1. Sacramento Valley Simulation Model (SVSim) and C2VSim
Linda Bond
California Department of Water Resources
Division of Integrated Regional Water Management
Thank you for the opportunity to talk today about the new Sacramento Valley Simulation Model (SVSim). SVSim is based on the existing DWR model, C2VSim, which I’ll also touch on today.
GRA and DWR Workshop
Stream Depletion Through the SGMA Lens:
Practical Solutions for a Complex Problem
August 29, 2017

2. Purpose of SVSim Model
Water Transfer Program - project-specific impacts of groundwater substitution transfers on stream depletion in the Sacramento Valley
SGM Program – stream depletion, water budgets, land subsidence, and water management scenarios
The purpose of the model was to develop a tool for Water Transfer Program for evaluating project-specific impacts of groundwater substitution transfers on stream depletion in the Sacramento Valley
In addition, the model, it’s data, and methods, should be useful for the Sustainable Groundwater Management Program for evaluating stream depletion, awa water budgets, land subsidence, and water management scenarios

3. SVSim and C2VSim Integrated Groundwater-Surface Water Models Using DWR’s IWFM Code
SVSim and C2VSim Differences:
Sacramento Valley vs. Central Valley
SVSim Explicitly simulates Water Transfer Wells
SVSim Grid and Layering
Updated Hydrogeology (new approach is being adopted for new versions of C2VSim)
SVSim and C2VSim are similar in most respects.
Both Models are Integrated Groundwater-Surface Water Models
They both use the Integrated Water Flow Model Code developed by DWR (IWFM)
They both use the same hydrologic inputs.
Many of you are familiar with C2VSim, so I’m going to focus on the new features that have been developed for SVSim
Model location: SVSim covers the Sac Valley while C2VSim covers the entire Central Valley
SVSim Explicitly simulates the Sacramento Valley Water Transfer Wells
Because technical focus of SVSim is to improve the calculation of stream depletion caused by pumping
3. SVSim has a finer grid adjacent to and beneath rivers and streams than C2VSim
4. Because Hydrogeology is a primary factor that controls the rate of stream depletion, we updated hydrogeology, using a new approach.
This new approach is now being adopted for the new versions of C2VSim.

4. SVSim Model Location Sacramento Valley
Physical boundaries on east, west, and north and at the base
Southern boundary flows specified with C2VSim
The SVSim Model encompasses entire area affected by groundwater pumping associated with water transfer projects in the Sacramento Valley
Bounded on the east, west, north, and at the base by physical boundaries.
Only artificial boundary is on the south, located beyond the Delta, where we will specify boundary flows using C2VSim.

5. SVSim Water Transfer Wells
Explicitly Simulates Groundwater Substitution Pumping
Extended southern boundary ensures effect of project pumping is not distorted
The model includes all of the Sac Valley Water Transfer projects.
The transfer project service area are shown in color and their wells are indicated by the small black dots.
The model expli….
The model extends 25 miles south of transfer projects to ensure analysis of project pumping is not distorted by boundary effects
If the area of influence of wells intercepts an artificial boundary, stream depletion will be overestimated or underestimate and will also distort the calibration of the model.
The intersection of drawdown and artificial boundaries is discussed in the literature, including the MODFLOW User Manual and Anderson/Woessner text on Applied GW Modeling
This is important because we want to ensure that the pumping we’re analyzing is fully within the model boundaries.
Southernmost well is 25 miles from the specified flux boundary
In other words, pumping has no affect at the boundary, which would distort the results of the analysis.

6. SVSim Grid and Layering
Finer grid adjacent to rivers
Multiple thin layers beneath streams improves accuracy of calculation of stream depletion
To improve the calculation of stream depletion, SVSim has been redesigned to have a very fine grid along streams (rivers and creeks) and multiple thin layers beneath the streams.
Layering is often thought of as just a way to represent geologic units or groundwater production zones. In fact, the grid and layering has an impact on how well the model can simulate flow gradients.
To determine how fine the grid and layering should be, we conducted numerical experiments.
Layering turned out to be the most sensitive parameter, particularly for wells located at a distance from streams. (1 mile)
The Baseline simulation, which represents a 20-layer model, produces stable stream depletion hydrographs.
We compared stream depletion hydrographs for models with different number of layers to this baseline.
A model with a thick top layer, represented by our 4 layer test model, would overestimate stream depletion for the first 5 years and up to 60% during the first year.
A 20 layer model was not feasible because of computer runtimes – the model would be too big.
So, we selected a 9-layer model design because is produced results almost as accurate as the baseline model and was computationally feasible.
9 Layers

7. LAYER-CAKE GROUNDWATER SYSTEM
Updated Hydrogeology Primary Controlling Factor for Rate of Stream Depletion Caused by Pumping
Alluvial basins do not have layer-cake hydrogeology
We also updated the hydrogeology for the model
Hydrogeology – hydraulic conductivity and storativity – are Primary Controlling Factors for groundwater flow, and, correspondingly, the Rate of Stream Depletion Caused by Pumping
We know, based on geologic and geomorphic studies that most GW basins in CA are alluvial basins – deposited by streams and overland flow.
Alluvial basins contain a matrix of Discontinuous Stream channels, flood basin deposits, and alluvial fans.
Alluvial basins are not structure like the textbook examples of layer-cake aquifers and aquitards.
They are not equivalent, and We know that GW flows differently through these two systems.
Source: Virtual campus in hydrology and water resources (
Chapter 3, Aquifers (Geological Conditions of Aquifers)
Figure The subenvironments of an alluvial flood plain cut by a meandering river channel (a) and of a braided alluvial outwash plain (b).
Fluvial deposits are the materials laid down by physical processes in river channels or on flood plains. The materials are also known as alluvial deposits. Fluvial materials occur in nearly all regions. In many areas aquifers of fluvial origin are important sources of water supply.
Alluvial deposits are basically of two kinds, whose characteristics are largely related to the morphology of the river channels which deposited them:
high-sinousity meandering channels (Figure 3.5a)
low-sinuosity braided channel complexes (Figure 3.5b)
“TEXTBOOK” EXAMPLE OF
LAYER-CAKE GROUNDWATER SYSTEM
SACRAMENTO VALLEY ALLUVIAL GROUNDWATER SYSTEM

8. Hydrogeologic Conceptual Model
Each Time-Correlated Unit Contains Matrix of Hydrogeologic Units
Geology ≠ Hydrogeology
We can see the pattern of the alluvial deposits in Surface Geology maps, road cuts and well logs.
This simplified figure shows the interweaving pattern of coarse stream deposits in yellow, clayey flood plain deposits in red, and mixed sand and clay alluvial fan deposits in orange. Variations of this pattern fills the entire basin.
This exposure of the Tuscan Formation shows the intermixture volcanic flows, channelized sand deposits, and clays from eroded ash.
Each Time-Correlated Geologic Unit Contains a Matrix of Hydrogeologic Units
In most cases, Geology ≠ Hydrogeology
So, our challenge was to develop a methodology that addresses the complexity of the hydrogeology
Graham Fogg calls this an connected-network.
California’s Alluvial Groundwater Basins
In most cases Geologic Units do not correlate to a Hydrogeologic
Time-Correlated Units Are Not Hydrogeologic Units
Rather than layer-cake geology, Graham Fogg describes alluvial systems as a “Connected Network” of discontinuous stream channels imbedded in a flood basin and alluvial fan deposits.
Maps of the Surface Geology Show a depositional pattern of interfingered
Stream Channels
Flood Basins
Alluvial Fans
Tuscan Formation Contrasting Hydrogeologic Deposits
Dense volcanic flows
Channelized sand deposits
Distal clays from eroded ash
Both he and the USGS have developed methods to structure models to represent this kind of GW structure.
GRAHAM FOGG: NEW CONCEPTUAL MODEL: CONNECTED NETWORK
Surface
Geology
Tuscan Formation

9. Translating Conceptual Model into Numerical Model Input
Characteristics of Alluvial System
Distribution of Coarse and Fine Sediments
Hydraulic Properties of Sediments
Depositional Structure of the Alluvial Basin
Methodology that addresses the complexity of the hydrogeology
We looked at how the hydrogeology could be simplified while preserving fundamental characteristics of the alluvial basin (geomorphology)
It’s not layer-cake geology so we can’t construct the typical cross sections where you assume deposits are laterally continuous
For each element of the model
Components of System
We simplified the deposits into a binary system (coarse and fine)
Distribution of Sediments (coarse and fine)
Hydraulic properties (of coarse and fine materials)
Depositional Structure (applied parameters that reflected lens-and-matrix deposition structure)
Characteristic Combined
To Calculate Aquifer Parameters For Model
With Power-law Averaging Equation

10. Distribution of Sediments: Texture Analysis
To determine the distribution of sediments in the basin, we performed a texture analysis.
We reviewed almost 4,700 well and boring logs, identifying the sequence and thickness of coarse and fine-grained materials
This figure show the location of the wells and boring logs
Over 2000 logs compiled USGS (red)
1500 high-quality borings DWR’s Div of Flood Management Levee Evaluation Program (blue)
760 logs collect by DWR including high quality DWR-installed and grant funded monitoring wells (green)
240 logs provided by YCWA (yellow)
Example of one of the High-Quality DWR Project Well Log Used in Texture Analysis
Classification:
Coarse grained = sands and gravels
Fine grained = silts and clays
4,700 LOGS ANALYZED

11. Hydraulic Properties of Coarse-grained and Fine-grained Sediments
Parameter
Material
Coarse-Grained
Fine-Grained Kh
Horizontal hydraulic conductivity
22 to 75
ft/d
3.3 to 11
Vertical Anisotropy (Kh/Kv)
10:1 Sy
Specific yield
0.15
0.05 Ss
Specific storage
2.0x10-6
1/ft
2.2x10-4
Once we identified the distribution of coarse and fine sediments, the next task was to assign hydraulic properties coarse and fine materials
Extensive data is available, in the form of specific capacity tests, for the Sacramento Valley. So, for Kh, we were able to develop depth-correlation analysis for the Kh for both coarse and fine grained materials.
We relied on published reports for the assignment of Vertical Anisotropy, Sy, and Ss.
Values Based on Depth-Correlation Analysis of 1500 Specific Capacity Tests
Values Based on Published Reports And Papers Applicable to Sacramento Valley

12. Based on 1500 Specific Capacity Tests
Example of Depth-Correlation Analysis
Hydraulic Conductivity Coarse-grained Sediments
Values Based On Analyses of field tests and Published Reports Applicable To Sacramento Valley
1. HYDRAULIC CONDUCTIVITY DEPTH-CORRELATION ANALYSIS OF 1,500 SPECIFIC-CAPACITY TESTS
Example of data distribution: Map showing specific capacity for wells with screen mid-points between 250 to 300 feet
Specific capacity can be translated into the transmissivity of the aquifer materials within the screened interval
Extensive data are available that inform the horizontal hydraulic conductivity of the groundwater system underlying the Sacramento Valley. Those data are the results of specific-capacity tests documented in the Well Completion Reports submitted by drillers to DWR.
Estimates of the horizontal hydraulic conductivity of the coarse-grained and fine-grained fractions of the aquifer material were extracted from reported test results. This resulted in a dataset of hydraulic conductivity values at about1,500 wells within the Sacramento Valley. The dataset in turn was analyzed to identify possible geographic and vertical patterns of horizontal hydraulic conductivity of coarse-grained and fine-grained fractions of aquifer material within the valley.
Based on 1500 Specific Capacity Tests

13. Depositional Structure of the Alluvial Basin
p = empirical parameter
that relates to connectivity
of flow paths within
groundwater system
NUMERICAL EXPERIMENTS:
LENSES MATRIX
RESULTS:
p = 0.93 for KH
p = for KV
Numerical experiments used to identify the factor that corresponds to a matrix of coarse and fine sediments.
Connected Hydrogeologic Units
The depositional structure of the basin– connectivity of flow paths within groundwater system-is reflected in the Power Law equation exponent.
We conducted a series of numerical experiments to determine the values that would best represent horizontal and vertical connectivity with the alluvial aquifer.
POWER-LAW EXPONENT
OBLIQUE VIEW OF FINITE ELEMENT MESH FOR NUMERICAL EXPERIMENTS
p = empirical parameter - that relates to the connectivity of flow paths within the groundwater system
Depositional structure of the groundwater system For the bulk horizontal hydraulic conductivity, p = 0.93 reasonably represents a range of textural bimodal structures, where the value is the average of the experimental results. For the bulk vertical hydraulic conductivity, p = reasonably represents a range of textural structures.

14. Updated Hydrogeology and Redesigned Grid and Layers
Resulting initial aquifer parameters for the model
Distribution of hydraulic conductivity
Example: Layer 1 Values
Improved Accuracy of
Stream Depletion Calculations
Layer 1

15. Using SVSim for GSPs DATA MODEL METHODS
DATA, METHODS, AND MODEL CAN BE USED FOR GSPs for the analysis of stream depletion and development of models
METHODS