Long-term Monthly Mean Research Articles

Statistical Connections between Large-Scale Climate Indices and Observed Mean and Extreme Temperatures in the US from 1948 to 2018

In order to better understand the extent to which global climate variability is linked to the frequency and intensity of heat waves and overall changes in temperature throughout the United States (US), correlations between long-term monthly mean, minimum, and maximum temperatures throughout the contiguous US on the one hand and low-frequency variability of multiple climate indices (CIs) on the other hand are analyzed for the period from 1948 to 2018. The Pearson’s correlation coefficient is used to assess correlation strength, while leave-one-out cross-validation and a bootstrapping technique (p-value) are used to address potential serial and spurious correlations and assess the significance of each correlation. Three parameters defined the sliding windows over which surface temperature and CI values were averaged: window size, lag time between the temperature and CI windows, and the beginning month of the temperature window. A 60-month sliding window size and 0 lag time resulted in the highest correlations overall; beginning months were optimized on an individual site basis. High (r ≥ 0.60) and significant (p-value ≤ 0.05) correlations were identified. The Western Hemisphere Warm Pool (WHWP) and El Niño/Southern Oscillation (ENSO) exhibited the strongest links to temperatures in the western US, tropical Atlantic sea surface temperatures to temperatures in the central US, the WHWP to temperatures throughout much of the eastern US, and atmospheric patterns over the northern Atlantic to temperatures in the Northeast and Southeast. The final results were compared to results from previous studies focused on precipitation and coastal sea levels. Regional consistency was found regarding links between the northern Atlantic and overall weather and coastal sea levels in the Northeast and Southeast as well as on weather in the upper Midwest. Though the MJO and WHWP revealed dominant links with precipitation and temperature, respectively, throughout the West, ENSO revealed consistent links to sea levels and surface temperatures along the West Coast. These results help to focus future research on specific mechanisms of large-scale climate variability linked to US regional climate variability and prediction potential.

The China Coastal Front from Himawari-8 AHI SST Data—Part 1: East China Sea

High-resolution (2 km) high-frequency (hourly) SST data from 2015–2020 provided by the Advanced Himawari Imager (AHI) onboard the Japanese Himawari-8 geostationary satellite positioned over 140.7°E were used to study spatial and temporal variability of the China Coastal Front (CCF) in the East China Sea. This dataset was processed with the Belkin and O’Reilly algorithm to generate long-term mean monthly maps of the SST gradient magnitude (GM) and frontal frequency (FF). The horizontal structure of the SST field in the vicinity of the CCF was also investigated from the cross-frontal distributions of SST along eight parallels between 31°N and 24°N. The monthly mean distributions of SST along these 8 parallels were used to determine inshore and offshore boundaries of the CCF and to calculate the CCF strength defined as the total cross-frontal step (range) dSST calculated as the difference between offshore and inshore SST. The CCF emerges in November, fully develops in December, and peaks in strength in January–February. The front’s fragmentation and shrinking/weakening begins in February and March, respectively. In winter (December–February), the front’s strength dSST exceeds 5 °C offshore the Zhejiang-Fujian coast and could be as high as 7.5 °C when nearshore waters cool down to 7 °C. In winter, the front’s strength decreases downstream between 31°N and 24°N. The CCF changes its physical nature as the seasons progress. In winter, the CCF is a water mass front between the cold and fresh water coming from the north and the warm and salty water coming from the south. In summer, the CCF becomes a coastal upwelling front maintained largely by southerly winds. In winter, the CCF’s cross-frontal structure in the SST field is ramp-shaped, with SST increasing monotonously in the offshore direction. In summer, the CCF’s cross-frontal structure in the SST field is V-shaped or U-shaped, featuring a minimum SST formed by cold upwelled water at some distance from the shore. Thus, the summer SST structure effectively consists of two parallel fronts, an inshore one and an offshore one, with a minimum SST in between. Across the inshore/offshore front, the SST decreases/increases in the offshore direction.

Effect of typical meteorological year selection on integrated daylight modeling and building energy simulation

The complete description of outdoor luminous and thermal environment is the basis for daylight utilization design with simulation tools. Nevertheless, Typical Meteorological Year (TMY) and generation method specifically developed for the energy simulation of daylight-utilized buildings is still unavailable currently. Luminous environment parameters have not been taken into consideration in existing TMY generation methods. In this study, the feasibility of existing TMY generation process has been examined. A generic office model implementing sided window daylighting is established. Historical meteorological data of Hong Kong from 1979 to 2007 have been collected and three existing weighting schemes are applied during the Typical Meteorological Month (TMM) selection procedures. Three TMY files for Hong Kong are generated and used to conduct integrated Climate-Based Daylight Modeling and building energy simulation. The result demonstrates that, on annual basis, the energy consumption results obtained from the generated TMY files are in good agreements with the long-term mean annual value. The maximum deviation of annual energy consumptions for the generated TMY files is only 1.8%. However, further analysis on monthly basis shows that all the three generated TMY files fail to fully represent the long-term monthly mean level. The maximum deviation of monthly energy consumptions for the generated TMY files can reach up to 11%. As the energy performance daylight utilization is subject to weather change, analysis on daily and monthly energy level is important, especially during design stage. The deficiency of existing TMM selection process and TMY generation method indicates the necessity to develop a corresponding typical weather data input with finer resolution for the energy simulation of daylight-related buildings.

Assessment of Land Surface Schemes from the WRF-Chem for Atmospheric Modeling in the Andean Region of Ecuador

Surface interactions occur near the land–atmosphere interface, thus affecting the temperature, convection, boundary layer, and stability of the atmosphere. A proper representation of surface interactions is a crucial component for numerical atmospheric and air quality modeling. We assessed four land surface schemes—1. 5–layer thermal diffusion scheme (1 5-Layer); 2. unified Noah land surface model (2 Noah); 3. rapid update cycle (3 RUC) land surface model; and 4. Pleim–Xiu land surface model (4 Pleim–Xiu)—from the Weather Research and Forecasting with Chemistry (WRF-Chem V3.2) model for the purposes of atmospheric modeling in Cuenca, which is a region with a complex topography and land use configuration and which is located in the Southern Andean region, in Ecuador. For this purpose, we modeled the meteorological and air quality variables during September 2014. It was found that the meteorological and short-term air quality variables were better modeled through the 2 Noah scheme. Long-term (mean monthly) air quality variables were better modeled by the 1 5–Layer and 3 RUC options. On average, the 2 Noah scheme was better at modeling meteorology and air quality. In addition, we assessed the 2 Noah scheme combined with the urban canopy model (UCM) (5 Noah UCM), which was developed as an option to represent the urban effects at a subgrid-scale. Results indicated that the performance of the 5 Noah UCM scheme was not better at modeling than the 2 Noah scheme alone. Moreover, the 5 Noah UCM scheme notably decreased the modeling performance for carbon monoxide and fine particulate matter. These results complement previous assessments of other schemes, allowing us to recommend a basic configuration of parameters for atmospheric modeling in the Andean region of Ecuador.

Comparative Study of Flood and Long-term Mean Monthly Flow Estimation Approaches: Case Studies of Six Basins in Nepal


 
 
 Most of Nepal's river basins have poor hydro- meteorological databases, with several river basins being ungauged. Thus, hydrological parameters need to be estimated using different types of computation methods. The primary goal of this study is to identify the most accurate method for calculating peak flood and long-term mean monthly flow among the most commonly used methods in Nepal. We compared the peak flood calculated using various flood computation formulas, such as Hydest, Modified Hydest, MHSP (Medium Hydropower Study Project) 1997, Modified Dickens, PCJ (Prem Chandra Jha) 1996, Rational, and Specific Discharge, to the flood calculated using gauged discharge data frequency analysis. We find that it is wise to use the Modified Hydest method in the khokana basin for all Return Periods (RPs) and in the Belkot basin (for RP ≤ 100 years), the Specific Discharge method in the Jamu basin, the MHSP 1997 method in the Belkot basin (for RP ≤ 100 years) and the Bagasoti Gaun basin (for RP ≤ 20 years). The PCJ 1996 method having the lowest cumulative value of Root Mean Square Error (RMSE) for the six studied catchments is suitable for Rabhuwa Bazar (for RP > 50 years) and Bagasoti Gaun (for RP >20 years). Similarly, the Modified Dickens method is suitable in the Bagasoti Gaun basin for RP ≤ 50 years. This paper also shows the performance of the Hydest and MHSP 1997 mean flow estimation methods and suggests different coefficients or constants to be used with the MHSP 1997, Modified MIP (Medium Irrigation Project), and Hydest methods to obtain more reliable long-term mean monthly flows. Overall, our study will help the designer choose a reliable method for design flow estimation. This study also shows that the flow obtained from even the most suitable methods needs to be adjusted. As a result, intensive research is required to adjust previous methods and develop the new one.
 
 

Evaluation of long-term monthly mean streamflow trend in the Mediterranean basins using different methods

Evaluation of long-term monthly mean streamflow trend in the Mediterranean basins using different methods

Projected changes in mean annual cycle of temperature and precipitation over the Czech Republic: Comparison of CMIP5 and CMIP6

The multi-model ensembles like CMIP5 or CMIP6 provide a tool to analyze structural uncertainty of climate simulations. Currently developed regional and local climate change scenarios for the Czech Republic assess the uncertainty based on state-of-the-art Global Climate Model (GCM) and Regional Climate Model (RCM) ensembles. Present study focuses on multi-model spread of projected changes in long-term monthly means and inter-annual variability of monthly mean minimum, mean and maximum daily air temperature and monthly mean precipitation. We concentrate in more detail on the simulation of CNRM-ESM2-1, the driving GCM for the convection permitting ALADIN-Climate/CZ simulation contributing to the local scenarios in very high resolution. For this GCM, we also analyze a mini-ensemble with perturbed initial conditions to evaluate the range of internal climate variability. The results for the Czech Republic reveal minor differences in model performance in the reference period whereas quite substantial inter-generation shift in projected future change towards higher air temperature and lower summer precipitation in CMIP6 comparing to CMIP5. One of the prominent features across GCM generations is the pattern of summer precipitation decrease over central Europe. Further, projected air temperature increase is higher in summer and autumn than in winter and spring, implying increase of thermal continentality of climate. On the other hand, slight increase of winter precipitation and tendency towards decrease of summer precipitation lead to projected decrease of ombric continentality. The end of 21st century projections also imply higher probability of dry summer periods, higher precipitation amounts in the cold half of the year and extremely high temperature in summer. Regarding the CNRM-ESM2-1, it is often quite far from the multi-model median. Therefore, we strictly recommend to accompany any analysis based on the simulation of nested Aladin-CLIMATE/CZ with proper uncertainty estimate. The range of uncertainty connected to internal climate variability based on one GCM is often quite large in comparison to the range of whole CMIP6 ensemble. It implies that when constructing climate change scenarios for the Central Europe region, attention should be paid not only to structural uncertainty represented by inter-model differences and scenario uncertainty, but also to the influence of internal climate variability.

Feasibility of Random Forest and Multivariate Adaptive Regression Splines for Predicting Long-Term Mean Monthly Dew Point Temperature

The accurate estimation of dew point temperature (Tdew) is important in climatological, agricultural, and agronomical studies. In this study, the feasibility of two soft computing methods, random forest (RF) and multivariate adaptive regression splines (MARS), is evaluated for predicting the long-term mean monthly Tdew. Various weather variables including air temperature, sunshine duration, relative humidity, and incoming solar radiation from 50 weather stations in Iran as well as their geographical information (or a subset of them) are used in RF and MARS as inputs. Three statistical indicators namely, root mean square error (RMSE), mean absolute error (MAE), and correlation coefficient (R) are used to assess the accuracy of Tdew estimates from both models for different input configurations. The results demonstrate the capability of the RF and MARS methods for predicting the long-term mean monthly Tdew. The combined scenarios in both the RF and MARS methods are found to produce the best Tdew estimates. The best Tdew estimates were obtained by the MARS model with the RMSE, MAE, and R of respectively 0.17°C, 0.14°C, and 1.000 in the training phase; 0.15°C, 0.12°C, and 1.000 in the validation phase; and 0.18°C, 0.14°C, and 0.999 in the testing phase.

Estimation of spatio-temporal groundwater storage variations in the Lower Transboundary Indus Basin using GRACE satellite

In the Lower Transboundary Indus Basin (LTIB), excessive groundwater is being consumed in combination with surface water to meet the increasing demand for irrigation and resulting in groundwater depletion. This study employed the generalized three-cornered hat method (GTCH) to assess uncertainty of different Gravity Recovery and Climate Experiment (GRACE) derived-terrestrial water storage anomalies (TWSA) measurements using GRACE (-TWSA) data and the variance variability in space and time of GRACE derived groundwater storage anomalies (GWSA) using Empirical Orthogonal Function (EOF) analysis, respectively. Furthermore, monthly GRACE derived GWSA were estimated using Global Land Data Assimilation System (GLDAS) model data and GWSA time series trend and depletion of groundwater storage were evaluated using the MannKendal (MK) trend test and Sen’s slope estimator in the LTIB. The TWSA and precipitation data depict seasonal characteristics with peaks in the summer and dips in the winter, which reflect variation in GWSA, respectively. Additionally, it is observed that the variations in GWSA exhibit a downward trend from 2003 to 2016. The results revealed that more than 80% of total variance variability was explained by the first 2 EOF modes. The results showed that GRACE derived GWSA is being depleted at a rate of 4.16 ± 0.26 mm per year (2.97 ± 0.19 km3 per year). Long-term monthly mean of GRACE derived GWSA showed remarkable agreements of correlation of determination (R2) with PCRaster Global Balance (PCR-GLOBWB) model 75% and WGHM (WaterGap Global Hydrological model) 81%. The GRACE-derived GWSA also showed a good correlation of 0.69 and 0.82 with in situ data on seasonal and annual scales, respectively. This study would be insightful to calculate the agro-economic impact of excessive groundwater withdrawal.

Machine Learning Modeling of Climate Variability Impact on River Runoff

The hypothesis of this study was one of existence of spatially organized links between the time series of river runoff and climate variability indices, describing the oscillations in the atmosphere–ocean system: ENSO (El Niño–Southern Oscillation), PDO (Pacific Decadal Oscillation), AMO (Atlantic Multidecadal Oscillation), and NAO (North Atlantic Oscillation). The global river flow reconstructions (ERA-20-CM-R) for 18 study areas on six continents and climate variability indices for the period 1901–2010 were used. The split-sample approach was applied, with the period 1901–2000 used for training and 2001–2010 used for testing. The quality measures used in this paper were mean absolute error, dynamic time warping, and top extreme events error. We demonstrated that a machine learning approach (convolution neural network, CNN) trained on climate variability indices can model the river runoff better than the long-term monthly mean baseline, both in univariate (per-cell) and multivariate (multi-cell, regionalized) settings. We compared the models to the baseline in the form of heatmaps and presented results of ablation experiments (test time ablation, i.e., jackknifing, and training time ablation), which suggested that ENSO is the primary determinant among the considered indices.

Evaluating the Suitability of Century-Long Gridded Meteorological Datasets for Hydrological Modeling

AbstractRecent advances in climate reanalyses have led to the development of meteorological products providing information from the beginning of the last century or even before. As these data sources might be of interest to practitioners in the event of missing data from meteorological stations, it is important to assess their usefulness for different applications. The main objective of this study is to investigate the ability of two long-term reanalysis datasets (CERA-20C and 20CR) and one long-term interpolated dataset (Livneh) for supporting hydrological modeling. The precipitation and temperature data of the three datasets were first compared, downscaled, and then used as inputs to the conceptual hydrological model HBV in 168 basins in the United States. The findings suggest that the quality of all three datasets decreases the further we go back in time. Models calibrated at the beginning of the time series, where the data quality is worse, are only able to capture the general properties of the time series and thus do not show a decrease in performance as the period between calibration and validation becomes larger. The opposite is true for models calibrated at the end of the time series, which show a clear decrease in performance toward the beginning of the century. While the hydrological model driven with the interpolated datasets achieved the best performance, the results obtained with the reanalysis datasets were still informative (i.e., better than the long-term monthly mean), and they matched the performance of the interpolated dataset in a few catchments in the northwestern United States.

Identification of possible dynamical drivers for long-term changes in temperature and rainfall patterns over Europe

This study looks into the question to what extent long-term change patterns of observed temperature and rainfall over Europe can be attributed to dynamical causes, in other words: Are the observed changes due to a change in frequency of the patterns or have the patterns’ dynamical properties changed? By using a combination of daily meteorological data and a European weather-type classification, the long-term monthly mean temperature and precipitation were calculated for each weather-type. Subsequently, the observed weather-type sequences were used to construct analogue time series for temperature and precipitation which only include the dynamical component of the long-term variability since 1961. The results show that only a fraction of about 20% of the past temperature rise since 1990, which for example amounted to 1 °C at the Potsdam Climate Station can be explained by dynamical changes, i.e. most of the weather-types have become warmer. Concerning long-term changes of seasonal rainfall patterns, a fraction of more than 60% is considerably higher. Moreover, the results indicate that for rainfall compared with temperature, the decadal variability and trends of the dynamical component follow the observed ones much stronger. Consequently, most of the explained seasonal rainfall variances can be linked to changes in weather-type sequences in Potsdam and over Europe. The dynamical contribution to long-term changes in annual and seasonal rainfall patterns dominates due to the fact that the alternation of wet and dry weather-types (e.g. the types Trough or High pressure over Central Europe), their frequencies and duration has significantly changed in the last decades.

Dynamic Scaling of the Generalized Complementary Relationship Improves Long-term Tendency Estimates in Land Evaporation

Most large-scale evapotranspiration (ET) estimation methods require detailed information of land use, land cover, and/or soil type on top of various atmospheric measurements. The complementary relationship of evaporation (CR) takes advantage of the inherent dynamic feedback mechanisms found in the soil-vegetation-atmosphere interface for its estimation of ET rates without the need of such biogeophysical data. ET estimates over the conterminous United States by a new, globally calibrated, static scaling (GCR-stat) of the generalized complementary relationship (GCR) of evaporation were compared to similar estimates of an existing, calibration-free version (GCR-dyn) of the GCR that employs a temporally varying dynamic scaling. Simplified annual water balances of 327 medium and 18 large watersheds served as ground-truth ET values. With long-term monthly mean forcing, GCR-stat (also utilizing precipitation measurements) outperforms GCR-dyn as the latter cannot fully take advantage of its dynamic scaling with such data of reduced temporal variability. However, in a continuous monthly simulation, GCR-dyn is on a par with GCR-stat, and especially excels in reproducing long-term tendencies in annual catchment ET rates even though it does not require precipitation information. The same GCR-dyn estimates were also compared to similar estimates of eight other popular ET products and they generally outperform all of them. For this reason, a dynamic scaling of the GCR is recommended over a static one for modeling long-term behavior of terrestrial ET.

The Development and Use of Isoscapes to Determine the Geographical Origin of Quercus spp. in the United States

The stable isotope ratios of oxygen, hydrogen, carbon and sulfur from extracted wood of 87 samples of oaks from the United States were analysed. Relationships with climate variables and the stable isotope ratios of the 69 training dataset samples were investigated to a monthly resolution using long-term monthly mean climate data from NASA and the University of East Anglia’s Climate Research Unit, in conjunction with forecast data for hydrogen and oxygen isotope ratios in precipitation. These relationships were used to construct model isoscapes for oxygen, hydrogen, carbon and sulfur for US oak with the aim of using them to forecast isotopic patterns in areas that were not sampled and predict values in samples not used to construct the models. The leading predictors for isoscape generation were oxygen isotope ratios in January precipitation for oak oxygen isotope ratios, hydrogen isotope ratios in July precipitation for oak hydrogen isotope ratios, water vapour in April for carbon isotope ratios, and reflected shortwave radiation in March in combination with sulfate concentration in May for oak sulfur isotopes. The generated isoscapes can be used to show regions an unknown sample may have originated from with a resolution dependent on the rarity of the stable isotope signature within the United States. The models were assessed using the data of 18 samples of georeferenced oak. The assessment found that 100% of oxygen, 94% of hydrogen, 78% of carbon, and 94% of sulfur isotope ratios in the 18 test dataset samples fell within two standard deviations of the isoscape models. Using the results of the isoscapes in combination found that there were 4/18 test samples which did not fall within two standard deviations of the four models, this is largely attributed to the lower predictive power of the carbon isoscape model in conjunction with high local variability in carbon isotope ratios in both the test and training data. The method by which this geographic origin method has been developed will be useful to combat illegal logging and to validate legal supply chains for the purpose of good practice due diligence.

Performance evaluation of Eta/HadGEM2-ES and Eta/MIROC5 precipitation simulations over Brazil

Climate change effects can have significant impacts worldwide. Extreme events can modify water availability and agricultural production, making climate change planning an essential task. The National Institute for Space Research (INPE in Portuguese) in Brazil has made a large dataset of regional climate model outputs (simulations and projections) available, which opens up many possibilities of carrying out high-resolution climate change studies. However, there is still no performance evaluation of the model-derived rainfall output against high-resolution ground-based observation data considering the Brazilian biomes. This paper attempts to fill this gap and evaluates the simulated precipitation throughout Brazil. We used gridded observed precipitation data and historical climate simulations from the Model for Interdisciplinary Research on Climate, version 5 (MIROC5) and from the Hadley Center Global Environment Model, version 2 (HadGEM2-ES), which were downscaled by the Eta RCM (Regional Climate Model). For the overlapping period (1980–2005), there is good agreement (PBIAS up to 10%) of downscaled annual simulations for the Amazon and Cerrado biomes and large biases (reaching 40%) in the Pampa biome, compared to the observations. Our results showed that HadGEM2-ES is capable of representing long-term mean monthly precipitation for large areas well, such as the Amazon and Cerrado. Furthermore, the Eta RCM has considerably improved the driving GCM MIROC5 simulations. In conclusion, we recommend using the HadGEM2-ES simulations for the Amazon, Eta/HadGEM2-ES for the Atlantic Forest, Cerrado, and Pampa, and Eta/MIROC5 for the Caatinga and Pantanal. Our study provides an overview of two downscaled simulation datasets in Brazil that may help verify the models' suitability for further climate change assessments.

Variability of Sunlight Duration in Tomsk in 1961–2018

Variations in the sunlight duration (SLD) in Tomsk are analyzed for the period from 1961 to 2018 and separately for 1961–1990 and 1981–2010. Data on clouds and total solar radiation obtained at the TOR-station of IAO SB RAS in 1996–2018 are used. The actual long-term monthly mean SLD ranges from 44 h in December to 317 h in June–July. The analysis of the long-term variation in SLD shows its increase from 1961 to 1989 and its decrease starting from 1999 due to an increase in the low cloud cover and high frequency of continuous clouds. The SLD in Tomsk in the modern period has increased relative to the historical period. Regression equations between SLD and the total solar radiation (Q) are derived.

El Niño and La Niña influence on mean river flows of southern South America in the 20th century

ABSTRACTThe influence of the El Niño Southern Oscillation (ENSO) phenomenon on monthly mean river flows of 12 rivers in the extreme south of South America in the 20th century is analysed. The original dataset of each river is divided into two subsets, i.e. warm ENSO events or El Niño, and cold ENSO events or La Niña. The elements of the subsets are composites of 24 consecutive months, from January of the year when the ENSO event begins to December of the following year. The ENSO signal is analysed by comparing the monthly mean value of each subset to the long-term monthly mean. The results reveal that, in general, monthly mean El Niño (La Niña) river flows are predominantly larger (smaller) than the long-term monthly mean in the rivers studied. The anomalies are more evident during the second half of the year in which the event starts and the first months of the following year.

Run-off\u2013erosion modelling and water balance in the Epit\xe1cio Pessoa Dam river basin, Para\xedba State in Brazil

Water scarcity and sediment yield scarcity are major environmental problems in the Brazilian semi-arid region that influence the quantity and quality of water used for water supply. Thus, the aim of the paper was to analyse the water balance and the sediment yield in the Epitácio Pessoa Dam river basin using the Soil and Water Assessment Tool model. Rainfall, streamflow, air temperature, air humidity data from 1970 to 1993, a soil type map, a land cover map, and a digital elevation model were used in this study. The model was calibrated and validated by comparing simulated mean monthly streamflow with observed long-term mean monthly streamflow for the Poço de Pedras and Caraúbas streamflow gauges after sediment yield values were estimated. The Soil and Water Assessment Tool model results indicated good performance by the model during the calibration, with an observed mean streamflow of 6.86 m3/s and a simulated streamflow of 5.68 m3/s for Poço de Pedras and 9.33 m3/s for Caraúbas. The mean sediment yield was 3.91 ton/ha/month. The results of the water balance analysis indicate a high rate of evapotranspiration in the basin (68% evapotranspiration), and 7% of the rainfall was converted to streamflow (5% streamflow and 2% base flow). Therefore, this study found that Soil and Water Assessment Tool could be effectively used for assessing the water balance components and sediment yield of the Epitácio Pessoa Dam river basin, Paraíba State, Brazil.

Spatially distributed potential evapotranspiration modeling and climate projections

Evapotranspiration integrates energy and mass transfer between the Earth's surface and atmosphere and is the most active mechanism linking the atmosphere, hydrosphsophere, lithosphere and biosphere. This study focuses on the fine resolution modeling and projection of spatially distributed potential evapotranspiration on the large catchment scale as response to climate change. Six potential evapotranspiration designed algorithms, systematically selected based on a structured criteria and data availability, have been applied and then validated to long-term mean monthly data for the Shannon River catchment with a 50m2 cell size. The best validated algorithm was therefore applied to evaluate the possible effect of future climate change on potential evapotranspiration rates. Spatially distributed potential evapotranspiration projections have been modeled based on climate change projections from multi-GCM ensembles for three future time intervals (2020, 2050 and 2080) using a range of different Representative Concentration Pathways producing four scenarios for each time interval. Finally, seasonal results have been compared to baseline results to evaluate the impact of climate change on the potential evapotranspiration and therefor on the catchment dynamical water balance. The results present evidence that the modeled climate change scenarios would have a significant impact on the future potential evapotranspiration rates. All the simulated scenarios predicted an increase in potential evapotranspiration for each modeled future time interval, which would significantly affect the dynamical catchment water balance. This study addresses the gap in the literature of using GIS-based algorithms to model fine-scale spatially distributed potential evapotranspiration on the large catchment systems based on climatological observations and simulations in different climatological zones. Providing fine-scale potential evapotranspiration data is very crucial to assess the dynamical catchment water balance to setup management scenarios for the water abstractions. This study illustrates a transferable systematic method to design GIS-based algorithms to simulate spatially distributed potential evapotranspiration on the large catchment systems.

Error inhomogeneity in the computation of spherical mean displacement

The traditional method for computing the mean displacement in latitude–longitude coordinates is a spherical meridional–zonal resultant displacement method (MRDM), which regards the displacement as the resultant vector of the meridional and zonal displacement components. However, there are inhomogeneity and singularity in the computation error of the MRDM, especially at high latitudes. Using the NCEP/NCAR long-term monthly mean wind and idealized wind fields, the inhomogeneity in the MRDM was accessed by using a great circle displacement computing method (GCDM) for non-iterative cases. The MRDM and GCDM were also compared for iteration cases by taking the trajectories from a three-time level reference method as the real trajectories. In the horizontal direction, the GCDM assumes that an air particle moves along its locating great circle and that the magnitude of the displacement equals the arc length of the great circle. The inhomogeneity of the MRDM is evaluated in terms of the horizontal distance error from the products of wind speed, lapse time, and angle difference from the GCDM displacement orient. The non-iterative results show that the mean horizontal displacement computed through the MRDM has both computational and analytical errors. The displacement error of the MRDM depends on the wind speed, wind direction, and the departure latitude of the air particle. It increases with the wind speed and the departure latitude. The displacement magnitude error has a four-wave pattern and the displacement direction error has a two-wave feature in the definition range of the wind direction. The iterative result shows that the displacement magnitude error and angle error of the MRDM and GCDM with respect to the reference method increase with the lapse time and have similar distribution patterns. The mean magnitude error and the angle error of the MRDM are nearly twice as large as those of the GCDM.