Amman, Jordan, th 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, PhD Department 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. W HY IS F OOD S ECURITY I MPORTANT IN THE MENA R EGION ?

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 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 st 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 Agriculture Water Conservation Practices Compost from Soil waste as Mulch on Potatoes (major crop in Lebanon) different rates ( 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) 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 P e = accumulated precipitation excess at time t P = accumulated rainfall depth at time t I a = initial abstractionI a = 0.2 S S = potential maximum retention

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

Transfer methods - Snyder Critical characteristics of UH: – Lag – Peak flow – Total time base Snyder Unit Hydrograph

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 Watersheds 1.Importing DEM file

Watershed Modeling System (WMS) Create WMS model of Orontes & Qaa Watersheds 2. Stream Network Delineation 3. Create outlet point 4. Delineate watershed Qaa watershed Orontes watershed

Watershed Modeling System (WMS) Create WMS model of Orontes & Qaa Watersheds 5. Create soil coverage based on Soil Map of Lebanon (1:50000)

Watershed Modeling System (WMS) Create WMS model of Orontes & Qaa Watersheds 5. Create soil coverage (Hydrologic Group Classification) 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 Watersheds 5. Create soil and 6. Landuse

Watershed Modeling System (WMS) Create WMS model of Orontes & Qaa Watersheds 7. Initialize the HEC-HMS model 8. Set Job Control Data (hourly time interval) 9.Create 2D grid for Mod-Clark model (250x250) 10. Compute time of concentration based 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 ) Watershed Time of Concentration (hours) Avg. Curve Number Assi Kaa

Watershed Modeling System (WMS) Create WMS model of Orontes & Qaa Watersheds 11. Compute CN of watershed Qaa watershed Orontes watershed Curve Number Grids

Watershed Modeling System (WMS) Create WMS model of Orontes & Qaa Watersheds 12. Define precipitation (User Hyetograph)

HEC-HMS 3.5 – Results (1) 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) Clark4.35/1/ : Mod Clark17.15/1/ : SCS11.15/1/ : Snyder (Cp=0.4)6.35/1/ : Transform model Peak discharge (CMS) Time of peak discharge Total discharge (MCM) Daily average discharge (CMS) Clark2.75/1/ : Mod Clark8.25/2/ : SCS6.75/1/2011 8: Snyder (Cp=0.4)3.95/1/2011 7:

Thank you Dr. Machlab, Dr. Jomaa, and Mrs. Masaad Dr. Dodge, Dr. Oweis