Inflows from mountainous, snow-covered catchments will have distinctly different hydrographs from those without snow. Inflow regimes will have a lag between precipitation falling in winter and the subsequent inflows of melt water to downstream lakes. Regions with high summer rainfall, when maximum snowmelt also occurs, will have resulting inflow records that show much higher maximum inflows and lower minimums, compared to the more moderated snowless catchments.
Another possible lag effect between precipitation and subsequent streamflows is that of storage of water as groundwater. In catchments with these lag effects, rainfall forecasting is often of limited use to the end user of the forecasts, as the streamflows utilised by users are greatly influenced by this storage of rainfall as snow or as groundwater, and the subsequent lag of inflows to downstream lakes. Encapsulating this lag effect in catchments with significant snow storage is one of the key differences between forecasting seasonal rainfall and streamflows.
Dettinger et al. (1999) noted that anomalous winter climate influences often do not register in the streamflow records until the springtime thaw several months later, due to snow storage in the catchment. They found that during spring, probabilities of upper-tercile mean daily flows are generally accentuated relative to the winter probabilities and they are generally more spatially coherent. Barlow and Tippett (2005) attempted to link the previous season's snowpack to the warm season river flows in Uzbekistan and Tajikistan. They used canonical correlation analysis between the cold season regional climate (zonal winds and precipitation) and the subsequent warm season river discharge. They found significant
correlations between the above mentioned variables, and say their scheme could be used to make operational forecasts. Cayan et al. (1999) examined links between SOI and streamflows in the United States, and found that the SOI leads the precipitation events by several months, and hydrologic lags (which they attributed mostly to snowmelt) delay the streamflow response by several more months, giving a longer available significant forecast period, over that of precipitation.
The skill of forecasting spring and summer streamflows in snowy catchments could be greatly enhanced by a better knowledge of the snow water equivalent stored in the catchment during winter. Estimating snow water equivalent in often remote catchments with significant orography is a difficult task. One international example of where snowpack water equivalent is estimated and inflow forecasts widely distributed to river users, is in Oregon in the USA (USDA, 2000). The Oregon Basin Outlook Report is produced monthly, and provides information on snowpack and streamflow predictions for five month lead times. Probabilistic forecasts include the range of possible streamflows for three month periods. Snow data are physically based, and comes from an extensive network of snow monitoring stations. The forecasts are said to be “Extremely accurate” (pers comm. Jon Lea, Hydrologist, USDA). Some work has been undertaken in New Zealand on the estimation of snow water equivalent, and the resulting inflows from snowmelt (Fitzharris and Garr 1995, Fitzharris 1992, Neale 1996, Fitzharris et al. 1998, Fitzharris and McAlevey 1999).
After the 1992 power shortages, the Otago University Consulting Group was contracted to produce a snow storage and runoff evaluation model for the Waitaki River catchment for the hydro electricity scheme owners, ECNZ. Before the development of the model, Fitzharris (1993) compared the HBV model (Bergstrom, 1992) and the Swiss Snow Melt Runoff (SMR) models (Martinec 1975, Martinec and Rango 1986), and considered their usefulness for South Island’s basins. The HBV model (Bergstrom, 1992) is a hydrological model first developed in the early 1970s to model snowmelt and runoff in an alpine catchment in Sweden. The model includes snow accumulation and melt estimates, soil moisture accounting, and river rating subroutines. Input data are precipitation and air temperature. It is usually run with daily time steps, but accumulates snowpack seasonally. Fitzharris (1993a) concluded that the Snow Melt Runoff Model (SRM) is the preferred model for South Island catchments, as it better handles snowmelt with rain events, has more
convenient features for updating, and is slightly superior for seasonal flow forecasting. He went on to say that by using snow covered area as an input it exerts more real time control over modelled melt down of the snowpack. It is estimated that 15% of annual inflows to the Waitaki river catchment are from snowmelt (Fitzharris, 1992). Knowledge of snowpack water equivalent is therefore a major component of any attempt to predict seasonal inflows to the Waitaki catchment. Direct measurements are hampered by difficult access to the mountains. Estimates of this resource have therefore traditionally been estimated by remote means.
Fitzharris (1993b) then proposed work to produce a snowmelt and runoff model. He stated the objectives of the research to be to quantify the melt down of the seasonal snow pack, to model daily snow melt and inflows for Lake Pukaki over spring and summer periods, to separate the snow melt, glacier melt, and rainfall components of daily inflows, and to prepare the way for forecasts of spring and summer inflows into hydro storage lakes. Fitzharris and Garr (1995) published the results from this model, named "SnowSim". They stated that the model calculates seasonal snow deposition, ablation, and accumulation, and that the model is based on daily temperature and precipitation data from long-established climate stations about the Southern Alps. Output from the model is given as daily specific net balance of snow at five elevation bands from 1000 to 2200 metres above sea level, and as total water stored as seasonal snow over several major river catchments. Model output, they claimed, is in general agreement when verified against the few historical observations of snowpack, and is tuned to the long-term water balance. They constructed a chronology of seasonal snow from 1931 to 1993, and found that area-averaged annual maxima average 366mm of water equivalent. They found no trend over time, but large inter-annual variability, from less than 200 to over 650mm water equivalent. They also stated that seasonal snowpack in the Lake Pukaki catchment can peak at any time from September to January. This model was upgraded several times over the following six years, and subsequent versions also give a three month inflow forecast, based on the size of the snowpack and past inflow data. However, the error bands on the inflow forecast were broad. Improvements to the algorithms of this model came as a result of a six week field study of snowmelt at high elevation in the catchment (Neale, 1996).
NIWA (1994) examined temperatures and their effect on inflows in the Waitaki catchment. They concluded that as winter temperatures for 1993 were predicted to be 0.5 ºC to 1.0 ºC
lower than average, they would expect a reduction in winter inflows into Lake Pukaki, and the Waitaki river system in general (due to additional snow storage) by 10% over what otherwise might have occurred. In hindsight, Lake Pukaki inflows for winter 1993 were actually higher than normal, but Niwa’s posited relationship between high (low) temperatures and high (low) inflows was verified. McAlevey (1998) developed a distributed seasonal snow model “SnowSim NZ”, for New Zealand. It provides spatial estimates of snow for every 1 km2 pixel in the seasonal snow zone of New Zealand (38000 km2). Inputs to each pixel are daily temperature and precipitation, interpolated from 41 climate stations around New Zealand. Interpolation procedures include the use of a neural network, an inverse distance weighted algorithm, and lapse rates. A study of the long term stored water coming from retreating glaciers in the Waitaki catchment estimated that 7.5 cumecs was contributed to Lake Pukaki inflows over the November to April period annually, and that this was likely to increase in the future (Purdie and Fitzharris, 1998).
Other work outside the Waitaki catchment includes a model designed by Barringer (1991), which simulated snowlines and snowfalls from daily meteorological data for the Remarkables Range, Queenstown, New Zealand. It was used to estimate snowline altitudes over the past 60 years with moderate success.
Gutzler (2000) tested the hypothesis that snowpack extent in the New Mexico area in North America may actually modulate the monsoon and exhibit an inverse correlation with summer rainfall anomalies. Results were significant for the 1961-90 climatic averaging period. This study considered a large snow covered region which would have significant influences on albedo and surface energy exchanges over a wide area. Similar effects are unlikely to be seen in New Zealand, where the snow covered area is comparatively small, and the dominant influence on precipitation is the strong mid-latitude westerlies and dramatic orography.
The other main approach to snow water equivalent estimation is through the use of remote sensing. Seidel et al. (1989) illustrated the use of National Oceanic and Atmospheric Administration (NOAA) satellite data in estimating snow cover extent in Switzerland. The estimation of snow water equivalent using standard depletion curves was then undertaken, and subsequent runoff estimations using the Snowmelt Runoff Model (SRM). Simulated runoffs correlated well to actual measurements. However, when Fitzharris and McAlevey
(1999) examined remote sensing of seasonal snow using satellite imagery in the South Island of New Zealand, they concluded the resolution was not sufficient to provide a complete snow climatology.
A more combined approach is that of Haefner et al. (1997). The management system utilised, the SRM-ETH, combined remote sensing of snow cover, runoff modelling, GIS, DEM, and computer graphics. It was tested in three basins in the Swiss Alps, and was deemed well suited to analyse snow hydrological processes, in particular runoff, to improve day-to-day water management practices, and to simulate future trends based on various climate scenarios.
While snow melt in a catchment is the most studied lag effect in seasonal streamflow forecasting, other lag effects between precipitation and runoff are also evident. Wedgbrow et al. (2002) attributed at least some of their success in forecasting seasonal streamflows in England to the “memory”, or lag effect of groundwater in the catchments studied. They stated that a hydrological model encompassing the geology of the sub-basin may lead to further understanding of this lag effect. McKerchar et al (1998) state that losses to groundwater are negligible in the Waitaki river catchment.