scholarly journals Assessment of rice yield gap under a changing climate in India

2020 ◽  
Author(s):  
Subhankar Debnath ◽  
Ashok Mishra ◽  
D. R. Mailapalli ◽  
N. S. Raghuwanshi ◽  
V. Sridhar
Keyword(s):  
Climate Change ◽  
Explicit Knowledge ◽  
Rice Yield ◽  
Management Strategies ◽  
Temporal Analysis ◽  
Future Climate ◽  
Historical Period ◽  
Change Scenario ◽  
Yield Gap ◽  
Climate Conditions

Abstract Climate change evokes future food security concerns and needs for sustainable intensification of agriculture. The explicit knowledge about crop yield gap at country level may help in identifying management strategies for sustainable agricultural production to meet future food demand. In this study, we assessed the rice yield gap under projected climate change scenario in India at 0.25° × 0.25° spatial resolution by using the Decision Support System for Agrotechnology Transfer (DSSAT) model. The simulated spatial yield results show that mean actual yield under rainfed conditions (Ya) will reduce from 2.13 t/ha in historical period 1981–2005 to 1.67 t/ha during the 2030s (2016–2040) and 2040s (2026–2050), respectively, under the RCP 8.5 scenario. On the other hand, mean rainfed yield gap shows no change (≈1.49 t/ha) in the future. Temporal analysis of yield indicates that Ya is expected to decrease in the considerably large portion of the study area (30–60%) under expected future climate conditions. As a result, yield gap is expected to either stagnate or increase in 50.6 and 48.7% of the study area during the two future periods, respectively. The research outcome indicates the need for identifying plausible best management strategies to reduce the yield gap under expected future climate conditions for sustainable rice production in India.

Projecting future vegetation change for northeast China using CMIP6

2020 ◽  
Author(s):  
Wei Yuan ◽  
Shuang-ye Wu ◽  
Shugui Hou
Keyword(s):  
Climate Change ◽  
Vegetation Change ◽  
Management Strategies ◽  
Future Climate ◽  
Future Climate Change ◽  
Vegetation Changes ◽  
Emission Scenarios ◽  
Climate Data ◽  
Climate Conditions ◽  
Temperature And Precipitation

<p>This study aims to establish future vegetation changes in the east and central of northern China (ECNC), an ecologically sensitive region in the transition zonal from humid monsoonal to arid continental climate. The region has experienced significant greening in the past several decades. However, few studies exist on how vegetation will change with future climate change, and great uncertainties exist due to complex, and often spatially non-stationary, relationships between vegetation and climate. In this study, we first used historical NDVI and climate data to model this spatially variable relationship with Geographically Weighted Logit Regression. We found that temperature and precipitation could explain, on average, 43% of NDVI variance, and they could be used to model NDVI fairly well. We then establish future climate change using the output of 11 CMIP6 models for the medium (SSP245) and high (SSP585) emission scenarios for the mid-century (2041-2070) and late-century (2071-2100). The results show that for this region, both temperature and precipitation will increase under both scenarios. By late-century under SSP585, precipitation is projected to increase by 25.12% and temperature is projected to increase 5.87<sup>o</sup>C in ECNC. Finally, we used future climate conditions as input for the regression models to project future vegetation (indicated by NDVI). We found that NDVI will increase under climate change. By mid-century, the average NDVI in ECNC will increase by 0.024 and 0.021 under SSP245 and SSP585. By late-century, it will increase by 0.016 and 0.006 under SSP245 and SSP585 respectively. Although NDVI is projected to increase, the magnitude of increase is likely to diminish with higher emission scenarios, possibly due to the benefit of precipitation increase being gradually encroached by the detrimental effects of temperature increase. Moreover, despite the overall NDVI increase, the area likely to suffer vegetation degradation will also expands, particularly in the western part of ECNC. With higher emissions and later into the century, region with low NDVI is likely to shift and/or expand north-forward. Our results could provide important information on possible vegetation changes, which could help to develop effective management strategies to ensure ecological and economic sustainability in the future.</p>

scholarly journals Identifying most promising agronomic adaptation strategies to close rainfed rice yield gap in future: a model-based assessment

2021 ◽  
Author(s):  
Subhankar Debnath ◽  
Ashok Mishra ◽  
D. R. Mailapalli ◽  
N. S. Raghuwanshi
Keyword(s):  
Climate Change ◽  
Crop Yield ◽  
Rice Yield ◽  
Negative Impact ◽  
Baseline Period ◽  
Adaptation Strategies ◽  
Change Scenario ◽  
Yield Gap ◽  
Rainfed Rice ◽  
Agronomic Adaptation

Abstract There is an increasing consensus that climate change may have a high negative impact on crop yield, and that it will affect farmers in developing and least developed counties the most. ‘Close the yield gap’ could be one of the promising options to address the issue of yield improvement. Better understanding of adaptation strategies and implication of the adaptations in crop yield are required to close the yield gap. In this study, the effectiveness of agronomic adaptation options on rainfed rice yield gap was evaluated for the baseline period (1981–2005) and two future periods (2016–2040 and 2026–2050) for India by using bias-corrected RegCM4 output and the Decision Support System for Agrotechnology Transfer (DSSAT) model. Results suggested that a combined adjustment of transplanting time (advancing by fortnight), crop spacing ((10 × 10) cm) and N-fertilizer application (140 kg/ha) was the best strategy as compared to single adaptation option to close the yield gap under the climate change scenario. The strategy improved rice yield by 37.5–168.0% and reduced average attainable yield gap among the cultivars from 0.74 to 0.16 t/ha under future climate projection. This study provides agronomic indications to rice growers and lays the basis for an economic analysis to support policy-makers, in charge of promoting the sustainability of the rainfed rice-growing systems.

scholarly journals STOCHASTIC TYPHOON MODEL AND ITS APPLICATION TO FUTURE TYPHOON PROJECTION

2011 ◽  
Vol 1 (32) ◽  
pp. 16 ◽  
Author(s):  
Tomohiro Yasuda ◽  
Hajime Mase ◽  
Shoji Kunitomi ◽  
Nobuhito Mori ◽  
Yuta Hayashi
Keyword(s):  
Climate Change ◽  
General Circulation ◽  
Circulation Model ◽  
Stochastic Modelling ◽  
Climate Scenario ◽  
Future Climate ◽  
Statistical Characteristics ◽  
Climate Change Signal ◽  
Change Scenario ◽  
Climate Conditions

This study presents a stochastic typhoon model (STM) for estimating the characteristics of typhoons in the present and future climate conditions. Differences of statistical characteristics between present and future typhoons were estimated from projections by an Atmospheric General Circulation Model (AGCM) under a climate change scenario and are taken into account in the stochastic modelling of future typhoons as a climate change signal. From the STM results which utilize the Monte Carlo simulation, it was found that the frequency of typhoon landfall in Osaka bay area, Japan, will decrease, although the mean value of atmospheric central pressure of typhoon will not change significantly. The arrival probability of stronger typhoons will increase in the future climate scenario.

Bias correction of extreme values of high resolution climate simulations for risk analysis.

2021 ◽  
Author(s):  
luis Augusto sanabria ◽  
Xuerong Qin ◽  
Jin Li ◽  
Robert Peter Cechet
Keyword(s):  
Climate Change ◽  
Bias Correction ◽  
Extreme Values ◽  
The Body ◽  
Future Climate ◽  
Return Periods ◽  
Climate Conditions ◽  
Mitigation And Adaptation ◽  
Climate Simulations ◽  
The Impact

Abstract Most climatic models show that climate change affects natural perils' frequency and severity. Quantifying the impact of future climate conditions on natural hazard is essential for mitigation and adaptation planning. One crucial factor to consider when using climate simulations projections is the inherent systematic differences (bias) of the modelled data compared with observations. This bias can originate from the modelling process, the techniques used for downscaling of results, and the ensembles' intrinsic variability. Analysis of climate simulations has shown that the biases associated with these data types can be significant. Hence, it is often necessary to correct the bias before the data can be reliably used for further analysis. Natural perils are often associated with extreme climatic conditions. Analysing trends in the tail end of distributions are already complicated because noise is much more prominent than that in the mean climate. The bias of the simulations can introduce significant errors in practical applications. In this paper, we present a methodology for bias correction of climate simulated data. The technique corrects the bias in both the body and the tail of the distribution (extreme values). As an illustration, maps of the 50 and 100-year Return Period of climate simulated Forest Fire Danger Index (FFDI) in Australia are presented and compared against the corresponding observation-based maps. The results show that the algorithm can substantially improve the calculation of simulation-based Return Periods. Forthcoming work will focus on the impact of climate change on these Return Periods considering future climate conditions.

scholarly journals An Assessment of Recent and Future Temperature Change over the Sichuan Basin, China, Using CMIP5 Climate Models

2017 ◽  
Vol 30 (17) ◽  
pp. 6701-6722 ◽  
Author(s):  
Daniel Bannister ◽  
Michael Herzog ◽  
Hans-F. Graf ◽  
J. Scott Hosking ◽  
C. Alan Short
Keyword(s):  
Climate Change ◽  
Sichuan Basin ◽  
Climate Models ◽  
Distribution Functions ◽  
Future Climate ◽  
First Century ◽  
Future Climate Change ◽  
Change Scenario ◽  
The Sichuan Basin ◽  
Twenty First Century

The Sichuan basin is one of the most densely populated regions of China, making the area particularly vulnerable to the adverse impacts associated with future climate change. As such, climate models are important for understanding regional and local impacts of climate change and variability, like heat stress and drought. In this study, climate models from phase 5 of the Coupled Model Intercomparison Project (CMIP5) are validated over the Sichuan basin by evaluating how well each model can capture the phase, amplitude, and variability of the regionally observed mean, maximum, and minimum temperature between 1979 and 2005. The results reveal that the majority of the models do not capture the basic spatial pattern and observed means, trends, and probability distribution functions. In particular, mean and minimum temperatures are underestimated, especially during the winter, resulting in biases exceeding −3°C. Models that reasonably represent the complex basin topography are found to generally have lower biases overall. The five most skillful climate models with respect to the regional climate of the Sichuan basin are selected to explore twenty-first-century temperature projections for the region. Under the CMIP5 high-emission future climate change scenario, representative concentration pathway 8.5 (RCP8.5), the temperatures are projected to increase by approximately 4°C (with an average warming rate of +0.72°C decade−1), with the greatest warming located over the central plains of the Sichuan basin, by 2100. Moreover, the frequency of extreme months (where mean temperature exceeds 28°C) is shown to increase in the twenty-first century at a faster rate compared to the twentieth century.

scholarly journals Evaluating catchment response to artificial rainfall from four weather generators for present and future climate

2018 ◽  
Vol 77 (11) ◽  
pp. 2578-2588 ◽  
Author(s):  
Hjalte Jomo Danielsen Sørup ◽  
Steffen Davidsen ◽  
Roland Löwe ◽  
Søren Liedtke Thorndahl ◽  
Morten Borup ◽  
...  
Keyword(s):  
Climate Change ◽  
Future Climate ◽  
Control Measures ◽  
Rainfall Time Series ◽  
Urban Water ◽  
Combined Sewer Overflows ◽  
Climate Conditions ◽  
Artificial Rainfall ◽  
Weather Generators ◽  
Catchment Responses

Abstract The technical lifetime of urban water infrastructure has a duration where climate change has to be considered when alterations to the system are planned. Also, models for urban water management are reaching a very high complexity level with, for example, decentralized stormwater control measures being included. These systems have to be evaluated under as close-to-real conditions as possible. Long term statistics (LTS) modelling with observational data is the most close-to-real solution for present climate conditions, but for future climate conditions artificial rainfall time series from weather generators (WGs) have to be used. In this study, we ran LTS simulations with four different WG products for both present and future conditions on two different catchments. For the present conditions, all WG products result in realistic catchment responses when it comes to the number of full flowing pipes and the number and volume of combined sewer overflows (CSOs). For future conditions, the differences in the WGs representation of the expectations to climate change is evident. Nonetheless, all future results indicate that the catchments will have to handle more events that utilize the full capacity of the drainage systems. Generally, WG products are relevant to use in planning of future changes to sewer systems.

Trading space for time: Assessment of tree habitat shifts under climate change using bioclimatic envelopes

2021 ◽  
Author(s):  
Katharina Enigl ◽  
Matthias Schlögl ◽  
Christoph Matulla
Keyword(s):  
Climate Change ◽  
Tree Species ◽  
Ips Typographus ◽  
Future Climate ◽  
Forest Damage ◽  
Climate Conditions ◽  
North East ◽  
Bioclimatic Variables ◽  
The Future ◽  
Forest District

<p>Climate change constitutes a main driver of altering population dynamics of spruce bark beetles (<em>Ips typographus</em>) all over Europe. Their swarming activity as well as development rate are strongly dependent on temperature and the availability of brood trees. Especially over the last years, the latter has substantially increased due to major drought events which led to a widespread weakening of spruce stands. Since both higher temperatures and longer drought periods are to be expected in Central Europe in the decades ahead, foresters face the challenges of maintaining sustainable forest management and safeguarding future yields. One approach used to foster decision support in silviculture relies on the identification of possible alternative tree species suitable for adapting to expected future climate conditions in threatened regions. </p><p>In this study, we focus on the forest district of Horn, a region in Austria‘s north east that is beneficially influenced by the mesoclimate of the Pannonian basin. This fertile yet dry area has been severely affected by mass propagations of <em>Ips typographus</em> due to extensive droughts since 2017, and consequently has suffered from substantial forest damage in recent years. The urgent need for action was realized and has expedited the search for more robust alternative species to ensure sustainable silviculture in the area.</p><p>The determination of suitable tree species is based on the identification of regions whose climatic conditions in the recent past are similar to those that are to be expected in the forest district of Horn in the future. To characterize these conditions, we consider 19 bioclimatic variables that are derived from monthly temperature and rainfall values. Using downscaled CMIP6 projections with a spatial resolution of 2.5 minutes, we determine future conditions in Horn throughout the 21st century. By employing 20-year periods from 2021 to 2100 for the scenarios SSP1-26, SSP2-45, SSP3-70 and SSP5-85,  and comparing them to worldwide past climate conditions, we obtain corresponding bioclimatic regions for four future time slices until the end of the century. The Euclidian distance is applied as measure of similarity, effectively yielding similarity maps on a continuous scale. In order to account for the spatial variability within the forest district, this procedure is performed for the colder northwest and the warmer southeast of the area, individually seeking similar bioclimatic regions for each of these two subregions. Results point to Eastern Europe as well as the Po Valley in northern Italy as areas exhibiting the highest similarity to the future climate in this North-Eastern part of Austria.</p>

Potential changes in monthly fire risk in the eastern Canadian boreal forest under future climate change

2009 ◽  
Vol 39 (12) ◽  
pp. 2369-2380 ◽  
Author(s):  
Héloïse Le Goff ◽  
Mike D. Flannigan ◽  
Yves Bergeron
Keyword(s):  
Climate Change ◽  
Forest Fires ◽  
Fire Risk ◽  
Weather Conditions ◽  
Future Climate ◽  
Fire Season ◽  
Future Climate Change ◽  
Change Scenario ◽  
Fire Weather Index ◽  
Fire Activity

The main objective of this paper is to evaluate whether future climate change would trigger an increase in the fire activity of the Waswanipi area, central Quebec. First, we used regression analyses to model the historical (1973–2002) link between weather conditions and fire activity. Then, we calculated Fire Weather Index system components using 1961–2100 daily weather variables from the Canadian Regional Climate Model for the A2 climate change scenario. We tested linear trends in 1961–2100 fire activity and calculated rates of change in fire activity between 1975–2005, 2030–2060, and 2070–2100. Our results suggest that the August fire risk would double (+110%) for 2100, while the May fire risk would slightly decrease (–20%), moving the fire season peak later in the season. Future climate change would trigger weather conditions more favourable to forest fires and a slight increase in regional fire activity (+7%). While considering this long-term increase, interannual variations of fire activity remain a major challenge for the development of sustainable forest management.

scholarly journals Hydrologic Regime Changes in a High-Latitude Glacierized Watershed under Future Climate Conditions

Water ◽  
2018 ◽  
Vol 10 (2) ◽  
pp. 128 ◽  
Author(s):  
Melissa Valentin ◽  
Terri Hogue ◽  
Lauren Hay
Keyword(s):  
High Latitude ◽  
Future Climate ◽  
Balance Model ◽  
Historical Period ◽  
Glacier Mass Balance ◽  
Hydrologic Regime ◽  
Climate Conditions ◽  
Snow Covered Area ◽  
Covered Area ◽  
Rcp 8.5

A calibrated conceptual glacio-hydrological monthly water balance model (MWBMglacier) was used to evaluate future changes in water partitioning in a high-latitude glacierized watershed in Southcentral Alaska under future climate conditions. The MWBMglacier was previously calibrated and evaluated against streamflow measurements, literature values of glacier mass balance change, and satellite-based observations of snow covered area, evapotranspiration, and total water storage. Output from five global climate models representing two future climate scenarios (RCP 4.5 and RCP 8.5) was used with the previously calibrated parameters to drive the MWBMglacier at 2 km spatial resolution. Relative to the historical period 1949–2009, precipitation will increase and air temperature in the mountains will be above freezing for an additional two months per year by mid-century which significantly impacts snow/rain partitioning and the generation of meltwater from snow and glaciers. Analysis of the period 1949–2099 reveals that numerous hydrologic regime shifts already occurred or are projected to occur in the study area including glacier accumulation area, snow covered area, and forest vulnerability. By the end of the century, Copper River discharge is projected to increase by 48%, driven by 21% more precipitation and 53% more glacial melt water (RCP 8.5) relative to the historical period (1949–2009).

scholarly journals Simulated Biomass, Climate Change Impacts, and Nitrogen Management to Achieve Switchgrass Biofuel Production at Diverse Sites in U.S.

Agronomy ◽  
2020 ◽  
Vol 10 (4) ◽  
pp. 503 ◽  
Author(s):  
Sumin Kim ◽  
Sojung Kim ◽  
Jaepil Cho ◽  
Seonggyu Park ◽  
Fernando Xavier Jarrín Perez ◽  
...  
Keyword(s):  
Climate Change ◽  
Panicum Virgatum ◽  
Warm Season ◽  
Field Trials ◽  
N Fertilization ◽  
N Fertilizer ◽  
Future Climate ◽  
Biofuel Production ◽  
Climate Conditions ◽  
Functional Relationships

Switchgrass (Panicum virgatum L.) is a C4, warm season, perennial native grass that has been strongly recommended as an ideal biofuel feedstock. Accurate forecasting of switchgrass yield across a geographically diverse region and under future climate conditions is essential for determining realistic future ethanol production from switchgrass. This study compiled a switchgrass database through reviewing the existing literature from field trials across the U.S. Using observed switchgrass data, a process-based model (ALMANAC) was developed. The ALMANAC simulation results showed that crop management had more effect on yield than location. The ALMANAC model consists of functional relationships that provide a better understanding of interactions among plant physiological processes and environmental factors (water, soil, climate, and nutrients) giving realistic predictions in different climate conditions. This model was used to quantify the impacts of climate change on switchgrass yields. Simulated lowland switchgrass would have more yield increases between Illinois and Ohio in future (2021–2050) under both Representative Concentration Pathway (RCP) 4.5 and 8.5 pathways with low N fertilizer inputs than high N fertilizer inputs. There was no significant effect of climate variability on upland simulated yields, which means that N fertilization is a key factor in controlling upland switchgrass yields under future climate conditions.

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