Elham Rahmani[1]

Dr.Abdolmajid Liaghat[1]– Prof.Ali Khalili[1]

[1] Irr. & Reclam. Eng. Faculty, Agro meteorology Department, university of Tehran, Tehran, Iran

10th International Meeting on Statistical Climatology (10IMSC)

Introduction

The main drought definition is the lack of gaining water in specific period of time and in a distinct region.

Drought causes extensive damages to agricultural products.

Rainfall has the major part in all definitions of drought indexes.

The climatic parameters are usually used for estimating crop products and predicting agricultural droughts .

In this research ,

the attempt is to define different models that relate drought indexes and climatic parameters combinatory on

crop yield, using classical (regression) methods.

Methodology

To develop regression models, different climatic parameters and drought indexes were used to relate them with crop yield.

Data were collected from north east of Iran, Tabriz and Miane synoptic stations.

Tabriz

Miane

Estimated Drought Indexes The Percentage of Normal Precipitation Index (PNPI)

Standard Index of Annual Precipitation (SIAP)

Converted Selenianov Hydrothermal Index (HT)

Nguyen Index (k)

Rainfall Anomaly Index (RAI)

Standardized Precipitation Index (SPI)

Shashko Moisture Drought Index (MD)

Transeau Index (Ih)

Single and Multivariate Regression Models

Developed ModelsIrrigated barley yield Tabriz(30 years)

Rain fed barley yieldTabriz(30 years)

Rain fed barley yield Tabriz(17 years)

Rain fed barley yield Miane(17 years)

Active temp. above 10°1-1-I1-1-R2-13-1Rainfall1-2-I1-2-R2-23-2

Relative humidity1-3-I1-3-R2-33-3

Average wind speed1-4-I1-4-R2-43-4

Sunshine hours1-5-I1-5-R2-53-5

Average min. temperature1-6-I1-6-R2-63-6

Average max. temperature1-7-I1-7-R2-73-7

Average mean temperature1-8-I1-8-R2-83-8

Evaporation1-9-I1-9-R2-93-9

Vapor pressure1-10-I1-10-R2-103-10

Hydrothermal index (HT)1-11-I1-11-R2-113-11

Shashko moisture drought Index (Md)1-12-I1-12-R2-123-12

Nguyen Index (k)1-13-I1-13-R2-133-13

Transeau Index (Ih)1-14-I1-14-R2-143-14

Standard index of annual precipitation (SIAP)1-15-I1-15-R2-153-15

The Percentage of normal precipitation index (PNPI)1-16-I1-16-R2-163-16

Rainfall Anomaly Index (RAI)1-17-I1-17-R2-173-17

SPI31-18-I1-18-R2-183-18

SPI61-19-I1-19-R2-193-19

SPI91-20-I1-20-R2-203-20

SPI121-21-I1-21-R2-213-21

SPI241-22-I1-22-R2-223-22

Climatic parameters1-23-I1-23-R2-233-23

Drought indexes1-24-I1-24-R2-243-24

Climatic parameters and drought indexes1-25-I1-25-R2-253-25

Evaluation of Model function indexes

ModelsR (test)RMSE (test)MBE (test)MBE %(test)

1-25-I0.730.560.234-0.632

1-23-R0.10.3160.056-0.067

1-25-R0.5290.3190.318-0.383

2-20.7890.8020.79-6.58

2-110.6950.6340.254-2.117

2-170.8670.6120.348-9.2

2-250.9370.8160.15-1.25

3-230.8190.7330.098-0.516

3-250.7950.7240.036-0.189

Conclusion The best multivariate regression model includes P, wind, Sunshine, t>=10, K

Among the drought indexes studied in this research, RAI, Ih, SPI24, K are the most effective ones on the crop yield estimation.

The multiple models include the combination of climatic parameters & drought indexes together are better for crop yield prediction.

To predict the yield of products, multiple models are better than simple models.

Thanks for your kind attention