UCL DEPARTMENT OF GEOGRAPHY

Climate change impacts on a Nile headwater catchment: the River Mitano, Uganda

Daniel Kingston, Richard Taylor, Martin Todd, Julian ThompsonDepartment of Geography, University College London

The River Mitano

• Basin size: 2098km2

• Headwater catchment of the Nile

• Located in southwestern Uganda

• River drains from relatively high peaks (~2500m) to Lake Edward (975m) in the East African Rift valley

• 79% catchment land use is agrarian

• Previous work: Mileham et al. – RCM-driven soil-moisture balance model– for SRES A2 scenario from HadCM3

GCM– For 2070-99 time horizon

Mileham 2008

Mitano climatology and hydrology

• Humid-tropical climate– Mean annual precipitation

(1965-79): 1190mm– Bi-model annual regime with

wet seasons from March-May, and Sept-Nov

– Monthly temperature (and evaporation) relatively constant throughout year

– PET > precipitation in 9 out of 12 months

• Discharge lags precipitation by 2-6 weeks

Mileham 2008

Scenarios analysed

• 1-6 °C increase in global mean temperature for HadCM3• 2 °C increase across all seven GCMs

– UKMO HadCM3 & HadGEM1, CCCMA, CSIRO, IPSL, MPI and NCAR

• All four SRES scenarios on HadCM3 (2040-69)• SRES A1b across all seven GCMs (2040-69)

• Not probabilistic, but ‘envelopes of non-discountable change’

Hydrological model

• SWAT (Soil and Water Assessment Tool)– Physically-based semi-distributed river basin scale model– Widely used and freely available– Runs within Arc GIS packages

• Manually calibrated for 1961-90 period– Using CRU TS3 0.5° lat/lon resolution gridded monthly climate

data– Weather generator

• Validated 1991-2005

Model set-up

• Basin defined from 3 arc-second SRTM DEM data• Land-cover derived from FAO Africover data set

– and modified to conform with internal SWAT land-classes

• Soil data from FAO global data base– No local data

• Potential evaporation (PET) calculated using the Hargreaves equation– (temperature-based)

Model calibration

• Calibration period 1961-90• Spearman correlation

coefficient: 0.61• Nash-Sutcliffe coefficient: 0.06

– Issues with CRU observational data…

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Model calibration (2)

• 6 precipitation gauges within the catchment for 1965-1980• For this period, the difference between gauged and gridded (CRU)

precipitation data is correlated with model discharge error– coefficient = 0.40

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• 1965-1980 modelled river discharge– Nash-Sutcliffe = 0.21– Correlation coefficient = 0.71

• 1991-2005 validation consistent with calibration period

Blue line= gridded minus gauged precipitation

Brown line = model minus observed river discharge

Prescribed increase in global mean temperature,on HadCM3

• With increasing global mean temperature:

– Increasing late-season flow– Decreasing early season flow

• Annual runoff:– No linear trend – balance

between decreasing early season flow and increasing late season flow

– Relatively small overall changes until 6 °C threshold

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HadCM3 prescribed warming: temperature vs precipitation

• Impact of temperature changes greater than precipitation in first wet season

• Precipitation-dominated signal in early part of second wet season

• Both are important for end of second wet season

Temperature climate change signal

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Overall climate change signal

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HadCM3 prescribed warming: groundwater climate change signal• % contribution of groundwater and

lateral flow to total discharge:– Little change at 2 °C– Decreasing GW flow in 1st wet

season at 4 & 6 °C• Link to strong temperature signal

in discharge• Little change otherwise

Overall climate change signal

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Groundwater contribution to streamflow

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Lateral flow contribution to streamflow

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HadCM3 prescribed warming: groundwater climate change signal

2 °C prescribed warming for all 7 GCMs• No consistency in direction

of change between GCMs– NCAR and CSIRO are the

wettest and driest (respectively)

• …either in seasonality or annual total

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2 °C prescribed warming: temperature vs precipitation

• Impact of changing temperature less than impact of changing precipitation

• Uncertainty in temperature change between GCMs is less than for precipitation– Agreement in direction of

temperature change for 10 months of the year

Temperature climate change signal

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SRES scenarios on HadCM3 for 2040-2069

• A1b, A2, B1 very similar:– increasing seasonality– increasing total annual flow

• B2 also shows increasing total annual flow, but in the context of reduced early season flow

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SRES emissions scenario

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SRES A1b across all GCMs for 2040-2069

• No consistency in direction of change between GCMs– …either in seasonality or

annual total

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Summary:uncertainty envelopes

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HadCM3 1-6 °C prescribed warming 2 °C prescribed warming (all GCMs)

SRES A1b (all GCMs)HadCM3 SRES scenarios

Solid line=baseline; dotted lines indicate upper and lower bounds of climate change signal

Summary

• Mixed results, but some common themes• GCM uncertainty > climate sensitivity and emissions uncertainty

– Consistent with findings of others• Emissions uncertainty relatively small for 2040-69

– but emissions scenarios likely to diverge towards 2100• No scenario shows substantial decrease in 2nd wet season discharge• Little change in annual low flow period• No consistent changes in early season 1st wet season flow at 2 °C or

A1b between GCMs• Only 1 scenario shows notable decrease in mean annual flow (CSIRO

at 2 °C )

Further work

• Model structure– Global hydrological model

• Some agreement of changes in seasonality and annual runoff

– Mileham et al:• For 2070-99, A2 scenario:

– Annual mean recharge increase by 14%

– Annual mean runoff increase by 84%

• Model parameterisation– Manual vs auto-calibration

techniques

GLOBAL MODEL

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CATCHMENT MODEL

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SRES A1b (2040-2069)

Further work (2)

• Consideration of non-climatic pressures on water resources:– impacts of changing basin population

alongside climate change– Land-use change (from SRES

scenarios)– Ecosystem requirements

(environmental flows)• Incorporation of measure of

storage/reliability– Theoretical reservoir…? (McMahon et

al. 2007)