Amman, Jordan, 20136th Annual Coordination Meeting

American University of Beirut and the regional advantage to support WLI partners for research, capacity building and

scaling technologies

Research Results 2014

Hadi H. Jaafar, PhDDepartment of Agriculture

American University of Beirut

Outline

• Food Security Problems• AUB and Department of Agriculture• Research Activities for this year– Field– Modeling

The Arab region is considered one of the most food insecure in the world.

WHY IS FOOD SECURITY IMPORTANT IN THE MENA REGION?

Arab Countries are the largest net cereal importers in the World

Source: Improving Food Security in Arab Countries (IFAD and WB, 2009)

Arab Countries import more cereals than all Asian countries combined.

MENA is short of arable land

Available natural renewable freshwater in the MENA region is low

Twelve MENA countries fall below the threshold of 1,000 cubic meters of water per capita annually

Arab countries have high stunting prevalence. The highest being in Yemen, Djibouti, Somalia and Sudan.

Source: Breisinger, C., O. Ecker, P. Al-Riffai, B. Yu. 2012. Beyond the Arab awakening: Policies and investment for poverty reduction and food security. International Food Policy Research Institute.

Micro-level Food Insecurity

To sum up: common FS problems across MENA• Limited food access/ stability /agriculture supply• Limited water supply• Rural poverty• Low food / preparedness/ vulnerability to shocks• Poor information systems/Poor FS Monitoring• Chronic malnutrition in food insecure groups

Looking at food systems is needed to address the challenges

and move forward

American University of Beirut Department of Agriculture

• AUB Founded in 1866• 1st Course in agriculture offered in 1912• In 1953, Dr. Samuel Edgecombe, the first Dean of

Agriculture at AUB, carefully selected a plot of 100 hectares of land for AUB's new Agriculture Research and Education Center (AREC).

Research, capacity building and scaling technologies at AUB

• Opportunities for working with DOA at AUB– Conducting Replicates for WLI experiments– Statistical Analysis of Results– Scientific writing (important to attract funding)– HUB for disseminating Technologies and Capacity

Building – Offers Training in Software (GIS courses, water

modeling software)– Precision Irrigation Training

Research Activities

• Field• Modeling

Conservation AgricultureWater Conservation Practices

• Compost from Soil waste as Mulch on Potatoes (major crop in Lebanon)

• different rates (200-400 tons/ha)• Compost is free– Just pay for transportation

Potatoes under Compost

• Total Yield of Potatoes

Straw as mulch on Potatoes

Advantages

• This system is simple• economical (no machinery, no soil bed

preparation, no digging or hilling, and high potato yield)

• sustainable (no contamination/pollution-no herbicides)

• saves water• appropriate for dry and urban areas (gardens)

and suitable for organic farming

Hydrologic Modeling

Objectives:- Modeling of Orontes & Qaa watersheds- Estimation of the total runoff from Orontes &

Qaa watersheds- Comparison between different models

Watershed Modeling

Precipitation

- Infiltration- Evaporation- Transpiration- Interception- Depression storage

…

Losses

Net Precipitation

Transfer Functions

Hydrograph(Flow vs time)

From Rainfall to Runoff

Hydrologic Model

Precipitation

Land surface data

Hydrograph

Losses method - SCS Curve Number

The Soil Conservation Service (SCS) Curve Number (CN) model estimates precipitation excess as a function of - cumulative precipitation- soil cover- land use - antecedent moisture

Pe = accumulated precipitation excess at time tP = accumulated rainfall depth at time tIa = initial abstraction Ia = 0.2 SS = potential maximum retention

𝑷 𝒆=(𝑷−𝑰 𝒂)

𝟐

𝑷− 𝑰𝒂+𝑺

Losses method - SCS Curve Number

Land use

Soil Type

Curve Number

Losses method - SCS Curve Number

SCS lag time equation

SCS time of concentration equation

𝒕 𝒍𝒂𝒈=𝑳𝟎 .𝟖(𝑺+𝟏)𝟎 .𝟕

𝟏𝟗𝟎𝟎×(%𝑺𝒍𝒐𝒑𝒆)𝟎 .𝟓

𝒕𝒄=𝟎 .𝟔×𝒕 𝒍𝒂𝒈

Transfer methods - SnyderCritical characteristics of UH:

– Lag– Peak flow– Total time base

Snyder Unit Hydrograph

𝒕𝒑=𝟓 .𝟓𝒕𝒓• - basin lag• - rainfall duration

𝒕𝒑𝑹=𝒕𝒑−𝒕 𝒓 −𝒕𝑹𝟒

• - duration of desired UH• - lag of desired UH

𝑼 𝒑

𝑨=𝑪

𝑪𝒑

𝒕𝒑• - peak of standard UH• A- watershed drainage area• - UH peaking coefficient• C – conversion constant

Watershed Modeling System (WMS)

• Developed by the Environmental Modeling Research Laboratory of Brigham Young University in cooperation with the U.S. Army Corps of Engineers Waterways Experiment Station and is currently being developed by Aquaveo LLC.

• Performs automated basin delineation

• Computes important basin parameters such as area slope runoff distances

• Serves as a graphical user interface for several hydraulic and hydrologic models

Watershed Modeling System (WMS)Create WMS model of Orontes & Qaa Watersheds1. Importing DEM file

Watershed Modeling System (WMS)Create WMS model of Orontes & Qaa Watersheds2. Stream Network Delineation3. Create outlet point4. Delineate watershed

Qaa watershed

Orontes watershed

Watershed Modeling System (WMS)Create WMS model of Orontes & Qaa Watersheds5. Create soil coverage based on Soil Map of Lebanon (1:50000)

Watershed Modeling System (WMS)Create WMS model of Orontes & Qaa Watersheds5. Create soil coverage (Hydrologic Group Classification)

Hydrologic Group

TextureHorizon Depth (cm)

TextureHorizon Depth (cm)

TextureHorizon Depth (cm)

Calcaric Gleysols A silty loam 0-20 silty loam 20-45 silty loam 45-150Areno-Eutric Leptosols B Sandy loam 0-50 sandstoneCalcaro-Mollic Leptosols B silt loam 0-20 silt loam 20-30Fluvic Cambisols B loam 0-25 loam 25-40 loam 40-80Haplic Arenosols B sandy loam 0-5 sandy loam 5--55Hyperskeletic Leptosols B loam 0-20 clay 20-65 clay 65+Skeletic Regosols B sandy loam 0-20 clay loam 20-80 clay loam 80-150Aridic calcisols C clay loam 0-20 clay loam 20-65Haplic Calcisols C clay loam 0-20 clay 20-100 clay 100-150Haplic Luvisols C Sandy clay loam 0-28 clay loam 28-42 clay 42-77Leptic Calcisols C clay loam 0-40 clay loam 40+Lithic Leptosols C Loam 0-30 limestoneVertic Cambisols C clay loam 0-10 sandy clay 10--70 sandy clay 70-90Aridic Regosols D clay 0-10 clay 10--40 clay 40-70Calci-Haplic Cambisols D silty clay 0-25 clay 25-60 clay 60-110Endocalcaro-Hyperskeletic Leptosols D clay 0-30 clay loam 30-135 sandy clay loam 135-160Humi-Eutric Cambisols D sandy clay 0-20 sandy clay 20-60 sandy clay 60-100Leptic Andosols D clay loam 0-20 basaltLeptic Luvisols D clay 0-20 clay 20-40Petric Calcisols D clay loam 0-30 marlRhodic Luvisols D clay 0-25 clay 25-60 clay 60-150Vertic Luvisols D clay 0-35 clay 35-45 clay 45-110

First layer 2nd layer 3rd layer

Some of soil types present in our study area with its corresponding texture and its hydrologic group classification

Watershed Modeling System (WMS)Create WMS model of Orontes & Qaa Watersheds5. Create soil and 6. Landuse

Watershed Modeling System (WMS)Create WMS model of Orontes & Qaa Watersheds7. Initialize the HEC-HMS model8. Set Job Control Data

(hourly time interval)

9. Create 2D grid for Mod-Clark model(250x250)

10. Compute time of concentrationbased on SCS lag time equation

11. Compute CN of watershed based on land use and soil groups

(gridded CN in ModClark model, average CN in HEC-HMS )

WatershedTime of

Concentration (hours)

Avg. Curve Number

Assi 22.6 58.3

Kaa 17.0 59.6

Watershed Modeling System (WMS)Create WMS model of Orontes & Qaa Watersheds11. Compute CN of watershed

Qaa watershedOrontes watershed

Curve Number Grids

Watershed Modeling System (WMS)Create WMS model of Orontes & Qaa Watersheds12. Define precipitation (User Hyetograph)

HEC-HMS 3.5 – Results (1)

0

5

10

15

20

25

30 0

2

4

6

8

10

12

14

16

18

20

Precipitation (MM) Losses (MM)Clark modClarkSCS Snyder (Cp=0.4)

Prec

ipit

ation

(mm

)

Tota

l Flo

w (m

3/s)

0

5

10

15

20

25

30 0

1

2

3

4

5

6

7

8

9

10

Precipitation (MM)Losses (MM)ClarkmodClark

Prec

ipit

ation

(mm

)

Tota

l Flo

w (m

3/s)

Orontes Watershed Qaa Watershed

HEC-HMS 3.5 – Results (2)Orontes Watershed Results

Qaa Watershed Results

Transform model Peak discharge (CMS)

Time of peak discharge

Total discharge (MCM)

Daily average discharge (CMS)

Clark 4.3 5/1/2011 19:00 0.7 1.8Mod Clark 17.1 5/1/2011 17:00 3.3 8.5SCS 11.1 5/1/2011 11:00 0.8 2.9Snyder (Cp=0.4) 6.3 5/1/2011 11:00 0.8 2.0

Transform model Peak discharge (CMS)

Time of peak discharge

Total discharge (MCM)

Daily average discharge (CMS)

Clark 2.7 5/1/2011 13:00 0.4 1.0Mod Clark 8.2 5/2/2011 13:00 1.3 3.5SCS 6.7 5/1/2011 8:00 0.4 1.4Snyder (Cp=0.4) 3.9 5/1/2011 7:00 0.4 1.0

Thank you

Dr. Machlab, Dr. Jomaa, and Mrs. Masaad

Dr. Dodge, Dr. Oweis