Snowmelt runoff forecasts in Colorado with remote sensing

Transcription

1 Hydrology in Mountainous Regions. I - Hydrologjcal Measurements; the Water Cycle (Proceedings of two Lausanne Symposia, August 1990). IAHS Publ. no. 193, Snowmelt runoff forecasts in Colorado with remote sensing A. RANGO & V. VAN KATWIJK USDA Hydrology Laboratory, Agricultural Research Service, Beltsville, Maryland U.S.A. J. MARTINEC Federal Institute for Snow and Avalanche Research, CH-7260 Weissfluhjoch/Davos, Switzerland ABSTRACT Some evidence exists to indicate that remote sensing of snow cover areal extent can be used to improve the performance of snowmelt runoff models. The Snowmelt Runoff Model (SRM) already accepts snow covered area and several additional models are being revised to accept this remote sensing input. Modified snow cover depletion curves have been developed to permit SRM snowmelt runoff forecasts of three types, namely, seasonal volume, short-term daily flows, and long-term daily flows. The method of forecasting was tested on the Rio Grande basin in Colorado for The method was evaluated to be successful in producing useful SRM forecast hydrographs. Forecast improvements resulted when the forecasted discharge was updated with actual observations every seven days. INTRODUCTION The transition from snowmelt runoff simulations to operational forecasts in real time is outlined by examples from basins in the Rocky Mountains. In order to forecast runoff using the Snowmelt-Runoff Model (SRM), air temperatures and precipitation amounts are required for the forecasting period. The third variable, the areal extent of the seasonal snow cover, is currently measured by satellites. The depletion curves of the snow coverage are projected into the future by using temperature forecasts and a set of so called modified depletion curves which should be derived for the basin in question. EXAMPLES OF AN OPERATIONAL FORECAST When a flood situation developed in the Spring of 1983 in Cache La Poudre basin (2732 km 2, m a.s.l.) of Colorado, a private company was asked by the local water authority to forecast the time and magnitude of the flood peak to allow sufficient time for instituting adequate protective measures. Special short-term meteorological forecasts of temperature and precipitation were prepared and aircraft flights over the basin were carried out in order to evaluate the snow covered areas separately for three elevation zones. The authors of the forecast reported (Jones et al., 1984) that the 1-3 day forecasts during the critical period by SRM were within 20% of the measured streamflow values. These short term forecasts do not require an extrapolation of the depletion curves of the snow coverage to an extended future period. Such extrapolation, however, is necessary if the day-by-day forecast should extend to several weeks or to the whole 627

2 A. Rango et al. 628 snowmelt season. Therefore, a method was developed (Martinec, 1985), to derive the course of the depletion curves from the forecasted trends of air temperatures. ROLE OF SNOW COVER MRPPING IK RUNOFF MODELLING The gradual decline of the areal extent is a typical feature of the seasonal snow cover in the ablation period. Before the advent of remote sensing it was difficult to measure this variable in remote mountain basins. In addition, measurements must be repeated periodically. Consequently, in order to minimize measurement problems, the first attempts were limited to small experimental basins. In view of this situation, most developers of snowmelt runoff models adapted rainfall runoff models to snow conditions without paying special attention to the actual areal extent of the snow cover. In a worldwide intercomparison of snowmelt runoff models conducted by the World Meteorological Organization (WMO) (1986) this proved to be a disadvantage. Only one model in the WMO intercomparison, SRM, used remote sensing input (satellite snow cover extent) for producing the simulations of snowmelt runoff. SRM achieved a better overall accuracy than the nonremote sensing models in the WMO study, especially when maximum inaccuracies for all years tested are considered as shown in Fig. 1. The definitions of the accuracy criteria in Fig. 1 are given in the WMO report (WMO, 1986). çn^(^{ E}( N0NR ' S ' f/^±_/ FIG. 1 Combined representation of model performance with regard to the coefficient of determination, R, the coefficient of gain from daily means, DG, and the runoff volume deviation, D v. The volumes of the prisms indicate the maximum inaccuracies of the respective models from results listed in the WMO snowmelt season tables. R.S. refers to a model with remote sensing input and NON R.S. to models without remote sensing inputs (after Martinec & Rango, 1989).

3 629 Snowmelt runoffforecasts with remote sensing Thus, the areal extent of the seasonal snow cover has proved to be a powerful tool in snowmelt runoff modelling. It is therefore worthwhile to reinforce the position of snow cover mapping in planning future remote sensing programs. At the same time, it is necessary to develop methods for using satellite snow cover data without delay and even with a projection to the future. In fact, several modellers participating in the WMO intercomparison are now incorporating remote sensing inputs into their models (Leavesley and Stannard, 1990; Fortin et al., 1990). FORECASTS OF SNOW COVERED AREAS As was explained elsewhere (Hall & Martinec, 1985), the conventional (snow cover versus time) depletion curves of snow covered areas do not unequivocally reveal the initial accumulation of snow. The future course of these curves cannot be forecasted because a shallow snow cover will decline rapidly and a deep snow cover slowly, with the same forecasted air temperatures. Modified snow cover depletion curves have been proposed (Martinec, 1985) to determine the snow accumulation at the start of the snowmelt season in terms of the areal average water equivalent. Figure 2 shows a set of these curves derived for the Rio Grande basin above Del Norte, Colorado (3419 km, m a.s.l.) (Rango & van Katwijk, 1990). If only a single year is available, with no direct measurements of the water equivalent, a modified depletion curve indicates the initial snow accumulation only after the snowmelt season is completed. With a set of curves, it is possible to compare, in real time, snow covered areas from satellites with the corresponding totalized snowmelt depths and thus determine, several weeks after the beginning of the snowmelt season, which modified depletion curve is valid in the current year. If the water equivalent of snow is measured in the basin, the proper modified depletion curve can be selected at the start of the snowmelt season by using these measurements as an index of the snow accumulation in the given year. It should be remembered that the effect of intermittent snowfalls during the snowmelt period is excluded. In other words, the energy input (represented by degree-days) spent for melting new snow does not count in computing the cumulative snowmelt in Fig. 2. SNOWMELT RUNOFF FORECASTS When an appropriate modified depletion curve for the current year is identified, it is possible to issue the following runoff forecasts: (a) Seasonal forecast of the runoff volume. The initial water volume stored in the snow cover corresponds to the area below the curve. The resulting water volume must be reduced by a runoff coefficient and increased by an expected contribution from precipitation. The seasonal forecast can be repeated on later dates according to the cumulative computed snowmelt depth to date or if snow coverage data becomes available.

4 A. Rango et al cm snow water equivalent over zone B CUMULATIVE SNOWMELT DEPTH (cm) CORRECTED FOR NEW SNOW FIG. 2 Nomograph of modified snow cover depletion curves indicating the estimated areal average water equivalent of snow (cm) on 1 April in elevation zone B ( m a.s.l.) of the Rio Grande basin above Del Norte, Colorado. (b) (c) Periodical short term forecast (e.g., weekly) of daily flows. Predetermined SRM parameters are used (Martinec & Rango, 1986) together with forecasted temperatures and precipitation. The future course of the conventional depletion curve of the snow coverage is evaluated from the modified depletion curve and from the temperature forecast. The model computations can be updated weekly by taking into account the actual discharge instead of the computed discharge. Seasonal forecast of daily flows. Predetermined SRM parameters are again used. The seasonal hydrograph can be computed for the following assumptions: average temperatures and precipitation to be expected, or various combinations of minimum and maximum values to be expected, or probable stochastic series of these two variables. For each temperature series to be applied, the modified depletion curve is again used to forecast the snow covered areas for the entire snowmelt season so that the daily values can be used in running the model. RUNOFF FORECASTS IN THE RIO GRANDE BASIN Apart from the three mentioned variables and the area-elevation curve of the basin, the following parameters must be predetermined to run the SRM model:

5 631 Snowmelt runoff forecasts with remote sensing degree-day factor [a] runoff coefficients for snow and rain [c s, c R ] temperature lapse rate [LR] critical temperature for snow/rain [T c ] time lag [TL] recession coefficient as a function of the current discharge [k] With SRM, runoff simulations with historical data are not used for arithmetical optimizing or calibration of the parameters, but rather to verify values which can be expected from the hydrological and physical point of view. Figure 3 shows a SRM simulation for the year 1983 which is one of the 10 years of historical simulations on the Rio Grande basin. After this historical verification, the following average parameter values are ready to be used in a forecast year: a = cm C~ 1 day~ 1 ; c s = ; c R = ; LR = C/100 m; T_ = C; TL = 14 hrs; and k = Q. To demonstrate the real time conditions, it would be appropriate to use temperatures and precipitation forecasts as they are issued by meteorological offices. Such data were not available in Colorado. They were also not available in a recent WMO project (Askew, 1989) which aimed at demonstrating real time conditions. The measured temperatures and precipitation were used, so that the only simulation of real time conditions consisted in data being distributed at the same time to model operators and the model runs had to be done without delay. NASH-SUTCLIFFE R VOLUMETRIC DIFF. D v % % 158 cc < I o to WWÊÉ. bw tftffî '< ' 'ADMEASURED m \ V*COMPUTED ÏSH <*%&* 258- ânhtflitf- Hay Jim Jly Aug Sep FIG. 3 Snowmelt runoff simulation for 1983 on the Rio Grande basin above Del Norte, Colorado using SRM. In this paper, in order to come closer to real time conditions, the U.S. Soil Conservation Service was consulted to furnish what could be considered as forecasted temperatures and precipitation for the test forecast year They directed us to use long term average daily

6 A. Rango et al S u. 158 o Ou < I \ A î MEASURED V AL C O M P U T E D>- l V "&, A, \ NASH-SUTCLIFFE R VOLUMETRIC DIFF. D v = 4,36? «HMHfW Apr Hay Jun Jly FIG. 4 Snowmelt runoff forecast for 1987 on the Rio Grande basin using SRM. MEASURED^ NASH-SUTCLIFFE R 2 = 0.90 VOLUMETRIC DIFF. D v % % COMPUTED»- \_J% '" Aug Sep FIG. 5 Snowmelt runoff forecast for 1987 on the Rio Grande basin using SRM and updates with actual streamflow every seven days. minima and maxima for temperature and 110% of average monthly precipitation totals scattered randomly through the respective months. Thus the snow covered areas had to be extrapolated, using the nomograph in Fig. 2 and long term average temperatures for each day of the year instead of correct temperatures.

7 633 Snowmelt runoff forecasts with remote sensing In view of this deteriorated quality of input variables, the "forecasted" hydrograph is less accurate than the simulated hydrograph, as shown in Fig. 4. It is possible, however, to improve it by a weekly updating, that is to say by starting each forecast for the next seven days using the last measured discharge (Rango & van Katwijk, 1989). Figure 5 shows that the agreement of daily flows in late May and June has been improved with this very limited kind of updating. CONCLUSIONS At the moment, few snowmelt runoff models include the areal distribution of the snow as obtained from remote sensing as an input. SRM profited from such remote sensing input in a WMO intercomparison of snowmelt runoff models when simulated hydrographs were required. In order to provide forecasts with SRM, it is necessary to develop modified snow cover depletion curves. When combined with temperature forecasts, the modified depletion curves can be used to project the snow cover extent into the forecast period. This makes possible three types of runoff forecasts, namely, seasonal forecasts of snowmelt volume, periodical short term forecasts of daily flows, and seasonal forecasts of daily flows. The method of forecasting was tested on the Rio Grande basin in Colorado for SRM was evaluated to be successful for producing forecast hydrographs. Forecast improvements resulted from updating the forecasted discharge with actual observations every seven days. REFERENCES Askew, A. J. (1989) Real-time intercomparison of hydrological models. New Directions for Surface Water Modeling (Proceedings of the Baltimore Symposium), IAHS Publication No. 181, Fortin, J-P., Villeneuve, J-P, Bocquillon, C., Leconte, R. S Harvey, K. D. (1990) HYDROTEL - A hydrological model designed to make use of remotely sensed and GIS data. Proceedings of the Workshop on Application of Remote Sensing in Hydrology, National Hydrology Research Institute, Saskatoon, Saskatchewan. Hall, D. K. & Martinec, J. (1985) Remote Sensing of Ice and Snow. Chapman and Hall, London, U.K. Jones, E. B., Frick, D. M., Barker, P. R. & Allum, J. (1984) Application of a snowmelt runoff model for flood prediction. Unpublished report. Resource Consultants, Inc., Fort Collins, Colorado. Leavesley, G. H. S Stannard, L. G. (1990) Application of remotely sensed data in a distributed-parameter watershed model. Proceedings of the Workshop on Application of Remote Sensing in Hydrology, National Hydrology Research Institute, Saskatoon, Saskatechewan. Martinec, J. (1985) Snowmelt runoff models for operational forecasts. Nordic Hydrology 16, Martinec, J. & Rango, A. (1986) Parameter values for snowmelt runoff modelling. Journal of Hydrology 84, Martinec, J. & Rango, A. (1989) Merits of statistical criteria for the performance of hydrological models. Water Resources Bulletin 25,

8 A. Rango et al. 634 Rango, A. & van Katwijk, V. (1990) Development and testing of a snowmelt-runoff forecasting technique. Water Resources Bulletin 26. World Meteorological Organization (1986) Intercomparison of models of snowmelt runoff. Operational Hydrology Report No. 23, Geneva, Switzerland.