“GIS-integrated Decision Support System (DSS) in Water Resources

Management”

Ivan Maximov, Ph.D

The Ministry of Natural Resources Russian Federal Water Resources Agency

STRUCTURE OF PRESENTATION

1. DSS (Decision Support Systems)

2. U.S EPA BASINS (Better Assessment Science Integrating point and Non-point Sources): Integrated GIS, data analysis and modeling system designed to support watershed based analysis.

3. Application of BASINS. Examples.

4. CONCLUSION

1.DSS (Decision Support Systems)

Q: What is a Decision Support System? Process? Tool?

As a process: …is a systematic method of leading decision-makers and other stakeholders through the task of considering all objectives and then evaluating options to identify a solution that best solves an explicit problem while satisfying as many objectives as possible.

As a tool: …consists of models, data, and point-and-click interfaces that connect decision-makers directly to the models and data they need to make informed, scientific decisions. A DSS collects, organizes, and processes information, and then translates the results into management plans that are comprehensive and justifiable

Q: What are the benefits of this technology for water resources management?

• Based on scientific data and models it can account for all stakeholder objectives, cause/effect relationships, risks, costs, and reliability, whereas traditional decision processes have had difficulty aggregating all of these considerations.

• Adaptable. Custom-designed. Scenario analysis and forecasts. Graphical interface links the decision-makers with the models.

• Aggregates all competing objectives to identify the best strategy – optimal solution.

STRUCTURE OF DSS (GENERAL CASE)

DATABASE

TSS

MonitoringSystems

Laws, regulations

DATA TOOLS AND

ASSESSMENT

MODELS

USER' S DEMAND, MODEL SELECTION, MODEL'S

LIMITATION

MIKE SHE PLOAD

HSPFSWAT

DECISION-MAKING

OU

TP

UT

R

ES

UL

TS

/P

OS

T-

PR

OC

ES

SIN

G

SCENARIO ANALYSIS

MET

HYD

WQ

TOPOSOL

AGR

GEOL

SOCLU

STATS GIS

MAPSINFO-ANALYTICAL

SYS.

SIMULATION

MODELING SYS.

SYNTHESIS

‘what if’

‘forecasts’

2. U.S EPA BASINS (Better Assessment Science Integrating

point and Non-point Sources)

• What Is BASINS?BASINS - Integrated into GIS, , multipurpose

environmental analysis system for performing watershed and water-quality-based studies.

• Main Objectives- To facilitate examination of environmental information- To support analysis of environmental systems- To provide a framework for examining management

alternatives

BASINSGIS Interface

Watershed DelineationAutomated w/ SA

QUAL2E WIN HSPFSWAT

Schematic Diagram of BASINS 3.0

GenScn(Post-processor)

Output and Analysis

Watershed DelineationManual - AV only.

PLOAD

Watershed Parameterization

Tools/Utilities

BASINS Spatially Distributed Data (Full set)

– Land use and land cover (shape and grid)

– Urbanized areas

– Populated place locations

– Reach file 1

– Reach file 3

– National Hydrographic Data (NHD)

– Major roads

– USGS hydrologic unit boundaries (accounting and catalog units)

– Drinking water supply sites– Dam sites– EPA region boundaries– State boundaries– County boundaries– DEM (shape and grid)– Ecoregions– NAQWA study unit boundaries– Managed area database (Federal

and Indian Lands)– Soil (STATSGO)

* Red color– those data that are required for model start-up

BASINS Environmental Monitoring Data

• Drinking water supply sites

• Water quality monitoring station summaries

• Bacteria monitoring station summaries

• Weather station sites• USGS gauging

stations• Dam sites• Classified shellfish

area• 1996 Clean water

needs survey

BASINS Point Source Data

• Permit compliance system sites (PCS)• Industrial facilities discharge sites• Toxic release inventory sites (annual releases)• Superfund national priority list sites• Mineral data• Hazardous and solid waste sites

Assessment Tools in BASINS •Target: provides broad-based evaluation of watershed water quality and point source loadings.

•Assess: watershed-based evaluation of specific water quality stations and/or discharges and their proximity to waterbodies.

•Data mining: dynamic link of data elements using a combination of tables and maps. Allows for visual interpretation of geographic and historical data.

•Watershed reporting: automated report generation with user-defined selection options.

3. APPLICATION OF BASINS. EXAMPLES

• HSPF (Hydrologic Simulation Program, FORTRAN)Project: Development of a local DSS - Integrated assessment of climate and land-use change effects on hydrology and water quality of the Great Miami River, OH, USA • SWAT (Soil and Water Assessment Tool).Project: To develop a DSS in order to decide how many and where to place water quality sampling stations in Swiss mezo-scale watershed (Thur River)

HSPF is a comprehensive, physically-based, continuous, lumped model that can simulate hydrologic and associated water quality processes on pervious and impervious land surfaces. Model is capable to model point and non-point source pollution. Good for mid-size watersheds. Has a history of successful application (Chesapeake Bay Program and others.)

Schematic structure of HSPF

LANDSACAPE DATA 

Windows Interface, WinHSPF GUI

Land Use and pollutant specific data

Meteorological Data

HSPF code Post Processing and

decision-making process

GIS ArcView

Point Sources

Land Use distribution

Stream Data

Hydrology in HSPF

Impervious Land Pervious Land

WaterBody

Runoff

Runoff

Interflow

Base flow

Surf

ace

Layers

Upp

erLo

wer

Gro

und

wat

er

Courtesy of Tetra Tech Inc.

LOCATION OF STUDY AREA

Basin drainage area= 5,385 sq.mi

MA

D R

GR

EA

T M

IAM

I R

TW

IN C

R

STILL

WA

TE

R R

INDIA

N C

R

SEVEN

MILE

CR

INDIAN LAKE

GR

EA

T M

IAM

I R

DARKE

LOGAN

BUTLER

MIAMI

CLARK

HARDIN

PREBLE

WAYNE

MERCER

GREENE

SHELBY

WARREN

RANDOLPH

AUGLAIZE

HAMILTON

FRANKLIN

BOONE

CHAMPAIGN

MONTGOMERY

DEARBORN

UNION

0 40 80 Miles

N

County boundaries

Streams

DEM Great Miami river

151 - 185

186 - 219

220 - 253

254 - 287

288 - 321

322 - 355

356 - 389

390 - 423

424 - 457

No Data

Legend

Digital Elevation ModelGMR basin

METHODOLOGY

Data collection

Characteristic of current water quantity and quality conditions

in the Great Miami River

Development, calibration and validation of GMR

hydrological model

Development, calibration and validation of GMR

water quality model

Hypothetical climate and land-use scenarios construction

Simulation of GMR hydrologic regime and water quality

Simulation of BMPs effect on water quality of Stillwater river

BASINS, HSPF, GenScn

BASINS, HSPF, WDMUtil

CLIMATE CHANGE ONLY

Simulation of GMR hydrologic regime and water quality

LAND USE CHANGE ONLY

Simulation of GMR hydrologic regime and water quality

COMBINED: CLIMATE AND LAND USE

HSPF, GenScn

HSPF, GenScn

Analysis of the results

Analysis of the results

HSPF, GenScn

HSPF, GenScn

Model development

USGS (National Water Information System), U.S EPA STORET, NCDC, US Census

Bureau, Ohio EPA, Miami conservancy group, National

Resource Inventory, Ohio Department of Development,

Stormwater managers resource center

Upper GMR basin divided into sub-basins

$

$

$$$ $

$

$ $

$$ $ $$$ $$$$ $$ $$ $ $$$$ $

$$

$$$

$

$$

6

2

27

13

12

1

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1815 19

3

58

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9

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20

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25

23

28

16

MA

D R

*A

GR

EA

T M

IAM

I R

ST

I LLW

AT

ER

R

BU

CK

CR

LORAMIE

CR

GREENVILLE CR

LO

ST

CR

PAINTER CRMU

D C

R

HO

NE

Y C

R

SP

RIN

G C

R

MOSQU

ITO CR

LUDLOW CR

NETTLE CR

KINGS CR

BEAVER CR

HARRIS CR

IND

IAN

CR

CH

AP

MAN

CR

KR

AUT C

R

SWAM

P CRMUDDY CR

DO

NN

EL

S C

R

BO

YD

CR

AN

DE

RS

ON

CR

INDIAN L

LEA

TH

ER

WO

OD

CR

0 30 60 Miles

N

Cataloging Unit Boundaries

Streams

USGS Gage Stations$

Permit Compliance System

Watershed

Subbasins

Legend

Scale

Dayton

Springfield

HSPF MODEL CALIBRATION AND VALIDATION

  Very Good Good FairHydrology/Flow <10 10-15 15-25

Sediment <20 20-30 30-45

Water temperature <7 8-12 13-18

Water Quality/Nutrients <15 15-25 25-35

Pesticides/Toxics <20 20-30 30-40

Calibration, 1985-1990 Validation, 1990-1995Observed

mean annual

flow (ft3/s)

Simulated mean

annual flow (ft3/s)

% Error between

observed and simulated

Observed mean

annual flow (ft3/s)

Simulated mean

annual flow (ft3/s)

% Error between

observed and simulated

Upper GMR at Dayton,

OH

2,494 2,305 7.5

(good)

2,659 2,922 9.8

(good)Lower

GMR at Hamilton,

OH

3,646 2,998 17.7

(fair)

3,728 3,150 15.0

(good)

STREAMFLOW

Great Miami River at Dayton, OH

Calibration

100)(

E%

1m,

1ms

n

ii

n

ii

X

XX

n

ii

n

ii

XX

XX

1

2mm

1

2ms

)(

)(1NS

Nash-Sutcliffe model efficiency

R=0.91; NS=0.74

R= 0.78

R= 0.96

Upper GMR

Lower GMR

Upper GMR

Lower GMR

Calibration and Validation of Water quality

CALIBRATION (1980-1985) VALIDATION (1985-1991)

Observed Simulated % Error Observed Simulated % Error

UPPER GMR

W T (F) 54.0 44.0 18.4 (fair) 53.4 44.8 16.1 (good)

DO (mg/l) 10.3 11.3 9.7 10.3 11.2 8.7

TP (mg/l) 0.34 0.33 2.9 (very good) 0.31 0.33 6.4(very good)

TAM (mg/l) 0.18 0.16 11.1(very good) 0.09 0.1 11.1(very good)

NO2+NO3 (mg/l)

3.50 3.12 10.8(very good) 3.80 3.20 15.7(good)

LOWER GMR

WT (F) 57.2 56.3 1.6(very good) 57.2 56.8 1.0(very good)

DO (mg/l) 9.9 9.4 5.0 10.5 9.3 11.4

TP (mg/l) 0.5 0.44 12.0(very good) 0.44 0.41 6.8(very good)

TAM (mg/l) 0.3 0.27 10.0(very good) 0.18 0.16 11.1(very good)

NO2+NO3 (mg/l)

4.18 3.61 13.6(very good) 3.9 3.44 11.7(very good)

WATER QUALITY

WTEMP DO

NO3+NO2 OrthoP

Hypothetical climate scenarios for HSPF simulations

Hot Scenario Group

Warm Scenario Group

Base Scenario(Current

Temperature and Precipitation)

Hot and Wet scenario(T+2oC, P+20%)

HW

Hot and Wet scenario(T+2oC, P+20%)

HW

Hot and Dry scenario(T+2oC, P-20%)

HD

Hot and Dry scenario(T+2oC, P-20%)

HD

Warm and Wet scenario(T+1.5oC, P+20%)

WW

Warm and Wet scenario(T+1.5oC, P+20%)

WW

Warm and Dry scenario(T+1.5oC, P-20%)

WD

Warm and Dry scenario(T+1.5oC, P-20%)

WD

DEVELOPMENT OF LAND USE CHANGE SCENARIO

Hypothetical land-use change scenario includes an overall 30% increase in urban area: by 20% in Upper GMR basin and by 32% in the Lower GMR.

Major Roads

Streams

MRLC Land Use

Urban

Barren or Mining

Transitional

Agriculture - Cropland

Agriculture - Pasture

Forest

Upland Shrub Land

Grass Land

Water

Wetlands

Legend

Current Land Usefor the UPPER GMRBasin (Base Case)

6

2

27

13

12

1

4

10

1815 19

3

58

7

17

22

14

9

26

20

24

11

21

25

23

28

16

Ma d R

iv er

St il lw

a t er Ri ver

Grea t M

iami R

i ver

Greenvil

le Cree

k

Buc

k Cr

eek

Mile Creek

Los

t Cre

e k

Loramie C

reek

Mud Run

Tu r tle Cr e ek

Muc

hini

ppi C

reek

Honey C

reek

Dugan

Run

Mad River

GREENVILLE

BELLEFONTAINE

URBANA

PIQUA

SIDNEY

DAYTONSPRINGFIELD

TROY

0 20 40 Miles

N

Scale

Base case

Major Roads

Streams

FUTURE LAND USE SCENARIO

Urban

Barren or Mining

Transitional

Agriculture - Cropland

Agriculture - Pasture

Forest

Upland Shrub Land

Grass Land

Water

Wetlands

LEGEND

Future HypotheticalLand Use Scenariofor the UPPER GMR

Basin

6

2

27

13

12

1

4

10

1815 19

3

58

7

17

22

14

9

26

20

24

11

21

25

23

28

16

Ma d R

iv er

St il lw

a t er Ri ver

Grea t M

iami R

i ver

Greenvil

le Cree

k

Buc

k Cr

eek

Mile Creek

Los

t Cre

e k

Loramie C

reek

Mud Run

Tu r tle Cr e ek

Muc

hini

ppi C

reek

Honey C

reek

Dugan

Run

Mad River

TROY

SPRINGFIELDDAYTON

SIDNEY

PIQUA

GREENVILLE

URBANA

BELLEFONTAINE

0 20 40 Miles

N

Scale

Future scenario

SIMULATIONS UNDER COMBINED CLIMATE AND LAND USE CHANGE SCENARIOS

UPPER GMR AT DAYTON, OH Mean annual flowft3/s (km3/year)

Difference between Base Case Scenario and Simulated combined Climate and Hypothetical

Land Use Scenario

Base Case Scenario 2347 (2.1) -

Hot and Dry Climate Scenario + Land Use Scenario (HD+LU)

 1688 (1.5)

 -28%

Hot and Wet Climate Scenario + Land Use Scenario (HW+LU)

 3775 (3.36)

 +61%

Warm and Dry Climate Scenario + Land Use Scenario (WD+LU)

 1863 (1.66)

 -21%

Warm and Wet Climate Scenario + Land Use Scenario (WW+LU)

 4112 (3.6)

 +75%

     

LOWER GMR AT HAMILTON, OH

Mean annual flowft3/s (km3/year)

Difference between Base Case Scenario and Simulated combined Climate and Hypothetical

Land Use Scenario

Base Case Scenario 2984 (2.66) -

Hot and Dry Climate Scenario + Land Use Scenario (HD+LU)

 2653 (2.36)

 -11%

Hot and Wet Climate Scenario + Land Use Scenario (HW+LU)

5080 (4.53)

 +70%

Warm and Dry Climate Scenario + Land Use Scenario (WD+LU)

 2928 (2.6)

 -2%

Warm and Wet Climate Scenario + Land Use Scenario (WW+LU)

 5464 (4.8)

 +83%

ST

RE

AM

FL

OW

Results from Water quality simulations under combined climate and land use change change scenarios:

  UPPER GMR AT DAYTON, OH

 Base Case

(Simulated)(Mean annual

values)

Difference between Base Case Scenario and Simulated combined Climate and

Hypothetical Land Use Scenario

Hot and Dry Climate Scenario + Land Use Scenario (HD+LU):

Water temperature (F)

DO (Mg/l)Total Phosphorus (Mg/l)

Total NH4 (Mg/l)

NO2+NO3 (Mg/l)

  

44.4 (47.8)11.2 (9.6)

0.33 (0.54)0.13 (0.16)3.16 (5.40)

  

+7.6%-14.0%+63.0%+9.0%

+12.9%

Hot and Wet Climate Scenario + Land Use Scenario (HW+LU):

Water temperature (F)

DO (Mg/l)Total Phosphorus (Mg/l)

Total NH4 (Mg/l)

NO2+NO3 (Mg/l)

  

44.4 (46.8)11.2 (10.1)0.33 (0.47)0.13 (0.10)3.16 (2.51)

  

+5.4%-9.8%

+42.0%+19.6%+22.8%

Warm and Dry Climate Scenario + Land Use Scenario (WD+LU):

Water temperature (F)

DO (Mg/l)Total Phosphorus (Mg/l)

Total NH4 (Mg/l)

NO2+NO3 (Mg/l)

  

44.4 (46.0)11.2 (10.2)0.33 (0.50)0.13 (0.14)3.16 (4.90)

  

+3.6%-8.9%

+51.0%+7.8%+9.2%

Warm and Wet Climate Scenario + Land Use Scenario (WW+LU):

Water temperature (F)

DO (Mg/l)Total Phosphorus (Mg/l)

Total NH4 (Mg/l)

NO2+NO3 (Mg/l)

  

44.4 (46.1)11.2 (11.1)0.33 (0.46)0.13 (0.12)3.16 (3.10)

  

+3.8%-1.0%

+39.0%+3.8%

+12.4%

WA

TE

R Q

UA

LIT

Y

Results from BMPs simulations (SIMULATED construction dry/wet detention ponds, wetlands, aquatic

buffers)

   No BMPs and

current conditions (base case)

 

 With BMPs and current conditions

(% change to base case)

 No BMPs and future land use scenario

With BMPs and future land-use

scenario(% change to

“No BMPs and future land use”)

Annual flow (ft3/s) at

Englewood, OH

 675

 612 (-9.3%)

 816

 715 (-12.3%)

Annual total phosphorus

(mg/l)

 0.37

 0.30 (-19.0%)

 0.53

 0.38 (-28.3%)

Annual ammonia

nitrogen (mg/l)

0.10 0.10 (no change)

0.11 0.10 (-10.0%)

Annual sum of nitrites and

nitrates (mg/l)

 3.50

 3.30 (-6.0%)

 3.58

 3.0 (-16.2%)

HSPF in DSS for Great Miami River basin

• The effectiveness of the integrated approach when simulating “what if” scenarios in the context of combined future climate and land use changes. The important consideration is examining the combined effects rather than individual.

• Better understanding the complex hydrological cycle and interplay of climate and land-use changes and their effects on the stream ecosystem.

FOR THEORY

PRACTICAL

• the REAL measures, which could be applied in attempt to alleviate and minimize the consequences of predicted negative impacts on water quality.• capability of the GIS-based U.S EPA BASINS and HSPF tool.

SWAT is a physically based model, capable of simulating long-term impacts, such as land-use changes, climate changes and agricultural management. SWAT has a history of successful applications.

Criteria for choosing this model: (a) model capabilities; (b) model accuracy; (c) model flexibility; and (d) data requirements.

• SWAT input data: land-use, soils, slope and climatological data.• SWAT models evapotraspiration, lateral subsurface flow, return flow from groundwater, surface runoff, pollutants loads, erosion and sediment yield:(a) Land phase of the hydrologic cycle (controls the amount of water, sediment, nutrient, and pesticide loadings to the main channel in each subbasin (for water budget):

SWt = SW + ∑ (Ri-Qi-ETi-Pi-QRi)SWt is the final soil water content; SW is the soil water content for plant uptake; R – precip (mm); Q – surface runoff (mm); ET – evapotraspiration (mm); P – percolation (mm); QR – return flow (mm)

(b) Routing phase – movement of water, sediments etc., through channel network of the watershed to outlet (QUAL2E).

Precipitation

(Rainfall & Snow)

Evaporation and Transpiration

Infiltration/plant uptake/ Soil moisture redistribution

Surface Runoff

Lateral Flow

Percolation to shallow aquifer

HYDROLOGICAL CYCLE IN SWAT

LOCATION OF STUDY AREA

Basin drainage area= 1,700 km2

Gage stations$T

Streams

Subbasins

LEGEND

$T

$T

$T

$T

$T

$T

$T

$T$T

$T$T

$T

$T

$T

$T

$T

$T

$T

$T

121

1310

36

164

157 11

148 9

5

2

Murg-W„ngi

Thur-Halden

Necker-NeckerThur-Btschwil

Murg-FrauenfeldThur-Andelfingen

Sitter-Appenzell

Aubach-Fischingen

Thur-Neu St. Johann

Sitter-Bernhardzell

Thur-Stein, Iltishag

Thur-Jonschwil, Mhlau

Thur-Lichtensteig, Flotz

0 10 20 Kilometers

Delineation of theThur River Basin

(16 subchatchments)

N

Scale

FLOW CALIBRATION, SUMMARY RESULTS (1991-1995)

1991-1995

Total Water Yield, mm

Surface runoff,

mm

Baseflow, mm

Mean annual flow, m3/s

Daily R (Monthly

R) between obs vs.

sim

Nash-Sutcliffe

coefficient

% Error

Observed 894 388 506 48.5 0.88 (0.92)

0.75 3.1

SWAT simulated

867 358 509 46.0

1991-1995 Summer, m3/s

%Error Fall, m3/s

% Error Winter, m3/s

% Error Spring, m3/s

% Error

Observed 50.6 +10.0 38.0 -5.2 48.7 -27.0 56.4 -10.4

SWAT 56.5 36.1 38.4 51.1

Very Good Good Fair

Hydrology/Flow <10 10-15 15-25

Sediment <20 20-30 30-45

Water temperature <7 8-12 13-18

Water Quality/Nutrients <15 15-25 25-35

Pesticides/Toxics <20 20-30 30-40

Target criteria for calibration (percent mean errors or differences between simulated and observed values )

MODEL CALIBRATION AND VALIDATION

FLOW CALIBRATION (comparison between simulated and observed values)

Calibration

r daily = 0.88, rmonthly = 0.92NS = 0.75

SIMULATED WATER BUDGET OF THE THUR RIVER

Streams

PRECIPITATION [mm] Average

999 - 1001

1001 - 1085

1085 - 1435

1435 - 1668

1668 - 1890

LEGEND

1

6

2

59

7

4

3

10

8

11

14

13

12

16

15

0 20 Kilometers

Thur River BasinPrecipitation

by subbasins, mm,1991-2000

N

Scale

#YAndelfingen

Streams

a.EVAPOTRASPIRATION [mm] Aver

406 - 410

411 - 516

517 - 540

541 - 591

592 - 627

LEGEND

1

6

2

59

7

4

3

10

8

11

14

13

12

16

15

0 20 Kilometers

Thur River BasinSimulated

Actual Evapotraspirationby subbasins, mm,

1991-2000

N

Scale

#YAndelfingen

P, mm ET, mm

Streams

GROUNDWATER CONTRIBUTION [mm] Aver

6 - 50

51 - 140

141 - 265

266 - 353

354 - 475

LEGEND

1

6

2

59

7

4

3

10

8

11

14

13

12

16

15

0 20 Kilometers

Thur River BasinSimulated

Groundwater Contribution to the flow

by subbasins, mm,1991-2000

N

Scale

#YAndelfingen

GW contribution, mm

Subbasins

Streams

Snow/ice melt amount (mm H2O), average

14 - 20

20 - 27

27 - 61

61 - 87

87 - 175

LEGEND

121

1310

36

164

157 11

148 9

5

2

0 10 20 Kilometers

SimulatedSnowmelt/Icemelt

amountsby subbasins, mm H2O,

Thur River Basin,1991-2000

N

Scale

#YAndelfingen

SNOWMELT, mmH2 0

SIMULATED WATER BUDGET (continued)

Streams

SURFACE RUNOFF CONTRIBUTION [mm] Aver

28 - 91

92 - 175

176 - 244

245 - 545

546 - 768

LEGEND

1

6

2

59

7

4

3

10

8

11

14

13

12

16

15

0 20 Kilometers

Thur River BasinSimulated

Surface runoff Contribution to the streamlowby subbasins, mm,

1991-2000

N

Scale

#YAndelfingen

SURFACE RUNOFF, mm

TSS calibration, 1991-1995: R = 0.78, NS = 0.60

TSS calibration, 1991-1995: R = 0.96, NS = 0.57

FLOW

SIMULATED TSS LOADS BY SUBBABSINS

Gage stations$T

Streams

Total Sediment Yield, metric tonnes

513 - 2909

2910 - 12371

12372 - 35292

35293 - 49348

49349 - 102646

Subbasins

LEGEND

$T

$T

$T

$T

$T

$T

$T

$T$T

$T$T

$T

$T

$T

$T

$T

$T

$T

$T

121

1310

36

164

157 11

148 9

5

2

Murg-W„ngi

Thur-Halden

Necker-NeckerThur-Btschwil

Murg-FrauenfeldThur-Andelfingen

Sitter-Appenzell

Aubach-Fischingen

Thur-Neu St. Johann

Sitter-Bernhardzell

Thur-Stein, Iltishag

Thur-Jonschwil, Mhlau

Thur-Lichtensteig, Flotz

0 10 20 Kilometers

SimulatedTotal Sediment

Yield by Subbasins,metric tonnes,

Thur River Basin,1991-2000

N

Scale

SIMULATED TN LOADS BY SUBBABSINS

Gage stations$T

Streams

Total Nitrogen Yields, kgN

16095 - 53436

53437 - 104879

104880 - 156425

156426 - 333093

333094 - 560155

Subbasins

LEGEND

$T

$T

$T

$T

$T

$T

$T

$T$T

$T$T

$T

$T

$T

$T

$T

$T

$T

$T

121

1310

36

164

157 11

148 9

5

2

Murg-W„ngi

Thur-Halden

Necker-NeckerThur-Btschwil

Murg-FrauenfeldThur-Andelfingen

Sitter-Appenzell

Aubach-Fischingen

Thur-Neu St. Johann

Sitter-Bernhardzell

Thur-Stein, Iltishag

Thur-Jonschwil, Mhlau

Thur-Lichtensteig, Flotz

0 10 20 Kilometers

SimulatedTotal Nitrogen

Loads by Subbasins,kgN,

Thur River Basin,1991-2000

N

Scale

Gage stations$T

Streams

Total Phosphorus Yields, kgP

552 - 2147

2148 - 4932

4933 - 12320

12321 - 28298

28299 - 66720

Subbasins

LEGEND

$T

$T

$T

$T

$T

$T

$T

$T$T

$T$T

$T

$T

$T

$T

$T

$T

$T

$T

121

1310

36

164

157 11

148 9

5

2

Murg-W„ngi

Thur-Halden

Necker-NeckerThur-Btschwil

Murg-FrauenfeldThur-Andelfingen

Sitter-Appenzell

Aubach-Fischingen

Thur-Neu St. Johann

Sitter-Bernhardzell

Thur-Stein, Iltishag

Thur-Jonschwil, Mhlau

Thur-Lichtensteig, Flotz

0 10 20 Kilometers

SimulatedTotal Phosphorus

Loads by Subbasins,kgP,

Thur River Basin,1991-2000

N

Scale

SIMULATED TP LOADS BY SUBBABSINS

• SUMMARY TABLE: SEDIMENT AND NUTRIENT LOADS BY LAND USE TYPE, THUR RIVER BASIN

IMPORTANT – LOADS PER LAND-USE

Land Use SYLD, tn

SYLD, SYLD

T-NO3,kg

N

T-NO3

, T-NO3 TN, kgN TN, TN

TP, kgP TP, TP

   tn/ha

(% contributi

on)  kgN/ha

(% contributi

on)  

kgN/ha

(% contributio

n)  kgP/ha

(% contributio

n)

FOREST 3,7760.02 0.9 91,484 0.54 30.7

106,664

0.63 5.0

1,869 0.01 0.9

PASTURE43,00

10.25 10.6 57,762 0.34 19.4

319,607

1.88 15.0

32,519 0.19 15.1

AGRICULTURE337,8

191.99 83.5 138,700 0.82 46.6

1,642,864

9.67 77.3

174,841 1.03 81.3

URML15,06

50.09 3.7 3,449 0.02 1.2 38,369

0.23 1.8

4,529 0.03 2.1

BROMGRASS/BAREN LAND 3,912

0.02 1.0 5,596 0.03 1.9 6,635

0.04 0.3 157 0.00 0.1

ORCHARDS 1,4800.01 0.4 911 0.01 0.3 10,427

0.06 0.5

1,176 0.01 0.5

• SUMMARY TABLE: TSS AND NUTRIENT LOADS BY LAND USE AND SUBBASINS, THUR RIVER BASIN

Sub # LU Area,ha SYLD, tn SYLD,tn/ha T-NO3, kg T-NO3, kgN/ha TN, kg TN, kgN/ha TP, kg TP, kgP/ha

1 FRST 4610 250 0.05 1003 0.22 2483 0.54 191 0.04AGGR 12758 35042 2.75 10050 0.79 153942 12.07 18974 1.49

2 PAST 5772 3198 0.55 11913 2.06 12733 2.21 150 0.03FRST 5651 576 0.10 8301 1.47 8448 1.50 23 0.00BROM 1747 2040 1.17 3194 1.83 3739 2.14 82 0.05AGGR 2676 4062 1.52 4433 1.66 13948 5.21 1191 0.45

3 PAST 580 1035 1.78 557 0.96 7934 13.67 913 1.57FRST 2347 33 0.01 1632 0.70 1939 0.83 41 0.02AGRR 3677 6840 1.86 3560 0.97 53514 14.55 6176 1.68URML 459 3701 8.06 710 1.55 8271 18.01 972 2.12ORCD 580 762 1.31 420 0.72 1089 1.88 575 0.99

4 PAST 521 5202 9.98 937 1.80 24333 46.70 2898 5.56FRST 3076 244 0.08 3328 1.08 5540 1.80 276 0.09AGRR 4366 43902 10.05 8203 1.88 211139 48.36 25124 5.75

5 PAST 3079 2360 0.77 11169 3.63 26253 8.53 1873 0.61FRST 6074 144 0.02 24667 4.06 25145 4.14 60 0.01AGRR 4870 4292 0.88 19097 3.92 43238 8.88 2999 0.62

6 PAST 991 7976 8.05 455 0.46 30980 31.27 3778 3.81FRST 3707 98 0.03 1100 0.30 2025 0.55 118 0.03AGRR 9527 81426 8.55 9474 0.99 273632 28.72 35018 3.68URML 993 7550 7.60 2000 2.01 23205 23.37 2751 2.77ORCD 963 441 0.46 296 0.31 1251 1.30 366 0.38

7 PAST 896 519 0.58 3466 3.87 7504 8.37 500 0.56FRST 2908 9 0.00 9096 3.13 9179 3.16 11 0.00AGRR 4384 2381 0.54 17406 3.97 36753 8.38 2395 0.55

8 PAST 2189 1834 0.84 7560 3.45 15973 7.30 1055 0.48FRST 2573 201 0.08 8339 3.24 8596 3.34 35 0.01AGRR 1712 5006 2.92 3146 1.84 23859 13.94 2547 1.49

9 PAST 3332 2353 0.71 6668 2.00 7261 2.18 107 0.03FRST 2606 360 0.14 3654 1.40 3748 1.44 13 0.00BROM 1335 1872 1.40 2402 1.80 2896 2.17 75 0.06AGRR 1453 5257 3.62 2742 1.89 18554 12.77 1952 1.34

10 FRST 4563 68 0.01 2066 0.45 2641 0.58 78 0.02AGRR 8679 19936 2.30 11915 1.37 141802 16.34 17363 2.00

SCENARIO ANALYSIS

No-Fertilizer

No-Tillage

No-Tillage +

No- Fertilizer

Summary results from agricultural management scenarios simulations

Base case scenario No-Fertilizer scenario No-Tillage scenario No-Fertilize rand No-Tillage scenarioConstituent Observed Simulated Simulated % difference Simulated % difference Simulated % difference

to Base case to Base case to Base caseTSS (tn) 9,291 8,510 8,533 - 7,059 -17.0 7,443 -12.5TN (tn) 200 191 162 -15.0 187 - 182 -5.0

NO3 (tn) 145 130 108 -17.0 115 -11.5 113 -13.0TP (tn) 14 16 13 -18.0 12 -25.0 12 -25.0TN/TP 14 12 12 16 15

• SWAT is capable to model alpine-pre-alpine watershed with acceptable degree of accuracy

• Results show relation between land-use and water quality parameters (winter wheat + summer pasture produced the highest sediment and nutrient loads)

• Spatial distribution, dynamic of nutrient and sediment loadings. Relative impacts of types of agricultural activities and land-uses on water resources.

THANK YOU FORATTENTION!