Effect of changing climate on rice water requirement in Guilan, north of Iran

In this study, the effectiveness of 15 global climate models (GCMs) for simulating weather data of Rasht synoptic station in the north of Iran was evaluated using a statistical downscaling approach. Downscaling of GCMs was performed using a stochastic weather generator model (LARS-WG5.5) and the best GCM (INCM3) was selected. The parameters such as precipitation, radiation, temperature and reference evapotranspiration were simulated using the selected GCMs for two periods of 2013–2042 and 2043–2072, and accordingly, the rice water requirement was estimated for the coming periods. Then, simulated results were compared with data in the baseline period (1981–2010). The results showed that reference evapotranspiration (ETo) for all the seasons will increase in the coming periods. The highest ETo increase (18.5–23.7 mm month–1) will occur in the spring. Also, the average rice water requirement will increase between 178 and 572 m3 ha–1 depending on the emission scenarios and future studied periods. The incremental changes in ETo and, consequently, in rice water requirement for the coming periods will occur as a result of the significant increase in temperature. The results of this study can be used by local planners as a correct view of water demand in the future.

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PreciPatch: A Dictionary-based Precipitation Downscaling Method

Remote Sensing ◽

10.3390/rs12061030 ◽

2020 ◽

Vol 12 (6) ◽

pp. 1030 ◽

Cited By ~ 1

Author(s):

Mengchao Xu ◽

Qian Liu ◽

Dexuan Sha ◽

Manzhu Yu ◽

Daniel Q. Duffy ◽

...

Keyword(s):

Climate Models ◽

Global Climate ◽

Super Resolution ◽

Global Climate Models ◽

Weather Data ◽

Downscaling Method ◽

Spatially Distributed ◽

Local Impact ◽

Global And Local ◽

Precipitation Events

Climate and weather data such as precipitation derived from Global Climate Models (GCMs) and satellite observations are essential for the global and local hydrological assessment. However, most climatic popular precipitation products (with spatial resolutions coarser than 10km) are too coarse for local impact studies and require “downscaling” to obtain higher resolutions. Traditional precipitation downscaling methods such as statistical and dynamic downscaling require an input of additional meteorological variables, and very few are applicable for downscaling hourly precipitation for higher spatial resolution. Based on dynamic dictionary learning, we propose a new downscaling method, PreciPatch, to address this challenge by producing spatially distributed higher resolution precipitation fields with only precipitation input from GCMs at hourly temporal resolution and a large geographical extent. Using aggregated Integrated Multi-satellitE Retrievals for GPM (IMERG) data, an experiment was conducted to evaluate the performance of PreciPatch, in comparison with bicubic interpolation using RainFARM—a stochastic downscaling method, and DeepSD—a Super-Resolution Convolutional Neural Network (SRCNN) based downscaling method. PreciPatch demonstrates better performance than other methods for downscaling short-duration precipitation events (used historical data from 2014 to 2017 as the training set to estimate high-resolution hourly events in 2018).

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Utilization of Climate Files Predicting Future Weather in Dynamic Building Performance Simulation – A review

Journal of Physics Conference Series ◽

10.1088/1742-6596/2069/1/012070 ◽

2021 ◽

Vol 2069 (1) ◽

pp. 012070

Author(s):

C N Nielsen ◽

J Kolarik

Keyword(s):

Climate Change ◽

Climate Models ◽

Dynamical Downscaling ◽

Global Climate ◽

Global Climate Models ◽

Weather Data ◽

Building Performance ◽

Comprehensive Overview ◽

Local Climate ◽

The Impact

Abstract As the climate is changing and buildings are designed with a life expectancy of 50+ years, it is sensible to take climate change into account during the design phase. Data representing future weather are needed so that building performance simulations can predict the impact of climate change. Currently, this usually requires one year of weather data with a temporal resolution of one hour, which represents local climate conditions. However, both the temporal and spatial resolution of global climate models is generally too coarse. Two general approaches to increase the resolution of climate models - statistical and dynamical downscaling have been developed. They exist in many variants and modifications. The present paper aims to provide a comprehensive overview of future weather application as well as critical insights in the model and method selection. The results indicate a general trend to select the simplest methods, which often involves a compromise on selecting climate models.

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Maize productivity analysis in response to climate change under different nitrogen management strategies

Journal of Agrometeorology ◽

10.54386/jam.v23i3.29 ◽

2021 ◽

Vol 23 (3) ◽

pp. 279-285

Author(s):

NISHANT K SINHA ◽

M. MOHANTY ◽

J. SOMASUNDARAM ◽

R. S. CHAUDHARY ◽

H. PATRA ◽

...

Keyword(s):

Management Practices ◽

Climate Models ◽

Global Climate ◽

N Uptake ◽

Management Strategies ◽

Central India ◽

Maize Yield ◽

Baseline Period ◽

Global Climate Models ◽

Productivity Analysis

The evaluation of climatic change impact on maize grain and biomass yield under different N management practices through a well-calibrated and validated APSIM model in Vertisol of central India has been made. Climate scenarios were derived from seven global climate models (GCM) for two representative concentration pathways (RCPs), i.e. RCP4.5 and RCP8.5, and two-time slices, i.e. 2050 and 2080. The five N scenarios, namely N0%, N50%, N100%, N150%, and 100% organic, were studied in different climatic scenarios. The probability of exceedance showed that N0%, N50%, N100%, N150%, and 100% organic treatments have a 50% chance of yield greater than 1.0, 3.40, 4.20, 4.45 and 3.84 t ha-1, respectively. The average reduction of maize yield was -44.4, -20, -19.7 - 17.9 and 22.5 per cent in N 0%, N 50%, N 100%, N 150%, 100% organic, respectively under RCP4.5 over the baseline period (1980-2010). For RCP8.5, the average reduction of maize yield in N 0%, N 50%, N 100%, N 150%, 100% organic was 41.2, 21.2, 20.8 20.6 and 23.1 per cent, respectively. Simulation results suggested that a higher decrease of maize yield in 100 per cent organic treatments than inorganic treatments is due to variability in N uptake.

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Blue Water in Europe: Estimates of Current and Future Availability and Analysis of Uncertainty

Water ◽

10.3390/w11030420 ◽

2019 ◽

Vol 11 (3) ◽

pp. 420 ◽

Cited By ~ 5

Author(s):

Alvaro Sordo-Ward ◽

Isabel Granados ◽

Ana Iglesias ◽

Luis Garrote

Keyword(s):

High Resolution ◽

Water Availability ◽

Climate Models ◽

Climate Model ◽

Global Climate ◽

Baseline Period ◽

Global Climate Models ◽

Blue Water ◽

Large Area

This study presents a regional assessment of future blue water availability in Europe under different assumptions. The baseline period (1960 to 1999) is compared to the near future (2020 to 2059) and the long-term future (2060 to 2099). Blue water availability is estimated as the maximum amount of water supplied at a certain point of the river network that satisfies a defined demand, taking into account specified reliability requirements. Water availability is computed with the geospatial high-resolution Water Availability and Adaptation Policy Assessment (WAAPA) model. The WAAPA model definition for this study extends over 6 million km2 in Europe and considers almost 4000 sub-basins in Europe. The model takes into account 2300 reservoirs larger than 5 hm3, and the dataset of Hydro 1k with 1700 sub-basins. Hydrological scenarios for this study were taken from the Inter-Sectoral Impact Model Inter-Comparison Project and included simulations of five global climate models under different Representative Concentration Pathways scenarios. The choice of method is useful for evaluating large area regional studies that include high resolution on the systems´ characterization. The results highlight large uncertainties associated with a set of local water availability estimates across Europe. Climate model uncertainties for mean annual runoff and potential water availability were found to be higher than scenario uncertainties. Furthermore, the existing hydraulic infrastructure and its management have played an important role by decoupling water availability from hydrologic variability. This is observed for all climate models, the emissions scenarios considered, and for near and long-term future. The balance between water availability and withdrawals is threatened in some regions, such as the Mediterranean region. The results of this study contribute to defining potential challenges in water resource systems and regional risk areas.

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Projected climate changes in four different regions in Colombia

ENVIRONMENTAL SYSTEMS RESEARCH ◽

10.1186/s40068-019-0161-1 ◽

2019 ◽

Vol 8 (1) ◽

Cited By ~ 1

Author(s):

Oscar D. Molina ◽

Christian Bernhofer

Keyword(s):

Relative Humidity ◽

Statistical Downscaling ◽

Minimum Temperature ◽

Climate Models ◽

Global Climate ◽

Meteorological Data ◽

Baseline Period ◽

Global Climate Models ◽

Reanalysis Dataset ◽

Representative Concentration Pathways

Abstract Background Considering the lack of research over this region the Statistical Downscaling Model (SDSM) was used as a tool for downscaling meteorological data statistically over four representative regions in the eastern side of Colombia. Data from the two Global Climate Models CanESM2 and IPSL-CM5A-MR, which are part of the CMIP5-project have been used to project future maximum and minimum temperature, precipitation and relative humidity for the periods 2021–2050 and 2071–2100. For both models, the Representative Concentration Pathways RCP2.6 and RCP8.5 were considered, representing two different possible future emission trajectories and radiative forcings. Predictor variables from the National Centre for Environmental Prediction (NCEP-DOE 2) reanalysis dataset, together with analyzed correlation coefficient (R) and root mean square error (RMSE) were used as performance indicators during the calibration and validation process. Results Results indicate that Maximum and minimum temperature is projected to increase for both Global Climate Models and both Representative Concentration Pathways; relative humidity shows a decreasing trend for all scenarios and all regions; and precipitation shows a slight decrease over three regions and an increase over the warmest region. As expected, the results of the simulation for the period 2071–2100 show a more drastic change when compared to the baseline period of observations. Conclusions The SDSM model proves to be efficient in the downscaling of maximum/minimum temperature as well as relative humidity over the studied regions; while showing a lower performance for precipitation, agreeing with the results for other statistical downscaling studies. The results of the projections offer good information for the evaluation of possible future-case scenarios and decision-making management.

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Maize productivity analysis in response to climate change under different nitrogen management strategies

Journal of Agrometeorology ◽

10.54386/jam.v23i3.54 ◽

2021 ◽

Vol 23 (3) ◽

pp. 279-285

Author(s):

NISHANT K SINHA ◽

M. MOHANTY ◽

J. SOMASUNDARAM ◽

R. S. CHAUDHARY ◽

H. PATRA ◽

...

Keyword(s):

Management Practices ◽

Climate Models ◽

Global Climate ◽

N Uptake ◽

Management Strategies ◽

Central India ◽

Maize Yield ◽

Baseline Period ◽

Global Climate Models ◽

Productivity Analysis

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Variation of Projected Atmospheric Water Vapor in Central Asia Using Multi-Models from CMIP6

Atmosphere ◽

10.3390/atmos11090909 ◽

2020 ◽

Vol 11 (9) ◽

pp. 909 ◽

Cited By ~ 1

Author(s):

Zhenjie Li ◽

Hui Tao ◽

Heike Hartmann ◽

Buda Su ◽

Yanjun Wang ◽

...

Keyword(s):

Water Vapor ◽

Central Asia ◽

Radiative Forcing ◽

Climate Models ◽

Global Climate ◽

Baseline Period ◽

High Elevation ◽

Global Climate Models ◽

The Caspian Sea ◽

Near Surface

Using data from the Integrated Global Radiosonde Archive Version 2 (IGRA2) and the Multi Model Ensemble (MME) of four global climate models (GCMs), named CanESM5, IPSL-CM6A-LR, MIROC6, and MRI-ESM2-0, within the framework of phase 6 of the Coupled Model Intercomparison Project (CMIP6), we analyzed the changes in atmospheric total column water vapor (TCWV) over Central Asia in the future (2021–2100) under SSP-RCPs scenarios: SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0, SSP4-3.4, SSP4-6.0, and SSP5-8.5, relative to baseline period (1986–2005). Results showed that the annual mean TCWV from IGRA2 was consistent with the model output from 1979 to 2014 in Central Asia. Besides, the spatial distribution of TCWV in Central Asia during the baseline period was consistent between the models. The regional average value of Central Asia was between 10.8 mm and 12.4 mm, and decreased with elevation. TCWV will increase under different SSP-RCPs from 2021 to 2040, but showed different trends after 2040. It will increase under SSP1-1.9 and SSP1-2.6 scenarios from 2021 to 2050, and decrease after that. It will grow from 2021 to 2055 under SSP4-3.4 scenario, and then stay essentially constant. Under SSP2-4.5 and SSP4-6.0 scenarios, TCWV will rise rapidly during 2021–2065, but the growth will decline from 2065 to 2100. TCWV will continue to increase under SSP3-7.0 and SSP5-8.5 scenarios, and the largest increase is projected under SSP5-8.5 scenario. Change in near-surface temperature (Ts) matched the change in TCWV, but changes in precipitation and evapotranspiration are not significant during 2021–2100. In spite of the large variations in TCWV under different SSP-RCPs, the dominant characteristic in all scenarios shows that a large TCWV increase is demonstrated over areas with small TCWV amounts during the baseline period. On the contrary, increases will be small where the TCWV amounts had been large during the baseline period. The change in TCWV is highly correlated to the increase in Ts in Central Asia. Under SSP2-4.5, SSP3-7.0, SSP4-3.4, SSP4-6.0, and SSP5-8.5 scenarios, the higher the temperature due to higher radiative forcing, the steeper the regression slope between TCWV and Ts change. It is closest to the theoretical value of the Clausius-Clapeyron equation under SSP3-7.0 and SSP5-8.5 scenarios, but not presented under other scenarios. Spatially, steeper regression slopes during 2021–2100 have been found around the Caspian Sea in the southwest and in the high-elevation areas in the southeast of Central Asia, which is likely related to the abundant local water supply for evaporation.

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GIS Modeling of Climate Change Impact on Saffron Water Requirement (the case of Ardabil Province)

Ukrainian Journal of Ecology ◽

10.15421/2017_114 ◽

2017 ◽

Vol 7 (4) ◽

pp. 255-262

Author(s):

Vahid Ashrafi

Keyword(s):

Climate Change ◽

Climate Change Impact ◽

Climate Models ◽

Potential Evapotranspiration ◽

Global Climate ◽

Evaluation Criteria ◽

Water Requirement ◽

Global Climate Models ◽

Gis Modeling ◽

Change Impact

In recent decades, climate change in Iran has led to the introduction of specific plants that have less water requirement than policymakers. Saffron cultivation has a long history in Iran and saffron was ranked as one of these figures on the agenda of policymakers. Here we presented the evaluation of Climate Change Impact on Saffron Water Requirement by GIS Modeling in Ardabil Provincel. We spot periods 1992-2017 and 2017-2040 as base and future period, respectively. For CCCSN, the data from five global climate models (GCMs) from the CGCM3T47 archive were selected that cover three ‘Representative Concentration Pathways’ (RCPs) scenarios. Potential evapotranspiration is estimated by Torrent White method. The accuracy of models at base period was determined by evaluation criteria, such as the RMSE, R2. Results showed that accuracy of CGCM3T47 model on A1B scenario was higher than other AOGCM models which used on base term. Also, it illustrated that water requirement will rise in all capable regions of state on 2040. In universal, average of addition of water requirement is 67, where in Germi, capital of state, we will have maximum variations by 95 mm ascension for the year 2040. Also, pole of production saffron in state, Bilasvar will have 40 mm ascension in saffron water requirement. Mean water requirement of saffron will be constantly increasing. In the meanwhile, the index of 425.52 mm for the year 2017 to 487.61 mm for the year 2040 were performed.

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