Effect of Snow Cover Conditions on the Hydrologic Regime: Case Study in a Pluvial‐Nival Watershed,Japan1

Abstract: Hydrologic monitoring in a small forested and mountainous headwater basin in Niigata Prefecture has been undertaken since 2000. An important characteristic of the basin is that the hydrologic regime contains pluvial elements year‐round, including rain‐on‐snow, in addition to spring snowmelt. We evaluated the effect of different snow cover conditions on the hydrologic regime by analyzing observed data in conjunction with model simulations of the snowpack. A degree‐day snow model is presented and applied to the study basin to enable estimation of the basin average snow water equivalent using air temperature at three representative elevations. Analysis of hydrological time series data and master recession curves showed that flow during the snowmelt season was generated by a combination of ground water flow having a recession constant of 0.018/day and diurnal melt water flow having a recession constant of 0.015/hour. Daily flows during the winter/snowmelt season showed greater persistence than daily flows during the warm season. The seasonal water balance indicated that the ratio of runoff to precipitation during the cold season (December to May) was about 90% every year. Seasonal snowpack plays an important role in defining the hydrologic regime, with winter precipitation and snowmelt runoff contributing about 65% of the annual runoff. The timing of the snowmelt season, indicated by the date of occurrence of the first significant snowmelt event, was correlated with the occurrence of low flow events. Model simulations showed that basin average snow water equivalent reached a peak around mid‐February to mid‐March, although further validation of the model is required at high elevation sites.     

EFFECT OF SIMULATED CLIMATE CHANGE ON SNOWMELT RUNOFF MODELING IN SELECTED BASINS1

ABSTRACT: The projected increase in the concentration of CO₂ and other greenhouse gases in the atmosphere is likely to result in a global temperature increase. This paper reports on the probable effects of a temperature increase and changes in transpiration on basin discharge in two different mountain snowmelt regions of the western United States. The hydrological effects of the climate changes are modeled with a relatively simple conceptual, semi-distributed snowmelt runoff model. Based on the model results, it may be concluded that increased air temperatures will result in a shift of snowmelt runoff to earlier in the snowmelt season. Furthermore, it is shown that it is very important to include the expected change in climate-related basin conditions resulting from the modeled temperature increase in the runoff simulation. The effect of adapting the model parameters to reflect the changed basin conditions resulted in a further shift of streamflow to April and an even more significant decrease of snowmelt runoff in June and July. If the air temperatures increase by approximately 5°C and precipitation and accumulated snow amounts remain about the same, runoff in April and May, averaged for the two basins, is expected to increase by 185 percent and 26 percent, respectively. The runoff in June and July will decrease by about 60 percent each month. Overall, the total seasonal runoff decreases by about 6 percent. If increased CO₂ concentrations further change basin conditions by reducing transpiration by the maximum amounts reported in the literature, then, combined with the 5°C temperature increase, the April, May, June, and July changes would average +230 percent, +40 percent, ?55 percent, and ?45 percent, respectively. The total seasonal runoff change would be +11 percent.     

Influence of manure application on surface energy and snow cover: field experiments

Application of manure to frozen and/or snow-covered soils of high-latitude, continental climate regions is associated with enhanced P losses to surface water bodies, but the practice is an essential part of most animal farming systems in these regions. Field experiments of the fates of winter-applied manure P are so difficult as to make them essentially impractical, so a mechanistic, modeling approach is required. Central to a mechanistic understanding of manure P snow-melt runoff is knowledge of snowpack disappearance (ablation) as affected by manure application. The objective of this study was to learn how solid manure applied to snow-covered fields modulates the surface energy balance and thereby snow cover ablation. Manure landspreading experiments were conducted in Arlington, WI during the winters of 1998 and 1999. Solid dairy manure was applied on top of snow at a rate of 70 Mg ha(-1) in 1998, and at 45 and 100 Mg ha(-1) in 1999. Results showed that the manure retarded melt, in proportion to the rate applied. The low-albedo manure increased absorption of shortwave radiation compared with snow, but this extra energy was lost in longwave radiation and turbulent flux of sensible and latent heat. These losses result in significant attenuation of melt peaks, retarding snowmelt. Lower snowmelt rates beneath manure may allow more infiltration of meltwater compared with bare snow. This infiltration and attenuated snowmelt runoff may partially mitigate the enhanced likelihood of P runoff from unincorporated winter-spread manure.     

Observed Changes in Climate and Streamflow in the Upper Rio Grande Basin

Observed streamflow and climate data are used to test the hypothesis that climate change is already affecting Rio Grande streamflow volume derived from snowmelt runoff in ways consistent with model‐based projections of 21st‐Century streamflow. Annual and monthly changes in streamflow volume and surface climate variables on the Upper Rio Grande, near its headwaters in southern Colorado, are assessed for water years 1958–2015. Results indicate winter and spring season temperatures in the basin have increased significantly, April 1 snow water equivalent (SWE) has decreased by approximately 25%, and streamflow has declined slightly in the April–July snowmelt runoff season. Small increases in precipitation have reduced the impact of declining snowpack on trends in streamflow. Changes in the snowpack–runoff relationship are noticeable in hydrographs of mean monthly streamflow, but are most apparent in the changing ratios of precipitation (rain + snow, and SWE) to streamflow and in the declining fraction of runoff attributable to snowpack or winter precipitation. The observed changes provide observational confirmation for model projections of decreasing runoff attributable to snowpack, and demonstrate the decreasing utility of snowpack for predicting subsequent streamflow on a seasonal basis in the Upper Rio Grande Basin.     

MODEL ACCURACY IN SNOWMELT-RUNOFF FORECASTS EXTENDING FROM 1 TO 20 DAYS1

ABSTRACT: This paper examines the performance of snowmelt-runoff models in conditions approximating real-time forecast situations. These tests are one part of an intercomparison of models recently conducted by the World Meteorological Organization (WMO). Daily runoff from the Canadian snowmelt basin Illecille. waet (1155 km², 509–3150 m a.s.l.) was forecast for 1 to 20 days ahead. The performance of models was better than in a previous WMO project, which dealt with runoff simulations from historical data, for the following reasons: (1) conditions for models were more favorable than a real-time forecast situation because measured input data and not meteorological forecast inputs were distributed to the modelers; (2) the selected test basin was relatively easy to handle and familiar from the previous WMO project; and (3) all kinds of updating were allowed so that some models even improved their accuracy towards longer forecast times. Based on this experience, a more realistic follow-up project can be imagined which would include temperature forecasts and quantitative precipitation forecasts instead of measured data.     

The Mass and Energy Exchange of a Tibetan Glacier: Distributed Modeling and Climate Sensitivity

Most glaciers in the Tibetan Plateau (TP) are not closely monitored for mass balance (MB) due to their inaccessibility, which makes it difficult to better understand the dynamics of glacial advancement or retreat. Surface energy budget, MB, and the resulting melt runoff were calculated for Zhadang glacier (5,710 m above sea level) of the central TP. Energy balance was calculated on 30‐m square grids for the summers of 2007 and 2008. On average, net radiation dominated the total energy source (66%) while the residual was supplied by sensible heat flux. More than 67% of the energy sink was available for melting on the glacier. Thus, less than 33% of the total energy was consumed by latent heat flux. A large and a slightly negative summer MB were calculated for the 2007 and 2008 summers, respectively. The high sensitivity of the glacier to air temperature may indicate that the lower than average seasonal temperature was more important than the increased precipitation for the slightly negative MB in the summer of 2008. Comparisons of glacial melt runoff indicated that rainfall and snowmelt were the dominant contribution to total runoff in the glacierized basin and the ice melting is also very important. Glacial melt calculation provides a basis for quantifying glacial melt‐runoff contribution to the river streamflow in the TP.     

Influence of manure application on surface energy and snow cover: model development and sensitivities

Winter landspreading is an important part of manure management in the U.S. Upper Midwest. Although the practice is thought to lead to excessive P runoff losses, surprisingly little has been learned from field experiments or current water quality models. We captured knowledge gained from winter manure landspreading experiments by modifying a mechanistic snow ablation model to include manure. The physically based, modified model simulated the observed delay in snow cover disappearance and surface energy balance changes caused by application of the manure. Additional model simulations of surface energy balance estimates of radiation and turbulent fluxes showed that during intense melting events the manure on top of snow significantly reduced the energy available for melt of the snow underneath, slowing melt. The effect was most pronounced when snowmelt was driven by both relatively high solar radiation and turbulent heat fluxes. High absorbed shortwave radiation caused significant warming of the manure, which led to substantial losses in turbulent fluxes and longwave radiation. Simulations of snowmelt also showed that manure applications between 45 and 100 Mg ha(-1) significantly reduced peak snowmelt rates, in proportion to the manure applied. Lower snowmelt rates beneath manure may allow more infiltration of meltwater compared with bare snow. This infiltration and attenuated snowmelt runoff may partially mitigate the enhanced likelihood of P runoff from unincorporated winter-spread manure.     

MONTHLY RUNOFF DISTRIBUTION RELATED TO BASIN GEOGRAPHY1

ABSTRACT: Mean monthly runoff from ungaged drainage basins that have significant snowpacks each year can be estimated quite well by assuming that the time duration between snowfall and snowmelt is the predominant factor in temporal runoff distribution. That time span is related to basin temperatures which are, in turn, functions of basin elevation and latitude. Regional hydrologic analyses of gaged basin data create regression equations for estimating runoff distribution by month. These equations then can be applied to ungaged basins. Basin latitude and mean elevation are two independent variables that can be used in estimating monthly runoff distributions.     

SHORT-TERM FORECASTING OF SNOWMELT RUNOFF USING ARMAX MODELS1

ABSTRACT: Time series models of the ARMAX class were investigated for use in forecasting daily riverflow resulting from combined snowmelt/rainfall. The Snowmelt Runoff Model (Martinec-Rango Model) is shown to have a form similar to the ARMAX model. The advantage of the ARMAX approach is that analytical model identification and parameter estimation techniques are available. In addition, previous forecast errors can be included to improve forecasts and confidence limits can be estimated for the forecasts. Diagnostic checks are available to determine if the model is performing properly. Finally, Kalman filtering can be used to allow the model parameters to vary continuously to reflect changing basin runoff conditions. The above advantages result in improved flow forecasts with fewer model parameters.     

A HYDROCLIMATOLOGICAL ANALYSIS OF THE RED RWER OF THE NORTH SNOWMELT FLOOD CATASTROPHE OF 19971

ABSTRACT: The flood hydroclimatology of the Grand Forks flood of April 1997, the most costly flood on a per capita basis for a major metropolitan area in United States history, is analyzed in terms of the natural processes that control spring snowmelt flooding in the region. The geomorphological characteristics of the basin are reviewed, and an integrated assessment of the hydroclimatological conditions during the winter of 1996 to 1997 is presented to gain a real‐world understanding of the physical basis of this catastrophic flood event. The Grand Forks flood resulted from the principal flood‐producing factors occurring at either historic or extreme levels, or at levels conducive to severe flooding. Above normal fall precipitation increased the fall soil moisture storage and reduced the spring soil moisture storage potential. A concrete frost layer developed that effectively reduced the soil infiltration capacity to zero. Record snowfall totals and snow cover depths occurred across the basin because of the unusual persistence of a blocking high circulation pattern throughout the winter. A severe, late spring blizzard delayed the snowmelt season and replenished the snow cover to record levels for early April. This blizzard was followed by a sudden transition to an extreme late season thaw due to the abrupt breakdown of the blocking circulation pattern. The presence of river ice contributed to backwater effects and affected the timing of tributary inflows to the main stem of the Red River. Only the absence of spring rains prevented an even more catastrophic flood disaster from taking place. This paper contributes to our understanding of the flood hydroclimatology of catastrophic flood events in an unusual flood hazard region that possesses relatively flat terrain, a north‐flowing river, and an annual peak discharge time series dominated by spring snowmelt floods.     

POSTFIRE SUSPENDED SEDIMENT FROM YELLOWSTONE NATIONAL PARK,WYOMING1

ABSTRACT: Wildfires in 1988 burned over 2000 square miles of the greater Yellowstone area in Montana and Wyoming in the largest fires in the history of Yellowstone National Park (YNP). A four-year postfire study to estimate fire-related changes in suspended sediment transport on the Yellowstone River and its principal tributary in YNP, the Lamar River, benefitted from a recently completed three-year prefire baseline study. Both studies took daily depth-integrated samples from April through September. Fire-related changes in suspended sediment were distinguished from natural climatic variations by two methods: comparison of forecast postfire sediment loads estimated with prefire sediment-rating equations to measured postfire loads; and by postfire changes in suspended sediment load expressed per unit volume runoff. Both methods indicated postfire sediment increases that varied according to season. The higher elevation Lamar River basin had little postfire increase in spring snowmelt season sediment but large increases in summer sediment load. The Yellowstone River had postfire increases in sediment load for the spring but did not reflect the large summer increases of its upstream tributary. The reasons for the difference in postfire snowmelt sediment response are unclear but may relate to basin elevation differences, the effects of unburned watersheds, and cooler postfire springs. The few high streamflow snowmelt events in the postfire period mitigated postfire sediment increases.     

INLET CONCENTRATION TIME NOMOGRAM FOR URBAN BASINS1

ABSTRACT: A physically-based nomogram to determine inlet concentration times for composite developed basins is presented. This nomogram is constructed from previously derived kinematic wave formulations of both catchment and gutter flow components of surface runoff. With specified physical properties of a basin and rainfall intensity-duration relationships, the inlet time can be obtained from the nomogram directly.     

Runoff Simulation of the Headwaters of the Yellow River Using The SWAT Model With Three Snowmelt Algorithms1

Abstract: The Soil and Water Assessment Tool (SWAT) model combined with different snowmelt algorithms was evaluated for runoff simulation of an 114,345 km² mountainous river basin (the headwaters of the Yellow River), where snowmelt is a significant process. The three snowmelt algorithms incorporated into SWAT were as follows: (1) the temperature‐index, (2) the temperature‐index plus elevation band, and (3) the energy budget based SNOW17. The SNOW17 is more complex than the temperature‐based snowmelt algorithms, and requires more detailed meteorological and topographical inputs. In order to apply the SNOW17 in the SWAT framework, SWAT was modified to operate at the pixel scale rather than the normal Hydrologic Response Unit scale. The three snowmelt algorithms were evaluated under two parameter scenarios, the default and the calibrated parameters scenarios. Under the default parameters scenario, the parameter values were determined based on a review of the current literature. The purpose of this type of evaluation was to assess the applicability of SWAT in ungauged basins, where there is little observed data available for calibration. Under the calibrated parameters scenario, the parameters were calibrated using an automatic calibration program, the Shuffled Complex Evolution (SCE‐UA). The purpose of this type of evaluation was to assess the applicability of SWAT in gauged basins. Two time periods (1975‐1985 and 1986‐1990) of monthly runoff data were used in this study to evaluate the performance of SWAT with different snowmelt algorithms. Under the default parameters scenario, the SWAT model with complex energy budget based SNOW17 performed the best for both time periods. Under the calibrated parameters scenario, the parameters were calibrated using monthly runoff from 1975‐1985 and validated using monthly runoff from 1986‐1990. After parameter calibration, the performance of SWAT with the three snowmelt algorithms was improved from the default parameters scenario. Further, the SWAT model with temperature‐index plus elevation band performed as well as the SWAT model with SNOW17. The SWAT model with temperature‐index algorithm performed the poorest for both time periods under both scenarios. Therefore, it is suggested that the SNOW17 model be used for modeling ungauged basins; however, for gauged basins, the SNOW17 and simple temperature‐index plus elevation band models could provide almost equally good runoff simulation results.     

EVAPOTRANSPIRATION AND SOIL MOISTURE DYNAMICS ON A SEMIARID PONDEROSA PINE HILLSLOPE1

ABSTRACT: Detailed measurements of soil moisture and ET in semiarid forest environments have not been widely reported in the literature. In this study, soil moisture and water balance components were measured over a four‐year period on a semiarid ponderosa pine hillslope, with evapotranspiration (ET) determined as the residual of measured precipitation, runoff, and change in soil moisture storage. ET accounts for approximately 95 percent of the water budget and has a distinctly bimodal annual pattern, with peaks occurring after spring snowmelt and during the late summer monsoon season, periods that coincide with high soil moisture. Weekly growing season ET rates determined by the hillslope water balance are found to be invariably below calculated potential rates. Normalized ET rates are linearly correlated (r²= 0.62) with soil moisture; therefore, a simple linear relation is proposed. Growing season soil moisture dynamics were modeled based on this relation. Results are in fair agreement (r²= 0.63) with the observed soil moisture data over the four growing seasons; however, for two dry summers with little surface runoff, much better results (r² > 0.90) were obtained.     

Use of observed hydroclimatic trends to constrain projections of snowmelt season runoff in the Rio Grande headwaters

A method is developed for choosing 21st Century streamflow projections among widely varying results from a large ensemble of climate model-driven simulations. We quantify observed trends in climate–streamflow relationships in the Rio Grande headwaters, which has experienced warming temperature and declining snowpack since the mid-20th Century. Prominent trends in the snowmelt runoff season are used to assess corresponding statistics in downscaled global climate model projections. We define “Observationally Consistent (OC)” simulations as those that reproduce historical changes to linear statistics of diminished snowpack–streamflow coupling in the headwaters and an associated increase in the contribution of spring season (post-peak snowpack) precipitation to streamflow. Only a modest fraction of the ensemble of simulations meets these consistency metrics. The subset of OC simulations projects significant decreases in headwaters flow, whereas the simulations that poorly replicate historical trends exhibit a much wider range of projected changes. These results bolster confidence in model-based projections of declining runoff in the Rio Grande headwaters in the snowmelt runoff season and offer an example of a methodology for evaluating model-based projections in basins with similar hydroclimates that have experienced pronounced climate changes in the recent historical record.     

THE ATMOSPHERIC WATER BALANCE AND THE HYDROLOGY OF LARGE RIVER BASINS1

The concept of using the atmospheric water balance technique in the study of the hydrology of large (greater than 10⁵km²) river basins is described. The atmospheric water balance technique consists of determining the spacial and time distributions and fluxes of water vapor through the atmospheric volume overlying the basin. The quantity precipitation minus evaporation at the earth's surface is determined as a residual of the computation. A review of the results of various experiments employing this technique is provided. The incorporation of the technique in a study of the hydrology of a large river basin is demonstrated by showing the results of a study of the hydrometeorology of the Upper Colorado River Basin. The example covers the study of eleven winter seasons, November through April, 1957–1968. The seasonal accumulation of water over the basin as determined by the atmospheric water balance is highly correlated with annual runoff. Correlation coefficient r = .8. The daily evaporation rate during dry days varies by a factor of two over the winter season, and is shown to be related to the incoming solar radiation intensity.     

AI-based runoff simulation based on remote sensing observations: A case study of two river basins in the United States and Canada

Data-driven techniques are used extensively for hydrologic time-series prediction. We created various data-driven models (DDMs) based on machine learning: long short-term memory (LSTM), support vector regression (SVR), extreme learning machines, and an artificial neural network with backpropagation, to define the optimal approach to predicting streamflow time series in the Carson River (California, USA) and Montmorency (Canada) catchments. The moderate resolution imaging spectroradiometer (MODIS) snow-coverage dataset was applied to improve the streamflow estimate. In addition to the DDMs, the conceptual snowmelt runoff model was applied to simulate and forecast daily streamflow. The four main predictor variables, namely snow-coverage (S-C), precipitation (P), maximum temperature (T_max), and minimum temperature (T_min), and their corresponding values for each river basin, were obtained from National Climatic Data Center and National Snow and Ice Data Center to develop the model. The most relevant predictor variable was chosen using the support vector machine-recursive feature elimination feature selection approach. The results show that incorporating the MODIS snow-coverage dataset improves the models' prediction accuracies in the snowmelt-dominated basin. SVR and LSTM exhibited the best performances (root mean square error = 8.63 and 9.80) using monthly and daily snowmelt time series, respectively. In summary, machine learning is a reliable method to forecast runoff as it can be employed in global climate forecasts that require high-volume data processing.     

DEVELOPMENT OF WATERSIIED MODELS FOR TWO SIERRA NEVADA BASINS USING A GEOGRAPHIC INFORMATION SYSTEM1

ABSTRACT: Techniques were developed using vector and raster data in a geographic information system (GIS) to define the spatial variability of watershed characteristics in the north-central Sierra Nevada of California and Nevada and to assist in computing model input parameters. The U.S. Geological Survey's Precipitation-Runoff Modeling System, a physically based, distributed-parameter watershed model, simulates runoff for a basin by partitioning a watershed into areas that each have a homogeneous hydrologic response to precipitation or snowmelt. These land units, known as hydrologic-response units (HRU's), are characterized according to physical properties, such as altitude, slope, aspect, land cover, soils, and geology, and climate patterns. Digital data were used to develop a GIS data base and HRIJ classification for the American River and Carson River basins. The following criteria are used in delineating HRU's: (1) Data layers are hydrologically significant and have a resolution appropriate to the watershed's natural spatial variability, (2) the technique for delineating HRU's accommodates different classification criteria and is reproducible, and (3) HRU's are not limited by hydrographic-subbasin boundaries. HRU's so defined are spatially noncontiguous. The result is an objective, efficient methodology for characterizing a watershed and for delineating HRU's. Also, digital data can be analyzed and transformed to assist in defining parameters and in calibrating the model.     

EVALUATION OF GREAT LAKES NET BASIN SUPPLY FORECASTS1

ABSTRACT: Evaluation of the Great Lakes Environmental Research Laboratory's (GLERL's) physically-based monthly net basin supply forecast method reveals component errors and the effects of model improvements for use on the Laurentian Great Lakes. While designed for probabilistic outlooks, it is assessed for giving deterministic outlooks along with other net basin supply forecast methods of the U.S. Army Corps of Engineers and Environment Canada, and with a stochastic approach commissioned by the Corps. The methods are compared to a simple clima-tological forecast and to actual time series of net basin supplies. Aetual net basin supplies are currently determined by estimating all components directly, instead of as water-balance residuals. This is judged more accurate and appropriate for both forecasting and simulation. GLERL's physically-based method forecasts component supplies while the other methods are based on residual supplies. These other methods should be rederived to be based on component supplies. For each of these other methods, differences between their outlooks and residual supplies are used as error estimates for the rederived methods and component supplies. The evaluations are made over a recent period of record high levels followed by a record drought. Net basin supply outlooks are better than climatology, and GLERL's physically-based method performs best with regard to either component or residual net basin supplies. Until advances are made in long-range climate outlooks, deterministic supply outlooks cannot be improved significantly.     

Antibiotic losses in leaching and surface runoff from manure-amended agricultural land

A 3-yr field study quantified leaching and runoff losses of antibiotics from land application of liquid hog (chlortetracycline and tylosin) and solid beef (chlortetracycline, monensin, and tylosin) manures under chisel plowing and no-tillage systems. The study was conducted in southwestern Wisconsin, a karst area with steep, shallow, macroporous soils. Relative mass losses of chlortetracycline, monensin, and tylosin were <5% of the total amount applied with manure. Chlortetracycline was only detected in runoff, whereas monensin and tylosin were detected in leachate and runoff. Highest concentrations of monensin and tylosin in the leachate were 40.9 and 1.2 microg L(-1), respectively. Highest chlortetracycline, monensin, and tylosin concentrations in runoff were 0.5, 57.5, and 6.0 microg L(-1), respectively. For all three antibiotics, >90% of detections and 99% of losses occurred during the non-growing season due to fall manure application and slow degradation of antibiotics at cold temperatures. During years of high snowmelt, runoff accounted for nearly 100% of antibiotic losses, whereas during years of minimal snowmelt, runoff accounted for approximately 40% of antibiotic losses. Antibiotic losses were generally higher from the no-tillage compared with chisel plow treatment due to greater water percolation as a result of macroporosity and greater runoff due to lack of surface roughness in the no-tillage plots during the non-growing season. The results from this study suggest that small quantities of dissolved antibiotics could potentially reach surface and ground waters in the Upper Midwestern USA from manure-amended shallow macroporous soils underlain with fractured bedrock.