Meteorological Parameters–Soil  Temperature Relations in a Sub-Tropical  Summer Grassland: Physically-Based  and Data-Driven Modeling

N.A.

Makale detayları

Meteorological Parameters–Soil Temperature Relations in a Sub-Tropical Summer Grassland: Physically-Based and Data-Driven Modeling

Yazarlar:

N.A.

Yayıncı:

Ataturk University

Dergi:

Atatürk Üniversitesi Ziraat Fakültesi Dergisi Vol.54 Issue 2, pp.48-56

Belge Tipi:

Makale

Yayın Tarihi:

5/2023

Belge Dili:

İngilizce

Document ID (Org No.):

2001289951

pISSN:

1300-9036

Anahtar kelimeler:

Özet:

 The knowledge of soil temperature dynamics at different depths is paramount for the agricultural industry because soil temperature impacts the physical, chemical, and biological processes in soil. A relationship between meteorological parameters and temperature at different depths in silt loam soil was assessed by using a physically based HYDRUS-1D model and a linear regression model. Soil temperature at 5, 10, 20, 30, and 50 cm soil layers, minimum and maximum air temperature, air pressure, relative humidity, dew point, rainfall, sunshine duration, wind speed, and evaporation data collected at a weather station were used. The correlation sensitivity for the input combinations was investigated. The quantitative evaluation based on mean absolute percentage error and R2 showed that the predictions of both linear regression model and HYDRUS-1D models were satisfactory. The R2 values at 5, 10, and 20 cm depths were 0.96, 0.94, and 0.88 for linear regression model, and 0.85, 0.86, and 0.78, for HYDRUS-1D model, respectively. Similarly, the mean absolute percentage error values for linear regression model were 0.81, 0.87, and 1.05, whereas 3.44, 2.87, and 3.73 at 5, 10, and 20 cm depths for HYDRUS-1D model, respectively. Generally, the accuracy of the models diminished with increasing the soil depth. At 30 cm soil depth, both models failed to estimate soil temperature accurately. The R2 and mean absolute percentage error values at 50 cm depth for linear regression model were 0.55 and 1.25 and 0.51 and 4.13 for HYDRUS-1D, respectively. The linear regression model performed better than the HYDRUS-1D model. Five independent variables mean air temperature, maximum humidity, rainfall, wind speed, and evaporation were found to significantly affect the summer-time soil temperature. Either of the methods can be used satisfactorily to predict soil temperature at 020 cm soil depth.

 The knowledge of soil temperature dynamics at different depths is paramount for the agricultural industry because soil temperature impacts the physical, chemical, and biological processes in soil. A relationship between meteorological parameters and temperature at different depths in silt loam soil was assessed by using a physically based HYDRUS-1D model and a linear regression model. Soil temperature at 5, 10, 20, 30, and 50 cm soil layers, minimum and maximum air temperature, air pressure, relative humidity, dew point, rainfall, sunshine duration, wind speed, and evaporation data collected at a weather station were used. The correlation sensitivity for the input combinations was investigated. The quantitative evaluation based on mean absolute percentage error and R2 showed that the predictions of both linear regression model and HYDRUS-1D models were satisfactory. The R2 values at 5, 10, and 20 cm depths were 0.96, 0.94, and 0.88 for linear regression model, and 0.85, 0.86, and 0.78, for HYDRUS-1D model, respectively. Similarly, the mean absolute percentage error values for linear regression model were 0.81, 0.87, and 1.05, whereas 3.44, 2.87, and 3.73 at 5, 10, and 20 cm depths for HYDRUS-1D model, respectively. Generally, the accuracy of the models diminished with increasing the soil depth. At 30 cm soil depth, both models failed to estimate soil temperature accurately. The R2 and mean absolute percentage error values at 50 cm depth for linear regression model were 0.55 and 1.25 and 0.51 and 4.13 for HYDRUS-1D, respectively. The linear regression model performed better than the HYDRUS-1D model. Five independent variables mean air temperature, maximum humidity, rainfall, wind speed, and evaporation were found to significantly affect the summer-time soil temperature. Either of the methods can be used satisfactorily to predict soil temperature at 020 cm soil depth.

 The knowledge of soil temperature dynamics at different depths is paramount for the agricultural industry because soil temperature impacts the physical, chemical, and biological processes in soil. A relationship between meteorological parameters and temperature at different depths in silt loam soil was assessed by using a physically based HYDRUS-1D model and a linear regression model. Soil temperature at 5, 10, 20, 30, and 50 cm soil layers, minimum and maximum air temperature, air pressure, relative humidity, dew point, rainfall, sunshine duration, wind speed, and evaporation data collected at a weather station were used. The correlation sensitivity for the input combinations was investigated. The quantitative evaluation based on mean absolute percentage error and R2 showed that the predictions of both linear regression model and HYDRUS-1D models were satisfactory. The R2 values at 5, 10, and 20 cm depths were 0.96, 0.94, and 0.88 for linear regression model, and 0.85, 0.86, and 0.78, for HYDRUS-1D model, respectively. Similarly, the mean absolute percentage error values for linear regression model were 0.81, 0.87, and 1.05, whereas 3.44, 2.87, and 3.73 at 5, 10, and 20 cm depths for HYDRUS-1D model, respectively. Generally, the accuracy of the models diminished with increasing the soil depth. At 30 cm soil depth, both models failed to estimate soil temperature accurately. The R2 and mean absolute percentage error values at 50 cm depth for linear regression model were 0.55 and 1.25 and 0.51 and 4.13 for HYDRUS-1D, respectively. The linear regression model performed better than the HYDRUS-1D model. Five independent variables mean air temperature, maximum humidity, rainfall, wind speed, and evaporation were found to significantly affect the summer-time soil temperature. Either of the methods can be used satisfactorily to predict soil temperature at 020 cm soil depth.

 Toprak sıcaklığı topraktaki fiziksel, kimyasal ve biyolojik süreçleri etkilediğinden, farklı derinliklerdeki toprak sıcaklığı dinamikleri bilgisi tarım endüstrisi için çok önemlidir. Bu çalışmada siltli tın tekstür sınıfına ait toprakların farklı derinliklerinde meteorolojik parametreler ile sıcaklık arasındaki ilişkiler, fiziksel tabanlı HYDRUS-1D modeli ve bir doğrusal regresyon modeli LRM kullanılarak değerlendirilmiştir. Çalışma alanında 5, 10, 20, 30 ve 50 cm derinliğindeki toprak katmanlarının sıcaklık değerleri ile meteoroloji istasyonundan alınan en düşük ve en yüksek hava sıcaklığı, basınç, çiğ oluşum noktası, yağış, güneşlenme süresi, rüzgar hızı verileri kullanılmıştır. Girdi kombinasyonları için korelasyon hassasiyeti araştırılmıştır. Çalışma sonucunda ortalama mutlak yüzde hatası OMYH ve R2ye dayalı kantitatif değerlendirmelerin hem LRM hem de HYDRUS-1D modellerinden elde edilen tahminlerin tatmin edici olduğunu göstermiştir. LRM modelinde 5, 10 ve 20 cm derinlik katmanlarındaki R2 değerlerinin sırasıyla 0,96, 0,94 ve 0,88 olduğu, HYDRUS-1D modelinde ise 0,85, 0,86 ve 0,78 olduğu tespit edilmiştir. Benzer şekilde OMYH değerleri 5, 10 ve 20 cm derinlik kademelerinde LRM için 0,81, 0,87 ve 1,05 iken, HYDRUS-1D modeli için 3,44, 2,87 ve 3,73 olarak hesaplanmıştır. Genel olarak modellerin doğruluğu toprak derinliğinin artmasıyla azalmış ve 30cmden daha derin katmanlarda her iki modelin de toprak sıcaklığını doğru bir şekilde tahmin edemediği belirlenmiştir. Toprak derinliğinin 50 cm olduğu katmanlarda R2 ve OMYH değerleri LRM modeli için 0,55 ve 1,25, HYDRUS-1D modeli için ise 0,51 ve 4,13 olmuştur. Çalışma sonucunda ayrıca LRM modelinin HYDRUS-1D modelinden daha iyi performans gösterdiği, beş bağımsız değişkenin ortalama hava sıcaklığı, maksimum nem, yağış, rüzgar hızı ve buharlaşma yaz mevsimindeki toprak sıcaklığını önemli ölçüde etkilediği, her iki yönteminde 0-20 cmlik toprak derinliğinde toprak sıcaklığını tahmin etmek için tatmin edici bir şekilde kullanılabileceği belirlenmiştir.

Makale detayları

Meteorological Parameters–Soil Temperature Relations in a Sub-Tropical Summer Grassland: Physically-Based and Data-Driven Modeling

Yazarlar:

N.A.

Yayıncı:

Ataturk University

Dergi:

Atatürk Üniversitesi Ziraat Fakültesi Dergisi Vol.54 Issue 2, pp.48-56

Belge Tipi:

Makale

Yayın Tarihi:

5/2023

Belge Dili:

İngilizce

Document ID (Org No.):

2001289951

pISSN:

1300-9036

Evaporation

humidity

HYDRUS-1D

linear regression model

wind speedBuharlaşma

nem

HYDRUS-1D

lineer regresyon modeli

rüzgar hızı

Sayı

Makale detayları

Yazarlar: N.A.

Yayıncı: Ataturk University

Dergi: Atatürk Üniversitesi Ziraat Fakültesi Dergisi Vol.54 Issue 2, pp.48-56

Belge Tipi: Makale

Yayın Tarihi: 5/2023

Belge Dili: İngilizce

Document ID (Org No.): 2001289951

pISSN: 1300-9036

Sayı

Yazarlar:

N.A.

Yayıncı:

Ataturk University

Dergi:

Atatürk Üniversitesi Ziraat Fakültesi Dergisi Vol.54 Issue 2, pp.48-56

Belge Tipi:

Makale

Yayın Tarihi:

5/2023

Belge Dili:

İngilizce

Document ID (Org No.):

2001289951

pISSN:

1300-9036