Snowmelt Model Research Articles

Using Commercial Satellite Imagery to Reconstruct 3 m and Daily Spring Snow Water Equivalent

AbstractSnow water equivalent (SWE) distribution at fine spatial scales (≤10 m) is difficult to estimate due to modeling and observational constraints. However, the distribution of SWE throughout the spring snowmelt season is often correlated to the timing of snow disappearance. Here, we show that snow cover maps generated from PlanetScope's constellation of Dove Satellites can resolve the 3 m date of snow disappearance across seven alpine domains in California and Colorado. Across a 5‐year period (2019–2023), the average uncertainty in the date of snow disappearance, or the period of time between the last date of observed snow cover and the first date of observed snow absence, was 3 days. Using a simple shortwave‐based snowmelt model calibrated at nearby snow pillows, the PlanetScope date of snow disappearance could be used to reconstruct spring SWE. Relative to lidar SWE estimates, the SWE reconstruction had a spatial coefficient of correlation of 0.75, and SWE spatial variability that was biased by 9%, on average. SWE reconstruction biases were then improved to within 0.04 m, on average, by calibrating snowmelt rates to track the spring temporal evolution of fractional snow cover observed by PlanetScope, including fractional snow cover over the full modeling domain, and across domain subsections where snowmelt rates may differ. This study demonstrates the utility of fine‐scale and high‐frequency optical observations of snow cover, and the simple and annually repeatable connections between snow cover and spring snow water resources in regions with seasonal snowpack.

Distributed modelling of snow and ice melt in the Naltar Catchment, Upper Indus basin

Energy balance distributed modelling in High Mountain Asia (HMA) is important to examine glaciological and hydrological processes and assess changes in streamflow in the current and future climate. In this study, the Physically based Distributed Snow Land and Ice Model (PDSLIM) using detailed observed meteorological data at hourly scale is employed to simulate the hydrological response of the Naltar catchment, 242.62 km2 in size, (in the Karakoram region in Pakistan to simulate its glaciers’ mass balance as well as daily runoff. The results exhibited overall satisfactory performance in terms of coefficient of determination (R2 = 0.96) and Nash-Sutcliffe Efficiency (NSE=0.95) modelled against satellite-based snow cover areas, for internal model verification, in eight years. The results of runoff simulations compared for external model verification, with observed daily discharge resulted in NSE 0.90 and 0.89 for calibration and validation period respectively. Flow composition analysis revealed that the streamflow regime of Naltar catchment is composed to 40 % by glacier runoff, 42 % by sub-surface runoff and 18 % by surface runoff. The eight year mean value of net mass balance exhibited a slightly negative mass balance (−0.810 ± 0.31 m w.e. a-1) less pronounced than that observed globally in several continental glaciers distinct from the Greenland and Antarctic ice sheets in the current climate. It seems that the ‘Karakoram anomaly’, i.e. the balanced to slightly positive glacier budgets observed in the region in the recent decades, a unique dynamics worldwide, has a moderate impact in the central Karakoram. Overall, the distributed energy-balance model PDSLIM, so far tested in the Alps, results to be a suitable tool to estimate energy and mass balance in the glacierized catchments of Karakoram and Himalaya and to better understand snow and ice melt runoff dynamics and floods in highly complex and glacierized mountain basins in the current and, in our research perspective, in the future climate.

Parsimonious streamflow forecasting system based on a dynamical systems approach

Operational hydrologic forecasting often relies on the use of physically-based computer models at the catchment scale. However, our understanding of streamflow generation is still evolving and incomplete, and oftentimes linearization assumptions must be made to make physically-based hydrologic and hydraulic models tractable. In addition to uncertainty in the physical runoff-generation mechanisms, many physically-based approaches do not formally assimilate observed streamflow data. Streamflow is their output, deterministically simulated from observed inputs (such as precipitation, temperature, etc.). Divergences between observed and simulated streamflow may be due to input uncertainty, calibration issues, or problems with the inherent physical process representation. Thus, data-driven approaches that are responsive to observed streamflow by including assimilation as an essential feature are promising in operational hydrology. In this study, we use a novel forcings-weighted k-Nearest Neighbor (kNN) nonlinear timeseries forecasting method developed in the context of the modeling of nonlinear dynamical systems. The rainfall-runoff transform is assumed to be a low-dimensional system of (unknown) nonlinear differential equations; the deterministic state space is reconstructed based on the streamflow timeseries; and a forecast is generated based on how “similar” the current hydrograph is to historical events encoded within the reconstructed state space, weighted by the similarity of forcings. We tested the model in a long-term test period with no real data assimilation, and in forecast mode with daily data assimilation. When used for forecasting, we also illustrate two different uncertainty quantification approaches: forecast postprocessing and internal-to-the-model ensemble generation. The method is conceptually and computationally simple, but offers promising results. We tested the model in three different scenarios. First, investigating a long-term test period with no real data assimilation in a rainfall-dominated research catchment at the Coweeta Hydrologic Lab in North Carolina, USA. The simulation exhibited an NSE of 0.73. Next, we included a conceptual snowmelt model to drive the long term simulation and studied boreal-temperate transition zone catchments in northern Minnesota, USA. The study area included a small (<0.1 mi2) reference research catchment at the Marcell Experimental Forest, and a large river basin (>19 k mi2) with incomplete forcings information. The kNN method yielded NSEs from 0.28 to 0.50. And finally, we conducted a pseudo-operational forecasting test for a large river basin in northern Minnesota, USA with a focus on performance forecasting a flood-of-record on the Rainy River. The test involved reforecasting, data assimilation, and implementation of ensemble and postprocessing bias correction uncertainty-quantification schemes. The technique demonstrated good performance capturing a flood-of-record on a large catchment (−5.7 to −3.3 % crest error with 1-day of lead time). For the entire test period in forecast mode, the 1-day lead time NSE was 0.99, going down to 0.79 for the 7-day lead time, showing its general utility as an operational forecasting tool.

Comparative Analysis of Snowmelt-Driven Streamflow Forecasting Using Machine Learning Techniques

The rapid advancement of machine learning techniques has led to their widespread application in various domains, including water resources. However, snowmelt modeling remains an area that has not been extensively explored. In this study, we propose a state-of-the-art (SOTA) deep learning sequential model, leveraging a Temporal Convolutional Network (TCN), for snowmelt forecasting of the Hindu Kush Himalayan (HKH) region. To evaluate the performance of our proposed model, we conducted a comparative analysis with other popular models, including Support Vector Regression (SVR), Long Short-Term Memory (LSTM), and Transformer models. Furthermore, nested cross-validation (CV) was used with five outer folds and three inner folds, and hyperparameter tuning was performed on the inner folds. To evaluate the performance of the model, the Mean Absolute Error (MAE), Root-Mean-Square Error (RMSE), R square (R2), Kling–Gupta Efficiency (KGE), and Nash–Sutcliffe Efficiency (NSE) were computed for each outer fold. The average metrics revealed that the TCN outperformed the other models, with an average MAE of 0.011, RMSE of 0.023, R2 of 0.991, KGE of 0.992, and NSE of 0.991 for one-day forecasts of streamflow. The findings of this study demonstrate the effectiveness of the proposed deep learning model as compared to traditional machine learning approaches for snowmelt-driven streamflow forecasting. Moreover, the superior performance of this TCN highlights its potential as a promising deep learning model for similar hydrological applications.

Mechanisms of spring freshet generation in southern Quebec, Canada

Seasonal forecasting of spring floods in snow-covered basins is challenging due to the ambiguity in the driving processes, uncertain estimations of antecedent catchment conditions, and the choice of predictor variables. In this study, we attempt to improve the prediction of spring flow peaks in southern Quebec, Canada, by studying the preconditioning mechanisms of runoff generation and their impact on inter-annual variations in the timing and magnitude of spring peak flow. Historical observations and simulated data from a hydrological and snowmelt model were used to study the antecedent conditions that control flood characteristics in 12 unregulated snow-dominated catchments. Maximum snow accumulation (peak SWE), snowmelt and rainfall volume and intensity, soil moisture, river baseflow, and the air freezing index, a proxy for soil freezing, were estimated during the pre-flood period. Stepwise multiple linear regression was used to identify the most relevant predictors and assess their relative contribution to interannual variability flood variability. Peak SWE was not by itself a strong predictor of spring flood magnitude and timing. The snowmelt rate during the pre-flood period was the most ubiquitous and skillful predictor of spring flood magnitude, followed by the rainfall intensity. The ‘soil memory’ effect, represented here by the simulated soil moisture content and freezing depth, was generally poorly related to flood characteristics. SWE and snowmelt dynamics dominated the interannual variability of flood magnitude in the southern and agricultural basins while rainfall intensity had a stronger influence in the northern, snowier forested basins. More complex machine learning models (random forest and support vector regression) did not perform better than the simple linear models to predict peakflow from antecedent hydroclimatic factors. These results highlight the impact of climate and land cover and use on spring flood generation mechanism and the moderate predictability potential of spring floods based on antecedent hydrological factors.

Estimating Stage-Frequency Curves for Engineering Design in Small Ungauged Arctic Watersheds

The design of hydraulic structures in the Arctic is complicated by shallow relief, which cause unique runoff processes that promote snow-damming and refreeze of runoff. We discuss the challenges encountered in modeling snowmelt runoff into two coastal freshwater lagoons in Utqiaġvik, Alaska. Stage-frequency curves with quantified uncertainty were required to design two new discharge gates that would allow snowmelt runoff flows through a proposed coastal revetment. To estimate runoff hydrographs arriving at the lagoons, we modeled snowpack accumulation and ablation using SnowModel which in turn was used to force a physically-based hydraulic runoff model (HEC-RAS). Our results demonstrate the successful development of stage-frequency curves by incorporating a Monte Carlo simulation approach that quantifies the variability in runoff timing and volume. Our process highlights the complexities of Arctic hydrology by incorporating significant delays in runoff onset due to localized snow accumulation and melting processes. This methodology not only addresses the uncertainty in snow-damming and refreeze processes which affect the arrival time of snowmelt inflow peaks, but is also adaptable for application in other challenging environments where secondary runoff processes are predominant.

Interannual Variations in Spring Snowmelt Timing of Alaskan Black Spruce Forests Using a Bulk‐Surface Energy Balance Approach

AbstractSpring snowmelt occurs for a short duration on an annual time scale, but their timings considerably affect the carbon and hydrological cycle in high‐latitude ecosystems. Here, we developed a simple snowmelt model, treating the ecosystem surface as a bulk‐surface layer. Energy fluxes across this bulk surface and the snow‐soil boundary determine snow temperature and the energy utilized for snowmelt. Parameterizing the bulk surface using decade‐long eddy covariance site data from two Alaskan open black spruce forests offered an opportunity to quantitatively evaluate meteorological drivers affecting snowmelt timings without the needs for detailed canopy information. The sensitivity analysis suggested that the total snowfall on the forest floor, ranging from 0.35 m in 2016 to about 1 m in 2018 and 2020, was the most crucial driver for snowmelt timing. This factor accounted for a 10‐day difference in the interannual variations in snow disappearance dates. The importance of the snowfall varied from year to year, and in 2013, the late snowmelt was characterized by low air temperatures, which increased sensible heat loss from the snowpack. The importance of atmospheric radiation was revealed in relatively warm years, such as 2016 and 2019. Our modeling approach necessitates adjusting one empirical parameter that reflects the heat conductivity from the bulk surface to the snow, based on observations. Nevertheless, despite this need for adjustment, the bulk‐surface approach helps identify important meteorological drivers underlying observed snowmelt within a simple theoretical framework.

Simulation of roof snow loads based on a multi-layer snowmelt model: Impact of building heat transfer

Simulation of roof snow loads based on a multi-layer snowmelt model: Impact of building heat transfer

Sensitivity analysis in the wavelet domain: a comparison study

Sensitivity analysis plays a pivotal role for the development and calibration of hydrological models, since they are often affected by equifinality. Despite a lot of effort has been placed for the development of effective sensitivity analysis methods, hydrological models remain over parametrized. We take advantage of the evidence that hydrological processes can be described as the superposition of effects occurring at different temporal scales (e.g., seasonal precipitation patterns, seasonal and daily snow and glacier melt, seasonal, daily and sub-daily water management operations) to develop a new framework to perform sensitivity analysis. We apply discrete and continuous wavelet transforms to disentangle hydrological signals occurring at different temporal scales and we take advantage of the different information stored at different temporal scales of the wavelet spectrum to perform a scale-dependent sensitivity analysis. This approach aims to increase the number of identifiable model parameters in comparison to standard sensitivity analysis performed in the time domain. As an exemplary problem, we apply the methodology to synthetic data describing surface water-groundwater interaction in rivers affected by hydropeaking (i.e., sudden fluctuations in the river stage due to hydropower production). The method could be applied also to other models displaying the superposition of processes with different intensities at different temporal scales such as ocean tide propagation in aquifers as well as snow and glacier melt models. The results indicate that considering multiple temporal scales allows us to increase the number of parameters that can be identified and hence calibrated with only a little increase in the computational effort.

Melt pond detection on landfast sea ice using dual co-polarized Ku-band backscatter

The onset of melt ponds on landfast first-year and multiyear sea ice is detected using dual co-polarized Ku-band spaceborne scatterometer data. Twenty-nine locations in the Canadian Arctic Archipelago are used to develop and test the methodology. The daily ScatSat-1 sigma-nought backscatter composite product with a spatial resolution of 2.216 km is used. Vertical (VV) and horizontal (HH) polarizations of these data are used to derive the co-polarized ratio (VV/HH). An increase in VV/HH is associated with an increase in melt pond fraction on the sea ice. This occurs because there is a larger increase in resonant Bragg scattering for VV than for HH when melt pond surfaces exhibit wind-generated capillary waves. VV backscatter magnitude is evaluated in time series using a peak-finding methodology to initiate detection of melt ponds with VV/HH. The peak-finding is based on geophysical controls on backscatter from snow-covered sea ice during the melt season. Daily time-series data from ScatSat-1 are used in conjunction with ERA-5 reanalysis data (2-m air temperature and wind) and a semi-empirical snowmelt model. Air temperature data are used to identify snow melt onset and to force the snowmelt model. Wind data are used to evaluate if the wind speed is sufficient for capillary wave formation. The NSCAT-4DS geophysical model function is used to model Ku-band backscatter from water, which is then used in a sea ice mixture model to simulate the VV/HH magnitude for a range of pond fractions and wind speeds. The potential for detecting pond onset is evaluated in relation to the noise inherent in the ScatSat-1 VV/HH sea ice measurements, which is found to be approximately 0.16 dB. When the melt pond fraction is sufficiently large to cause VV/HH to exceed the noise, pond onset is detected. Cloud-free Sentinel-2 optical satellite imagery is used for validation; specifically, the near-infrared albedo. We find an overall mean bias of −0.5 days, and an absolute deviation of 1.4 days, when comparing our method to a near-infrared method for detecting pond onset. The mean biases (absolute deviations) for first-year and multiyear sea ice are −0.3 (1.5) and −0.8 (1.2) days, respectively. Results show that the temporal resolution, spatial resolution, and efficacy of this method can provide robust sea ice melt pond onset timing for landfast sea ice regions of the Arctic.

Snowmelt estimation using an empirical radiation model

This study developed a novel approach for estimating snowmelt using an empirical radiation model, referred to as the Empirical Radiation Snowmelt Model, or ERSM. ERSM assumes that energy available to melt snow is equivalent to the sum of the shortwave radiation absorbed by the snowpack and the net longwave radiation at the snow surface. ERSM has two components. The first component is an empirically determined relationship between the observed daily snowmelt and the daily potential shortwave radiation. The longwave radiation component is implicitly estimated through its influence on the observed snowmelt. The daily potential shortwave radiation was found using two calculated parameters: the top-of-the-atmosphere shortwave radiation and the albedo of the snow surface. This first component simulates the average snowmelt that will occur for a given level of the potential shortwave radiation. The second component of ERSM estimates the difference between the average snowmelt simulated from the potential shortwave radiation (first component) and the actual snowmelt that occurred on any day. Observations from 20 SNOTEL11SNOTEL-U.S. Department of Agriculture, Natural Resources Conservation Service Snow Telemetry site. sites in the Truckee River Watershed were used to develop separate ERSM parameters for each site. ERSM was able to robustly predict snowmelt for all sites considered in this study. The error statistics vary with the magnitude of the observed daily snowmelt and the best results were found for magnitudes at which the majority of snowmelt occurs. A particularly valuable application of ERSM is its use to estimate the melt factor required by temperature index snowmelt models. ERSM can explain a large degree of the observed variance of the temperature index melt factor.

Simulation and statistical analysis of ground snow loads based on a multi-layer snow accumulation and melt model

Ground snow loads, the study of which relies on the snowpack data from historical records of meteorological stations, are the basis of calculating the roof snow loads. In the absence of observation data, the snow accumulation and melt model, also called the snowmelt model, can be used to simulate the annual extreme samples of ground snow loads in each locality, and then the ground snow loads under a given recurrence interval can be obtained through probability analysis. In this paper, a multi-layer snowmelt model is used to input meteorological data such as precipitation, air temperature, wind speed, and relative humidity to obtain detailed information on the long-term snowpack, including snow density, snow depth, and snow water equivalent (SWE), which overcomes the problem of insufficient sample data for observed snow loads. Firstly, the precision of multi-layer snowmelt is checked through data of snow depth and SWE. Then, based on the model, meteorological stations in thirty-seven localities of six Chinese regions are selected to simulate the ground snow loads for nearly 60 years. Subsequently, the maximum snow depth and its occurrence time are compared, and the relationship between snow depth, snow density, and SWE in each region is analyzed. Finally, a variety of probabilistic models are used to calculate the ground snow loads at 50-year recurrence intervals. The goodness of fit of each probabilistic model for the snow loads sample is examined, and the calculated values of ground snow loads are compared with recommended values by the Chinese code. The results indicate that the snowmelt model can accurately reproduce the detailed process of ground snow events, which can be used to obtain the samples of ground snow load extreme. Due to gravity compaction, ground snow loads corresponding to the maximum depth in a certain year differ from the maximum ground snow loads in that year, which requires more attention. The goodness of fit is higher and the results are larger than the values of the code when generalized extreme distribution and logarithmic normal distribution are adopted to estimate extreme ground snow loads.

Understanding wind-driven melt of patchy snow cover

Abstract. The representation of snow processes in most large-scale hydrological and climate models is known to introduce considerable uncertainty into the predictions and projections of water availability. During the critical snowmelt period, the main challenge in snow modeling is that net radiation is spatially highly variable for a patchy snow cover, resulting in large horizontal differences in temperatures and heat fluxes. When a wind blows over such a system, these differences can drive advection of sensible and latent heat from the snow-free areas to the snow patches, potentially enhancing the melt rates at the leading edge and increasing the variability of subgrid melt rates. To get more insight into these processes, we examine the melt along the upwind and downwind edges of a 50 m long snow patch in the Finseelvi catchment, Norway, and try to explain the observed behavior with idealized simulations of heat fluxes and air movement over patchy snow. The melt of the snow patch was monitored from 11 June until 15 June 2019 by making use of height maps obtained through the photogrammetric structure-from-motion principle. A vertical melt of 23 ± 2.0 cm was observed at the upwind edge over the course of the field campaign, whereas the downwind edge melted only 3 ± 0.4 cm. When comparing this with meteorological measurements, we estimate the turbulent heat fluxes to be responsible for 60 % to 80 % of the upwind melt, of which a significant part is caused by the latent heat flux. The melt at the downwind edge approximately matches the melt occurring due to net radiation. To better understand the dominant processes, we represented this behavior in idealized direct numerical simulations, which are based on the measurements on a single snow patch by Harder et al. (2017) and resemble a flat, patchy snow cover with typical snow patch sizes of 15, 30, and 60 m. Using these simulations, we found that the reduction of the vertical temperature gradient over the snow patch was the main cause of the reductions in vertical sensible heat flux over distance from the leading edge, independent of the typical snow patch size. Moreover, we observed that the sensible heat fluxes at the leading edge and the decay over distance were independent of snow patch size as well, which resulted in a 15 % and 25 % reduction in average snowmelt for, respectively, a doubling and quadrupling of the typical snow patch size. These findings lay out pathways to include the effect of highly variable turbulent heat fluxes based on the typical snow patch size in large-scale hydrological and climate models to improve snowmelt modeling.

Improvements in the degree-day model, incorporating forest influence, and taking China’s Tianshan Mountains as an example

Study regionKunes River Basin in the western Tianshan Mountains, China. Study focusThe study used atmospheric meteorological data such as temperature, radiation and snow melt rate for areas with different degrees of openness. The climatic parameters, snow melt process, and major environmental controls were studied. By analysing an improved degree-day model that simulated the forest subcanopy, a subcanopy snowmelt model was constructed. New hydrological insights for the regionThe net shortwave radiation of snowy subcanopy surfaces is lower than that of open sites, whilst the net longwave radiation is higher. This suggests that the influence of longwave radiation on the snowmelt process in the subcanopy is greater than that of shortwave radiation. Furthermore, due to the relatively high night-time temperatures in the subcanopy, the influence of the water recession process was significant. In this study, net longwave radiation and water recession were added to a traditional degree-day model. The simulation showed that the improved degree-day model responded well. The R2 of each station was > 0.88, the Ens was > 0.87, and the PBIAS was 0.63 %− 8.44 %, providing a simple and feasible method for the simulation of snowmelt runoff.

Comparison of sequential and variational assimilation methods to improve hydrological predictions in snow dominated mountainous catchments

• Assimilation of streamflow and snow data using variational and sequential techniques in a hydrological model. • Satellite snow coverage products are assimilated together with streamflow data. • Variational techniques outperform sequential techniques in two mountainous basins. • Average relative gains in variational assimilation are 49% for streamflow and 28% for snow data. Snowmelt modeling is vital issue for proper operational flow forecasting systems but still challenging in upstream mountainous basins due to limited observations and associated uncertainties. Data assimilation is a valuable tool to improve model state estimates to improve hydrological forecast. While assimilation of snow has been increasingly implemented in research, particularly using sequential data assimilation methods, less attention has been dedicated to compare different types of assimilation techniques. This study aims at comparing the well-known Ensemble Kalman Filter (EnKF) sequential data assimilation and the less known Moving Horizon Estimation (MHE) variational assimilation. Initial conditions of the hydrological model states are improved by assimilating in-situ streamflow and remotely sensed snow covered area data into the Hydrologiska Byråns Vattenbalansavdelning (HBV) conceptual rainfall-runoff model. The study is applied to improve runoff predictions over two mountainous basins: one in the uppermost catchment of the Karasu river, in Eastern Turkey, and the second in an alpine basin located in the headwaters of the Genil River in the northern flank of the Sierra Nevada Mountain in Southern Spain. The forecast models are tested in a hindcasting condition, i.e. closed loop environment, which simulates real-time forecasting systems. The results are analyzed using forecast verification metrics. Results showed that streamflow and snow state predictions using MHE outperform the commonly used EnKF based on the continuous ranked probability score (CRPS).

Development and parameter estimation of snowmelt models using spatial snow-cover observations from MODIS

Abstract. Given the importance of snow on different land and atmospheric processes, accurate representation of seasonal snow evolution, including distribution and melt volume, is highly imperative to any water resources development trajectories. The limitation of reliable snowmelt estimation in mountainous regions is, however, further exacerbated by data scarcity. This study attempts to develop relatively simple extended degree-day snow models driven by freely available snow-cover images. This approach offers relative simplicity and a plausible alternative to data-intensive models, as well as in situ measurements, and has a wide range of applicability, allowing for immediate verification with point measurements. The methodology employs readily available MODIS composite images to calibrate the snowmelt models on spatial snow distribution in contrast to the traditional snow-water-equivalent-based calibration. The spatial distribution of snow-cover is simulated using different extended degree-day models with parameters calibrated against individual MODIS snow-cover images for cloud-free days or a set of images representing a period within the snow season. The study was carried out in Baden-Württemberg (Germany) and in Switzerland. The simulated snow-cover data show very good agreement with MODIS snow-cover distribution, and the calibrated parameters exhibit relative stability across the time domain. Furthermore, different thresholds that demarcate snow and no-snow pixels for both observed and simulated snow cover were analyzed to evaluate these thresholds' influence on the model performance and identified for the study regions. The melt data from these calibrated snow models were used as standalone inputs to a modified Hydrologiska Byråns Vattenbalansavdelning (HBV) without the snow component in all the study catchments to assess the performance of the melt outputs in comparison to a calibrated standard HBV model. The results show an overall increase in Nash–Sutcliffe efficiency (NSE) performance and a reduction in uncertainty in terms of model performance. This can be attributed to the reduction in the number of parameters available for calibration in the modified HBV and an added reliability of the snow accumulation and melt processes inherent in the MODIS calibrated snow model output. This paper highlights that the calibration using readily available images used in this method allows for a flexible regional calibration of snow-cover distribution in mountainous areas with reasonably accurate precipitation and temperature data and globally available inputs. Likewise, the study concludes that simpler specific alterations to processes contributing to snowmelt can contribute to reliably identify the snow distribution and bring about improvements in hydrological simulations, owing to better representation of the snow processes in snow-dominated regimes.

Probabilistic study of snow loads on flat roofs considering the effects of wind at representative sites in China

Based on meteorological data, this paper simulates the evolution of ground and roof snow loads over time in winter. The snowmelt model is used to simulate the ground snow load, and the snow transport model is employed in cooperation with the snowmelt model to simulate the flat roof snow load. Through the simulations, samples of annual maximum ground snow loads, annual maximum roof snow loads, and ground-to-roof conversion factors for at least 60 years at 50 representative snowy sites in China are obtained. First, the statistical characteristics of the above samples are analysed. Then, the probabilistic models with different parameter estimation methods are used to fit the samples, and each model's goodness of fit is evaluated by using K-S test, Akaike information criterion (AIC), and Q-Q plot. The results show that the Generalized Extreme Value (GEV) distribution based on Maximum Likelihood Estimation (MLE) of parameters is the preferred probabilistic model for snow loads both on the ground and roofs. However, none of the models selected in this paper can accurately describe the ground-to-roof conversion factor. Therefore, this paper recommends using the R-year ground and roof snow loads obtained by the probabilistic model for calculating the ground-to-roof conversion factor. Finally, the ground-to-roof conversion factor is found to be quite scattered for the most of the studied sites, thus it could be more reasonable to use site-specific values estimated by the method proposed in this paper rather than a uniform value in consideration of safety and economy of the structure.

Analyzing influences of snowfall on electric field at the interface of composite cross-arm with finite element method

The composite cross-arm was used in large numbers in power systems due to its safety and economic advantages. The interfaces were the weak link in the property of composite cross-arm. However, the influence of severe weather such as ice and snow on the interface property was not clear, which would bring potential risks to operation safety for composite cross-arm. To this end, the finite element method was used to study the characteristics of the electric field at the FRP-silicone rubber and FRP-RPUF interfaces under snowfall conditions in this paper. Firstly, the snow and snowmelt models of the composite cross-arm were established by combining the natural snowfall conditions and the classical snowed model, then the influence of snowed degrees, snowmelt stages and dry-band on the interfaces electric field was analyzed. The results show that the snow without melting basically does not change the interface electric field. During the snowmelt stages, the average electric field intensity of the FRP-silicone rubber interface increases and the maximum electric field intensity of the two interfaces decreases. The distortion of the interface electric field caused by the dry-band in the middle snowmelt stage is the most serious, at this time, the electric fields at FRP-silicone rubber interfaces reach a maximum of 14.72 kV/cm and 8.53 kV/cm respectively. When the length of dry-band reaches or exceeds the distance between the high-voltage fitting and the eighth large umbrella skirt, it basically no longer causes the distortion of interface electric field. Compared to the FRP-RPUF interface, the FRP-silicone rubber interface is more severely affected by snowfall. The results can provide a reference for improving the interface property and ensuring the safety of the composite cross-arm in severe weather conditions.

Quantification of Precipitation and Evapotranspiration Uncertainty in Rainfall-Runoff Modeling

Mountainous watersheds have always been a challenge for modelers due to large variability and insufficient ground observations, which cause forcing data, model structure, and parameter uncertainty. This study employed Differential Evolution Adaptive Metropolis (DREAM) algorithm which utilizes Markov Chain Monte Carlo (MCMC) approach to account for forcing data uncertainty. A conceptual degree day snowmelt model, MIKE 11 NAM (Nedbor Afstromnings Model), was used to simulate snowmelt runoff from Ilgaz basin, with an area of 28.4 km2 area, located in the northern part of Turkey. The mean elevation is around 1700 m and the basin is covered with broadleaf forest and has mainly brown soil with a high water holding capacity. Precipitation and evapotranspiration (ET) values were optimized in combination with model parameters conditioned on observed discharges and corrected values of input data were utilized for calibration and validation. Results showed that the observed precipitation was over-estimated by almost 10%, while evapotranspiration calculated by Penman–Monteith method was underestimated. The mean values of storm and ET multipliers were obtained as 1.14 and 0.84, respectively. When only parameter uncertainty was considered, calibration did not yield Nash–Sutcliffe Efficiency (NSE) greater than 0.64. However, when forcing data uncertainty was incorporated in the DREAM approach, an improved value of NSE (0.84) was obtained. After calibration and treatment of forcing data errors, the model yielded reasonable prediction uncertainty bounds and well-defined posterior distributions of NAM model parameters. Main objectives of the study are to assess the applicability of MIKE 11 NAM model to the selected catchment. In addition, the importance of errors in the input forcing variables to the model is demonstrated.

Weekly Monitoring and Forecasting of Hydropower Production Coupling Meteo-Hydrological Modeling with Ground and Satellite Data in the Italian Alps

This paper presents a system for supporting hydropower production on mountainous areas. The system couples the outputs of a numerical weather prediction model and a snow melting and accumulation temperature-based model. Several procedures are presented for interpolating meteorological variables and calibrating and validating model parameters that can be generalized to any other mountainous area where the estimation of current and forecasted snow water equivalent and melting amount is required. The system reliability has been assessed through the validation of three components: spatial interpolation of meteorological data, mathematical modeling, and quantitative meteorological forecast. The results show that good accuracy of meteorological data spatial interpolation can be achieved when the data from snow gauges are used for assessing the precipitation lapse rate at higher altitudes, and the temperature lapse rate is computed from data at each time step. The temperature-based hydrological model proved to be effective in simulating lake inflow water volume and energy production. No clear result has been found for snow melt forecast due to the difficulties in providing reliable quantitative weather forecast in complex alpine area.