Yağışın mekânsal dağılışında deterministik ve stokastik yöntemler: Mauritius örneği, Doğu Afrika

Abstract

Yağış, mekânsal ve zamansal ölçekte büyük değişkenlik gösteren en önemli iklim parametrelerinden biridir. Yağışın doğru bir biçimde modellenmesi, hidrolojik çalışmalar, kuraklık ve sel gibi olayların tahmin edilmesi, yerüstü ve yeraltı su kaynakları miktarının tahmini, su kaynaklarının kirlenmesi ile ilişkili pek çok araştırmanın en önemli bölümünü oluşturur. Bu sebeple, yağışın modellenmesinde çok sayıda enterpolasyon yöntemleri uygulanmakta ve birbirleriyle karşılaştırılarak doğru modeller oluşturulmaktadır. Bu çalışmada, 1981–2010 dönemine ait 53 meteoroloji istasyonunun verileri kullanılarak Doğu Afrika’nın Mauritius ada ülkesinin yıllık ortalama toplam yağış dağılış modeli deterministik yöntemlerden, Thiessen Polygon (TP) ve Inverse Distance Method (IDW) ile stokastik yöntemlerden Ordinary Kriging (OK) kullanılarak gerçekleştirilmiştir. Yağış modellerinin doğruluğu Çapraz Geçerlilik (Cross-Validation) yöntemiyle test edilmiş ve modellerin karşılaştırılmasında Ortalama Hata (Mean Error, ME), Ortalama Mutlak Hata (Mean Absolute Error, MAE), Kök Ortalama Kare Hata (Root Mean Square Error, RMSE), Belirleyicilik Katsayısı (Detemination Coefficient, R2)’ndan yararlanılmıştır. Stokastik bir yöntem olan Ordinary Kriging (OK) -17,66 ME, 527,21 MAE, 329,53 mm RMSE ve 0.88 R2değerleri ile en yüksek performans sonucunu vermiştir. Buna karşın deterministik yöntemlerinden biri olan Thiessen Polygon (TP) -78,83 ME, 453,92 MAE, 621,58 mm RMSE ve 0,60 R2değerleriyle en düşük performans değerini göstermiştir. Buna göre, stokastik yöntem sonucu oluşturulan yağış modelinin, deterministik yöntemler kullanılarak oluşturulan yağış modellerine kıyasla doğru bir yağış modeli oluşturduğu sonucuna ulaşılmıştır

Keywords

• Yağış, mekânsal enterpolasyon, deterministik yöntem, stokastik yöntem, Kriging, Mauritius

Deterministic and stochastic methods to analyse the spatial distribution of precipitation: The case of Mauritius, East Africa

Abstract

Precipitation is one of the most important climatic parameters displaying significant changes across space and time. The accurate modeling of precipitation has become an important part of climate research for hydrological studies, the forecast of events such as droughts and floods and the estimation of ground and surface water resources. For this reason, several interpolation methods have been applied and compared for the accurate generation of models. In this study, the spatial distribution of annual mean total precipitation of Mauritius, located east of Africa, was investigated by applying deterministic methods, namely Thiessen Polygon (TP) and Inverse Distance Method (IDW), and stochastic methods, namely Ordinary Kriging (OK), using precipitation data from 53 meteorological stations for the period 1981–2010. The accuracy of the models was tested using the Cross Validation method and the models were compared using the Mean Error (ME), Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and the Coefficient of Determination (R2). The stochastic method, OK, provided the highest performance results, generating ME, MAE, RMSE and R2 values of -17,66, 527,21, 329,53 mm and 0,88 respectively. In contrast, the deterministic method, Thiessen Polygon (TP), generated the lowest performance results, generating ME, MAE, RMSE, R2 values of -78,83, 453,92, 621,58 mm and 0,60 respectively. Therefore, according to the results obtained, it can be concluded that stochastic methods provide more accurate models as compared to deterministic methods

Keywords

• Precipitation, spatial interpolation, deterministic methods, stochastic methods, Kriging, Mauritius

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