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.

An increase in extreme rainfall frequency across the midwestern United States has been accompanied by an increase in damaging floods. The US has over 90,000 dams, more than 75% of which are small and rarely used for flood mitigation. Recent research focused on operating these ponds for flood reduction using gated outlets, a technique known as activated distributed storage, has confirmed its potential for reducing flood impacts. Here, the authors build upon this work by developing a hydrologic model to simulate the active management of a distributed network of 130 ponds that employs up to 18 h of forecasted rainfall for operational decision making, a process known as forecast-informed reservoir operation (FIRO). Using five observed rainfall events and a single dam operations scheme, the effects of using FIRO for real-time gate operations on both downstream peak flows and basin wide storage utilization are evaluated. Simulation results that use the high-resolution rapid refresh (HRRR) product, were compared to those that (1) use no rainfall forecasts for decision making; and (2) use 18 h of observed rainfall mimicking an ideal forecast. Regardless of forecast accuracy or rainfall accumulation, shorter forecast lead times result in operational decisions that release water early in an event, vacating storage, while longer lead times result in increased storage throughout an event, thus reducing downstream flows. These results indicate that rainfall forecasts may not be solely capable of addressing the complexities governing a distributed storage network's ability to release water. This suggests that a more nuanced approach, utilizing optimal control of the storage network is required to unlock the technique's full potential.

Forecasting landslide deformation is challenging due to influence of various internal and external factors on the occurrence of systemic and localized heterogeneities. Despite the potential to improve landslide predictability, deep learning has yet to be sufficiently explored for complex deformation patterns associated with landslides and is inherently opaque. Herein, we developed a holistic landslide deformation forecasting method that considers spatiotemporal correlations of landslide deformation by integrating domain knowledge into interpretable deep learning. By spatially capturing the interconnections between multiple deformations from different observation points, our method contributes to the understanding and forecasting of landslide systematic behavior. By integrating specific domain knowledge relevant to each observation point and merging internal properties with external variables, the local heterogeneity is considered in our method, identifying deformation temporal patterns in different landslide zones. Case studies involving reservoir-induced landslides and creeping landslides demonstrated that our approach (1) enhances the accuracy of landslide deformation forecasting, (2) identifies significant contributing factors and their influence on spatiotemporal deformation characteristics, and (3) demonstrates how identifying these factors and patterns facilitates landslide forecasting. Our research offers a promising and pragmatic pathway toward a deeper understanding and forecasting of complex landslide behaviors. (c) 2025 Institute of Rock and Soil Mechanics, Chinese Academy of Sciences. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/ 4.0/).

Pipelines are the primary mode of oil and gas transport in cold regions. Differential frost heaving of frozen and non-frozen soil masses can damage such pipelines, posing economic and environmental risks. The present study investigates the mechanical behaviors of buried pipelines under differential frost heaving forces. A discrete forecasting model of these mechanical behaviors based on frost heaving springs is proposed. The relationship between the frost heaving amount and force at any moment is established using the Takashi empirical equation and the corresponding development of frost depth. On this basis, the properties of nonlinear frost heaving springs are disclosed. A model of pipeline mechanical state is derived to understand the deformation and stress at any moment, allowing the dynamic prediction of mechanical behaviors. The model is applied to two case studies involving the Caen and Alaska buried pipelines. The modeling results agree well with measurements taken at these pipelines, and the discrete descriptions of their mechanical modes are effective. A sensitivity analysis of the modeling results for pipelines of different size was conducted, providing a theoretical foundation for the optimal design of buried pipelines in cold regions.

Uncertainty plays a key role in hydrological modeling and forecasting, which can have tremendous environmental, economic, and social impacts. Therefore, it is crucial to comprehend the nature of this uncertainty and identify its scope and effects in a way that enhances hydrological modeling and forecasting. During recent decades, hydrological researchers investigated several approaches for reducing inherent uncertainty considering the limitations of sensor measurement, calibration, parameter setting, model conceptualization, and validation. Nevertheless, the scope and diversity of applications and methodologies, sometimes brought from other disciplines, call for an extensive review of the state-of-the-art in this field in a way that promotes a holistic view of the proposed concepts and provides textbook-like guidelines to hydrology researchers and the community. This paper contributes to this goal where a systematic review of the last decade's research (2010 onward) is carried out. It aims to synthesize the theories and tools for uncertainty reduction in surface hydrological forecasting, providing insights into the limitations of the current state-of-the-art and laying down foundations for future research. A special focus on remote sensing and multi-criteria-based approaches has been considered. In addition, the paper reviews the current state of uncertainty ontology in hydrological studies and provides new categorizations of the reviewed techniques. Finally, a set of freely accessible remotely sensed data and tools useful for uncertainty handling and hydrological forecasting are reviewed and pointed out.

Economic and human losses from flooding have had a significant global impact. Undeveloped nations often require extended periods to recover from flood-related damage, exacerbating the climate poverty trap, specifically in flood-prone regions. To address this issue, early warning systems (EWS) provide lead time for preparedness and measures to reduce vulnerability. However, EWS are mainly empirical at large scales and often do not incorporate hydrodynamic behaviors in flood forecasting, at least in developing regions with a lack of information. This study presents an open-source system integrating a hydrodynamic model with satellite rainfall data (PERSIANN PDIR-Now) and weather prediction data (GFS). It functions as a near real-time Digital Twin (DT) and Early Warning System for high-resolution flood forecasting. Simulated data can be compared with gauge stations in real-time through the model monitoring interface. A proof-of-concept was made by assessing the model capabilities in two case studies. First, the system simulated two consecutive extreme events (hurricanes ETA and IOTA) over the Sula Valley, Honduras, showing fidelity in streamflow responses. Second, the system worked as a DT and EWS to monitor the current and future hydrological states for two periods in 2022 and 2023. Results indicate that satellite data coupled with DT can provide up-to-date system conditions for flood forecasts for regions of lack of data for extreme rainfall events. This tool offered insights to enhance civil protection and societal engagement through warning dissemination against extreme events to build resilience to cope with the increasing magnitude and frequency of disasters in regions with data scarcity.

Extreme temperatures can cause severe disruptions to society, from negative health consequences to infrastructure damage. Accurate and timely weather forecasts contribute to minimising these detrimental effects, by supporting early-warning systems. In this context, information on the expected performance of the forecasts is valuable. Here, we investigate whether there is a relationship between the persistence of atmospheric circulation patterns in the Euro-Atlantic sector and forecast skill for temperatures and temperature extremes in Europe. We first apply an objective method to compute the persistence of large-scale atmospheric patterns in European Centre for Medium-Range Weather Forecasts (ECMWF) subseasonal retrospective forecasts. We find that the forecasts successfully predict atmospheric persistence up to time-scales of approximately two weeks. We next investigate the relationship between the persistence of an atmospheric state and the practical predictability of temperature in terms of the error in surface temperature forecasts. The relationship between the two varies depending on season and location. Nonetheless, in a number of cases atmospheric persistence provides potentially valuable information on the practical predictability of temperature. We specifically highlight the cases of wintertime temperature forecasts up to three weeks lead time and wintertime cold spells up to roughly two weeks lead time.

In contrast to boreal winter when extratropical seasonal predictions benefit greatly from ENSO-related teleconnections, our understanding of forecast skill and sources of predictability in summer is limited. Based on 40 years of hindcasts of the Canadian Seasonal to Interannual Prediction System, version 3 (CanSIPSv3), this study shows that predictions for the Northern Hemisphere summer surface air temperature are skillful more than 6 months in advance in several midlatitude regions, including eastern Europe-Middle East, central Siberia-Mongolia-North China, and the western United States. These midlatitude regions of statistically significant predictive skill appear to be connected to each other through an upper-tropospheric circumglobal wave train. Although a large part of the forecast skill for the surface air temperature and 500-hPa geopotential height is attributable to the linear trend associated with global warming, there is signifi- cant long-lead seasonal forecast skill related to interannual variability. Two additional idealized hindcast experiments are performed to help shed light on sources of the long-lead forecast skill using one of the CanSIPSv3 models and its uncoupled version. It is found that tropical ENSO-related sea surface temperature (SST) anomalies contribute to the forecast skill in the western United States, while land surface conditions in winter, including snow cover and soil moisture, in the Siberian and western U.S. regions have a delayed or long-lasting impact on the atmosphere, which leads to summer forecast skill in these regions. This implies that improving land surface initial conditions and model representation of land surface processes is crucial for the further development of a seasonal forecasting system.

The C & ocirc;a region in inner-northern Portugal heavily relies on viticulture, which is a cornerstone of its economy and cultural identity. Understanding the intricate relationship between climatic variables and wine production (WP) is crucial for adapting management practices to changing climatic conditions. This study employs machine learning (ML), specifically random forest (RF) regression, to predict grapevine yields in the C & ocirc;a region using high-resolution climate data for 2004-2020. SHAP (SHapley Additive exPlanations) values are used to potentially explain the non-linear relationships between climatic factors and WP. The results reveal a complex interplay between predictors and WP, with precipitation emerging as a key determinant. Higher precipitation levels in April positively impact WP by replenishing soil moisture ahead of flowering, while elevated precipitation and humidity levels in August have a negative effect, possibly due to late-season heavy rainfall damaging grapes or creating more favorable conditions for fungal pathogens. Moreover, warmer temperatures during the growing season and adequate solar radiation in winter months favor higher WP. However, excessive radiation during advanced growth stages can lead to negative effects, such as sunburn. This study underscores the importance of tailoring viticultural strategies to local climatic conditions and employing advanced analytical techniques such as SHAP values to interpret ML model predictions effectively. Furthermore, the research highlights the potential of ML models in climate change risk reduction associated with viticulture, specifically WP. By leveraging insights from ML and interpretability techniques, policymakers and stakeholders can develop adaptive strategies to safeguard viticultural livelihoods and stable WP in a changing climate, particularly in regions with a rich agrarian heritage, such as the C & ocirc;a region.

A landslide involves the downward movement of a mass of rock, debris, earth, or soil. Landslides happen when gravitational forces and other types of shear stresses on a slope surpass the shear strength of the materials. Additionally, landslides can be triggered by processes that weaken the shear strength of the slope's material. Shear strength primarily depends on two factors such as frictional strength, which is the resistance to movement between the interacting particles of the slope material, and cohesive strength, which is the bonding between those particles. A landslide is a terrible natural disaster that causes much damage to both human life and the economy. It often occurs in steep mountainous areas or hilly regions, ranging in scale from medium to large. It progresses slowly (20-50 mm/year), but when it occurs, it can move at a speed of 3 m/s. Therefore, early detection or prevention of this disaster is an essential and significant task. This paper developed a method to collect and analyze data, with the purpose of determining the possibility of landslide occurrences to reduce its potential losses. center dot The proposed method is convenient for users to grasp information of landslide phenomenon. center dot A machine learning model is applied to forecast landslide phenomenon. center dot Internet of things (IoT) system is utilized to manage and send a warning text to individual email address and mobile devices.