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In most arid and semiarid environments, groundwater is one of the precious resources threatened by water table decline and desiccation, thus it must be constantly monitored. Identifying the causes in uencing the variations of the subsurface water level, such as meteorological drought, is one approach for monitoring these uctuations. In the present study, the effect of two meteorological drought indices SPI and SPEI on the uctuations of the underground water level was evaluated, as was their relationship with the drought index of the subsurface water level (SWI) using multivariate linear regression and M5 decision tree regression. After calculating climatic and hydrological drought indicators in a 6-month time window for a long-term statistical period , the semi-deep aquifers of Golestan province, which is located in northern Iran, were considered as a research location for this purpose. The results demonstrated that the effect of meteorological drought does not immeddergiately manifest in the changes of the subsurface water table and the hydrological drought index. By adding the meteorological drought index with a 6-month lag step, the average air temperature, and the total rainfall from the previous 6 months as new variables, the correlation with the SWI index increases, so that in the best-case scenario, the M5 decision tree model provides the best result in predicting the SWI index. The second half of the year yielded a coe cient of determination of 0.92 and an error value of RMSE = 0.27 for the SPEI index. Among the meteorological drought indicators, the SPEI index, which is based on precipitation and evapotranspiration, created a stronger link with the SWI index, which highlights the signi cance of potential evapotranspiration. It is a warning that, as a result of global warming, subsurface water tables in this region may fall in the future.
In most arid and semiarid environments, groundwater is one of the precious resources threatened by water table decline and desiccation, thus it must be constantly monitored. Identifying the causes in uencing the variations of the subsurface water level, such as meteorological drought, is one approach for monitoring these uctuations. In the present study, the effect of two meteorological drought indices SPI and SPEI on the uctuations of the underground water level was evaluated, as was their relationship with the drought index of the subsurface water level (SWI) using multivariate linear regression and M5 decision tree regression. After calculating climatic and hydrological drought indicators in a 6-month time window for a long-term statistical period , the semi-deep aquifers of Golestan province, which is located in northern Iran, were considered as a research location for this purpose. The results demonstrated that the effect of meteorological drought does not immeddergiately manifest in the changes of the subsurface water table and the hydrological drought index. By adding the meteorological drought index with a 6-month lag step, the average air temperature, and the total rainfall from the previous 6 months as new variables, the correlation with the SWI index increases, so that in the best-case scenario, the M5 decision tree model provides the best result in predicting the SWI index. The second half of the year yielded a coe cient of determination of 0.92 and an error value of RMSE = 0.27 for the SPEI index. Among the meteorological drought indicators, the SPEI index, which is based on precipitation and evapotranspiration, created a stronger link with the SWI index, which highlights the signi cance of potential evapotranspiration. It is a warning that, as a result of global warming, subsurface water tables in this region may fall in the future.
Bir yere ait meteorolojik olayların uzun yıllık ortalamaları olarak bilinen iklim olgusu bölgeye ait hava şartlarının karakteristik yapısında ve değişikliklere bağlı olarak da farklı bitki topluluklarının teşekkül etmesinde oldukça önemli bir role sahiptir. Bilim insanları tarafından çok sayıdaki iklim tipine ait benzer ve farklı yönleri tespit edebilmek amacıyla çeşitli iklim sınıflandırmaları yapılmıştır. İklim tipleri sıcaklık, yağış ve nem gibi meteorolojik olaylara bakılarak sınıflandırılabilir. Thornthwaite yöntemi de iklim tipini belirlemek için kullanılan sınıflandırma metotlarından biridir. Bu çalışmada, meteorolojiden temin edilen uzun süreli iklim verileri ile Thornthwaite iklim belirleme yöntemi kullanılarak Bursa ili ve ilçelerine ait toplam 18 istasyonda iklim tipleri ve su bilançoları belirlenmeye çalışılmıştır. Thornthwaite yöntemine göre yapılan analizler sonucunda Büyükorhan ilçesinin “Yarı Kurak”; Gemlik, Harmancık, İznik, Karacabey ve Yenişehir ilçelerinin “Yarı Nemli, Yarı Kurak”; Gürsu, İnegöl, Kestel, Mustafakemalpaşa, Mudanya, Orhangazi, Osmangazi, Orhaneli, Nilüfer ve Yıldırım ilçelerinin “Yarı Nemli”; Keles ilçesinin “Nemli”, Uludağ istasyonuna ait verilerin ise “Çok Nemli” iklim sınıfında olduğu tespit edilmiştir. Uludağ’ın “Düşük Sıcaklıkta, Karasal iklime yakın” diğer ilçelerin ise “Orta Sıcaklıkta, Okyanus iklimine yakın” olduğu belirlenmiştir. Çalışma sonucu veriler incelendiğinde araştırma alanında genel itibariyle yaz mevsiminde su eksikliği, kış mevsiminde ise Uludağ’da su fazlalığı diğer ilçelerde ise orta seviyede suyun olduğu görülmüştür.
Climate change and human activities have increased droughts, especially overgrazing and deforestation, which seriously threaten the balance of terrestrial ecosystems. The ecological carrying capacity and vegetation cover in the arid zone of Xinjiang, China, are generally low, necessitating research on vegetation response to drought in such arid regions. In this study, we analyzed the spatial and temporal characteristics of drought in Xinjiang from 2001 to 2020 and revealed the response mechanism of SIF to multi-timescale drought in different vegetation types using standardized precipitation evapotranspiration index (SPEI), solar-induced chlorophyll fluorescence (SIF), normalized difference vegetation index (NDVI), and enhanced vegetation index (EVI) data. We employed trend analysis, standardized anomaly index (SAI), Pearson correlation, and trend prediction techniques. Our investigation focused on the correlations between GOSIF (a new SIF product based on the Global Orbital Carbon Observatory-2), NDVI, and EVI with SPEI12 for different vegetation types over the past two decades. Additionally, we examined the sensitivities of vegetation GOSIF to various scales of SPEI in a typical drought year and predicted future drought trends in Xinjiang. The results revealed that the spatial distribution characteristics of GOSIF, normalized difference vegetation index (NDVI), and enhanced vegetation index (EVI) were consistent, with mean correlations with SPEI at 0.197, 0.156, and 0.128, respectively. GOSIF exhibited the strongest correlation with SPEI, reflecting the impact of drought stress on vegetation photosynthesis. Therefore, GOSIF proves advantageous for drought monitoring purposes. Most vegetation types showed a robust response of GOSIF to SPEI at a 9-month scale during a typical drought year, with grassland GOSIF being particularly sensitive to drought. Our trend predictions indicate a decreasing trend in GOSIF vegetation in Xinjiang, coupled with an increasing trend in drought. This study found that compared with that of the traditional greenness vegetation index, GOSIF has obvious advantages in monitoring drought in the arid zone of Xinjiang. Furthermore, it makes up for the lack of research on the mechanism of vegetation GOSIF response to drought on multiple timescales in the arid zone. These results provide strong theoretical support for investigating the monitoring, assessment, and prediction of vegetation response to drought in Xinjiang, which is vital for comprehending the mechanisms of carbon and water cycles in terrestrial ecosystems.
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