Soil moisture and soil temperature, reflecting a synthetic climate regime, are vitally important for climate change assessments and adaption. As historical in situ measurements of soil states are ...extremely scarce and spatially uneven, reanalysis products play an increasingly important role in filling these gaps. The focus of this paper is on water–heat covariations in reanalysis products and a joint evaluation of soil moisture and soil temperature in five widely used atmospheric and land reanalyses is presented using in situ observations from 25 networks during various periods from 1979 to 2017. At the network scale, the five reanalyses show statistically significant correlations with observations, and the European Centre for Medium‐Range Weather Forecasts ERA5 shows higher skills than the other four products and a significant improvement over its predecessor. The National Centers for Environmental Prediction Climate Forecast System Reanalysis performs better in terms of long‐term trends. The most skilful signals in the five reanalyses are the seasonal cycles, with correlation coefficients of over 0.9. However, long‐term trends are substantially weaker than the observed trends and still tend to perform poorly over the high latitudes during cold seasons. Soil temperature reanalyses show even better skills, with mean correlation coefficients over 0.9 between anomalies; ERA5 shows enhanced annual ranges toward the high latitudes and altitudes. A joint evaluation of soil temperature and soil moisture showed physically consistent water–heat covariations in the soil in conjunction with atmospheric fluxes during the growing season over the Northern Hemisphere. This report suggests a good future for reanalysis products and their potential role in land surface climate change assessments.
Water–heat covariations and joint evaluations of soil moisture and soil temperature in five atmospheric and land reanalyses are presented using in situ observations from 25 networks during various periods from 1979 to 2017. ERA5 shows higher skills than the other four products and a significant improvement over its predecessor. Soil water–heat covariations in the reanalyses are physically consistent in conjunction with atmospheric fluxes during the growing season over the Northern Hemisphere.
Soil microbes make up a significant portion of the genetic diversity and play a critical role in belowground carbon (C) cycling in terrestrial ecosystems. Soil microbial diversity and organic C are ...often tightly coupled in C cycling processes; however, this coupling can be weakened or broken by rapid global change. A global meta‐analysis was performed with 1148 paired comparisons extracted from 229 articles published between January 1998 and December 2021 to determine how nitrogen (N) fertilization affects the relationship between soil C content and microbial diversity in terrestrial ecosystems. We found that N fertilization decreased soil bacterial (−11%) and fungal diversity (−17%), but increased soil organic C (SOC) (+19%), microbial biomass C (MBC) (+17%), and dissolved organic C (DOC) (+25%) across different ecosystems. Organic N (urea) fertilization had a greater effect on SOC, MBC, DOC, and bacterial and fungal diversity than inorganic N fertilization. Most importantly, soil microbial diversity decreased with increasing SOC, MBC, and DOC, and the absolute values of the correlation coefficients decreased with increasing N fertilization rate and duration, suggesting that N fertilization weakened the linkage between soil C and microbial diversity. The weakened linkage might negatively impact essential ecosystem services under high rates of N fertilization; this understanding is important for mitigating the negative impact of global N enrichment on soil C cycling.
Microbial diversity plays a vital role in soil C cycling as microbes control soil biochemical processes. However, in this meta‐analysis we found that soil microbial diversity is strongly negatively related to soil C content (mainly SOC, MBC and DOC), and their correlation coefficients decreased with increasing N fertilization rate and experimental duration, suggesting that the linkage between microbial diversity and soil C is weakened by N fertilization rate and duration. This global meta‐analysis presents evidence that long‐term N fertilization led to the decoupling between microbial diversity and soil C.
Savonius rotor is simple in design and easy to fabricate at a lower cost. The basic driving force of Savonius rotor is drag. The drag coefficient of a concave surface is more than the convex surface. ...Hence, the advancing blade with concave side facing the water flow would experience more drag force than the returning blade, thus forcing the rotor to rotate. Net driving force can be increased by reducing the reverse force on the returning blade. This can be realized by providing flow obstacle to the returning blade. The objective of the present work is to find out the optimal position of the deflector plate upstream to the flow which would result in maximum power generated by the rotor. Experimental investigations are carried out to study the influence of the location of the deflector plate on the performance of a modified Savonius rotor with water as the working medium at a Reynolds number of 1.32
×
10
5. Eight different positions of the deflector plate are attempted in this study. Results conclude that deflector plate placed at its optimal position increases the coefficient of power by 50%. Maximum coefficient of power is found to be 0.21 at a tip speed ratio of 0.82 in the presence of deflector plate. Two stage and three stage modified Savonius rotors are tested to study the influence of deflector plate at the optimal position. Maximum coefficient of power improves by 42%, 31% and 17% with deflector plate for two stage 0° phase shift, 90° phase shift and three stage modified Savonius rotor respectively.
Using data collected from collocated hillslopes in central Iowa, the United States, the authors (1) explored the spatial variability of runoff coefficient at the event scale by examining the ...relationships between the standard deviation and coefficient of variation of runoff coefficient and the mean and (2) analyzed the temporal persistence of spatial pattern of runoff coefficient using Spearman rank and Pearson correlation coefficient. This study considered 12 cropland hillslopes with 0–20% native prairie vegetation coverage distributed at different hillslope locations. Seventy runoff events over the period 2008–2011 were investigated, of which 51 occurred during crop active growing season, when the hydrologic responses of crops and prairie vegetation are similar. For these events, the spatial coefficient of variation had a median value of 0.80, which indicate high variation of event‐scale runoff coefficients across neighboring hillslopes. This spatial variation largely cannot be consistently explained by the individual hillslope structural properties investigated. The standard deviation and mean of runoff coefficient showed a convex upward relationship across the range of runoff coefficients, with the maximum standard deviation value at the mean runoff coefficient of about 0.48. The coefficient of variation exponentially decreased with increasing runoff coefficient. For 71% of the cases, the results of both correlation analyses were statistically significant (p ≤ 0.05), which indicate stable spatial pattern of runoff coefficient across events. This temporal persistence could be disrupted under extremely dry and wet conditions. The spatial variation‐mean empirical relation and the temporal persistence of spatial pattern provide insight for parameterizing spatial variability of runoff coefficient in distributed hydrologic models.
Key Points
Event‐scale runoff coefficient was highly variable across neighboring hillslopes with spatial separation ranging from tens of meters to 3000 m
Spatial variability of runoff coefficient followed predictable patterns with respect to spatial mean of runoff coefficient
Temporal persistence of the spatial pattern of runoff coefficient was observed and it could be disrupted under extremely dry and wet conditions
Vertical axis wind turbines can be successfully installed in low wind speed conditions but its detailed starting characteristics in terms of starting torque, starting time and dynamic performances ...have not been investigated thoroughly which is important for increasing the energy yield of such turbines. Amongst their designs, H-Darrieus rotor, in spite of having good power coefficient, possesses poor self-starting features as symmetrical blade profiles are used most of the times. Instead of using symmetrical blades if unsymmetrical or cambered blades are used with high solidity, then starting performance of H-Darrieus rotor along with its power coefficients can be improved. Though this performance improvement measures are correlated with improvement in the starting characteristics, a detailed work in this direction would be useful and for this reason the present work has been carried out. Three types of blade designs have been considered; two unsymmetrical blades namely S815 and EN0005 and one conventional symmetrical NACA 0018 blade, and experiments are conducted using a centrifugal blower test rig for three-bladed H-Darrieus rotors using these three considered blades at low wind streams (4 m/s, 6 m/s and 8 m/s). Considering reality, the effects of flow non-uniformity and turbulence intensity on the rotor performance at optimum condition as well as flow physics have also been studied. It has been found that unsymmetrical S815 blade rotor has higher dynamic torque and higher power coefficient than unsymmetrical EN0005 and symmetrical NACA 0018 blade H-Darrieus rotors.
•High solidity unsymmetrical blade H-Darrieus rotors are investigated in low wind stream.•Such H-Darrieus rotors are investigated for applications other than power generation.•Static and dynamic performances of such rotors are evaluated.•Effects of non-uniform flow and steady turbulence are investigated on the rotor performance.•Optimum aspect ratio of such rotor was found out for the rotor highest dynamic performances.
The Soil Moisture Active Passive (SMAP) mission has delivered valuable sensing of surface soil moisture since 2015. However, it has a short time span and irregular revisit schedules. Utilizing a ...state‐of‐the‐art time series deep learning neural network, Long Short‐Term Memory (LSTM), we created a system that predicts SMAP level‐3 moisture product with atmospheric forcings, model‐simulated moisture, and static physiographic attributes as inputs. The system removes most of the bias with model simulations and improves predicted moisture climatology, achieving small test root‐mean‐square errors (<0.035) and high‐correlation coefficients >0.87 for over 75% of Continental United States, including the forested southeast. As the first application of LSTM in hydrology, we show the proposed network avoids overfitting and is robust for both temporal and spatial extrapolation tests. LSTM generalizes well across regions with distinct climates and environmental settings. With high fidelity to SMAP, LSTM shows great potential for hindcasting, data assimilation, and weather forecasting.
Plain Language Summary
Soil moisture is the water content in soil, and it is a critical component of the water cycle. It controls whether crops or wild vegetation can function properly, the risk of wildfire, and the likelihood of floods. The NASA satellite SMAP, launched in 2015, measures soil moisture near the ground surface over Earth at high accuracy. SMAP data are of great value to global communities to which soil moisture is relevant. However, since it was launched only recently, there is only little overlap with other data sets, which limits its use. To extend SMAP's observations in time, we employ a “deep learning” technology called Long Short‐Term Memory (LSTM), which is one of the pillars of artificial intelligence and is bringing revolutionary changes in many scientific fields and our daily lives. We use LSTM to learn patterns of soil moisture dynamics and where physics‐based models make mistakes in describing moisture changes. This approach shows great promise for projecting SMAP observations into the long past: because soil moisture has a short memory, 2 years of data seem sufficient to train LSTM to successfully capture its dynamics. Because LSTM can handle large and diverse data, it offers valuable alternatives to older statistical methods.
Key Points
With 2 years of data, SMAP L3 data can be extended at high fidelity using a deep learning network (LSTM), showing potential for hindcasting
Despite significant, spatially varying bias in Land Surface Models, LSTM can remove bias, correct moisture climatology, and capture extremes
LSTM is more generalizable than simpler methods, and its strength seems to derive from its memory and ability to accommodate large data
A total of 14 chemical transport models (CTMs) participated in
the first topic of the Model Inter-Comparison Study for Asia (MICS-Asia)
phase III. These model results are compared with each other and ...an extensive
set of measurements, aiming to evaluate the current CTMs' ability in
simulating aerosol concentrations, to document the similarities and
differences among model performance, and to reveal the characteristics of
aerosol components in large cities over East Asia. In general, these CTMs
can well reproduce the spatial–temporal distributions of aerosols in East
Asia during the year 2010. The multi-model ensemble mean (MMEM) shows
better performance than most single-model predictions, with correlation
coefficients (between MMEM and measurements) ranging from 0.65 (nitrate,
NO3-) to 0.83 (PM2.5). The
concentrations of black carbon (BC), sulfate
(SO42-), and PM10 are
underestimated by MMEM, with normalized mean biases (NMBs) of −17.0 %,
−19.1 %, and −32.6 %, respectively. Positive biases are simulated
for NO3- (NMB = 4.9 %), ammonium
(NH4+) (NMB = 14.0 %), and PM2.5
(NMB = 4.4 %). In comparison with the statistics calculated from
MICS-Asia phase II, frequent updates of chemical mechanisms in CTMs during
recent years make the intermodel variability of simulated aerosol
concentrations smaller, and better performance can be found in reproducing
the temporal variations of observations. However, a large variation (about a
factor of 2) in the ratios of SNA (sulfate, nitrate, and ammonium) to
PM2.5 is calculated among participant models. A more intense secondary
formation of SO42- is simulated by Community Multi-scale Air Quality (CMAQ)
models, because of the higher SOR (sulfur oxidation ratio) than other
models (0.51 versus 0.39). The NOR (nitric oxidation ratio) calculated by all
CTMs has larger values (∼0.20) than the observations,
indicating that overmuch NO3- is
simulated by current models. NH3-limited condition (the mole ratio of
ammonium to sulfate and nitrate is smaller than 1) can be successfully
reproduced by all participant models, which indicates that a small reduction
in ammonia may improve the air quality. A large coefficient of variation
(CV > 1.0) is calculated for simulated coarse particles,
especially over arid and semi-arid regions, which means that current CTMs
have difficulty producing similar dust emissions by using different dust
schemes. According to the simulation results of MMEM in six large Asian
cities, different air-pollution control plans should be taken due to
their different major air pollutants in different seasons. The MICS-Asia
project gives an opportunity to discuss the similarities and differences of
simulation results among CTMs in East Asian applications. In order to acquire
a better understanding of aerosol properties and their impacts, more
experiments should be designed to reduce the diversities among air quality
models.
Determining the importance of independent variables is of practical relevance to ecologists and managers concerned with allocating limited resources to the management of natural systems. Although ...techniques that identify explanatory variables having the largest influence on the response variable are needed to design management actions effectively, the use of various indices to evaluate variable importance is poorly understood. Using Monte Carlo simulations, we compared six different indices commonly used to evaluate variable importance; zeroâorder correlations, partial correlations, semipartial correlations, standardized regression coefficients, Akaike weights, and independent effects. We simulated four scenarios to evaluate the indices under progressively more complex circumstances that included correlation between explanatory variables, as well as a spurious variable that was correlated with other explanatory variables, but not with the dependent variable. No index performed perfectly under all circumstances, but partial correlations and Akaike weights performed poorly in all cases. Zeroâorder correlations was the only measure that detected the presence of a spurious variable, whereas only independent effects assigned overlap areas correctly once the spurious variable was removed. We therefore recommend using zeroâorder correlations to eliminate predictor variables with correlations near zero, followed by the use of independent effects to assign overlap areas and rank variable importance.
This study evaluates the performance of six atmospheric reanalyses (ERA-Interim, ERA5, JRA-55, CFSv2, MERRA-2, and ASRv2) over Arctic sea ice from winter to early summer. The reanalyses are evaluated ...using observations from the Norwegian Young Sea Ice campaign (N-ICE2015), a 5-month ice drift in pack ice north of Svalbard. N-ICE2015 observations include surface meteorology, vertical profiles from radiosondes, as well as radiative and turbulent heat fluxes. The reanalyses simulate surface analysis variables well throughout the campaign, but have difficulties with most forecast variables. Wintertime (January–March) correlation coefficients between the reanalyses and observations are above 0.90 for the surface pressure, 2-m temperature, total column water vapor, and downward longwave flux. However, all reanalyses have a positive wintertime 2-m temperature bias, ranging from 1° to 4°C, and negative (i.e., upward) net longwave bias of 3–19 W m−2. These biases are associated with poorly represented surface inversions and are largest during cold-stable periods. Notably, the recent ERA5 and ASRv2 datasets have some of the largest temperature and net longwave biases, respectively. During spring (April–May), reanalyses fail to simulate observed persistent cloud layers. Therefore they overestimate the net shortwave flux (5–79 W m−2) and underestimate the net longwave flux (8–38 W m−2). Promisingly, ERA5 provides the best estimates of downward radiative fluxes in spring and summer, suggesting improved forecasting of Arctic cloud cover. All reanalyses exhibit large negative (upward) residual heat flux biases during winter, and positive (downward) biases during summer. Turbulent heat fluxes over sea ice are simulated poorly in all seasons.
Celotno besedilo
Dostopno za:
BFBNIB, DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Motivated essentially by the recent works of Srivastava et al. 10, Frasin and Aouf 6, Xu et al. 15, and other authors, the authors of the present sequel investigate the coefficient estimate problems ...associated with an interesting general subclass BΣh,p(λ) of analytic and bi-univalent functions in the open unit disk U, which is introduced here. In particular, for functions belonging to this general class BΣh,p(λ), the problems involving the estimates on the first two Taylor–Maclaurin coefficients |a2| and |a3| are investigated. The results presented in this paper generalize and improve the aforecited recent works of Frasin and Aouf 6 and Xu et al. 15 (see also a closely-related earlier investigation by Srivastava et al. 10).
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