Meteorological drought risk in the Daqing River Basin, North China: current observations and future projections

Mu, Wenbin; Yu, Fuliang; Han, Yuping; Ma, Weixi; Zhao, Yong

doi:10.1007/s00477-020-01845-6

Cited by 6 publications

(2 citation statements)

References 55 publications

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“…The Standardized Precipitation Index (SPI) is a widely used drought index for measuring and predicting meteorological drought. Several studies in the literature have investigated the use of SPI for drought forecasting, such as research conducted by Dixit et al (2022), Mu et al (2020), Celik et al (2018, Dinç et al (2016), Ilgar (2010), andYeğiner (2005).…”

Section: Introductionmentioning

confidence: 99%

Meteorological Drought Modelling by Explainable Artificial Intelligence

Baykal

Terzi

Yıldırım

et al. 2023

Preprint

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The accurate prediction of drought events is important to relieve the negative consequences on water resources, ecosystems, and agriculture. Machine learning algorithms require less time and minimum input and are promising methods to predict drought because it is relatively less complicated than conventional models. In this study, the standardized precipitation index (SPI) is calculated for 3-, 6-, 9- and 12- months by using the monthly precipitation data of the Isparta, Eğirdir, Senirkent, Uluborlu, and Yalvaç meteorology measurement stations in Isparta, Turkey between 1964-2019. The calculated SPI values were used to develop the drought models by machine learning algorithms (Random Forest, K-Nearest Neighbors, Extra Trees Regression, Support Vector Regression, Gradient Boosting, and Extreme Gradient Boost). The R2 and RMSE values are used in the assessment of the model results. In general, these algorithms gave good results for all calculated drought periods. Also, the results showed that the best result is obtained as 0.80 R2 by using Extra Trees algorithms for testing set of 12- month model. The relationship between model results and input parameters was interpreted using the Shapley Additive exPlanations algorithm one of the explainable artificial intelligences. It was seen that the most effective input on the Isparta station SPI values is Senirkent station.

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Section: Introductionmentioning

confidence: 99%

Meteorological Drought Modelling by Explainable Artificial Intelligence

Baykal

Terzi

Yıldırım

et al. 2023

Preprint

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“…Lakes provide a main source of potable water; serve various functions, such as irrigation, aquaculture, storage and regulation, tourism and ecological balance; and play an important role in regional ecosystems and global biogeochemical cycles [4], [5]. These important effects are especially evident in Daqing Lakes, the largest lake gathering place in Northeast China [6], [7]. The water quality of lakes can directly influence the survival of human beings and sustainable development of society [8].…”

Section: Introductionmentioning

confidence: 99%

Quantifying Turbidity Variation for Lakes in Daqing of Northeast China Using Landsat Images From 1984 to 2018

Wang

Song

Wen

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Water turbidity is an important proxy to measure water quality and environmental conditions. Based on extensive field data and Landsat data (2011-2018) ，this study developed a retrieval model suitable for turbidity. The determination coefficient (R 2 ) of the model was 0.946 and the root mean square error (RMSE) was 23.82 NTU. The model was implemented to obtain the turbidity information of hundreds of lakes in Northeast China . The results revealed the distinctive spatial pattern of water turbidity values of the lakes (i.e., high turbidity in the south; low turbidity in the northwest; and moderate turbidity in the east). In terms of temporal pattern, the water turbidity values of most lakes trended downward at an average rate of 1.39 NTU/a (P< 0.05) with obvious seasonal differences (i.e., decreased from May to the lowest in July, and then increased from September onwards). Finally, we quantitatively examined how several typical factors affect turbidity variation at different scales. We found that water turbidity was highly correlated with NDVI (R=0.56, P< 0.001), followed by water temperature and wind speed (0.02

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