Generation of Ensemble Streamflow Forecasts Using an Enhanced Version of the Snowmelt Runoff Model1

Harshburger, Brian J., Von P. Walden, Karen S. Humes, Brandon C. Moore, Troy R. Blandford, and Albert Rango, 2012. Generation of Ensemble Streamflow Forecasts Using an Enhanced Version of the Snowmelt Runoff Model. Journal of the American Water Resources Association (JAWRA) 48(4): 643‐655. DOI: 10.1111/j.1752‐1688.2012.00642.x Abstract: As water demand increases in the western United States, so does the need for accurate streamflow forecasts. We describe a method for generating ensemble streamflow forecasts (1‐15 days) using an enhanced version of the snowmelt runoff model (SRM). Forecasts are produced for three snowmelt‐dominated basins in Idaho. Model inputs are derived from meteorological forecasts, snow cover imagery, and surface observations from Snowpack Telemetry stations. The model performed well at lead times up to 7 days, but has significant predictability out to 15 days. The timing of peak flow and the streamflow volume are captured well by the model, but the peak‐flow value is typically low. The model performance was assessed by computing the coefficient of determination (R²), percentage of volume difference (Dv%), and a skill score that quantifies the usefulness of the forecasts relative to climatology. The average R² value for the mean ensemble is >0.8 for all three basins for lead times up to seven days. The Dv% is fairly unbiased (within ±10%) out to seven days in two of the basins, but the model underpredicts Dv% in the third. The average skill scores for all basins are >0.6 for lead times up to seven days, indicating that the ensemble model outperforms climatology. These results validate the usefulness of the ensemble forecasting approach for basins of this type, suggesting that the ensemble version of SRM might be applied successfully to other basins in the Intermountain West.     

Trends in Western U.S. Snowpack and Related Upper Colorado River Basin Streamflow1

Miller, W. Paul and Thomas C. Piechota, 2011. Trends in Western U.S. Snowpack and Related Upper Colorado River Basin Streamflow. Journal of the American Water Resources Association (JAWRA) 47(6):1197–1210. DOI: 10.1111/j.1752‐1688.2011.00565.x Abstract: Water resource managers in the Western United States (U.S.) are currently faced with the challenge of adapting to unprecedented drought and uncertain impacts of climate change. Recent research has indicated increasing regional temperature and changes to precipitation and streamflow characteristics throughout the Western U.S. As such, there is increased uncertainty in hydroclimatological forecasts, which impact reservoir operations and water availability throughout the Western U.S., particularly in the Colorado River Basin. Previous research by the authors hypothesized a change in the character of precipitation (i.e., the frequency and amount of rainfall and snowfall events) throughout the Colorado River Basin. In the current study, 398 snowpack telemetry stations were investigated for trends in cumulative precipitation, snow water equivalent, and precipitation events. Observations of snow water equivalent characteristics were compared to observations in streamflow characteristics. Results indicate that the timing of the last day of the snow season corresponds well to the volume of runoff observed over the traditional peak flow season (April through July); conversely, the timing of the first day of the snow season does not correspond well to the volume of runoff observed over the peak flow season. This is significant to water resource managers and river forecasters, as snowpack characteristics may be indicative of a productive or unproductive runoff season.     

A Streamflow Forecasting Framework using Multiple Climate and Hydrological Models1

Abstract: Water resources planning and management efficacy is subject to capturing inherent uncertainties stemming from climatic and hydrological inputs and models. Streamflow forecasts, critical in reservoir operation and water allocation decision making, fundamentally contain uncertainties arising from assumed initial conditions, model structure, and modeled processes. Accounting for these propagating uncertainties remains a formidable challenge. Recent enhancements in climate forecasting skill and hydrological modeling serve as an impetus for further pursuing models and model combinations capable of delivering improved streamflow forecasts. However, little consideration has been given to methodologies that include coupling both multiple climate and multiple hydrological models, increasing the pool of streamflow forecast ensemble members and accounting for cumulative sources of uncertainty. The framework presented here proposes integration and offline coupling of global climate models (GCMs), multiple regional climate models, and numerous water balance models to improve streamflow forecasting through generation of ensemble forecasts. For demonstration purposes, the framework is imposed on the Jaguaribe basin in northeastern Brazil for a hindcast of 1974‐1996 monthly streamflow. The ECHAM 4.5 and the NCEP/MRF9 GCMs and regional models, including dynamical and statistical models, are integrated with the ABCD and Soil Moisture Accounting Procedure water balance models. Precipitation hindcasts from the GCMs are downscaled via the regional models and fed into the water balance models, producing streamflow hindcasts. Multi‐model ensemble combination techniques include pooling, linear regression weighting, and a kernel density estimator to evaluate streamflow hindcasts; the latter technique exhibits superior skill compared with any single coupled model ensemble hindcast.     

Developing Subseasonal to Seasonal Climate Forecast Products for Hydrology and Water Management

We describe a new effort to enhance climate forecast relevance and usability through the development of a system for evaluating and displaying real‐time subseasonal to seasonal (S2S) climate forecasts on a watershed scale. Water managers may not use climate forecasts to their full potential due to perceived low skill, mismatched spatial and temporal resolutions, or lack of knowledge or tools to ingest data. Most forecasts are disseminated as large‐domain maps or gridded datasets and may be systematically biased relative to watershed climatologies. Forecasts presented on a watershed scale allow water managers to view forecasts for their specific basins, thereby increasing the usability and relevance of climate forecasts. This paper describes the formulation of S2S climate forecast products based on the Climate Forecast System version 2 (CFSv2) and the North American Multi‐Model Ensemble (NMME). Forecast products include bi‐weekly CFSv2 forecasts, and monthly and seasonal NMME forecasts. Precipitation and temperature forecasts are aggregated spatially to a United States Geological Survey (USGS) hydrologic unit code 4 (HUC‐4) watershed scale. Forecast verification reveals appreciable skill in the first two bi‐weekly periods (Weeks 1–2 and 2–3) from CFSv2, and usable skill in NMME Month 1 forecast with varying skills at longer lead times dependent on the season. Application of a bias‐correction technique (quantile mapping) eliminates forecast bias in the CFSv2 reforecasts, without adding significantly to correlation skill.     

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.     

The Water‐Year Water Balance of the Colorado River Basin

Model‐estimated monthly water balance components (i.e., potential evapotranspiration, actual evapotranspiration, and runoff (R)) for 146 United States (U.S.) Geological Survey 8‐digit hydrologic units located in the Colorado River Basin (CRB) are used to examine the temporal and spatial variability of the CRB water balance for water years 1901 through 2014 (a water year is the period from October 1 of one year through September 30 of the following year). Results indicate that the CRB can be divided into six subregions with similar temporal variability in monthly R. The water balance analyses indicated that approximately 75% of total water‐year R is generated by just one CRB subregion and that most of the R in the basin is derived from surplus (S) water generated during the months of October through April. Furthermore, the analyses show that temporal variability in S is largely controlled by the occurrence of negative atmospheric pressure anomalies over the northwestern conterminous U.S. (CONUS) and positive atmospheric pressure anomalies over the southeastern CONUS. This combination of atmospheric pressure anomalies results in an anomalous flow of moist air from the North Pacific Ocean into the CRB, particularly the Upper CRB. Additionally, the occurrence of extreme dry and wet periods in the CRB appears to be related to variability of the Atlantic Multidecadal Oscillation and the Pacific Decadal Oscillation.     

Exploring Persistence in Streamflow Forecasting

In this study, the authors explore three persistence approaches in streamflow forecasting motivated by the need for forecasting model skill evaluation. The authors use streamflow observations with 15 min resolution from the year 2008 to 2017 at 140 United States Geological Survey streamflow gauges monitoring the streams and rivers over the State of Iowa. The spatial scale of the basins ranges from about 7 to 37,000 km². The study explores three approaches: simple persistence, gradient persistence, and anomaly persistence. The study shows that persistence forecasts skill has strong dependence on basin scales and weaker but non‐negligible dependence on geometric properties of the river network for a given basin. Among the three approaches explored, anomaly persistence shows highest skill especially for small basins, under about 500 km². The anomaly persistence can serve as a benchmark for model evaluations considering the effect of basin scales and geometric properties of river network of the basin. This study further reiterates that persistence forecasts are hard‐to‐beat methods for larger basin scales at short to medium forecast range.     

EVALUATION OF PRACTICES IN WATER SUPPLY FORECASTING1

ABSTRACT: Snowmelt runoff is a primary source of water supply in much of the Western United States. Multipurpose planning requires long-range forecasts and the accuracy of the forecasts has a significant effect on economic benefits. In an effort to increase the accuracy of snowrnelt runoff forecasts, selected practices in water supply forecasting were evaluated. These practices include 1) using multiple regression in developing forecasting models;2) using a model that was calibrated to make forecasts an April 1 for making forecasts at other times;3) using maximum snow water equivalent measurements in forecast equations; and 4) using weighted snow water equivalent measurements for making forecasts. The results of a case study indicate that forecasting accuracy is significantly affected by these practices. Goodness-of-fit statistics may not be indicative of the accuracy of forecasts when the prediction equations are used to make forecasts for dates other than that used in calibration. The use of maximum snow water equivalentmeasurements and weighted averages did not improve forecast accuracy.     

Mid‐Range Streamflow Forecasts Based on Climate Modeling – Statistical Correction and Evaluation1

Abstract: Mid‐range streamflow predictions are extremely important for managing water resources. The ability to provide mid‐range (three to six months) streamflow forecasts enables considerable improvements in water resources system operations. The skill and economic value of such forecasts are of great interest. In this research, output from a general circulation model (GCM) is used to generate hydrologic input for mid‐range streamflow forecasts. Statistical procedures including: (1) transformation, (2) correction, (3) observation of ensemble average, (4) improvement of forecast, and (5) forecast skill test are conducted to minimize the error associated with different spatial resolution between the large‐scale GCM and the finer‐scale hydrologic model and to improve forecast skills. The accuracy of a streamflow forecast generated using a hydrologic model forced with GCM output for the basin was evaluated by forecast skill scores associated with the set of streamflow forecast values in a categorical forecast. Despite the generally low forecast skill score exhibited by the climate forecasting approach, precipitation forecast skill clearly improves when a conditional forecast is performed during the East Asia summer monsoon, June through August.     

Does Including Soil Moisture Observations Improve Operational Streamflow Forecasts in Snow‐Dominated Watersheds?

Changing climate and growing water demand are increasing the need for robust streamflow forecasts. Historically, operational streamflow forecasts made by the Natural Resources Conservation Service have relied on precipitation and snow water equivalent observations from Snow Telemetry (SNOTEL) sites. We investigate whether also including SNOTEL soil moisture observations improve April‐July streamflow volume forecast accuracy at 0, 1, 2, and 3‐month lead times at 12 watersheds in Utah and California. We found statistically significant improvement in 0 and 3‐month lead time accuracy in 8 of 12 watersheds and 10 of 12 watersheds for 1 and 2‐month lead times. Surprisingly, these improvements were insensitive to soil moisture metrics derived from soil physical properties. Forecasts were made with volumetric water content (VWC) averaged from October 1 to the forecast date. By including VWC at the 0‐month lead time the forecasts explained 7.3% more variability and increased the streamflow volume accuracy by 8.4% on average compared to standard forecasts that already explained an average 77% of the variability. At 1 to 3‐month lead times, the inclusion of soil moisture explained 12.3‐26.3% more variability than the standard forecast on average. Our findings indicate including soil moisture observations increased statistical streamflow forecast accuracy and thus, could potentially improve water supply reliability in regions affected by changing snowpacks.     

Climatic Fluctuations and Forecasting of Streamflow in the Lower Colorado River Basin1

Abstract:  Water‐resource managers need to forecast streamflow in the Lower Colorado River Basin to plan for water‐resource projects and to operate reservoirs for water supply. Statistical forecasts of streamflow based on historical records of streamflow can be useful, but statistical assumptions, such as stationarity of flows, need to be evaluated. This study evaluated the relation between climatic fluctuations and stationarity and developed regression equations to forecast streamflow by using climatic fluctuations as explanatory variables. Climatic fluctuations were represented by the Atlantic Multidecadal Oscillation (AMO), Pacific Decadal Oscillation (PDO), and Southern Oscillation Index (SOI). Historical streamflow within the 25‐ to 30‐year positive or negative phases of AMO or PDO was generally stationary. Monotonic trends in annual mean flows were tested at the 21 sites evaluated in this study; 76% of the sites had no significant trends within phases of AMO and 86% of the sites had no significant trends within phases of PDO. As climatic phases shifted in signs, however, many sites had nonstationary flows; 67% of the sites had significant changes in annual mean flow as AMO shifted in signs. The regression equations developed in this study to forecast streamflow incorporate these shifts in climate and streamflow, thus that source of nonstationarity is accounted for. The R² value of regression equations that forecast individual years of annual flow for the central part of the study area ranged from 0.28 to 0.49 and averaged 0.39. AMO was the most significant variable, and a combination of indices from both the Atlantic and Pacific Oceans explained much more variation in flows than only the Pacific Ocean indices. The average R² value for equations with PDO and SOI was 0.15.     

A Simple Framework for Incorporating Seasonal Streamflow Forecasts into Existing Water Resource Management Practices1

Gong, Gavin, Lucien Wang, Laura Condon, Alastair Shearman, and Upmanu Lall, 2010. A Simple Framework for Incorporating Seasonal Streamflow Forecasts Into Existing Water Resource Management Practices. Journal of the American Water Resources Association (JAWRA) 46(3):574-585. DOI: 10.1111/j.1752-1688.2010.00435.x Abstract: Climate-based streamflow forecasting, coupled with an adaptive reservoir operation policy, can potentially improve decisions by water suppliers and watershed stakeholders. However, water suppliers are often wary of straying too far from their current management practices, and prefer forecasts that can be incorporated into existing system modeling tools. This paper presents a simple framework for utilizing streamflow forecasts that works within an existing management structure. Climate predictors are used to develop seasonal inflow forecasts. These are used to specify operating rules that connect to the probability of future (end of season) reservoir states, rather than to the current storage, as is done now. By considering both current storage and anticipated inflow, the likelihood of meeting management goals can be improved. The upper Delaware River Basin in the northeastern United States is used to demonstrate the basic idea. Physically plausible climate-based forecasts of March-April reservoir inflow are developed. Existing simulation tools and rule curves for the system are used to convert the inflow forecasts to reservoir level forecasts. Operating policies are revised during the forecast period to release less water during forecasts of low reservoir level. Hindcast simulations demonstrate reductions of 1.6% in the number of drought emergency days, which is a key performance measure. Forecasts with different levels of skill are examined to explore their utility.     

Evaluation of the Temporal Transferability of a Model Describing Dissolved Solids in Streams of the Upper Colorado River Basin1

Kenney, Terry A. and Susan G. Buto, 2012. Evaluation of the Temporal Transferability of a Model Describing Dissolved Solids in Streams of the Upper Colorado River Basin. Journal of the American Water Resources Association (JAWRA) 48(5): 1041‐1053. DOI: 10.1111/j.1752‐1688.2012.00667.x Abstract: The application of a nonlinear least‐squares regression model describing the sources and transport of dissolved solids in streams of the Upper Colorado River Basin, and that was calibrated using data from water year 1991, was evaluated for use in predicting annual dissolved‐solids loads for the years 1974 through 1998. Simulations for each water year were run using annual climate data. To evaluate how well the model captures the observed annual variability across the basin, differences in predicted annual dissolved‐solids loads for each simulated year and 1991 were compared with differences in monitored annual loads. The temporal trend of the differences between predicted annual loads for the simulated years and the load for 1991 generally followed the trend of the monitored loads. The model appears to underpredict the largest annual loads and overpredict some of the smaller annual loads. An underprediction bias for wetter years was evident in the residuals as was an overprediction bias, to a lesser degree, for drier years. A regression analysis on the residuals suggests that the underprediction bias is associated with precipitation differences from 1991 and with previously defined downward trends in dissolved‐solids concentrations in the basin. In general, given the representative climatic conditions, the model adequately performs throughout the period examined. However, the model is most transferable to years with climatic conditions similar to 1991.     

USING CLIMATE FORECASTS FOR WATER MANAGEMENT: ARIZONA AND THE 1997–1998 EL NIÑO1

ABSTRACT: Unrelenting pressure on limited surface water supplies requires increasingly sophisticated water management approaches. Climate forecasts of seasonal precipitation and temperature are potentially useful, but the operational water management community currently underutilizes them. However, some agencies in Arizona took unprecedented advantage of forecasts for a potentially wet winter during the 1997–1998 El Niño event. This study investigates use of this information through a series of semi‐structured in‐depth interviews with key personnel from agencies responsible for emergency management and water supply; their jurisdictions ranged from urban to rural and local to regional. Interviews investigated information acquisition, interpretation, and incorporation into specific decisions and actions. While unprecedented actions were taken by some water management agencies and no agencies implemented inappropriate measures, some missed opportunities for more effective response, primarily through inaction. This study reveals a variety of technical factors and institutional characteristics affecting forecast use. Study findings emphasize the need for: (a) closer ongoing relationships between forecast producers and users, (b) increased institutional flexibility to exploit the increasing skill of seasonal climate forecasts, (c) demonstration projects of effective forecast use, and (d) a regional forum to facilitate information transfer between the hydro‐climatic research community and operational water managers.     

DOWNSCALED CLIMATE AND STREAMFLOW STUDY OF THE SOUTHWESTERN UNITED STATES1

ABSTRACT: Downscaling coarse resolution climate data to scales that are useful for impact assessment studies is receiving increased attention. Basin-scale hydrologic processes and other local climate impacts related to water resources such as reservoir management, crop and forest productivity, and ecosystem response require climate information at scales that are much finer than current and future GCM resolutions. The Regional Climate System Model (RCSM) is a dynamic downscaling system that has been used since 1994 for short-term precipitation and streamflow predictions and seasonal hindcast analysis with good skill. During the 1997–1998 winter, experimental seasonal forecasts were made in collaboration with the NOAA Climate Prediction Center and UCLA with promising results. Preliminary studies of a control and 2°CO₂ perturbation for the southwestern U.S. have been performed.     

Hydroclimatology of the Mississippi River Basin

Model estimated monthly water balance (WB) components (i.e., potential evapotranspiration, actual evapotranspiration, and runoff [R]) for 848 United States (U.S.) Geological Survey 8‐digit hydrologic units located in the Mississippi River Basin (MRB) are used to examine the temporal and spatial variability of the MRB WB for water years 1901 through 2014. Results indicate the MRB can be divided into nine subregions with similar temporal variability in R. The WB analyses indicated ~79% of total water‐year MRB runoff is generated by four of the nine subregions and most of the R in the basin is derived from surplus (S) water during the months of December through May. Furthermore, the analyses showed temporal variability in S is largely controlled by the occurrence of negative atmospheric pressure anomalies over the western U.S. and positive atmospheric pressure anomalies over the eastern U.S. coast. This combination of atmospheric pressure anomalies results in an anomalous flow of moist air from the Gulf of Mexico into the MRB. In the context of paleo‐climate reconstructions of the Palmer Drought Severity Index, since about 1900 the MRB has experienced wetter conditions than were experienced during the previous 500 years.     

Forecasting the Probability of Future Groundwater Levels Declining Below Specified Low Thresholds in the Conterminous U.S.

We present a logistic regression approach for forecasting the probability of future groundwater levels declining or maintaining below specific groundwater‐level thresholds. We tested our approach on 102 groundwater wells in different climatic regions and aquifers of the United States that are part of the U.S. Geological Survey Groundwater Climate Response Network. We evaluated the importance of current groundwater levels, precipitation, streamflow, seasonal variability, Palmer Drought Severity Index, and atmosphere/ocean indices for developing the logistic regression equations. Several diagnostics of model fit were used to evaluate the regression equations, including testing of autocorrelation of residuals, goodness‐of‐fit metrics, and bootstrap validation testing. The probabilistic predictions were most successful at wells with high persistence (low month‐to‐month variability) in their groundwater records and at wells where the groundwater level remained below the defined low threshold for sustained periods (generally three months or longer). The model fit was weakest at wells with strong seasonal variability in levels and with shorter duration low‐threshold events. We identified challenges in deriving probabilistic‐forecasting models and possible approaches for addressing those challenges.     

BASIN SCALE WATER MANAGEMENT AND FORECASTING USING ARTIFICIAL NEURAL NETWORKS1

ABSTRACT: Water scarcity in the Sevier River Basin in south‐central Utah has led water managers to seek advanced techniques for identifying optimal forecasting and management measures. To more efficiently use the limited quantity of water in the basin, better methods for control and forecasting are imperative. Basin scale management requires advanced forecasts of the availability of water. Information about long term water availability is important for decision making in terms of how much land to plant and what crops to grow; advanced daily predictions of streamflows and hydraulic characteristics of irrigation canals are of importance for managing water delivery and reservoir releases; and hourly forecasts of flows in tributary streams to account for diurnal fluctuations are vital to more precisely meet the day‐to‐day expectations of downstream farmers. A priori streamflow information and exogenous climate data have been used to predict future streamflows and required reservoir releases at different timescales. Data on snow water equivalent, sea surface temperatures, temperature, total solar radiation, and precipitation are fused by applying artificial neural networks to enhance long term and real time basin scale water management information. This approach has not previously been used in water resources management at the basin‐scale and could be valuable to water users in semi‐arid areas to more efficiently utilize and manage scarce water resources.     

FORECASTING DRY SEASON STREAMFLOW ON THE PEACE RIVER AT ARCADIA,FLORIDA, USA1

ABSTRACT: The Peace River at Arcadia, Florida, is a municipal water supply supplement for southwestern Florida. Consequently, probabilities of encountering low flows during the dry season are of critical importance. Since the association between Pacific Ocean sea surface temperatures (SSTs) and seasonal streamflow variability in the southeastern United States is well documented, it is reasonable to generate forecasts based on this information. Here, employing historic records of minimum, mean, and maximum flows during winter (JFM) and spring (AMJ), upper and lower terciles define “above normal,”“normal,” and “below normal” levels of each variable. A probability distribution model describes the likelihood of these seasonal variables conditioned upon Pacific SSTs from the previous summer (JAS). Model calibration is based upon 40 (of 50) years of record employing stratified random sampling to ensure equal representation from each decade. The model is validated against the remaining 10 samples and the process repeated 100 times. Each conditional probability distribution yields varying probabilities of observing flow variables within defined categories. Generally, a warm (cold) Pacific is associated with higher (lower) flows. To test model skill, the forecast is constrained to be the most probable category in each calibration year, with significance tested by chi‐square frequency tables. For all variables, the tables indicate high levels of association between forecast and observed terciles and forecast skill, particularly during winter. During spring the pattern is less clear, possibly due to the variable starting date of the summer rainy season. This simple technique suggests that Pacific SSTs provide a good forecast of low flows.     

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.