Simulated Biomass Research Articles

Corrosion Performance of HVOF-deposited Ni-based Coatings in Simulated Biomass Fired Boiler Environment at 700 °C

Corrosion Performance of HVOF-deposited Ni-based Coatings in Simulated Biomass Fired Boiler Environment at 700 °C

Insight into the synergistic reaction mechanism of biomass pseudo components and low-density polyethylene for the production of light aromatics through co-catalytic fast pyrolysis over hierarchical HZSM-5

Co-feeding of waste plastics during biomass catalytic fast pyrolysis can improve remarkably the yield of aromatic hydrocarbons. In this work, the simulated biomass by using its model components of cellulose, xylan, and lignin at different proportions was Co-CFP with LDPE to investigate the synergistic reaction mechanism between biomass and low-density polyethylene (LDPE) over hierarchical HZSM-5. Results showed that biomass and its model components were oxygen-enriched feedstock, while LDPE was the hydrogen-enriched feedstock with H/Ceff over 2. The synergistic effect between biomass model components and LDPE was ordered as cellulose and LDPE > xylan and LDPE > lignin and LDPE. Because pyrolysis of cellulose produced higher yield of furans than that of xylan and lignin which acted as the key synergistic compounds in Diels-Alder reaction to form light aromatics. The transfer of hydrogen free radical from the LDPE-derived hydrocarbons to the biomass-derived oxygenates promoted the deoxygenation reaction of oxygenates in hierarchical HZSM-5. Diels-Alder reaction between furans and olefins was the key synergistic reaction which highly improved the yield of hydrocarbon aromatics. In general, the difference of the pyrolysis oxygenated intermediate among the biomass pseudo components resulted in different yields of light aromatics, where cellulose presented highest extent of synergistic reaction to LDPE.

Impacts of climate change on forest growth in saline-alkali land of Yellow River Delta, North China

Climate change is an important factor affecting forest growth. Therefore, approaching the impacts of climate change on forest growth is of great significance to ameliorate this degraded land and push up forestry development. This paper initially probes the impacts of climate change on tree growth in Yellow River Delta region and responds of different tree species on the change. In this study, five species of 22-year-old trees were selected, and the tree biomass was measured by standard site methods and tree ring sampling to pursue the impacts of climate change on forest growth. Besides, growth models of the different tree species were established and verified using Robinia pseudoacacia as an example. The results showed: (1) In the Yellow River Delta, the most adapted tree species are Fraxinus chinensis and R. pseudoacacia. (2) Precipitation is the main meteorological factor affecting tree growth, while temperature and air pressure are also significantly correlated with tree growth. (3) Linear and power function models can simulate tree growth well. From the verification results, the modified R. pseudoacacia biomass is 294.54 t/ha, and the simulated biomass of the linear function model is close to the value. It is expected that the research not only provides a theoretical basis for forestry development in saline lands, but also helps to rehabilitate saline-alkali lands and cope with climate change.

Carbon Formation in the Reforming of Simulated Biomass Gasification Gas on Nickel and Rhodium Catalysts

Biomass gasification gas contains hydrocarbons that must be converted to CO and H2 prior to the utilization of the gas in a synthesis unit. Autothermal or steam reforming operating with a nickel or noble metal catalyst is a feasible option to treat the gas, but the harsh reaction conditions may lead to the formation of solid carbon. This study discusses the effects of pressure, time-on-stream, and ethylene content on the carbon formation on nickel and rhodium catalysts. The experiments were carried out with laboratory-scale equipment using reaction conditions that were closely simulated after a pilot-scale biomass gasifier. The results indicated that ethylene content above 20,000 vol-ppm and the increased pressure would increase the carbon formation, although there were differences between the rhodium and nickel catalysts. However, carbon formation was significantly more pronounced on the nickel catalyst when the reaction time was increased from 5 h to 144 h. The type of carbon was found to be primarily encapsulating and graphitic. The formation of whisker carbons (also known as carbon nanotubes) was not observed, which is consistent with the literature as the feed gas contained H2S. It was concluded that utilizing a noble metal catalyst as the front layer of the catalyst bed could lower the risk for carbon formation sufficiently to provide stable long-term operation.

Simulating Cotton Growth and Productivity Using AquaCrop Model under Deficit Irrigation in a Semi-Arid Climate

AquaCrop is a water-driven model that simulates the effect of environment and management on crop production under deficit irrigation. The model was calibrated and validated using three databases and four irrigation treatments (i.e., 100%ET, 80%ET, 70%ET, and 50%ET). Model performance was evaluated by simulating canopy cover (CC), biomass accumulation, and water productivity (WP). Statistics of root mean square error (RMSE) and Willmott’s index of agreement (d) showed that model predictions are suitable for non-stressed and moderate stressed conditions. The results showed that the simulated biomass and yield were consistent with the measured values with a coefficient of determination (R2) of 0.976 and 0.950, respectively. RMSE and d-index values for canopy cover (CC) were 2.67% to 4.47% and 0.991% to 0.998% and for biomass were 0.088 to 0.666 ton/ha and 0.991 to 0.999 ton/ha, respectively. Prediction of simulated and measured biomass and final yield was acceptable with deviation ˂10%. The overall value of R2 for WP in terms of yield was 0.943. Treatment with 80% ET consumed 20% less water than the treatment with 100%ET and resulted in high WP in terms of yield (0.6 kg/m3) and biomass (1.74 kg/m3), respectively. The deviations were in the range of −2% to 11% in yield and −2% to 4% in biomass. It was concluded that AquaCrop is a useful tool in predicting the productivity of cotton under different irrigation scenarios.

Optimization of fermentation conditions for sourdough by three different lactic acid bacteria using response surface methodology

This study aimed to investigate optimal fermentation conditions for sourdough by freeze-dried Lactobacillus curvatus N19, Weissella cibaria N9 and Lactobacillus brevis ED25 isolated from Turkish sourdough previously. The central composite rotational design was applied to the optimization of fermentation parameters (temperature and time). The fermentation was carried out under a simulated sourdough system and biomass concentration, total acidity, and lactic and acetic acid formation were chosen as response variables. Results showed that the models developed for all variables were significant (p < 0.05) and there was no lack of fit in any of quantifications (p > 0.05), indicating the suitability for representing the relationship between variables and factors. While both of the independent parameters were effect the response, fermentation time was the most significant factor influencing the response. The validation experiments using the optimized condition showed a good agreement between the experimental and predicted values except the lactic and acetic acid formation for W. cibaria N9. In conclusion, freeze-dried L. curvatus N19 can be used as a starter culture to sourdough fermentation for bread industry due to optimum fermentation conditions (29oC temperature and 23h time).

Quantifying the effects of varietal types × management on the spatial variability of sorghum biomass across US environments

AbstractRegional‐scale estimations of sorghum biomass production allow identification of optimum genotype×environment×management (G×E×M) combinations for bioenergy generation. The objective of this study was to determine the degree of contributions of G, E, and M toward variability in sorghum biomass in the United States. Using the Agricultural Production Systems sIMulator in a grid computing platform, biomass was simulated for irrigated and rainfed conditions for 30 years across the United States for four sorghum varietal types (grain—GS, sudangrass—SS, photosensitive—PS, and photo‐insensitive—PI). Simulated biomass was assessed by environments clustered using the sum of intercepted solar radiation (ir), mean of temperature stress factor (tp) and water stress factor (sw). Simulated biomass ranged from 5.8 t ha−1 (GS‐rainfed) to 27.5 t ha−1 (PI‐irrigated). Under high‐temperature environments (mean annual temperature = 25°C), rainfed biomass between 40 and 80 days after planting (DAP) was strongly correlated with sw (r = 0.64–0.86) and irrigated biomass with ir (r = 0.68–0.81). Under low‐temperature environments (mean annual temperature = 18°C) after 40 DAP, tp and ir had greater effects than sw (r = 0.55–0.82). Biomass variance was mainly explained by varietal type (50%–76%) in all environments×irrigation combinations, except in the high‐ and mid‐temperature environments under rainfed conditions where rainfall had the major effect (25%–45%). However, when mean temperature during the growing season decreased from 25°C (high environments) to 18°C (low environments), the contribution of mean temperature to biomass variance increased from 7% to 34% (rainfed) and from 4% to 36% (irrigated). Varietal type had the larger interactions with other factors independently of the environment and irrigation. We demonstrated a need to quantify (i) the main G×E×M drivers of biomass variability based on environmental stress factors and (ii) the variance contribution of these drivers on sorghum biomass. Our regional‐scale estimations are key inputs for future robust biomass projections of energy sorghum genotypes integrating G×E×M under climate change scenarios.

High Temperature Corrosion Resistance of Fecral/Stainless Steels Bulk/Overlay Weld Coatings in Simulated Biomass Environment at 600 °C

High Temperature Corrosion Resistance of Fecral/Stainless Steels Bulk/Overlay Weld Coatings in Simulated Biomass Environment at 600 °C

Effect of the Presence of HCl on Simultaneous CO2 Capture and Contaminants Removal from Simulated Biomass Gasification Producer Gas by CaO-Fe2O3 Sorbent in Calcium Looping Cycles

This study investigated the effect of HCl in biomass gasification producer gas on the CO2 capture efficiency and contaminants removal efficiency by CaO-Fe2O3 based sorbent material in the calcium looping process. Experiments were conducted in a fixed bed reactor to capture CO2 from the producer gas with the combined contaminants of HCl at 200 ppmv, H2S at 230 ppmv, and NH3 at 2300 ppmv. The results show that with presence of HCl in the feeding gas, sorbent reactivity for CO2 capture and contaminants removal was enhanced. The maximum CO2 capture was achieved at carbonation temperatures of 680 °C, with efficiencies of 93%, 92%, and 87%, respectively, for three carbonation-calcination cycles. At this carbonation temperature, the average contaminant removal efficiencies were 92.7% for HCl, 99% for NH3, and 94.7% for H2S. The outlet contaminant concentrations during the calcination process were also examined which is useful for CO2 reuse. The pore structure change of the used sorbent material suggests that the HCl in the feeding gas contributes to high CO2 capture efficiency and contaminants removal simultaneously.

Evaluation of palm-oil fly ash derived lithium silicate for CO2 sorption under simulated gasification conditions

CO2 utilization through high temperature CO2 enhanced sorption processes is a promising pathway to achieve widespread decarbonisation and to be successful, highly performing CO2 acceptors have to be developed. In this work, a fly ash derived lithium silicate material (Li-FA) was evaluated as CO2 sorbent under simulated biomass gasification conditions. The CO2 uptake tests were carried out using a high-pressure wire mesh apparatus between 380 °C and 700 °C, 15 bar and in presence of steam, H2, CO and CO2. The Li-FA sorbent was able to selectively adsorb CO2 with a maximum uptake of 4.2 mmol CO2/g at 700 °C under 4.5 bar CO2 (15 bar P total). The stability of the Li-FA CO2 sorbent was tested under more realistic exposition time in a 10-cycles experiment at 700 °C. Adsorption was very fast (138 mg CO2/g in average) requiring less than 20 s to achieve about 95 % of the total CO2 uptake of 3.2 mmol/g after 10 cycles and only 2 min to desorb the CO2 at 710 ° C and 15 bar. SEM/elemental mapping revealed that the good elemental dispersion was retained unchanged after 10 cycles. The kinetic analysis demonstrated that the sorption constants k1 and k2 were larger than those of similar high-temperature sorbents, which combined to their stability and CO2 uptake capacity offer promising application in CO2 sorption enhanced processes.

Effects of sample gear on estuarine nekton assemblage assessments and food web model simulations

Long-term fisheries-independent sampling data inform population status and trends of species-specific biomass and are often used to drive biomass-based food web models such as the Comprehensive Aquatic Systems Model (CASM). Indicators such as total biomass and mean trophic level derived from these data and from CASM outputs inform management and facilitate assessments of on-going and predicted coastal change and restoration activities on fisheries, but rely on consistent sampling to enable comparisons across space and time. Changes in coastal estuarine gradients, combined with the availability of new sampling technologies, highlight a need to assess the potential consequences of changing sampling technologies on fisheries data and the cascading impact on model outputs. In Louisiana, USA, CASM models are used to inform coastal restoration projects, relying on 40 years of fisheries-independent data derived from 50′ seine sampling. However, alternative use of electrofishers as a sampling method has been proposed to replace the seine sampling. In this study, we examine data from concurrent seine and electrofisher sampling in Barataria Basin, Louisiana, and compare biomass, assemblage data and CASM outputs related to species biomass, food web structure and energy cycling. In a paired comparison of data in 2018–2019, the electrofisher captured higher total catch and diversity compared to the seine. The electrofisher samples were dominated by shrimp (grass, white, brown) and larger bodied fish, while seine samples were dominated by smaller-bodied fish (i.e., anchovy, menhaden). Ecosystem indicators derived from running the CASM using biomass data from seine and electrofisher sampling separately in two different simulation exercises provide contrasting results. In Simulation Exercise 1, the use of different datasets (long-term CASM calibration, 2018–2019 seine, 2018–2019 electrofisher) to initialize the CASM biomasses did not result in large or long-running changes in the simulated biomasses over time. In contrast, in Simulation Exercise 2, CASM model outputs using adjusted gear ratios indicated changes in biomass structure when using electrofisher data, with a doubling of total food web biomass due to the increased shrimp count, and a 13% increase in total energy flow through the food web. Conversions based on area and gear efficiency for overall catch may be useful in maintaining the continuity of historical data. However, differences in species-specific catch due to gear selectivity could have large consequences for constructing and calibrating fish and ecosystem models and remain difficult to reconcile. These differences in assemblages, and estimated biomasses for key food web species, suggest careful consideration in changing gears.

Parameter uncertainty dominates C-cycle forecast errors over most of Brazil for the 21st century

Abstract. Identification of terrestrial carbon (C) sources and sinks is critical for understanding the Earth system as well as mitigating and adapting to climate change resulting from greenhouse gas emissions. Predicting whether a given location will act as a C source or sink using terrestrial ecosystem models (TEMs) is challenging due to net flux being the difference between far larger, spatially and temporally variable fluxes with large uncertainties. Uncertainty in projections of future dynamics, critical for policy evaluation, has been determined using multi-TEM intercomparisons, for various emissions scenarios. This approach quantifies structural and forcing errors. However, the role of parameter error within models has not been determined. TEMs typically have defined parameters for specific plant functional types generated from the literature. To ascertain the importance of parameter error in forecasts, we present a Bayesian analysis that uses data on historical and current C cycling for Brazil to parameterise five TEMs of varied complexity with a retrieval of model error covariance at 1∘ spatial resolution. After evaluation against data from 2001–2017, the parameterised models are simulated to 2100 under four climate change scenarios spanning the likely range of climate projections. Using multiple models, each with per pixel parameter ensembles, we partition forecast uncertainties. Parameter uncertainty dominates across most of Brazil when simulating future stock changes in biomass C and dead organic matter (DOM). Uncertainty of simulated biomass change is most strongly correlated with net primary productivity allocation to wood (NPPwood) and mean residence time of wood (MRTwood). Uncertainty of simulated DOM change is most strongly correlated with MRTsoil and NPPwood. Due to the coupling between these variables and C stock dynamics being bi-directional, we argue that using repeat estimates of woody biomass will provide a valuable constraint needed to refine predictions of the future carbon cycle. Finally, evaluation of our multi-model analysis shows that wood litter contributes substantially to fire emissions, necessitating a greater understanding of wood litter C cycling than is typically considered in large-scale TEMs.

Hot corrosion behavior of an arc sprayed Fe-based amorphous coating in a simulated biomass firing environment

Coating technologies are cost-effective solutions to the Cl-induced hot corrosion problems in biomass boilers. In this study, an arc sprayed Fe-Cr-Mo amorphous coating was fabricated and exposed to a stimulated biomass firing condition at 500 °C and 600 °C. Fe and Cr in the coating were vulnerable to chlorination and continuously evaporated, leaving Cr depletion zones. The distribution of Mo was inhomogeneous, and Mo-rich zones were resistant to gaseous corrosive species but susceptible to electrochemical attack as temperature increased. The crevices in the coating were detrimental to its corrosion resistance by acting as corrodent diffusing channels.

A three dimensional, full life cycle, anchovy and sardine model for the North Aegean Sea (Eastern Mediterranean): Validation, sensitivity and climatic scenario simulations

We present the development of a 3D full-lifecycle, individual-based model (IBM) for anchovy and sardine, online coupled to an existing hydrodynamic/biogeochemical low-trophic level (LTL) model for the North Aegean Sea. It was built upon an existing 1D model for the same species and area, with the addition of a horizontal movement scheme. In the model, both species evolve from the embryonic stage (egg+yolk sac larva) to the larval, juvenile, and adult stages. Somatic growth is simulated with the use of a “Wisconsin” type bioenergetics model and fish populations with an adaptation of the ‘super individuals’ (SI) approach. For the reference simulation and model calibration, in terms of fish growth and population biomass, the 2000-2010 period was selected. Interannual biomass variability of anchovy was successfully represented by the model, while the simulated biomass of sardine exhibited low variability and did not satisfactorily reproduce the observed interannual variability from acoustic surveys. The spatial distribution of both species’ biomass was in relatively good agreement with field data. Additional single-species simulations revealed that species compete for food resources. Temperature sensitivity experiments showed that both species reacted negatively to a temperature increase. Anchovy, in particular, was more affected since its spawning and larval growth periods largely overlap with the period of maximum yearly temperature and low prey concentration. Finally, simulation experiments using IPCC climatic scenarios showed that the predicted temperature increase and zooplankton concentration decrease in the future will negatively affect anchovy, resulting in sardine prevalence.

EFFECT of surface treatment on corrosion control of P91 hydrothermal aqueous solutions

ABSTRACT The proven efficacy of surface treatment on ferritic-martensitic (F/M) steels in subcritical and supercritical water environments has spawned interest in its application for the construction of biomass hydrothermal liquefaction (HTL) conversion reactors. The objective of this paper is to determine the relative extent to which well-established surface treatments, including mechanical abrasion (baseline), sandblasting, shot peening, and pickling, affect corrosion of the steels in a simulated HTL biomass conversion environment. These treatments were administered to P91 steel for the purpose of improving corrosion resistance towards that exhibited by Type 304 austenitic stainless steel. Samples were immersed in a simulated HTL conversion environment at 310 °C and 10 MPa for 10 days using a static autoclave testing system. None of the surface treatments administered improved corrosion control of P91. The structure and chemistry of the oxide scale formed during immersion were not altered by the surface treatments administered, i/e., none of which promoted of the formation of a more protective Cr-enriched scale. Shot peening was particularly problematic as it promoted the formation of Cr-rich carbides in the near-surface region, which, when incorporated into the oxide scale that formed during high temperature immersion, served to reduce the amount Cr available for oxidization.

A modelling approach to simulate Chlorophyta and Cyanobacteria biomasses based on historical data of a Brazilian urban reservoir

A process-based model was used to simulate a hindcast based on the worst historical water quality condition of a tropical urban reservoir. Paranoá Lake is located in Brasília-DF, Brazil, and went through intense eutrophication in the 70s and 90s, with an important cyanobacterial bloom event in 1978. The parameters of phytoplankton were calibrated, focusing on the group of Chlorophyta (green algae) and Cyanobacteria (blue-green algae) at four depths (1, 10, 15 and 20m). The results indicated that the model was able to reproduce the Cyanobacteria biomass in comparison with the observations (RMSE=22-29.10-3 mgC L-1). On the other hand, the simulated Chlorophyta biomass showed good agreement with the observed data only in the bottom layer (RMSE=29. 10-3 mgC L-1 at 20m). In the hindcast simulation, the model was able to predict a significant increase in cyanobacterial biomass facing a water quality deterioration. In the meantime, the simulated Chlorophyta biomass decreased, which may indicate the phytoplankton group succession in response to the environmental conditions.

Forest management required for consistent carbon sink in China\u2019s forest plantations

BackgroundForest is the largest biomass carbon (C) pool in China, taking up a substantial amount of atmospheric carbon dioxide. Although it is well understood that planted forests (PFs) act as a large C sink, the contribution of human management to C storage enhancement remains obscure. Moreover, existing projections of forest C dynamics suffer from spatially inconsistent age and type information or neglected human management impacts. In this study, using developed PF age and type maps and data collected from 1371 forest plantation sites in China, we simulated biomass C stock change and quantified management impacts for the time period 2010–2050.ResultsResults show that future forest biomass C increment might have been overestimated by 32.5%–107.5% in former studies. We also found that age-related growth will be by far the largest contributor to PF biomass C increment from 2010 to 2050 (1.23 ± 0.002 Pg C, 1 Pg = 1015 g = 1 billion metric tons), followed by the impact of human management (0.57 ± 0.02 Pg C), while the contribution of climate is slight (0.087 ± 0.04 Pg C). Besides, an additional 0.24 ± 0.07 Pg C can be stored if current PFs are all managed by 2050, resulting in a total increase of 2.13 ± 0.05 Pg C.ConclusionsForest management and age-related growth dominate the biomass C change in PFs, while the effect of climatic factors on the accumulation is minor. To achieve the ambitious goal of forest C stock enhancement by 3.5 Pg from 2020 to 2050, we advocate to improve the management of existing forests and reduce the requests for more lands for forest expansion, which helps mitigate potential conflicts with agricultural sectors. Our results highlight that appropriate planning and management are required for sustaining and enhancing biomass C sequestration in China’s PF.

Multiple configurations and fluctuating trophic control in the Barents Sea food-web.

The Barents Sea is a subarctic shelf sea which has experienced major changes during the past decades. From ecological time-series, three different food-web configurations, reflecting successive shifts of dominance of pelagic fish, demersal fish, and zooplankton, as well as varying trophic control have been identified in the last decades. This covers a relatively short time-period as available ecological time-series are often relatively short. As we lack information for prior time-periods, we use a chance and necessity model to investigate if there are other possible configurations of the Barents Sea food-web than those observed in the ecological time-series, and if this food-web is characterized by a persistent trophic control. We perform food-web simulations using the Non-Deterministic Network Dynamic model (NDND) for the Barents Sea, identify food-web configurations and compare those to historical reconstructions of food-web dynamics. Biomass configurations fall into four major types and three trophic pathways. Reconstructed data match one of the major biomass configurations but is characterized by a different trophic pathway than most of the simulated configurations. The simulated biomass displays fluctuations between bottom-up and top-down trophic control over time rather than persistent trophic control. Our results show that the configurations we have reconstructed are strongly overlapping with our simulated configurations, though they represent only a subset of the possible configurations of the Barents Sea food-web.

Improving winter wheat biomass and evapotranspiration simulation by assimilating leaf area index from spectral information into a crop growth model

Data assimilation, a state-of-the-art method that merges remote sensing data with a dynamic model to improve model performance, has been widely used in land surface process modeling. Application of data assimilation under various water conditions can provide insight of crop response to different water supply rates, which is useful for agricultural water management in arid and semi-arid regions. For this purpose, we developed a generic data assimilation methodology by integrating both the Shuffled Complex Evolution (SCE) and the Ensemble Kalman Filter (EnKF) algorithms into the Simple Algorithm For Yield and Evapotranspiration (SAFYE) model to provide improved simulation of winter wheat biomass and yield, and simulation of evapotranspiration (ET) under different water-supply scenarios. An experiment with nine irrigation scenarios was conducted during the 2013—2015 growing cycles. Field spectral data were employed to retrieve the leaf area index (LAI), which was then used as a single state variable to determine other parameters in the SAFYE model using a global optimization algorithm. Time-series LAI was eventually assimilated in the SAFYE model based on the EnKF algorithm to improve overall model simulation. The results showed that the simulated crop growth dynamics followed the measurements well in most cases when the estimated LAI was assimilated. The accuracy of simulated biomass at the daily step was improved, with the maximum RMSE decreased from 199.4 to 123.8 g m−2 and from 466.6 to 393.4 g m−2 for the 2013–2014 and 2014–2015 growing seasons respectively. A good agreement was also achieved between the estimated and field measured grain yield (R2 = 0.901, RMSE= 31.9 g m−2, RRMSE=6.55%) for both growing seasons. The simulation of soil water content in the top 0—20 cm soil layer was better (RMSE: 3.3—5.0 mm) than that of 0—100 cm layer (RMSE: 11.7—29.6 mm). Accuracy of the simulated ET under early-stage water deficit scenarios was lower than that under other scenarios, with a positive mean relative error of 14% (3.4—24.3%) during two growing seasons. This study demonstrates the great potential of coupling remote sensing data to improve the performance of SAFYE model in modeling winter wheat growth.

Climate change impacts on rice yield of a large-scale irrigation scheme in Malaysia

The yield of lowland rice is prone to climatic factors and farming practices, among which water management and weed control are crucial. Crop simulation models can help evaluating the impacts of these factors and practices on yield growth. This study assessed the impacts of climate, irrigation practices, and weed control measures on the yield of lowland rice by using FAO-AquaCrop model. Necessary data were generated by well-planned field experiments during two consecutive rice-growing seasons (off-/dry season: January–April and main-/wet season: July–October) in 2017. Ensemble climate data was downscaled from eighteen General Circulation Models (GCMs) under three Representative Concentration Pathway (RCP) scenarios (RCP4.5, RCP6.0 and RCP8.5) both for baseline (1976–2005) and future (2010–2099) periods. AquaCrop model was calibrated and validated with observed data with regard to Normalized Root-Mean-Square Error (NRMSE), Prediction Error (Pe), Mean Absolute Error (MAE), and Index of Agreement (d). The model simulated biomass, yield and productivity of rice satisfactorily. It predicted 13.6%, 14.1% and 15.3% increase in yield under RCP4.5, RCP6.0 and RCP8.5 emission scenario, respectively for the off-season in the future compared to the baseline period. The corresponding increase in yield is 13.9%, 14.5% and 15.2% for the main-season. The model simulated reduced yield under water stress and poor weed control, with yield reduction mounting to 65% under worse weed control. However, proper weed control and water management practices will likely augment yield under changing climate. The stress exerted by rising temperature on yield will likely be compensated by the ensuing increase in CO2 fertilization. The predicted susceptibility of rice yield to weed infestation and water stress under changing climate will help devising proper management practices for sustainable rice productivity in the future.