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.

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.

Rapid atmospheric warming changes the thermal conditions of permafrost over the Northern Hemisphere (NH), including ground temperature warming and ground ice thawing. This warming and thawing of ice-rich permafrost damages existing infrastructure and poses a threat to sustainable development. Bearing capacity (BC) loss and ground subsidence (GS) due to permafrost thawing are two major risks to the infrastructure and key indexes for risk assessment. However, current information on the BC and GS is too coarse, restricted to the Arctic, and scarce for future periods. The aim of this study was to address these gaps by presenting spatial data on the BC and GS for current and future periods across the NH at a resolution of 1 km. A machine learning-based approach was developed to simulate permafrost thermal dynamics under four climate scenarios (SSPs 1-2.6, 2-4.5, 3-7.0, and 5-8.5). The associated changes in the BC and GS were estimated based on changes in the permafrost temperature at or near the depth of zero annual amplitude (MAGT) and active-layer thickness (ALT). The results indicate a continuous increase in MAGT and ALT by 2.3 degrees C (SSPs1-2.6) to 7.6 degrees C (SSPs5-8.5) and 16.0 cm (SSPs1-2.6) to 51.0 cm (SSPs5-8.5), respectively, at the end of the 21ts century. This permafrost degradation will lead to a high potential BC loss of 37.8% (SSPs1-2.6) to 40.2% (SSPs5-8.5) on average over 2041-2060, and up to 60.5% (SSPs1-2.6) to 92.2% (SSPs5-8.5) in 2081-2100. The produced average GS is approximately 1.0 cm in 2021-2040, and further up to 1.5 cm (SSPs1-2.6) to 4.7 cm (SSPs5-8.5) in 2081-2100, with notable variations across the permafrost region. These forecasts provide new opportunities to assess future permafrost changes and associated risks and costs with climate warming.

This article presents the results of field study near a Northern Railway embankment (Hanovey station) in a field work area of the Geocryology Department (Moscow State University), where we performed cone penetration tests, measured the thickness of the active layer and soil temperatures, monitored settlement of the embankment, and performed laboratory tests. A mathematical model was compiled in the Qfrost program based on these data taking the unevenness of the snow cover in the study area into account. Calculations of the temperature regime of the embankment until 2050 taking climate change into account (according to the RCP 4.5 scenario), showed that the thickness of the talik at the embankment will increase by 40% in 30 years and without taking this factor into account, by 17%. This article also discusses the features of the position and structure of the embankment, as well as the composition and properties of frozen soils, which significantly affect the stability of the embankment.

Reliable subseasonal forecasts of high summer temperatures would be very valuable for society. Although state-of-the-art numerical weather prediction (NWP) models have become much better in representing the relevant sources of predictability like land and sea surface states, the subseasonal potential is not fully realized. Complexities arise because drivers depend on the state of other drivers and on interactions over multiple time scales. This study applies statistical modeling to ERA5 data, and explores how nine potential drivers, interacting on eight time scales, contribute to the subseasonal predictability of high summer temperatures in western and central Europe. Features and target temperatures are extracted with two variations of hierarchical clustering, and are fitted with a machine learning (ML) model based on random forests. Explainable AI methods show that the ML model agrees with physical understanding. Verification of the forecasts reveals that a large part of predictability comes from climate change, but that reliable and valuable subseasonal forecasts are possible in certain windows, like forecasting monthly warm anomalies with a lead time of 15 days. Contributions of each driver confirm that there is a transfer of predictability from the land and sea surface state to the atmosphere. The involved time scales depend on lead time and the forecast target. The explainable AI methods also reveal surprising driving features in sea surface temperature and 850 hPa temperature, and rank the contribution of snow cover above that of sea ice. Overall, this study demonstrates that complex statistical models, when made explainable, can complement research with NWP models, by diagnosing drivers that need further understanding and a correct numerical representation, for better future forecasts.

Marine heatwaves (MHWs) are becoming increasingly frequent and intense around China, impacting marine ecosystems and coastal communities. Accurate forecasting of MHWs is crucial for their management and mitigation. In this study, we assess the forecasting ability of the global eddy-resolving ocean forecast system LICOM Forecast System (LFS) for the MHW events in October 2021 around China. Our results show that the 1-day lead forecast by the LFS accounts for up to 79% of the observed MHWs, with the highest skill during the initial and decay periods. The forecasted duration and intensity of the MHW event are consistent with observations but with some deviations in specific regions of the Yellow and South China seas. A detailed analysis of the heat budget reveals that the forecasted shortwave radiation flux is a key factor in the accuracy of the forecasted MHW duration and intensity. The oceanic dynamic term also greatly contributes to the accuracy in the southern Yellow Sea. In addition, the increasing bias of the forecasted duration and intensity with lead time are mainly caused by the underestimated shortwave radiation. Our findings suggest that improving the accuracy of oceanic dynamic processes and surface radiation fluxes in the LFS could be a promising direction to enhance the forecasting ability of marine extreme events such as MHWs.

Human activities have substantially altered present-day flow regimes. The Headwater Area of the Yellow River (HAYR, above Huanghe'yan Hydrological Station, with a catchment area of 21,000 km(2) and an areal extent of alpine permafrost at similar to 86%) on the northeastern Qinghai-Tibet Plateau, Southwest China has been undergoing extensive changes in streamflow regimes and groundwater dynamics, permafrost degradation, and ecological deterioration under a warming climate. In general, hydrological gauges provide reliable flow records over many decades and these data are extremely valuable for assessment of changing rates and trends of streamflow. In 1998-2003, the damming of the Yellow River by the First Hydropower Station of the HAYR complicated the examination of the relations between hydroclimatic variables and streamflow dynamics. In this study, the monthly streamflow rate of the Yellow River at Huanghe'yan is reconstructed for the period of 1955-2019 using the double mass curve method, and then the streamflow at Huagnhe'yan is forecasted for the next 20 years (2020-2040) using the Elman neural network time-series method. The dam construction (1998-2000) has caused a reduction of annual streamflow by 53.5-68.4%, and a more substantial reduction of 71.8-94.4% in the drier years (2003-2005), in the HAYR. The recent removal of the First Hydropower Station of the HAYR dam (September 2018) has boosted annual streamflow by 123-210% (2018-2019). Post-correction trends of annual maximum (Q(Max)) and minimum (Q(Min)) streamflow rates and the ratio of the Q(Max)/Q(Min) of the Yellow River in the HAYR (0.18 and 0.03 m(3).(-)s(-1).yr(-1) and -0.04 yr(-1), respectively), in comparison with those of precorrection values (-0.11 and -0.004 m(3).s(-1).yr(-1) and 0.001 yr(-1), respectively), have more truthfully revealed a relatively large hydrological impact of degrading permafrost. Based on the Elman neural network model predictions, over the next 20 years, the increasing trend of flow in the HAYR would generally accelerate at a rate of 0.42 m(3).s(-1).yr(-1). Rising rates of spring (0.57 m(3).s(-1).yr(-1)) and autumn (0.18 m(3).s(-1).yr(-1)) discharge would see the benefits from an earlier snow-melt season and delayed arrival of winter conditions. This suggests a longer growing season, which indicates ameliorating phonology, soil nutrient availability, and hydrothermal environments for vegetation in the HAYR. These trends for hydrological and ecological changes in the HAYR may potentially improve ecological safety and water supplies security in the HAYR and downstream Yellow River basins.

Aim Climate change results in increasing temperature and modified precipitation regimes in the High Arctic. Models can help anticipate the consequences on future biotic dynamics, e.g. vegetation. In rugged terrain, forecasts should consider fine-scale spatial variability in environmental conditions that are proximally linked to plant performance. Here, we forecasted Arctic plant community response to future climate change using high-resolution environmental variables. Location Methods Zackenberg in the High Arctic of Greenland. Using a combination of remote-sensing data and field measurements, we interpolated soil moisture and temperature at 1 m resolution together with spatial data on snow cover and solar radiation. We calibrated stacked species distribution models (S-SDMs) with data from 200 vegetation plots. To explore the sensitivity of Arctic communities to climate change, we forecasted these models under simulated increases in temperature and changes (positive or negative) in snow cover and soil moisture, corresponding to more winter and/or summer precipitation and higher frequencies of summer droughts. Results Main conclusions S-SDMs associated with high-resolution environmental variables were able to reproduce the spatial variation in species richness and plant community structure along a mountain slope in Zackenberg. Model forecasts under climate change revealed that most species reacted to a combination of changes in soil moisture and temperature, and changes in these two variables resulted in an extensive restructuring of the distributions of species assemblages. In most scenarios, a gradual homogenization of communities was forecasted due to shrub expansion. Increasing temperatures and altered soil moisture were predicted to turn the currently highly heterogeneous tundra landscape at Zackenberg into homogenous dwarf-shrub tundra. Such homogenization of vegetation communities may have profound ramifications for species, interaction webs, and ecosystem processes via modifications to the surface albedo, energy and water balance, as well as snow accumulation and permafrost.

Increasing global warming has led to the incremental retreat of glaciers, which in turn affects the water supply of the rivers dependent on glacier melts. This is further affected by the increases in flooding that is attributable to heavy rains during the snowmelt season. An accurate estimation of streamflow is important for water resources planning and management. Therefore, this paper focuses on improving the streamflow forecast for Kaidu River Basin, situated at the north fringe of Yanqi basin on the south slope of the Tianshan Mountains in Xinjiang, China. The interannual and decadal scale oceanic-atmospheric oscillations, i.e.,Pacific decadal oscillation (PDO), North Atlantic oscillation (NAO), Atlantic multidecadal oscillation (AMO), and El Nino-southern oscillation (ENSO), are used to generate streamflow volumes for the peak season (April-October) and the water year, which is from October of the previous year to September of the current year for a period from 1955-2006. A data-driven model, least-square support vector machine (LSSVM), was developed that incorporated oceanic atmospheric oscillations to increase the streamflow lead time. Based on performance measures, predicted streamflow volumes are in agreement with the measured volumes. Sensitivity analyses, performed to evaluate the effect of individual and coupled oscillations, revealed a stronger presence of coupled PDO, NAO, and ENSO indices within the basin. The AMO index shows a pronounced effect when individually compared with the other oscillation modes. Additionally, model-forecasted streamflow is better than that for climatology. Overall, very good streamflow predictions are obtained using the SVM modeling approach. Furthermore, the LSSVM streamflow predictions outperform the predictions obtained from the most widely used feed-forward back-propagation models, artificial neural network, and multiple linear regression. The current paper contributes in improving the streamflow forecast lead time, and identified a coupled climate signal within the basin. The increased lead time can provide useful information to water managers in improving the planning and management of water resources within the Kaidu River Basin. (C) 2013 American Society of Civil Engineers.

Prediction of snowmelt has become a critical issue in much of the western United States given the increasing demand for water supply, changing snow cover patterns, and the subsequent requirement of optimal reservoir operation. The increasing importance of hydrologic predictions necessitates that traditional forecasting systems be re-evaluated periodically to assure continued evolution of the operational systems given scientific advancements in hydrology. The National Weather Service (NWS) SNOW17, a conceptually based model used for operational prediction of snowmelt, has been relatively unchanged for decades. In this study, the Snow-Atmosphere-Soil Transfer (SAST) model, which employs the energy balance method, is evaluated against the SNOW17 for the simulation of seasonal snowpack (both accumulation and melt) and basin discharge. We investigate model performance over a 13-year period using data from two basins within the Reynolds Creek Experimental Watershed located in southwestern Idaho. Both models are coupled to the NWS runoff model [SACramento Soil Moisture Accounting model (SACSMA)] to simulate basin streamflow. Results indicate that while in many years simulated snowpack and streamflow are similar between the two modeling systems, the SAST more often overestimates SWE during the spring due to a lack of mid-winter melt in the model. The SAST also had more rapid spring melt rates than the SNOW17 7, leading to larger errors in the timing and amount of discharge on average. In general, the simpler SNOW17 performed consistently well, and in several years, better than, the SAST model. Input requirements and related uncertainties, and to a lesser extent calibration, are likely to be primary factors affecting the implementation of an energy balance model in operational streamflow prediction. (C) 2008 Elsevier B.V. All rights reserved.