Climate Reanalysis Datasets Research Articles

Evaluating and Predicting Meteorological Drought Using Different Climate Reanalysis Datasets over New South Wales, Australia

AbstractDroughts are one of the most disastrous natural hazards, primarily due to their persistence and spatial distribution. Drought prediction is one of the key challenges for effective drought management and to do so, studies often involve the use of station-based data which are effective only in regions with high-gauge density. Therefore, there is growing interest in the use of interpolated climatic grids to predict droughts. In recent decades, drought conditions have been aggravated by climate change and for that reason the use of climatic variables is important to accurately predict droughts. The analysis of any aspect of drought can be affected by the choice of data and drought index. Therefore, this study aims to identify the most suitable dataset and drought index for the New South Wales (NSW) region of Australia. The present study evaluates various precipitation datasets (Climate Research Unit (CRU), ERA-5, and Scientific Information for Land Owners (SILO)) and their corresponding variations on the Standardised Precipitation Index (SPI) at different time scales. Based on the findings, CRU was used to predict meteorological drought using machine learning techniques. The different machine learning models are Support Vector Regression, Random Forest and Artificial Neural Networks. The results suggest SVM to be the best performing model among these models for predicting SPI at short time scales (1 month and 3 month) and ANN to be the best performing model for long-term scales (6 months and 12 months). Such findings depict the capabilities of different models in examining drought characteristics and confirming the use of interpolated climatic grids thereby assisting in regional drought management.

Proper orthogonal decomposition reduced-order model of the global oceans

AbstractA reduced-order model (ROM) of the global oceans is developed by projecting the hydrostatic Boussinesq equations of motion onto a proper orthogonal decomposition (POD) basis. Three-dimensional POD modes are calculated from the ocean fields of an ensemble climate reanalysis dataset. The coefficients in the POD ROM are calculated using a regression approach. The performance of various POD ROM configurations are assessed. Each configuration is derived from an alternate sea-water equation of state, linking the density and temperature fields. POD ROM variants incorporating an equation of state in which density is a quadratic function of temperature, are able to reproduce the statistics of the large-scale structures at a fraction of the computational cost required to numerically simulate this flow. Due to the speed and efficiency of calculation, such reduced-order models of the global geophysical system will enable researchers and policy makers to assess the physical risk for a broader range of potential future climate scenarios.

Tracking Forest Disturbance in Northeast China’s Cold-Temperate Forests Using a Temporal Sequence of Landsat Data

Cold-temperate forests (CTFs) are not only an important source of wood but also provide significant carbon storage in China. However, under the increasing pressure of human activities and climate change, CTFs are experiencing severe disturbances, such as logging, fires, and pest infestations, leading to evident degradation trends. Though these disturbances impact both regional and global carbon budgets and their assessments, the disturbance patterns in CTFs in northern China remain poorly understood. In this paper, the Genhe forest area, which is a typical CTF region located in the Inner Mongolia Autonomous Region, Northeast China (with an area of about 2.001 × 104 km2), was selected as the study area. Based on Landsat historical archived data on the Google Earth Engine (GEE) platform, we used the continuous change detection and classification (CCDC) algorithm and considered seasonal features to detect forest disturbances over nearly 30 years. First, we created six inter-annual time series seasonal vegetation index datasets to map forest coverage using the maximum between-class variance algorithm (OTSU). Second, we used the CCDC algorithm to extract disturbance information. Finally, by using the ECMWF climate reanalysis dataset, MODIS C6, the snow phenology dataset, and forestry department records, we evaluated how disturbances relate to climate and human activities. The results showed that the disturbance map generated using summer (June–August) imagery and the enhanced vegetation index (EVI) had the highest overall accuracy (88%). Forests have been disturbed to the extent of 12.65% (2137.31 km2) over the last 30 years, and the disturbed area generally showed a trend toward reduction, especially after commercial logging activities were banned in 2015. However, there was an unusual increase in the number of disturbed areas in 2002 and 2003 due to large fires. The monitoring of potential widespread forest disturbance due to extreme drought and fire events in the context of climate change should be strengthened in the future, and preventive and salvage measures should be taken in a timely manner. Our results demonstrate that CTF disturbance can be robustly mapped by using the CCDC algorithm based on Landsat time series seasonal imagery in areas with complex meteorological conditions and spatial heterogeneity, which is essential for understanding forest change processes.

The Development of a Hailstone Disdrometer and Its Preliminary Observation in Aksu, Xinjiang

Hailfall is a severe local weather event that can cause great economic losses as well as the loss of people’s property; however, it is still difficult for domestic meteorological stations to comprehensively observe hail, and domestic independently developed hail observation instruments are still scarce. To help enable better automatic hail observations, a new independently developed hailstone disdrometer based on the acoustic principle, which can be used to measure the hailstone number and particle size and to calculate the corresponding equivalent liquid precipitation of hailstones, is proposed in this paper. The characteristics of hailstones were preliminarily analyzed using observation data from two hailstone disdrometers installed in Aksu, Xinjiang, where three hail events were observed via the hailstone disdrometer in the summer of 2023. By analyzing the development of deep convection clouds using the Fengyun 4A satellite-based cloud-top brightness temperature, and synoptic conditions based on the fifth-generation global climate reanalysis dataset produced by the European Centre for Medium-Range Weather Forecasts (the ECMWF ERA5 dataset), the hail formation mechanism was investigated in detail for one hailfall event. Accurate hail observations are an important basis for understanding spatiotemporal hail variation. The hailstone disdrometer proposed in this study offers a useful approach for domestic hail observation to provide first-hand hail information for the inspection of weather modification effects and disaster prevention and reduction.

ASSESSMENT OF SOLAR PV OUTPUT PERFORMANCE WITH VARYING TILT ANGLES AND WEATHER DATA FROM ERA5: CASE OF MUNTINLUPA CITY, PHILIPPINES

Abstract. Solar photovoltaic (PV) technology has been gaining popularity in the Philippines as an alternative source of sustainable energy. In such technology, module tilt angle and weather conditions are among the system parameters that have substantial impacts on PV system performance. Previous studies have considered either tilt angles or weather conditions, but not the combined impact of these two parameters on solar PV power output. The objective of this study is to examine the effects of weather variables and tilt angle on the output of solar photovoltaic (PV) systems in Muntinlupa City, the Philippines. Three 120W monocrystalline solar PV panels were used and set up to three different tilt angles (i.e., 5°, 10°, and 15°). The fifth generation of the ECMWF's global climate and weather reanalysis (ERA5) dataset was used to gather hourly weather information such as surface solar radiation, wind speed, wind direction, relative humidity, ambient temperature, and total precipitation. Three principal components (PC), which together account for 95% of the variability, were identified using principal components analysis (PCA), which was used to address multicollinearity among the weather parameters. To assess the effects of tilt angle, time, and PCs on solar PV production, analysis of covariance (ANCOVA) was carried out. Results show that all weather variables, except for wind speed and total precipitation, have a significant impact on solar PV production with configuration producing the best results. Moreover, a significant difference in mean solar PV production was observed among the three tilt angles. From 6:00 AM to 2:00 PM, solar PV output gradually increases and declines thereafter. Outputs this study can help in optimizing the design and configuration of solar PV systems in the Philippines by considering weather variables and module tilt angle. Lastly, this study provides useful information for system designers, installers, and policymakers in improving energy generation and utilization, encouraging the use of renewable energy sources, and advancing sustainable energy objectives.

A conceptual model for evaluating the stability of high-altitude ice-rich slopes through coupled thermo-hydro-mechanical simulation

The collapse of glacial, permafrost, and ice-rich moraine slopes in high-altitude mountainous areas not only threatens downstream residents and infrastructure but also displaces ice mass to lower and warmer elevations, accelerating glacial ablation to some extent. Despite being considered rare, ice-rich slopes collapse more frequently than commonly thought due to climate change. For example, two adjacent mountain glaciers in the Aru Range of the Tibetan Plateau, characterized by large volumes (68 × 106 m3 and 83 × 106 m3, respectively) and low surface slope angles (12.3° and 12.9°, respectively), collapsed surprisingly in 2016. While the mechanisms behind these collapses have garnered broad attention, the ability to quantitatively assess the instability of ice-rich slopes remains limited due to the complex interplay of multi-physical processes. Taking the Aru glacier collapses as reference cases, this paper presents a conceptual model, implemented through coupled thermo-hydro-mechanical simulation, to evaluate the stability of high-altitude ice-rich slopes due to climate change, rainfall and ice ablation. Results indicate that the methodology captures the effects of temperature change, rainfall and meltwater on the instability events well, demonstrating promising potential in evaluating potential collapse zones of ice-rich slopes similar to the Aru glaciers. Furthermore, the role of climate change in the well-known Aru events is demonstrated using a state-of-the-art global climate reanalysis dataset. Findings reveal that the increase in liquid water infiltrating the Aru glaciers since 2010 was a critical factor leading to the instability events.

Energy fluxes and vertical heat transfer in the Southern Baltic Sea

IntroductionThis study investigates the dynamics of energy fluxes and vertical heat transfer in the Southern Baltic Sea, emphasizing the significant role of the dicothermal layer in modulating the penetration of the thermocline and the propagation rates of thermal energy. The research aims to elucidate the complex patterns of solar energy absorption, its conversion into sea surface temperature (SST), and the transference of this energy deeper into the marine environment.MethodsData were collected through 93 monitoring cruises by the Institute of Oceanology of the Polish Academy of Sciences (IOPAN) from 1998 to 2023, using a high-resolution towed probe technique alongside Argo floats data for the Baltic Proper from 2020 to 2023. ERA5 climate reanalysis dataset and NEMOv4.0 ocean model forecasts were also utilized for a comprehensive analysis of VITE, Top Net Short-Wave Radiation, SST, and energy budget across the Southern Baltic Sea.Results and discussionThe Southern Baltic Sea functions as a net energy sink, with an average energy budget of 5.48 W m-2, predominantly absorbing energy during daylight and emitting it from September to February. A 59-day lag between peak solar energy and VITE peak was observed, followed by an additional 6-day delay before peak SST. The study further reveals a 15-day delay in temperature phase shift per 10 meters depth due to the dicothermal layer's influence on thermal energy propagation, extending to 35 days in the Central and Northern Baltic. Heat transfer is significantly affected by the levels of the thermocline and halocline, with regional variations in advection-driven seasonal signals. The pronounced thermal inertia and the critical role of the dicothermal layer underscore the complexity of thermal energy distribution in the Southern Baltic Sea.

Global evidence of rapid flash drought recovery by extreme precipitation

Flash drought affects agricultural activities and water availability. However, the rate of flash drought development and termination and their controlling mechanisms remain mostly unexplored. Using climate reanalysis (ERA5) datasets, we examine the flash drought development and recovery rates in seventeen climate regions across the globe during the 1981–2020 period. In most global climate regions, flash drought recovery (25.2 percentile/pentad) is faster than its development rate (17.2 percentile/pentad). The tropical and sub-tropical humid areas, particularly eastern North America, northern South America, South Asia, Southeast Asia, and the Islands groups, are the hotspots of rapid flash drought development and faster recovery rates. In most climate regions, flash drought development and recovery rates have considerably increased during the recent two decades. Pluvial events (heavy-to-extreme precipitation) associated with increased soil moisture and decreased atmospheric aridity vapor pressure deficit are the primary driver of the rapid flash drought recovery. Globally, 10 of 17 regions showed the dominance of extreme precipitation in flash drought recovery, primarily due to an increase in the frequency of extreme precipitation. A fraction of flash droughts terminated by extreme precipitation has increased significantly across the most regions during 1981–2020. Considering the increase in flash drought frequency, development rate, and rapid termination, the compound risk of flash droughts followed by extreme precipitation and flooding has enhanced. The abrupt transition from flash drought to wet conditions makes drought and flood management more challenging, with consequences for agriculture and water resources.

Brief communication: Significant biases in ERA5 output for the McMurdo Dry Valleys region, Antarctica

Abstract. The ERA5 climate reanalysis dataset plays an important role in applications such as monitoring and modeling climate system changes in polar regions, so the calibration of the reanalysis to ground observations is of great relevance. Here, we compare the 2 m air temperature time series of the ERA5 reanalysis and the near-surface bias-corrected reanalysis to the near-ground air temperature measured at 17 automatic weather stations (AWSs) in the McMurdo Dry Valleys, Antarctica. We find that the reanalysis data have biases that change with the season of the year and do not clearly correlate with elevation. Our results show that future work should rely on secondary observations to calibrate when using the ERA5 reanalysis in polar regions.

Making climate reanalysis and CMIP6 data processing easy: two “point-and-click” cloud based user interfaces for environmental and ecological studies

Climate reanalysis and climate projection datasets offer the potential for researchers, students and instructors to access physically informed, global scale, temporally and spatially continuous climate data from the latter half of the 20th century to present, and explore different potential future climates. While these data are of significant use to research and teaching within biological, environmental and social sciences, potential users often face barriers to processing and accessing the data that cannot be overcome without specialist knowledge, facilities or assistance. Consequently, climate reanalysis and projection data are currently substantially under-utilised within research and education communities. To address this issue, we present two simple “point-and-click” graphical user interfaces: the Google Earth Engine Climate Tool (GEEClimT), providing access to climate reanalysis data products; and Google Earth Engine CMIP6 Explorer (GEECE), allowing processing and extraction of CMIP6 projection data, including the ability to create custom model ensembles. Together GEEClimT and GEECE provide easy access to over 387 terabytes of data that can be output in commonly used spreadsheet (CSV) or raster (GeoTIFF) formats to aid subsequent offline analysis. Data included in the two tools include: 20 atmospheric, terrestrial and oceanic reanalysis data products; a new dataset of annual resolution climate variables (comparable to WorldClim) calculated from ERA5-Land data for 1950-2022; and CMIP6 climate projection output for 34 model simulations for historical, SSP2-4.5 and SSP5-8.5 scenarios. New data products can also be easily added to the tools as they become available within the Google Earth Engine Data Catalog. Five case studies that use data from both tools are also provided. These show that GEEClimT and GEECE are easily expandable tools that remove multiple barriers to entry that will open use of climate reanalysis and projection data to a new and wider range of users.

Surface flux equilibrium estimates of evaporative fraction and evapotranspiration at global scale: Accuracy evaluation and performance comparison

Accurate estimation of terrestrial evapotranspiration (ET) is crucial to understand the water cycle and partitioning of turbulent energy fluxes at the land surface. However, large-scale ET estimation is always difficult to achieve due to the complexity of controlling factors and the heterogeneity of landscapes. A recent model, referred to as the surface flux equilibrium (SFE) model, proposes that evaporative fraction (EF, the ratio of ET to available energy at the surface) can be estimated accurately from near-surface specific humidity and air temperature, which makes it applicable to common weather stations and climate reanalysis datasets. However, previous validation only focused on the evaluation of this model at site and basin scale for ET estimation, making the real performance of the SFE model for EF estimation heretofore unknown. There also exists a gap in the comparison between the SFE model and other existing products in the representation of global ET patterns and variation trend. Against this background, a comprehensive validation was performed in this study to evaluate the accuracy of the SFE model for both EF and ET estimation. The validation against 136 flux towers worldwide shows that the ground-based SFE model achieved EF estimation with a root-mean-square-error (RMSE) of 0.22 and 0.16 at daily and monthly scale, respectively. The corresponding RMSE of ET estimation was 0.94 mm/day and 16.37 mm/month. The application of the SFE model on a global scale was achieved by using two reanalysis products (ERA5-Land and GLDAS-CLSM) as inputs respectively. The results indicate that the SFE model did hold potential to reduce the RMSE of these two products for ET estimation, but the cost was the decrease in correlation coefficient (r). The comparison with five existing global ET products (BESS, GLEAM, AVHRR, MOD16, and SSEBop) shows that the SFE model performed on the basis of ERA5-Land product has achieved moderate accuracy among these products. Judged from r, mean absolute error, RMSE, and bias, its accuracy was ranked as fourth, fourth, second, and second, respectively. However, a further evaluation over different land cover types suggests that the ERA-based SFE model has significant superiority over cropland, relative superiority over forest and savannas, and significant inferiority over shrubland and grassland. This analysis is the first global validation of the SFE model for EF estimation as well as its comparison with existing global ET products. The validation analysis proves that this simple model has generally achieved comparable accuracy to existing complex models in the representation of global ET patterns and variation trend.

Tropical cyclone genesis prediction based on support vector machine considering effects of multiple meteorological parameters

Most coastal regions worldwide face great challenges from tropical cyclones (TCs). Accurate prediction of TC genesis is critical for conducting risk assessments and disaster mitigation. Compared to conventional statistical models, this study introduces a machine-learning-based approach utilizing a support vector machine (SVM) to predict TC genesis. Historical TC genesis data from the Japan Meteorological Agency (JMA), China Meteorological Administration (CMA), and Joint Typhoon Warning Center (JTWC) are utilized. Five meteorological parameters are extracted from climate reanalysis datasets: absolute vorticity, relative humidity, vertical velocity relative sea surface temperature, and vertical wind shear. A high-dimensional mapping between meteorological parameters and TC genesis labels is performed before classification training. The effects of proportions of positive/negative samples and training/testing sets on model performance are examined, aiming to identify the optimal solution. Different meteorological parameter combinations tested to achieve the highest classification accuracy. Temporal and spatial outcomes of TC predictions are compared with historical data to verify model accuracy. The proposed approach enables simultaneous determination of TC genesis locations and counts, significantly enhancing computational efficiency. It also uses meteorological parameters more comprehensively considering the underlying physical mechanisms. In future climate change scenarios, forecasted meteorological data can be directly incorporated to derive TC genesis patterns.

Chronic Kidney Disease Among Agricultural Workers in Taiwan: A Nationwide Population-Based Study

Chronic kidney disease (CKD) in agricultural communities is a significant public health issue. We aimed to investigate the epidemiology of CKD among Taiwanese farmers and its association with outdoor heat exposure. A nested case-control study was conducted on participants in the National Adult Health Examination (NAHE) from 2012 to 2018. The farming occupation was identified through National Health Insurance data. The primary outcomes of interest were the development of CKD, defined as a decreased estimated glomerular filtration rate (eGFR) with diagnosis by physicians, and CKD of undetermined etiology (CKDu), defined as CKD excluding common traditional etiologies. We calculated the county-wide average ambient temperature from a climate reanalysis dataset (ERA5-Land). All CKD cases were matched 1:2 to non-CKD participants by age and biological sex. We estimated the odds ratios (ORs) of CKD and CKDu for farmers and changes in mean ambient temperature (°C) before the examination. We identified 844,412 farmers and 3,750,273 nonfarmers. Among 24.9% of farmers and 7.4% of nonfarmers with reduced kidney function, only 1 in 7 received a diagnosis of CKD. The farming occupation was independently predictive of CKDu (OR= 1.09, 95% confidence interval [CI]= 1.001-1.18) but not CKD. Increased ambient temperature (°C) was associated with a higher risk of CKD (OR= 1.023, 95% CI= 1.017-1.029), with particularly strong associations observed among middle-aged participants and diabetics. Taiwanese farmers might have a higher risk of developing CKDu. Outdoor heat exposure is associated with the development of CKD, and middle-aged participants and those with diabetes are more vulnerable than the general population.

Effect of temperature on carbon accumulation in northern lake systems over the past 21,000 years

Introduction: Rising industrial emissions of carbon dioxide and methane highlight the important role of carbon sinks and sources in fast-changing northern landscapes. Northern lake systems play a key role in regulating organic carbon input by accumulating carbon in their sediment. Here we look at the lake history of 28 lakes (between 50°N and 80°N) over the past 21,000 years to explore the relationship between carbon accumulation in lakes and temperature changes.Method: For this study, we calculated organic carbon accumulation rates (OCAR) using measured and newly generated organic carbon and dry bulk density data. To estimate new data, we used and evaluated seven different regression techniques in addition to a log-linear model as our base model. We also used combined age-depth modeling to derive sedimentation rates and the TraCE-21ka climate reanalysis dataset to understand temperature development since the Last Glacial Maximum. We determined correlation between temperature and OCAR by using four different correlation coefficients.Results: In our data collection, we found a slightly positive association between OCAR and temperature. OCAR values peaked during warm periods Bølling Allerød (38.07 g·m−2·yr−1) and the Early Holocene (40.68 g·m−2·yr−1), while lowest values occurred during the cold phases of Last Glacial Maximum (9.47 g·m−2·yr−1) and Last Deglaciation (10.53 g·m−2·yr−1). However, high temperatures did not directly lead to high OCAR values.Discussion: We assume that rapid warming events lead to high carbon accumulation in lakes, but as warming progresses, this effect appears to change as increased microbial activity triggers greater outgassing. Despite the complexity of environmental forcing mechanisms affecting individual lake systems, our study showed statistical significance between measured OCAR and modelled paleotemperature for 11 out of 28 lakes. We concluded that air temperature alone appears to drive the carbon accumulation in lakes. We expected that other factors (catchment vegetation, permafrost, and lake characteristics) would influence accumulation rates, but could not discover a conclusive factor that had a statistical significant impact. More data available on long-term records from northern lake systems could lead to more confidence and accuracy on the matter.

Estimation of Surface-Subsurface Water Balance in Lower Tapi River Basin Using Gridded Data and Station-Based Observed Data

ABSTRACT Water is an essential resource. An understanding of catchment water balance is crucial to ensure water availability in the region. Hydrological models are credible tools for water balance assessment of a region. These models are data intensive. The traditional hydrological modeling approach relies on station-based observed data. The availability of sufficient long-term consistent observed data is often limited. On the other hand, climate reanalysis data for a long timeperiod is available from multiple data sources. This study estimates runoff characteristics for Lower Tapi River Basin in western India, with the help of a Soil Conservation Service Curve Number method. The catchment water balance in terms of runoff, baseflow, and recharge at sub-basin scale is worked out with the help of station-based and climate reanalysis datasets. The model output are calibrated against observed river gauging data. A reasonable fit between model-simulated and observed runoff is obtained in terms of duration and magnitude of runoff peak. Tested model is applied to future climate scenarios. The simulations show an increase in surface runoff, recharge, baseflow and total runoff for future time period. Results reveal that climate changes have significant impact on hydrology of the basin.

Assessing Predictions of Australian Offshore Wind Energy Resources from Reanalysis Datasets

Offshore wind farms are a current area of interest in Australia due to their ability to support its transition to renewable energy. Climate reanalysis datasets that provide simulated wind speed data are frequently used to evaluate the potential of proposed offshore wind farm locations. However, there has been a lack of comparative studies of the accuracy of wind speed predictions from different reanalysis datasets for offshore wind farms in Australian waters. This paper assesses wind speed distribution accuracy and compares predictions of offshore wind turbine power output in Australia from three international reanalysis datasets: BARRA, ERA5, and MERRA-2. Pressure level data were used to determine wind speeds and capacity factors were calculated using a turbine bounding curve. Predictions across the datasets show consistent spatial and temporal variations in the predicted plant capacity factors, but the magnitudes differ substantially. Compared to weather station data, wind speed predictions from the BARRA dataset were found to be the most accurate, with a higher correlation and lower average error than ERA5 and MERRA-2. Significant variation was seen in predictions and there was a lack of similarity with weather station measurements, which highlights the need for additional site-based measurements.

Climate change effect on water temperature and evaporation in Lake Qarun

ABSTRACT Climate change can have significant effects on the water and heat budgets of lakes and reservoirs. This study examined the effect of climate change on Lake Qarun through analyzing the temporal variation in lake surface temperature and evaporation from the lake. The analysis was based on the remote-sensing dataset MOD11A1 and the ERA5 climate reanalysis dataset. Daily surface temperatures for 1-km MOD11A1 pixels were filtered to remove outliers, averaged to obtain mean lake temperature, and then aggregated to compute the monthly average lake temperature. Lake evaporation was estimated by applying an un-mixing algorithm to ERA5 evaporation fluxes for mixed land-lake pixels and land-only pixels. The resulting monthly evaporation from the lake was corrected using a regression model based on evaporation estimates available in the literature for Lake Qarun. The regression model performance was confirmed through the Nash–Sutcliffe efficiency coefficient, correlation coefficient, and root mean square error. Over the period 1980 to 2019, findings show that annual evaporation ranged between 140 and 190 cm/year. Maximum average monthly evaporation for the study period is 21 cm in July while the minimum is 6.7 cm in February from 1980 to 2019. For the average lake surface temperature, it varied between ~31.9 and 33.9°C over the period 2000 to 2019. For the same time period, average maximum and minimum monthly temperatures were 44°C in August and 20.1°C in January. It is concluded that the average increase in evaporation and lake surface temperature is 37 mm/decade and 0.4°C/decade, respectively. The increase in both lake surface temperature and evaporation is likely due to climate change effects. However, using datasets with a larger temporal record for lake surface temperature, further studies can be applied for confirmation. Also, further confirmation can be obtained by applying the same analysis to other lakes in Egypt.

Hydrological and water resources investigations of the Mereb-Gash river basin in Eritrea

Introduction. Adaptation and mitigation of climate change and its consequences through effective water resources management, among others, is essential for achieving sustainable development goals. The construction of water infrastructure facilities and the maintenance of existing ones which can be done with up-to-date hydrological information are required. Therefore, this study aims at obtaining unique information and choosing effective tools for stream flow simulations in Mereb-Gash river basin.
 
 Materials and methods. To this end, physically-based semi-distributed and conceptual models are investigated using climate reanalysis datasets and geomorphologic catchment characteristics. Prior to feeding model forcing variables, their statistical and spatial patterns, methods of potential evapotranspiration estimations, and basin drought conditions are studied. Two or more approaches have been employed for each of these tasks.
 
 Results. GIUH-Nash model has the potential to produce acceptable values in most cases irrespective of sources and resolutions of digital elevation models, but it is found to be sensitive to the type of algorithms selected for stream network generations. The continuous simulation models reveal considerable overestimation of most of the precipitation in the reanalysis datasets, which in turn has a significant effect on other variables, such as potential evapotranspiration and thereby leading to a substantial discrepancy between simulated and observed.
 
 Conclusions. Despite the fact that reanalysis dataset has a great advantage over ground-based observations in terms of their simplicity and accessibility, the research results have shown the need for a critical attitude to both sources of initial data. To minimize the uncertainties of mathematical models and thereby to improve modeling capabilities, there is no qualitative alternative to restoring existing as well as new ground-based observation stations.

Energy assessment of potential locations for OWC instalation at the Portuguese coast

This work aims to determine the exploitable wave energy resource at five potential sites close to harbour protection facilities at the Portuguese coast, namely at the Azores archipelago, at Madeira Island and at Sines, on the coast of mainland Portugal. For that purpose, a third-generation wave model SWAN is used to transfer the offshore estimates of sea wave conditions to those points over the last 40 years. Sea states and wind fields are provided by the climate reanalysis datasets ERA5. Using sea states as boundary conditions and wind fields as forcings in the numerical domains of the SWAN model, the sea states were propagated shoreward, in order to estimate and analyse the wave conditions in the regions of interest. By combining the average energy flux per unit length of wave front and the probability of occurrence of each sea state, the average exploitable annual energy per unit length of wave crest can be computed. The variability of this energy flux is analysed since it is of paramount importance for the efficiency of Wave Energy Converters (WEC). This assessment showed that the best location for the installation of dual-chamber OWC devices is at the Azores archipelago.

Variability in Antarctic surface climatology across regional climate models and reanalysis datasets

Abstract. Regional climate models (RCMs) and reanalysis datasets provide valuable information for assessing the vulnerability of ice shelves to collapse over Antarctica, which is important for future global sea level rise estimates. Within this context, this paper examines variability in snowfall, near-surface air temperature and melt across products from the Met Office Unified Model (MetUM), Regional Atmospheric Climate Model (RACMO) and Modèle Atmosphérique Régional (MAR) RCMs, as well as the ERA-Interim and ERA5 reanalysis datasets. Seasonal and trend decomposition using LOESS (STL) is applied to split the monthly time series at each model grid cell into trend, seasonal and residual components. Significant systematic differences between outputs are shown for all variables in the mean and in the seasonal and residual standard deviations, occurring at both large and fine spatial scales across Antarctica. Results imply that differences in the atmospheric dynamics, parametrisation, tuning and surface schemes between models together contribute more significantly to large-scale variability than differences in the driving data, resolution, domain specification, ice sheet mask, digital elevation model and boundary conditions. Despite significant systematic differences, high temporal correlations are found for snowfall and near-surface air temperature across all products at fine spatial scales. For melt, only moderate correlation exists at fine spatial scales between different RCMs and low correlation between RCM and reanalysis outputs. Root mean square deviations (RMSDs) between all outputs in the monthly time series for each variable are shown to be significant at fine spatial scales relative to the magnitude of annual deviations. Correcting for systematic differences results in significant reductions in RMSDs, suggesting the importance of observations and further development of bias-correction techniques.