MANAGING WATER SUPPLIES DURING DROUGHT: TRIGGERS FOR OPERATIONAL RESPONSES

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1 MANAGING WATER SUPPLIES DURING DROUGHT: TRIGGERS FOR OPERATIONAL RESPONSES Selene Fisher Kramer, Chin, and Mayo, Consulting Engineers And Richard N. Palmer University of Washington INTRODUCTION In the past, municipal water supply planning has focused on assuring reliable sources of water to meet all but the most extreme demands. Systems have been designed to deliver peak daily demands by evaluating distribution limitations and to meet long-term demands by developing sufficient storage or other reliable sources. As demand increased, new facilities were constructed to both meet the peak demands and seasonal (or over seasonal) requirements. Today, limitations on acceptable new reservoir sites, depletion and restriction of groundwater sources, rising development costs, and growing public concern for environmental impacts has refocused the emphasis from new sources to better management of existing supplies. Without new sources, and despite better management, the struggle among competing users has intensified and extreme events, such as drought, make the allocation of water more difficult and the impacts more significant. To urban water managers in the U.S. these impacts are most significant when they cause deviations from normal operation. The timing and extent of these management changes is a primary focus of this paper. Water supply shortages are a function of water availability and demand, which in turn depend on the physical, hydrologic, and climatological characteristics of the system. Water supply systems vary dramatically throughout the US and this impacts the appropriate management responses. For instance, reservoir storage in the Colorado River system is approximately four times the average annual inflow (four years of streamflow and precipitation can be stored). As a result, below normal streamflow over a short time period is buffered by the reservoir storage; even several years of deficient precipitation may not adversely affect water use (Changnon and Easterling 1989). Conversely, in more humid regions where precipitation is distributed throughout the year, reservoir storage is typically less than the average annual inflow, so these systems may be sensitive to shortfalls occurring over only a few months (Lettenmaier et al. 1990). Outside the US, droughts may be viewed quite differently. For example, in Bali, six days without rain is considered a drought, whereas in Libya, two years without rains signals a drought (Dracup et al. 1980). Identifying the onset of a drought can be subtle, requiring detection of depletions of supplies and/or increases in demand. This can be made more difficult by the inability to make reliable, long-term weather forecasts and the lack of practice in managing drought. A delay in instituting a drought response can limit the range of possible actions if drought does occur. In contrast, early preventative actions may result in needless expenses or a loss of revenue. Water managers must appear poised to take appropriate action but not willing to impose unnecessary responses when they are not warranted (Wright et al. 1986). Numerous utilities and government organizations throughout the country have explored the use of triggers (a pre-defined system condition that initiates management action). Examples of triggers to support decision-making include: the Massachusetts Water Resources Authority (which serves the Boston area), Northern New Jersey, the Interstate Commission on the Potomac River Basin (which facilitates supply operations in the Washington, D.C., northern Virginia, and southern Maryland area), the Bureau of Water Works for the City of Portland, the Seattle Water Department, and Denver Water. Triggers such as these can help identify the onset or increased severity of a drought and thus function to initiate drought response measures ( US Army Corps of Engineers 1991). The use of trigger can simplify the decision-making required during stressful periods (Nault et al. 1990). Triggers can help mitigate the impacts of drought by clearly defining the conditions requiring action and encourage timely response of actions to avoid conflict (Hrezo et al. 1986). 14

2 In the remainder of this paper, a specific drought indicator for municipal water supply is presented and its application as a trigger to initiate demand management is illustrated. First, previously developed drought indices are reviewed, as well as their use in drought management. Next, the indicator Days of Supply Remaining is presented and its calculation illustrated. A case study applying the indicator is then provided followed by results of the study. INDICATORS OF DROUGHT Generally, hydrologic indicators used to define triggers are physical measures of a system, such as reservoir storage, streamflow levels, or groundwater supply. Reservoir storage is useful because it is relatively easy to determine. However, it can underestimate drought severity for several reasons: reservoir levels may not reflect increased demands associated with dry periods, it may be inappropriate for small reservoirs, and many reservoirs are operated on a rule curve which may disguise early drought indications (Titlow 1987). Streamflow levels are often used as drought indicators. Streamflow can be related to the total moisture of a basin as well, since it is a function of soil moisture, groundwater levels, runoff and precipitation (Dracup, et al. 1980). However, streamflow may not be appropriate in some instances. Small streams may respond too quickly to short periods of dryness, large streams may react too slowly to the onset of drought, and streamflow can be strongly influenced by basin characteristics, such as the natural topography and geology, and the manmade development. Groundwater is a major source of water in many parts of the country, and its elevation or drawdown level is often used as a drought indicator. As an indicator, groundwater level may be limited by a poor understanding of the aquifer stratigraphy and recharge rates, as well as other factors that may influence groundwater level (Johnson and Kohne 1993). Meteorologic indicators are based on climatological influences, including precipitation, air temperature, or evaporation. Since hydrologic indices, such as streamflow, are a function of meteorological conditions, such as rainfall, measuring precipitation may provide a more direct indication of drought. Since rainfall measurements alone do not indicate the antecedent conditions of moistness or dryness, some indicators make use of mean air temperature to reflect this climatic moisture demand. A significant drawback to indicators based on rainfall is that precipitation varies widely over short distances, and few regions have enough observation stations to accurately measure the spatial variations and get a representative rainfall amount (Titlow, 1987). Agricultural indicators typically consider soil water and crop parameters, such as soil moisture in the top 100 cm of soil, crop yields, or cumulative precipitation and temperature since the beginning of spring. Perhaps the best known of these indices is the Palmer Index, which refers to either the Palmer Drought Severity Index (PDSI) or the Palmer Hydrologic Drought Index (PHDI). The PDSI attempts to track weather patterns and the PHDI tracks hydrologic factors such as soil moisture, lake level, or streamflow (Mather 1985). Fundamentally, the index provides monthly values corresponding to degrees of wetness or dryness, where drought is a function of the weighted differences between actual precipitation and precipitation requirement. The precipitation requirement is a function of water balance terms: potential evapotranspiration, soil recharge, surface runoff, and soil moisture loss. Although precipitation is widely used in agricultural regions as a measure of drought severity (Alley 1985), it is an empirical method with several of the Palmer index limitations. All of the indices described may provide a partial description of drought, but none define it completely. Attempts to correlate an indicator with a particular level of drought severity observed in a region have resulted in several multiple-parameter indicators. Some proposed composite indicators are the method of truncation (Chang and Kleopa 1991), the Water Availability Index (Davis and Holler 1987), and the Surface Water Supply Index (Dezman et al. 1982). The method of truncation uses historic records of streamflow, precipitation, groundwater drawdown, lake elevation, and temperature. The historic data are sorted in ascending order, and trigger levels are determined from the truncation level specified (Figure 1). For example, if Stage 1 Drought is defined as the 70% level, this corresponds to streamflows which are less than 70% of the flows. When using this method, drought events of higher severity are nested inside drought events of lower severity, i.e., a 90% drought implies the occurrence of a 70 and 80% drought (if those are the trigger levels selected). The highest level of severity is the one of interest, since a water management system must deal with the most severe shortage that occurs. 15

3 The Water Availability Index (WAI) was developed for several basins in the southeast and relates current water availability to historical availability during periods of drought by measuring the deviation-from-normal rainfall over the prior four months. The WAI is multiplied by a constant to make the index fall between 0 and 10, with zero being normal conditions and ten indicating severe drought. Hoke (1989) included a factor for minimum and maximum desirable pool elevation, and Raney (1991) included volume and drainage area factors to account for multiple reservoirs within one watershed. The Surface Water Supply Index (SWSI) was developed specifically for mountainous, snowmelt regions where the Palmer Index is not appropriate. The SWSI is a function of precipitation, representing the potential water supply; snowpack, representing the major water source; and reservoir level, representing the currently held water supply. Streamflow is substituted for snowpack in the summer months. Several limitations were identified by Shafer and Dezman (1982) who noted that since the SWSI is based on historical records, it is sensitive to data collection problems, discontinuance of stations, occurrence of extreme events outside the range of conditions used to develop the SWSI, and changes in water management activities. INDICATORS IN DROUGHT MANAGEMENT Use of triggers for drought management requires comparing a forecast of supply and demand. If a water supply has few stresses (i.e., supply exceeds demands or drought events are very infrequent), little management is needed to prepare or respond to water shortages. In this case, simple indicators, such as reservoir storage or cumulative precipitation, compared to normal may be adequate. For example, the city of Manchester, Connecticut uses only reservoir storage, expressed as a percentage of normal season capacity, as an index. In a community primarily dependent on a single supply source, this measure gives sufficient indication of drought status (California 1991). Conversely, a highly stressed system with competing users, demands near system yield, or frequent drought events requires early and frequent management action to preserve the available water supplies. This system may require a more complex triggering method, utilizing several indicators or a statistically based index. Table 1 reviews some of the indicators used or proposed for drought management at selected sites in the U.S. All of the indicators described in Table 1 are essentially hydrologic and compare the measured or calculated factor to some norm. In their calculation, they do not account for the operating rules of multiple use systems, allocation between users, institution of cutbacks, or any of the other social concerns affecting water managers during droughts. A more dynamic indicator relating supply and demand is needed, one that monitors drought onset and severity and triggers agency response to reduce drought impacts, within the confines of the system operating environment. THE DAYS OF SUPPLY REMAINING INDEX Days of Supply Remaining (DSR) is suggested here as an appropriate variable upon which to trigger water management decisions. DSR includes current reservoir storage, forecasted future inflows from precipitation and snowmelt, and predicted demands for municipal supply and instream flows. It can be utilized effectively to provide an objective measure of the system's state and simplify communication among interested parties, which can become confused by the various units used to describe system needs and states (acre feet, million gallons per day, cubic feet per second, etc). DSR describes the water supply status, is easily understood by decision-makers and water users, and can be compared to the trigger levels to determine drought status. The indicator is robust, in that as new sources of supply or new demands occur, the index remains appropriate. DSR is calculated by predicting future inflows and demands and determining when the inflows and existing supply will no longer be adequate to meet demands. A simplified example, shown in Table 2, illustrates this calculation. The DSR are calculated at the beginning of each time step, when forecasts are made of the subsequent weeks inflows and demands. Inflows are added and demands subtracted for each successive week until the remaining supply is negative (inadequate to meet demand). In this example, there are 100 units of storage at the beginning of the time step. Forecasts show that demand cannot be met six weeks from the beginning of the forecast period. There is adequate supply for the current week plus five weeks, six total weeks, which equals 42 days of supply remaining. If a water manager were presented with the data in this example, the agency might try to reduce demand 5%, which would maintain adequate levels of storage to last until the expected reservoir refill 7 weeks from now. Alternatively, a 16

4 manager could choose to pursue an additional supply source, or undertake other demand and supply management options. With perfect forecasting abilities (as is assumed in many optimization models), a water manager knows exactly when and what type of restrictions to implement (if that is the management option of choice) to minimize drought impacts. In reality, forecasting weather (temperature, snowmelt, rainfall, etc.) is much less reliable as the timeframe for the forecast is extended (Wright et al. 1986). An index, developed analytically and incorporating measures of risk, identifies when a drought period begins and when it increases in severity. SYSTEM DESCRIPTION The DSR index was evaluated in a simulation model (Fisher 1993, Fisher and Palmer 1995) of the Seattle system, a system supplied primarily from the Cedar and Tolt Rivers, located in Northwestern Washington state. The Seattle Water Department (SWD) currently supplies water to about 1.2 million residents through both direct and purveyor sales. The demand (primarily residential) peaks in late spring and summer, primarily due to increased outdoor water use and coinciding with reduced precipitation. The municipal demand is supplied approximately 70% from the Cedar River and 30% from the Tolt River. Groundwater supplies are very modest and used mainly to meet a portion of the summer peak demands. In addition to water supply, the Seattle system also provides water for instream fish flows in both the Cedar and Tolt Rivers, lake level maintenance in Lake Washington with releases from the Cedar River, and navigation through the Chittenden Locks. The Cedar and Tolt Rivers both have one major reservoir, used for the purposes suggested above, in addition to flood control. The reservoirs are drawn-down during the winter and are allowed to refill in March when the danger of flooding subsides, and typically the refill cycle is complete by early June. Rainfall in autumn is retained to meet continuing demand until the high flow season begins again ( Seattle Water Department 1986). The storage capacity of the SWD reservoirs is small relative to the amount of annual runoff generated in the watershed with annual streamflow approximately four times larger than total storage. Although the Seattle system does have the capability for some supply augmentation through wells and dead storage pumping, this analysis neglects these options. Response activities in this study focus on demand reduction, which is a function of time of year. During the summer, increased residential demand is due almost entirely to outdoor water use; therefore, a larger percent of restrictions can be achieved. In winter, however, residential demand is almost entirely indoor use and reductions in demand are more difficult. The Seattle system is vulnerable to drought under three hydrologic scenarios. A light winter snowpack combined with below average spring rains results in an inadequate spring runoff to refill the reservoirs from the winter flood protection level to the higher summer operating level. This can lead to shortfalls in any month, especially the high demand months of June through August (Water Shortage Contingency Plan 1993). A drought may also develop if a warm spring causes an early runoff that must be released, rather than stored, because operating pools are still at lowered flood control levels. If the early runoff is followed by a warm, dry summer, resulting in higher than normal demands, the remaining stored water can be quickly depleted (Karpack 1992). The final hydrologic scenario is a normal year followed by a dry fall. If the autumn rains begin late or are significantly below normal, the remaining storage after summer s high demand period may not be adequate, leading to shortfalls in November and December ( Seattle Water Department 1986). SIMULATION MODEL A system operations model was constructed for this study, using a week as the appropriate time-step. Operations were modeled as a function of time of year, reservoir storage levels, unconstrained and curtailed demand, streamflow forecasts and a prescribed trigger value. The DSR index was calculated and compared to triggers to determine water supply status and the appropriate response activities taken. Performance was evaluated with respect to the actual hydrological inputs received (the historical record). Inflows and demands are forecast for every two weeks up to a maximum of eighteen subsequent weeks. Eighteen weeks represent an estimate of the longest reasonable forecast period for system inflows and demands. Inflows are defined as the predicted inflows into reservoir, and supplemental flows downstream of the reservoirs. Demands are defined as the required instream flow releases for fish, spill, and the proportion of each reservoir allocated for municipal demands. The index is calculated by subtracting demands and adding inflows until the remaining supply in either reservoir becomes 17

5 negative. The Cedar and Tolt Rivers are considered independently, because the Cedar can not supply water for the Tolt instream flow needs and vice versa. Hence, it is possible that the Tolt basin, for example, will be unable to meet instream needs when there is still water in the Cedar system, resulting in a DSR somewhat lower than if the two systems were considered individually (or synergistically, after Hirsch et al. 1977). The resulting number of weeks is multiplied by seven to convert to days of supply remaining in the system. The model is a simulation program, rather than an optimization. A simulation was selected for this study because of the conflicting objectives for water use, a nonlinear function for reservoir continuity (the Cedar River Reservoir has significant groundwater losses due to seepage) and convex economic loss function. Figure 4 presents a pseudo flowchart of the program, and major assumptions of the program are listed in Table 3. DEVELOPMENT OF TRIGGER LEVELS Since the SWD supplies water for conflicting needs, there is no single system measure of performance preferred by all stakeholders. In the absence of a single, non-inferior set of trigger levels, a weighting scheme was applied to select the best set of trigger levels. Various objectives were examined, including minimizing impacts solely of municipal demands, Cedar River instream flows or Tolt River instream flows, and combinations of these priorities. In the case study, the trigger levels were determined with the simulation model using the historical record as the predicted and actual inflows. Forecasting with perfect information should result in minimum impacts, since the system responds precisely as anticipated. A four-stage drought response plan, similar to that in use by the Seattle Public Utilities, is modeled. This response plan prescribes the water savings associated with each stage of curtailment ranging from voluntary restrictions to curtailments limiting outdoor lawn watering. A trigger scenario using Days of Supply Remaining was created by prescribing when various stages of water-use restrictions would be implemented. For the various scenarios, Stage 1 began with between 25 to 175 days of supply remaining and subsequent stages were triggered when the supply decreased by 10 to 30 days. For example, one scenario had Stage 1 curtailments initiated at 140 days of supply remaining, Stage 2 at 120 days, Stage 3 at 100 and Stage 4 at 80 (Table 4 and Figure 5). Three hundred and twenty sets of trial trigger levels were tested in the simulation assuming perfect forecasting ability. 175 Days of Supply Remaining was chosen as the maximum "look-ahead" as this is the average length of time from initial snow pack to the end of the snowmelt in the Seattle system. Scenarios were explored and tested in an iterative fashion. To limit the investigation to policies that were practical, all sets of triggers that created shortfalls from demand in excess of one hundred million gallons per day were eliminated. A further constraint eliminated all scenarios that resulted in a Cedar River instream flow requirement (IFR) shortfall of greater than 5 cfs. In this fashion, the number of feasible trigger scenarios was reduced from 320 to 82. Table 4 shows the range of trigger levels for each stage before and after the elimination. For this multi-objective problem, an approach was needed to select the set of trigger levels that represents the best compromise among the competing users. In this study, a simple weighting method was used. Parameters were normalized using the maximum calculated value of system performance by the maximum observed over all 320 trials. Weights were assigned in order of priority, where the highest priority factor receives the highest weight, hence the lowest weighted sum is the best solution for the defined objective. In the absence of clearly defined rights and relative importance in the Cedar/Tolt system, eight objectives were examined to determine the effect on the reduced triggers scenarios. These objectives are summarized in Table 5. Note that these priority assignments are subordinate to the water allocation rules encoded in the simulation. These objectives were used to select a best compromise operating rule, but inherent in the results is the higher priority given to IFR in the allocation of available water. INDEX PERFORMANCE The benefits of using the DSR index were assessed by comparing the impacts in the system operating with the index to the impacts resulting from the system operated without the index. Since the future flows cannot be forecast perfectly, predicting the days of supply remaining must utilize a forecast of inflow. The index was evaluated by comparing system behavior over the historical record with different predicted inflows. The status quo condition was defined as the system with no institutionalized restrictions to curtail demand. Full demands are met without hedging against future shortages, resulting in fewer, but much larger, shortfalls. 18

6 Several simple predicted inflows were used, including the mean flow and the 10, 20, 30, 40, and 50 percentile flows. The lower percentile flows represent a pessimistic forecast of flows with the higher percentiles representing decreasing conservatism. Perfect information illustrates how well the index and triggers could perform if all available information is accurate and incorporated into the decision process. Less than perfect information illustrates how conservative forecasts impact the effectiveness of the DSR index as a decision aid. By evaluating the triggers selected in the first step with different inflow sequences, the effects of increasingly less-conservative streamflow estimates can be assessed. During the drought susceptible period in Seattle, July through November, streamflows are driven by precipitation (rather than snowmelt runoff, as earlier in the year) for which forecasts of greater than two weeks are typically very poor. In the absence of rainfall and rainfall predictions, percentile flows represent a reasonable substitute. The system performance was evaluated using measures reflecting the primary system considerations: municipal supply and instream flows. These included the number of days and magnitude of supply shortfalls and the number of days of demand restrictions and at which stages for municipal demands. Instream flow measures were similar: number of days and magnitude flow is below normal and at or below critical levels. RESULTS OF TRIGGER LEVEL SELECTION Naturally, no single set of triggers was uniformly superior for all of the performance measures. The municipal impacts were as predicted: with early drought stage declarations (Stage 1 curtailments triggered with the index set high), the number and magnitude of actual shortfall events was decreased, but the time spent under restrictions was increased. These trends were the same whether there was quick or slow progression into successive stages (stages triggered 10 to 30 days apart). Thus, when combining all of the performance measures, municipal impacts were minimized with moderately high trigger levels that progress rapidly from Stage 1 to 4. For example, Objective #1 (Table 5) and identified as Muni in Figure 5, has trigger levels of 130, 120, 110, and 105 days for Stages 1 through 4, respectively. Time spent in restrictions (Objective #4) is minimized by triggering drought stages as late as possible, thus the high Stage 1 trigger level. These are shown in Figure 5 as Days and are set at 115, 110, 100, and 90 days, successively. Although the impact trends were similar for the instream flow requirements on the Cedar River, there were fewer overall instances of shortfalls. This was due primarily to the higher priority assigned to IFR releases in the reservoir allocations and to the availability of downstream local inflows to help supply instream flows. One item which did impact the Cedar River flows was the declaration of Stage 3 or 4 droughts, which could drive the IFR to critical flows to match the severe restrictions under which the municipal demands were placed. Therefore, when only instream flow impacts were considered (Objective #2) it was best to restrict municipal demand as early as possible with high Stage 1 triggers, but delay triggering Stage 3 and 4 as long as possible. These triggers are shown in Figure 5 as IFR and are set at 170, 140, 110, and 85 days. The results were the same when the Tolt River IFR impacts, Objective #3, were considered singly. The combined objectives considered both the municipal and instream results. Once a drought occurs, overall impacts are reduced when demands are reduced quickly, but not too early, avoiding long periods of possibly unnecessarily restricted demands. As an example, Objective #5, shown as IFR -> Muni in Figure 5, has trigger levels at 140, 130, 115, and 110 days. The other objectives had very similar results. EVALUATION OF DSR INDEX AS A DECISION AID Once established, the trigger levels for DSR were used in the simulation program with the various predicted inflows. Each scenario identified in the previous step was used, but the trends associated with differences in predicted inflows were similar. The impacts occurring with mean and 50% flows are very similar. 30% and 40% flows also have similar results, although most impacts are higher than the mean and 50% flow results. All of these flow sets have at least one incidence of 100% municipal shortfall, although the average shortfall does decrease with percentile flow level. The maximum Cedar River IFR shortfall is fairly constant across the flow predictions, increasing slightly with increased percentile. The 10 and 20 percentile flows show the greatest difference in results between sets of triggers. As expected, the deviations from normal demand are high compared to other predicted flows, because the conservative flow estimates institute restrictions more often. As a consequence, the lowest municipal shortfalls are also observed in the 10 and 20 percentile flow results. The magnitude of instream flow deviations from normal 19

7 does not vary much with percentile level, but the shortfalls from expected are lower in the 10 and 20% results. The status quo condition (with no index and no hedging) has deviations from normal municipal demand only when there is inadequate supply to meet demand, so shortfalls and deviations are the same. The status quo has more shortfall events than the tests using the DSR index, and the magnitude of shortfall is greater, both in terms of the maximum and the cumulative shortfall. The cumulative shortfall is an order of magnitude higher with no triggers than if the index is used with the 10 percentile flows. The Cedar River instream impacts under status quo are quite similar to the results achieved with low triggers and mean flows, but are higher than with any other predicted flows. The Tolt River impacts are of the same magnitude as any of the inflows, predicted or perfect. Figures 6 and 7 provide a summary of the relative system performance with the Muni triggers for the different predicted inflows. Results with predicted inflows have utilized the DSR index to institute restrictions when indicated by the trigger levels. The status quo uses no predicted flows, no index, and no demand restrictions. Demand shortfalls result from supply inadequacies. An examination of the weekly output identified the maximum municipal shortfalls as occurring in the fall, when instream flow requirements increase rapidly while municipal demand drops off slowly, and remaining storage and inflows were insufficient to meet both needs. In real droughts, the instream flows would likely be returned to the higher winter levels more slowly, until inflows are replenished by autumn rains. CONCLUSIONS A significant challenge in water supply management is determining the onset of a drought. Early action can extend the period over which supplies can be delivered; however, they may result in a response to a condition that never materializes. Drought indicators and triggers are useful decision support tools in determining when to initiate management action. In this paper, the index Days of Supply Remaining (DSR) was defined. By predicting future inflows and demands under current operating policies, an indicator of the water supply condition is determined. It has the advantage of including operating priorities and providing an answer in easily understood units. To develop and assess the utility of the DSR index, a test case was performed with a simulation model of the Seattle water supply. Two steps of development were required: determining the index levels at which response activities should be triggered, and evaluating the effect of using the index as a decision tool under different predicted flows. Trigger scenarios were evaluated by comparing the system performance using a perfect forecasting ability. Screening techniques such as minimum performance levels or weighted sums can help identify the general range of triggers against which to compare the index. Since it is difficult to find perfect trigger scenarios that satisfy all users, emphasis should be placed on developing an index that is acceptable to decision-makers and on considering a range of trigger levels in concert with other observations, such as snowpack or cumulative precipitation. Different trigger levels depending on season may be appropriate. When a system is dependent on highly variable, poorly predicted inflows (precipitation), using some prediction method correlated to the historical record provides a good guide to inflows. For this case study it was shown that it is better to be conservative with these predictions in a municipal supply system. The results show that using the DSR index can improve the management of water. The index triggers provide valuable insight on when to initiate hedging against prolonged shortages to minimize specific impacts. However, the DSR index alone, in the absence of factors correlating with physical system status, may be insufficient. The suggested approach requires iterative simulations rather than a single optimization, which may be viewed as a shortcoming. However, incorporation of the nuances of real time management currently preclude either linear or dynamic programming formulations of this complex problem. REFERENCES Alley, W.M. (1985). The Palmer Drought Severity Index as a Measure of Hydrologic Drought, Water Resources Bulletin, 21(1), Bower, B.T., Hufschmidt, M.M., and Reedy, W.W. (1962). Operating Procedures: Their Role in the Design of Water-Resource Systems by Simulation Analyses, in Design of Water Resource Systems, A. Maass, M.M. Hufschmidt, R. Dorfman, H.A. Thomas, Jr, S.A. Marglin, and G.M. Fair, eds., Harvard University Press, Cambridge, MA. Chang, T.J. and Kleopa, X.A. (1991). A Proposed 20

8 Method for Drought Monitoring, Water Resources Bulletin, 27(2), Changnon, Jr, S.A. and Easterling, W.E. (1989) Measuring Drought Impacts: The Illinois Case, Water Resources Bulletin, 25(1), Davis, C.P., Watson III, R.M., and Holler, A.G. (1989). Southeastern Drought of A Federal Perspective, Proc., National Water Conference, ASCE, New York, NY, Davis, P.C., and Holler, A.G. (1987). Southeastern Drought of Lessons Learned, Proc., Engineering Hydrology Symposium, A.D. Feldman, ed., ASCE, New York, NY, Dezman, L.E., Shafer, B.A., Simpson, H.D., and Danielson, J.A. (1982). Development of a Surface Water Supply Index - A Drought Severity Indicator for Colorado, Proc., International Symposium on Hydrometeorology, American Water Resources Association, Bethseda, MD, Dracup, J.A., Lee, K.S., and Paulson, E.G. (1980). On the Definition of Droughts, Water Resources Research, 16(2), Fisher, S.M. (1993) Days-of-Supply-Remaining as an Indicator of Drought Severity in Water Supply Planning and Management, Masters thesis, University of Washington, Seattle, WA. Fisher, S.M. and Palmer, R.N., (1995) "Managing Water Supplies during Drought, The Search for Triggers," Proceedings of the 22nd Annual National Conference, Water Resources Planning and Management Division of ASCE, Cambridge, Massachusetts, Garen, D.C. (1993). Revised Surface-Water Supply Index for Western United States, Journal of Water Resources Planning and Management, 119(4), Managing Droughts Through Triggering Mechanisms, Journal AWWA, 78(6), Johnson W.K. and Kohne, R.W. (1993) Susceptibility of Reservoirs to Drought Using Palmer Index, Journal of Water Resources Planning and Management, 119(3), Karpack, L.M. (1992) The Use of Simulation Modeling to Evaluate the Effect of Regionalization on Water System Performance, Masters thesis, University of Washington, Seattle, WA. Kibler, D.F., Shaffer, G.L., and White, E.L. (1987) Analysis of Drought Indicators in Pennsylvania, Proc. Engineering Hydrology Symposium, A.D. Feldman, ed., ASCE, New York, NY, Lettenmaier, D.P., Wood, E.F., and Parkinson, D.B. (1990) Operating the Seattle Water System During the 1987 Drought, Journal AWWA, 82(5), Draft Drought Management Plan. (1989) Massachusetts Water Resources Authority, 1989, Commonwealth of Massachusetts, Boston, MA. Mather, J.R. (1985) Drought Indices for Water Managers, Publications in Climatology, 38(1), 77 pages. Nault, R.J., Gaewski, P., and Wall, D.J. (1990) Drought Indication and Response, in Optimizing the Resources for Water Management, Proc., 17th Annual National Conf. R.M. Khanbilvardi and T.C. Gooch, eds., ASCE, New York, NY, Raney, D.C. (1991) A Water Availability Index for Reservoir Management, Proc., Environmental Engineering Conf., P.A. Krenkel, ed., ASCE, New York, NY, COMPLAN (Seattle Comprehensive Regional Water Plan). (1986) Vol. I, Summary, Vol. VI, Conservation Plan, Seattle Water Department, City of Seattle, WA. Hirsch, R.M., Cohon, J.L., and ReVelle, C.S. (1977) Gains from Joint Operation of Multiple Reservoir Systems, Water Resources Research, 13(2), Hoke, Jr, J.P. (1989) Savannah River Basin Drought Contingency Planning, Proc., National Water Conference, ASCE, New York, NY, Hrezo, M.S., Bridgeman, P.G., and Walker, W.R. (1986) Shafer, B.A. and Dezman, L.E. (1982) Development of a Surface Water Supply Index (SWSI) to Assess the Severity of Drought Conditions in Snowpack Runoff Areas, Proc. Western Snow Conference, Colorado State University, Fort Collins, CO,

9 Shih, G., (1987) Mapping of Drought Status in South Florida, Proc., Engineering Hydrology Symposium, A.D. Feldman, ed., ASCE, New York, NY, Smith, R.L. and Lampe, L.K. (1981) A Drought Alert System, A Drought Contingency Manual for Kansas Public Water Utilities, Department of Civil Engineering, University of Kansas, for Kansas Public Water Utility, Department of Health and Environment. South Fork Tolt River Hydroelectric Project, Settlement Agreement (1988) Federal Energy Regulatory Commission, Washington D.C. Titlow, III, J.K. (1987) A Precipitation-Based Drought Index for the Delaware River Basin, Publications in Climatology, 40(2), 68 pages. Urban Drought Guidebook (1991) State of California, Department of Water Resources, Water Conservation Office. US Army Corps of Engineers (1991) The National Study of Water Management During Drought, A Research Assessment, Institute for Water Resources, IWR Report 91-NDS-3. Water Shortage Contingency Plan (draft) (1993) Supplement to the SWD Water Supply Plan, Seattle, WA. Water Supply Plan (draft) (1992) Seattle Water Department, City of Seattle, WA. Wilhite, D.A. (1982) Measuring Drought Severity and Assessing Impact, Proc., International Symposium on Hydrometeorology, American Water Resources Association, Bethseda, MD, Wright, J.R., Houck, M.H., Diamond, J.T., and Randall, D. (1986) Drought Contingency Planning, Civil Engineering Systems, 3(4), Selene Fisher is currently employed by Kramer, Chin and Mayo, located in Seattle, Washington. She worked previously as an Aerospace Engineer for Rockwell International. She received her B.S. from California Sate Polytechnic University in 1985 and her M.S. from the Department of Civil Engineering at the University of Washington in Dr. Richard Palmer is a professor of Civil Engineering at the University of Washington, and received his PhD from Johns Hopkins University in 1979 and a M.S. from Standford University in

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