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Most slope collapse accidents are indicated by certain signs before their occurrence, and unnecessary losses can be avoided by predicting slope deformation. However, the early warning signs of slope deformation are often misjudged. It is necessary to establish a method to determine the appropriate early warning signs in sliding thresholds. Here, to better understand the impact of different scales on the early warning signs of sliding thresholds, we used the Fisher optimal segmentation method to establish the early warning signs of a sliding threshold model based on deformation speed and deformation acceleration at different spatial scales. Our results indicated that the accuracy of the early warning signs of sliding thresholds at the surface scale was the highest. Among them, the early warning thresholds of the blue, yellow, orange, and red level on a small scale were 369.31 mm, 428.96 mm, 448.41 mm, and 923.7 mm, respectively. The evaluation accuracy of disaster non-occurrence and occurrence was 93.25% and 92.41%, respectively. The early warning thresholds of the blue, yellow, orange, and red level on a large scale were 980.11 mm, 1038.16 mm, 2164.63 mm, and 9492.75 mm, respectively. The evaluation accuracy of disaster non-occurrence and occurrence was 97.22% and 97.44%, respectively. Therefore, it is necessary to choose deformation at the surface scale with a large scale as the sliding threshold. Our results effectively solve the problem of misjudgment of the early warning signs of slope collapse, which is of great significance for ensuring the safe operation of water conservation projects and improving the slope deformation warning capability.

The focus of this paper is on the grassland productivity response to drought under the background of climate change. There is an established lag impact on the response of grassland ecosystems to drought events, which may have additional effects on subsequent drought events. Meanwhile, due to climate change interference, the influence of drought on grassland productivity over the past 50 years is not simply equal to the algebraic sum of all the historical drought events. In the Inner Mongolia grassland, precipitation deficit plays a leading role in causing drought. Therefore, taking into consideration the impacts of drought lag effect and climate change, in this paper, we focus on the net influence of drought on grassland productivity over the past 50 years on the basis of long-term precipitation deficit, we identify the interference effect from different climate factors (precipitation and temperature) by using different scenario simulation tests, and therefore, further clarify the net influence on the grassland productivity of Inner Mongolia over the past 50 years.

Slope collapse is one of the most severe natural disaster threats, and accurately predicting slope deformation is important to avoid the occurrence of disaster. However, the single prediction model has some problems, such as poor stability, lower accuracy and data fluctuation. Obviously, it is necessary to establish a combination model to accurately predict slope deformation. Here, we used the GFW-Fisher optimal segmentation method to establish a multi-scale prediction combination model. Our results indicated that the determination coefficient of linear combination model, weighted geometric average model, and weighted harmonic average model was the highest at the surface spatial scale with a large scale, and their determination coefficients were 0.95, 0.95, and 0.96, respectively. Meanwhile, RMSE, MAE and Relative error were used as indicators to evaluate accuracy and the evaluation accuracy of the weighted harmonic average model was the most obvious, with an accuracy of 5.57%, 3.11% and 3.98%, respectively. Therefore, it is necessary to choose the weighted harmonic average model at the surface scale with a large scale as the slope deformation prediction combination model. Meanwhile, our results effectively solve the problems of the prediction results caused by the single model and data fluctuation and provide a reference for the prediction of slope deformation.