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Public Interest Energy Research Program: Final Project Report

cover of report Integrated Forecast and Reservoir Management (INFORM) for Northern California: System Development and Initial Demonstration

Publication Number: CEC-500-2006-109
Publication Date: April 2007
PIER Program Area: Energy-Related Environmental Research

The executive summary, abstract and table of contents for this report are available below. This publication is available as an Adobe Acrobat Portable Document Format File. In order to download, read and print PDF files, you will need a copy of the free Acrobat Reader software installed in and configured for your computer. The software can be downloaded from Adobe Systems Incorporated's website.

Download Report in Acrobat PDF ( 263 pages, 10.4 megabytes - Note size! )

Appendices

Appendices A to D
A: Summary of Proceedings for the INFORM Oversight and Implementation Committee Meetings
B: Validation Figures for the Application of the Downscaling Precipitation Model to the Folsom Lake Drainage
C: Reliability-Diagram Tables for CFS-Conditioned and Unconditioned ESP for INFORM Reservoir Inflows
D: INFORM Project Hydrometeorological Database
( 94 pages, 2.7 megabytes - note size )

Appendices F to I
F: Plots from the Evaluation of the INFORM Stand-Alone Distributed Hydrologic Model
G: Selected Reservoir, Hydropower Facility, and Demand Data
H: Historical Analog Streamflow Forecasting Model
I: River Index Calculation and Water Year Characterization
( 34 pages, 3.4 megabytes - note size )



Abstract

This report describes the first three-year phase of the Integrated Forecast and Reservoir Management (INFORM) project. The primary INFORM objective is to demonstrate the utility of present-day meteorological/climate and hydrologic forecasts for the Northern California river and reservoir system, including all major reservoirs on the Trinity, Sacramento, Feather, American, and San Joaquin rivers, and the Sacramento-San Joaquin Delta. In close collaboration with water forecast and management agencies of the region, a software system was designed and implemented in a distributed manner, with components that ran at various agency and research centers. The system contains real-time, short-range forecast components; off-line longer-range forecast components; and off-line decision components that span forecast and decision time scales from hours to seasons. In all cases, forecast uncertainty was explicitly characterized and used for risk-based decision support. Extensive tests with historical data and an initial five-month period of operational "dry run" testing for the wet season of 2005-2006 showed that system components perform well and clearly demonstrated the value of the system in advancing the current state of forecast, management, and planning operations in the region. The main recommendation is to continue the demonstration of the INFORM system for two to three more years to reliably quantify real-time performance and utility for planning and management and to explore more fully the various applications to which the system is suited.

Keywords: Ensemble precipitation forecasting, ensemble temperature forecasting, ensemble flow forecasting, risk-based decision support, adaptive reservoir management, INFORM, Integrated Forecast and Reservoir Management



Executive Summary

Introduction

Considerable investments have been made toward improving the quality and applicability of climate, synoptic, and hydrologic forecast information, and earlier retrospective studies have demonstrated clearly that the management of water resource systems with large reservoirs can potentially benefit from such information. However, before this project no focused program has ever aimed to quantify and demonstrate these benefits in an operational environment. There are three main reasons why this has not been previously accomplished:

  1. Synoptic and climate forecasts include substantial uncertainty, and their effective use in management requires procedures that explicitly account for that uncertainty both in flow forecast and decision models/processes.

  2. Existing reservoir management procedures depend on presently available information and operate under set institutional constraints, so that nontrivial technical and institutional changes are required to use information of a different type (i.e., improved hydrologic, synoptic, or climate timescale forecasts).

  3. The development and application of such systems requires that the technical teams maintain a close relationship with the operational users and have a clear understanding of their operational environment.

As a result, up to this point few reservoir managers have been able or willing to dedicate the considerable effort required to use new approaches and realize the benefits of improved forecast information.

Purpose

The purpose of the Integrated Forecast and Reservoir Management (INFORM) Project was to demonstrate increased water-use efficiency in Northern California water resources operations through the innovative application of meteorological/climate, hydrologic, and decision science.

Project Objectives

In accordance with its purpose, the particular objectives of INFORM are to:

  1. Implement a prototype integrated forecast-management system for the primary Northern California reservoirs, both for individual reservoirs as well as system-wide.

  2. Demonstrate the utility of meteorological/climate and hydrologic forecasts through near-real-time tests of the integrated system with actual data and management input by comparing its economic and other benefits to those accruing from current management practices for the same hydrologic events.

Project Outcomes

To achieve the general objectives of the INFORM project, the authors performed the following technical tasks:

  • Created the Oversight and Implementation Committee for project oversight and assistance with system implementation and tests.

  • Developed, implemented, and performed validation of climate and weather INFORM components for Northern California with historical data and real-time data.

  • Developed, implemented, and performed validation of hydrologic INFORM reservoir-inflow forecasts with historical and real-time data for all major reservoirs of Northern California.

  • Developed, implemented, and performed validation of decision INFORM components with historical and real-time data for the Northern California water resources management system.

  • Integrated INFORM system climate, hydrology, and decision components and performed initial operational tests producing real-time ensemble forecasts out to lead times of 16 days four times daily for the 2005-2006 wet season.

  • Performed assessments of the integrated forecast-decision system value via retrospective simulation experiments.

  • Held INFORM design, assessment, and training meetings with operational forecast and management agency staff.

Conclusions

There are several technical and specific conclusions that have been drawn from the outcomes of the project in the areas of meteorology/climate, hydrology, and decision science. These conclusions are detailed in the report (Chapter 7). The most important conclusion of the report is that, with available real-time availability of forecast information from the National Centers for Environmental Prediction and with real-time observed precipitation and temperature (as well as hydrologic model state values from the California Nevada River Forecast Center), integrated forecast-management systems are realizable as effective operational decision-support tools for management and planning of California water resources. Such systems assist water managers in translating forecasts and their uncertainty into a range of effective risk-based policies. In addition, these systems can advance current operational practices by (1) incorporating forecast uncertainty in decisions on a range of time scales, and (2) allowing for regional coordination of management decisions.

Recommendations

Perhaps the most important recommendation arising from this work is to continue the INFORM demonstration experiments for two or (more usefully) three additional operational seasons beyond the system "dry run" wet season of 2005-2006 in continued close collaboration with the forecast and management partner agencies in Northern California. These additional operational seasons are necessary for the reliable evaluation of the INFORM system performance and utility in specific situations, for the application of any system corrections and adjustments that appear necessary from system evaluation, for the establishment of a protocol for its operational use by the collaborating agencies, and for exploring alternative applications for the system that have been suggested by sponsor agencies.

A second overarching recommendation pertains to the use of the INFORM system in a stand-alone mode for climate change simulations. The INFORM system closely emulates several of the actual forecast and management procedures used in routine operations in Northern California. As such, it constitutes a realistic simulation system for impact analysis in this region using the output of state-of-the-science global climate models that predict climatic variability and change. Such impacts include potential future climatic influences on precipitation, temperature, and snowmelt and runoff patterns in the Sierra Nevada resolved on the scale of INFORM catchments (from hundreds to thousands of square kilometers); the effects of increased demand scenarios; and the effectiveness of alternative management scenarios for improved water-use efficiency.

Benefits to California

A significant benefit of this first phase of INFORM for Northern California is its contribution toward integrating operational water supply forecast and management activities by federal and state agencies toward increased water use efficiency. The mutual technology transfer and science cooperation between research centers and operational agencies is another. Lastly, even in its current prototype form, the INFORM system provides a unique resource for operational and management agencies in Northern California. These agencies may benefit by using this system to evaluate potential decision policies pertaining to the use of Northern California's water supply during real-time operations and for seasonal planning, both for present and future years.



Table of Contents

Preface.. iv
Abstract xvii
Executive Summary 1
1.0 Introduction 4
1.1. Background and Overview 4
1.2. Project Objectives 5
1.3. Feasibility Studies 7
1.4. Report Organization 11
2.0 Integrated System Design and Implementation 12
2.1. Overview of INFORM System 12
2.2. Processing of Available Operational NCEP Data 14
2.3. GFS-based Ensemble Forecasts 16
2.4. CFS-based Ensemble Forecasts 20
2.5. INFORM DSS Reservoir System 23
2.6. INFORM DSS Overview 25
2.7. INFORM DSS Implementation Aspects 27
2.7.1. Database 27
2.7.2. Data Processing and Utility Tools 27
2.7.3. Interface Functions 28
3.0 Weather and Climate Downscaling Models 29
3.1. Introduction 29
3.2. Formulation of Orographic Rainfall Enhancement Model 30
3.2.1. Potential Theory Updrafts 30
3.3. Evaluation of Orographic Rainfall Enhancement Model with Data 36
3.3.1. American River Watershed 36
3.3.2. Other INFORM Watersheds 37
3.4. Formulation of Surface Air Temperature Model 47
3.4.1. Model Equations 47
3.4.2. Model Domain and Input 53
3.5. Evaluation of Surface Air Temperature Model with Data 54
3.6. Formulation of Probabilistic Climate Forecast Downscaling 57
3.7. Evaluation of Probabilistic Climate Forecast Downscaling 58
3.7.1. Unconditional ESP 58
3.7.2. ESP Conditional on CFS Forecasts 68
4.0 Hydrologic Models 83
4.1. Introduction 83
4.2. Formulation of Hydrologic Model Components 83
4.2.1. Snow Accumulation and Ablation Model 84
4.2.2. Sacramento Soil Moisture Accounting Model (SAC-SMA) 86
4.2.3. Unit Hydrograph and Channel Routing Procedures 87
4.2.4. Stand-Alone Distributed Hydrologic Model 87
4.3. Hydrologic Model Application Basins 88
4.3.1. Basin Representations for CNRFC Operational Hydrologic Models 89
4.3.2. Basin Representations for the Stand-Alone Distributed Hydrologic Models 91
4.4. INFORM Hydrometeorological Database 99
4.5. Snow Model Sensitivities 100
4.5.1. Sensitivity to Temperature Data 100
4.5.2. Sensitivity to Model Parameters 103
4.6. Evaluation of Hydrologic Models 106
4.6.1. Performance Measures 107
4.6.2. Evaluation of CNRFC Operational Models 108
4.6.3. Evaluation of the Stand-Alone Hydrologic Model 118
5.0 Decision Support System 129
5.1. Background and Overview 129
5.2. Near-Real-Time Operations: Turbine Load Dispatching Model 129
5.3. Short-Range Reservoir Management 133
5.4. Mid-Range Reservoir Management 138
5.5. Long-range Planning 140
5.5.1. System Simulation Model 145
5.5.2. Simulation Model Validation 153
5.5.3. System Optimization Model 158
5.6. Scenario and Policy Assessment Models 172
5.6.1. Mid-range Scenario and Policy Assessment Examples 175
5.6.2. Long-range Scenario and Policy Assessment Examples 181
6.0 Assessments 192
6.1. Introduction 192
6.2. Reservoir Inflow Simulations 193
6.3. Precipitation and Temperature Forecasts for INFORM Catchments 194
6.3.1. MAP Ensemble Forecasts 200
6.3.2. MAT Ensemble Forecasts 206
6.4. Reservoir Inflow Forecasts 211
6.5. Overall Assessment of INFORM Real-Time Short-Range Forecasts 215
6.6. Integrated Forecast-Decision Assessments 219
7.0 Conclusions and Recommendations 228
7.1. Overarching Conclusions 228
7.2. Specific Conclusions 229
7.2.1. Forecast Component 229
7.2.2. Decision Component 231
7.3. Overarching Recommendations 234
7.4. Specific Recommendations 234
7.4.1. Forecast Component 234
7.4.2. Decision Component 235
7.5. Benefits to California 236
8.0 References 237
9.0 Glossary 242


Appendices

A: Summary of Proceedings for the INFORM Oversight and Implementation Committee Meetings
B: Validation Figures for the Application of the Downscaling Precipitation Model to the Folsom Lake Drainage
C: Reliability-Diagram Tables for CFS-Conditioned and Unconditioned ESP for INFORM Reservoir Inflows
D: INFORM Project Hydrometeorological Database
E: Plots from the Evaluation of the CNRFC Operational Hydrologic Model
F: Plots from the Evaluation of the INFORM Stand-Alone Distributed Hydrologic Model
G: Selected Reservoir, Hydropower Facility, and Demand Data
H: Historical Analog Streamflow Forecasting Model
I: River Index Calculation and Water Year Characterization



List of Figures

Figure 1. Study area in Northern California and major reservoirs used to manage water resources for conservation, energy production, and flood damage mitigation 6
Figure 2. Reliability diagram of the unconditional (ESP) and conditional (ECHAM3 5) prediction frequencies that Folsom Lake inflow is in the lower tercile of its distribution. Vertical bars indicate 95% bounds due to sampling uncertainty. The period of record is 1970–1992, with ensemble forecasts issued every five days. 9
Figure 3. Values of Folsom Lake multiple objectives for various forecast scenarios and using the decision model of the integrated forecast-control system. Results for ECHAM3-5 are similar to those for ECHAM3-10. 10
Figure 4. Schematic diagram of the distributed INFORM system configuration with data links indicated. Black arrows signify real-time data links, while grey arrows signify off-line data links 13
Figure 5. Schematic of INFORM forecast component processing 16
Figure 6. Schematic of processing flow associated with ingesting GFS ensemble forecasts and other information into the INFORM forecast component 18
Figure 7. Real-time spatial depiction of INFORM major reservoir inflow watersheds on a Google Earth display 21
Figure 8. Real-time ensemble-average 24-hr precipitation forecast over the INFORM domain superimposed on Google Earth displays. The precipitation scale is in inches/day. 21
Figure 9. Real-time INFORM short- and long-term ensemble forecasts for Folsom Lake inflows and for a forecast preparation time of 3/1/06 00Z 24
Figure 10. A schematic of the INFORM reservoir and river system 24
Figure 11. INFORM DSS modeling framework 25
Figure 12. Mean areal updraft for Folsom Lake watershed as a function of direction angle from North (shown in degrees) for a unit 700-mbar wind inflow. The arrows indicate the direction from where the 700-mbar wind is blowing, and the magnitude of the mean areal updraft as a fraction of the incoming wind magnitude (contours of equal mean areal updraft are shown as concentric circles with indicated magnitude). Terrain slope is averaged over 10 km intervals. 33
Figure 13. Linear regression equations between six-hourly downscaled (predictor) and observed estimates (predictant) of mean areal precipitation for the Folsom Lake sub-basins 38
Figure 14. As in Figure 13, but for daily data 39
Figure 15. Map of INFORM sub-basins. The sub-basins with the CNRFC code name are used for the evaluation of the orographic precipitation component. 40
Figure 16. Scatter grams of observed and simulated precipitation and associated regression lines and parameter values for INFORM region basins 42
Figure 17. Cumulative distribution functions of six-hourly mean areal precipitation amounts (observed in blue and simulated in red) for several basins of the INFORM region 44
Figure 18. Interpolated field (no model used) and downscaled field (using the temperature downscaling model) for the Northern California region for a specific 6-hour period in April 1992 (during the melting period). Both fields have a resolution of 10 km and are produced using the same 100 km-scale forcing information. 55
Figure 19. Six-hourly observed (red line and symbols) and simulated (red line and symbols) temperature for the Chester and Weed (WED) sites and for the wet season 1997–1998 56
Figure 20. Theory of ensemble precipitation forecast downscaling from CFS precipitation forecasts 59
Figure 21. Distribution of N-day inflow volume anomalies for Oroville Reservoir 61
Figure 22. Reliability diagram for Folsom Reservoir 30-day inflow volumes in the lower tercile (left-hand column) and upper tercile (right-hand column) of the observed distribution 62
Figure 23. Reliability diagram, as in Figure 22, but for Trinity Reservoir 30-day inflow volumes 62
Figure 24. Reliability diagram, as in Figure 22, but for Shasta Reservoir 30-day inflow volumes 63
Figure 25. Reliability diagram, as in Figure 22, but for Oroville Reservoir 30-day inflow volumes 63
Figure 26. Cumulative probability distribution functions of rain-plus-melt values segregated according to high (red solid line) and low (dashed blue line) CFS monthly precipitation forecasts for Trinity and for each month of the year 73
Figure 27. As in Figure 26, but for New Bullards Bar Reservoir drainage on the Yuba River 75
Figure 28. As in Figure 26, but for the Folsom Reservoir drainage on the American River 76
Figure 29. As in Figure 26, but for the Oroville Reservoir drainage on the Feather River 77
Figure 30. As in Figure 26, but for the Shasta Reservoir drainage on the Sacramento and Pit Rivers 78
Figure 31. Reliability diagram for Folsom Reservoir 60-day inflow volumes in the lower tercile (left-hand column) and upper tercile (right-hand column) of the observed distribution. ESP conditioned on CFS. 79
Figure 32. Reliability diagram, as in Figure 31, but for New Bullards Bar reservoir inflows 79
Figure 33. Reliability diagram, as in Figure 31, but for Oroville reservoir inflows 80
Figure 34. Reliability diagram, as in Figure 31, but for Shasta reservoir inflows 80
Figure 35. Reliability diagram, as in Figure 31, but for unconditional ESP with 15 ensemble members. Folsom reservoir inflows. 81
Figure 36. Reliability diagram, as in Figure 35, but for New Bullards Bar reservoir inflows 81
Figure 37. Reliability diagram, as in Figure 35, but for Oroville reservoir inflows 82
Figure 38. Reliability diagram, as in Figure 35, but for Shasta reservoir inflows 82
Figure 39. Location of four major Sierra Nevada reservoir watersheds within Northern California 88
Figure 40. Schematic structure of the simulation model of the American River drainage into Folsom Reservoir for the CNRFC operational hydrologic model 90
Figure 41. As in Figure 40, except representing the operational hydrologic model for the Trinity River drainage into Trinity Lake 90
Figure 42. As in Figure 40 except representing the operational hydrologic model for the Sacramento River drainage into Shasta Reservoir 91
Figure 43. As in Figure 40, except representing the operational hydrologic model for the Feather River drainage into Oroville Reservoir 92
Figure 44. (a) Representation of the stand-alone distributed model of the American River drainage to Folsom Reservoir. (b) Schematic structure of routing network in this representation. 95
Figure 45. As in Figure 44, except representing the distributed model for the Trinity Lake watershed 96
Figure 46. As in Figure 44, except representing the distributed model for the Shasta Reservoir watershed 97
Figure 47. As in Figure 44, except representing the distributed model for the Oroville Reservoir watershed 98
Figure 48. Schematic of the temperature (abscissa) relation to elevation (ordinate) by the moist adiabatic lapse rate 101
Figure 49. Snow water equivalent (SWE) in the Upper South Fork American River for water year 1980 as a function of systematic perturbation in the MAT: (a) 8 simulations in elevation zones with MAT that ranges from -5 to +3 oC; (b) areal weighted average of the perturbed simulation. The nominal simulation that uses the observed (unperturbed) MAT values is shown in red. 102
Figure 50. Snow water equivalent (SWE) in the Upper South Fork American River for water year 1980 as a function of random perturbation of the MAT. The unperturbed MAT simulation is provided in red. 102
Figure 51. Two-parameter snow depletion curve. Parameters a and b are the angles with respect to the x- and y-axis, respectively, as shown. 104
Figure 52. The nominal simulation of SWE (blue) compared to SWE corresponding to a 50% overestimation (red) and 50% underestimation (black) of the parameter value 105
Figure 53. Example of the observed (blue) and simulated (red) flows in cubic meters per second (m3/s) for Water Year 1971 for the total flow at Folsom Dam and the outlet of the sub-watersheds of the South, Middle, and North Forks of the American River 109
Figure 54. Daily flow simulated by the operational hydrologic model versus observed flow in m3/s for the American River catchments 111
Figure 55. Cumulative distribution of the observed (blue) and simulated (red) flows over the length of the simulation for the American River catchments and for the operational hydrologic model 112
Figure 56. Observed (blue) and simulated (red) duration curves of Box-Cox transformed flow for the American River and for the operational hydrologic model 113
Figure 57. Scatter plots of the simulated versus observed flows at a monthly scale for the American River catchments 114
Figure 58. Observed and simulated monthly mean flows (+/- 1 standard deviation shown by “x”) expressed as a fraction of the annual flow volume for the American River catchments 115
Figure 59. (a) Observed (black) and simulated (red) time trace of the spring pulse. (b) Annual differences between the observed and simulated spring pulse timing (given in days). 116
Figure 60. Daily snow water equivalent from snow sensors (dots) compared with simulated snow water equivalent from the operational model (solid line) for four different Water Years (1988–1991) and for the South Fork of the American River sub-watershed 117
Figure 61. Comparison of observed (FNF, in blue) and simulated (red) inflows to Folsom Reservoir for the INFORM stand-alone distributed hydrologic model 125
Figure 62. Mean daily observed (FNF) versus simulated inflows to Folsom Reservoir for the stand-alone distributed hydrologic model 125
Figure 63. Observed (FNF, blue) and simulated (red) cumulative inflows to Folsom Reservoir for Water Years 1961–1999 126
Figure 64. Monthly scatter plots of simulated inflows versus FNF flow for Folsom Reservoir in Box Cox transformed units for the stand-alone distributed hydrologic model 128
Figure 65. Observed (FNF) and simulated monthly mean inflows (+/- 1 standard deviation for Folsom Reservoir, expressed as a fraction of annual flow volume. Presented is the simulation with the stand-alone distributed hydrologic model. 128
Figure 66. Plant power generation as function of hourly discharge and reservoir level for a) Trinity, b) Shasta, c) Oroville, and d) Folsom 132
Figure 67. Plant daily generation as function of daily release and reservoir level for a) Trinity, b) Shasta, c) Oroville, and d) Folsom 136
Figure 68. Typical hourly power generation schedules for a) Trinity, b) Shasta, c) Oroville, and d) Folsom 137
Figure 69. Mid-range model example run for Trinity. A 3-month forecast horizon from 1/1/1981. 141
Figure 70. Mid-range model example run for Shasta. A 3-month forecast horizon from 1/1/1981. 142
Figure 71. Mid-range model example run for Oroville. A 3-month forecast horizon from 4/1/1981. 143
Figure 72. Mid-range model example run for Folsom. A 3-month forecast horizon from 1/1/1965. 144
Figure 73. American River spatial aggregation 154
Figure 74. Delta outflow comparisons 155
Figure 75. X2 Location comparisons 155
Figure 76. Reservoir Storage Comparisons: (a) Trinity, (b) Shasta, (c) Oroville, (d) Folsom, and (e) New Melones 158
Figure 77. Long-range inflow forecasts from March 1, 2006 167
Figure 78. Long-range inflow forecast vs. historical means 168
Figure 79. Planning tradeoffs: a) Carry-over storage vs. demand; b) Energy generation vs. demand 169
Figure 80. Reservoir storage and release sequences associated with tradeoff point 3 170
Figure 81. Energy generation sequences associated with tradeoff point 3 171
Figure 82. X2 location sequences associated with tradeoff points 3 and 5 172
Figure 83. Delta outflow associated with tradeoff point 3 173
Figure 84. Scenario and policy assessment models 174
Figure 85. Mid-range assessments: Trinity elevation and release sequences 176
Figure 86. Mid-range assessments: Shasta elevation and release sequences 177
Figure 87. Mid-range assessments: Oroville elevation and release sequences 178
Figure 88. Mid-range assessments: Folsom elevation and release sequences 179
Figure 89. Long-range assessments: Reservoir elevation sequences 184
Figure 90. Long-range assessments: Reservoir release sequences 185
Figure 91. Long-range assessments: Energy generation sequences 186
Figure 92. South export and deficit sequences 187
Figure 93. X2 location sequences 188
Figure 94. Delta outflow sequences 189
Figure 95. INFORM hydrologic model 6-hour simulations of Folsom reservoir inflow using CNRFC-estimated MAP and MAT time series for the period 10/5/2005–3/15/2006 (blue dashed line). The red line signifies the corresponding CNRFC full natural flow (FNF) estimates (observations). 195
Figure 96. As in Figure 95, but for the New Bullards Bar reservoir inflow on the Yuba River 196
Figure 97. As in Figure 95, but for the Oroville reservoir inflow 197
Figure 98. As in Figure 95, but for the Shasta reservoir inflow 198
Figure 99. As in Figure 95, but for the Trinity reservoir inflow 199
Figure 100. Highest and lowest averages of ensemble members over the indicated forecast lead time period at each valid time (blue dashed lines). The INFORM downscaling component produced these forecasts in real time for the upper modeling area of the American River North Fork. The panels also show the corresponding observed averages estimated by CNRFC (red lines). Forecast lead times range from 12 hours to 5 days. 202
Figure 101. As in Figure 100, but for the Middle Fork of the American River 203
Figure 102. (Upper six panels) As in Figure 100, but for the upper area of the Pit River catchment within the Shasta reservoir drainage area. (Lower six panels) as in Figure 100, but for the lower area of the Pit River catchment. 204
Figure 103. As in Figure 100, but for the upper area of the Sacramento River catchment with outlet at Delta, California 205
Figure 104. (Upper six panels) As in Figure 100, but for MAT over the upper area of the Pit River catchment within the Shasta reservoir drainage area. (Lower six panels) as in Figure 100, but for MAT over the lower area of the Pit River catchment. 207
Figure 105. As in Figure 104, but for the upper and lower areas of the Sacramento River with outlet at Delta, California 208
Figure 106. As in Figure 104, but for the North Fork Feather River with outlet at Pulga in the Oroville reservoir drainage 209
Figure 107. As in Figure 104, but for the Indian Creek in the Oroville reservoir drainage 210
Figure 108. Folsom highest and lowest ensemble member average reservoir inflow forecasts (blue dashed lines) for forecast lead times from 1 day (upper panel) to 5 days (lower panel). Corresponding CNRFC FNF estimates are shown in each case (red line). 212
Figure 109. As in Figure 108, but for the New Bullards Bar reservoir inflow on the Yuba River and for a forecast lead time of 12 hours 213
Figure 110. As in Figure 108, but for the Oroville reservoir inflow 214
Figure 111. As in Figure 108, but for the Shasta reservoir inflow 216
Figure 112. As in Figure 108, but for the Trinity reservoir inflow 217
Figure 113. As in Figure 109, but for Shasta reservoir inflows (upper panel) and for Folsom reservoir inflows (lower panel) and for a 2-day forecast lead time. Input of bias adjusted MAPs through a single factor. 223
Figure 114. Integrated, mid-range assessments: Trinity elevation and release sequences 224
Figure 115. Integrated, mid-range assessments: Shasta elevation and release sequences 225
Figure 116. Integrated, mid-range assessments: Oroville elevation and release sequences 226
Figure 117. Integrated, mid-range assessments: Folsom elevation and release sequences 227



List of Tables

Table 1. Reliability scores of forecasting Folsom Lake inflow volumes 9
Table 2. GFS Forecast Data for INFORM 17
Table 3. Statistical performance of simplified orographic model in wet season (1969–1992) 36
Table 4. Names and code numbers of drainage basins used in the evaluation of downscaled precipitation 40
Table 5. Frequency of occurrence of zero and low precipitation 41
Table 6. Second moment statistics of mean areal precipitation observations and simulations for basins of the INFORM region 46
Table 7. Second moment properties of simulates and observed mean areal precipitation for southwesterly and northwesterly wind events 47
Table 8. CFS grid points and watershed centroids (degrees) 69
Table 9. Tercile values for CFS-forecast monthly precipitation 69
Table 10. Validation results for Trinity 72
Table 11. Validation results for New Bullards Bar 72
Table 12. Validation results for Folsom 72
Table 13. Validation results for Oroville 74
Table 14. Validation results for Shasta 74
Table 15. Hydrologic Model Parameters 85
Table 16. Drainage areas of CNRFC major catchments 93
Table 17. Properties of stand-alone distributed model catchments 94
Table 18. Daily values of evapotranspiration demand for the Folsom Reservoir Drainage Area 99
Table 19. Nominal values of snow model parameters 103
Table 20. Performance statistics for the historical simulation of the operational hydrologic model 110
Table 21. Parameters of the snow and soil model components used for the Folsom Reservoir stand-alone distributed model simulation 120
Table 22. As in Table 21, except for the Trinity Lake inflow simulation 121
Table 23. As in Table 21, except for the Shasta Reservoir inflow simulation 122
Table 24. As in Table 21, except for the Oroville Reservoir inflow simulation 123
Table 25. Performance statistics for the historical simulation of the INFORM distributed hydrologic model 124
Table 26. Mid-range assessment statistics 180
Table 27. Long-range assessment: Reservoir statistics 190
Table 28. Long-range assessment: Hydropower and spillage statistics 190
Table 29. Long-range assessment: Water supply statistics 191
Table 30. Long-range assessment: Maximum X2 location statistics 191
Table 31. Integrated, mid-range assessments statistics 222
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