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For many of the Siberian rivers, and the Upper Ob in particular, 70–80% of the volume of the annual water runoff is formed during the spring flood. Thus, factors influencing the formation of water runoff during the spring flood are paramount. We explain changes in the isotopic composition of the Upper Ob surface waters by changing different components’ contribution to the runoff water discharge over the spring flood period. We suggest estimating the time of meltwater flow from the Upper Ob watershed to the outlet section using the difference between the date of the complete melting of the snow cover in the catchment area and the date of the maximum light isotope composition of water in the outlet section. We show that a sharp short-term weighting of the isotopic composition of water in the river at the end of the first phase of the flood may be associated with the influx of autumn soil moisture, displaced from the soils by snowmelt waters.

In order to help develop a better understanding of relevant catchment processes, this paper presents the changes in physico-chemical features of the Wieprz River water during the spring snowmelt flood of 2006. The obtained results showed that the groundwater sampled from the springs and the water sampled from the river had a similar and quite stable composition of the basic physicochemical features in the period of solely groundwater feeding (the river is fed only with the water coming from underground sources). The physico-chemical composition of river water during snowmelt depended on the contribution of surface runoff in total outflow and the flood phase. The correlation coefficients between the discharge in the Wieprz River and the concentrations in the studied indices were significantly negative: pH, SEC, HCO3, Ca, Mg, Na, Sr, SiO2, Cl, SO4, F. Significantly positive correlations associated with an increase in discharge were observed in the case of: K, NO3, NO2, total organic carbon, chemical oxygen demand and biochemical oxygen demand. Step and bidirectional responses were noted during the snowmelt flood in the case of the content of NH4 and PO4. W pracy przedstawiono zmiany cech fizyczno-chemicznych wody rzeki Wieprz w Guciowie (SE Polska) w czasie wiosennego wezbrania roztopowego 2006 roku. Wyniki badań wykazały, że wody gruntowe pobrane ze źródeł oraz wody pobrane z koryta rzeki Wieprz w okresie wyłącznego zasilania podziemnego, miały zbliżony i stabilny skład podstawowych wskaźników fizyczno-chemicznych. Podczas roztopów wartości parametrów fizyczno-chemicznych wody w rzece były uzależnione od stopnia przemarznięcia pokrywy glebowej, udziału spływu powierzchniowego w odpływie całkowitym oraz fazy wezbrania. Współczynniki korelacji między przepływem wody w rzece Wieprz a stężeniem badanego wskaźnika były istotne ujemne w przypadku: pH -0,78, SEC -0,92, TH -0,92, HCO3 -0,93, Ca -0,89, Mg -0,88, Na -0,81, Sr -0,87, SiO2 -0,81, Cl -0,87, SO4 -0,83, F -0,59. Istotnie dodatnie zależności związane ze zwiększeniem przepływu zanotowano w przypadku: K 0,73, NO3 0,71, NO2 0,58, TOC 0,62, COD 0,63 i BOD 0,62. Skokową i różnokierunkową reakcję podczas wezbrania roztopowego notowano w przypadku: NH4 0,34 i PO4 0,13.

The SWAT model has been widely used to simulate snowmelt runoff in cold regions thanks to its ability of representing the effects of snowmelt and permafrost on runoff generation and confluence. However, a core method used in the SWAT model, the temperature index method, assumes both the dates for maximum and minimum snowmelt factors and the snowmelt temperature threshold, which leads to inaccuracies in simulating snowmelt runoff in seasonal snowmelt regions. In this paper, we present the development and application of an improved temperature index method for SWAT (SWAT+) in simulating the daily snowmelt runoff in a seasonal snowmelt area of Northeast China. The improvements include the introduction of total radiation to the temperature index method, modification of the snowmelt factor seasonal variation formula, and changing the snowmelt temperature threshold according to the snow depth derived from passive microwave remote sensing data and temperature in the seasonal snowmelt area. Further, the SWAT+ model is applied to study climate change impact on future snowmelt runoff (2025–2054) under the climate change scenarios including SSP2.6, SSP4.5, and SSP8.5. Much improved snowmelt runoff simulation is obtained as a result, supported by several metrics, such as MAE, RE, RMSE, R2, and NSE for both the calibration and validation. Compared with the baseline period (1980–2019), the March–April ensemble average snowmelt runoff is shown to decrease under the SSP2.6, SSP4.5, and SSP8.5 scenario during 2025–2054. This study provides a valuable insight into the efficient development and utilization of spring water resources in seasonal snowmelt areas.

A warming climate is expected to have an impact on the magnitude and timing of river floods; however, no consistent large-scale climate change signal in observed flood magnitudes has been identified so far. We analyzed the timing of river floods in Europe over the past five decades, using a pan-European database from 4262 observational hydrometric stations, and found clear patterns of change in flood timing. Warmer temperatures have led to earlier spring snowmelt floods throughout northeastern Europe; delayed winter storms associated with polar warming have led to later winter floods around the North Sea and some sectors of the Mediterranean coast; and earlier soil moisture maxima have led to earlier winter floods in western Europe. Our results highlight the existence of a clear climate signal in flood observations at the continental scale.

The contributions of heavy rainfall and/or rapid snow and glacier melting can be quantified to help understand the evolution and recession of flood formation. In this study, the hydrologic components of river discharge in the Manas River Basin were simulated using an energy balance snowmelt model called ?UEBGrid.? The model can identify surface water inputs from rain, evaporation and snow and glacial melting. The 3-h downscaled China meteorological forcing dataset and available geographic information related to basin topography, and vegetation characteristics were used to drive the model. The surface water inputs from rain, snowmelt and glacier ice melt to the river discharge during the rain-on-snow flood on 20 July 2004, and the snowmelt flood on 29 March 2011, were explored. Detailed analyses of the temporal distributions of the three surface water inputs and their contributions to river discharge during two typical floods at a 3-h scale are presented herein. The results show that (1) changes in the hydrologic components were connected to variations in either air temperature or precipitation or a combination of the two. In early spring, sharply rising temperatures likely accelerated the snow melting rate and caused the area to be prone to flooding. In late July, torrential rainfall was the most common cause of flooding. The increased rainfall in the preceding period increased the runoff rate, thereby reducing the runoff delay time by approximately one day. (2) In the flooding example, the contributions of rain, snowmelt and glacier ice melt to the river discharge varied strongly by flood type and showed obvious seasonal characteristics. The surface water inputs to the mountain front discharge were 90% sourced from snowmelt, less than 10% sourced from the glacial melt and less than 1% sourced from effective rainfall during spring flooding. In contrast, the surface water inputs were 90% sourced from effective rainfall and 10% sourced from glacial melt during the torrential rainfall flooding process. These results can help us understand the water sources and magnitude of river discharge when floods occur. Hourly surface water inputs from rain, snowmelt and glacier ice melt to river discharge that lead to hydrologic extremes (flood events) may be of value for flood predictions, climate change risk assessments and other related applications. This study can aid in preventing flood hazards and mitigating flood disasters.

Climate change due to anthropogenic greenhouse gas emissions is expected to increase the frequency and intensity of precipitation events, which is likely to affect the probability of flooding into the future. In this paper we use river flow simulations from nine global hydrology and land surface models to explore uncertainties in the potential impacts of climate change on flood hazard at global scale. As an indicator of flood hazard we looked at changes in the 30-y return level of 5-d average peak flows under representative concentration pathway RCP8.5 at the end of this century. Not everywhere does climate change result in an increase in flood hazard: decreases in the magnitude and frequency of the 30-y return level of river flow occur at roughly one-third (20–45%) of the global land grid points, particularly in areas where the hydrograph is dominated by the snowmelt flood peak in spring. In most model experiments, however, an increase in flooding frequency was found in more than half of the grid points. The current 30-y flood peak is projected to occur in more than 1 in 5 y across 5–30% of land grid points. The large-scale patterns of change are remarkably consistent among impact models and even the driving climate models, but at local scale and in individual river basins there can be disagreement even on the sign of change, indicating large modeling uncertainty which needs to be taken into account in local adaptation studies.

The hydrological effect of snow over Eurasia is important for regulating regional and global climate through affecting land–atmosphere energy exchange. Based on observational and reanalysis datasets, this study investigates the effect of spring Eurasian snowmelt on the following summer rainfall over eastern China during the period of 1979–2018. The results show that a substantial meridional dipole pattern of summer rainfall anomalies over eastern China is closely associated with the preceding spring snowmelt anomalies over Eurasia, especially over remote Siberia. Excessive snowmelt anomalies over Siberia in spring could result in a wetter local soil condition from spring until the following summer, thereby increasing latent heat fluxes and reducing local surface temperature, and vice versa. Then, the anomalous summer surface cooling over Siberia increases the meridional gradient of temperature between the Eurasian midlatitudes and high latitudes, which intensifies the Eurasian atmospheric baroclinicity and motivates the eddy‐induced geopotential height responses along with the significant wave propagations spreading from the Eurasian high latitudes to Lake Baikal. As a result, excessive spring snowmelt anomalies over Siberia tend to be accompanied with an anomalous anticyclone circulation to the east of Lake Baikal and an anomalous cyclonic circulation over southeastern China in the following summer. This could lead to a meridional dipole pattern of summer rainfall anomalies over eastern China, with deficient rainfall over northern China and slightly excessive rainfall over southern China. The present findings highlight the lagged effect of spring Eurasian snowmelt on summer climate over eastern China, with implications for the regional seasonal climate prediction.

Rain-on-snow (ROS) events are multivariate hydrometeorological phenomena that require a combination of rain and snowpack, with complex processes occurring on and within the snowpack. Impacts include floods and landslides, and rain may freeze within the snowpack or on bare ground, potentially affecting vegetation, wildlife, and permafrost. ROS events occur mainly in high-latitude and mountainous areas, where sparse observational networks hinder accurate quantification—as does a scalemismatch between coarse-resolution (50–100 km) reanalysis products and localized events. Variability in the rain–snow temperature threshold and temperature sensitivity of snowmelt adds additional uncertainty. Here the high-resolution (1 km) se- Norge hydrometeorological dataset, capturing complex topography and drainage networks, is utilized to produce the first large-scale climatology of ROS events for mainland Norway. For daily data spanning 1957–2016, suitable rain and snowpack thresholds for defining ROS events are applied to construct ROS climatologies for 1961–90 and 1981–2010 and to investigate trends. Differing ROS characteristics are found, reflecting Norway’s diverse climates. Relative to 1961–90, events in the 1981–2010 period decrease most in the southwest low elevations in winter, southeast in spring, and north in summer (consistent with less snow cover in a warming climate) and increase most in the southwest high elevations, centralmountains, and north in winter–spring (consistent with increased precipitation and/or more snow falling as rain in a warming climate). Winter–spring events also broadly correlate with the North Atlantic Oscillation, and the Scandinavia pattern—and more so with the Arctic Oscillation, particularly in the southern mountain region where long-term ROS trends are significant (+0.50 and +0.33 daily ROS counts per kilometer squared per decade for winter and spring).

Snow is a critical contributor to Ontario's water-energy budget, with impacts on water resource management and flood forecasting. Snow water equivalent (SWE) describes the amount of water stored in a snowpack and is important in deriving estimates of snowmelt. However, only a limited number of sparsely distributed snow survey sites (n=383) exist throughout Ontario. The SNOw Data Assimilation System (SNODAS) is a daily, 1 km gridded SWE product that provides uniform spatial coverage across this region; however, we show here that SWE estimates from SNODAS display a strong positive mean bias of 50 % (16 mm SWE) when compared to in situ observations from 2011 to 2018. This study evaluates multiple statistical techniques of varying complexity, including simple subtraction, linear regression and machine learning methods to bias-correct SNODAS SWE estimates using absolute mean bias and RMSE as evaluation criteria. Results show that the random forest (RF) algorithm is most effective at reducing bias in SNODAS SWE, with an absolute mean bias of 0.2 mm and RMSE of 3.64 mm when compared with in situ observations. Other methods, such as mean bias subtraction and linear regression, are somewhat effective at bias reduction; however, only the RF method captures the nonlinearity in the bias and its interannual variability. Applying the RF model to the full spatio-temporal domain shows that the SWE bias is largest before 2015, during the spring melt period, north of 44.5∘ N and east (downwind) of the Great Lakes. As an independent validation, we also compare estimated snowmelt volumes with observed hydrographs and demonstrate that uncorrected SNODAS SWE is associated with unrealistically large volumes at the time of the spring freshet, while bias-corrected SWE values are highly consistent with observed discharge volumes.

Floods are one of the deadliest weather-related natural disasters in the continental United States (CONUS). Given that rainfall intensity and the amount of CONUS population exposed to floods is expected to increase in the future, it is critical to understand flood characteristics across the CONUS. Therefore, the purpose of this study is to develop a flood-producing storm climatology over the CONUS from 2002 to 2013 to better understand rainfall characteristics of these storms and spatiotemporal differences across the country. Flood reports from the NCEI Storm Events Database are grouped by causative meteorological event and are merged with a database of stream-gauge-indicated floods to provide a robust indication of significant hydrologic events with a meteorological linkage. High-resolution Stage IV rainfall data were matched to 5559 flood episodes across the CONUS to identify rainfall characteristics of flood-producing storms in a variety of environments. This storm climatology indicates that flash flood–producing storms frequently occur with high rainfall accumulations in the summer east of the Rockies. Slow-rise flood-producing storms frequently occur in the spring–early summer (winter), with high rainfall accumulations over the northern and central CONUS (Pacific Northwest) due to rain-on-snowmelt, synoptic systems, and mesoscale convective systems (atmospheric rivers). Hybrid flood-producing storms, sharing characteristics of flash and slow-rise floods, frequently occur in spring–summer and have high rainfall accumulations in the central CONUS, Northeast, and mid-Atlantic. Results from this climatology may provide useful for emergency managers, city planners, and policy makers seeking efforts to protect their communities against risks associated with flood-producing storms.