Setting up SWAT to quantify water-related ecosystem services in a large East African watershed

Benedikt Notter, University of Bern, Switzerland Setting up SWAT to quantify water-related ecosystem services in a large East African watershed 5th International SWAT Conference August 5 7, Boulder, Colorado

Contents 1. Introduction 2. The study area 3. Materials and methods 4. Results and discussion 5. Conclusions and outlook

Contents 1. Introduction 2. The study area 3. Materials and methods 4. Results and discussion 5. Conclusions and outlook

The Ecosystem Services Concept Ecosystem services are the benefits people obtain from ecosystems. These include products (such as food) and actual services (such as waste assimilation). (Millennium Ecosystem Assessment 2003) increasingly popular concept in policy and research however, needs operationalization

Conceptual framework for ecosystem service quantification Valuation by stakeholders at different levels for different purposes Interface: Ecosystem services & corresponding inputs Level PHYSIOLOGICAL PRODUCTIVE SOCIO-CULTURAL INTRINSIC GLOBAL Stable climate Clean air Tourism/Recreation Disaster prevention Sanitation Drinking water LOCAL Food Non-valued output Hydropower production Agricultural production Requirements: Quantity, Quality, Location, Timing Means to pursue or preserve a way of life Service-providing unit (SPU) Environmental service (valued output) Nature conservation Service-supporting unit (SSU) Ecosystem and ecosystem functions Carbon sequestration Erosion regulation Soil formation Water regulation Primary production Climate regulation

Resulting modelling challenges Spatial compatibility of model outputs with stakeholder information High modelling resolution (ecosystem services should be transferrable within smallest spatial units) Strategies to deal with lack of high quality data and resulting uncertainty

4 strategies to meet these challenges: 1)Reducing data limitations by combining datasets from different sources, and using GIS tools to determine and implement most appropriate preprocessing technique 2)Implementing minor modifications to SWAT2005 model code 3)Developing a subbasin delineation procedure that takes into account administrative units and elevation zones while minimizing the number of subbasins created

4 strategies to meet these challenges: 4)Applying the SUFI-2 algorithm for calibration and uncertainty assessment, and additionally including uncertainty in measured inputs like rainfall, temperature, point sources, and diversion amounts in the uncertainty analysis

Contents 1. Introduction 2. The study area 3. Materials and methods 4. Results 5. Conclusions and outlook

The Pangani Basin Urban centres & industry Small-scale farming Large-scale farming Nyumba ya Mungu Pastoralism Hydropower generation Hale New Pangani Falls

Contents 1. Introduction 2. The study area 3. Materials and methods 4. Results and discussion 5. Conclusions and outlook

Data collection and quality control 3 main types of data: Hydro-met. data: Daily rainfall, max/min temperature, discharge, point source inputs (large springs), granted diversion amounts imported to DB and quality-controlled using methods described by Feng et al. (2004) Spatial data: DEM (SRTM), Land use & soils (FAO), Satellite images (GeoCover spatial reference), digitized 1:250 000 map sheets Stakeholder demand for ecosystem services: Census, Household Budget Survey, Agricultural sample census

Input pre-processing: Time-series data SWAT2005: no interpolation of met. variables pre-processing outside SWAT & using processed data as pseudogauge inputs Time-series interpolation tool: - allows taking into account elevation effects - cross-validation to determine appropriate interpolation technique

From SWAT2005 to SWAT-P Corrected error in auto-irrigation routine Changed dormancy threshold for tropical latitudes to avoid unintended dormancy Introduced floodplain routine analogous to existing wetland routine Introduced correction factors for rainfall, temperature, point source inputs and maximum diversion amounts, in order to assess uncertainty with SUFI-2

From SWAT2005 to SWAT-P Changed order of removal of water for consumptive use and irrigation consumptive use is first in SWAT-P Increased number of HRUs and number of gauge records that can be processed in a run (HRUs were limited to 6300 by some fixed-size arrays these limitations were removed; the maximum number of gauge record files was changed from 18 to 30)

Model configuration High spatial resolution and compatibility with socio-economic data while minimizing number of spatial units (i.e. computing time) AML tool featuring: Weighted flow accumulation to form different sizes of subbasins Land unit inputs so subbasin outlets are created at intersection of land unit borders with streams

Model configuration (cont.) Land unit inputs so their borders form additional subbasin boundaries additional subbasins within physical subbasins linked through zero-length pseudo streams to ensure correct water routing Coverages (subbasins, streams) output by AML can be used in ArcSWAT interface (Winchell et al. 2007)

Calibration/validation and uncertainty assessment Calibration using monthly measured discharge data from 16 stations, mostly from between 1980 and 2005 SUFI-2 as optimization algorithm: aggregates uncertainty in model concept, inputs and parameters starting with large parameter ranges, iteratively decreases these ranges aim is to maximize the p-factor (=percentage of data bracketed by 95% uncertainty interval) while minimizing the r-factor (=width of the interval)

in this study: additional SWAT-P parameters describing uncertainty in measured inputs (rainfall, temperature, point sources, maximum diversions) calibrated with SUFI-2 initial sensitivity analysis yields 16 parameters sensitive to Q to be calibrated: Parameter name Description v PCOR.sub Correction factor for precipitation (introduced in SWAT-P) v TCOR.sub Correction factor for temperature (introduced in SWAT-P) v ALPHA_BF.gw Base flow alpha factor [days] v GW_DELAY.gw Groundwater delay time [days] v GWQMN.gw Threshold depth of water in the shallow aquifer for return flow to occur [mm] v CH_K2.rte Effective hydraulic conductivity in the main channel [mm/h] v RCHRG_DP.gw Deep aquifer percolation fraction v PSCOR.sub Correction factor for point source inflow (introduced in SWAT-P) v DIVCOR.hru Correction factor for maximum diversion for irrigation (introduced in SWAT-P) r CH_N2.rte Manning's n value for main channel r CN2.hru SCD runoff curve number for moisture condition II r SOL_K.sol Soil conductivity [mm/h] r SOL_AWC.sol Soil available water storage capacity [mm H 2 O / mm soil] r SOL_BD.sol Soil bulk density [g/cm 3 ] r ESCO.hru Soil evaporation compensation factor r EPCO.hru Plant evaporation compensation factor

Parameters varied in a regional approach by 11 parameter zones based on climate, topography, and geology; mountain zones internally further subdivided into higher and lower zone for groundwater parameters

Contents 1. Introduction 2. The study area 3. Materials and methods 4. Results and discussion 5. Conclusions and utlook

Time-series data interpolation Cross-validation results: IDW as overall best-performing technique, closely followed by Kriging Slight improvement when using elevation as secondary variable 0 Daily rainfall 0 Mean monthly rainfall 0 Mean annual rainfall 0 Temperature RMSE / Mean parameter value 1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4 5 5 5 5 IDW Kriging Spline ED-IDW IDW Kriging Spline ED-IDW IDW Kriging Spline ED-IDW IDW Kriging Spline ED-IDW

Better agreement of rainfall map with observed vegetation patterns when including elevation: Pre-processing reduced uncertainty in met. inputs, but it still needs to be included in uncertainty assessment

Model configuration 1853 physical (topographical) subbasins 3820 model subbasins due to elevation band subdivisions 21 052 HRU s Maximum spatial detail while keeping computational demand low (similar spatial detail with conventional subbasin delineation tools produced >10 000 subbasins) Model outputs are spatially compatible with socio-economic information

Model configuration Partitioning of GW into shallow and deep aquifer Human/industrial/livestock consumption removed via.wus files; returned via point sources, with loadings according to degree of pollution after use GW discharge from shallow aquifer via usual SWAT groundwater routine Irrigation of crops using auto-irrigation subroutine!!!!! GW discharge from deep aquifer through point sources (reccnst command) Elevation band subbasin Reach segment linking elevation band subbasins Physical subbasin boundary Physical reach! Major spring

Calibration and validation results so far: Upper Basin only (upstream Nyumba ya Mungu dam) Calibration: 7 of 8 stations with NSE > 0.5 Average p-factor = 70% Average r-factor = 0.61 SD of measured data Validation: 4 of 6 stations with NSE > 0.5 Average p-factor = 66% Average r-factor = 0.78 SD of measured data

Contents 1. Introduction 2. The study area 3. Materials and methods 4. Results and discussion 5. Conclusions and outlook

Study has shown that: SWAT can be set up to quantify waterrelated ecosystem services under the starting assumption, and produce satisfactory results, but some changes in the model code became necessary using a more flexible subbasin delineation procedure, SWAT can be run at high spatial detail while minimizing computational demand however, separate input files for each spatial unit get cumbersome with increasing model complexity considering development towards higher resolution models, a change to table input format could be beneficial

SUFI-2 has shown to produce good results with a quite low number of runs (3 iterations à 300 500 runs in the upper basin); inclusion of additional parameters allows assessing uncertainty in contexts of low data quality newest version of software (SWAT- CUP 2.1.4) allows varying precipitation inputs directly through its interface (without using the correction factor introduced with SWAT-P)

Yet to do: Derivation of ecosystem service availability Definition and assessment of change scenarios

End of presentation Thank you for your attention!

2.5000 2.0000 1.5000 1.0000 0.5000 0.0000 1/1/1998 3/1/1998 5/1/1998 7/1/1998 9/1/1998 11/1/1998 1/1/1999 3/1/1999 5/1/1999 7/1/1999 9/1/1999 11/1/1999 1/1/2000 3/1/2000 5/1/2000 7/1/2000 9/1/2000 11/1/2000 No auto-irrigation Auto-irrigation/SWAT2005.exe Auto-irrigation/SWAT-P

PET & ET [mm], SW [mm/100], LAI 12 10 8 6 4 2 HRU 134 (lowest CHAR), SWAT2005.exe SWAT-P.exe 250000 14000 12000 200000 10000 150000 8000 100000 6000 4000 50000 2000 Biomass [kg/ha] 0 00 1 30 59 88 117 146 175 204 233 262 291 320 349 13 42 71 100 129 158 187 187 216 216 245 245 274 274 303 303 332 332 361 361 Simulation day PET ET LAI Soil water BIOM

Requirements for delivery of benefits (1) Quantity and timing: 10 Flow duration curve 9 8 98% reliability level for domestic use Discharge [m3/s] 7 6 5 4 3 2 1 0 96% reliability level for livestock ~ 70% reliability level for most crops Criteria for river flow vary with available storage capacity! 0 10 20 30 40 50 60 70 80 90 100 Percentage of time flow equalled or exceeded

From SWAT2005 to SWAT-P Corrected error in auto-irrigation routine

From SWAT2005 to SWAT-P Corrected error in auto-irrigation routine Changed dormancy threshold for tropical latitudes to avoid unintended dormancy

1st SWAT modelling results Performance measure Charongo Ngomberi Daily r 2 0.72 0.82 Daily NSE 0.70 0.81 Monthly r 2 0.84 0.94 Monthly NSE 0.82 0.93 Total Q dev. (Sim Obs) [%] -0.40-2.93

SWAT outputs: Areal contributions to river flow Notter 2007

Input pre-processing: Spatial data

Example GIS layer: river network

Output: Natural Resources Monitoring & GIS databases Relational Distributed database to all Collection contributing of GIS data containing hydromet. & metadata, created institutions; Introduction & training on monitoring data & combining the best functions use for updating PBWO staff information carried out from in each & quality workshop controlon 24. November source dataset 2007