Assimilation Dataset Research Articles

Impact of 4D Seismic Data Assimilation in the Production Forecasting of a Heavy Oil Turbidite Sandstone Field Offshore Brazil

The incorporation of 4D seismic data can be done at model characterization and data assimilation steps, the latter applying the observed data to update the model properties. In this work, we evaluate the impact of 4DS data in the production forecasting results investigating the influence on these distinct steps. We employ a prior model set generated without 4DS data and another prior set with 4DS in the characterisation. We perform, then, the DA in three different ways: (1) using only well history data with a prior set without 4DS; (2) using well history and 4DS data with this same prior set; and (3) using well history and 4DS data with the prior set generated with 4DS data. We employ the Ensemble Smoother with Multiple Data Assimilation method for data assimilation and implement a procedure to adjust the well productivities and injectivities after DA. This work assesses the differences among the three DA cases in terms of field oil production forecasting, model attributes and model misfits, the latter evaluated in terms of the assimilated data set and of a different data set for validation purposes only. We apply the work to a real heavy oil turbiditic field located offshore Brazil. The case application demonstrates a successful data assimilation study on a real field integrating well history and 4DS data. The results show that the utilisation of 4DS in both model characterisation and DA steps brought value to the final uncertainty reduction results. The posterior model ensemble with 4DS data in model characterization and DA provided the lowest model misfits overall to the full data set, being the best choice for application in field production forecasting studies. The case without 4DS data provided significantly more pessimistic field oil production than the cases with 4DS, making very evident the importance of employing 4DS at uncertainty reduction workflows.

Attention Pyramid Convolutional Neural Network Optimized with Big Data for Teaching Aerobics

Aerobics has emerged as a widely embraced cardiovascular exercise, fostering improved fitness through rhythmic movements that enhance heart rate, stamina, endurance, and cardiovascular health. Effective instruction by skilled professionals is crucial for maximizing the benefits of aerobics, ensuring participants' correct and safe performance. This study introduces the concept of Aerobics Movement Teaching, emphasizing its pivotal role in college physical education. The proposed method, Attention Pyramid Convolutional Neural Network optimized with big data for teaching Aerobics (AP-CNN-BTA), focuses on enhancing aesthetic ability and overall human development. Data from the Simple Ocean Data Assimilation Data Set are collected, preprocessed, and utilized in the teaching process using the Attention Pyramid Convolutional Neural Network, specifically designed for efficient aerobics instruction in coastal areas. The resulting data are stored in the cloud and accessed through a human interface unit. The implementation, carried out in Python, undergoes evaluation using various metrics, including accuracy, computational time, sensitivity, specificity, precision, and ROC analysis. The simulation results demonstrate a remarkable improvement, with the proposed technique achieving 36.52%, 39.55%, and 43.75% higher accuracy compared to existing methods such as Exploration on and thinking about aesthetic infiltration in aerobics teaching in colleges and universities(ETAI-AT-CU), sea level height depending on big data of the Internet of Things along aerobics teaching in coastal regions(SLH-BD-IOT-ATC), and impact of deep learning-based curriculum's ideological and political integration on sports aerobics instruction design(IP-CDL-TDTA), respectively. This research contributes to advancing the efficacy of aerobics instruction, particularly in coastal regions, and underscores the significance of comprehensive student development through high-quality education.

Two-decadal climate impacts on growth of major forest types of Eastern Himalaya

Forests affect regional climates, livelihoods and global cycles of water, carbon and nitrogen. Anthropogenic activities and climatic change affect forest health and national growth. Therefore, developing effective forest management plans requires understanding of the drivers of forest growth. The primary objective of this study was to understand the long-term effect of abiotic factors on the growth of forests in the region. This study used Moderate Resolution Imaging Spectroradiometer (MODIS) data for vegetation indices like NDVI and EVI and NASA's Land Assimilation datasets (wind speed, evapotranspiration, soil moisture and temperature) to understand their role on forest growth through statistical techniques such as Pearson's correlation and Multiple Linear Regression. The study examined the relationship between standard monthly vegetation indices and abiotic variables (i.e., moisture and temperature at different soil profiles up to 2 m depth, land surface temperature, evapotranspiration, relative humidity, wind velocity, and air temperature at 2 m height) in selected forests of the Eastern Himalayas for two decades (2001–2020, n = 240). Rainfall, temperature, and other associated factors significantly affected forest growth in the region. It was observed that rainfall alone affected forest growth in the region. However, its impact was maximum after two months of the rain events, reflecting a significant lag effect. Soil moisture at different depths affected vegetation growth in all forest types. Reduced soil moisture had a more significant effect on old-growth forests than younger forests. Multiple Linear Regression models developed with the abiotic factors explained higher variability in forest growth. In conclusion, this study reveals that rainfall, temperature, and their associated variables significantly affected forest growth in the study area. The study has significant implications for forest management in the region for formulating better strategies to mitigate climate change effects on forests in the region in future.

Interannual Migration of the Summertime Eastward Jet in the South China Sea Associated With the Upper Layer Thickness Distribution

AbstractThe summertime eastward jet (SEJ) in the South China Sea (SCS) can be regarded as the boundary between the southern and northern upper circulation of the SCS in climatology. The dynamic process of SEJ migration in response to the variation of SCS summer monsoon (SCSSM) strength on the interannual scale is investigated, based on the Simple Ocean Data Assimilation (SODA) dataset and by a 1.5‐layer reduced gravity model. Analysis shows that SEJ position is closely related to SCSSM strength, with southward/northward migration of SEJ coinciding with stronger/weaker SCSSM. Besides migration of wind stress curl (WSC) zero‐line and north‐south difference of WSC variation, the north‐south difference of the SCS upper layer thickness (ULT) in summer, that is, thick in the southern SCS and thin in the northern SCS, also plays a significant role in the dynamic process of SEJ migration. It is the joint effect of wind strength and ULT distribution. With the variation of SCSSM strength, the thinner ULT in the northern SCS corresponds to the larger magnitude of potential vorticity (PV) variation inputted by wind stress, which leads to more pronounced strengthening/weakening of the western boundary current (WBC) in the northern SCS than that in the southern SCS. Then, the north and south WBCs compete with each other until the upper layer circulation reaches a new equilibrium state, and SEJ migrates southward/northward accordingly.

Reconstructing tephra fall deposits via ensemble-based data assimilation techniques

Abstract. In recent years, there has been a growing interest in ensemble approaches for modelling the atmospheric transport of volcanic aerosol, ash, and lapilli (tephra). The development of such techniques enables the exploration of novel methods for incorporating real observations into tephra dispersal models. However, traditional data assimilation algorithms, including ensemble Kalman filter (EnKF) methods, can yield suboptimal state estimates for positive-definite variables such as those related to volcanic aerosols and tephra deposits. This study proposes two new ensemble-based data assimilation techniques for semi-positive-definite variables with highly skewed uncertainty distributions, including aerosol concentrations and tephra deposit mass loading: the Gaussian with non-negative constraints (GNC) and gamma inverse-gamma (GIG) methods. The proposed methods are applied to reconstruct the tephra fallout deposit resulting from the 2015 Calbuco eruption using an ensemble of 256 runs performed with the FALL3D dispersal model. An assessment of the methodologies is conducted considering two independent datasets of deposit thickness measurements: an assimilation dataset and a validation dataset. Different evaluation metrics (e.g. RMSE, MBE, and SMAPE) are computed for the validation dataset, and the results are compared to two references: the ensemble prior mean and the EnKF analysis. Results show that the assimilation leads to a significant improvement over the first-guess results obtained from the simple ensemble forecast. The evidence from this study suggests that the GNC method was the most skilful approach and represents a promising alternative for assimilation of volcanic fallout data. The spatial distributions of the tephra fallout deposit thickness and volume according to the GNC analysis are in good agreement with estimations based on field measurements and isopach maps reported in previous studies. On the other hand, although it is an interesting approach, the GIG method failed to improve the EnKF analysis.

Creation and Verification of a High-Resolution Multi-Parameter Surface Meteorological Assimilation Dataset for the Tibetan Plateau for 2010–2020 Available Online

The Qinghai–Tibet Plateau (QTP) is a crucial component of the global climate system, influencing the regional and global climate through complex thermal and dynamic mechanisms. The high-altitude region, which is the largest part of the extra-polar cryosphere, encompasses extensive mountain glaciers, permafrost, and seasonally frozen land, making it highly sensitive to global climate change. However, the challenging environmental conditions, such as the harsh terrain and high altitude, coupled with sparse weather station distribution and weak observatory representation, make it difficult to accurately quantify the atmospheric conditions and land–atmosphere coupling systems and their effects on the surrounding areas. To address these challenges, we utilized the Weather Research and Forecasting (WRF) model and a three-dimensional variational (3DVAR) assimilation method to create a high-resolution assimilated dataset (HRAD). The QTP-HRAD, covering the spatial range of 70 to 110°E and 25 to 40°N, was validated using both surface weather station observations and the European Center for Medium-Range Weather Forecasts Reanalysis V5, and can now be utilized for further studies on land–atmosphere interactions, water cycling and radiation energy transfer processes, and extreme weather events in the region.

Evaluation of Earth Observations and In Situ Data Assimilation for Seasonal Hydrological Forecasting

AbstractEarth observations (EOs) are a valuable complement to in situ measurements in hydrology because they provide information in locations where direct measurements are unavailable or prohibitively expensive to make. Recent advances have enabled the assimilation of data sets of different physical variables into hydrological models to better estimate states and fluxes. Along with the meteorological forcings, the assimilated data exert controls on forecasts; it is therefore important to apportion the contributions from the forcing and assimilated data. Quality assessment is sometimes conducted in the pseudo‐reality, which neglects observations and model limitations. Here we introduce a diagnostic framework that accounts for observations to assess the sources of skill and infer the seasonal importance of assimilated and forcing data. We test the framework with a forcing data set from a downscaled Global Circulation Model and assimilate four EO and two in situ data sets to initialize the forecasts. We investigate the hydrological response and seasonal predictions over a Swedish snowmelt‐dominated catchment using the HYPE model over 2001–2015. The framework allows assessing the improvement in seasonal skill due to the different assimilated data and meteorological forcing. For the studied catchment, all EO and in situ data sets add information to the final forecast. The lead times during which data assimilation influences forecast skill also differ between data sets and seasons for example, assimilating snow water equivalent impacts the forecast for more than 20 weeks during winter. Lastly, assimilated data sets are generally more important to streamflow forecasting skill than meteorological forcing in the studied snow‐dominated catchment.

Using a deep-learning approach to infer and forecast the Indonesian Throughflow transport from sea surface height

The Indonesian Throughflow (ITF) connects the tropical Pacific and Indian Oceans and is critical to the regional and global climate systems. Previous research indicates that the Indo-Pacific pressure gradient is a major driver of the ITF, implying the possibility of forecasting ITF transport by the sea surface height (SSH) of the Indo-Pacific Ocean. Here we used a deep-learning approach with the convolutional neural network (CNN) model to reproduce ITF transport. The CNN model was trained with a random selection of the Coupled Model Intercomparison Project Phase 6 (CMIP6) simulations and verified with residual components of the CMIP6 simulations. A test of the training results showed that the CNN model with SSH is able to reproduce approximately 90% of the total variance of ITF transport. The CNN model with CMIP6 was then transformed to the Simple Ocean Data Assimilation (SODA) dataset and this transformed model reproduced approximately 80% of the total variance of ITF transport in the SODA. A time series of ITF transport, verified by Monitoring the ITF (MITF) and International Nusantara Stratification and Transport (INSTANT) measurements of ITF, was then produced by the model using satellite observations from 1993 to 2021. We discovered that the CNN model can make a valid prediction with a lead time of 7 months, implying that the ITF transport can be predicted using the deep-learning approach with SSH data.

Significant Difference of the Summertime Eastward Jet Enhancement in the South China Sea During the El Niño Developing Summers of 1982 and 1997

AbstractThe interannual variation of the South China Sea (SCS) summertime eastward jet (SEJ) contributes to the regional climate and ecology. Based on the Simple Ocean Data Assimilation data set, this study conducts a case analysis of SEJ in the SCS during two El Niño developing years, 1982 and 1997. In both years, the summer‐mean circulation presented a stronger SEJ with an obvious dipole pattern; however, the enhancement of SEJ was significantly weaker in 1997 than that in 1982. The reasons for enhanced SEJ and the difference of the enhancement magnitude (e.g., 256% and 52%) between 1982 and 1997 are explored. The climatological summer‐mean southwesterly wind stress is strongest in the middle SCS and decreases northward and southward (i.e., an arch‐shaped structure), forming a wind stress curl field conducive to the generation of SEJ. There is a low‐level anticyclonic anomaly during El Niño developing summer, with its northern flank enhancing the SCS monsoon. However, the structures of wind anomaly in the SCS are different in 1982 and 1997, which may relate to the earlier or later development of El Niño events modulated by the eastward migration of Madden‐Julian oscillation. In 1982, the northern flank of the anomalous anticyclone strengthened the climatological arch‐shaped structure, effectively enhancing the wind stress curls and SEJ. Whereas, in 1997, the northward deviation of the northern flank weakened the arch‐shaped structure north of 11°N, reduced the positive wind stress curl around 11°N–14.2°N, and caused the weaker enhancement of SEJ in 1997 than in 1982.

Assimilation of Both Column- and Layer-Integrated Dust Opacity Observations in the Martian Atmosphere.

A new dust data assimilation scheme has been developed for the UK version of the Laboratoire de Météorologie Dynamique Martian General Circulation Model. The Analysis Correction scheme (adapted from the UK Met Office) is applied with active dust lifting and transport to analyze measurements of temperature, and both column‐integrated dust optical depth (CIDO), τ ref (rescaled to a reference level), and layer‐integrated dust opacity (LIDO). The results are shown to converge to the assimilated observations, but assimilating either of the dust observation types separately does not produce the best analysis. The most effective dust assimilation is found to require both CIDO (from Mars Odyssey/THEMIS) and LIDO observations, especially for Mars Climate Sounder data that does not access levels close to the surface. The resulting full reanalysis improves the agreement with both in‐sample assimilated CIDO and LIDO data and independent observations from outside the assimilated data set. It is thus able to capture previously elusive details of the dust vertical distribution, including elevated detached dust layers that have not been captured in previous reanalyzes. Verification of this reanalysis has been carried out under both clear and dusty atmospheric conditions during Mars Years 28 and 29, using both in‐sample and out of sample observations from orbital remote sensing and contemporaneous surface measurements of dust opacity from the Spirit and Opportunity landers. The reanalysis was also compared with a recent version of the Mars Climate Database (MCD v5), demonstrating generally good agreement though with some systematic differences in both time mean fields and day‐to‐day variability.

The Impact of Seasonality and Response Period on Qualifying the Relationship between Ecosystem Productivity and Climatic Factors over the Eurasian Steppe

As climate change intensifies, surface vegetation productivity and carbon exchange between terrestrial ecosystems and the atmosphere are significantly affected by the variation of climatic factors. Due to the sensitivity of grasslands to these climatic factors, it is crucial to understand the response of vegetation greenness, or carbon exchange within grasslands, to environment factor dynamics. In this study, we used solar-induced chlorophyll fluorescence (SIF), precipitation (P), vapor pressure deficit (VPD), evaporative stress (ES), and root zone soil moisture (RSM) derived from remote sensing, reanalysis, and assimilation datasets to explore the response of vegetation greenness within Eurasian Steppe to climatic factors. Our results indicated deseasonlization based on the Seasonal-Trend decomposition using Loess (STL) method, which was an effective means to remove the seasonality disturbances that affect the qualification of the relationship between SIF and the four climatic factors. The response of SIF had a time lag effect on these climatic factors, and the longer the response period, the greater the impact on the correlation of SIF with P, VPD, ES, and RSM. We also found, among the four factors, that the response of SIF to ES was the timeliest. The findings of this study emphasized the impact of the seasonality and time lag effect on the dynamic response between variables, and provided references to the attribution and monitoring of vegetation greenness and ecosystem productivity.

Creating 1-km long-term (1980–2014) daily average air temperatures over the Tibetan Plateau by integrating eight types of reanalysis and land data assimilation products downscaled with MODIS-estimated temperature lapse rates based on machine learning

Air temperature (Tair) is critical to modeling environmental processes (e.g. snow/glacier melting) in high-elevation areas of the Tibetan Plateau (TP). To resolve the issue that Tair observations are scarce in the TP western part and at high elevation, many studies have estimated daily air temperatures by using MODIS land surface temperature (LST) and various reanalysis datasets. These estimates are however inadequate for supporting high-resolution long-term hydrological simulations or climate analysis due to the high cloud cover, short time span or low spatial resolution. To improve the Tair estimation, this study develops a novel machine-learning based method that uses the Gradient Boosting model to efficiently integrate observations from high-elevation stations with eight widely used air temperature reanalysis and assimilation datasets (i.e., NNRP-2, 20CRV2c, JRA-55, ERA-Interim, MERRA-2, CFSR, ERA5 and GLDAS2) downscaled with remote sensing-based temperature lapse rates (TLR). This method is used to generate a new dataset of daily air temperature with the 1-km resolution for the period of 1980–2014. To overcome the problem that TLR derived from limited stations may be unreliable, a new TLR estimation method is developed to first estimate spatially continuous monthly TLRs from MODIS LST and then downscale daily mean Tair from eight reanalysis and assimilation datasets to obtain Tair at the 1-km resolution using the MODIS-estimated TLRs. The Gradient Boosting (GB) model is selected for integrating the eight downscaled Tair and five other auxiliary variables. The models are trained and validated using observations from 100 common stations (i.e. China Meteorology Administration stations) and 13 independent high-elevation stations (4 on glaciers). The results show that the proposed TLR estimation method can efficiently reduce exceptional TLRs in the meantime keeping acceptable downscaling accuracy. The downscaled Tair from JRA-55 is the best among the eight downscaled datasets followed by ERA-Interim, MERRA-2, CFSR and others. Finally, the GB-integrated Tair further outperforms the downscaled JRA-55 Tair with the mean root-mean-squared-deviation (RMSD) of 1.7 °C versus 2.0 °C, especially in high-elevation stations with mean RMSD of 1.9 °C versus 2.7 °C. Both the MODIS-estimated TLR and the high-elevation training observations are demonstrated to significantly improve the air temperature estimation accuracy of the GB model in high-elevation stations. This study also provides a framework for integrating multiple reanalysis and assimilation temperature data with elevation correction in mountainous regions that is not restricted to the TP.

Comparative Analysis of Water-Energy Cycle Processes Based on High-Resolution Assimilation Dataset of the Water-Energy Cycle in China Data Over Different Underlying Surfaces in Qinghai-Tibet Plateau

We used a High-Resolution Assimilation Dataset of the water-energy cycle in China (HRADC) to study the land-atmosphere interactions and meteorological characteristics in inhomogeneous underlying surface of the Qinghai-Tibet Plateau (QTP). Three different underlying surfaces (i.e., grassland, open shrubland, and barren or sparsely vegetated) of the QTP are selected and the meteorological elements on each underlying surface grid are averaged. We compared and analyzed the Green Vegetation Fraction (GVF), precipitation, soil moisture and soil temperature, and energy fluxes for three different land-use types in QTP. The results showed that the vegetation coverage of HRADC showed a gradual decrease trend from southeast to northwest throughout the Qinghai-Tibet Plateau. The GVF of the grassland in the southeast can reach more than 60% in summer, and only about 20% in sparse vegetation areas. HRADC can well reproduce the seasonal change trend of soil temperature and soil moisture in different underlying surfaces. The annual variation trend of soil temperature shows that the time of the deep soil temperature reaching the peak value lags behind the shallow layer. The annual averaged soil moisture over grassland is higher than that of open shrubland and barren land, which is consistent with the plateau precipitation distribution. The peak value of sensible heat flux over grassland is only 80 W·m-2 in April, and the latent heat flux can reach 90 W·m-2, and the net radiation of the barren land can reach 210 W·m-2 in July. This study is important to discover the water-energy cycle characteristics of QTP.

Drought propagation in Northern China Plain: A comparative analysis of GLDAS and MERRA-2 datasets

Drought impact is closely related to water deficit phases within the hydrological cycle, and to drought propagation (the complex evolutions from meteorological drought to hydrological drought and to soil moisture drought). Besides the qualitative description, the quantitative analysis of drought propagation is still limited so far. Therefore, in this study, the propagation from meteorological to hydrological and to soil moisture droughts have been quantitatively analyzed. In particular, run theory has been utilized for quantifying key drought features including its duration and magnitude. The two available land assimilation datasets from 1981–2018 were selected for comparison purpose in Northern China Plain. Our results showed that although drought events identified by two datasets were not the same, both datasets revealed that meteorological drought occurred more frequently than hydrological and soil moisture droughts. And both demonstrated meteorological drought, hydrological drought and soil moisture drought were not synchronous. In particular, more than 91.89% of meteorological droughts led to hydrological droughts, and more than 87.10% of hydrological droughts caused soil moisture droughts. Furthermore, linear models showed the best for drought duration and magnitude. We found when meteorological drought was prolonged or shortened by one month, more than 91.89% probability it would lead to the extension or shortening of hydrological drought by 0.992 months with 1.687 unit in magnitude. And if the duration of hydrological drought changed by one month, more than 80.56% probability of the soil moisture drought duration would change by 1.006 months with 0.992 unit in magnitude. The results of duration and magnitude fitted well across the whole study area. Building on the above drought evolution information, practical drought mitigation measures and early warning system could be established.

Effect of spring soil moisture over the Indo-China Peninsula on the following summer extreme precipitation events over the Yangtze River basin

Extreme precipitation events (EPEs) over the Yangtze River basin (YRB) exert widespread impacts on regional ecological environment and people’s life. Using observed precipitation, atmospheric reanalysis, and land assimilation datasets, the present study explores the relationship between the summer EPEs over the YRB and the Meiyu front and their possible linkages with the preceding spring soil moisture anomalies over the Indo-China Peninsula (ICP). The analyses show that both the frequency and intensity of summer EPEs over the YRB are closely associated with the mean intensity of the Meiyu front, which exhibits a significant negative correlation with the soil moisture anomalies in the preceding spring over the ICP. An abnormally drier soil over the ICP in spring would evidently raise air temperature by suppressing local evapotranspiration, and vice versa. Owing to a strong memory of the ICP soil moisture, the persistent anomalous heating would elevate local geopotential height in summer, inducing an excessive westward extension of the Western Pacific subtropical high. Accordingly, a strengthened southwesterly wind at the lower troposphere brings abundant warm–wet air to the YRB, intensifying the mean Meiyu front. This is also verified by the diagnosis of vertical motion (omega) equation. As a result, the risk of summer EPEs (both the frequency and intensity) over the YRB would increase (decrease) with an abnormally drier (wetter) ICP soil during the preceding spring. For the summer EPEs over the YRB, our results suggest that the spring ICP soil moisture can be used as an important seasonal predictor.

Variations of the North Equatorial Current Bifurcation and the SSH in the Western Pacific Associated With El Niño Flavors

AbstractThis study presents different variations of the North Equatorial Current Bifurcation (NECB) in the development of the canonical El Niño, the Central Pacific El Niño I (CP‐I El Niño), and the Central Pacific El Niño II (CP‐II El Niño) in autumn and investigates the dynamic mechanism by analyzing Simple Ocean Data Assimilation data sets. It is suggested that the meridional shifts of the NECB are negatively related to the sea surface height (SSH) near the coast of the Philippines. The NECB shifts northward in autumn of the development phase of both canonical and CP‐II El Niño events, whereas it moves insignificantly for CP‐I El Niño events. During the development phase of canonical and CP‐II El Niños, the remote positive wind stress curl anomalies in the central and western tropical Pacific between 12°N and 15°N are responsible for the variations of the NECB, which can induce the westward Rossby waves of negative SSH anomalies and thus result in the northward shift of the NECB several months later. However, the local wind stress curl anomalies near the Philippine coast are significantly negative and result in locally positive SSH anomalies for CP‐I El Niños. Such anomalous positive SSHs are offset by the negative counterparts that result from the remote wind forcing in the western tropical Pacific. Therefore, the meridional shifts of the NECB are not significant in autumn for CP‐I El Niños. A one and a half layer‐reduced gravity model is used to conduct experiments to further confirm the above results.

Responses of soil moisture to regional climate change over the Three Rivers Source Region on the Tibetan Plateau

AbstractIn this study, soil moisture data from two reanalysis datasets (ERA‐Interim, ERA5), a satellite soil moisture product from the European Space Agency (ESA) and three assimilation datasets from the Global Land Data Assimilation System (GLDAS), that is, GLADS‐NOAH025, GLADS‐NOAH10, and GLDAS‐CLM were compared with the observed soil moisture data in Maqu and Maduo stations over the Three Rivers Source Region (TRSR) on the Tibetan Plateau in China. Comparative statistical parameters between the soil moisture observations and the products, including the root mean square error, correlation coefficient (R), and standard deviation ratio were calculated to evaluate the products in different seasons. It was found that most products overestimated the soil moisture values, among which GLADS‐NOAH025 had relatively good consistency with the observations in the surface soil layer (0–10 cm), whereas GLDAS‐CLM agreed well with the observations in the 10–40 cm layer. Both ERA5 and ERA‐interim soil moisture overestimated soil moisture at all levels. The responses of soil moisture to regional climate change over the TRSR were also investigated by using the GLDAS‐NOAH025 soil moisture product, the ESA soil moisture product, and the observations during the nonfreezing period. The results show that the trends of soil moisture are consistent with the precipitation changes in the main central part of the TRSR. A significant positive correlation was found between soil moisture and precipitation in most regions of the TRSR, which indicates that soil moisture increases with the increase of precipitation, except in the northwest TRSR, where the variation of soil moisture was possibly influenced by both the precipitation changes and the increase of evapotranspiration.

Evaluation and Analysis of Grid Precipitation Fusion Products in Jinsha River Basin Based on China Meteorological Assimilation Datasets for the SWAT Model

Highly accurate and high-quality precipitation products that can act as substitutes for ground precipitation observations have important significance for research development in the meteorology and hydrology of river basins. In this paper, statistical analysis methods were employed to quantitatively assess the usage accuracy of three precipitation products, China Meteorological Assimilation Driving Datasets for the Soil and Water Assessment Tool (SWAT) model (CMADS), next-generation Integrated Multi-satellite Retrievals for Global Precipitation Measurement (IMERG) and Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA), for the Jinsha River Basin, a region characterized by a large spatial scale and complex terrain. The results of statistical analysis show that the three kinds of data have relatively high accuracy on the average grid scale and the correlation coefficients are all greater than 0.8 (CMADS:0.86, IMERG:0.88 and TMPA:0.81). The performance in the average grid scale is superior than that in grid scale. (CMADS: 0.86(basin), 0.6 (grid); IMERG:0.88 (basin),0.71(grid); TMPA:0.81(basin),0.42(grid)). According to the results of hydrological applicability analysis based on SWAT model, the three kinds of data fail to obtain higher accuracy on hydrological simulation. CMADS performs best (NSE:0.55), followed by TMPA (NSE:0.50) and IMERG (NSE:0.45) in the last. On the whole, the three types of satellite precipitation data have high accuracy on statistical analysis and average accuracy on hydrological simulation in the Jinsha River Basin, which have certain hydrological application potential.

Conversion features of evapotranspiration responding to climate warming in transitional climate regions in northern China

Global surface evapotranspiration is one of the most significant components of the response of the water cycle to a warming climate. However, trends in surface evapotranspiration differ considerably from the trend in climate warming according to recent studies, with some studies even showing an opposing trend. The reason for this difference in the response of surface evapotranspiration to climate warming is still not completely understood. We validated the gridded FLUXNET evapotranspiration dataset and the Global Land Surface Assimilation Dataset (GLDAS) against evapotranspiration data observed in northern China using the eddy covariance system. The response of surface evapotranspiration to an increase in temperature varied with the type of climate (classified by the amount of precipitation) and the trend of surface evapotranspiration with warming showed similar features to the transitions between these climate types. The climate type with precipitation in the range of 250–350 mm was the most sensitive to the effects of warming on evapotranspiration. In more humid climates, surface evapotranspiration increased with increasing temperature, whereas in drier climates surface evapotranspiration decreased with increasing temperature. A similar response of evapotranspiration to increasing temperatures was also observed elsewhere in regions of climate transition. There are two main mechanisms of evapotranspiration: (1) an increase in temperature resulting in a direct increase in potential evapotranspiration; and (2) an increase in temperature resulting in a loss of soil moisture due to the increase in evapotranspiration, which in turn will indirectly suppress surface evapotranspiration due to the loss of vegetation.

Influence of underwater hydrodynamics on oil and gas blowouts off Amazon River Mouth

This study is focused on analyzing the behavior of oil/gas plumes from blowouts into deepwater, located at the northern Brazil continental shelf. The Regional Ocean Modeling System (ROMS) model is used to simulate ocean dynamics in the region 60.5°-24.0°W/5°S-16°N with 0.25° of resolution, 32 vertical levels and considering the discharges of the Amazon and Pará Rivers. The ROMS output are compared to Simple Ocean Data Assimilation (SODA) dataset. Three points were selected to make the numerical simulations, located at (50°W, 5.25°N), (44.5°W, 0.5°N) and (42.75°W, 1°S). The time step suggested by Lee and Cheung (1990) was adjusted due to the particular oceanographic conditions at each point, in which, the initial velocity tends to zero and the coefficient 0.1 of the original equation was replaced by 0.0250 and 0.0375. All the plumes behaved as type 3. The seasonal current speed was small from the bottom to the surface, usually not exceeding 0.25 ms-1; the maximum displacement of the plumes from its point of origin was not greater than 1 m. The mean plumes diameter on the surface ranged 54 - 79.7 m and the arrival time to the surface was from 7.25 to 8.05 hours.