Time Lag Effect Research Articles

Impact of network investor sentiment and news arrival on jumps

This paper studied the influence of news announcements and network investor sentiment on Chinese stock index and index futures market jumps. A machine learning text analysis algorithm was employed to measure investor forum sentiment. It was found that news arrivals were an important reason for jump occurrences, jumps were significantly associated with network investor sentiment, and while occasionally the news and network investor sentiment resulted in simultaneous market jumps, they appeared to be relatively independent. The network investor sentiment time-lag and asymmetric effects were also tested, from which it was found that network investor sentiment had a significant asymmetric effect on the jumps, but time-lag effects had little influence. News announcements and the top 25% of the extreme network sentiments were found to explain more than 50% of the jumps, with extreme sentiments tending to increase the volatility of the news-related jumps and persistently influencing returns after the news-related jumps.

Global trends in total fertility rate and its relation to national wealth, life expectancy and female education

ObjectivesAlong with the development of the times and progress of the society, the total fertility rate (TFR) markedly changed in each country. Therefore, it is critical to describe the trend of TFR and explore its influencing factors. However, previous studies did not consider the time lag and cumulative effect in the associations between the influencing factors and TFR. Thus, our study aimed to analyze the associations from a new dimension.MethodsThe study was employed using national-level data from the World Bank and United Nations Development Programme. Distributed lag non-linear models with 5-year lag were used to examine the independent associations between the relevant factors and TFR.ResultsThe cumulative exposure-TFR curves were inverted U-shaped for log gross domestic product (GDP) per capita and life expectancy at birth, while the cumulative exposure-response curves were approximately linear for female expected years of schooling and human development index (HDI). However, it is worth noting that in the developed regions, TFR increased slightly with the high level of GDP per capita, female expected years of schooling and HDI.ConclusionsNowadays, with the growth of GDP per capita, life expectancy at birth, female expected years of schooling and HDI, TFR are on a drastic downward trend in most regions. Besides, with the development of society, when levels of the factors continued to increase, TFR also showed a slight rebound. Therefore, governments, especially those in developing countries, should take measures to stimulate fertility and deal with a series of problems caused by declining TFR.

Growth peak of vegetation and its response to drought on the Mongolian Plateau

Global change in recent decades has caused severe degradation of grassland ecosystems in arid and semi-arid regions in the world. In the context of global change, the maximum gross primary production (GPPmax) and its response to drought on the Mongolian Plateau (MP) remain unclear. Here, we used long time-series datasets (temperature, precipitation, GPP) and calculated GPPmax, timing of GPPmax (TGM), and Standardized Precipitation Evapotranspiration Index (SPEI) to explore the changes in peak growth of vegetation and its response to drought on the MP from 1982 to 2018. Our results show that GPPmax and TGM presented high spatial heterogeneity. The mean GPPmax was 336 g C·m−2 over the past three decades, with a decreasing trend at a rate of 0.32 g C·m−2·year−1; the mean TGM was on DOY (day of year) 197, with little year-to-year change, TGM received the time-lag effect (mostly 1, 2, 10 months in time scale) of drought was found in 35.3% of the MP, while the cumulative effect of drought on TGM occurred only 16.3% of the MP. These results reveal changes in vegetation growth peaks on the MP and their response to drought over three decades and can contribute to our understanding of the response and feedbacks of MP vegetation to global change.

Short-range reservoir inflow forecasting using hydrological and large-scale atmospheric circulation information

Short and long range reservoir inflow forecast is essential for efficient real time operational planning, scheduling of hydroelectric power system and management of water resources. Large-scale climate phenomenon indices have a strong influence on hydrological processes under complex weather conditions, and it should be considered to forecast reservoir inflow for efficient dam operation strategies. This study aims to explore the relevance of large-scale climate phenomenon indices in improving the reservoir inflow prediction at short-term time scales. This paper presents a simple and effective framework to combine various data-driven machine learning (ML) algorithms for short-range reservoir inflow forecasting. Random Forest (RF), Gradient Boosting Regressor (GBR), K-Nearest Neighbors Regressor (KNN), and Long Short-Term Memory (LSTM) were employed for predicting daily reservoir inflows considering various climate phenomenon indices (e.g., Arctic Oscillation, North Atlantic Oscillation, and Southern Oscillation Index) and hydroclimatic variables (precipitation), accounting for time-lag effects. After training the individual ML algorithm, a framework was developed to create an ensemble model using a robust weighted voting ensemble method to quantify forecasting uncertainty and to improve the model performance by combining the inflow results of the single ML model and the highest vote is chosen based on the weights assigned to the single ML model. The developed framework was examined in two distinct reservoirs located in India and California, USA. The ensemble model consistently outperformed the standalone RF, GBR, KNN, and LSTM in predicting high (flood control and monsoon seasons) and low flows (runoff and non-monsoon seasons) of both study reservoirs. The demonstrated short-term reservoir forecasting model allows reservoir operators to adapt and add regional hydrological and large-scale climate indices in real-time decision-making. The presented framework can be applied for any reservoir inflow forecasting.

Attribution analysis of land degradation in Hainan Island based on geographical detector

Rapid and accurate surveys of land are essential for protecting ecosystems. This study aimed to survey the spatial–temporal land degradation patterns and explore its driving mechanism based on a geographical detector in Hainan Island from 2000 to 2020. First, the areas of forest, cultivated land, and construction land were analysed, and their mutation year was determined through the Mann-Kendall abrupt test. Then, change vector analysis was used to create a double-evaluation system by combining the normalized difference vegetation index and net primary productivity. Finally, the geographical detector was used to identify the driving mechanism of different land-use types. The results show that 2013 was the mutation year of the three land-use types. From 2000 to 2013 (Stage 1), the land degradation areas of forest, cultivated land, construction land, grassland, and unutilised land reached 1977.13, 613.25, 165.81, 261.06, and 4.25 km2, respectively. From 2013 to 2020 (Stage 2), the degraded land area accounted for only 0.91 % of the island's total area. From the single factor perspective, the elevation (DEM), least distance to residential area (LDP), and variety of mean annual temperature (VTem) were found to be the dominant factors of land degradation of cultivated land and grassland, forest, construction land in Stage 1. On the other hand, in Stage 2, the main factor of land degradation was the variety of the density of GDP (VGDP) in cultivated land, grassland, and construction land, while it was the variety of mean annual precipitation (VPre) in the forest. Moreover, all pair-factors provided a higher determinant power for degraded land areas than a single factor. From the interaction effect perspective in Stage 1, VPOP ∩ LDR (i.e. the variety of the density of population density and least distance to roads or railways), LDP ∩ LDR, and VTem ∩ LDR were the most significant pair-factors of the interaction effect on land degradation of cultivated land, forest and grassland, and construction land. In Stage 2, the most significant pair-factors of the interaction effect in different land-use types were DEM ∩ VPOP and VPre ∩ POP in degraded cultivated land, VPre ∩ DEM and VPre ∩ LDR in degraded forest land, DEM ∩ VTem in degraded grassland, and VGDP ∩ LDR in degraded construction land. Given the time-lag effects of factors and the attribution analysis of land degradation, we suggest limiting the construction of roads and residential areas, increasing biodiversity, and sheltering forests to protect terrestrial ecosystems in the future.

A perception of the nexus “resistance, recovery, resilience” of vegetations responded to extreme precipitation pulses in arid and semi-arid regions: A case study of the Qilian Mountains Nature Reserve, China

Unprecedented pulses of extreme precipitation due to climate change are causing significant stresses and impacts on regional and even global ecosystems. However, the relationship of vegetation response to this disturbance is unclear, such as phase characteristics, extent, timing, and degree. We summarize the nexus between vegetation resistance, recovery, and resilience under three stages of extreme precipitation pulses: duration, lagging, and post-disturbance, and then construct a pragmatic scheme to quantify and validate this complex relationship based on precipitation and Normalized Difference Vegetation Index (NDVI) data for the Qilian Mountains Nature Reserve (QMNR) from 2000 to 2020. The results show that the four extreme precipitation pulses were spring 2010 (118.98 mm), summer 2007 (312.25 mm), autumn 2010 (109.74 mm), and winter 2018 (6.84 mm). Extreme precipitations had a significant effect on vegetation in at least 98.5 % of the area, and there was also a two-month time lag effect. Specifically, the percentage of negative vegetation resistance in the face of four seasons of extreme precipitation pulses was 18.3 %, 2.0 %, 15.4 %, and 21.7 %, respectively, compared to negative recovery rates of 4.8 %, 11.9 %, 17.8 % and 10.2 % respectively, resilience was even more severe, with 20.1 %, 10.9 %, 16.1 % and 16.3 % of vegetation failing to rebound to normal levels within two months. The negative resistance, negative recovery, and weak resilience of vegetation under short-term extreme precipitation pulses are approximately 4.8, 3.7, and 5.3 times more fierce than long-term vegetation degradation. A total of 62 % of the four seasonal areas of severe negative resistance, severe negative recovery, and weak resilience were located in areas of moderate and significant steepness, which confirms that extreme precipitation pulses cause serious degradation of vegetation. Response of vegetation under extreme precipitation pulses is perceived, quantified, and validated in this study, which is essential for addressing climate change.

Time-Lag Effect of Climate Conditions on Vegetation Productivity in a Temperate Forest–Grassland Ecotone

Climate conditions can significantly alter the vegetation net primary productivity (NPP) in many of Earth’s ecosystems, although specifics of NPP–climate condition interactions, especially time-lag responses on seasonal scales, remain unclear in ecologically sensitive forest–grassland ecotones. Based on the Moderate-Resolution Imaging Spectroradiometer (MODIS) and meteorological datasets, we analyzed the relationship between NPP and precipitation, temperature, and drought during the growing season (April–August), considering the time-lag effect (0–5 months) at the seasonal scale in Hulunbuir, Inner Mongolia, China from 2000 to 2018. The results revealed a delayed NPP response to precipitation and drought throughout the growing season. In April, the precipitation in the 4 months before (i.e., the winter of the previous year) explained the variation in NPP. In August, the NPP in some areas was influenced by the preceding 1~2 months of drought. The time-lag effect varied with vegetation type and soil texture at different spatial patterns. Compared to grass and crop, broadleaf forest and meadow exhibited a longer legacy of precipitation during the growing season. The length of the time-lag effects of drought on NPP increased with increasing soil clay content during the growing season. The interaction of vegetation types and soil textures can explain 37% of the change in the time-lag effect of the NPP response to PPT on spatial pattern. Our findings suggested that preceding precipitation influences vegetation growth at the early stages of growth, while preceding drought influences vegetation growth in the later stages of growth. The spatial pattern of the time lag was significantly influenced by interaction between vegetation type and soil texture factors. This study highlights the importance of considering the time-lag effects of climate conditions and underlying drivers in further improving the prediction accuracy of NPP and carbon sinks in temperate semiarid forest–grassland ecotones.

Environmental risks and sphingolipid signatures in adult asthma and its phenotypic clusters: a multicentre study

BackgroundAdult asthma is phenotypically heterogeneous with unclear aetiology. We aimed to evaluate the potential contribution of environmental exposure and its ensuing response to asthma and its heterogeneity.MethodsEnvironmental risk was evaluated...

Improved soil–structure interaction model considering time-lag effect

An important aspect of soil– structure interaction analysis is establishing a high-precision conversion relationship between the dynamic impedance in the frequency domain and impulse response function in the time domain. Accordingly, an improved Nakamura model was developed in this study to convert dynamic impedance to an impulse response function. The causes and distribution rules of repeated phase angles were investigated based on the traveling time-lag effect, rendering the improved method more robust. In addition, the impulse response function and interaction force can achieve high fitting accuracy by stripping the singular term from the conventional term. In this study, the time-domain impulse response function and interaction force were derived based on the force–displacement relationship. Further, the accuracy and stability of the improved method were verified using a semi-infinite bar on an elastic foundation. Then, the effects of frequency interval, maximum frequency, and shape of the coefficient matrix on the impulse response function and interaction force were studied by considering the analytical solution of a spherical cavity embedded in full space as an example. Finally, two interaction systems were used to verify the applicability of the improved method in analyzing the soil–structure dynamic interaction.

The influence of investment lag on company value in a non-financing constrained region

ABSTRACT We focus on the impact of the investment time lag on a company’s value. We set up the original model in the start-up phase and the mature phase, which reflects the effect of investment time lag on the company’s value without financing constraints. Furthermore, based on the model, we obtain the explicit expressions of the company’s value in the start-up phase and the mature phase. In the empirical analysis, considering financing costs, investment opportunities and cash flow, we use PVAR to investigate how investment time lag affects the company’s value.

Model-scale tests to examine water pressures acting on potentially buoyant underground structures in clay strata

Throughout the service life, underground structures are subjected to transient and sustained hydrostatic pressures. The reservoir impoundment results in an increase in water level, as well as hydraulic gradient, which can endanger the uplift performance of infrastructure. In uplift design, a reduction factor is often suggested for buoyant force acting on underground structures in clays due to the time lag effect. However, the mechanism of pore pressure generation in clays is not fully understood. This investigation presents a novel U-shaped test chamber to assess the pore pressure generation with time in the horizontal branch subjected to an increase in reservoir level in the left vertical branch. A mathematical model is developed to explain the time lag effect of pore pressure generation. The test program also involves the evaluation of uplift pressure acting on foundation model in the right vertical branch due to adjacent reservoir impoundment. It is found that the time lag effect of pore pressure generation in clays can be observed irrespective of hydraulic gradient, but a higher hydraulic gradient can lead to a faster response in pore pressure sensors. A reduction factor of 0.84–0.87 should be considered to reduce the conservatism of uplift design.

Mid- to Long-Term Runoff Prediction Based on Deep Learning at Different Time Scales in the Upper Yangtze River Basin

Deep learning models are essential tools for mid- to long-term runoff prediction. However, the influence of the input time lag and output lead time on the prediction results in deep learning models has been less studied. Based on 290 schemas, this study specified different time lags by sliding windows and predicted the runoff process by RNN (Recurrent Neural Network), LSTM (Long–short-term Memory), and GRU (Gated Recurrent Unit) models at five hydrological stations in the upper Yangtze River during 1980–2018 at daily, ten-day, and monthly scales. Different models have different optimal time lags; therefore, multiple time lags were analyzed in this paper to find out the relationship between the time intervals and the accuracy of different river runoff predictions. The results show that the optimal time-lag settings for the RNN, LSTM, and GRU models in the daily, ten-day, and monthly scales were 7 days, 24 ten days, 27 ten days, 24 ten days, 24 months, 27 months, and 21 months, respectively. Furthermore, with the increase of time lags, the simulation accuracy would stabilize after a specific time lag at multiple time scales of runoff prediction. Increased lead time was linearly related to decreased NSE at daily and ten-day runoff prediction. However, there was no significant linear relationship between NSE and lead time at monthly runoff prediction. Choosing the smallest lead time could have the best prediction results at different time scales. Further, the RMSE of the three models revealed that RNN was inferior to LSTM and GRU in runoff prediction. In addition, RNN, LSTM, and GRU models could not accurately predict extreme runoff events at different time scales. This study highlights the influence of time-lag setting and lead-time selection in the mid- to long-term runoff prediction results for the upper Yangtze River basin. It is recommended that researchers should evaluate the effect of time lag before using deep learning models for runoff prediction, and to obtain the best prediction, the shortest lead-time length can be chosen as the best output for different time scales.

Temporal effects of climatic factors on vegetation phenology on the Loess Plateau, China

AbstractStudying the start (SOS) and end (EOS) of the vegetation growing season can improve vegetation prediction under climate change. Climatic factors have temporal effects on vegetation growth, including the no time effect (no), time-lag effect (lag), time-accumulation effect (acc), and both time-lag and -accumulation effects (lagacc). The linear regression equations between SOS/EOS and climatic factors were constructed for the Loess Plateau (LP). Subsequently, we analyzed the effects of single and multiple climatic factors on vegetation phenology under four temporal effect scenarios and investigated the response of vegetation phenology to the time-lag and time-accumulation effects of climatic factors, under the lagacc scenario. Among the four temporal effects, lagacc explained the effects of climate on vegetation phenology to the greatest degree, and it is the optimal temporal effect for simulating the relationship between vegetation phenology and climate on the LP. Moreover, the explanation degrees of multiple climatic factors were higher than those of single climatic factors across different temporal effects and vegetation types. Simultaneously considering multiple climatic factors improved predictability of their impact on vegetation phenology. Under lagacc, responses of SOS to temperature (TMP) and precipitation (PRE) exhibited 1.44 ± 0.43/3.49 ± 0.85 and 1.38 ± 0.30/3.38 ± 0.71 months lag/accumulation, respectively, and responses of EOS to TMP and PRE exhibited 1.35 ± 0.17/2.37 ± 0.34 and 1.59 ± 0.19/4.16 ± 0.50 months lag/accumulation, respectively, across the entire LP. The results show that both lagacc and multiple climatic factors require consideration when establishing relationships between climatic factors and vegetation phenology.

Evaluation of the Groundwater and Irrigation Quality in the Zhuoshui River Alluvial Fan between Wet and Dry Seasons

The Zhuoshui River alluvial fan is one of the most important groundwater and agricultural areas in Taiwan. Abundant groundwater resources are the main source of domestic water supply and irrigation water. However, groundwater recharge and groundwater quality have been greatly affected under extreme climate and hydrological conditions. Hence, the quality of groundwater has been a topic of concern to the public. In this study, groundwater level and groundwater quality data of the Zhuoshui River alluvial fan from 2008 to 2020 were used to divide the wet and dry season groups according to the sampling dates. An independent samples t-test was used to evaluate the differences in the mean groundwater level and the mean concentration between the wet and dry seasons. The test results show that there was no statistically significant difference in the mean groundwater level between the wet and dry seasons. This may result from the time lag effects of groundwater recharge. Except for groundwater temperature, bicarbonate, and total organic carbon (TOC), there were no significant differences among the mean concentrations of other groundwater quality parameters in Aquifer 1 and Aquifer 2 between the wet and dry seasons. In terms of the alluvial fan location, although the soil texture, land utilization, cropping systems, and hydrogeology of the proximal, mid-, and distal fan may affect groundwater quality variations, it seems that only Aquifer 1 is affected by surface water infiltration, resulting in significant differences in mean groundwater temperature, mean concentrations of major ions, and nitrate between the wet and dry seasons, whereas Aquifer 2 is less affected. At the same time, owing to the geological conditions and intensive cultivation in the Zhuoshui River alluvial fan, nitrate and arsenic could represent a high risk to the public’s health if groundwater is used as a source for domestic water supply or irrigation water in the distal fan area, whether in the wet season or dry season. Meanwhile, due to global climate change and uneven droughts and floods, the hydrological conditions of the so-called “wet season” and “dry season” are obviously different from those in the past. Compared with precipitation, groundwater level may be a better indicator for understanding variations in groundwater quality.

Performance evaluation of two-stage production systems with time-lag effects: An application in the horticulture industry

In standard data envelopment analysis (DEA), it is assumed that inputs of a specific production period are used to generate outputs of the same period. However, in some practical examples, time-lag effects exist between inputs and outputs. The inputs of one period are used to generate outputs for several periods, or inputs of several periods are used to create outputs for one period. In this paper, we present some new DEA models for performance assessment of network production systems with time-lag effects. An empirical application in the horticulture sector in Iran shows the usefulness and capabilities of our proposed approach.

Effect of time-lag on two mutually competing plant populations under allelochemicals

This paper examines a bionomic model of competing organisms in the presence of allelochemicals with differing time-dependent densities. A competition mathematical model is proposed in this paper. It is shown that when one plant produces an allelochemical, it gives stimulatory effect to itself and inhibitory effect to the other plant. Equilibrium points are calculated and stability analysis is performed about non-zero equilibrium point. Hopf-bifuraction is observed with the help of delay parameter introducing in term of allelochemicals. Model is verified with already existing data for the effect of allelochemical on plant growth by Zahid et.al (2016). The numerical results are substantiated using MATLAB.

Exploring the causal relationship between urbanization and air pollution: Evidence from China

• Urbanization has both negative and positive effects on air pollution. • The effects of urbanization on PM 2.5 concentrations could last for 6–8 years. • PM 2.5 concentrations have decoupled from urbanization since 2015 in China. • Different stages of urbanization have divergent impacts on air pollution. Urbanization and air quality are both closely linked to sustainable development and human well-being. However, few studies discussed the improvement effect, stage characteristics, and time-lag effects of urbanization on air quality. It is not clear whether China's air pollution has decoupled from urbanization. Based on the population, economic, land, and social dimensions, we analyzed the relationship between China's urbanization and air pollution using fully modified least squares, Granger causality test, impulse response functions, and variance decomposition. The results show that urbanization has both negative and positive effects on air quality, which is related to the urbanization stages and policies. From 2000 to 2012, each 1% increase in population, economic, land, and social urbanization increased PM 2.5 concentration by -0.28%, 0.71%, 0.04%, and 0.16%, respectively. However, from 2013 to 2019, the effects were mostly not significant. The increasing urbanization rate could reduce PM 2.5 concentrations during the entire period. Bi-directional long-term causality between population, economic, and land urbanization and PM 2.5 concentration existed before 2013, after which causality became uni-directional, and the effects of PM 2.5 concentrations on urbanization weakened. The impact of urbanization could last for 6–8 years. PM 2.5 concentrations were decoupled from all dimensions of urbanization after 2015 in China. This study provides a useful reference for government decision-making and also gives insights and methods to other studies.

Development of a Portable Infrared-Type Noncontact Blood Pressure Measuring Device and Evaluation of Blood Pressure Elevation during Driving

Hypertension has been established as a major risk factor for cardiovascular morbidity and mortality. Therefore, prevention of hypertension is an urgent matter to maintain people’s health and avoid death, and out-of-office blood pressure measurement is said to be an integral part of the diagnosis and management of hypertension. Hypertension not only causes loss of productivity and economic loss but is also a major cause of road accidents. Therefore, it is important to develop an in-vehicle noncontact blood pressure measurement system for drivers. In addition to measurement accuracy, proper detection timing is also important, and there must be no difference between noncontact and contact detection times. In this study, we introduce an infrared cuffless and portable noncontact blood pressure monitoring system, its measurement principle, and performance evaluation. A total of 13 male adults participated in the experiment to evaluate the effect of time lag between the use of the infrared blood pressure monitoring system and the contact blood pressure monitoring system using a driving simulator. The changepoint method was applied to detect the first change point in the blood pressure time series data caused by the unexpected first appearance of the vehicle. The results showed that the detection time of the developed system was about 2.5 s shorter than that of the contact-type continuous blood pressure measurement system, with no significant difference.

Transfer function models for instantaneous internal cooling loads to describe time lag effect of conversion process

For the better operation of air-conditioning systems and the improvement of office building thermal environment, internal heat loads resulting from occupants, equipment, and lightings need to be accurately quantified because they are main components of space cooling loads. However, existing methods are hard to describe the complicated variation characteristics of internal cooling loads and time lag effect of conversion processes between electric energy and internal cooling loads, leading to limited application and unstable accuracy. To tackle this problem, in this study, transfer function models for instantaneous internal cooling loads were firstly established. Second, an experiment was conducted based on a chamber with good insulation. Third, experimental data and radiant time factors were employed in model discretization, parameter identification, and order decrease steps to acquire unknown parameters and develop models as second-order and third-order discrete transfer functions. Fourth, a simulation was conducted to validate the models, and a mean absolute percentage error of 8.08% was attained. Finally, a comparison between proposed and previously developed methods was carried out to evaluate model performance. The proposed models achieved a maximum internal cooling loads reduction of 75.57%, and the maximum time lag resulting from the heat storage characteristics of indoor thermal masses was approximately 30 min. With two easily accessible input parameters, the proposed models exhibited promising applicability and feasibility during the real-time operation of buildings. This paper provides a theoretical basis for accurately obtaining instantaneous internal cooling loads and thus is meaningful in improving building thermal environment and enhancing comfort levels.

Development Of A Streamline-Based Heat Transport Model For Thermal Oil-Recovery Simulation

Fluid transport calculations based on streamlines have been used successfully for years to model two-phase in compressible flow simulations", The pressures for defining the streamlines are obtained by assuming that the reservoir fluids and rock are incompressible and that flow is in the steady state, which yields a time-independent equation that can be solved to define the fixed pressure distribution. Streamline tracking is performed with the pressure field to advance saturations or compositions. In this approach, the changing pressure field and the movement of fluids are not tightly connected, which results in inaccuracies in the solution.The streamline approach has recently been extended to various applications, such as compositional and black oil problems for updating the composition and saturation, In cases, a non-linear equation for the pressure is solved assuming unsteady-state flow but compressible fluids and rock, followed by solving the conservation equations in sequence or fully implicitly, i.e. the pressure and the saturation equations are solved together along each streamline. In this approach, most of the physical parameters that depend on the pressure changes are accounted for throughout the solution.The major limitation of the streamline method is that applicability is restricted to convective problems only. In practice, the contribution of physical diffusion due to gravitational and capillary forces must be considered in modeling a reservoir undergoing a displacement process. The model including diffusion cannot be solved using one dimensional (1D) streamlines. The operator splitting technique has been proposed to avoid this restriction, The idea is to isolate the convective flow from the diffusion due to gravity for separate solution. The first part is calculated along the common streamline trajectories and the second part is determined by the direction of gravity.Based on recent advances in streamline based simulation techniques, we have extended the methods to the thermal oil-recovery simulation. Modeling thermal processes is difficult due to the many complex mechanisms, high degree of non-linearity, and requirements for appropriate thermodynamic formulation to account for the changes in properties with temperature and pressure. The present study approached the problem from a different angle in the streamline framework. An operator splitting technique was applied to handle the heat diffusion due to gravity, capillary, and conduction effects, and the implicit method was used for solving the highly non- linear convective streamline and diffusive equations. A practical rule was introduced to select the time step for pressure updates to reduce the time-lag effects on the coefficients in the phase conservation equations.A sequential thermal simulator, which solves the pressure and heat equations sequentially, was developed and tested for simulations of hot water-flooding in heavy-oil reservoirs. First we performed simulation with a two dimensional (2D) heterogeneous reservoir to evaluate the main characteristics of the streamline method such as the number of streamlines, the grid refinement along the streamlines, and the time step size. Then we performed three-dimensional (3D) simulation to examine how the gravity mechanism affects the production performance. The solutions obtained using a commercial thermal simulator were used to compare and validate the developed model.