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.

Cotton Yield and Climate Change Adaptation in Pakistan: Application of Multinomial Endogenous Switching Regression Model

2021 ◽  
Vol 7 (3) ◽  
Author(s):  
Muhammad Masood Anwar ◽  
Aisha Siddiqua ◽  
Aftab Anwar ◽  
Jamshaid Ur Rehman
Keyword(s):  
Climate Change ◽  
Regression Model ◽  
Management Practices ◽  
Household Survey ◽  
Switching Regression ◽  
Cotton Yield ◽  
Sources Of Information ◽  
Cotton Production ◽  
Endogenous Switching Regression ◽  
Endogenous Switching

Purpose:Cotton is the backbone of Pakistan economy, as country is the 4th largest producer of cotton in the world. Despite this importance there is steep decline in cotton production over time due to climate change. The need to evaluate the potential of adaptation in improving cotton yield has necessitated this study. Design/Methodology/Approach:This study is based on the farm household survey of four cotton producing districts, two from each Punjab and Sindh that were purposively selected from heat stress regions of Pakistan. Data were analyzed through multinomial endogenous switching regression model and treatment effect framework. Findings:Farm management practices were evaluated for their significance in reducing adverse impacts of climatic extremes on cotton yield. Adaptation in the combination of first three strategies observed to be the most successful strategies in increasing yield. Implications/Originality/Value:For effective adaptation access to credit and extension, education, farming experience, and sources of information revealed to be important predictors

scholarly journals Application of LARS - WG downscaling model for building climate change scenarios in the Srepok watershed

2014 ◽  
Vol 17 (2) ◽  
pp. 108-122
Author(s):  
Khoi Nguyen Dao ◽  
Nhung Thi Hong Nguyen ◽  
Canh Thanh Truong
Keyword(s):  
Climate Change ◽  
General Circulation ◽  
Emission Scenario ◽  
Circulation Model ◽  
Climate Change Scenarios ◽  
Climate Variables ◽  
Climate Data ◽  
Policy Makers ◽  
Rain Gauges ◽  
Temperature And Precipitation

There are statistical downscaling methods such as: SDSM, LARS-WG, WGEN…, used to convert information on climate variables from the simulation results of General Circulation Model (GCM) to build climate change scenarios for local region. In this study, we used the LARS-WG model and HadCM3 GCM for two emission scenarios: B1 (low emission scenario) and A1B (medium emission scenario) to generate future scenarios for temperature and precipitation at meteorological stations and rain gauges in the Srepok watershed. The LARS-WG model was calibrated and validated against observed climate data for the period 1980-2009, and the calibrated LARS-WG was then used to generate future climate variables for the 2020s (2011-2030), 2055s (2046-2065), and 2090s (2080-2099). The climate change scenarios suggested that the climate in the study area will become warmer and drier in the future. The results obtained in this study could be useful for policy makers in planning climate change adaptation strategies for the study area.

scholarly journals Case study on the use of dynamically downscaled climate model data for assessing water security in the Lower Hunter region of the eastern seaboard of Australia

2016 ◽  
Vol 66 (2) ◽  
pp. 177
Author(s):  
Natalie Lockart ◽  
Garry Willgoose ◽  
George Kuczera ◽  
Anthony S. Kiem ◽  
AFM Kamal Chowdhury ◽  
...  
Keyword(s):  
Climate Change ◽  
General Circulation ◽  
Potential Evapotranspiration ◽  
Circulation Model ◽  
Water Security ◽  
Climate Modelling ◽  
Security Assessment ◽  
Large Role ◽  
Eastern Seaboard

A key aim of the Eastern Seaboard Climate Change Initiative (ESCCI) is under-standing the effect of climate change on the eastern seaboard of Australia, and the implications for climate change adaptation in this area. The New South Wales (NSW) / Australian Capital Territory (ACT) Regional Climate Modelling project (NARCliM) has produced three dynamically downscaled reanalysis climate datasets along with 12 downscaled general circulation model (GCM) projections of current (1990–2009) and future climate. It is expected that the NARCliM dataset will be used for many climate change impact studies including water security assessment. Therefore, in this study we perform a case study investigation into the usefulness and limitations of using NARCliM data for water security assessment, using the Lower Hunter urban water supply system managed by Hunter Water Corporation. We compare streamflow and reservoir levels simulated using NARCliM rainfall and a gridded historical rainfall dataset (AWAP) and focus our analysis on the differences in the simulated streamflow and reservoir levels. We show that when raw (i.e. not bias-corrected) NARCliM rainfall and potential evapotranspiration (PET) data is used to simulate streamflow and reservoir storage levels, some of the NARCliM datasets produce unrealistic results when compared with the simulations using AWAP; for example, some NARCliM datasets simulate reservoirs at or near empty while the AWAP reservoir simulations rarely drop below 60%. The bias-corrected NARCliM rainfall (corrected to AWAP) produces estimates of streamflow and reservoir levels that have a closer, but still inconsistent, match with the streamflow and reservoir levels simulated using AWAP directly. The inconsistency between the simulations using bias-corrected rainfall and historical AWAP rainfall is potentially because while bias-correction reduces systematic deviations it does not fix temporal rainfall sequencing issues. Additionally, the NARCliM PET is not bias-corrected and using bias-corrected rainfall with uncorrected PET in hydrological models results in physical inconsistencies in the rainfall-PET relationship and simulated streamflow. We demonstrate that rainfall plays a large role in the streamflow simulations, while PET seems to play a large role in the reasonableness of the simulated reservoir dynamics by determining the evaporation losses from the reservoirs. The downscaled GCM datasets that simulate the greatest average PET for 1990–2009 show reservoirs often (unrealistically) near empty. This study highlights the need to assess the validity of all climate data for the applications required, with a focus on long-term statistics for reservoir modelling and ensuring realism and coherence across all projected variables.

Bioclimatic approach to assessing the potential impact of climate change on two flea beetle (Coleoptera: Chrysomelidae) species in Canada

2017 ◽  
Vol 149 (5) ◽  
pp. 616-627 ◽  
Author(s):  
O. Olfert ◽  
R.M. Weiss ◽  
R.H. Elliott ◽  
J.J. Soroka
Keyword(s):  
Climate Change ◽  
General Circulation ◽  
Insect Pests ◽  
Flea Beetle ◽  
Circulation Model ◽  
Simulation Models ◽  
Economic Losses ◽  
Climate Change Scenarios ◽  
Climate Variables ◽  
The Impact

AbstractBoth the striped flea beetle, Phyllotreta striolata (Fabricius), and crucifer flea beetle, Phyllotreta cruciferae (Goeze) (Coleoptera: Chrysomelidae), are invasive alien species to North America. In western Canada, they are the most significant insect pests of cruciferous (Brassicaceae) crops. Climate is the one of the most dominant factors regulating the geographic distribution and population density of most insect species. Recent bioclimatic simulation models of the two flea beetle species fostered a better understanding of how the two species responded to selected climate variables. They demonstrated that selected climate variables increased population densities and geographic range of the two species. General circulation model inputs were applied in this study to assess the impact of a changing climate on the response of P. cruciferae and P. striolata populations. Model output, using the climate change scenarios, predicted that both P. cruciferae and P. striolata populations will shift north in future climates and the degree of geographic overlap between these two species will be greater than for current climate. This suggests that the two species could potentially cause economic losses over an expanded area in the future.

scholarly journals Cotton yield and Climate Change Adaptation in Pakistan: Application of Multinomial Endogenous Switching Regression Model

2021 ◽  
Vol 7 (3) ◽  
pp. 491-502
Author(s):  
Aisha Siddiqua ◽  
Aftab Anwar ◽  
Muhammad Masood Anwar ◽  
Jamshaid Ur Rehman
Keyword(s):  
Climate Change ◽  
Regression Model ◽  
Management Practices ◽  
Household Survey ◽  
Switching Regression ◽  
Cotton Yield ◽  
Sources Of Information ◽  
Cotton Production ◽  
Endogenous Switching Regression ◽  
Endogenous Switching

Purpose: Cotton is the backbone of Pakistan economy, as country is the 4th largest producer of cotton in the world. Despite this importance there is steep decline in cotton production over time due to climate change. The need to evaluate the potential of adaptation in improving cotton yield has necessitated this study. Design/Methodology/Approach: This study is based on the farm household survey of four cotton producing districts, two from each Punjab and Sindh that were purposively selected from heat stress regions of Pakistan. Data were analyzed through multinomial endogenous switching regression model and treatment effect framework. Findings: Farm management practices were evaluated for their significance in reducing adverse impacts of climatic extremes on cotton yield. Adaptation in the combination of first three strategies observed to be the most successful strategies in increasing yield. Implications/Originality/Value: For effective adaptation access to credit and extension, education, farming experience, and sources of information revealed to be important predictors

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.

“Modes of Variability” and Climate Change

2009 ◽  
Vol 22 (10) ◽  
pp. 2639-2658 ◽  
Author(s):  
Grant Branstator ◽  
Frank Selten
Keyword(s):  
Climate Change ◽  
Stochastic Model ◽  
General Circulation ◽  
Climate Changes ◽  
Circulation Model ◽  
Meridional Wind ◽  
Model Atmosphere ◽  
Linear Pattern ◽  
Modes Of Variability ◽  
Mean State

Abstract A 62-member ensemble of coupled general circulation model (GCM) simulations of the years 1940–2080, including the effects of projected greenhouse gas increases, is examined. The focus is on the interplay between the trend in the Northern Hemisphere December–February (DJF) mean state and the intrinsic modes of variability of the model atmosphere as given by the upper-tropospheric meridional wind. The structure of the leading modes and the trend are similar. Two commonly proposed explanations for this similarity are considered. Several results suggest that this similarity in most respects is consistent with an explanation involving patterns that result from the model dynamics being well approximated by a linear system. Specifically, the leading intrinsic modes are similar to the leading modes of a stochastic model linearized about the mean state of the GCM atmosphere, trends in GCM tropical precipitation appear to excite the leading linear pattern, and the probability density functions (PDFs) of prominent circulation patterns are quasi-Gaussian. There are, on the other hand, some subtle indications that an explanation for the similarity involving preferred states (which necessarily result from nonlinear influences) has some relevance. For example, though unimodal, PDFs of prominent patterns have departures from Gaussianity that are suggestive of a mixture of two Gaussian components. And there is some evidence of a shift in probability between the two components as the climate changes. Interestingly, contrary to the most prominent theory of the influence of nonlinearly produced preferred states on climate change, the centroids of the components also change as the climate changes. This modification of the system’s preferred states corresponds to a change in the structure of its dominant patterns. The change in pattern structure is reproduced by the linear stochastic model when its basic state is modified to correspond to the trend in the general circulation model’s mean atmospheric state. Thus, there is a two-way interaction between the trend and the modes of variability.

scholarly journals Climate Change Patterns of Wild Blueberry Fields in Downeast, Maine over the Past 40 Years

Water ◽  
2021 ◽  
Vol 13 (5) ◽  
pp. 594
Author(s):  
Rafa Tasnim ◽  
Francis Drummond ◽  
Yong-Jiang Zhang
Keyword(s):  
Climate Change ◽  
Vegetation Index ◽  
Potential Evapotranspiration ◽  
Growing Season ◽  
Spatial Average ◽  
Climate Variables ◽  
Threshold Values ◽  
The Past ◽  
Wild Blueberry ◽  
Crop Health

Maine, USA is the largest producer of wild blueberries (Vaccinium angustifolium Aiton), an important native North American fruit crop. Blueberry fields are mainly distributed in coastal glacial outwash plains which might not experience the same climate change patterns as the whole region. It is important to analyze the climate change patterns of wild blueberry fields and determine how they affect crop health so fields can be managed more efficiently under climate change. Trends in the maximum (Tmax), minimum (Tmin) and average (Tavg) temperatures, total precipitation (Ptotal), and potential evapotranspiration (PET) were evaluated for 26 wild blueberry fields in Downeast Maine during the growing season (May–September) over the past 40 years. The effects of these climate variables on the Maximum Enhanced Vegetation Index (EVImax) were evaluated using Remote Sensing products and Geographic Information System (GIS) tools. We found differences in the increase in growing season Tmax, Tmin, Tavg, and Ptotal between those fields and the overall spatial average for the region (state of Maine), as well as among the blueberry fields. The maximum, minimum, and average temperatures of the studied 26 wild blueberry fields in Downeast, Maine showed higher rates of increase than those of the entire region during the last 40 years. Fields closer to the coast showed higher rates of warming compared with the fields more distant from the coast. Consequently, PET has been also increasing in wild blueberry fields, with those at higher elevations showing lower increasing rates. Optimum climatic conditions (threshold values) during the growing season were explored based on observed significant quadratic relationships between the climate variables (Tmax and Ptotal), PET, and EVImax for those fields. An optimum Tmax and PET for EVImax at 22.4 °C and 145 mm/month suggest potential negative effects of further warming and increasing PET on crop health and productivity. These climate change patterns and associated physiological relationships, as well as threshold values, could provide important information for the planning and development of optimal management techniques for wild blueberry fields experiencing climate change.

scholarly journals Meteorological modes of variability for fine particulate matter (PM2.5) air quality in the United States: implications for PM2.5 sensitivity to climate change

2012 ◽  
Vol 12 (6) ◽  
pp. 3131-3145 ◽  
Author(s):  
A. P. K. Tai ◽  
L. J. Mickley ◽  
D. J. Jacob ◽  
E. M. Leibensperger ◽  
L. Zhang ◽  
...  
Keyword(s):  
Climate Change ◽  
General Circulation ◽  
Transport Model ◽  
Circulation Model ◽  
Multiple Linear Regression Model ◽  
The United States ◽  
Chemical Transport Model ◽  
Modes Of Variability ◽  
The Us ◽  
Cyclone Frequency

Abstract. We applied a multiple linear regression model to understand the relationships of PM2.5 with meteorological variables in the contiguous US and from there to infer the sensitivity of PM2.5 to climate change. We used 2004–2008 PM2.5 observations from ~1000 sites (~200 sites for PM2.5 components) and compared to results from the GEOS-Chem chemical transport model (CTM). All data were deseasonalized to focus on synoptic-scale correlations. We find strong positive correlations of PM2.5 components with temperature in most of the US, except for nitrate in the Southeast where the correlation is negative. Relative humidity (RH) is generally positively correlated with sulfate and nitrate but negatively correlated with organic carbon. GEOS-Chem results indicate that most of the correlations of PM2.5 with temperature and RH do not arise from direct dependence but from covariation with synoptic transport. We applied principal component analysis and regression to identify the dominant meteorological modes controlling PM2.5 variability, and show that 20–40% of the observed PM2.5 day-to-day variability can be explained by a single dominant meteorological mode: cold frontal passages in the eastern US and maritime inflow in the West. These and other synoptic transport modes drive most of the overall correlations of PM2.5 with temperature and RH except in the Southeast. We show that interannual variability of PM2.5 in the US Midwest is strongly correlated with cyclone frequency as diagnosed from a spectral-autoregressive analysis of the dominant meteorological mode. An ensemble of five realizations of 1996–2050 climate change with the GISS general circulation model (GCM) using the same climate forcings shows inconsistent trends in cyclone frequency over the Midwest (including in sign), with a likely decrease in cyclone frequency implying an increase in PM2.5. Our results demonstrate the need for multiple GCM realizations (because of climate chaos) when diagnosing the effect of climate change on PM2.5, and suggest that analysis of meteorological modes of variability provides a computationally more affordable approach for this purpose than coupled GCM-CTM studies.

scholarly journals Impact of climate change on sediment yield in the Mekong River basin: a case study of the Nam Ou basin, Lao PDR

2013 ◽  
Vol 17 (1) ◽  
pp. 1-20 ◽  
Author(s):  
B. Shrestha ◽  
M. S. Babel ◽  
S. Maskey ◽  
A. van Griensven ◽  
S. Uhlenbrook ◽  
...  
Keyword(s):  
Climate Change ◽  
Sediment Yield ◽  
General Circulation ◽  
Climate Models ◽  
Greenhouse Gas Emission ◽  
Circulation Model ◽  
Sediment Flux ◽  
Seasonal Temperature ◽  
Impact Of Climate Change ◽  
The Impact

Abstract. This paper evaluates the impact of climate change on sediment yield in the Nam Ou basin located in northern Laos. Future climate (temperature and precipitation) from four general circulation models (GCMs) that are found to perform well in the Mekong region and a regional circulation model (PRECIS) are downscaled using a delta change approach. The Soil and Water Assessment Tool (SWAT) is used to assess future changes in sediment flux attributable to climate change. Results indicate up to 3.0 °C shift in seasonal temperature and 27% (decrease) to 41% (increase) in seasonal precipitation. The largest increase in temperature is observed in the dry season while the largest change in precipitation is observed in the wet season. In general, temperature shows increasing trends but changes in precipitation are not unidirectional and vary depending on the greenhouse gas emission scenarios (GHGES), climate models, prediction period and season. The simulation results show that the changes in annual stream discharges are likely to range from a 17% decrease to 66% increase in the future, which will lead to predicted changes in annual sediment yield ranging from a 27% decrease to about 160% increase. Changes in intra-annual (monthly) discharge as well as sediment yield are even greater (−62 to 105% in discharge and −88 to 243% in sediment yield). A higher discharge and sediment flux are expected during the wet seasons, although the highest relative changes are observed during the dry months. The results indicate high uncertainties in the direction and magnitude of changes of discharge as well as sediment yields due to climate change. As the projected climate change impact on sediment varies remarkably between the different climate models, the uncertainty should be taken into account in both sediment management and climate change adaptation.

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