Original research questions from project brief:

1. How can the consequences of improved rainwater management (RMS) systems be anticipated (and measured)? What methods are appropriate under different circumstances?

2. How can the contribution of improved RMS be assessed relative to the contributions of other factors?

3. How can research on performance be used to further improve RMS design?

N4 Activity update, May 2012

N4 Activity update, May 2012

N4 team (9 staff; 4 consultants; 10 students):

TL/hydrology/modelling: Charlotte MacAlisterHydrology/modelling: Solomon Seyoum, Dan Fuka, Zach Easton,

Tammo Steenhuis, Francisco Flores Soils/crop productivity: Teklu ErkossaLivestock productivity: Amare Haileslassie, Don PedenEconomics/Livelihoods: Kinde Getnet, Nancy JohnstonEconomic data review: Gerba LetaSpatial Analysis/data: Yenenesh Abebe

Students: MSc - Bedasa Eba (also N2), Ayele Abebe (also N2), Alemayehu Wudneh, Bamlaku Desalegn, Getnet Taye, Negasa Bane, Addisu Asfaw, Nurelegn Mekuriaw.PhD - Abeyou Wale, Haimanote Bayabil

2012 budget: $359K reducing to $319K

N4 ‘themes’:

• Developing hydrological (process based) and water resource models of the BNB to anticipate the impact of (large scale) RMS implementation

• Assessing sediment and nutrient transport, loss and contamination

• Investigating crop-water and livestock-water productivity > relating to RMS potential

• Livelihoods and poverty impact analysis

• Economic assessment of the water, sediment and agronomic components of the primary farming approaches, and modelling anticipated impacts of potential RMS on livelihoods in the BNB

• Linking to N3 targeting for recommendation of appropriate ‘development domains’ for RMS

• Analysis of policy implications of basin scale implementation of RMS

N4 Activity update, May 2012

*Biophysical *Socio-economic

Biophysical Impacts of RMS

• Improvements in RMS optimize distribution of rainfall amongst different hydrologic components to:- increase water availability > less loss and more water storage (soil, surface,

GW)- reduce evaporation and increase transpiration i.e. crop water productivity =

fodder availability and improved livestock water productivity- improve soil conditions (reduce sediment loss) and reverse land degradation

• Evaluate current status of hydrologic components in relation to rainfall from global data

• Initialize hydrologic and water resource models to evaluate impacts of RMS on water availability, sediment load, soil moisture (and groundwater recharge)

N4 Activity update, May 2012

Challenges:

• Properly describing hydro-physical processes e.g. runoff

• Parameterizing hydrological components (P, ET, soil moisture etc)

• Representing RMS practices in the process based model (SWAT)

• Accurate representation of plant water use at large scale (LU-LC)

• Reliable sediment data

• Sediment routing in reservoirs within WEAP model

• Definition of RMS scenarios for impact modelling

• Linking hydrological, water resource and economic models

N4 Activity update, May 2012

Rainfall

Vegetation Land Surface

Water Body

Soil

Aquifer

Stream

Canopy Evaporation

Soil Evaporation

Evaporation

Transpiration

Watershed discharge

Baseflow

Overland flow

Interflow

Percolation

Throughfall

Infiltration

Capillary Rise

Capillary Rise

Optimizing rainfall partitioning and quantifying rainfall-runoff processes

Unproductive water use

TARGET/Productive water use

Transfers

Minimize:

Maximize:

Optimize:

Proportion of Rainfall Contributing to Major Hydrologic Components (Climate Forecast System Reanalysis, 31 year mean)

Proportion of Rainfall Contributing to Major Hydrologic Components (Climate Forecast System Reanalysis, 31 year mean - Wet Season )

Hydrological Units are defined by a coincidence of soil type and landuse

Soils

Landuse

Hydrological Response Units

Traditional SWAT

So runoff here is calculated the same..

…as here

but we know this is not the case

swat_hrusSURQ [mm]

1.978 - 4.014

4.014 - 4.440

4.440 - 4.783

4.783 - 5.214

5.214 - 5.75

swat_vsa_hrusSURQ [mm]

0 - 0.939

0.939 - 2.201

2.201 - 4.532

4.532- 8.868

8.868- 10.213

SWAT-VSA Runoff (mm)

SWAT Runoff (mm)

0-0.93 0.93-2.20 2.20-4.53 4.53-8.86 8.86-10.21

0.66-0.93 0.93-2.20 2.20-4.53 4.53-8.86 8.86-10.21

a b

Incorporating Topographic Index in the SWAT model iiii

ii TTI

Tln

tanln

‘Automating’ the Topographic Index in ArcGIS

TI

Global Soil – to be

replaced by Masterplan

soil coverage

HRU’s

SWAT-WEAP Interface (WEAP schema)

SWAT sub-catchmentsDams / ReservoirsIrrigation demandsRiver Networks

Next: how to parameterize the RMS in the SWAT process model…..

Anticipating RMS impacts on Crop Water Productivity – on and off site Aim Objective Activities Status

1. Estimate on-site effects

Understand effect on crop water productivity

• Estimate crop water productivity (CWP) of Vertisols under current and alternative RMS scenarios

• Model baseline for major crops completed• Vertisol areas suitable for different management

alternatives identified and mapped

• Estimate CWP of other soils under selected RMS scenarios

• Assessment of current RMS at different landscape positions in the selected catchments completed

• Model baseline for CWP under way with MSc students

• Identify determinants of CWP • Data analysis under way to identify determinant factors of CWP based on the above at the landscape level

Quantify soil erosion as an indicator of land degradation

• Measure sediment and nutrient transport and loss at selected landscapes

• Sediment sampling and laboratory analysis completed• Data preparation and analysis underway

• Relate nutrient loss to yield loss or replacement cost at landscape level

• Crop response functions for selected crops to nitrogen and phosphorus established at landscapes level

• Further analysis underway

• Extrapolate to larger / basin scale • Method to be discussed and agreed upon

2. Estimate off-site effects

a. Hydrology and water availability

• Estimate differential Crop Water Requirement under the RMS scenarios

• Estimate the impact on flow at sub-catchment levels

• Method to be discussed and agreed upon

b. Water quality Measure sediment and nutrient load • Sediment and nutrient concentration measured at sub-catchment level

3. Contribute to capacity building

Support and supervise graduate students from partner institutions

Provide research topics, financial support and guidance

• 6 students finalizing their thesis on two thematic areas: crop water productivity Sediment and nutrient loss

Activities Who is responsible Status Comment

Livestock management practices and its implications on rain water use efficiencies

Ayele/Amare/Alan Data collected, cleaned, analyzed and draft report in progress

Partly under revision

Livestock feed sourcing and feeding strategies and its implications on rain water use efficiencies

Bedasa/Amare/Alan Data collected, cleaned, analyzed and draft report in progress

Strategies to integrate livestock into agricultural rain water management

Amare/Kebebe/Alan Hypothesis developed, (data secured- from student), model selected and sample model run

Econometrics models ( GLM, and Tobit)

Anticipating economic impacts of RMS on households and catchments:

1. Establish a baseline of current situation using HH, hydrological/sediment and secondary data (at hydrological unit scale or HRU)Done:• Primary HH data gathered at Jeldu, Diga, Fogera• ECOSAUT populated for Jeldu and Fogera• Preliminary analysis completed for JelduChallenges:• ‘Validating’ the model and analysis• Incorporating crop, sediment and runoff data from 3 sites

2. Scenario development with N2, N3 and stakeholders

3. Extrapolation of economic impacts of RMS scenarios to larger scale

N4 Activity update, May 2012

Analysis of policy implications of basin scale implementation of RMS

No output so far……..