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Publications of AgriMetSoft
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Prediction of meteorological drought in arid and semi-arid regions using PDSI and SDSM: a case study in Fars Province, Iran
Springer-Verlag
10.1007/s40333-020-0095-5
Abstract
Drought is one of the most significant environmental disasters, especially in arid and semi-arid regions. Drought indices as a tool for management practices seeking to deal with the drought phenomenon are widely used around the world. One of these indicators is the Palmer drought severity index (PDSI), which is used in many parts of the world to assess the drought situation and continuation. In this study, the drought state of Fars Province in Iran was evaluated by using the PDSI over 1995-2014 according to meteorological data from six weather stations in the province. A statistical downscaling model (SDSM) was used to apply the output results of the general circulation model in Fars Province. To implement data processing and prediction of climate data, a statistical period 1995-2014 was considered as the monitoring period, and a statistical period 2019-2048 was for the prediction period. The results revealed that there is a good agreement between the simulated precipitation (R2>0.63; R2, determination coefficient; MAE<0.52; MAE, mean absolute error; RMSE<0.56; RMSE, Root Mean Squared Error) and temperature (R2>0.95, MAE<1.74, and RMSE<1.78) with the observed data from the stations. The results of the drought monitoring model presented that dry periods would increase over the next three decades as compared to the historical data. The studies showed the highest drought in the meteorological stations Abadeh and Lar during the prediction period under two future scenarios representative concentration pathways (RCP4.5 and RCP8.5). According to the results of the validation periods and efficiency criteria, we suggest that the SDSM is a proper tool for predicting drought in arid and semi-arid regions.
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Rainfed wheat (Triticum aestivum L.) yield prediction using economical, meteorological, and drought indicators through pooled panel data and statistical downscaling
Ecological Indicators
10.1016/j.ecolind.2019.105991
Abstract
Agriculture productions play significant roles in economic development. Extreme weather events, especially drought under climate change conditions, can affect future crop production. Nowadays, researchers are trying to apply modeling approaches for estimating future changes on amounts of crop yields. This study employed pooled panel data to simulate the most effective meteorological drought indices, economic and meteorological variables on rainfed wheat yield. The observation period was 1990–2016 for several meteorological data, besides SPI (Standardized Precipitation Index) and SPEI (Standardized Precipitation Evapotranspiration Index) drought indices in monthly and yearly scales. The available economic variables during the study period were yearly guaranteed wheat prices (Rial/kg) and area under cultivation (ha). In this research, first, the most effective variables were selected according to the efficiency criteria and stepwise regression. Then by using pooled panel data, a relation was estimated between yield and the independent variables. Finally, with future downscaled variables, the amount of wheat yield was determined for the next 20 years (2019–2038). The GFDL- ESM2M and MIROC5 models under RCP45 and RCP85 were run, and MIROC5 under RCP45 was selected as the best model, for the evaluation period. The results revealed that guaranteed wheat prices, yearly precipitation and sunshine hours, the area under cultivation, and SPI of October were identified as the most effective variables on wheat yield through the Panel model. By using the projection weather variables and the pooled panel model, we achieved that the amount of rainfed wheat yield would be increased over two next decades at Mashhad, Sabzevar, and Torbat H. locations.
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Climate data clustering effects on arid and semi-arid rainfed wheat yield: a comparison of artificial intelligence and K-means approaches
International Journal of Biometeorology
10.1007/s00484-019-01699-w
Abstract
Clustering algorithms are critical data mining techniques used to analyze a wide range of data. This study compares the utility of ant colony optimization (ACO), genetic algorithm (GA), and K-means methods to cluster climatic variables affecting the yield of rainfed wheat in northeast Iran from 1984 to 2010 (27 years). These variables included sunshine hours, wind speed, relative humidity, precipitation, maximum temperature, minimum temperature, and the number of wet days. Seven climatic factors with higher correlations with detrended rainfed wheat yield were selected based on Pearson correlation coefficient significance (P value < 0.1). Three variables (i.e., sunshine hours, wind, and average relative humidity) were excluded for clustering. In the next step based on Pearson correlation (P value < 0.05) between the yield, and the seven climate attributes, fitness function, and silhouette index, only four attributes with higher correlation in its cluster were selected for reclustering. Four climate attributes had an extensive association with yield, so we used four-dimensional clustering to describe the common characteristics of low-, medium-, and high-yielding years, and this is the significance of this research that we have done four-dimensional clustering. The silhouette index showed that the best number of clusters for each station was equal to three clusters. At the last step, reclustering was done through the best-selected method. The results yielded that GA was the best method.
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Comparing the Performance of Dynamical and Statistical Downscaling on Historical Run Precipitation Data over a Semi-Arid Region
Asia-Pacific Journal of Atmospheric Sciences
10.1007/s13143-019-00112-1
Abstract
Precise evaluations of climate model precipitation outputs are valuable for making decisions regarding agriculture, water resource, and ecosystem management. Many downscaling techniques have been developed in the past few years for projection of weather variables. We need to apply dynamical and statistical downscaling (DD and SD) to bridge the gap between the coarse resolution general circulation model (GCM) outputs and the need for high-resolution climate information over a semi-arid region. We compare the requirements of DD (RegCM4) and SD (Delta) approaches, evaluate the historical run of NNRP1 data in comparison with station data, and analyze the changes in wet days and precipitation values through both methods during 1990–2010. In this study, we did not want to use prediction data under different scenarios of climate change, and we have just applied observed data to assess the amount of precise of NNRP1 data, over the observed period. SD method requires less time and computing power than DD. The DD approach performs better over the evaluation period according to efficiency criteria. In general, the Pearson correlation in DD with observation data in evaluation period was higher than (r > 0.72 and R2 > 0.52) SD (r > 0.65 and R2 > 0.41) over three study stations. Similarly, MAE and NSE show better results from DD relative to SD. SD underestimates the number annual mean wet-days for all three stations examined. DD overestimates a number of annual mean wet-days, but with less deviation from the observed mean.
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Prediction of climate variables by comparing the k-nearest neighbor method and MIROC5 outputs in an arid environment
Journal: Climate Research
10.3354/cr0154
Abstract
The goal of this study was to compare the ability of the k-Nearest Neighbors (k-NN) approach and the downscaled output from the MIROC5 model for generating daily precipitation (mm), and daily maximum and minimum temperature (Tmax, and Tmin) (C) for an arid environment. For this study data from the easternmost province of Iran, South Khorasan was used for a period from 1986 to 2015. We also used an ensemble method to decrease the uncertainty of the k-NN approach. Although based on the initial evaluation MIROC5 had better results, we also used the output result of kNN alongside the MIROC5's data to generate future weather data for the period 2018 to 2047. NSE (Nash-Sutcliffe eficiency) between MIROC5 estimates and observed monthly Tmax ranged from 0.86 to 0.92, and 0.89-0.93 for Tmin over the evaluation period (2006-2015). K-NN performed less well, with NSE between k-NN estimates and observed Tmax ranging from 0.54 to 0.64, and from 0.75-0.78 for Tmin. The MIROC5 simulated precipitation was close to observed historical values (NSE between 0.06 and 0.07); the k-NN simulated precipitation was less accurate (NSE between -0.36 and -0.14). For these arid regions, the k-NN precipitation results compared poorly to the MIROC5 downscaling. MIROC5 predicts increases in monthly Tmin and Tmax in summer and autumn and decreases in winter and spring and decreases in winter monthly precipitation under RCP4.5 over the 2018-2047 period of this study. This study showed that the k-NN method cannot be used for generating future data in comparison to the outputs of the MIROC5 model for arid environments.
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Evaluation of different gridded rainfall datasets for rainfed wheat yield prediction in an arid environment
Journal: International Journal of Biometeorology
10.1007/s00484-018-1555-x
Abstract
The accuracy of daily output of satellite and reanalysis data is quite crucial for crop yield prediction. This study has evaluated the performance of APHRODITE (Asian Precipitation-Highly-Resolved Observational Data Integration Towards Evaluation), PERSIANN (Rainfall Estimation from Remotely Sensed Information using Artificial Neural Networks), TRMM (Tropical Rainfall Measuring Mission), and AgMERRA (The Modern-Era Retrospective Analysis for Research and Applications) precipitation products to apply as input data for CSM-CERES-Wheat crop growth simulation model to predict rainfed wheat yield. Daily precipitation output from various sources for 7 years (2000–2007) was obtained and compared with corresponding ground-observed precipitation data for 16 ground stations across the northeast of Iran. Comparisons of ground-observed daily precipitation with corresponding data recorded by different sources of datasets showed a root mean square error (RMSE) of less than 3.5 for all data. AgMERRA and APHRODITE showed the highest correlation (0.68 and 0.87) and index of agreement (d) values (0.79 and 0.89) with ground-observed data. When daily precipitation data were aggregated over periods of 10 days, the RMSE values, r, and d values increased (30, 0.8, and 0.7) for AgMERRA, APHRODITE, PERSIANN, and TRMM precipitation data sources. The simulations of rainfed wheat leaf area index (LAI) and dry matter using various precipitation data, coupled with solar radiation and temperature data from observed ones, illustrated typical LAI and dry matter shape across all stations. The average values of LAImax were 0.78, 0.77, 0.74, 0.70, and 0.69 using PERSIANN, AgMERRA, ground-observed precipitation data, APHRODITE, and TRMM. Rainfed wheat grain yield simulated by using AgMERRA and APHRODITE daily precipitation data was highly correlated (r2 ≥ 70) with those simulated using observed precipitation data. Therefore, gridded data have high potential to be used to supply lack of data and gaps in ground-observed precipitation data.
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Prediction of effective climate change indicators using statistical downscaling approach and impact assessment on pearl millet (Pennisetum glaucum L.) yield through Genetic Algorithm in Punjab, Pakistan
Journal: Ecological Indicators, Volume 90, July 2018, Pages 569–576
10.1016/j.ecolind.2018.03.053
Abstract
Climate change involves long term changes in climate including increase in temperature, elevated CO2 and uneven distribution of rainfall quantity and periodicity. Though daily mean temperature (Tmean) is considered a widely useful index of assessment of climate change, averaging can obscure some of the variations expected in diurnal temperature range (DTR). The aim of this study was to evaluate the effectiveness of DTR relative to Tmean as a metric for predicting millet yield using a combination of a historical dataset (1980–2010) and two climate model (MIROC5 and GFDL) projections for 2017–2046 under RCP 4.5 and 8.5 across two different environments (arid, Layyah and semi-arid, Faisalabad) of Punjab, Pakistan. Provincial datasets of pearl millet yields were collected and checked for an empirical relationship between Tmean, DTR and crop yield. The mean of projections showed increasing DTR relative to baseline in both environments. Projected Tmax and Tmin were highly correlated (0.90–0.99) for both environments and climate models. MIROC5 predicted Tmax and Tmin well and GFDL performed efficiently in predicting precipitation by 2046. The data also showed more hot days in future decades and erractic rainfall pattern by 2046 in both environments. The Genetic Algorithm (GA) appeared to be a good approach to assess climate change impact on pearl millet yield in Punjab, Pakistan, predicting negative yield impacts (11–12%) due to future warming. We suggest broadening tests of this method to other cases around the world, with similar climate regimes.
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Predictive value of Keetch-Byram Drought Index for cereal yields in a semi-arid environment
Journal: Theoretical and Applied Climatology, pp 1–10
10.1007/s00704-017-2315-2
Abstract
Meteorological drought indices associated with soil moisture status have potential for varying applications including predictive power for crop yields estimation. The Keetch-Byram Drought Index (KBDI) was initially developed to estimate forest flammability, based on quantification of the moisture deficiency in upper soil layer as a function of daily precipitation and maximum air temperature. In this study, we characterized the utility of KBDI to accurately trace and monitor vegetation change and crop yield fluctuation in a semi-arid environment. It is tried to find any temporal association for both the 16-day MODIS-derived NDVI and KBDI from 2002 to 2012 and the correlation between KBDI and wheat and barley yield from 1984 to 2010. Correlation between KBDI and NDVI showed a general seasonal pattern with strongest correlation in mid-growing season, but this varied across study locations. Warmer locations with very sparse vegetation showed weaker association between KBDI and NDVI. Although a robust correlation between KBDI and winter cereal crop yield was not achieved based on winter (wet and cold season) data, spring cereal crop yield was correlated with KBDI.
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Estimation of meteorological drought indices based on AgMERRA precipitation data and station-observed precipitation data
Journal: Journal of Arid Land, December 2017, Volume 9, Issue 6, pp 797–809
10.1007/s40333-017-0070-y
Abstract
Meteorological drought is a natural hazard that can occur under all climatic regimes. Monitoring the drought is a vital and important part of predicting and analyzing drought impacts. Because no single index can represent all facets of meteorological drought, we took a multi-index approach for drought monitoring in this study. We assessed the ability of eight precipitation-based drought indices (SPI (Standardized Precipitation Index), PNI (Percent of Normal Index), DI (Deciles index), EDI (Effective drought index), CZI (China-Z index), MCZI (Modified CZI), RAI (Rainfall Anomaly Index), and ZSI (Z-score Index)) calculated from the station-observed precipitation data and the AgMERRA gridded precipitation data to assess historical drought events during the period 1987–2010 for the Kashafrood Basin of Iran. We also presented the Degree of Dryness Index (DDI) for comparing the intensities of different drought categories in each year of the study period (1987–2010). In general, the correlations among drought indices calculated from the AgMERRA precipitation data were higher than those derived from the station-observed precipitation data. All indices indicated the most severe droughts for the study period occurred in 2001 and 2008. Regardless of data input source, SPI, PNI, and DI were highly inter-correlated (R2=0.99). Furthermore, the higher correlations (R2=0.99) were also found between CZI and MCZI, and between ZSI and RAI. All indices were able to track drought intensity, but EDI and RAI showed higher DDI values compared with the other indices. Based on the strong correlation among drought indices derived from the AgMERRA precipitation data and from the station-observed precipitation data, we suggest that the AgMERRA precipitation data can be accepted to fill the gaps existed in the station-observed precipitation data in future studies in Iran. In addition, if tested by station-observed precipitation data, the AgMERRA precipitation data may be used for the data-lacking areas.
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