Hydrologic modeling to quantify watershed functioning and predict

the sensitivity to change

A discussion of ideas towards an integrated Water Sustainability and Climate Project

David G TarbotonUtah State University

dtarb@usu.eduwww.engineering.usu.edu/dtarb

Outline

• Some philosophy– Question driven– Appropriate simplification

• An example

• A framework for thinking about water balance and change

• Concluding thoughts

My Research Focus

• Hydrologic Information System• Terrain Analysis Using Digital Elevation Models• Climate Change Impacts on Hydrology and Stream Ecology• Snow melt modeling• Modeling the impacts of land cover change on streamflow• The Great Salt Lake• Trends in Streamflow

Advancing the capability for hydrologic prediction by developing models that take advantage of new information and process understanding enabled by new technology.

The questions that we ask as scientists shape everything that follows. They can lead us to see the world in new ways, or mundane ones. They can spur the development of new approaches, or the recyclingof established ones. They can focus our attention in useful directions, or leave us wandering aimlessly.

WATERS Network: How can we protect ecosystems and better

manage and predict water availability and quality for future generations, given changes to the water

cycle caused by human activities and climate trends?

http://www.watersnet.org/docs/WATERS_Network_SciencePlan_2009May15.pdf

Grand ChallengesHydrologic Sciences:

Closing the water balance

Social Sciences: People, institutions, and

their water decisions

Engineering: Integration of built environment

water system

Measurement of stores, fluxes, flow paths and

residence times

Water quality data for water throughout natural

and built environment

Synoptic scale surveys of human behaviors and

decisions

How is fresh water availability changing, and

how can we understand and predict these changes?

How can we engineer water infrastructure to be

reliable, resilient and sustainable?

How will human behavior, policy design and institutional decisions affect and be affected by changes

in water?

Resources needed to answer these questions and transform water science to address the Grand Challenges

http://www.watersnet.org/docs/WATERS_Network_SciencePlan_2009May15.pdf

From the Preface:

•I explore and evaluate the biophysical relationship between ambient climate and the form and function of the associated vegetation•Land surface atmosphere boundary conditions are “interactive”•Theoretical generation of these atmospheric boundary conditions which are necessarily highly idealized•Monteith (1981) “progress can be made only if the number of variables is held to a minimum”•Many details neglected•XXX may find approach naïve and be offended•YYY may welcome the reduction of an intricate multidisciplinary problem to a small set of simple, albeit approximate rules•In multidisciplinary endeavors, all the rich scientific detail of each contributing field can’t be retained in their joining, lest the resulting complexity negate the utility of the result

We need to strike a tractable balance in the representation of process

complexity (from different disciplines)

• Building an integrated model (and associated data/information system):– is a way to encode and encapsulate

knowledge and test hypotheses– is a way to formalize communication across

disciplines– is a journey in research– is a process of discovery– should involve frequent iteration and adaption

Example: A distributed catchment-scale water resources planning model

The Water Resources Inventory Area 1 (WRIA 1) Nooksack hydrologic model for decision support

Evapotranspiration(ET)

Rai

nfa

ll-R

uno

ff T

ran

sfor

ma

tion

Snow

Up

we

lling

Re

char

ge

Th

rou

gh-

fall

Precipitation Input

Rain/snow separation

Snow

Canopy interception store

Vadose zone Soil Store

Groundwater Saturated Zone

Channel flow

Snowfall or rain with snow present

Rain

Su

rfa

ce r

uno

ff

Sn

ow

me

lt

Residual PET not satisfied from interception

Ba

seflo

w

Other Weather Inputs (Temperature, Wind,

Humidity)

Water Management

Irrigation

Withdrawals

Non Irrigation Users

Non Irrigation Return Flows

Sprinkler

Drip

Groundwater pumping

Groundwater return flow

Surface return flow

Surface withdrawal

Potential Evapotranspiration

(PET)

Artificial Drainage

Integrated model of Hydrologic, Water Management and Consumption processes at each “catchment”

• Competition for water resources among users

• Human activities can alter water balance having effects on stream ecosystems and water quality

• Simulation modeling used to quantify the likely impacts of water management choices

• Enhanced TOPMODEL (Beven and Kirkby, 1979 and later) applied to each subwatershed model element.

• Kinematic wave routing of

subwatershed inputs through

stream channel network.• Vegetation based

interception component.• Modified soil zone• Infiltration excess• GIS parameterization

Rainfall – Runoff Transformation

Soil parameter look up by zone code

Soil derived parameters

TEXTURE CLASS

TEXTURE NAME

ksat

(m/hr)porosity n f (m) b

fc

wilt

1

21 sand 0.6336 0.395 0.1 4.1 0.173 0.068 0.222 0.1052 loamy sand 0.5616 0.410 0.1 4.4 0.179 0.075 0.231 0.1043 sandy loam 0.1249 0.435 0.2 4.9 0.248 0.115 0.187 0.1344 silt loam 0.0259 0.485 0.8 5.3 0.368 0.180 0.117 0.1885 silt 0.0259 0.485 0.8 5.3 0.368 0.180 0.117 0.1886 loam 0.0250 0.451 0.5 5.4 0.313 0.155 0.138 0.1587 sandy clay loam 0.0227 0.420 0.3 7.1 0.299 0.175 0.121 0.1238 silty clay loam 0.0061 0.477 0.4 7.8 0.357 0.219 0.120 0.1389 clay loam 0.0088 0.476 0.6 8.5 0.391 0.250 0.085 0.140

10 sandy clay 0.0078 0.426 0.2 10.4 0.316 0.220 0.110 0.09611 silty clay 0.0037 0.492 0.5 10.4 0.408 0.284 0.084 0.12512 clay 0.0046 0.482 0.4 11.4 0.400 0.287 0.082 0.11313 Organic materials 0.6336 0.395 0.1 4.1 0.173 0.068 0.222 0.10514 Water 0.0004 1.000 0.0 1.0 0.003 0.000 0.997 0.00315 Bedrock 0.0004 0.100 0.5 15.0 0.088 0.068 0.012 0.02016 Other 0.0004 0.400 0.3 7.0 0.276 0.160 0.124 0.115

silt loamsilt loamsilt loamsilt loam

silty clay loamsilty clay loam

silty claysilty claysilty claybedrock

Table of Soil Hydraulic Properties – Clapp Hornberger 1978

0.1170.1170.1170.1170.1200.1200.0840.0840.0840.012

1

Depth weighted average

zfoeKK

KExponential decrease with depth

0.02600.02600.02600.02600.00600.00600.00370.00370.00370.0000

…

ZONE CODE f Ksat f (m)

1

2

4 4.43 222.09 0.68 0.12 0.1710 2.05 228.11 0.76 0.11 0.1811 46.23 681.10 0.39 0.13 0.1414 1.06 284.00 0.72 0.11 0.1717 1.06 1207.37 0.23 0.18 0.1321 1.06 350.10 0.45 0.14 0.1626 5.33 211.84 0.70 0.11 0.1730 2.04 233.59 0.75 0.11 0.1834 1.06 3.60 0.01 1.00 0.0047 2.10 240.09 0.72 0.12 0.1888 29.82 702.70 0.19 0.15 0.1291 18.20 154.94 0.44 0.11 0.1494 10.39 201.74 0.46 0.13 0.1596 1.06 242.40 0.42 0.13 0.1599 1.06 259.20 0.79 0.12 0.19

102 38.85 158.26 0.43 0.08 0.12

Soil Grid LayersJoined to Polygon Layer

f & K

Zone Code Polygon Layer

VEG CLASS CC (m) CR Albedo Description

0 0 1 0.23 unclassified1 0.003 3 0.14 Evergreen Needleleaf Forest2 0.003 3 0.14 Evergreen Broadleaf Forest3 0.003 3 0.14 Deciduous Needleleaf Forest4 0.003 3 0.14 Deciduous Broadleaf Forest5 0.003 3 0.14 Mixed Forest6 0.002 2 0.2 Closed Shrublands7 0.0015 1.5 0.2 Open Shrublands8 0.0015 1.5 0.2 Woody Savannah9 0.0015 1.5 0.2 Savannahs

10 0.001 1 0.26 Grasslands11 0.001 1 0.1 Permanent Wetlands12 0.001 1 0.26 Croplands13 0.001 1 0.3 Urban/Developed14 0.0015 1.5 0.2 Natural Vegetation15 0.001 1 0.6 Snow and Ice16 0 1 0.2 Barren or Sparsely Vegetated17 0 1 0.08 Water Bodies

Vegetation derived parameters

Historic (pre-settlement)

Existing

Water

Ice/Snow

Low Intensity Residential

High Intensity Residential

Commercial/Industrial/Transportation

Bare Rock/Sand/Clay

Quarries/ Strip Mines/ Gravel Pits

Transitional

Deciduous Forest

Evergreen Forest

Mixed Forest

Shrubland

Orchards/Vineyards/Other

Grassland

Pasture/Hay

Row Crops

Small Grains

Fallow

Urban/Recreational Grass

Dairy

Woody Wetlands

Emergent Herbacious Wetlands

Water

Ice/Snow

Low Intensity Residential

High Intensity Residential

Commercial/Industrial/Transportation

Bare Rock/Sand/Clay

Quarries/ Strip Mines/ Gravel Pits

Transitional

Deciduous Forest

Evergreen Forest

Mixed Forest

Shrubland

Orchards/Vineyards/Other

Grassland

Pasture/Hay

Row Crops

Small Grains

Fallow

Urban/Recreational Grass

Dairy

Woody Wetlands

Emergent Herbacious Wetlands

Distributed Energy Balance Snowmelt Model

)W/W(A/WW maxaafasc

mpgh

elelisnQQQQ

QQQQ

dt

dU

EMPPdt

dWrsr

sc

Luce, C. H. and D. G. Tarboton, (2004), "The Application of Depletion Curves for Parameterization of Subgrid Variability of Snow," Hydrological Processes, 18: 1409-1422, DOI: 10.1002/hyp.1420.

Area subject to artificial drainage

Water ManagementSources•Reservoir•Groundwater•Stream

•Withdrawal limited by availability and right priority

Reservoir

Stream

Uses•IrrigationoSoil moisture demand

driven•Non IrrigationoPer capita driven

•Diversions

Urban Agriculture

Groundwater

Precipitation Input and Interpolation

Streamflow gauges used in calibration

Calibration

67

14

2 35

89

11

4

54

40

12

38

67

4822

44

88

175

97

57 55

24

7556

128

73

13

47

32

176

84

3033

168

392627

17

49

6872

104

70

92

34

98

50

85

151

79

109

20

90

16

152159

10

23

87

6365

166171

51

89

145

53

116

36

124

28

148

93

66

164

43

5877

172

127

8081

31

60

154

35

76

153

9994

74

120

2515

180

41

149

96

62

78

177

126

101

8391

167

115

179

150

114

135

18

52

156

95

105123

147

21

86

4245

82

163170

37

125

138

5971

137

155

102

29

117

200

129 130

100

64

173

113

165161

19

4669

61

141

178

157

112

169

106103

174

0 105Miles

±1961-2005

NWM/Historic

0.95 - 0.96

0.96 - 1.00

1.00 - 1.05

1.05 - 1.87

Figure 4. Ratio of simulated existing streamflow with no water management to simulated historic streamflow, 30 year average over the years 1961-2005 at each node of the WRIA 1 surface water quantity model.

The impact on streamflow of present land use

67

14

2 35

89

11

4

54

40

12

38

67

4822

44

88

175

97

57 55

24

7556

128

73

13

47

32

176

84

3033

168

392627

17

49

6872

104

70

92

34

98

50

85

151

79

109

20

90

16

152159

10

23

87

6365

166171

51

89

145

53

116

36

124

28

148

93

66

164

43

5877

172

127

8081

31

60

154

35

76

153

9994

74

120

2515

180

41

149

96

62

78

177

126

101

8391

167

115

179

150

114

135

18

52

156

95

105123

147

21

86

4245

82

163170

37

125

138

5971

137

155

102

29

117

200

129 130

100

64

173

113

165161

19

4669

61

141

178

157

112

169

106103

174

0 105Miles

±1961-2005

Existing Managed/NWM

0.52 - 0.80

0.80 - 0.90

0.90 - 0.96

0.96 - 1.04

1.04 - 2.57

Figure 19. Ratio of simulated streamflow under existing conditions to simulated streamflow under existing conditions without water management.

The impact on streamflow of present water management and use

Jan70 Jan71 Jan72 Jan73 Jan74 Jan75 Jan760

10

20

30

40

50

60

cfs

UsersNo users

Jan70 Jan71 Jan72 Jan73 Jan74 Jan75 Jan760

5

10

15

20Drainage 87 user withdrawals

cfs

IrrigationSelf Supplied ResidentialSelf Supplied CITDairy

Figure 24. Streamflow at ProjnodeID=164, Drainage 87, Deer Creek.

Figure 25. Existing conditions simulation of user withdrawals from Deer Creek Drainage (Drainage 87)

Jan60 Jan70 Jan80 Jan90 Jan00 Jan100

0.5

1

1.5

2x 10

7 Lake Whatcom active storage

m3

ExistingFBO

Jan70 Jan71 Jan72 Jan73 Jan74 Jan75 Jan760

200

400

600

800

1000

1200

1400

cfs

ExistingFBO

Existing and Full Build Out scenario simulations of Lake Whatcom active storage.

Discharge from Lake Whatcom (Node 246).

Jan70 Jan71 Jan72 Jan73 Jan74 Jan75 Jan760

200

400

600

800

1000

1200

cfs

ExistingHistoricFull Build Out

Figure 35. Simulated Historic, Existing and Full Build Out Bertrand Creek Streamflow (ProjNodeID=515)

Table 1. Bertrand Water Balance: 1961-2005.

Annual inches of water over area of drainage Historic

Existing NWM Existing

Full Build Out

(1) Precipitation 57.87 57.87 57.87 57.87 (2) Evapotranspiration 33.49 26.08 29.26 28.47 (3) Streamflow (drainage outlet) 24.37 31.79 28.17 27.94 (4) Baseflow (included in streamflow) 21.2 24.7 22.7 22.4 (5) Irrigation Withdrawals 0.00 0.00 5.80 5.71 (6) Non Irrigation Withdrawals 0.49 1.62 (7) Return flows 0.04 0.15 Closure (1)-(2)-(3)-(6)+(7) 0.01 -0.01 -0.02 -0.01 Flow Rate (cfs) Streamflow 75.32 98.24 87.06 86.33 Self Supplied Residential 0.41 0.62

Self Supplied Commercial, Industrial and Transportation 0.79 4.11 Irrigation 17.92 17.63 Dairy 0.30 0.29 Return Flows 0.13 0.47

ProjNode 401 October

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0 5 10 15 20 25 30 35

cfs

Exc

eed

ance

Existing

Existing NWM

Historic

Full Buildout

ProjNode 401 March

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0 20 40 60 80 100 120 140 160

cfs

Exc

eed

ance

Existing

Existing NWM

Historic

Full Buildout

ProjNode 515 October

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0 10 20 30 40 50 60 70

cfs

Exc

eed

ance

Existing

Existing NWM

Historic

Full Buildout

ProjNode 515 March

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0 50 100 150 200 250 300

cfsE

xcee

dan

ce

Existing

Existing NWM

Historic

Full Buildout

Figure 12. Flow duration curves for October and March in Bertrand Creek

Impact on Water Balance

Jan60 Jan70 Jan80 Jan90 Jan00 Jan100

200

400

600

800

1000

1200

1400

1600

1800C

umu

lati

ve i

nch

es

PQ Existing NWMQ HistoricE Existing NWME HistoricArtificial Drainage

Deer Creek cumulative water balance components simulated under Historic and Existing conditions without water management.

Impact of a trans-basin diversion

Feb75 Mar75 Apr75 May75100

200

300

400

500

600

700

cfs

With DiversionNo management

Streamflow at ProjnodeID=185, Drainage 109, location of Middle Fork Diversion

Jan70 Jan71 Jan72 Jan73 Jan74 Jan75 Jan760

20

40

60

80

100

120

cfs

With DiversionNo management

Streamflow at ProjnodeID=519, Drainage 163, location where Middle Fork Diversion discharges into Anderson Creek.

The impact on streamflow of future development

Ratio of mean streamflow simulated under Full Buildout conditions to mean streamflow simulated under existing conditions.

Hydrology

Biogeochemistry

Engineered treatment works

Water resources infrastructure

Geomorphology

Human behaviorHuman

systems

Natural systems

Demands &

actions on

water

Policies and facilities

Hydrologic /

Biogeochemical

interactions

Landscape & climate

changes

Hydrologic /

Geomorphologic

Interactions

Infrastructure interactions

Water demands & discharges

We need to understand the overall functioning of coupled natural and human system water

systems

Humid AridEnergy Limited Water Limited

R/P

A general framework for thinking about the overall water balance and change impacts

S=P-Q-E P=Q+EE/P

Eva

pora

tive

Fra

ctio

n

Dryness (Available Energy /Precip)

1

E=R Energy limited upper bound

E=P Water limited upper bound

Q/P

Following Budyko, M. I., (1974), Climate and Life, Academic, San Diego, 508 p.

Budyko [1974] partitioning of input water P into the evapotranspiration fraction, E/P, the residual of which is discharge Q. Dryness or aridity is quantified in terms of R/P. As dryness increases, the evapotranspiration fraction increases. For the same R/P the evaporative fraction is greater when retention is greater as retained water has more opportunity to evaporate or transpire.

Eva

potr

ansp

iratio

n fr

actio

n

Dryness (available energy /precip)

1

humid arid

energy limited water limited

R/P

E/P E = R : energy limited upper boundlarge

small

Retention or Residence time

medium

E = P : water limited upper bound

Milly/Budyko Model – Framework for predictions and hypothesis testing

Milly, P.C.D. and K.A. Dunne, 2002, Macroscale water fluxes 2: water and energysupply control of their interannual variability, Water Resour. Res., 38(10).

Increasing Retention/Soil capacity

Q/P

Increasing variability in P – both seasonally and with storm events

Increasing variability in soil capacity or areas of imperviousness

Precise observations of Precipitation, Runoff, Soil Moisture, Energy Balance, Water Storage required to discriminate among these hypotheses

• Explains 88% of geographic variance

• Remaining 12% difference is consistent with uncertainty in model input and observed runoff

Uncalibrated Runoff Ratio

Low

High

Milly, P. C. D., (1994), "Climate, Soil Water Storage, and the Average Annual Water Balance," Water Resources Research, 30(7): 2143-2156.

Some suggestions for working together to solve regional water problems

Pick a place. Synergy from multiple studies in a common location.

Compelling Science. Societal importance. Shared data systems. Long term commitment.

GSL Level,GSL Level,Volume, &Volume, &

AreaArea

GSL SystemGSL System

Air Humidity

Lake Evaporation

Surface Salinity &

Temperature

Soil MoistureAnd

Groundwater

Air Temperature

Solar Radiation

Mountain Snow pack

Watershed Evapotranspiration

GSL Salt Load

Land Cover Land Use

Precipitation

Development, Growth, Water Resources

Management

Pumping

Summary thoughts on stimulating interdisciplinary water science collaboration

• Communication• Enabling Technology

– Putting data in the system should make an individual researchers job easier

– Enhance sharing by enabling analysis otherwise not available

• Maps and Geographic Information Systems are important for synthesis

• Advancement of water science is critically dependent on integration of water information

The Place

Upmanu Lall

A land of promise, where the glistening snows of winter give rise to a rumble of springtime meltwater and a silent explosion of vegetation that gradually regulates the water supply till the summer sun cooks all but the hardiest, leaving towering pines gently whispering in dry summer winds. Every drop that survives traces a pathway to union with the terminal lake. It is in this land that the human hand increasingly redirects flow, seeking to prolong nature’s dance, promoting alfalfa at the expense of wetlands. The byproducts of a thriving human civilization in this contrived oasis increasingly change the radiative equilibrium, the nutrient cycles and the fundamental chemistry of all media they come in contact with, increasing the fragility of the tenuous balance between water and life. Of all environments on Earth, it is the high-mountain desert that presents us with the grandest and most delicate of playgrounds, where we must learn the rules of Nature and adapt our ways to the many climes that powerfully shape the terrain. As we ponder issues of sustainability and change in the 21st century, the Great Salt Lake Basin Observatory emerges as a critical window into life at the margin – rugged, explosive, exciting and yet deceptively serene.