Snowmelt Period Research Articles

Isotopic Constraints on Nitrous Oxide Emissions From the US Corn Belt

AbstractAgriculture is the dominant source of anthropogenic nitrous oxide (N2O) –a greenhouse gas and a stratospheric ozone depleting substance. The US Corn Belt is a large global N2O source, but there remain large uncertainties regarding its source attribution and biogeochemical pathways. Here, we interpret high frequency stable N2O isotope observations from a very tall tower to improve our understanding of regional source attribution. We detected significant seasonal variability in δ15Nbulk (6.47–7.33‰) and the isotope site preference (δ15NSP = δ15Nα–δ15Nβ, 18.22–25.19‰) indicating a predominance of denitrification during the growing period but of nitrification during the snowmelt period. Isotope mixing models and atmospheric inversions both indicate that indirect emissions contribute substantially (>35%) to total N2O emissions. Despite the relatively large uncertainties, the upper bound of bottom‐up indirect emission estimates are at the lower bound of the isotopic constraint, implying significant discrepancies that require further investigation.

Algorithmically detected rain-on-snow flood events in different climate datasets: a case study of the Susquehanna River basin

Abstract. Rain-on-snow (RoS) events in regions of ephemeral snowpack – such as the northeastern United States – can be key drivers of cool-season flooding. We describe an automated algorithm for detecting basin-scale RoS events in gridded climate data by generating an area-averaged time series and then searching for periods of concurrent precipitation, surface runoff, and snowmelt exceeding predefined thresholds. When evaluated using historical data over the Susquehanna River basin (SRB), the technique credibly finds RoS events in published literature and flags events that are followed by anomalously high streamflow as measured by gauge data along the river. When comparing four different datasets representing the same 21-year period, we find large differences in RoS event magnitude and frequency, primarily driven by differences in estimated surface runoff and snowmelt. Using dataset-specific thresholds improves agreement between datasets but does not account for all discrepancies. We show that factors such as meteorological forcing and coupling frequency, as well as choice of land surface model, play roles in how data products capture these compound extremes and suggest care is to be taken when climate datasets are used by stakeholders for operational decision-making.

Season-dependent climate sensitivity of the surface runoff of major rivers in Changbai Mountain

Due to the sensitivity of runoff to changes in seasonal snowpack, the changes in surface runoff within high-latitude and high-elevation areas are more complex than in other areas. However, this sensitivity is often overlooked when correlating changes in runoff with meteorological variables. Here, we analyze changes in surface runoff during snowmelt and snowless periods and their sensitivity to climate change from 1960 to 2019 in the upstream watersheds of the Songhua, Yalu, and Tumen Rivers in Changbai Mountain. Most runoff indices, except for low flow in the Yalu River, exhibited decreasing trends during snowmelt and snowless periods. Precipitation was the primary variable responsible for surface runoff during snowless and snowmelt periods. Notably, rainfall replenished all runoff indices during snowless periods, particularly under high flow conditions. The average and high flows were also primarily supplemented by rainfall during snowmelt periods. However, low flow was positively correlated with snowmelt in the Songhua and Tumen Rivers and with temperature in the Songhua, Yalu and Tumen Rivers during snowmelt periods. Additionally, extreme runoff events were closely associated with extreme precipitation. These findings demonstrate the seasonal and index-dependent climate sensitivity of surface runoff within Changbai Mountain, offering insights into the surface runoff response mechanism to climate change in high-altitude mountainous areas. Under future climate change conditions, frequent and intense extreme precipitation events may increase the risk of flooding, owing to the high sensitivity of high flows to precipitation, posing a serious threat to regional ecological security.

Simulation and Prediction of Snowmelt Runoff in the Tangwang River Basin Based on the NEX-GDDP-CMIP6 Climate Model

In this study, the future snowmelt runoff in the chilly northeast region’s Tangwang River Basin was simulated and predicted using the SWAT model, which was built and used based on the NEX-GDDP-CMIP6 climate model. This study conducted a detailed analysis of the spatial and temporal distribution characteristics of snowmelt runoff using high-resolution DEM, land use, and soil data, along with data from historical and future climatic scenarios. Using box plots and the Bflow digital filtering approach, this study first determined the snowmelt runoff period before precisely defining the snowmelt periods. Sensitivity analysis and parameter rate determination ensured the simulation accuracy of the SWAT model, and the correlation coefficients of the total runoff validation period and rate period were 0.75 and 0.76, with Nashiness coefficients of 0.75 for both. The correlation coefficients of the snowmelt runoff were 0.73 and 0.74, with Nashiness coefficients of 0.7 and 0.68 for both, and the model was in good agreement with the measured data. It was discovered that while temperatures indicate an increasing tendency across all future climate scenarios, precipitation is predicted to increase under the SSP2-4.5 scenario. The SSP2-4.5 scenario predicted a decreasing trend regarding runoff, while the SSP1-2.6 and SSP5-8.5 scenarios showed an increasing trend with little overall change and the SSP5-8.5 scenario even showed a decrease of 6.35%. These differences were evident in the monthly runoff simulation projections. Overall, the findings point to the possibility that, despite future climate change having a negligible effect on the hydrological cycle of the Tangwang River Basin, it may intensify and increase the frequency of extreme weather events, creating difficulties for the management of water resources and the issuing of flood warnings. For the purpose of planning water resources and studying hydrological change in this basin and other basins in cold regions, this study offers a crucial scientific foundation. An in-depth study of snowmelt runoff is of great practical significance for optimizing water resource management, rational planning of water use, spring flood prevention, and disaster mitigation and prevention, and provides valuable data support for future research on snowmelt runoff.

Hydrological Regime of Rivers in the Periglacial Zone of the East European Plain in the Late MIS 2

At the end of the Pleniglacial and the first half of the Late Glacial period, approximately between 18 and 14 ka BP, rivers of the central and southern parts of the East European Plain had channels up to 10 times as large as the present day channels of the same rivers. These ancient channels, called large meandering palaeochannels, are widespread in river floodplains and low terraces. The hydrological regime of these large rivers is of great interest in terms of the palaeoclimatology of the late Marine Isotope Stage 2 (MIS 2). In this study, we aimed at quantitative estimation of maximum flood discharges of rivers in the Dnepr, Don and Volga basins in the late MIS 2. To approach this, we used massive measurements of the morphometric characteristics of large palaeochannels on topographic maps and remote sensing data—palaeochannel width, meander wavelength and their relationships with river flow parameters. The runoff depth of the maximum flood, which corresponds to the maximum depth of daily snow thaw during the snowmelt period, was obtained for unit basins with an area of <1000 km2. The mean value for the southern megaslope of the East European Plain was 44.2 mm/day (6 times the modern value), with 46 mm/day for the Volga River (5.5 times), 45 mm/day (6.3 times) for the Don River and 39 mm/day (8 times the modern value) for the Dnepr River basins. In general, the Dnepr basin was drier than the Don and Volga basins, which corresponds well to the modern distribution of humidity. At the same time, the westernmost part of the Dnepr River basin was relatively wet in the past, and the decrease in humidity from the past to the modern situation was greater there than in the eastern and central regions. The obtained results contradict the prevailing ideas, based mainly on climatic modeling and palynological data, that the climate of Europe was cold and dry during MIS 2. The reason is that palaeoclimatic reconstructions were made predominantly for the LGM epoch (23–20 ka BP). On the East European Plain, the interval 18–14 ka BP is rather poorly studied. Our results of paleoclimatological and palaeohydrological reconstructions showed that the Late Pleniglacial and the first half of the Late Glacial period was characterized by a dramatic increase in precipitation and river discharge relative to the present day.

Tower-based C-band radar measurements of an alpine snowpack

Abstract. To better understand the interactions between C-band radar waves and snow, a tower-based experiment was set up in the Idaho Rocky Mountains for the period of 2021–2023. The experiment objective was to improve understanding of the sensitivity of Sentinel-1 C-band backscatter radar signals to snow. The data were collected in the time domain to measure the backscatter profile from the various snowpack and ground surface layers. The data show that scattering is present throughout the snow volume, although it is limited for low snow densities. Contrasting layer interfaces, ice features and metamorphic snow can have considerable impact on the backscatter signal. During snowmelt periods, wet snow absorbs the signal, and the soil backscatter becomes negligible. A comparison of the vertically integrated tower radar data with Sentinel-1 data shows that both systems have similar temporal behavior, and both feature an increase in backscatter during the dry-snow period in 2021–2022, even during weeks of nearly constant snow depth, likely due to morphological changes in the snowpack. The results demonstrate that C-band radar is sensitive to the dominant seasonal patterns in snow accumulation but that changes in microstructure, stratigraphy, melt–freeze cycles and snow wetness may complicate satellite-based snow depth retrievals.

Lake Wellington and West Walker River in Great Basin of western United States: History and genesis

ABSTRACTClosed basins within the Great Basin of the western United States were home to numerous lakes during the Pleistocene. One of these paleolakes along the western edge of the Great Basin, Lake Wellington, once filled a 10 × 25‐km expanse of Smith Valley to depths approaching 90 m. This and other lakes that existed during the Pleistocene are generally considered to be pluvial, indicating contemporaneity with either or all a period of cooler climate, increased rainfall and snowmelt, and relatively reduced rates of evaporation as compared to today. Here we combine the results of 36Cl terrestrial cosmogenic nuclide surface exposure dating with soils and geomorphic observations to suggest Lake Wellington was not a pluvial lake but instead the result of a large landslide prior to ~43 ± 15 ka along the West Walker River where it exited Smith Valley. The observations collected also reveal an ancestral course of the West Walker River ~85 m above the current river grade. Attributing the elevation difference to incision caused by active 0.05 ± 0.01 mm a−1 uplift of the underlying Singatse and assuming the ancestral course followed the same path as today places the age of the paleoriver course at ~1.7 Ma.

The fate of 17β-estradiol in snowmelt from a field with a history of manure application: A laboratory simulation and field study

17β-estradiol is a naturally occurring estrogen, and livestock manure applied to agricultural fields is a major source to the environment. Liquid swine manure is widely applied to agricultural fields in the Canadian Prairies, a region where the majority of the annual runoff occurs during a brief snowmelt period over frozen soil. Transport of estrogens from manure amendments to soil during this important hydrological period is not well understood but is critical to mitigating the snowmelt-driven offsite transport of estrogens. This study quantified the concentration and load of 17β-estradiol in snowmelt from an agricultural field with a history of manure application under manure application methods: no manure applied, manure applied on the sub-surface, and on the surface, using a laboratory simulation study with flooded intact soil cores and a field study during snowmelt. A higher concentration of 17β-estradiol was in the laboratory simulation than in the field (mean laboratory pore water = 1.65 ± 1.2 μg/L; mean laboratory flood water = 0.488 ± 0.58 μg/L; and mean field snowmelt = 0.0619 ± 0.048 μg/L). There were no significant differences among manure application methods for 17β-estradiol concentration. Laboratory pore water concentrations significantly increased over time, corresponding with changes in pH. In contrast, there was no significant change in the field snowmelt concentrations of 17β-estradiol over time. However, for both laboratory simulation experiments and field-based snowmelt experiments, mean concentrations of 17β-estradiol were higher with subsurface than surface-applied manure, and the cumulative load of 17β-estradiol was significantly higher in the sub-surface than in surface applied. The mean cumulative load from the field study across all treatments (6.91 ± 3.7 ng/m2) approximates the magnitude of 17β-estradiol that could be mobilized from manured fields. The sub-surface application of manure seems to increase the persistence of 17β-estradiol in soil, thus enhancing the potential loss to snowmelt runoff.

Intensified warming suppressed the snowmelt in the Tibetan Plateau

Understanding how hydrological factors interrelate is crucial when examining the impact of climate warming on snowmelt. However, these connections are often overlooked, leading to an unclear relationship between temperature and snowmelt. This study investigates the complex interplay between temperature and snowmelt in the Tibetan Plateau from 1961 to 2020, focusing on how extreme high-temperature events affect the frequency of extreme snowmelt. Using a structural equation model, we detected three temperature-related factors that predominantly influenced snowmelt and extreme snowmelt. The annual average temperature was found to have a significant indirect impact on snowmelt, mediated by changes in snowfall, snow depth and snow cover. By contrast, high-temperature days (daily maximum temperatures exceeding the 90th percentile) and heat waves (at least three consecutive high-temperature days) negatively affected extreme snowmelt directly or indirectly. The direct effect of increasing extreme temperature events was associated with an earlier onset of high-temperature periods, which accelerated snowmelt and shortened the duration of extreme snowmelt periods. Additionally, the reduction in snow cover owing to warming emerged as a main factor suppressing snowmelt and extreme snowmelt frequencies. We also revealed spatiotemporal variations in the temperature‒snowmelt relationship that highly depended on changes in snowmelt patterns. The study elucidated why warming suppresses snowmelt and extreme snowmelt events in the Tibetan Plateau, highlighting the mediating roles of snow-related and phenological factors. The findings will provide scientific support for climate simulation and water management policymaking in alpine regions worldwide.

Rolling forecast of snowmelt floods in data-scarce mountainous regions using weather forecast products to drive distributed energy balance hydrological model

Snowmelt flood forecasting plays a crucial role in water resource management and utilization in arid regions. However, the accuracy of current snowmelt flood forecasts falls short of requirements due to complex mountainous terrain and limited observational data. In this study, We conducted a snowmelt flood simulation and forecasting experiment in a mountainous watershed in northern Xinjiang. First, we used site observations, ERA5-land reanalysis data and WorldClim data to produce historical meteorological forcing, calibrated the VIC-CAS model and improved routing model using historical observed runoff data, and conducted a detailed simulation of the hourly snowmelt flood process in the basin. Then, the latest grid forecast weather product developed by the China Meteorological Administration was integrated into the system to conduct a rolling forecast test of the snowmelt floods in the next 72 h during the snowmelt period in 2023. The remotely sensed snow cover and the freezing level measured by the sounding balloon were assimilated into the model in real time to correct the deviation of runoff simulation. The results show that during the model calibration period, the KGE of the simulated monthly runoff in the Kayertes River basin (KRB) from 2006 to 2011 was 0.83 and the NSE was 0.86. In the continuous hourly runoff simulation during the 2022 snowmelt period, the model well captured the intraday fluctuations of snowmelt runoff in the KRB, with the daily peak flow error within ± 20 % and the peak occurrence time controlled within ± 4 h. In the rolling forecast test in 2023, the daily peak flow error is within ± 20 % (the peak occurrence error during the peak period is even controlled within ± 5 %), and the peak occurrence time is within ± 4 h. Real-time assimilation of freezing level measured by sounding balloons and remotely sensed snow cover can effectively correct the deviations in simulated temperature and snow cover. This study can provide insights for runoff prediction in other high-altitude mountainous areas where data are scarce.

Effects of reduced snowpack due to climate warming on abiotic and biotic soil properties in alpine and boreal forest systems

Reduction in snow cover, depth, onset, and duration of seasonal snow in mid-latitude regions due to climate warming has multiple global and local scale ecosystem impacts. These effects include modulations of the hydrological cycles and increases in land surface solar radiation absorption due to decreased albedo. Changes in snow cover characteristics also affect underlying soils. Snow has an insulating effect on soils by decoupling air and soil temperatures, thus seasonal snow cover reduction leads to overall lower soil temperatures and an increase in freeze-thaw cycles. This is especially prominent during the fall and spring thaw seasons when the snow cover is not as extensive. This in turn has downstream impacts on soil physical, chemical, and biological properties. Among these impacts are soil moisture reduction, temperature, frost regimes, soil pH shifts, and alteration in nutrient flux dynamics during winter, snowmelt period and the following summer growing season. These changes in soil physicochemical properties due to snowpack reduction can then impact the biological soil properties via increased plant root mortality, reduced abundance and diversity of soil arthropods, and shifts in composition, abundance and activity of soil microbial communities. All these soil biotic factors can in turn alter the dynamics of soil nutrient fluxes and future greenhouse gas emissions. Here, we integrate data on the effects of snow cover reduction on abiotic and biotic soil properties, with focus on temperate alpine and forest ecosystems and with an outlook on future impacts.

Effect of freeze-thaw cycles on aggregate breakdown of typical black soil during transportation

During the snowmelt period, the external erosive forces are dominated by freeze-thaw cycles and snowmelt runoff. These forces may affect soil structure and aggregate stability, thereby influencing snowmelt erosion. The process of snowmelt runoff can lead to the breakdown of aggregates during their transportation. However, few studies examined the effects of freeze-thaw cycles on the breakdown of aggregates during transportation. Focusing on 5-7 and 3-5 mm soil aggregates of typical black soil region in Northeast China, we analyzed the composition of water-stable aggregates, mean weight diameter (MWD), normalized mean weight diameter (NMWD), as well as breakdown rate of soil aggregates (BR) under different freeze-thaw cycles (0, 1, 5, 10, 15 and 20 times) and different transport distances (5, 10, 15, 20, 25 and 30 m). We further investigated the contribution (CT) of both freeze-thaw cycles and transport distances to BR. The results showed that: 1) After freeze-thaw cycles, the 5-7 and 3-5 mm aggregates were mainly composed of particles with a diameter of 0.5-1 mm. With increasing frequency of freeze-thaw cycles, the MWD generally showed a downward trend. Moreover, under the same number of freeze-thaw cycles, the NMWD of 3-5 mm aggregates was higher than that of 5-7 mm aggregates. 2) As the transport distance increased, the BR of 5-7and 3-5 mm aggregates gradually increased. Compared that under control group, the BR under one freeze-thaw cycle increased by 59.7%, 32.2%, 13.7%, 6.2%, 13.4%, 7.5%, and 60.0%, 39.0%, 18.4%, 13.0%, 6.3%, 6.1% at the condition of 5, 10, 15, 20, 25 and 30 m transport distances, respectively. However, with increasing frequency of freeze-thaw cycles, the BR increased slowly. 3) The breakdown of soil aggregates was mainly influenced by the transport distance (CT=54.6%) and freeze-thaw cycles (CT=26.2%). Freeze-thaw cycles primarily altered the stability of soil aggregates, which in turn affected the BR. Therefore, during the snowmelt period, freeze-thaw cycles reduced the stability of soil aggregates, leading to severe breakdown of soil aggregates during snowmelt runoff process. This made the soil more susceptible to migration with snowmelt runoff, which triggered soil erosion. Therefore, more attention should be paid on the prevention of soil erosion during snowmelt period.

Nutrient and sediment retention by riparian vegetated buffer strips: Impacts of buffer length, vegetation type, and season

Farming activities in watersheds can significantly increase sediment and nutrient loads, threatening aquatic ecosystems. Riparian vegetated buffer strips (RVBS) have emerged as a promising strategy to capture and sequester these pollutants. While previous research highlights the effectiveness of woody vegetation in reducing nutrient loads, an essential knowledge gap exists regarding the comparative efficiency of different vegetation types (woody, shrubs and grasses) in trapping nutrients and reducing transport from watersheds to waterbodies. To address this knowledge gap, a multi-year field investigation was carried out with the following objectives: (a) to evaluate the influences of RVBS length (2, 5, 10, 14 and 18 m) and vegetation type (woody, grass, shrub, and no buffer) on reducing nutrient and sediment concentrations in surface runoff across different seasons, and (b) to compare soil microbiological characteristics among the different buffer treatments. The findings reveal significant insights into the effectiveness of different RVBS configurations in mitigating nutrient and sediment concentrations. Woody RVBS demonstrated exceptional resilience, particularly during snowmelt periods, significantly outperforming non-woody vegetation types. In contrast, RVBS with grasses often showed limited effectiveness in reducing nutrient levels in overland flow. Importantly, the results highlight the remarkable capacity of woody vegetation within 18-metre RVBS, exhibiting 100% sediment trapping efficiency (STE), 89.8% total nitrogen (TN) removal, and 92.82% total phosphorus (TP) removal. Comparatively, grass treatments displayed lower efficiencies (STE:86%, TN:72.3%, TP: 77.8%), followed by shrubs (STE: 62%, 43.9% and 48.8%), and the control with no buffer (STE: 27%, TN: 20.6%, TP: 23.17%). The superior efficacy of woody vegetation can be attributed to its deep rooting systems and higher organic matter content in the soil compared to grass and shrub areas. Additionally, woody treatments exhibited higher levels of organic carbon, nutrients, and microbial activities in the soil, indicating greater potential for nutrient cycling. High-throughput sequencing revealed shifts in bacterial community diversity and biomass in vegetation treatments compared to the control bare soil. These findings underscore the potential of RVBS incorporating woody vegetation or grass to mitigate nutrient and sediment concentrations by enhancing infiltration and plant uptake processes. Nevertheless, RVBS effectiveness may vary, particularly during snowmelt periods. Future studies should explore RVBS design and management approaches in natural systems, with a specific focus on optimizing the effectiveness of grass treatments during snowmelt.

Modification and Validation of the Soil–Snow Module in the INM RAS Climate Model

This paper describes the modification of a simple land snow cover module of the INM RAS climate model. The possible liquid water and refreezing of meltwater in the snow layer are taken into account by the proposed parameterization. This is particularly important for modelling the transition season, as this phenomenon is mainly observed during the formation and melting of the snow cover when the surface temperature fluctuates around 0 °C. The snow density evolution simulation is also added. This parameterization is implemented in the INM-CM snow module and verified on observation data using the ESM-SnowMIP-like protocol. As a result, the INM-CM mean climate snow melt periods are refined, particularly in middle and high latitudes. The snow-covered area according to the model is also improved. In the future, a modified version of the land snow module can be used, coupled with a snow albedo model that takes into account snow metamorphism. This module can also be applied to sea ice snow.

Effect of water movement in frozen soil on long-term hydrological simulation of Soil and Water Assessment Tool in cold climate watershed in Hokkaido, Japan

ABSTRACT Despite the unique hydrological properties exhibited by frozen soil, its characteristics are often oversimplified in most distributed hydrological models, resulting in major errors in material cycle predictions in cold climate regions. This study aims to modify the Soil and Water Assessment Tool (SWAT), a semi-distributed hydrological model that originally assumes no water movement in frozen soil layers, by incorporating the dynamic change of soil permeability based on the degree of soil freezing, and to understand the effect of water movement in frozen soil on watershed hydrology. SWAT with frozen soil permeability (SWAT-FSP) was developed by adding two modules to the original SWAT: a heat conduction equation that considers the phase change of ice and liquid water and a frozen-soil percolation model that considers dynamic decrease in soil permeability due to soil freezing. The original SWAT and SWAT-FSP were set up in the Tokoro River watershed in northern Japan. The river flow and soil water dynamics during the snowmelt period (March–May) simulated using the original SWAT and SWAT-FSP were compared. The modified model significantly alters the soil water dynamics simulation to generate earlier soil water drainage and reduces the soil water content during the snowmelt period in years featuring weak soil freezing. Consequently, the SWAT-FSP estimated an earlier increase in river flow at the beginning of the snowmelt period and a smaller river flow in the middle of the snowmelt period as compared with the original SWAT. The modified model improved the overall performance in river flow simulation for the snowmelt period (March–May) of 2012–2019. The Nash-Sutcliffe efficiency obtained during the calibration period by the original SWAT was 0.602, and during the validation period it was 0.367. For the SWAT-FSP, it was 0.659 during calibration, and 0.639 during validation. Meanwhile, the SWAT-FSP showed unsatisfactory performance in 2014 and 2015, with underestimated snow water equivalent, indicating that improvements in the snowmelt/accumulation module may further enhance its accuracy. Our results showed that the dynamic change of soil permeability governed by the degree of soil freezing is an important factor affecting the water cycle of cold climate watersheds with seasonally frozen soil.

A Tale of Two Catchments: Causality Analysis and Isotope Systematics Reveal Mountainous Watershed Traits That Regulate the Retention and Release of Nitrogen

AbstractMountainous watersheds are characterized by variability in functional traits, including vegetation, topography, geology, and geomorphology, which determine nitrogen (N) retention, and release. Coal Creek and East River are two contrasting catchments within the Upper Colorado River Basin that differ markedly in total nitrate (NO3−) export. The East River has a diverse vegetation cover, and sinuous floodplains, and is underlain by N‐rich marine shale. At 0.21 ± 0.14 kg ha−1 yr−1, the East River exports ∼3.5 times more NO3− relative to the conifer‐dominated Coal Creek (0.06 ± 0.02 kg ha−1 yr−1). While this can partly be explained by the larger size of the East River, the distinct watershed traits of these two catchments imply different mechanisms controlling the aggregate N‐export signal. A causality analysis shows physical and biogenic processes were critical in determining NO3− export from the East River catchment. Stable isotope ratios of NO3− (δ15NNO3 and δ18ONO3) show the East River catchment is a strong hotspot for biogeochemical processing of NO3− at the hillslope soil‐saprolite. By contrast, the conifer‐dominated Coal Creek retained nearly all atmospherically deposited NO3−, and its export was controlled by catchment hydrological traits (i.e., snowmelt periods and water table depth). The conservative N‐cycle within Coal Creek is likely due to the abundance of conifer trees, and smaller riparian regions, retaining more NO3− overall and reduced processing prior to export. This study highlights the value of integrating isotope systematics to link watershed functional traits to mechanisms of watershed element retention and release.

Extending the utility of space-borne snow water equivalent observations over vegetated areas with data assimilation

Abstract. Snow is a vital component of the earth system, yet no snow-focused satellite remote sensing platform currently exists. In this study, we investigate how synthetic observations of snow water equivalent (SWE) representative of a synthetic aperture radar remote sensing platform could improve spatiotemporal estimates of snowpack. We use a fraternal twin observing system simulation experiment, specifically investigating how much snow simulated using widely used models and forcing data could be improved by assimilating synthetic observations of SWE. We focus this study across a 24∘×37∘ domain in the western USA and Canada, simulating snow at 250 m resolution and hourly time steps in water year 2019. We perform two data assimilation experiments, including (1) a simulation excluding synthetic observations in forests where canopies obstruct remote sensing retrievals and (2) a simulation inferring snow distribution in forested grid cells using synthetic observations from nearby canopy-free grid cells. Results found that, relative to a nature run, or assumed true simulation of snow evolution, assimilating synthetic SWE observations improved average SWE biases at maximum snowpack timing in shrub, grass, crop, bare-ground, and wetland land cover types from 14 %, to within 1 %. However, forested grid cells contained a disproportionate amount of SWE volume. In forests, SWE mean absolute errors at the time of maximum snow volume were 111 mm and average SWE biases were on the order of 150 %. Here the data assimilation approach that estimated forest SWE using observations from the nearest canopy-free grid cells substantially improved these SWE biases (18 %) and the SWE mean absolute error (27 mm). Simulations employing data assimilation also improved estimates of the temporal evolution of both SWE and runoff, even in spring snowmelt periods when melting snow and high snow liquid water content prevented synthetic SWE retrievals. In fact, in the Upper Colorado River region, melt-season SWE biases were improved from 63 % to within 1 %, and the Nash–Sutcliffe efficiency of runoff improved from −2.59 to 0.22. These results demonstrate the value of data assimilation and a snow-focused globally relevant remote sensing platform for improving the characterization of SWE and associated water availability.

Совершенствование решения задач оптимизации размещения посевов сельскохозяйственных культур в севооборотах хозяйств Белгородской области с учётом интенсивности смыва почвы

The article proposes a new way to solve the problem of optimizing the placement of agricultural crops on eroded lands in protective crop rotations. Optimality criteria for the problem: the volume of the washed-off soil should strive to a minimum. The required variables – the areas are given as a percentage of the total arable land area and are limited to minimum and maximum values. For the new method, a detailed description of the database compilation matrices in Microsoft Excel and the "Excel Solver" (Solution Search) application add-in is provided. The methodology for calculating the potential soil washout under agricultural crops in crop rotations has been revised, depending on the availability of crops on arable land during the snowmelt period. A unified formula has been developed for washing away soil under spring and winter crops. Keywords: OPTIMIZATION PROBLEMS, STRUCTURE OF ACREAGE, EROSION CONTROL ORGANIZATION OF THE TERRITORY, ECONOMIC AND MATHEMATICAL MODEL, WATER EROSION OF LAND, SOIL FLUSHING

Improving cascade reservoir inflow forecasting and extracting insights by decomposing the physical process using a hybrid model

Accurate and reliable inflow forecasting is essential for efficient reservoir operation, which serves various purposes such as flood control, hydropower generation, water supply and irrigation. Although data-driven models have been vastly used to improve forecasting, the lack of interpretability and physical insights, particularly for neural network models, limit their trustworthiness and usability. In this study, we introduce an interpretable hybrid model to decompose inflow into precipitation-runoff and routing processes. This approach not only improves model performance but also facilitates the extraction of valuable hydrological insights. When applied to the Missouri River Basin cascade reservoir systems, the hybrid model demonstrates strong performance when compared to the state-of-the-art hydrologic model HEC-HMS and the widely used Long Short-Term Memory (LSTM) model in predicting reservoir inflow, especially for lower and smaller reservoirs in the cascade reservoir systems. For the upper and larger reservoirs, it achieves substantial improvement in Nash-Sutcliffe efficiency, ranging from 0.05 to 0.08 and 0.28 to 0.32, except for Fort Peck reservoir, when compared with the LSTM and HEC-HMS models. The decomposition of physical processes enables the hybrid model to represent each component more effectively, thereby leading to improved results in simulation and forecasting. Furthermore, the hybrid model shows how meteorological information updates the memory state of the LSTM unit and routing parameters, which in turn influences inflow dynamics, particularly in situations involving snowmelt periods. The proposed approach also allows the extraction of valuable hydrological insights, including the assessment of the contributions of precipitation-runoff processes versus routing to inflow dynamics, dynamic water travel time, and routing coefficients. The case studies have demonstrated the promising capabilities of this interpretable hybrid model in improving reservoir inflow forecasting and providing meaningful hydrological insights into reservoir inflow dynamics.

Features of the mineralogical composition of solod and solod meadow-chestnut soil, the case of soils of the liman depression at Dzhanybek research station of the Institute of Forest Science RAS

The structure of the soil cover at the territory of the clayey semidesert of the Northern Caspian Sea is represented by triparted soil complex and peculiar soils like solod. Solods are formed in relief elements that are constant for this territory – mesodepressions, which are referred to limans. Two soil profiles were studied in the bottom of the liman – solod, and on its peripheral part – solodized meadow-chestnut soil. The formation of the solod profile occurs because of periodic moistening with fresh water and systematic flooding during the snowmelt period. The second soil is in the zone of transit of surface water to the liman. The soil profiles have the same type of eluvialilluvial distribution of the clay fraction, while differentiation is more distinct in solod. Mineralogical analysis established the same type of qualitative composition of clay and clastogenic minerals in both soils. Clay minerals are represented by illite, smectite, mixed-layer illite-smectite, accompanied by kaolinite and chlorite. Clastogenic minerals are represented by quartz, mica, K-feldspar, plagioclase, and chlorite. Quartz and feldspars are accumulated in the solodized horizons of the soil profiles; the content of quartz and feldspars decreases in the textural horizons, while the content of mica and chlorite increases. The distribution of the smectite phase in the solod profile reveals a difference in the ratio of illite-smectite packets, structures with a low content of smectite packets predominate in the solod part of the profile, and the proportion with a swelling packet in mixed-layer minerals increases in textural horizons. The solodized meadow-chestnut soil is distinguished by a smaller thickness of the solodized horizon, a higher content of the smectite component, and a calm character of the profile differentiation of clay and clastogenic minerals. In both soils, labile minerals with signs of superdispersion are recorded in the solodized and textural horizons. The superdispersion of labile minerals has different causes. In solod, it is associated with the action of low-mineralized water and the hydrolysis of soil minerals during the wet seasons of the year, as well as with the intense regime of intrasoil weathering in situ, and in the solodized meadow-chestnut soil, with the passage of the stage of solonetz soils. Differences in the smectite component were also revealed: in the solodized horizons of solod, it is represented by individual smectite and the mixed-layer phase of illitesmectite; and in the meadow-chestnut soil – by mixed-layer illite-smectite.