How Valuing Water Related Ecosystem Services Could Improve

Hydrologic Sustainability?

Driss Ennaanay, Ph.D. Natural Capital Project,

Stanford University

SCARCE – December 2010

Outline

• Natural Capital Project

• InVEST Water Models

• Applications

Nature to Society…Global or Local? 60% of global ES in decline (Millennium Assessment)

$33 Trillion/y (Costanza et al. 1997 Nature)

2 forest patches: $60K/year (Ricketts et al. 2004. PNAS)

22 others (just for pollination!)

Most Policy WindowsMedium spatial scale

Short timelineStandard approach

Most Policy WindowsMedium spatial scale

Short timelineStandard approach

Global or Local?

Needs…?

• Relevant to many kinds of decisions• Scenario based• Applicable anywhere on the globe• With minimal data• Flexible scale• Biophysical and economic• Biodiversity and multi-services

Make conservation economically attractive– Develop science and policy tools to address

ecosystem services

– Apply tools in important places

– Support policies to maintain / pay for services

– Change the way ecosystems are viewed

InVEST

Bridging Global & Local…

The Natural Capital Project

Stakeh

olde

r En

gagemen

t Identify ObjectivesIdentify Objectives

Develop ScenariosDevelop Scenarios

Compile DataCompile Data

Post‐AnalysesPost‐Analyses

Biophysical ModelsBiophysical Models

Economic ModelsEconomic ModelsInVE

ST

Natural Capital Project Approach

History…

20072007 20082008 20092009 20102010

Project Launch: Terrestrial and FW modeling

First public release of InVESTPNAS Special Issue

Frontiers Special IssueHainan Island App

China, CA, HI, OR, Tanzania

Global, MN published

Marine Initiative

CAMEO: 3 new US sites

WCVI AppBelize App

Colombia, Ecuador, Uganda, Sumatra Apps

Amazon Application

OUP Book in press

First public release?

InVESThttp://invest.ecoinformatics.org

Ecosystem Services?

Map, quantify and value multiple ecosystem services

• Biodiversity, • Water yield for hydropower production• Avoided reservoir sedimentation• Water purification: nutrient retention• Carbon sequestration & storage• Managed timber production• Crop pollination

“…. Remember that all models are wrong; the practical question is how wrong do they have to be to not be useful…”

Box and Draper, Empirical Model‐Building,

Modelers: …. What’s our addition to make them more useful? …

Simplicity vs. Complexity…

Tier 1 Tier 2

Simple Complex

Models

Data

Tier 3

Annual

Data Hungry

Hourly

Data Poor

Water Purification

Storm Peak Mitigation, 

Irrigation, Baseflow, , Groundwater recharge

Hydropower

Water yield 

Sediment

Valuation

Land use land cover

Soil 

Digital Elevation Model

Climate: PPT, PET

Water yield

Sediment 

Nutrient

Energy 

Others 

Ret.$

Pur. $

E. $

Root dep

th

Leaf type

Seasonality

Water Availability

Precipitation

Yield

EvaporationRain

Snow

Fog

Groundwater Recharge

Inflow

Plant type

Water Yield Model

Transpiration

 

xjxjx

xjx

x

xj

RR

RP

AET11

1

++

+=

ω

ω

x

xx P

AWCZhang=ωx

xxj P

ETokcR ⋅=

⎟⎟⎠

⎞⎜⎜⎝

⎛−=

x

xjxjx P

AETPY 1.

kc = vegetation coefficientETo = potential evapotranspirationP = precipitation

Zhang = seasonality factorAWC = available water contentP = precipitation

Water Yield ModelAt Pixel Level

• Variables – Land use/land cover– Precipitation (rain and snow, not fog)– PET– AWC– Vegetation coefficient– Root depth– Soil depth

Water Yield Model

Testing …

Irrigation

Urban Consumption

Irrigation

Hydropower Model

Water Transfer

Catchment /Basin Water Balance

Subtracts water used by others

Estimates amount of power generated by water available for hydropower

Calculates yield at the pixel scale as difference between precipitation and evapotranspiration

Calculates net present value

• Variables (7 required, 10 optional)– Land use/land cover– Precipitation (rain and snow, not fog)– PET– AWC– Vegetation coefficient– Root depth– Soil depth– Consumptive use by LULC– Points of interest– Calibration coefficient– Turbine efficiency– Reservoir management fraction– Head– Production costs– Energy price– Timeframe– Discount rate

Water Yield & Hydropower Production Models

Maths…

⎟⎟⎠

⎞⎜⎜⎝

⎛−=

x

xjxjx P

AETPY 1.

Vin = Y - ud

εd = Vin βγhd2.72e-3

NPVHd = (peεd -TCd) x∑ 1(1+r)t

t = 0

T - 1

corn

forest

wheat

forest

Stream

Cumulative Sediment  Yield

PCLSKRUSLE ....=

Subtracts ‘allowed’ sediment

Calculates sediment export and retention at each pixel based on USLE and routing

Calculates present value

• Variables (8 required, 5 optional)– Land use/land cover– Rainfall erosivity– Soil erodability– Crop factor– Management factor– DEM– Sediment retention efficiency– Slope threshold– Reservoir dead volume– Points of interest– Sediment removal cost– Timeframe– Discount rate

Avoided Reservoir Sedimentation Models

Valuation

SEDxD = Rx ⋅ Kx ⋅ SLx ⋅ 1− Cx ⋅ Px( )( )+ SEx USLEy (1− SEz )z=y+1

x −1

∏y =1

x −1

∑⎛

⎝ ⎜ ⎜

⎞

⎠ ⎟ ⎟

SEDRETxD = SEDxD − Sa

PVSRxD = (SEDRETx × MCD ) ×1

1+ r( )tt =0

T∑

Longtang: 2003, 2006, 2007 Santan: 2003, 2006, 2007Jiabao: 2003, 2006, 2007 Baoqiao: 2003, 2006, 2007

Sedimentation retention model

y = 1.23xR² = 0.74p < 0.01

0

50

100

150

200

250

300

350

400

450

0 50 100 150 200 250

Sim

ulat

ed so

il lo

ss (1

03t)

Observed soil loss (103 t)

ARS Testing

Water Purification: Nutrient Retention

Slope

Water Yield -

Downslope Retention

corn

forest

wheat

forest

Stream

Cumulative Nutrient Loading

Water Purification: Nutrient Retention

Valuation

)_1(**)(_CNL

LoadAnnretainedpCostValuewp xx −=

Longtang: 2001, 2008 Songtao: 2001, 2008Longjiang: 2008 Baoqiao: 2001, 2008

Water purification model – TP

y = 0.81xR² = 0.94p < 0.01

0

100

200

300

400

500

600

0 100 200 300 400 500 600 700

Sim

ulat

ed to

tal p

hosp

horu

s (t)

Observed total phosphorus (t)

WP:NR Testing

Longtang: 2003, 2008 Songtao: 2003, 2008Longjiang: 2008 Baoqiao: 2001, 2008

Water purification model – TN

y = 1.11xR² = 0.92p < 0.01

0

1000

2000

3000

4000

5000

6000

7000

0 1000 2000 3000 4000 5000 6000

Sim

ulat

ed to

tal n

itrog

en (

t)

Observed total nitrogen (t)

WP:NR Testing

Applications: How InVEST is helping Enhance Hydrologic Sustainability?

Water Fund: Water for Life

Service providers: Local communities managing / owning watersheds

Buyers: water utilities, hydropower, sugarcane plantations etc. 

Services: water flow regulation, water purification, erosion control

Motivations: Avoid costs of water treatment, ensure regular supply

PROVIDERS USERS

Methodology

• Prioritization index for 4 BMPs.

• Ranking Landscape for each BMP

• InVEST application of Sediment and Water Yield models for each Investment Portfolio

Sediment Reduction for each Portfolio

Both Services …

Sumatra Master Plan

• Significant Palm expansion

• New Environmental Law

• WWF‐ Vision

• Governmental Development Plan

• Sustainability? 

Fig w.1. Distribution of water yield for Vision scenario 

Fig w.2. Difference between water yield per pixel in the Sumatra vision and the government plan

Sumatra-Water Yield

Fig w.3. Spatial Distribution of exported sediment to stream for Vision scenario 

Fig w.4. Difference between sediment exported per pixel in the Sumatra vision and the government plan

Sumatra Sediment Yield

Fig w.5. Spatial Distribution of exported phosphorus to stream for Current scenario 

Sumatra Nutrients

Fig w.6. Spatial Distribution of exported nitrogen to stream for Current scenario 

Conclusions

InVEST water models are Distributed GIS-based models

They are based on widely used hydrologic laws, using minimal data

Provide biophysical and valuation functions

Testing and verification show satisfactory results for different models.

Next Steps…

• More Tier 1 models: Storm Peak Mitigation, Baseflow and Groundwater recharge

• In‐stream Processes in these Sediment Retention and Water Purification models. 

• Tier 2 Platform (daily time step and bundled services)

THANKS

Natural Capital Project: 

www.naturalcapitalproject.org

InVEST:  invest.ecoinformatics.org

driss@stanford.edu