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A simple analytical vortex model is presented and used to study an idealized wind turbine rotor in uniform and sheared inflow, respectively. Our model predicts that 1D momentum theory should be applied locally when modelling a non-uniformly loaded rotor in a sheared inflow. Hence the maximum local power coefficient (expressed with respect to the local, upstream velocity) of an ideal rotor is not affected by the presence of shear. We study the interaction between the wake vorticity generated by the rotor and the wind shear vorticity and find that their mutual interaction results in no net generation of axial vorticity: the wake effects and the shear effects exactly cancel each other out. This means that there are no resulting cross-shear-induced velocities and therefore also no cross-shear deflection of the wake in this model.

The power performance of a wind turbine in complex terrain is studied by means of Large Eddy Simulations (LES). The simulations show that the turbine performance is significantly different compared to what should be expected from the available wind. The reason for this deviation is that the undisturbed flow field behind the turbine is non-homogeneous and therefore results in a very different wake development and induction than seen for a turbine in flat homogeneous terrain.

Operational forecasts of groundwater levels provide critical real-time knowledge during extreme events, such as floods and droughts. This study proposes a Long Short-Term Memory based Encoder-Decoder (LSTM-ED) model for multi-step-ahead groundwater level forecasting. The LSTM-ED is a well-suited architecture for sequence-to-sequence modelling tasks but has not yet been applied to forecast groundwater levels. The proposed LSTM-ED model is designed in the context of the Danish online monitoring system grundvandsstanden.dk to serve as operational groundwater level forecasting system. In the encoder LSTM model, sequences of past precipitation, temperature and groundwater levels are processed to initialize the decoder LSTM model which, in addition takes in forecast sequences of precipitation and temperature to output a sequence of groundwater levels. We train LSTM-ED models individually for each well, with all data aggregated to daily timescale. We demonstrate the performance of the LSTM-ED architecture for numerous wells from grundvandsstanden.dk and test varying lead times of up to 30 days. The LSTM-ED model forecasts are contrasted with simple benchmark models as well as with a sequence-to-sequence LSTM model that does not incorporate forecasts of precipitation and temperature for outputting the groundwater sequence. Initial results underpin that integrating forecasts of precipitation and temperature is a crucial component, especially for wells with shallow intakes where surface and sub-surface processes are well connected. The sequence-to-sequence LSTM model yields similar accuracy as the simple benchmark models, whereas accuracy clearly improves for the LSTM-ED model. Overall, this study highlights the potential of LSTM-ED models as an operational tool for multi-step-ahead forecasting of groundwater levels.

Providing adequate and equitable sanitation to all by 2030 is central to achieving Sustainable Development Goal 6 (SDG6). Pit-latrines provide a low-cost, accessible form of sanitation, there has, therefore, been a significant increase in the rapidly growing Malawian population using pit latrines, largely driven by a reduction in open defecation. Whilst open defecation reduction is critical in managing waterborne pathogens and other contaminants, pit latrines can also result in both microbial and nutrient contamination of groundwater; faecal contamination of groundwater, resulting in contaminated boreholes, has already been documented in Malawi.To forecast the level of pit-latrine usage in Malawi, we evaluate the trends in Malawian sanitary provision using linear modelling to estimate that currently 500,000 people gain access to sanitation in Malawi every year, requiring approximately 93,000 new pit-latrines to be constructed annually to accommodate this shift. The associated increase in pit-latrine density creates a heightened threat of borehole contamination and a key public health concern.We also examine the nature of pit-latrine management and usage, presenting the results of a national survey of over 200,000 sanitary facilities. Whilst pit-latrines are usually associated with faecal contaminants, we found that 82.3% of pit-latrines had materials other than faecal waste deposited including rubbish, plastics, and oils; these present a danger of micropollutant contamination. Furthermore, we find that sustainable practises to manage waste deposited in pit-latrines, such as pit-latrine emptying, have low adoption.Pit-latrine usage is already causing groundwater contamination in Malawi, this will only be exacerbated by our projected increase in pit-latrine usage as Malawi manages a growing population and actively pushes to eliminate open defecation.This research is thanks to research and collaboration with the Government of Malawi with funding by the Scottish Government under the Scottish Government. Climate Justice Fund Water Futures Programme.

Pesticides are contaminants of priority concern that continue to present a significant risk to drinking water quality. While pollution mitigation in catchment systems is considered a cost-effective alternative to costly drinking water treatment, the effectiveness of pollution mitigation measures is uncertain and needs to be able to consider local biophysical, agronomic, and social aspects. We developed a probabilistic decision support tool (DST) based on spatial Bayesian belief networks (BBNs) that simulates inherent pesticide leaching risk to ground- and surface water quality to inform field-level pesticide mitigation strategies in a small (3.1 km2) drinking water catchment with limited observational data. The DST accounts for the spatial heterogeneity in soil properties, topographic connectivity, and agronomic practices; the temporal variability of climatic and hydrological processes; and uncertainties related to pesticide properties and the effectiveness of management interventions. The rate of pesticide loss via overland flow and leaching to groundwater and the resulting risk of exceeding a regulatory threshold for drinking water was simulated for five active ingredients. Risk factors included climate and hydrology (e.g. temperature, rainfall, evapotranspiration, and overland and subsurface flow), soil properties (e.g. texture, organic matter content, and hydrological properties), topography (e.g. slope and distance to surface water/depth to groundwater), land cover and agronomic practices, and pesticide properties and usage. The effectiveness of mitigation measures such as the delayed timing of pesticide application; a 10 %, 25 %, or 50 % reduction in the application rate; field buffers; and the presence/absence of soil pan on risk reduction were evaluated. Sensitivity analysis identified the month of application, the land use, the presence of buffers, the field slope, and the distance as the most important risk factors, alongside several additional influential variables. The pesticide pollution risk from surface water runoff showed clear spatial variability across the study catchment, whereas the groundwater leaching risk was uniformly low, with the exception of prosulfocarb. Combined interventions of a 50 % reduced pesticide application rate, management of the plough pan, delayed application timing, and field buffer installation notably reduced the probability of a high risk of overland runoff and groundwater leaching, with individual measures having a smaller impact. The graphical nature of BBNs facilitated interactive model development and evaluation with stakeholders to build model credibility, while the ability to integrate diverse data sources allowed a dynamic field-scale assessment of “critical source areas” of pesticide pollution in time and space in a data-scarce catchment, with explicit representation of uncertainties.

There is an urgent demand for assessments of climate change impact on the hydrological cycle at high spatialresolution. In particular, the impacts on shallow groundwater levels, which can lead to both flooding and drought, havemajor implications for agriculture, adaptation and urban planning. Predicting such hydrological impacts is typicallyperformed using physically-based hydrological models. However, such models are computationally expensive, especially athigh spatial resolutions. This study is based on the Danish national groundwater model, setup as a distributed, integrated surface-subsurface model at500 m horizontal resolution. Recently, a version at a higher resolution of 100 m was created; amongst others, to betterrepresent the uppermost groundwater table and to meet end user demands for water management and climate adaptation. Theincrease in resolution of the hydrological model, however, also increases computational bottleneck. To evaluate climatechange impact, a large ensemble of climate models was run with the 500 m hydrological model, while performing the sameensemble run with the 100 m resolution nation-wide model was deemed infeasible. The desired outputs at 100 m resolutionwere produced by developing a novel, hybrid downscaling method based on machine learning. Hydrological models for five subcatchments, covering around 9 % of Denmark and selected to represent a range ofhydrogeological settings, were run at 100 m resolution with forcings from a reduced ensemble of climate models. RandomForest algorithms were established using the simulated climate change impacts (future – present) on water table depth at 100m resolution from those submodels as training data. The trained downscaling algorithms then were applied to create nation-wide maps of climate change-induced impacts on theshallow groundwater table at 100 m resolution. These downscaled maps were successfully validated against results from avalidation submodel at 100 m resolution excluded from training the algorithms, and compared to the impact signals from the500 m hydrological model across Denmark. The suggested downscaling algorithm also opens for the spatial downscaling of other model outputs. It has the potential forfurther applications where, for example, computational limitations inhibit running distributed hydrological models at fineresolutions.

BackgroundEvolving In situ methods are showing results for sustainable and efficient plume remediation of groundwater contaminations. By injecting reactive components such as oxidation agents, zero valent iron, substrate and/or bacteria, a treatment zone (TZ) is established. In the TZ, the contamination degrades into harmless components by chemical and/or biological processes. Successful in situ remediation depends on contact between injectants and contamination. Yet, monitoring the spreading of the injectant is difficult by point sampling. The cross-borehole geophysical method DCIP (Direct Current, Induced Polarisation) allows for detailed spatial information on subsurface electrical resistivity and induced polarisation properties. The information can be used to assess the success of the injection and the development over time. Furthermore, the IP properties can be used to infer spatial information on hydraulic conductivity, which can be used in planning of the in situ remediation and in quantification of contaminant mass discharge (CMD) at the site. The objective of this study is to develop a cost-efficient method for detailed spatial and temporal monitoring of in situ remediation and to develop better tools to retrieve spatial subsurface information, able to assist and improve CMD based monitoring. ApproachA TZ in a plume of chlorinated ethenes was established by injecting the micro zerovalent iron product and a bacterial culture into the groundwater. A network of 9 geophysical and 16 monitoring wells was established. Cross-borehole DCIP measurements and water samples were taken before and shortly after injection and during the following year. Soil cores were sampled for chemical analysis of iron shortly after injection, and slug tests and grain size analysis. Data from water samples, soil cores and hydraulic tests were compared to the geophysical measurements to assess correlation between water chemistry and electrical resistivity from cross-borehole DCIP. The hydraulic properties inferred from hydraulic tests and cross-borehole DCIP were compared. The hydraulic properties with uncertainties and the contamination data were used to estimate the CMD through the TZ. Results The changes in electrical conductivity and specific water quality parameters caused by the injection, showed a strong correlation with the geophysical model. The observed correlation enabled a coherent, detailed understanding of both spatial and temporal spreading of the injected components, resulting in a re-injection. Hydraulic tests and hydraulic properties inferred from cross borehole DCIP showed a very good correlation, and applying the hydraulic properties inferred from cross borehole DCIP reduced the uncertainty of the CMD estimate before and after injection. In conclusion, cross borehole DCIP has the potential to improve planning and monitoring of in situ groundwater remediation and to reduce uncertainty of CMD estimation and thereby strengthen CMD as a metric in risk assessment.

Blockage effects due to the interaction of five wind turbines in a row are investigated through both Reynolds-averaged Navier–Stokes simulations and site measurements. Since power performance tests are often carried out at sites consisting of several turbines in a row, the objective of this study is to evaluate whether the power performance of the five turbines differs from that of an isolated turbine. A number of simulations are performed, in which we vary the turbine inter-spacing (1.8, 2 and 3 rotor diameters) and the inflow angle between the incoming wind and the orthogonal line to the row (from 0 to 45∘). Different values of the free-stream velocity are considered to cover a broad wind speed range of the power curve. Numerical results show consistent power deviations for all five turbines when compared to the isolated case. The amplitude of these deviations depends on the location of the turbine within the row, the inflow angle, the inter-spacing and the power curve region of operation. We show that the power variations do not cancel out when averaging over a large inflow sector (from −45 to +45∘) and find an increase in the power output of up to +1 % when compared to the isolated case under idealised conditions (neutral atmospheric conditions, no vertical wind shear or ground effects). We simulate power performance “measurements” with both a virtual mast and nacelle-mounted lidar and find a combination of power output increase and upstream velocity reduction, which causes an increase of +4 % in the power coefficient under idealised conditions. We also use measurements from a real site consisting of a row of five wind turbines to validate the numerical results. From the analysis of the measurements, we also show that the power performance is impacted by the neighbouring turbines. Compared to when the inflow is perpendicular to the row, the power output varies by +1.8 % and −1.8 % when the turbine is the most downwind and upwind of the line, respectively.

Pesticides are contaminants of priority concern that continue to present a significant risk to drinking water quality. While pollution mitigation in catchment systems is considered a cost-effective alternative to costly drinking water treatment, the effectiveness of pollution mitigation measures is uncertain and needs to be able to consider local biophysical, agronomic, and social aspects. We developed a probabilistic decision support tool (DST) based on spatial Bayesian Belief Networks (BBN) that simulates inherent pesticide leaching risk to ground- and surface water quality to inform field-level pesticide mitigation strategies in a small drinking water catchment (3.1 km2) with limited observational data. The DST accounts for the spatial heterogeneity in soil properties, topographic connectivity, and agronomic practices; temporal variability of climatic and hydrological processes as well as uncertainties related to pesticide properties and the effectiveness of management interventions. The rate of pesticide loss via overland flow and leaching to groundwater and the resulting risk of exceeding a regulatory threshold for drinking water was simulated for five active ingredients. Risk factors included climate and hydrology (temperature, rainfall, evapotranspiration, overland and subsurface flow), soil properties (texture, organic matter content, hydrological properties), topography (slope, distance to surface water/depth to groundwater), land cover and agronomic practices, pesticide properties and usage. The effectiveness of mitigation measures such as delayed timing of pesticide application; 10 %, 25 % and 50 % reduction in application rate; field buffers; and presence/absence of soil pan on risk reduction were evaluated. Sensitivity analysis identified the month of application, land use, presence of buffers, field slope and distance as the most important risk factors, alongside several additional influential variables. Pesticide pollution risk from surface water runoff showed clear spatial variability across the study catchment, while groundwater leaching risk was uniformly low, with the exception of prosulfocarb. Combined interventions of 50 % reduced pesticide application rate, management of plough pan, delayed application timing and field buffer installation notably reduced the probability of high-risk from overland runoff and groundwater leaching, with individual measures having a smaller impact. The graphical nature of the BBN facilitated interactive model development and evaluation with stakeholders to build model credibility, while the ability to integrate diverse data sources allowed a dynamic field-scale assessment of ‘critical source areas’ of pesticide pollution in time and space in a data scarce catchment, with explicit representation of uncertainties.

The environmental fate and transport of nitrogen and phosphorus nutrient species leached from agroecosystems are largely influenced by the hydrogeological setting, which dictates the distribution of groundwater flow pathways, residence times, and physio-chemical properties of the subsurface. Traditional conceptual models tend to oversimplify these relationships, and their application towards river catchment nutrient management promotes insufficient characterisation of hydrogeological heterogeneity, which is subsequently not accounted for. Until recently, very little hydrogeological information and conceptual understanding existed for groundwater systems within the postglacial basement terranes of Scotland and Northern Ireland, due to an abundance of surface water resources and prevalence of poorly productive bedrock aquifers. Recent research has demonstrated the role of geological heterogeneity in determining the contaminant transport behaviour of these hard-rock aquifers, where the presence of weathering and fracturing can potentially result in the rapid delivery of nutrients to rural water supplies and groundwater-dependent ecosystems.We aim to further elucidate the role of hydrogeological setting in river catchment nutrient dynamics to improve agricultural sustainability in geologically heterogeneous agricultural regions. This will be achieved by developing conceptual models of nutrient fate and transport for two contrasting agricultural river catchments. Here, we present preliminary conceptual models based on a literature review of groundwater systems within the same geological terranes, analysis of hydrochemical monitoring data, and accounting for catchment-specific features through desk studies of geological and airborne geophysical surveys.The River Ythan is a groundwater-dominated lowland catchment within Scotland’s arable belt, designated a Nitrate Vulnerable Zone due to the eutrophication of its estuary. This catchment is geologically complex, with a variably metamorphosed and sheared Precambrian basement with igneous intrusions ranging from ultrabasic rocks to granite. This complexity is enhanced by the significant preservation of Tertiary weathering profiles and an extensive but discontinuous cover of glacial deposits derived from the saprolites. The superficial deposits create a shallow aquifer system characterized by oxic, well-mixed groundwaters with high nitrate concentrations. The bedrock groundwater bodies feature lower nitrate concentrations with variable denitrification rates, resulting from the relationships between lithology, tectonics, and weathering.Two upland headwater sub-catchments of the Upper Bann River (Co. Down, Northern Ireland) drain either side of the contact between a granodiorite laccolith and Lower Palaeozoic metasedimentary rocks within an elevated drumlinoid landscape. Here, diffuse phosphorus exports to surface waters have not experienced the same extent of decline observed in storm runoff phosphorus following the implementation of nutrient management policies. Anoxic groundwaters favourable for denitrification may result in the release of previously adsorbed (legacy) phosphorus following the reductive dissolution of Fe (hydr)oxides. These conditions are generated by (a) confinement by thick, drumlinised clayey tills; and (b) bedrock structures promoting deep groundwater flow.The site-specific conceptual models will be further developed through multi-scale geophysical characterisation of hydrogeological heterogeneity and constrained by the catchment-scale distribution of residence times derived from stable (2H, 18O) and radioactive (3H) isotope compositions of groundwaters. These refined conceptual models can guide the development of numerical groundwater models and spatially targeted nutrient management.

Pesticides are contaminants of priority concern regulated under the EU Water Framework Directive 2000 (WFD) and its daughter Directives. Article 7 of the WFD promotes a ‘prevention-led’ approach that prioritises pollution prevention at source rather than costly drinking water treatment.However, the effectiveness of pollution mitigation measures in catchment systems is uncertain and catchment management needs to consider local biophysical, agronomic, and social aspects. Local risk assessment and management of water contamination in drinking water catchments informed by process-based models is costly and often hindered by lack of data. Therefore, spatial risk indices have been developed to evaluate the intrinsic risk from pesticide pollution. However, these risk assessment approaches do not explicitly account for uncertainties in complex processes and their interactions. In this study, we developed a probabilistic decision support tool (DST) based on spatial Bayesian Belief Networks (BBN) to inform field-level pesticide mitigation strategies in a small drinking water catchment (3.5 km2) with limited observational data. The DST accounts for the spatial heterogeneity of soil properties, topographic connectivity, and agronomic practices; temporal variability of climatic and hydrological processes as well as uncertainties related to pesticide properties and the effectiveness of management interventions. Furthermore, the graphical nature of the BBN facilitated interactive model development and evaluation with stakeholders, while the ability to integrate diverse data sources allowed an effective use of available data.The risk of pesticide loss via two pathways (overland flow and leaching to groundwater) was simulated for five active ingredients. Risk factors included climate and hydrology (temperature, rainfall, evapotranspiration, overland and subsurface flow), soil properties (texture, organic matter content, hydrological properties), topography (slope, distance to surface water/depth to groundwater), landcover and agronomic practices, pesticide properties and usage. The effectiveness of mitigation measures such as delayed pesticide application timing; 10%, 25% and 50% application rate reduction; field buffers; and presence/absence of soil pan on risk reduction were evaluated.Sensitivity analysis identified the application rates, rainfall, and overland flow connectivity as the most important risk factors. Pesticide pollution risk from surface water runoff showed clear spatial variability across the study catchment, while groundwater leaching risk was more uniform. Combined interventions of 50% reduced pesticide application rate, management of plough pan, delayed application timing and field buffer installation reduced the probability of high-risk from overland flow in several fields.The DST provided a probabilistic dynamic field-scale assessment of ‘critical risk areas’ of pesticide pollution in time and space and is easily transferable to neighbouring catchments.

By use of transient and distributed groundwater-surface water flow models, simulated time series of stream discharge and groundwater level for monitoring networks, groundwater bodies and river reaches have been analysed for a historical period and four different future scenarios toward 2100 in two large-scale catchments in Denmark. The purpose of the climate scenarios has been to qualify the existing knowledge on how future climate change most likely will impact hydrology, groundwater status and Ecological Quality Elements (EQR- Ecological flow in rivers). Another purpose has been to identify whether foreseen climate changes will be detected by the surface water and groundwater monitoring networks, and to which degree the River Basin Management Plan measures for supporting the goal of good quantitative status are robust to the projected changes in water balance and ecological flow. The developed hydrological models were run with climate inputs based on selected RCP4.5 and RCP8.5 climate model runs (RCP8.5 wet, median, dry and RCP4.5 median). Changes in groundwater quantitative status and ecological flow metrics were calculated based on 30-year model runs driven by RCP8.5 for 2071-2100 (RCP4.5 for 2041-70) and compared to 1981-2010.Overall the four scenarios results in very significant water balance changes with increased precipitation: 3% to 27%, evapotranspiration: 6% to 17%, groundwater recharge: 0% to 49%, drainage flow: 0% to 71%, baseflow: 0% to 31% and overland flow: 16% to 281%. For one catchment an increase in abstraction of 23% to 171% due to an increase in irrigation demand by 36% to 113% is foreseen. The results have wide implications for groundwater flooding risks, quantitative status and ecological flow metrics. Most sensitive is changes in ecological flow conditions in rivers for fish, showing a relative high probability for decreased state for 10-20% of the reaches for the RCP8.5 wet and dry scenarios due to more extreme hydrological regimes toward 2071-2100. Maximum monthly runoff is increased for winter months by 100% for RCP8.5 wet and median scenarios and around 10% for RCP8.5 dry scenario. Annual maximum daily flows is simulated to increase by up to a factor of five, and late summer low flows decreased.Impacts on groundwater levels and water balances of groundwater bodies will be significant, with increased seasonal fluctuations and also increased maximum and decreased minimum groundwater levels for 30 year periods for 2071-2100 compared to 1981-2010.More rain, both when we look back on historical data and when we look forward with latest climate projections will result in more frequent flooding from groundwater and streams in the future. At the same time, the temperature and thus evapotranspiration rises. This means that in the long term we will have increased challenges with drought and increased irrigation demands on sandy soils while evapotranspiration will also increase on the clayey soils. This will result in greater fluctuation in the flow and groundwater levels between winters and summers, and between wet and dry years, challenging sustainable groundwater abstraction and maintaining good quantitative status of groundwater bodies.

Groundwater dating and travel time distributions are important tools and data for assessment of the vulnerability of water supply wells towards pollution from the surface. Here we present selected results from more than 30 water supply and monitoring wells from major Danish water companies. The wells were recently sampled and investigated using multiple environmental tracers including 85Kr, 39Ar, 3H/3He, 14C, SF6, CFCs and noble gases and different groundwater modeling techniques. The results demonstrate the value of groundwater dating and travel time estimations for the assessment of the history and fate of contaminants in the subsurface. This information is crucial for the assessment of the efficiency of measures to mitigate pollution of groundwater by harmful substances such as pesticides, nitrate and a large range of emerging contaminants. We demonstrate how groundwater ages and travel time distributions can be used to assess the vulnerability or susceptibility of water supply wells towards pollution, and how level specific sampling in long well screens can provide additional important information for assessment of the vulnerability of deep and shallow parts of a water supply well. Potential applications of the estimated travel time distributions include 1) improved management of well fields 2) development of pumping strategies and well screens minimizing the risk of pollution of drinking water wells, and 3) assessment of the adequacy of regulations established by authorities to protect valuable groundwater resources against pollution.

For load calculations on wind turbines it is usually assumed that the turbulence approaching the rotor does not change its statistics as it goes through the induction zone. We investigate this assumption using a nacelle-mounted forward-looking pulsed lidar that measures low frequency wind fluctuations simultaneous at distances between one half and three rotor diameters upstream. The measurements show that below rated wind speed the low-frequency wind variance is reduced by up to 10 % at one half rotor diameter upstream and above rated enhanced by up to 20 %. A quasi-steady model that takes into account the change of thrust coefficient with wind speed explains these variations partly. Large-eddy simulations of turbulence approaching an actuator disk model of a rotor support the finding that the slope of the thrust curve influences the low-frequency fluctuations.

Precipitation gauge catch correction is often given very little attention in hydrological modelling compared to model parameter calibration. This is critical because significant precipitation biases often make the calibration exercise pointless, especially when supposedly physically-based models are in play. This study addresses the general importance of appropriate precipitation catch correction through a detailed modelling exercise. An existing precipitation gauge catch correction method addressing solid and liquid precipitation is applied, both as national mean monthly correction factors based on a historic 30 yr record and as gridded daily correction factors based on local daily observations of wind speed and temperature. The two methods, named the historic mean monthly (HMM) and the time–space variable (TSV) correction, resulted in different winter precipitation rates for the period 1990–2010. The resulting precipitation datasets were evaluated through the comprehensive Danish National Water Resources model (DK-Model), revealing major differences in both model performance and optimised model parameter sets. Simulated stream discharge is improved significantly when introducing the TSV correction, whereas the simulated hydraulic heads and multi-annual water balances performed similarly due to recalibration adjusting model parameters to compensate for input biases. The resulting optimised model parameters are much more physically plausible for the model based on the TSV correction of precipitation. A proxy-basin test where calibrated DK-Model parameters were transferred to another region without site specific calibration showed better performance for parameter values based on the TSV correction. Similarly, the performances of the TSV correction method were superior when considering two single years with a much dryer and a much wetter winter, respectively, as compared to the winters in the calibration period (differential split-sample tests). We conclude that TSV precipitation correction should be carried out for studies requiring a sound dynamic description of hydrological processes, and it is of particular importance when using hydrological models to make predictions for future climates when the snow/rain composition will differ from the past climate. This conclusion is expected to be applicable for mid to high latitudes, especially in coastal climates where winter precipitation types (solid/liquid) fluctuate significantly, causing climatological mean correction factors to be inadequate.