scholarly journals Mitigating future climate change effects on wheat and soybean yields in Central region of Madhya Pradesh by shifting sowing dates

2021 ◽  
Vol 21 (4) ◽  
pp. 468-473
Author(s):  
Ankit Balvanshi ◽  
H. L. Tiwari
Keyword(s):  
Climate Change ◽  
General Circulation ◽  
Crop Yields ◽  
Circulation Model ◽  
Planting Date ◽  
Madhya Pradesh ◽  
Coefficient Of Determination ◽  
Future Climate Change ◽  
Yield Model ◽  
Adaptation Measure

The present work focuses on (1) estimation of future yield of wheat and soybean crop under RCPs scenario 2.6, 4.5 and 8.5 for years 2020, 2050 and 2080 using FAO AquaCrop yield simulating model and (2) assessment of shifting planting date as adaptation measure to mitigate climate change impact for Sehore district, Madhya Pradesh. Statistically downscaled General Circulation Model CanESM2 data was used as input to AquaCrop for generation of future data. The AquaCrop yield model was first checked for its suitability and accuracy in prediction of yield for years 2000–2015, model nash sutcliffe efficiency 0.79, 0.84, RMSE 300.7, 104.4 and coefficient of determination (R2) 0.91, 0.88 were obtained for wheat and soybean crops, respectively. The results depicts that RCP 8.5 shows the highest impact with reduction in wheat and soybean yield for projected year 2080. Under the changed climate, shifting planting date from of wheat from 15th November to 30th November and 1st July to 10th July for soybean resulted in least decline in crop yields and surfaced as a practical adaptation measure for sustaining future yields.

scholarly journals Assessing Future Impacts of Climate Change on Streamflow within the Alabama River Basin

Climate ◽  
2021 ◽  
Vol 9 (4) ◽  
pp. 55
Author(s):  
Joseph E. Quansah ◽  
Amina B. Naliaka ◽  
Souleymane Fall ◽  
Ramble Ankumah ◽  
Gamal El Afandi
Keyword(s):  
Climate Change ◽  
River Basin ◽  
21St Century ◽  
General Circulation ◽  
Goodness Of Fit ◽  
Circulation Model ◽  
Coefficient Of Determination ◽  
Future Climate Change ◽  
Model Version ◽  
Monthly Streamflow

Global climate change is expected to impact future precipitation and surface temperature trends and could alter local hydrologic systems. This study assessed the likely hydrologic responses and changes in streamflow due to future climate change within the Alabama River Basin (ARB) for the mid-21st century 2045 (“2030–2060”) and end-21st century 2075 (“2060–2090”). Using an integrated modeling approach, General Circulation Model (GCM) datasets; the Centre National de Recherches Météorologiques Climate Model 5 (CNRM-CM5), the Community Earth System Model, version 1–Biogeochemistry (CESM1- BGC.1), and the Hadley Centre Global Environment Model version 2 (HADGEM2-AO.1), under medium Representative Concentration Pathway (RCP) 4.5, and based on World Climate Research Program (WCRP)’s Couple Model Intercomparison Phase 5 (CMIP5), were assimilated into calibrated Soil and Water Assessment Tool (SWAT). Mann–Kendall and Theil Sen’s slope were used to assess the trends and magnitude of variability of the historical climate data used for setting up the model. The model calibration showed goodness of fit with minimum Nash–Sutcliffe Efficiency (NSE) coefficient values of 0.83 and Coefficient of Determination (R2) of 0.88 for the three gages within the ARB. Next, the research assessed changes in streamflow for the years 2045 and 2075 against that of the reference baseline year of 1980. The results indicate situations of likely increase and decrease in mean monthly streamflow discharge and increase in the frequency and variability in peak flows during the periods from the mid to end of the 21st century. Seasonally, monthly streamflow increases between 50% and 250% were found for spring and autumn months with decreases in summer months for 2045. Spring and summer months for 2075 resulted in increased monthly streamflow between 50% and 300%, while autumn and spring months experienced decreased streamflow. While the results are prone to inherent uncertainties in the downscaled GCM data used, the simulated dynamics in streamflow and water availability provide critical information for stakeholders to develop sustainable water management and climate change adaptation options for the ARB.

scholarly journals Evaluation of Climate Change Impacts on Cotton Yield using Cropsyst and Regression Models

2018 ◽  
Vol 8 ◽  
pp. 1433-1451 ◽  
Author(s):  
Pantazis Georgiou ◽  
Panagiota Koukouli
Keyword(s):  
Climate Change ◽  
Regression Models ◽  
General Circulation ◽  
Management Practices ◽  
Potential Evapotranspiration ◽  
Circulation Model ◽  
Planting Date ◽  
Climate Variables ◽  
Cotton Yield ◽  
Cotton Production

The regional as well as the international crop production is expected to be influenced by climate change. This study describes an assessment of simulated potential cotton yield using CropSyst, a cropping systems simulation model, in Northern Greece. CropSyst was used under the General Circulation Model CGCM3.1/T63 of the climate change scenario SRES B1 for time periods of climate change 2020-2050 and 2070-2100 for two planting dates. Additionally, an appraisal of the relationship between climate variables, potential evapotranspiration and cotton yield was done based on regression models. Multiple linear regression models based on climate variables and potential evapotranspiration could be used as a simple tool for the prediction of crop yield changes in response to climate change in the future. The CropSyst simulation under SRES B1, resulted in an increase by 6% for the period 2020-2050 and a decrease by about 15% in cotton yield for 2070-2100. For the earlier planting date a higher increase and a slighter reduction was observed in cotton yield for 2020-2050 and 2070-2100, respectively. The results indicate that alteration of crop management practices, such as changing the planting date could be used as potential adaptation measures to address the impacts of climate change on cotton production.

Reevaluation of Design Waves Off the Western Indian Coast Considering Climate Change

2016 ◽  
Vol 50 (1) ◽  
pp. 88-98 ◽  
Author(s):  
Pentapati Satyavathi ◽  
Makarand C. Deo ◽  
Jyoti Kerkar ◽  
Ponnumony Vethamony
Keyword(s):  
Climate Change ◽  
Global Warming ◽  
General Circulation ◽  
Generalized Pareto Distribution ◽  
Circulation Model ◽  
Offshore Structures ◽  
Future Climate Change ◽  
Extreme Value Analysis ◽  
Value Analysis ◽  
Wave Data

AbstractKnowledge of design waves with long return periods forms an essential input to many engineering applications, including structural design and analysis. Such extreme or long-term waves are conventionally evaluated using observed or hindcast historical wave data. Globally, waves are expected to undergo future changes in magnitude and behavior as a result of climate change induced by global warming. Considering future climate change, this study attempts to reevaluate significant wave height (Hs) as well as average spectral wave period (Tz) with a return period of 100 years for a series of locations along the western Indian coastline. Historical waves are simulated using a numerical wave model forced by wind data extracted from the archives of the National Center for Environmental Prediction and the National Center for Atmospheric Research, while future wave data are generated by a state-of-the-art Canadian general circulation model. A statistical extreme value analysis of past and projected wave data carried out with the help of the generalized Pareto distribution showed an increase in 100-year Hs and Tz along the Indian coastline, pointing out the necessity to reconsider the safety of offshore structures in the light of global warming.

scholarly journals Impact of GCM structure uncertainty on hydrological processes in an arid area of China

2017 ◽  
Vol 49 (3) ◽  
pp. 893-907 ◽  
Author(s):  
Gonghuan Fang ◽  
Jing Yang ◽  
Yaning Chen ◽  
Zhi Li ◽  
Philippe De Maeyer
Keyword(s):  
Climate Change ◽  
General Circulation ◽  
Assessment Tool ◽  
Circulation Model ◽  
Hydrological Processes ◽  
Future Climate Change ◽  
Management Decision ◽  
Structural Uncertainty ◽  
Rcp Scenarios ◽  
The Impact

Abstract Quantifying the uncertainty sources in assessment of climate change impacts on hydrological processes is helpful for local water management decision-making. This paper investigated the impact of the general circulation model (GCM) structural uncertainty on hydrological processes in the Kaidu River Basin. Outputs of 21 GCMs from the Coupled Model Intercomparison Project Phase 5 (CMIP5) under two representative concentration pathway (RCP) scenarios (i.e., RCP4.5 and RCP8.5), representing future climate change under uncertainty, were first bias-corrected using four precipitation and three temperature methods and then used to force a well-calibrated hydrological model (the Soil and Water Assessment Tool, SWAT) in the study area. Results show that the precipitation will increase by 3.1%–18% and 7.0%–22.5%, the temperature will increase by 2.0 °C–3.3 °C and 4.2 °C–5.5 °C and the streamflow will change by −26% to 3.4% and −38% to −7% under RCP4.5 and RCP8.5, respectively. Timing of snowmelt will shift forward by approximately 1–2 months for both scenarios. Compared to RCPs and bias correction methods, GCM structural uncertainty contributes most to streamflow uncertainty based on the standard deviation method (55.3%) while it is dominant based on the analysis of variance approach (94.1%).

Linkage between Biodiversity, Land Use Informatics and Climate Change

2011 ◽  
pp. 1-22
Author(s):  
Yongyut Trisurat ◽  
Rajendra P. Shrestha ◽  
Rob Alkemade
Keyword(s):  
Climate Change ◽  
Land Use ◽  
General Circulation ◽  
Biological Diversity ◽  
Circulation Model ◽  
Biodiversity Loss ◽  
Historical Background ◽  
Future Climate Change ◽  
Change Models ◽  
Living Organisms

Biodiversity is the variety and variability among living organisms and ecological complexes in which they occur, and it can be divided into three levels – gene, species and ecosystems. Biodiversity is an essential component of human development and security in terms of proving ecosystem services, but also it is important for its own right to exist in the globe. Failure to conserve and use biological diversity in a sustainable manner would result in degrading environments, new and more rampant illnesses, deepening poverty and a continued pattern of inequitable and untenable growth. This chapter provides a coherent presentation of the essential concepts, key terminology, historical background of biodiversity, and drivers to biodiversity loss, especially land use/land cover and climate change. A number of land use change models and a general circulation model for prediction of future climate change and its effects on individuals, populations, species, and ecosystems are briefly described. The chapter also introduces the structure of the book including summaries of each chapter.

scholarly journals Assessing the climate change impact on hydrological response in the Gorganrood River Basin, Iran

2017 ◽  
Vol 9 (3) ◽  
pp. 421-433 ◽  
Author(s):  
Hamed Rouhani ◽  
Marayam Sadat Jafarzadeh
Keyword(s):  
Climate Change ◽  
River Basin ◽  
General Circulation ◽  
Assessment Tool ◽  
Circulation Model ◽  
Low Flow ◽  
Future Climate Change ◽  
Monthly Precipitation ◽  
Climate Change Scenarios ◽  
Annual Scale

Abstract A general circulation model (GCM) and hydrological model SWAT (Soil and Water Assessment Tool) under forcing from A1B, B1, and A2 emission scenarios by 2030 were used to assess the implications of climate change on water balance of the Gorganrood River Basin (GRB). The results of MPEH5C models and multi-scenarios indicated that monthly precipitation generally decreases while temperature increases in various parts of the basin with the magnitude of the changes in terms of different stations and scenarios. Accordingly, seasonal ET will decrease throughout the GRB over the 2020s in all seasons except in summer, where a slight increase is projected for A1B and A2 scenarios. At annual scale, average quick flow and average low flow under the B1, A1B, and A2 scenarios are projected to decrease by 7.3 to 12.0% from the historical levels. Over the ensembles of climate change scenarios, the simulations project average autumn total flow declines of ∼10% and an overall range of 6.9 to 13.2%. In summer, the components of flow at the studied basin are expected to increase under A2 and A1B scenarios but will slightly decrease under B1 scenario. The study result addresses a likelihood of inevitable future climate change.

Evaluation of climate change impacts and adaptation strategies for maize cultivation in the Himalayan foothills of India

2015 ◽  
Vol 6 (3) ◽  
pp. 596-614 ◽  
Author(s):  
Proloy Deb ◽  
Anthony S. Kiem ◽  
Mukand S. Babel ◽  
Sang Thi Chu ◽  
Biplab Chakma
Keyword(s):  
Climate Change ◽  
General Circulation ◽  
Circulation Model ◽  
Maize Yield ◽  
Future Climate ◽  
Future Climate Change ◽  
Daily Maximum ◽  
Study Region ◽  
Supplementary Irrigation ◽  
The Future

This study evaluates the impacts of climate change on rainfed maize (Zea mays) yield and evaluates different agro-adaptation measures to counteract its negative impacts at Sikkim, a Himalayan state of India. Future climate scenarios for the 10 years centered on 2025, 2055 and 2085 were obtained by downscaling the outputs of the HadCM3 General Circulation Model (GCM) under for A2 and B2 emission scenarios. HadCM3 was chosen after assessing the performance analysis of six GCMs for the study region. The daily maximum and minimum temperatures are projected to rise in the future and precipitation is projected to decrease (by 1.7 to 22.6% relative to the 1991–2000 baseline) depending on the time period and scenarios considered. The crop simulation model CERES-Maize was then used to simulate maize yield under future climate change for the future time windows. Simulation results show that climate change could reduce maize productivity by 10.7–18.2%, compared to baseline yield, under A2 and 6.4–12.4% under B2 scenarios. However, the results also indicate that the projected decline in maize yield could be offset by early planting of seeds, lowering the farm yard manure application rate, introducing supplementary irrigation and shifting to heat tolerant varieties of maize.

scholarly journals Climate Variability and Corn Yields in Semiarid Ceará, Brazil

2007 ◽  
Vol 46 (2) ◽  
pp. 226-240 ◽  
Author(s):  
Liqiang Sun ◽  
Huilan Li ◽  
M. Neil Ward ◽  
David F. Moncunill
Keyword(s):  
General Circulation ◽  
Goodness Of Fit ◽  
Crop Yields ◽  
Circulation Model ◽  
Coefficient Of Determination ◽  
Climate Index ◽  
Corn Yield ◽  
Climate Variables ◽  
Potential Predictability ◽  
Weather Index

Abstract Understanding of climate influence on crop yields can help in the design of policies to reduce climate-related vulnerability in many parts of the world, including the target of this case study—the state of Ceará, Brazil. The study has examined the relationships between climate variations and corn yields and, in addition, has estimated the potential predictability of corn yields in Ceará drawing on the now well-established seasonal predictability of the region’s climate based on prevailing patterns of sea surface temperature (SST), especially in the tropical Atlantic and tropical Pacific Oceans. The relationships between corn yields and climate variables have been explored using observed data for the period of 1952–2001. A linear regression–based corn-yield model was evaluated by comparing the model-simulated yields with the observations using three goodness-of-fit measures: the coefficient of determination, the index of agreement, and the mean absolute error. A comparative performance analysis was carried out on several climate variables to determine the most appropriate climate index for simulating corn yields in Ceará. A weather index was defined to measure the severity of drought and flooding conditions in the growing season for corn. The analysis indicated that the weather index is the best climate parameter for simulating corn yields in Ceará. The observed weather index can explain 56.8% of the variance of the observed corn yields. High potential predictability of the weather index was revealed by the evaluation of an ensemble of 10 runs with the NCEP Regional Spectral Model nested into the ECHAM4.5 atmospheric general circulation model, driven with observed SSTs in each season for the period of 1971–2000. Whereas these runs are based on the actual observed SST pattern in each season, other studies have shown that persistence of SST over several months is sufficient for a true predictive capability. The aim here was to show that the SST-forced component of climate variation does translate into the weather features that are important for crop yields. Indeed, the results demonstrate the striking extent to which the year-to-year changes in SST force local climate characteristics that can specify the year-to-year variations in corn yields. The variance of corn yield explained by the SST-driven model was 49.5%.

scholarly journals Pastoral suitability driven by future climate change along the Apennines

2015 ◽  
Vol 10 (3) ◽  
pp. 109 ◽  
Author(s):  
Camilla Dibari ◽  
Giovanni Argenti ◽  
Francesco Catolfi ◽  
Marco Moriondo ◽  
Nicolina Staglianò ◽  
...  
Keyword(s):  
Climate Change ◽  
Random Forest ◽  
General Circulation ◽  
Circulation Model ◽  
Maximum Temperature ◽  
Future Climate Change ◽  
Machine Learning Model ◽  
Land Cover Map ◽  
Gis Environment ◽  
Sres Scenarios

This work aims at evaluating the impacts of climate change on pastoral resources located along the Apennines chain. To this end, random forest machine learning model was first calibrated for the present period and then applied to future conditions, as projected by HadCM3 general circulation model, in order to simulate possible spatial variation/shift of pastoral areas in two time slices (centred on 2050 and 2080) under A2 and B2 SRES scenarios. Pre-existent spatial database, namely Corine land cover map and WorldClim, were integrated and harmonised in a GIS environment in order to extract climate variables (mean seasonal precipitation, mean maximum temperature of the warmest month and minimum temperature of the coldest month) and response variables (presence/absence of pastures) to be used as model predictors. Random forest model resulted robust and coherent to simulate pastureland suitability under current climatology (classification accuracy error=19%). Accordingly, results indicated that increases in temperatures coupled with decreases in precipitation, as simulated by HadCM3 in the future, would have impacts of great concern on potential pasture distribution. In the specific, an overall decline of pasturelands suitability is predicted by the middle of the century in both A2 (–46%) and B2 (–41%) along the entire chain. However, despite alarming reductions in pastures suitability along the northern (–69% and –71% under A2 and B2 scenarios, respectively) and central Apennines (–90% under both scenarios) by the end of the century, expansions are predicted along the southern areas of the chain (+96% and +105% under A2 and B2 scenarios, respectively). This may be probably due to expansions in pastures dominated by xeric and thermophiles species, which will likely benefit from warmer and drier future conditions predicted in the southern zone of the chain by the HadCM3. Hence, the expected climate, coupled with an increasing abandonment of the traditional grazing practices, will likely threat grassland biodiversity as well as pastoral potential distribution currently dominating the Apennines chain.

The Arctic cryosphere in the Mid-Pliocene and the future

2008 ◽  
Vol 367 (1886) ◽  
pp. 49-67 ◽  
Author(s):  
Daniel J Lunt ◽  
Alan M Haywood ◽  
Gavin L Foster ◽  
Emma J Stone
Keyword(s):  
Climate Change ◽  
General Circulation ◽  
Ice Sheet ◽  
Greenland Ice Sheet ◽  
Circulation Model ◽  
Future Climate ◽  
Arctic Climate ◽  
The Arctic ◽  
Future Climate Change ◽  
The Future

The Mid-Pliocene ( ca 3 Myr ago) was a relatively warm period, with increased atmospheric CO 2 relative to pre-industrial. It has therefore been highlighted as a possible palaeo-analogue for the future. However, changed vegetation patterns, orography and smaller ice sheets also influenced the Mid-Pliocene climate. Here, using a general circulation model and ice-sheet model, we determine the relative contribution of vegetation and soils, orography and ice, and CO 2 to the Mid-Pliocene Arctic climate and cryosphere. Compared with pre-industrial, we find that increased Mid-Pliocene CO 2 contributes 35 per cent, lower orography and ice-sheet feedbacks contribute 42 per cent, and vegetation changes contribute 23 per cent of Arctic temperature change. The simulated Mid-Pliocene Greenland ice sheet is substantially smaller than that of modern, mostly due to the higher CO 2 . However, our simulations of future climate change indicate that the same increase in CO 2 is not sufficient to melt the modern ice sheet substantially. We conclude that, although the Mid-Pliocene resembles the future in some respects, care must be taken when interpreting it as an exact analogue due to vegetation and ice-sheet feedbacks. These act to intensify Mid-Pliocene Arctic climate change, and act on a longer time scale than the century scale usually addressed in future climate prediction.

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