The coastal zone of southeast Alaska contains thousands of streams and rivers that drain one of the wettest, carbon‐rich, and most topographically varied regions in North America. Watersheds draining temperate rainforests, peatlands, glaciers, and three large rivers that flow from the drier interior of the Yukon Territory and British Columbia discharge water and dissolved organic carbon (DOC) into southeast Alaskan coastal waters. This area, which we have designated the southeast Alaska drainage basin (SEAKDB), discharges about twice as much water as the Columbia or Yukon Rivers. An understanding of the timing, location, and source of water and DOC guides research to better understand the influence of terrestrial outputs on the adjacent marine systems. Additionally, a spatially extensive understanding of riverine DOC flux will improve our understanding of lateral losses related to terrestrial carbon cycling. We estimate 1.17 Tg C yr−1 of DOC enters the adjacent marine system along with 430 km2 of freshwater that influences estuary, shelf, and Gulf of Alaska hydrology. We estimate that 23% to 66% of the DOC entering coastal waters is bioavailable and may influence metabolism and productivity within the marine system. The combination of the large and spatially distributed water and DOC input, long and complex shoreline, large enclosed estuarine volume, and bounded nearshore coastal currents suggests that the physiographic structure of southeast Alaska may have a significant impact on the metabolism of riverine DOC in coastal marine ecosystems.

Moore, R.D. (Dan), J.W. Trubilowicz, and J.M. Buttle, 2011. Prediction of Streamflow Regime and Annual Runoff for Ungauged Basins Using a Distributed Monthly Water Balance Model. Journal of the American Water Resources Association (JAWRA) 48(1): 32‐42. DOI: 10.1111/j.1752‐1688.2011.00595.x Abstract:  Prediction of streamflow in ungauged basins is a global challenge, but is particularly an issue in physiographically complex regions like British Columbia (BC), Canada. The objective of this study was to assess the accuracy of a simple water balance model that can be run using existing spatial datasets. The model was developed by modifying an existing monthly water balance model to account for interception loss from forest canopy, glacier melt, and evaporation from lakes. The model was run using monthly climate normals from the ClimateBC application, which have a horizontal resolution of 400 m. Each ClimateBC grid cell was classified as forest, open land, glacier or water surface based on provincial scale digital maps of biogeoclimatic zones, glaciers, and water. The output was monthly mean runoff from each grid cell. These values were integrated within the catchment boundaries for streams gauged by the Water Survey of Canada. Annual runoff was predicted with modest accuracy: after updating the predicted runoff by interpolating errors from neighboring gauged streams, the mean absolute error was 25.4% of the gauged value, and 52% of the streams had errors less than 20%. However, the model appears to be quite robust in distinguishing between pluvial, hybrid, and melt‐dominated hydroclimatic regimes, and therefore has promise as a tool for catchment classification.

Abstract:Meteorological observations at high elevations in mountainous regions are often lacking. One opportunity to fill this data gap is through the use of downscaled output from weather reanalysis models. In this study, we tested the accuracy of downscaled output from the North American Regional Reanalysis (NARR) against high-elevation surface observations at four ridgetop locations in the southern Coast Mountains of British Columbia, Canada. NARR model output was downscaled to the surface observation locations through three-dimensional interpolation for air temperature, vapour pressure and wind speed and two-dimensional interpolation for radiation variables. Accuracy was tested at both the 3-hourly and daily time scales. Air temperature displayed a high level of agreement, especially at the daily scale, with root mean square error (RMSE) values ranging from 0.98 to 1.21°C across all sites. Vapour pressure downscaling accuracy was also quite high (RMSE of 0.06 to 0.11 hPa) but displayed some site specific bias. Although NARR overestimated wind speed, there were moderate to strong linear relations (r 2 from 0.38 to 0.84 for daily means), suggesting that the NARR output could be used as an index and bias-corrected. NARR output reproduced the seasonal cycle for incoming short-wave radiation, with Nash-Sutcliffe model efficiencies ranging from 0.78 to 0.87, but accuracy suffered on days with cloud cover, resulting in a positive bias and RMSE ranged from 42 to 46 Wm À 2 . Although fewer data were available, incoming long-wave radiation from NARR had an RMSE of 19 Wm À 2 and outperformed common methods for estimating incoming long-wave radiation. NARR air temperature showed potential to assist in hydrologic analysis and modelling during an atmospheric river storm event, which are characterized by warm and wet air masses with atypical vertical temperature gradients. The incorporation of a synthetic NARR air temperature station to better represent the higher freezing levels resulted in increased predicted peak flows, which better match the observed run-off during the event.

[1] Low-cost, low-power wireless sensor networks (mote networks) have the potential to revolutionize data collection methods in hydrology. They promise the ability to monitor catchments at very high spatial and temporal resolution with flexible sampling schemes, real time data processing and high levels of quality control. We operated an experimental network of 41 motes monitoring seven different parameters each at 15 min intervals for 10 months in a small forested catchment in southwestern British Columbia, Canada, to determine if this emerging technology is suitable for use by hydrologists in its current form. Our particular interests were ease of setup, sampling reliability, power consumption, and hardware resilience. We found that while motes gave the ability to monitor a catchment at resolution levels that were previously impossible, they still need to evolve into an easier to use, more reliable platform before they can replace traditional data collection methods.

Rain‐on‐snow events have generated major floods around the world, particularly in coastal, mountainous regions. Most previous studies focused on a limited number of major rain‐on‐snow events or were based primarily on model results, largely due to a lack of long‐term records from lysimeters or other instrumentation for quantifying event water balances. In this analysis, we used records from five automated snow pillow sites in south coastal British Columbia, Canada, to reconstruct event water balances for 286 rain‐on‐snow events over a 10‐year period. For large rain‐on‐snow events (event rainfall >40 mm), snowmelt enhanced the production of water available for run‐off (WAR) by approximately 25% over rainfall alone. For smaller events, a range of antecedent and meteorological factors influenced WAR generation, particularly the antecedent liquid water content of the snowpack. Most large events were associated with atmospheric rivers. Rainfall dominated WAR generation during autumn and winter events, whereas snowmelt dominated during spring and summer events. In the majority of events, the sensible heat of rain contributed less than 10% of the total energy consumed by snowmelt. This analysis illustrated the importance of understanding the amount of rainfall occurring at high elevations during rain‐on‐snow events in mountainous regions.

Abstract:Hydrologic classification is useful for data organization, transfer of model parameters and estimation of hydrologic sensitivity to disturbance and climatic change. Stream-flow regime has frequently been used as a basis for classification, typically by mapping regimes defined by stream-flow data from a gauging network. As an alternative, we hypothesized that ecological classification systems can predict stream-flow regime because they are based on the same characteristics that control run-off generation (soils, climate and topography). A multivariate regression tree (MRT) was used to relate stream-flow regime to the fractional coverages of the Biogeoclimatic Ecological Classification (BEC) zones within the catchment for gauged streams in British Columbia, Canada. Although the MRT identified a realistic set of regimes, only a small number of BEC zones were used as predictors, reflecting bias in the gauging network. To avoid this bias, we used a water balance model to compute mean monthly stream flow for 932 ungauged basins in British Columbia that were generated with areas between 10 and 1000 km 2 ; these monthly stream flows were used to train an MRT model based on BEC zone coverages. This model predicted the regime at gauged basins nearly as accurately as the water balance model for pluvial, nival and glacier-supported nival regimes. Difficulties occurred in smaller basins and in specific regions where the local BEC zones were not included as predictors. Coastal hybrid nivo-pluvial regimes were poorly predicted. With further development, ecological classification systems could have great value as a tool for hydrologic classification for both research and operational applications.

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