Improvement of Interpolation Using Information From Rainfall Stations and Comparison of Hydroclimate Changes (1913-1938)/(1986-2016)

Souheila Kezouh,M‘hamed Ait-oubelli,Dalila Smadhi,Ahcène Semar,Karim Ouamer-ali,Hakim Bachir

doi:10.33794/qjas.2021.129350.1002

Abstract

The primary objective of this study is to use a better method for rainfall mapping in areas with low density rain gauge networks. Secondly, to identify and study hydro-climatic change in the semi-arid high plains of eastern Algeria on the basis of a comparative mapping approach. The latter concerns the annual rainfall map produced by the authors of this paper for the period studied (1986/2016) and the annual rainfall map for the period 1913/1938, prepared by Chaumont and Paquin (1971). The results of this analysis show that isohyets between 300 mm and 350 mm cover a large part of the study area, they occupy an area of 14444 Km², followed by isohyets between 200 mm and 300 mm with an area of 5298 Km². In addition, the comparative analysis between the periods showed that hydro-climatic change was clear for the 200 mm, 300 mm and 400 mm isohyets, whereas there are no major changes for the 500 mm and above isohyets. Data processing based on a combination of statistical and geostatistical analysis (multiple linear regression and kriging) has once again shown the value of taking into account other parameters in the design of rainfall maps, such as geomorphological and geographical parameters.

Highlights

Climatic random parameters constitute constraints that may affect directly or indirectly the performance of the economy
We focused on reproducing the observed properties of rain fields, through the numerical tools of simulation
The measurements of the rainfall heights of the 1986-2016 period, recorded in the selected rainfall network, showed that the average annual rainfall varies from one station to another

Summary

Introduction

Climatic random parameters constitute constraints that may affect directly or indirectly the performance of the economy. They limit by their natures the interventions of the decision-makers at the macroeconomic or microeconomic level [1]. In this context, it is important to study the climate, statistically, while considering it as an intrinsic factor with defined and uniform characteristics [2]. The rains fluctuate in space and time its among and distribution are affected by many factors, including geographical, seasonal variations, temperature, atmospheric pressure and topography [3]. The necessity to take into account the temporal and spatial

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Conclusion