies have shown that hydrological models do not perform well when applied to periods with climate conditions that differ from those during model calibration. This has important implications for the application of these models in climate change impact studies. The causes of the low transferability to changed climate conditions have, however, only been investigated in a few studies. Here we revisit a study in Austria that demonstrated the inability of a conceptual semi-distributed HBV-type model to simulate the observed discharge response to increases in precipitation and air temperature. The aim of the paper is to shed light on the reasons for these model problems. We set up hypotheses for the possible causes of the mismatch between the observed and simulated changes in discharge and evaluate these using simulations with modifications of the model. In the baseline model, trends of simulated and observed discharge over 1978–2013 differ, on average over all 156 catchments, by 95±50 mm yr−1 per 35 years. Accounting for variations in vegetation dynamics, as derived from a satellite-based vegetation index, in the calculation of reference evaporation explains 36±9 mm yr−1 per 35 years of the differences between the trends in simulated and observed discharge. Inhomogeneities in the precipitation data, caused by a variable number of stations, explain 39±26 mm yr−1 per 35 years of this difference. Extending the calibration period from 5 to 25 years, including annually aggregated discharge data or snow cover data in the objective function, or estimating evaporation with the Penman–Monteith instead of the Blaney–Criddle approach has little influence on the simulated discharge trends (5 mm yr−1 per 35 years or less). The precipitation data problem highlights the importance of using precipitation data based on a stationary input station network when studying hydrologic changes. The model structure problem with respect to vegetation dynamics is likely relevant for a wide spectrum of regions in a transient climate and has important implications for climate change impact studies.

Bias correction methods have now emerged as the most commonly used approach when applying climate model outputs to impact studies. However, comparatively much fewer studies have looked at the limitations of bias correction caused by the very nature of the climate system. Two main sources of errors can affect the efficiency of bias correction over a future period: climate sensitivity and internal variability of the climate system. The former is related to differences in the forcing response between a climate model and the real climate system, whereas the latter results from the chaotic nature of the climate system. Using a “pseudo-reality” approach, this study investigates the contribution of these two sources of error to remaining biases of climate model after bias correction for future periods. The pseudo-reality approach uses one climate model as a reference dataset to correct other climate models. Results indicate that bias correction is beneficial over the reference period and in near future periods. However, large biases remain in future periods. The difference in climate sensitivities is the main contributor to the remaining biases in corrected data. Internal variability affects the near and far future similarly and may dominate in the near future, especially for precipitation. The impact of differences in climate sensitivity between the reference dataset and climate model data cannot be eliminated, while the impact of internal variability can be lessened by using a reference period for as long as possible to filter out low-frequency modes of variability.

Extreme temperature events derived from the global climate models (GCMs) are used for climate change impact studies in several sectors, especially in agriculture. Reducing the uncertainty in simulated crop yields based on extreme temperatures is an important task for reliable and relevant adaptation decisions. This study compared the performance of selected six global climate models in simulating temperature extreme events over Indian region for the 1976–2005 period. For this, performance statistics such as root mean square error, correlation coefficient, and agreement index were compared spatially and spatiotemporally. The study reveals that all the six models overestimate minimum and maximum temperature extremes for most parts of Central India, which resulted in hot bias. However, these models show a cold bias in simulating low temperature extremes over the Himalayan region. Further the study indicates that GFDL-ESM2M and MIROC-ESM-CHEM have performed relatively better in capturing temperature extreme events over Indian region among the six models that were found to closely simulate the observed climatology.

The MOSARH21 project evaluated the future impacts of climate change on streamflows from the French tributaries of the River Rhine based on recent climate projections (CMIP5 experiment). The adopted methodology relies on two hydrological models (GRSD and LARSIM), used jointly with an ensemble of climate projections disaggregated at the local scale. The impacts were quantified with several indicators describing rivers regimes, floods and low flows. The evolutions of mean interannual streamflow indicate a slight increase, which could even become important for the radiative scenario RCP 8.5 (the most pessimistic one in terms of climate warming). Flood could intensify in the near future (2021-2050). In the far future (2071-2100), the evolution of floods indicators is more uncertain, as the hydrological projections diverge. Low flows could be decreasing in the near future. Their evolution in the far future is more uncertain, going from drastic decrease to sensible rise depending on the chosen scenario. The results were compared to those from previous impact studies performed on the basin. Given the methodological differences and the obtained results, we can conclude that the MOSARH21 project is consistent with the conclusions of the previous studies.

This study outlines the immediate impact on local fisheries of the incident that took place on 21st April 2015. A barge capsized in the Nile in Upper Egypt’s Qena governorate after colliding with the foundations of the city's Dandara Bridge. The capsized barge was carrying 500 tons of phosphate (pre-industrial, inert material) for processing in a local factory. The study aimed to record any apparent effect of the incident on the fisheries in the immediate site vicinity and downstream. Also, it described the status quo of the fisheries around the incident location to compare it with the situation before the accident to confirm any short-term impact and to establish a baseline for any future, long-term, impact studies. Data were collected on fish assemblage structure in the incident site vicinity, the number of fishing boats and fishing methods that were applied to catch them. Furthermore, some fisheries biological parameters were assessed. The main conclusions derived from the study were that fishing in the vicinity of the incident is artisanal with small boats and simple fishing gears. The fisheries biological parameters of the area’s fishery are comparable to that of other similar environs, and that the incident did not have an apparent short term effect on the fishery profile of the area. The main recommendations resulted from this study were that the impact of the incident on fish at the molecular level should be studied and that the state of fisheries in the area should be followed up to detect any long-term impact of the incident.

Current climate change impact studies on coffee have not considered impact on coffee typicities that depend on local microclimatic, topographic and soil characteristics. Thus, this study aims to provide a quantitative risk assessment of the impact of climate change on suitability of five premium specialty coffees in Ethiopia. We implement an ensemble model of three machine learning algorithms to predict current and future (2030s, 2050s, 2070s, and 2090s) suitability for each specialty coffee under four Shared Socio-economic Pathways (SSPs). Results show that the importance of variables determining coffee suitability in the combined model is different from those for specialty coffees despite the climatic factors remaining more important in determining suitability than topographic and soil variables. Our model predicts that 27% of the country is generally suitable for coffee, and of this area, only up to 30% is suitable for specialty coffees. The impact modelling showed that the combined model projects a net gain in coffee production suitability under climate change in general but losses in five out of the six modelled specialty coffee growing areas. We conclude that depending on drivers of suitability and projected impacts, climate change will significantly affect the Ethiopian speciality coffee sector and area-specific adaptation measures are required to build resilience.

A large number of General Circulation Models (GCMs) are currently available for modelling the atmospheric conditions over the Earth. However, there is a large variability in the future climate pred...

ESA deployed the first Doppler wind lidar in space within its Earth Explorer Mission Aeolus in August 2018. Since then global wind profiles have been acquired for the improvement of weather forecasts on short and medium term. Current calibration and validation activities indicate the demanded positive impact from different forecast centers. In addition, Aeolus provides profiles of atmospheric backscatter and extinction coefficients that are supposed to become input for air quality models. The talk will provide an overview of the encountered technical challenges in flight and some preliminary results from impact studies.

Climate scenarios generated from GCMs taken as input to various climate change impact assessment studies. These GCMS data have lots of bias which may result in uncertain predictions of various impact studies. So, it is necessary to provide bias correct data as input to reduce the uncertainties in these studies. Therefore, this study is compared with two methods of quantile mapping bias correction technique: linear and tricube for Delhi location over 30 years’ (1976–2005) period for temperature and rainfall. GCM(CSIRO-MK3) model simulated climate data were bias corrected using both approaches. These bias-corrected data were compared yearly, and also, its parameterwise statistics (RMSE) were calculated. Based on the analysis, this study indicates that both the methods (linear and tricube) have the ability to remove the bias from climate data. But tricube method performed better among both the methods. Tricube method along with the wet_false combination can remove bias more near to the observed data as compared to combining with wet_true condition.

ispartof: INTERNATIONAL JOURNAL OF CLIMATOLOGY vol:41 pages:E428-E444 status: published

State-of-the-art hydrological climate impact assessment involves ensemble approaches to address uncertainties. For precipitation, a wide range of climate model runs is available. However, for particular meteorological variables used for the calculation of potential evapotranspiration (ETₒ), availability of climate model runs is limited. It is preferred that climate model runs are considered coupled when calculating changes in precipitation and ETₒ amounts, in order to preserve the internal physical consistency. This results in constraints on the maximum ensemble size. In this paper, we investigate the correlation between climate change signals of precipitation and ETₒ. It is found that, for two medium-sized catchments in Belgium, uncoupling climate model runs used for calculation of change signals of precipitation and ETₒ amounts does not result in a significant bias for changes in extreme flow. With these results, future impact studies can be conducted with larger ensemble sizes, resulting in a more complete uncertainty estimation.

Climate scenarios derived from the global climate models (GCMs) are used for climate change impact studies in several sectors including agriculture, hydrological, and health. Globally, more than 50 climate models exist and choosing suitable models based on reproducibility of observed weather for a study region is a challenging task. This step is important to reduce uncertainty. This study compared the simulation performance of 12 global climate models for temperatures and rainfall in past 30 years over Indian region. For this, Priority index from Fuzzy Analytic Hierarchy Process (FAHP) and Reliability index were used and both methods were compared. Study revealed all 12 models overestimated minimum and maximum temperatures in most regions of India, which resulted in hot bias especially in northern region. However, models showed significant cold bias for the Himalayan region. In general, simulated rainfall was underestimated by many GCMs. The analysis indicated that FAHP method is good to shortlist GCMs based on their spatial and temporal performance in reproducing observed weather. Among 12 models, NORESM1 model has performed better in reproducing maximum temperature. The IPSL-LR, FIO-ESM, GFDL-CM3, and MIROC5 models have performed better for minimum temperature. In case of rainfall, CSIRO, MIROC5, HADGEM2, GFDL-ESM 2 M, and IPSL-LR have performed better as compared to other models.

Rainfall variability plays an important role in many socio-economic activities such as food security, livelihood and farming in Ghana. Rainfall impact studies are thus very crucial for proper management of these key sectors of the country. This paper examines the seasonal and annual rainfall variability in the four agro-ecological zones of Ghana from the CHIRPS V2 rainfall time series spanning a period of 1981-2015. The rainfall indices were computed with the aid of the FClimDex package whereas the trends of these indices were further tested using the Mann Kendall trend test. The results show good agreement (r ≥ 0.7) between CHIRPS V2 and gauge in almost all portions of country although high biases were observed especially in DJF season over parts of the Northeastern (NE) portions of the country. The mean seasonal rainfall climatology over the country is observed to be in the range of 20 - 80 mm, 60 - 200 mm, 100 - 220 mm and 40 - 180 mm in DJF, MAM, JJA and SON seasons respectively with high intensities of rainfall dominating Southwestern portions of the country. The trend analysis revealed positive trends of consecutive dry days in the Transition, Forest and Coastal zones and negative trends in the Savannah zone of the country. Decreasing trends of consecutive wet days are observed over the Savannah, Transition and Coastal zones whereas increasing trends dominate the Forest zone. Savannah, Forest and Transition zones show weak increasing trends of the number of heavy rainfall days whilst weak decreasing trends are observed over the Coastal zone of the country. Similarly, weak increasing trends of the number of very heavy rainfall days are observed over all the agro-ecological zones except in the Transition zone. It is observed that the annual wet day rainfall total has increasing trend in the Savannah and Forest zones of the country whereas decreasing trends cover the remainder of the zones. The trends of these indices in the agro-ecological zones were all significant at a significant value of 0.05. This paper assessed the performance of the CHIRPS V2 rainfall data over the region and reports on the biases in seasonal rainfall amounts which are limited in previous studies. These findings have adverse impacts on rain-fed agricultural practices, water resource management and food security over the country.

Background: The Bagmati River is the rain-fed river in the basin of Nepal. The climate change in rainfall patterns may lead to drought or flashflood in this basin. Drought is a silent and pervasive hazard due to the deficit of water availability. It may have adverse impact on society leading to impact on environment, culture, political and other functions of the region.Objective: This study aims to assess the future drought in the Bagmati River Basin, Nepal.Materials and Methods: Providing Regional Climates for Impact Studies precipitation data was obtained from Department of Hydrology and Meteorology, Kathmandu. The Generalized Extreme Distribution was fitted to respective total precipitations in 3 time-scales using EasyFit software. Standardized Precipitation Index (SPI) method was used to derive SPI for winter drought, SPI for summer drought and SPI for long-term (annual) drought.Results: The results of data analysis showed that winter moderate drought episodes may occur in years 2035, 2042, 2048, 2049, 2051 and 2053. Likewise, summer severe drought episode may occur in 2046. The year 2046 also indicated long-term extreme drought. Moreover, 2030, 2031, 2035, 3040 and 2053 may be long-term moderate drought episodes years in future.Conclusion: There may be winter moderate drought, summer severe drought and a long-term extreme as well as moderate drought during the future period 2030-2060.Nepalese Journal of Statistics, Vol. 2, 75-88

Observation impact studies have received increasing amounts of research attention. The impacts of observations on numerical weather prediction (NWP) are highly dependent on assimilation algorithm, prediction system, and observation source. Therefore, the major NWP centers worldwide have each developed their own diagnostic techniques to assess observation impacts. However, similar diagnostic techniques have not yet been developed in China. In this study, a diagnostic technique was exploited with the randomized perturbation method in the Global/Regional Assimilation and Prediction System (GRAPES) 3DVAR system, and then applied to evaluate observation impacts for various regions of the world. It was found that a reasonable and stable estimation could be obtained when the number of perturbations was greater than 15. Because of differences in observations in the Northern and Southern Hemispheres, refractivity data from GNSS radio occultation (GNSS-RO), satellite radiance, and atmospheric motion vector data had more impact in the Southern Hemisphere than in the Northern Hemisphere. However, radiosonde data, aircraft, and surface data were more important in the Northern Hemisphere. Low-impact observation points were located in data-rich areas, whereas high-impact observation points were located in data-poor areas. In the equatorial region, the contributions of observations to the analysis were smaller than those in the nonequatorial regions because of the lack of proper mass–wind balance relationship. Radiosondes contributed the largest impact in China and its surrounding regions, with contributions of radiosondes and GNSS-RO data exceeding 60% of the total contributions, except for wind speed below 700 hPa.

Although composite materials are now used throughout the marine industry their susceptibility to impact events is still an unresolved problem. The complex nature of the problem in terms of the distinct material and impact event parameters specific to marine applications, damage tolerance and durability has been discussed in parts I, II and III of this review. Here, work addressing marine composite impact scaling and strain-rate effects, and impact studies not explicitly concerning marine applications yet considering laminates typical of the marine industry have been reviewed. Together with parts I, II and III, this paper gives a comprehensive review of ‘marine impact on marine composites’, providing a valuable resource for the marine industry and research fields.

When selecting a subset of climate change scenarios (GCM models), the priority is to ensure that the subset reflects the comprehensive range of possible model results for all variables concerned. Though many studies have attempted to improve the scenario selection, there is a lack of studies that discuss methods to ensure that the results from a subset of climate models contain the same range of uncertainty in hydrologic variables as when all models are considered. We applied the Katsavounidis–Kuo–Zhang (KKZ) algorithm to select a subset of climate change scenarios and demonstrated its ability to reduce the number of GCM models in an ensemble, while the ranges of multiple climate extremes indices were preserved. First, we analyzed the role of 27 ETCCDI climate extremes indices for scenario selection and selected the representative climate extreme indices. Before the selection of a subset, we excluded a few deficient GCM models that could not represent the observed climate regime. Subsequently, we discovered that a subset of GCM models selected by the KKZ algorithm with the representative climate extreme indices could not capture the full potential range of changes in hydrologic extremes (e.g., 3-day peak flow and 7-day low flow) in some regional case studies. However, the application of the KKZ algorithm with a different set of climate indices, which are correlated to the hydrologic extremes, enabled the overcoming of this limitation. Key climate indices, dependent on the hydrologic extremes to be projected, must therefore be determined prior to the selection of a subset of GCM models.

A new bias-corrected statistically downscaled product, namely, the NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP), has recently been developed by NASA to help the scientific community in climate change impact studies at local to regional scale. In this work, the product is validated over India and its added value as compared to its CMIP5 counterpart for the NCAR CCSM4 model is analyzed, followed by climate change projections under the RCP8.5 global warming scenario using the two datasets for the variables daily maximum 2-m air temperature (Tmax), daily minimum 2-m air temperature (Tmin), and rainfall. It is found that, overall, the CCSM4-NEX-GDDP significantly reduces many of the biases in CCSM4-CMIP5 for the historical simulations; however, some biases such as the significant overestimation in the frequency of occurrence in the lower tail of the Tmax and Tmin still remain. In regard to rainfall, an important value addition in CCSM4-NEX-GDDP is the alleviation of the significant underestimation of rainfall extremes found in CCSM4-CMIP5. The projected Tmax from CCSM4-NEX-GDDP are in general higher than that projected by CCSM4-CMIP5, suggesting that the risks of heat waves and very hot days could be higher than that projected by the latter. CCSM4-NEX-GDDP projects the frequency of occurrence of the upper extreme values of historical Tmax to increase by a factor of 100 towards the end of century (as opposed to a factor of 10 increase projected by CCSM4-CMIP5). In regard to rainfall, both CCSM4-CMIP5 and CCSM4-NEX-GDDP project an increase in annual rainfall over India under the RCP8.5 global warming scenario progressively from the near term through the far term. However, CCSM4-NEX-GDDP consistently projects a higher magnitude of increase and over a larger area as compared to that projected by CCSM4-CMIP5. Projected daily rainfall distributions from CCSM4-CMIP5 and CCSM4-NEX-GDDP suggest the occurrence of events that have no historical precedents. Worth noting is that the extreme daily rainfall values projected by CCSM4-NEX-GDDP are two to three times larger than that projected by CCSM4-CMIP5.

The paper presents a study of the impact of oil mineral-based and synthetic-based oils on the efficiency of the single stage cycloidal gear. Experimental studies were carried out for different loads and input speeds of the gears. The reference levels were the results of theoretical calculations of efficiency of the cycloidal gear obtained at the corresponding values of loads and input speeds as in the experiment.

This study proposes a method to estimate the timing of human-induced climate change (HICC) emergence from internal climate variability (ICV) for hydrological impact studies based on climate model ensembles. Specifically, ICV is defined as the inter-member difference in a multi-member ensemble of a climate model in which human-induced climate trends have been removed through a detrending method. HICC is defined as the mean of multiple climate models. The intersection between HICC and ICV curves is defined as the time of emergence (ToE) of HICC from ICV. A case study of the Hanjiang River watershed in China shows that the temperature change has already emerged from ICV during the last century. However, the precipitation change will be masked by ICV up to the middle of this century. With the joint contributions of temperature and precipitation, the ToE of streamflow occurs about one decade later than that of precipitation. This implies that consideration for water resource vulnerability to climate should be more concerned with adaptation to ICV in the near-term climate (present through mid-century), and with HICC in the long-term future, thus allowing for more robust adaptation strategies to water transfer projects in China.

Study region Catchments: Wolzerbach, Raab; Styria; Austria. Study focus The study was carried out to investigate the fitness for purpose of the high-resolution Austrian National Climate datasets (OKS15) in hydrological applications. Provided as a standard for climate impact studies in Austria in 2016, the potential of the OKS15 data for hydrological impact studies has not previously been assessed. To fill this research gap, we evaluated the OKS15 datasets for the recent climate period (1961–2005) to determine their suitability for hydrological analyses. Hydro-meteorological indicators of the OKS15 datasets, as well as of the data used to obtain them by bias correcting and downscaling the EURO-CORDEX models, were assessed at different spatio-temporal scales and compared to the raw EURO-CORDEX data. The hydrological model WaSiM was driven with the different datasets to generate the hydrological simulations used for validation against long-term observations. New hydrological insights for the region Hydro-meteorological indicators obtained from OKS15 models are comparable to observations and associated with less uncertainty than the raw EURO-CORDEX data. We thus consider the OKS15 dataset basically appropriate for hydrological impact studies. However, despite bias correction, some biases in the OKS15 data remain and differences in timing, magnitude and spatial characteristics lead to deviations in hydrological indicators at different scales.

Water scarcity is a critical issue in Iberia but few studies have investigated future drought over the region. The few that do are continental or global studies and do not report the range of possible future projections. In this paper, historical drought and future projections of drought were examined, using observed and downscaled rainfall data from climate models, for the main Iberian international basins (Douro, Tagus and Guadiana). Two drought indices were used, the standardized precipitation index (SPI) and the drought severity index (DSI). However, problems were found in the distribution fitting required for the calculation of SPI which render this index inadequate for assessing droughts in Iberia. The skill of CMIP5 climate models in representing historical drought for the Douro, Tagus and Guadiana is very variable with some not showing enough persistence of dry conditions, and others simulating droughts that are too long and too severe. Nevertheless, the observed range of drought conditions for these basins was encompassed by the ensemble members. Furthermore, we found no relationship between performance in simulating mean large-scale circulation and model ability to reproduce the historical drought characteristics for the region. All models project an intensification of drought conditions for the three basins. However, some only project small increases, while most project extreme multi-year droughts by the end of the century. We found that climate model future projections are neither related to their performance in simulating historical drought nor to their large-scale circulation patterns. This result emphasises the need for climate change impact studies to take into account the whole range of climate model projections in order to provide the best information for robust adaptation. Choosing one or a subset of climate models based on historical performance would not lead to a credible and justified reduction in uncertainty.

Abstract. Climate change impact studies typically require a reference climatological dataset providing a baseline period to assess future changes. The reference dataset is also used to perform bias correction of climate model outputs. Various reliable precipitation datasets are now available over regions with a high-density network of weather stations such as over most parts of Europe and in the United States. In many of the world’s regions, the low-density of observation stations (or lack thereof) renders gauge-based precipitation datasets highly uncertain. Satellite, reanalysis and merged products can be used to overcome this limitation. However, each dataset brings additional uncertainty to the reference climate. This study compares ten precipitation datasets over 1091 African catchments to evaluate dataset uncertainty contribution in climate change studies. The precipitation datasets include two gauged-only products (GPCC, CPC), four satellite products (TRMM, CHIRPS, PERSIANN-CDR and TAMSAT) corrected using ground-based observations, three reanalysis products (ERA5, ERA-I, and CFSR) and one merged product of gauge, satellite, and reanalysis (MSWEP).Each of those datasets was used to assess changes in future streamflows. The climate change impact study used a top-down modelling chain using 10 CMIP5 GCMs under RCP8.5. Each climate projection was bias-corrected and fed to a lumped hydrological model to generate future streamflows over the 2071-2100 period. A variance decomposition was performed to compare GCM uncertainty and reference dataset uncertainty for 51 streamflow metrics over each catchment. Results show that dataset uncertainty is much larger than GCM uncertainty for most of the streamflow metrics and over most of Africa. A selection of the best performing reference datasets (credibility ensemble) significantly reduced the uncertainty attributed to datasets, but remained comparable to that of GCMs in most cases. Results show also relatively small differences between datasets over a reference period can propagate to generate large amounts of uncertainty in the future climate.

In this study, we perform an evaluation of PRIMAVERA high-resolution (25–50 km) Global Climate Models (GCMs) relative to CORDEX Regional Climate Models (RCMs) over Europe (12–50 km resolutions). It is the first time such assessment is performed for regional climate information using ensembles of GCMs and RCMs at similar horizontal resolutions. We perform this exercise for the distribution of daily precipitation contributions to rainfall bins over Europe under current climate conditions. Both ensembles are evaluated against high quality national gridded observations in terms of resolution and station density. We show that PRIMAVERA GCMs simulate very similar distribution to CORDEX RCMs that CMIP5 cannot because of their coarse resolutions. PRIMAVERA and CORDEX ensembles generally show similar strengths and weaknesses. They are of good quality in summer and autumn in most European regions, but tend to overestimate precipitation in winter and spring. PRIMAVERA show improvements in the latter bias by reducing mid-rain rate biases in Central and Eastern Europe. Moreover, CORDEX simulate less light rainfall than PRIMAVERA in most regions and seasons, which improves this common GCM bias. Finally, PRIMAVERA simulate less heavy precipitation than CORDEX in most regions and seasons, especially in summer. PRIMAVERA appear to be closer to observations. However, when we apply an averaged precipitation undercatch error of 20 %, CORDEX become closer to these synthetic datasets. Considering 50 km resolution GCM or RCM datasets over Europe results in large benefits compared to CMIP5 models for impact studies at the regional scale. The effect of increasing resolution from 50 km to 12 km in CORDEX simulations is, in comparison, small in most regions and seasons outside mountainous regions (due to the importance of orography) and coastal regions (mostly depending on the resolution of the land-sea contrast). Now that GCMs are able to reach the level of information provided by CORDEX RCMs run at similar resolutions, there is an opportunity to better coordinate GCM and RCM simulations for future model intercomparison projects.

A growing number of urban health impact studies use Community Multiscale Air Quality (CMAQ) models for air pollution exposure estimation, although the performance of CMAQ models is likely to be affected by multiple parameters, including the configuration setting of the study domain. We presented an approach for CMAQ model uncertainty assessment with respect to domain size and reported spatial and temporal variations of CMAQ model performance over two study domains, a relatively small domain (DS) and a large domain (DL). Specifically, we simulated daily PM2.5 concentrations over two domains during 2011 and quantified the difference between the model predictions. The model performance was assessed by comparing modeled PM2.5 against measured PM2.5 values at monitoring sites located in the region of overlap for each domain. The results suggest that the CMAQ simulations over two domains were in good agreement across the study area except in southwestern areas. We also found that the overall model performance was better for CMAQ simulations with a large domain relative to the smaller domain. Based on our findings, we recommend applying a large domain for PM2.5 simulations, particularly for urban health risk assessments conducted over summer months, which generally contain more emissions.

Study region: Pra River Basin (PRB) located in Ghana Study focus: Now, hydrological and climate change impact studies are shifting towards reliance on openly accessible databases particularly for regions with limited observed datasets. There is therefore the need to evaluate the reliability of such datasets in order to reduce modelling uncertainties and boost confidence in modelling results. We present an evaluation of the performance of nine CORDEX RCA4 Regional Climate Model simulations in replicating the observed precipitation for a 31-year period (1975–2005). New hydrological insights for the region: On the annual timescale, CanESM2, IPSL, CNRM-CM5 and HadGEM2-ES reproduced minimal annual mean biases (0.8–18.4 %) and thus selected for the seasonal and monthly timescale analysis. Generally, with the exception of spring (March, April and May), all the selected models were able to simulate quite well the seasonal climatology of the Pra River Basin (PRB) with noticeable distinctions in the reproducibility of the spatial patterns, variability and magnitude of the observed data. The multi-model ensemble means indicated strong correlation with observations (r > 0.75) but with weak spatial variability (σ < 0.25). It is recommended that for climate impact assessment and hydrologic modelling studies, multi-model ensembles of CanESM2, IPSL, CNRM-CM5 and HadGEM2-ES be used. However, on singular basis, the CanESM2 and HadGEM2-ES RCA4 simulation outputs present better representation of the climate of the basin.

Watersheds located in the Catskill Mountains of southeastern New York State contribute about 90% of the water to the New York City water supply system. Recent studies show that this region is experiencing increasing trends in total precipitation and extreme precipitation events. To assess the impact of this and other possible climatic changes on the water supply, there is a need to develop future climate scenarios that can be used as input to hydrological and reservoir models. Recently, stochastic weather generators (SWGs) have been used in climate change adaptation studies because of their ability to produce synthetic weather time series. This study examines the performance of a set of SWGs with varying levels of complexity to simulate daily precipitation characteristics, with a focus on extreme events. To generate precipitation occurrence, three Markov chain models (first, second, and third orders) were evaluated in terms of simulating average and extreme wet days and dry/wet spell lengths. For precipitation magnitude, seven models were investigated, including five parametric distributions, one resampling technique, and a polynomial-based curve fitting technique. The methodology applied here to evaluate SWGs combines several different types of metrics that are not typically combined in a single analysis. It is found that the first-order Markov chain performs as well as higher orders for simulating precipitation occurrence, and two parametric distribution models (skewed normal and mixed exponential) are deemed best for simulating precipitation magnitudes. The specific models that were found to be most applicable to the region may be valuable in bottom-up vulnerability studies for the watershed, as well as for other nearby basins.

Vegetation diversity and interaction is thought to have a beneficial effect on ecosystem functioning, particularly improving ecosystem resistance to drought. This is of significant importance in the context of a warmer world, as extreme events such as droughts become more likely. Most of the studies performed so far on vegetation interaction are based on observations. Here we use the land surface model JULES to study the potential of vegetation mixing to mitigate the negative effect of drought events on the land surface through interaction, a mechanism which is difficult to study in situ at large scales. Using a set of simulations with mixed and unmixed vegetation, we show that the carbon, water, and energy fluxes are significantly affected by vegetation competition for water resources. The interaction is in general beneficial for the ecosystem carbon assimilation due to a better use of water resources. This benefit is highest when traits between vegetation types concerning resource competition overlap least. For a tree-grass combination, mixing improves carbon assimilation by 5% to 8% during summer. The NPP benefit of mixing increases further under progressively more resource-limited conditions up to an inflection point with a benefit of 14%, after which it falls back to zero under extremely dry conditions. Mixing also tends to reduce the inter-annual variability of the ecosystem carbon sink and therefore improves the resistance of the ecosystem. Our results highlight the importance of vegetation interaction in climate simulations and impact studies, and the potential of vegetation mixing as a mitigation tool.

Postprocessing of climate model outputs is usually performed to remove biases prior to performing climate change impact studies. The evaluation of the performance of bias correction methods is routinely done by comparing postprocessed outputs to observed data. However, such an approach does not take into account the inherent uncertainty linked to natural climate variability and may end up recommending unnecessary complex postprocessing methods. This study evaluates the performance of bias correction methods using natural variability as a baseline. This baseline implies that any bias between model simulations and observations is only significant if it is larger than the natural climate variability. Four bias correction methods are evaluated with respect to reproducing a set of climatic and hydrological statistics. When using natural variability as a baseline, complex bias correction methods still outperform the simplest ones for precipitation and temperature time series, although the differences are much smaller than in all previous studies. However, after driving a hydrological model using the bias-corrected precipitation and temperature, all bias correction methods perform similarly with respect to reproducing 46 hydrological metrics over two watersheds in different climatic zones. The sophisticated distribution mapping correction methods show little advantage over the simplest scaling method. The main conclusion is that simple bias correction methods appear to be just as good as other more complex methods for hydrological climate change impact studies. While sophisticated methods may appear more theoretically sound, this additional complexity appears to be unjustified in hydrological impact studies when taking into account the uncertainty linked to natural climate variability.

This study compares the multivariate predictions of daily temperature, temperature range, precipitation, surface wind and solar radiation of a single-model analogue approach with an original multi-model analogy over 12 regions in Europe and Maghreb. Both approaches are based on two-level analogue models where atmospheric predictors are either dynamic or thermodynamic. In the multi-model approach, independent analogue models with predictand-specific predictors are used. In the single-model one, a unique analogue model and its associated set of predictors is applied to all predictands. Testing numerous large-scale predictors, we first identify the best predictor sets for each modelling strategy. Those obtained for the single-model approach are significantly different from those of the predictand-specific models. This is especially the case for local temperature and wind speed. Both methods perform similarly for precipitation, temperature range and radiation. We next assess the ability of both approaches to simulate physically coherent multivariate weather scenarios. With the single-model method, weather scenarios are obtained for each prediction day from observations sampled simultaneously on one analogue day. The physical consistency between variables is thus automatically fulfilled each day. This allows the single-model method to reproduce well the observed inter-predictand correlations, especially the significant correlations between radiation and precipitation and between radiation and temperature range. These results suggest a hybrid analogue model using a single-model for radiation, temperature range and precipitation, combined with a univariate approach for wind. Two options are proposed for temperature for which either the predictand-specific method or a single-model approach with an additional correction are conceivable. This hybrid approach leads to a possible compromise between reasonable univariate prediction skills and realistic inter-predictands correlations, both classically required for many impact studies.

The parameter uncertainty of process-based models has received little attention in climate change impact studies. This paper aims to integrate parameter uncertainty into simulations of climate change impacts on forest net primary productivity (NPP). We used either prior (uncalibrated) or posterior (calibrated using Bayesian calibration) parameter variations to express parameter uncertainty, and we assessed the effect of parameter uncertainty on projections of the process-based model 4C in Scots pine (Pinus sylvestris) stands under climate change. We compared the uncertainty induced by differences between climate models with the uncertainty induced by parameter variability and climate models together. The results show that the uncertainty of simulated changes in NPP induced by climate model and parameter uncertainty is substantially higher than the uncertainty of NPP changes induced by climate model uncertainty alone. That said, the direction of NPP change is mostly consistent between the simulations using the standard parameter setting of 4C and the majority of the simulations including parameter uncertainty. Climate change impact studies that do not consider parameter uncertainty may therefore be appropriate for projecting the direction of change, but not for quantifying the exact degree of change, especially if parameter combinations are selected that are particularly climate sensitive. We conclude that if a key objective in climate change impact research is to quantify uncertainty, parameter uncertainty as a major factor driving the degree of uncertainty of projections should be included.

Summary Impact studies of the hydrological response of future climate change are important for the water authorities when risk assessment, management and adaptation to a changing climate are carried out. The objective of this study was to model the combined effect of land use and climate changes on hydrology for a 486 km 2 catchment in Denmark and to evaluate the sensitivity of the results to the choice of hydrological model. Three hydrological models, NAM, SWAT and MIKE SHE, were constructed and calibrated using similar methods. Each model was forced with results from four climate models and four land use scenarios. The results revealed that even though the hydrological models all showed similar performance during calibration, the mean discharge response to climate change varied up to 30%, and the variations were even higher for extreme events (1th and 99th percentile). Land use changes appeared to cause little change in mean hydrological responses and little variation between hydrological models. Differences in hydrological model responses to land use were, however, significant for extremes due to dissimilarities in hydrological model structure and process equations. The climate model choice remained the dominant factor for mean discharge, low and high flows as well as hydraulic head at the end of the century.

The multiple downscaled scenario products allow us to assess the uncertainty of the variations of precipitation and temperature in the current and future periods. Probabilistic assessments of both climatic variables help better understand the interdependence of the two and thus, in turn, help in assessing the future with confidence. In the present study, we use ensemble of statistically downscaled precipitation and temperature from various models. The dataset used is multi-model ensemble of 10 global climate models (GCMs) downscaled product from CMIP5 daily dataset using the Bias Correction and Spatial Downscaling (BCSD) technique, generated at Portland State University. The multi-model ensemble of both precipitation and temperature is evaluated for dry and wet periods for 10 sub-basins across Columbia River Basin (CRB). Thereafter, copula is applied to establish the joint distribution of two variables on multi-model ensemble data. The joint distribution is then used to estimate the change in trends of said variables in future, along with estimation of the probabilities of the given change. The joint distribution trends vary, but certainly positive, for dry and wet periods in sub-basins of CRB. Dry season, generally, is indicating a higher positive change in precipitation than temperature (as compared to historical) across sub-basins with wet season inferring otherwise. Probabilities of changes in future, as estimated from the joint distribution, indicate varied degrees and forms during dry season whereas the wet season is rather constant across all the sub-basins.

In regions of sparse gauge networks, satellite rainfall products are mostly used as surrogate measurements for various rainfall impact studies. Their potential to complement rain gauge measurements is influenced by the uncertainties associated with them. This study evaluates the performance of satellites and merged rainfall products over Ghana in order to provide information on the consistency and reliability of such products. Satellite products were validated with gridded rain gauge data from the Ghana Meteorological Agency (GMet) on various time scales. It was observed that the performance of the products in the country are mostly scale and location dependent. In addition, most of the products showed relatively good skills on the seasonal scale (r &gt, 0.90) rather than the annual, and, after removal of seasonality from the datasets, except ARC2 that had larger biases in most cases. Again, all products captured the onsets, cessations, and spells countrywide and in the four agro-ecological zones. However, CHIRPS particularly revealed a better skill on both seasonal and annual scales countrywide. The products were not affected by the number of gauge stations within a grid cell in the Forest and Transition zones. This study, therefore, recommends all products except ARC2 for climate impact studies over the region.

Projections of runoff from global multi-model ensembles provide a valuable basis for the estimation of future hydrological extremes. However, projections suffer from uncertainty that originates from different error sources along the modeling chain. Hydrological impact studies have generally partitioned these error sources into global impact and global climate model (GIM and GCM, respectively) uncertainties, neglecting other sources, including scenarios and internal variability. Using a set of GIMs driven by GCMs under different representative concentration pathways (RCPs), this study aims to partition the uncertainty of future flows coming from GIMs, GCMs, RCPs, and internal variability over the CONterminous United States (CONUS). We focus on annual maximum, median, and minimum runoff, analyzed decadally over the twenty-first century. Results indicate that GCMs and GIMs are responsible for the largest fraction of uncertainty over most of the study area, followed by internal variability and to a smaller extent RCPs. To investigate the influence of the ensemble setup on uncertainty, in addition to the full ensemble, three ensemble configurations are studied using fewer GIMs (excluding least credible GIMs in runoff representation and GIMs accounting for vegetation and CO2 dynamics), and excluding intermediate RCPs. Overall, the use of fewer GIMs has a minor impact on uncertainty for low and medium flows, but a substantial impact for high flows. Regardless of the number of pathways considered, RCPs always play a very small role, suggesting that improvement of GCMs and GIMs and more informed ensemble selections can yield a reduction of projected uncertainties.

Photovoltaics (PV) market is the fastest growing electrical energy generation industry. Well integrated distributed photovoltaics (PVs) will help tomorrow's energy system become more sustainable. This paper provides a comprehensive review on previous distributed generation (DG) PV impact studies on Low Voltage (LV) networks. The aim is to conduct the first-ever study on the Maltese islands on a real Low Voltage (LV) network for the Island of Gozo in Malta, considering real load and PV profiles in 15-minute resolution. The methodology uses a Monte-Carlo approach for statistical evaluation of two possible PV penetration that is close to and further away from substation. Voltage impacts are analyzed using European Standard EN50160 for voltage magnitude +/− 10% and overloading capabilities are assessed through utilization factor. This study is conducted within MCAST Energy Research Group supported by Enemalta PLC through transfer of knowledge from WISE-PV project.

Las estaciones de servicio (E/S) pueden aportar contaminantes al aire, suelo y los cuerpos de agua. Con la finalidad de analizar la concentración biodisponible (bio) y residual (res) de; y la fracción orgánica (FO) en partículas atmosféricas sedimentables recolectadas E/S de la ciudad, se tomaron muestras en tres estaciones, considerando tres lugares de muestreo (entrada, surtidor y salida), durante tres semanas. Para la determinación de metales biodisponibles, las muestras (