Permafrost is undergoing widespread degradation affected by climate change and anthropogenic factors, leading to seasonal freezing and thawing exhibiting interannual, and fluctuating differences, thereby impacting the stability of local hydrological processes, ecosystems, and infrastructure. To capture this seasonal deformation, scholars have proposed various InSAR permafrost deformation models. However, due to spatial-temporal filtering smoothing high-frequency deformation and the presence of approximate assumptions in permafrost models, such differences are often difficult to accurately capture. Therefore, this paper applies an InSAR permafrost monitoring method based on moving average models and annual variations to detect freezing and thawing deformation in the Russian Novaya Zemlya region from 2017 to 2021 using Sentinel-1 data. Most of the study area's deformation rates remained between 10 and 10 mm/yr, while in key oil extraction areas, they reached -20 mm/yr. Seasonal deformation amplitudes were relatively stable in urban areas, but reached 90 mm in regions with extensive development of thermokarst lakes, showing a significant increasing trend. To validate the accuracy of the new method in capturing seasonal deformations, we used seasonal deformations obtained from different methods to retrieval the Active Layer Thickness (ALT), and compared them with field ALT measurement data. The results showed that the new method had a smaller RMSE and improved accuracy by 5% and 30% in two different ALT observation areas, respectively, compared to previous methods. Additionally, by combining the spatial characteristics of seasonal deformation amplitudes and ALT, we analyzed the impact of impermeable surfaces, confirming that human-induced surface hardening alters the feedback mechanism of perennial frozen soil to climate.

Real-time flood forecasting updating is essential in improving the forecasting performance and preventing flood damages. The advanced dynamic system response curve (DSRC) method has been validated to be effective by adjusting precipitation based on simulated streamflow errors. However, the time lag between input-output signals is not explicitly considered in the original DSRC, resulting in the problem that the most recent precipitation information is not utilized in updating the forecasting. Moreover, regularization techniques are normally introduced in DSRC to ensure the numerical stability of error estimation, however, the commonly used Ridge estimator can result in excessive adjustment of precipitation. To address these critical issues, we proposed an improved precipitation adjustment framework (DSRC-ARMA) that integrated the DSRC method and the autoregressive-moving average (ARMA) model, such that the most recent precipitation information can be used for a complete precipitation adjustment. Moreover, alternative regularized estimators (i.e., the Lasso and Elastic Net estimators) were introduced and cross-compared to prevent the excessive adjustment issue. The performance of the proposed framework was evaluated in two basins in China. The results showed that the DSRC-ARMA method outperformed the original DSRC method in terms of overall goodness-of-fit (e.g., Nash-Sutcliffe efficiency improved from 0.94 f 0.03 to 0.95 f 0.04 and 0.89 f 0.05 to 0.91 f 0.05, respectively in Dapoling (DPL) and Jianyang (JY) basin) and particularly capturing the peak flows (relative error of peak flow decreased from 13.6 f 7.3 % to 5.2 f 3.7 % and from 10.1 f 7.8 % to 5.9 f 3.5 % in DPL and JY, respectively). For different regularized estimators, the Ridge estimator was most suitable for the rainfall events without intermittent non-rainfall time segments (due to its veracity feature); while the Lasso estimator performed better for intermittent rainfall events, due to its feature of sparsity that can confine non-rainfall period errors to be zeros and thus avoid excessive adjustment. Overall, the proposed precipitation adjustment framework holds the potential to enhance the real-time flood forecasting accuracy, thereby offering a valuable approach for flood mitigation.