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Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling SAHM

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Monthly drought prediction based on ensemble models.

Muhammad Haroon Shaukat¹, Ijaz Hussain¹, Muhammad Faisal^2,3

¹Department of Statistics, Quaid-i-Azam University, Islamabad, Pakistan.

|November 16, 2020

Summary

This summary is machine-generated.

This study introduces an ensemble approach for drought prediction in Pakistan, using climate information to improve accuracy. The Conditional Ensemble Drought Prediction (CEDP) model, incorporating climate indices, proved most effective for monthly drought analysis.

Keywords:

Conditional ensemble drought prediction Copulas Equal ensemble drought prediction Standardized precipitation index Weighted ensemble drought prediction

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Area of Science:

Environmental Science
Climatology
Hydrology

Background:

Droughts pose significant risks to society, economy, and environment, exacerbated by changing weather patterns.
Pakistan has experienced severe droughts in recent decades, necessitating improved prediction methods.
A comprehensive drought analysis integrating climate information was lacking for this region.

Purpose of the Study:

To develop and evaluate an ensemble approach for monthly drought prediction in Pakistan.
To define and analyze wet and dry events using climate data.
To assess the effectiveness of different ensemble models and statistical methods for drought analysis.

Main Methods:

Drought events were initially identified using the Standardized Precipitation Index (SPI-3).
Three ensemble models were employed: Equal Ensemble Drought Prediction (EEDP), Weighted Ensemble Drought Prediction (WEDP), and Conditional Ensemble Drought Prediction (CEDP).
The CEDP model utilized four copula families (Frank, Clayton, Gumbel, Joe) to model dependencies between climate indices and precipitation, with the Joe copula showing best performance.

Main Results:

The Conditional Ensemble Drought Prediction (CEDP) model demonstrated superior accuracy and uncertainty management compared to EEDP and WEDP.
Climate indices were found to be correlated with the weather patterns of the studied meteorological stations.
Extreme drought events occurred at rates of 1.44% (Multan), 0.57% (Bahawalpur), 2.59% (Barkhan), and 1.71% (Khanpur), with extremely wet events at 2.3%, 1.72%, 0.86%, and 2.86%, respectively.

Conclusions:

The CEDP model, integrating climate information, offers a robust method for monthly drought prediction.
Understanding drought patterns through climate data enhances future agricultural and water resource management strategies.
The study highlights the importance of advanced statistical techniques like copulas in climate-related hazard analysis.