Flood Vulnerability Assessment Using Multi- Criteria Approach: a Case Study from North Gaza

اﻟﺠـﺎﻣﻌــــــــــﺔ اﻹﺳـــــﻼﻣﯿــﺔ ﺑﻐــﺰة The Islamic University of Gaza

ﻋﻤﺎدة اﻟﺒﺤﺚ اﻟﻌﻠﻤﻲ واﻟﺪراﺳﺎت اﻟﻌﻠﯿﺎ Deanship of Research and Graduate Studies

ﻛـﻠﯿــــــــــــــــــــﺔ اﻟﮭﻨﺪﺳــــــــــــــــــﺔ Faculty of Engineering

Master of Water Resources Management ﻣﺎﺟﺴﺘﯿـــــﺮ إدارة ﻣﺼـــــــﺎدر ﻣﯿــــﺎه

Flood Vulnerability Assessment Using Multi- Criteria Approach: A Case Study from North Gaza

ﺘﻘییم ﻤواطن ﻀعﻒ أﻤﺎﻛن اﻟتﻌرض ﻟﻠﻔیضﺎﻨﺎت �ﺎﺴتخدام ﻨﻬﺞ ﻤتﻌدد اﻟمﻌﺎﯿیر: دراﺴﺔ ﺤﺎﻟﺔ ﺸمﺎل ﻗطﺎع ﻏزة

By Dalya Taysir Matar

Supervised by

Dr. Khalil Al Astal Dr. Tamer Eshtawi Assistant Professor in Water Assistant Professor in Water Resources Engineering Resources Engineering

A thesis submitted in partial fulfillment of the requirements for the degree of Master of Water Resources Management

March /2019 إﻗــــــــــــــرار

أﻨﺎ اﻟموﻗﻊ أدﻨﺎﻩ ﻤﻘدم اﻟرﺴﺎﻟﺔ اﻟتﻲ ﺘحمﻞ اﻟﻌنوان:

Flood Vulnerability Assessment Using Multi-Criteria Approach: A Case Study from North Gaza

أﻗر �ﺄن ﻤﺎ اﺸتمﻠت ﻋﻠیﻪ ﻫذﻩ اﻟرﺴﺎﻟﺔ إﻨمﺎ ﻫو ﻨتﺎج ﺠﻬدي اﻟخﺎص، �ﺎﺴتثنﺎء ﻤﺎ ﺘمت اﻹﺸﺎرة إﻟیﻪ ﺤیثمﺎ ورد، وأن

ﻫذﻩ اﻟرﺴﺎﻟﺔ �كﻞ أو أي ﺠزء ﻤنﻬﺎ ﻟم �ﻘدم ﻤن ﻗبﻞ ا ﻻ ﺨ ر� ن ﻟنیﻞ درﺠﺔ أو ﻟﻘب ﻋﻠمﻲ أو �حثﻲ ﻟدى أي ﻤؤﺴسﺔ

ﺘﻌﻠیمیﺔ أو �حثیﺔ أﺨرى.

Declaration

I understand the nature of plagiarism, and I am aware of the University’s policy on this.

The work provided in this thesis, unless otherwise referenced, is the researcher's own work and has not been submitted by others elsewhere for any other degree or qualification.

اﺴم اﻟطﺎﻟب: داﻟﯿﮫ ﺗﯿﺴﯿﺮ ﻣﻄﺮ :Student's name

اﻟتوﻗیﻊ: :Signature

ا ﻟ ت ﺎ ر� ﺦ : :Date

Abstract

Flooding is one of the prevalent natural disasters that cause serious damage to the population. The objective of this study was to assess the flood vulnerability in North Gaza area. This study has been done using three models: 1) Analytical Hierarchy process (AHP), 2) ArcGIS, and 3) SewerGEMs. To model and to predict the flood vulnerability areas in North Gaza, an integrated GIS and AHP techniques were used. The flood vulnerability mapping was produced using various flood vulnerability causative criteria such as social including poverty and culture and a population density of the area, coping, household structural, and physical including rainfall quantities, soil type, land use, and network availability and reliability. Moreover, the existing stormwater drainage system was investigated using SewerGEMs. The main method used for the delineation of the catchment areas of North Gaza governorate was Arc Hydro model under the ArcGIS environment. The main input data of the modeling was elevation points, digital elevation model (DEM), study area position. The catchment areas at ponds and depression zones were developed by the end of the model. The final results of catchments delineation were used to estimate the amount of the runoff using the Soil Conservation Service (SCS) method in SewerGEMs and compare the generated amount of runoff with the system capacity. From the pairwise judgment of each level at AHP model, it was clear that the most likely criteria to increase the vulnerability of the area was the physical criteria with a relative probability of 71.79% while the social, the household structure and the coping vulnerability were having less contribution to increase flood vulnerability of the area with relative weight of 15.18%, 7.67%, and 5.36% respectively. Furthermore, the most dominant sub-criteria were the drainage and slope with a relative weight of 22.69% and 18% respectively. It also showed high consistency in judicial opinions. As well as the results provided the most favorable and less favorable alternative actions to mitigate the vulnerability which shows that integrated stormwater management was the best alternative with a relative weight of 55.8% while the relocation option was ranked as the option before the do nothing with a relative weight of 19.2% due to high cost and limited land. Results of vulnerability mapping showed that nearly 45% of North Gaza are among highly vulnerable toward flood. While almost 70% of the population in North Gaza are expected to expose to the high flood risk. On the other hand, the assessment of the existing drainage pipelines presents that 40% of pipelines capacity is inadequate and the storage capacity of all the storm ponds are insufficient except for Abu Rashid pond. In that improvement and upgrading of the existing system are highly recommended. By the end, intervention prioritized and the procedure was also developed according to the degree of vulnerability, the highest vulnerable areas were proposed to conduct integrated stormwater management plan (ISWMP) while doing nothing option was proposed for the lowest vulnerable areas. In general, this research provides guidance on what the decision makers should consider when assessing vulnerability, setting mitigation options in response to hazards and giving a recommended prioritize support.

III

ﻣﻠﺨﺺ اﻟﺪراﺳﺔ

ﺗﻌﺪ اﻟﻔﯿﻀﺎﻧﺎت ھﻲ واﺣﺪة ﻣﻦ اﻟﻜﻮارث اﻟﻄﺒﯿﻌﯿﺔ اﻟﺴﺎﺋﺪة اﻟﺘﻲ ﺗﺘﺴﺒﺐ ﻓﻲ أﺿﺮار ﺟﺴﯿﻤﺔ ﻟﻠﺴﻜﺎن. ﺗﮭﺪف ھﺬه اﻟﺪراﺳﮫ إﻟﻰ ﺗﻘﯿﯿﻢ ﻣﻮاطﻦ اﻟﻀﻌﻒ ﺗﺠﺎه اﻟﻔﯿﻀﺎﻧﺎت ﻓﻲ ﻣﻨﻄﻘﺔ ﺷﻤﺎل ﻗﻄﺎع ﻏﺰة. ﺗﻢ إﺟﺮاء ھﺬه اﻟﺪراﺳﺔ ﻣﻦ ﺧﻼل ﺛﻼﺛﺔ ﻧﻤﺎذج: 1) ﻧﻤﻮذج اﻟﺘﺴﻠﺴﻞ اﻟﮭﺮﻣﻲ اﻟﺘﺤﻠﯿﻠﻲ (AHP)، 2) ﻧﻈﻢ اﻟﻤﻌﻠﻮﻣﺎت اﻟﺠﻐﺮاﻓﯿﮫ (SewerGEMs (3 ،(ArcGIS. ﻟﻨﻤﺬﺟﺔ وﺗﻮﻗﻊ ﻣﻨﺎطﻖ اﻟﻀﻌﻒ ﻓﻲ ﺷﻤﺎل ﻗﻄﺎع ﻏﺰة، ﺗﻢ اﺳﺘﺨﺪام ﺗﻘﻨﯿﺎت ﻧﻈﻢ اﻟﻤﻌﻠﻮﻣﺎت اﻟﺠﻐﺮاﻓﯿﺔ واﻟﺘﺴﻠﺴﻞ اﻟﮭﺮﻣﻲ اﻟﻤﺘﻜﺎﻣﻠﺔ. وﻗﺪ ﺗﻢ رﺳﻢ ﺧﺮاﺋﻂ اﻟﻀﻌﻒ ﺗﺠﺎه اﻟﻔﯿﻀﺎﻧﺎت ﺑﺎﺳﺘﺨﺪام ﺑﻌﺾ اﻟﻤﻌﺎﯾﯿﺮ اﻟﻤﺴﺒﺒﺔ ﻟﻠﻀﻌﻒ ﻣﺜﻞ اﻟﻤﻌﺎﯾﯿﺮ اﻻﺟﺘﻤﺎﻋﯿﮫ اﻟﺘﻲ ﺗﺸﻤﻞ اﻟﻜﺜﺎﻓﺔ اﻟﺴﻜﺎﻧﯿﺔ ﻟﻠﻤﻨﻄﻘﺔ ، وﻣﻌﺎﯾﯿﺮ اﻟﺘﺄﻗﻠﻢ ، ﻣﻌﺎﯾﯿﺮاﻟﮭﯿﺎﻛﻞ اﻟﺒﻨﺎﺋﯿﺔ ﻟﻠﻤﻨﺎزل ، اﻟﻤﻌﺎﯾﯿﺮاﻟﻔﯿﺰﯾﺎﺋﯿﺔ واﻟﺘﻲ ﺗﺸﻤﻞ ﻣﯿﻮل اﻟﻤﻨﻄﻘﺔ، ﻛﻤﯿﺎت ﺳﻘﻮط اﻟﻤﻄﺮ، ﻧﻮع اﻟﺘﺮﺑﺔ، اﺳﺘﺨﺪام اﻷراﺿﻲ وﺗﻮاﻓﺮ ﺷﺒﻜﺎت اﻟﺘﺼﺮﯾﻒ وﻣﺪى ﻗﺪرﺗﮭﺎ اﻻﺳﺘﯿﻌﺎﺑﯿﺔ. ﻋﻼوة ﻋﻠﻰ ذﻟﻚ، ﺗﻢ ﺧﻼل اﻟﺪراﺳﺔ دراﺳﺔ ﻧﻈﺎم ﺗﺼﺮﯾﻒ ﻣﯿﺎه اﻷﻣﻄﺎراﻟﺤﺎﻟﻲ واﻟﺘﺤﻘﯿﻖ ﻣﻦ ﻗﺪرﺗﮫ اﻻﺳﺘﯿﻌﺎﺑﯿﺔ ﺑﺎﺳﺘﺨﺪام SewerGEMs. ﺣﯿﺚ ﺗﻢ اﻋﺘﻤﺎد ﻧﻤﻮذج Arc Hydro ﻟﺪى ﺑﯿﺌﺔ ال Arc GIS ﻟﺘﺤﺪﯾﺪ ﻣﻨﺎطﻖ ﻣﺴﺘﺠﻤﻌﺎت اﻟﻤﯿﺎه ﻓﻲ ﻣﺤﺎﻓﻈﺔ ﺷﻤﺎل ﻏﺰة. ﺑﯿﺎﻧﺎت اﻹدﺧﺎل اﻟﺮﺋﯿﺴﯿﺔ اﻟﻤﺴﺘﺨﺪﻣﮫ ﻟﻠﻨﻤﺬﺟﺔ ھﻲ ﻧﻘﺎط اﻻرﺗﻔﺎع، ﻧﻤﻮذج اﻻرﺗﻔﺎع اﻟﺮﻗﻤﻲ (DEM)، ﺣﺪود ﻣﻨﻄﻘﺔ اﻟﺪراﺳﺔ. ﻓﻲ ﻧﮭﺎﯾﺔ اﻟﻨﻤﺬﺟﮫ ﺗﻢ ﺗﺤﺪﯾﺪ ﻣﻨﺎطﻖ ﻣﺴﺘﺠﻤﻌﺎت اﻟﻤﯿﺎه ﻟﺒﺮك ﺗﺠﻤﯿﻊ ﻣﯿﺎه اﻻﻣﻄﺎر وﻣﻨﺎطﻖ اﻟﻔﯿﻀﺎن ﻛﻤﺎ ﺗﻢ اﺳﺘﺨﺪام اﻟﻨﺘﺎﺋﺞ اﻟﻨﮭﺎﺋﯿﺔ اﻟﺴﺎﺑﻘﺔ ﻟﺘﻘﺪﯾﺮ ﻛﻤﯿﺔ اﻟﺠﺮﯾﺎن اﻟﺴﻄﺤﻲ ﺑﺎﺳﺘﺨﺪام طﺮﯾﻘﺔ Soil Conservation Service (SCS) ﻟﺪي SewerGEMs وﻣﻘﺎرﻧﺔ ﻛﻤﯿﺔ اﻟﺠﺮﯾﺎن اﻟﻨﺎﺗﺠﺔ ﻣﻊ ﻗﺪرة اﺳﺘﯿﻌﺎب اﻟﺸﺒﻜﺔ (ﻧﻈﺎم اﻟﺘﺼﺮﯾﻒ). أظﮭﺮت ﻧﺘﺎﺋﺞ اﻟﻤﻘﺎرﻧﺔ اﻟﻤﺰدوﺟﺔ ﻟﻜﻞ ﻣﺴﺘﻮى ﻓﻲ ﻧﻤﻮذج اﻟﺘﺴﻠﺴﻞ اﻟﮭﺮﻣﻲ أن اﻟﻤﻌﺎﯾﯿﺮ اﻟﻔﯿﺰﯾﺎﺋﯿﺔ ھﻲ اﻷﻛﺜﺮ ﺗﺄﺛﯿﺮاً ﻋﻠﻰ رﻓﻊ ﺿﻌﻒ اﻟﻤﻨﻄﻘﺔ ﺗﺠﺎه اﻟﻔﯿﻀﺎﻧﺎت ﺑﻨﺴﺒﺔ 71.79٪، ﺑﯿﻨﻤﺎ ﻛﺎﻧﺖ اﻟﻤﻌﺎﯾﯿﺮ اﻻﺟﺘﻤﺎﻋﯿﮫ، اﻟﮭﯿﻜﻞ اﻟﺒﻨﺎﺋﻲ ﻟﻠﻤﻨﺎزل وﻣﻌﺎﯾﯿﺮ اﻟﺘﻜﯿﻒ أﻗﻞ ﻣﺴﺎھﻤﺔ ﻓﻲ زﯾﺎدة ﺿﻌﻒ اﻟﻤﻨﻄﻘﮫ ﺗﺠﺎه اﻟﻔﯿﻀﺎﻧﺎت ﺑﻨﺴﺒﺔ 15.18%، 7.67% و 5.36% ﻋﻠﻰ اﻟﺘﻮاﻟﻲ. ﻣﻦ ﻧﺎﺣﯿﺔ أﺧﺮى، ﻛﺎﻧﺖ اﻟﻤﻌﺎﯾﯿﺮ اﻟﻔﺮﻋﯿﺔ اﻷﻛﺜﺮ ﺗﺄﺛﯿﺮاً ھﻲ ﺷﺒﻜﺎت اﻟﺘﺼﺮﯾﻒ وﻣﯿﻞ اﻟﻤﻨﻄﻘﮫ ﺑﻨﺴﺒﮫ 22.69% و 18% ﻋﻠﻰ اﻟﺘﻮاﻟﻲ. ﻛﻤﺎ أظﮭﺮت اﻟﻨﺘﺎﺋﺞ اﺗﺴﺎﻗًﺎ ًﻛﺒﯿﺮا ﻓﻲ آراء اﻟﺨﺒﺮاء اﻟﺬﯾﻦ ﻗﺎﻣﻮا ﺑﺘﺤﺪﯾﺪ اﻷوﻟﻮﯾﺎت واﻟﻤﻘﺎرﻧﮫ ﺑﯿﻦ اﻟﻤﻌﺎﯾﯿﺮ. ﻛﻤﺎ أظﮭﺮت اﻟﻨﺘﺎﺋﺞ اﻹﺟﺮاءات اﻟﺒﺪﯾﻠﺔ اﻷﻛﺜﺮ ﻣﻼءﻣﺔ واﻷﻗﻞ ﺗﻔﻀﯿﻼً ﻟﻠﺘﺨﻔﯿﻒ ﻣﻦ اﻟﻀﻌﻒ واﻟﺘﻲ أوﺿﺤﺖ أن اﻹدارة اﻟﻤﺘﻜﺎﻣﻠﺔ ﻟﻤﯿﺎه اﻷﻣﻄﺎر ھﻲ اﻟﺒﺪﯾﻞ اﻷﻓﻀﻞ ﺑﻮزن ﻧﺴﺒﻲ ﯾﺒﻠﻎ 55.8٪ ﺑﯿﻨﻤﺎ ﺗﻢ ﺗﺼﻨﯿﻒ ﺧﯿﺎر إﻋﺎدة اﻟﺘﻮطﯿﻦ ﻛﺨﯿﺎرأﺧﯿﺮ ﻗﺒﻞ ﻋﺪم اﺗﺨﺎذ أي إﺟﺮاءات ﻟﻠﺘﺨﻔﯿﻒ ﺑﻮزن ﻧﺴﺒﻲ 19.2٪ ﺑﺴﺒﺐ اﻟﺘﻜﻠﻔﺔ اﻟﻌﺎﻟﯿﺔ واﻷراﺿﻲ اﻟﻤﺤﺪودة. أظﮭﺮت ﻧﺘﺎﺋﺞ ﺧﺮاﺋﻂ اﻟﻀﻌﻒ أن 45% ﻣﻦ ﺷﻤﺎل ﻗﻄﺎع ﺗﺼﻨﻒ ﺿﻤﻦ اﻟﻤﻨﺎطﻖ اﻷﻛﺜﺮ ﺿﻌﻔﺎً ﺗﺠﺎه اﻷﻣﻄﺎر. ﻓﻲ ﺣﯿﻦ أن ﻣﺎ ﯾﻘﺮب ﻣﻦ 70 ٪ ﻣﻦ اﻟﺴﻜﺎن ﻓﻲ ﺷﻤﺎل ﻏﺰة ﻣﻦ اﻟﻤﺘﻮﻗﻊ أن ﯾﺘﻌﺮﺿﻮا ﻟﺨﻄﺮ اﻟﻔﯿﻀﺎﻧﺎت اﻟﻌﺎﻟﯿﺔ. ﻣﻦ ﻧﺎﺣﯿﺔ أﺧﺮى، أظﮭﺮت ﻧﺘﺎﺋﺞ ﺗﻘﯿﯿﻢ ﺷﺒﻜﺎت اﻟﺘﺼﺮﯾﻒ أن 40% ﻣﻦ ﺷﺒﻜﺎت ﺗﺼﺮﯾﻒ ﻣﯿﺎه اﻷﻣﻄﺎر ﺗﻌﺎﻧﻲ ﻣﻦ ﻗﺼﻮر ﻓﻲ اﻟﻘﺪرة اﻻﺳﺘﯿﻌﺎﺑﯿﺔ وﻋﺪم ﻛﻔﺎﯾﺔ اﻟﻘﺪرة اﻟﺘﺨﺰﯾﻨﯿﺔ ﻷﺣﻮاض ﺗﺠﻤﯿﻊ ﻣﯿﺎه اﻷﻣﻄﺎر ﺑﺎﺳﺘﺜﻨﺎء ﺑﺮﻛﺔ أﺑﻮ راﺷﺪ اﻷﻣﺮ اﻟﺬي ﯾﺘﻄﻠﺐ ﺗﺤﺴﯿﻦ اﻟﻨﻈﺎم اﻟﺤﺎﻟﻲ ورﻓﻊ ﻣﺴﺘﻮاه. ﻓﻲ اﻟﻨﮭﺎﯾﺔ ﺘم ﺘطﻮﯿر أوﻟوﯿﺎت اﻟﺘدﺨل وإﺠراءاﺘﮭﺎ ﺤﺴب درﺠﺔ اﻟﻀﻌف، واﻗﺘرﺤت ﺘﻨﻔﯿذ ﺨطﺔ ﻤﺘﮐﺎﻤﻟﺔ ﻹدارة ﻤﯿﺎه اﻷﻣﻄﺎر (ISWMP) ﻓﻲ اﻟﻤﻨﺎطﻖ اﻷﻛﺜﺮ ﺿﻌﻔﺎً، ﻓﻲ ﺤﯿن ﺘم اﻗﺘراح ﻋﺪم اﺟﺮاء أي ﻧﻮع ﻣﻦ اﻟﺘﺪﺧﻞ ﻓﻲ اﻟﻤﻨﺎطﻖ اﻷﻗل ﺿﻌﻔﺎً. ﺑﺸﻜﻞ ﻋﺎم ، ﯾﻘﺪم ھﺬا اﻟﺒﺤﺚ إرﺷﺎدات ﺣﻮل ﻣﺎ ﯾﺠﺐ ﻋﻠﻰ ﺻﻨﺎع اﻟﻘﺮار ﻣﺮاﻋﺎﺗﮫ ﻋﻨﺪ ﺗﻘﯿﯿﻢ ﻣﻮاطﻦ اﻟﻀﻌﻒ، وﺗﺤﺪﯾﺪ ﺧﯿﺎرات اﻟﺘﺨﻔﯿﻒ اﺳﺘﺠﺎﺑﺔ ﻟﻠﻤﺨﺎطﺮ وﺗﺤﺪﯾﺪ أوﻟﻮﯾﺎت اﻟﺪﻋﻢ.

Dedication

I dedicate this project to Allah my creator Almighty for giving me strength, and a unique enthusiasm for achieving this success.

I also dedicate this work to my mother and my father for their unconditional encouragement and support whose made it possible for me to complete what I have started.

To my beloved brothers, sisters, and friends for their whole-hearted supporting, helping and standing by me.

To the unknown soldier who affected me during this quest and stands by me when things look bleak.

Thank you.

My love for all of you will never be quantified.

Acknowledgment

As a Moslem, I am offering my greatest gratitude to Allah SWT for his grace, blessings, guidance, and help which could be no justification for the existence of my project without him.

I would like to extend my highest gratitude to my supervisors Dr. Khalil Al Astal and Dr. Tamer Eshtawi, who always gave me their best support and guidance.

These special thanks also go to the experts for their cooperation in providing me their judgment on assessing the vulnerability.

Many thanks also go to municipal key informants for their support, cooperation and providing me with the primary and secondary data, which help me to do a lot of research and know a new thing about the study area.

My amazing family, the greatest gifts anybody could ever ask, for their abundant support especially in my academic life. I have always been blessed by them for giving me all prayers, spirit, and courage not only this but also always of enough willingness to cultivate me with huge support and encouragement to accomplish my studies and now to finish up this thesis project.

My gratitude never stops here, because there are many people, I am grateful for in my journey. They are too many for me to mention them name by name but all I can say is that I never took anybody’s even smallest support for granted. Every one of them I am most grateful for helping me in a direct or indirect way.

Table of Contents

DECLARATION ...... I II ...... ﻧﺘﯿﺠﺔ اﻟﺤﻜﻢ ﻋﻠﻰ اﻷطﺮوﺣﮫ ABSTRACT ...... III IV ...... ﻣﻠﺨﺺ اﻟﺪراﺳﺔ DEDICATION ...... V ACKNOWLEDGMENT ...... VI TABLE OF CONTENTS ...... VII LIST OF TABLES ...... X LIST OF FIGURES...... XII LIST OF ABBREVIATIONS ...... XVI CHAPTER 1 INTRODUCTION ...... 1 1.1 BACKGROUND AND CONTEXT ...... 2 1.2 PROBLEM DESCRIPTION ...... 3 1.3 AIM AND OBJECTIVES ...... 4 1.3.1 Research Aim ...... 4 1.3.2 Research Objectives ...... 4 1.4 BRIEF METHODOLOGY ...... 5 1.5 GENERAL FRAMEWORK ...... 6 CHAPTER 2 LITERATURE REVIEW ...... 8 2.1 INTRODUCTION ...... 9 2.2 STORMWATER RUNOFF...... 9 2.3 FACTORS AFFECTING RUNOFF ...... 9 2.3.1 Land Cover and Land-Use ...... 9 2.3.2 Soil Type ...... 11 2.4 STORMWATER RUNOFF COMPUTATIONS ...... 11 2.4.1 Method 1: SCS Dimensionless Unit Hydrograph method ...... 12 2.4.2 Method 2: Rational Method...... 15 2.5 MULTICRITERIA ANALYSIS METHOD ...... 19 2.6 CATEGORIZATION OF THE MCA METHODS ...... 21 2.7 COMPARISON AMONG MCA METHODS ...... 23 2.8 ANALYTICAL HIERARCHY PROCESS (AHP) ...... 26 2.9 WEIGHTED LINEAR COMBINATION METHOD ...... 31

VII

2.10 FLOOD RISK ASSESSMENT...... 31 2.11 FLOOD VULNERABILITY ASSESSMENT ...... 32 2.12 PREVIOUS RELATED STUDIES ...... 33 CHAPTER 3 STUDY AREA ...... 44 3.1 GENERAL INFORMATION ...... 45 3.2 HYDROLOGY ...... 47 3.3 CLIMATE...... 48 3.3.1 Temperature ...... 48 3.3.2 Humidity ...... 48 3.3.3 Wind ...... 48 3.3.4 Evaporation ...... 49 3.3.5 Rainfall ...... 49 3.4 SOIL ...... 53 3.5 LAND USE ...... 55 3.6 EXISTING STORMWATER DRAINAGE SYSTEM IN NORTH GAZA GOVERNORATE. 58 3.7 EXISTING WASTEWATER DRAINAGE SYSTEM IN NORTH GAZA GOVERNORATE. 61 CHAPTER4 METHODOLOGY ...... 65 4.1 INTRODUCTION ...... 66 4.2 DEVELOPING THE AHP – FLOOD VULNERABILITY MAPPING ...... 68 4.2.1 Building the AHP Model ...... 68 4.3 ASSESSMENT OF EXISTING STORMWATER DRAINAGE INFRASTRUCTURE ...... 76 4.3.1 Data collection and Desk review ...... 77 4.3.2 Data Base Development ...... 77 4.3.3 Selection Evaluation of Criteria ...... 78 4.3.4 Creation of data Layer and Attributes ...... 78 4.3.5 GIS process ...... 81 4.3.6 Developing a hydraulic-hydrologic model ...... 83 4.3.7 Intervention procedure for the vulnerable areas ...... 85 CHAPTER 5 RESULTS AND DISCUSSION ...... 86 5.1 INTRODUCTION ...... 87 5.2 BUILDING THE AHP MODEL ...... 87 5.2.1 Hierarchy main goal ...... 87 5.2.2 Analytical Hierarchy structures ...... 88 5.2.3 Pairwise comparison ...... 89

VIII

5.2.4 Column summary ...... 91 5.2.5 Normalization and weighting ...... 92 5.2.6 Consistency of judgments ...... 96 5.2.7 Flood vulnerability Mapping ...... 99 5.3 CATCHMENTS AREA DELINEATION ...... 121 5.3.1 Investigate watersheds intersect to North Governorate ...... 121 5.3.2 Arc Hydro Model ...... 121 5.3.3 Identify the main watershed for North Gaza ...... 123 5.3.4 Verification ...... 123 5.4 THE RESILIENCE OF WASTEWATER NETWORKS...... 128 5.5 DEVELOPING THE HYDROLOGIC MODEL ...... 136 5.5.1 Rainfall Intensity ...... 136 5.5.2 Catchment building ...... 136 5.5.3 Intersection with the existing stormwater system ...... 138 5.5.4 Soil and land use ...... 140 5.6 MODELING THE CURRENT SITUATION ...... 141 5.7 CONCLUSION ...... 149 5.8 RECOMMENDATIONS ...... 150 5.9 FUTURE DEVELOPMENT ...... 151 REFERENCES ...... 152 APPENDICES ...... 163

List of Tables

Table 2-1: The initial infiltration capacity rate of dry soils ...... 11 Table 2-2: The final infiltration capacity rate of soils ...... 11 Table 2-3: Characteristics of soils assigned to soil groups ...... 13 Table 2-4: Runoff curve number for different urban characteristics (SCS CN method) ...... 14 Table 2-5: Runoff curve number for different urban characteristics (SCS CN method) (continued) ...... 15 Table 2-6: General runoff coefficients for the rational method ...... 16 Table 2-7: Intensity duration relationship for various hydrological periods of Gaza City ...... 18 Table 2-8: MCA methods comparison ...... 24 Table 2-9: Table of relative scores ...... 28 Table 2-10: Nine-point intensity of importance scale, modified from ...... 29 Table 2-11: Selected criteria and metrics used to measure them ...... 36 Table 2-12: Group criteria weights and their respective standard deviation (SD) and interquartile range (IQR). An IQR of 20% or less indicates consensus, 20-30 % indicates moderate divergence, 30-40% significant divergence, and < 40% strong divergence...... 37 Table 2-13: Ranking of urban flood casing criteria to obtain the pairwise comparison matrix ...... 38 Table 2-14: A matrix of pair-wise comparison of five criteria for the AHP process. 42 Table 2-15: Determined normalization of relative criterion weights ...... 42 Table 2-16: Summary of previous studies ...... 42 Table 3-1: Population of North Gaza municipalities (2016) ...... 45 Table 3-2: Rainfall stations location, and average and total values (mm/yr) ...... 52 Table 3-3: Average rainfall values in selected years (mm/yr) ...... 52 Table 3-4: Classification and characteristics of the different soil types in the Gaza strip...... 53 Table 3-5: Comparison of calculated LULC total surface area and percentages for 2004, 2010...... 56 Table 3-6: Data of wastewater pumping stations ...... 63 Table 4-1: Sample of Comparison matrix ...... 69 Table 4-2: Random Index (RI) used to compute consistency ration (CR) ...... 72 Table 4-3: Flood Vulnerability Ranking for the case study area ...... 73 Table 4-4: Collected Data ...... 79

Table 5-1: Comparison Matrix level 3 ...... 90 Table 5-2: Matrix comparison level 4 (Social) ...... 90 Table 5-3: Column summary of matrix level 3 ...... 91 Table 5-4: Column summary of matrix level 4 (Social)...... 92 Table 5-5: Column summary of matrix level 4 (Coping) ...... 92 Table 5-6: Column summary of matrix level 4 (Structural) ...... 92 Table 5-7: Column summary of matrix level 4 (Physical) ...... 92 Table 5-8: Normalization and relative priorities calculation matrix level 3 ...... 93 Table 5-9: Normalization and relative priorities calculation matrix level 3 (Social) 94 Table 5-10: Normalization and relative priorities calculation matrix level 3 (Coping) ...... 94 Table 5-11: Normalization and relative priorities calculation matrix level 3 (Structural) ...... 94 Table 5-12: Normalization and relative priorities calculation matrix level 3 (Physical) ...... 94 Table 5-13: The priority rank of each vulnerability element in the hierarchy ...... 95 Table 5-14: Consistency calculation matrix level 3 ...... 97 Table 5-15: Consistency calculation matrix level 4 (Social) ...... 98 Table 5-16: Consistency calculation matrix level 4 (Coping) ...... 98 Table 5-17: Consistency calculation matrix level 4 (Structural) ...... 98 Table 5-18: Consistency calculation matrix level 4 (Physical) ...... 98 Table 5-19: Summary of weight and rate of flood vulnerability criteria ...... 117 Table 5-20: Area percent of each flood vulnerability category ...... 120 Table 5-21: District flood vulnerability categorization ...... 120 Table 5-22: Classification of sub-catchment areas according to the existing stormwater and wastewater network ...... 129 Table 5-23: Summary of the sub-catchment zoning, area, and classification ...... 130 Table 5-24: Strom catchment area added to the wastewater pumping station ...... 135 Table 5-25: Summary of the Catchments flow, volume, SCS number and Time of concentration ...... 137 Table 5-26: Summary of ponds capacity of the system ...... 144

List of Figures

Figure 1-1: Brief Flow Chart Methodology...... 6 Figure 2-1: land use and surface runoff ...... 10 Figure 2-2: SCS 24-hour storm distribution type curves ...... 12 Figure 2-3: Rainfall intensity-duration-frequency ...... 17 Figure 2-4: Steps of Multi-criteria analysis ...... 21 Figure 2-5: Multicriteria methods ...... 22 Figure 2-6: General structure of the analytical hierarchy process (AHP) for multi- criteria decision making ...... 27 Figure 2-7: Hierarchical structure for a four-level problem ...... 27 Figure 2-8: Analytical Hierarchy Process steps ...... 28 Figure 2-9: Flood potential map ...... 34 Figure 2-10: Map of flood-vulnerable areas in Hadejia Jama’are River Basin ...... 35 Figure 2-11: AHP hierarchical tree ...... 36 Figure 2-12: A three- levels hierarchical structure of the characteristics of the parameters that represent urban flood vulnerability ...... 38 Figure 2-13: Decision hierarchy model for social vulnerability assessment ...... 40 Figure 2-14: Flood risk map...... 41 Figure 3-1: North governorate location map ...... 45 Figure 3-2: North Gaza districts...... 46 Figure 3-3: Topographic contour lines for North Gaza governorate ...... 46 Figure 3-4: NASA DEM map ...... 47 Figure 3-5: Average monthly rainfall and evaporation in Gaza Strip between 1980/2005...... 49 Figure 3-6: Average precipitation from the year 1976 to 2017 over the Gaza Strip by Thiessen Method ...... 50 Figure 3-7: Annual rainfall for Gaza Strip between 1976 and 2017 ...... 51 Figure 3-8: Average annual rainfall for North Gaza governorate rain stations between 1976 and 2017 ...... 51 Figure 3-9: Classification of soil map for the North Gaza Strip ...... 55 Figure 3-10: Land use land cover for the Gaza Strip in the year 2004 and 2010 ...... 56 Figure 3-11: Regional plan for Gaza Governorate 2005-2020, ...... 57 Figure 3-12: Northern governorate land use map for 2016 ...... 58 Figure 3-13: Stormwater infiltration basins in Northern governorate...... 60 Figure 3-14: Existing Stormwater network at the Northern Governorate ...... 61

XII

Figure 3-15: Existing wastewater networks in the northern governorate ...... 62 Figure 3-16: The existing wastewater pumping system in the northern governorate 62 Figure 3-17: Wastewater pumping station and their service area ...... 64 Figure 3-18: Routes of a pumping station in North Gaza ...... 64 Figure 4-1: Research Project Methodology ...... 67 Figure 4-2: Flood vulnerability map derivation process ...... 76 Figure 4-3: Catchment Area Delineation process...... 82 Figure 5-1: AHP Hierarchy model for flood vulnerability in North Gaza ...... 89 Figure 5-2: Relative priorities level 3...... 94 Figure 5-3: Relative priorities level 4...... 95 Figure 5-4: Available ground surface Elevation Points for north Gaza governorate ...... 100 Figure 5-5: The derived DEM based on the elevation map and NASA map ...... 100 Figure 5-6: Raster map of a) Slope map b) reclassified Slope map ...... 101 Figure 5-7: Raster map of simplifying slope classes...... 101 Figure 5-8: a) Vector map of soil type; b) Raster map of reclassified soil type ...... 102 Figure 5-9: a) Vector map of rainfall data; b) Raster map of reclassified rainfall data ...... 103 Figure 5-10: a) Vector map of network reliability; b) Raster map of reclassified network reliability ...... 104 Figure 5-11: a) Vector map of land use; b) Raster map of reclassified land use ..... 105 Figure 5-12: a) Vector map of population density; b) Raster map of reclassified population density ...... 106 Figure 5-13: a) Vector map of social; b) Raster map of reclassified social ...... 107 Figure 5-14: a) Vector map of coping; b) Raster map of reclassified coping ...... 108 Figure 5-15: a) Vector map of the household structure; b) Raster map of the reclassified household structure ...... 109 Figure 5-16: Raster map of sub-criteria weights ...... 110 Figure 5-17: Raster maps of a) Population density normalization, b) Poverty/culture normalization ...... 111 Figure 5-18: Raster maps of a) Household structure normalization, b) Coping normalization ...... 112 Figure 5-19: Raster maps of a) Network normalization, b) Slope normalization .... 112 Figure 5-20: Raster maps of a) Rainfall normalization, b) Land Use normalization ...... 113 Figure 5-21: Raster maps of Soil type normalization ...... 113

XIII

Figure 5-22: Raster map of weight and rate a) Social; b) Population density; c) Summation map of population density and poverty/culture map ...... 114 Figure 5-23: Summation map of social criteria (population density and poverty/culture) ...... 114 Figure 5-24: Raster map of weight and rate a) Coping; b) Household structure ..... 115 Figure 5-25: Raster map of weight and rate a) Network; b) Slope ...... 115 Figure 5-26: Raster map of weight and rate a) Rainfall; b) ...... 116 Figure 5-27: Raster map of weight and rate of Soil Type ...... 116 Figure 5-28: Summation map of physical criteria (network, slope, rainfall, land use and soil type) ...... 117 Figure 5-29: Flood vulnerability map of North Gaza governorate ...... 119 Figure 5-30: Main watersheds of the Gaza Strip ...... 121 Figure 5-31: Some intermediate results for Arc Hydro Model ...... 122 Figure 5-32: The delineated catchments including five main watersheds ...... 123 Figure 5-33: The delineated catchments intersection with the built-up area ...... 124 Figure 5-34: The main delineated catchment and its flow zones including flood areas ...... 124 Figure 5-35: Findings and recommendation during the verification...... 126 Figure 5-36: The main urbanized catchment including flood areas ...... 127 Figure 5-37: The main urbanized catchment intersects with land use and streets and its flow zones ...... 127 Figure 5-38: Zoning of the main urbanized catchment ...... 128 Figure 5-39: Classification of the sub-catchment areas according to the existing stormwater and wastewater network ...... 129 Figure 5-40: Areas with combined WWN in the main urbanized catchment and intersect with wastewater pumping station service area...... 132 Figure 5-41: The service area of the wastewater pumping station ...... 132 Figure 5-42: Overlay the areas with combined WWN layer and catchments of the pumping station layer ...... 133 Figure 5-43: Intersect of the combined WWN and catchments of the pumping station ...... 133 Figure 5-44: Stormwater catchment added to the wastewater pumping ...... 134 Figure 5-45: Land Use of the stormwater catchments added to the wastewater pumping station ...... 135 Figure 5-46: Cumulative SCS 24-Hour Storm Distribution for North Gaza ...... 136 Figure 5-47: Catchments and inlets in SewerGEMs model ...... 137 Figure 5-48: Catchments intersect with the existing stormwater system ...... 139

XIV

Figure 5-49: Catchments, inlets, stormwater pipelines, the main flow direction, and stormwater ponds ...... 139 Figure 5-50: Land use of the middle catchment ...... 140 Figure 5-51: Combined map of Soil type and Land use of the middle catchment .. 141 Figure 5-52: Simulated stormwater pipelines in SewerGEMs ...... 142 Figure 5-53: Simulated stormwater pipelines for Um Al Nasser Pond ...... 142 Figure 5-54: Simulated stormwater pipelines for Bite Lahia 01 Pond ...... 143 Figure 5-55: Simulated stormwater pipelines for Bite Lahia 02, Khalaf and Abu Rashid Ponds ...... 143 Figure 5-56: Simulated stormwater pipelines for Al Nazla Pond ...... 144 Figure 5-57: Overflowing drainage parts with respect to flood vulnerability map . 145 Figure 5-58: Flooding intervention procedure ...... 146 Figure 5-59: Flooding intervention priority ...... 147 Figure 5-60: Overlay AHP and SewerGEMs result ...... 147

List of Abbreviations

AHP Analytic Hierarchy Process ANP Analytic Network Process CAMP Coastal Aquifer Management Program CMWU Coastal Municipality Water Utility CR Consistency Ration CN Runoff Curve Number DEM Digital Elevation Model EIA Environmental Impact Assessment ELECTRE Elimination Et Coixtraduisant La Realite GIS Geographic Information System GS Gaza Strip HWE House of Water and Environment IDF Intensity Duration Frequency Curve IDW Inverse Distance Weight IQR Interquartile Range LCA Life Cycle Assessment LCLU Land Cover and Land Use MAUT Multi-Attribute Utility Theory MCA Multi-Criteria Analysis MCM Million Cubic Meters MOA Ministry of Agriculture MoLG Ministry of Local Government MoP Ministry of Planning NAIADE Novel Approach to Imprecise Assessment and Decision Environment NGOs Non-Governorate Organizations NWWTP North Wastewater Treatment Plant PROMETHEE Preference Ranking Organization Method for Enrichment Evaluation PWA Palestinian Water Authority PCBS Palestinian Central Bureau of Statistics RI Random Index SAW Simple Additive Weighting SCS Soil Conservation Service SD Standard Deviation SMAA Stochastic Multiobjective Acceptability Analysis SMART Simple Multi-Attribute Rated Technique WHO World Health Organization WLC Weighted Linear Combination WWTP Wastewater Treatment Plant

XVI

1 Chapter 1 Introduction

Chapter 1 Introduction

1.1 Background and Context

Floods are one of the most recurring and destructive natural hazards, affecting human lives and causing massive economic damage worldwide (Khan et. al., 2011). Recently It was clear that flood risks will not subside in the future spatially with the frequent change in climate, frequency, flood intensity, and urban growth which in turn will threaten many areas in the world (Jonkman and Dawson, 2012). According to Ouama and Tateishi (2014), floods occur due to intense rainfall that cause rapid accumulation of runoff downstream. Since flooding was top-ranked amongst the incidents of natural disasters in terms of a huge number of globally affected people and facilities, hydrologic and science of natural hazard were mainly concern about it (Borga, et. at., 2011). UN office for disaster risk reduction (2019) reported that flood affects the largest number of people, 35.4 million people and caused death for 2,859 people worldwide. European Academies’ Science Advisory Council (Easac) says. stated that climatological events such as storms have increased by more than a third this decay and are being recorded twice as frequently as in the 1980s. Moreover, Global floods and extreme rainfall events have surged by more than 50% this decade, and are now occurring at a rate four times higher than in 1980, according to a new report. In that, the trends towards extremes are continuing (Arthur Neslen, 2018). The cost of annual global flood damages was estimated at over $50 billion in 2013 alone (Centre for Research on the Epidemiology of Disasters, 2015) and is expected to more than double within the next twenty years from increasing populations and intensification of precipitation extremes (Doocy et al., 2013) In the Gaza Strip (GS), each season of heavy winter carries the threat of temporary displacement, property damages and health issues due to flooding and poor housing conditions. Many key factors that hinder the ability of the relevant actors to reduce vulnerability and respond effectively. For a fruitful understanding of the flood vulnerability, enough well-controlled management of flood risk is highly demanded in the fact that hazards can only be

2 classified as a disaster if its impact expanded to affect the community or vulnerable system (Reilly, 2009). At this research project, a determination of flood-vulnerable areas in the north part of the Gaza strip as a case study was investigated producing a flood-vulnerable map for the north area using a multi-criteria approach (MCA) method in conjunction with geographic information system GIS. This approach provides more flexible and more accurate information to the decision makers, in order to understand the factors driving vulnerability in a spatially explicit manner to assist decision makers regarding flood disaster in terms of mitigation measures.

1.2 Problem Description

The records of rainfall of the Gaza Strip is mainly collected from 12 rain stations. Most of the recent studies show the average quantities of annual rainfall varies from 413 mm/yr in the north to 254 mm/yr in the south of the Gaza Strip (MoA, 2017) which is considered the highest in GS. Most of the rainfall occurs in the period from October to March. During the last 5 years, in Gaza strip heavy rains were recorded which cause temporary displacement, losses of property and health risks due to flooding and poor housing conditions, for example, Alexa storm in 2013 forced more than 5000 capita in the Gaza Strip to evacuate their homes. Potential damages increase in regions rather than others due to social, infrastructure, environmental and economic development. The severity and frequency of flood are expected to increase, due to the climate change causing heavy rains. Thus, a proper understanding of flood vulnerability is critical to enhancing resilient societies, leading to further mitigation that is efficient strategies. However, integrating the dimensions of vulnerability in an overarching framework is complex due to conceptual and methodological constraints. Challenges always remain in (1) the selection of criteria to represent the vulnerability, (2) the determination of the importance of each criterion, (3) the data availability, and (4) the results validation (Müller et al., 2011). Many authors stated that flood vulnerability using indicator-based method is easier to use and understand. It depends on social vulnerability (Fekete, 2009; Frigerio and de

Amicis, 2016), economic vulnerability (Kienberger et al., 2009), and physical vulnerability (Godfrey et al., 2015; Kappes et al., 2012). Worldwide indicator-based method spatially the Analytical Hierarchy Approach (AHP) was widely used for flood risk and vulnerability assessment. In many cases, it was combined with GIS since it is providing a powerful tool that can be used in multi-criteria decision analysis. In GS a few recent studies determine the flood risk assessment of rainfall. One of them is implemented by Action Against Hunger (ACF) and still in progress, but there is no any recent studies that determine flood vulnerability assessment on GS using the multicriteria approach with integration with Geographic Information System.

In that, local decision makers require tools for better understanding and assessment of flooding vulnerability. Thus, in this study, a multi-criteria decision approach was developed which concentrate on the qualitative approach of vulnerability in flood risk assessment in North Gaza

1.3 Aim and Objectives

At this part, the aim and the objectives of this research will be exhibited as described below.

1.3.1 Research Aim The main goal of this study is to assist decision-makers regarding flood mitigation measures by developing a flood vulnerability map of the North Governorate of the Gaza Strip using Multicriteria Evaluation Approach integrated with GIS.

1.3.2 Research Objectives The main objectives of this research are summarized as follows: 1. Develop a participatory MCA for evaluating flood vulnerability while considering the interdependency among the criteria. 2. Develop a flood vulnerability mapping for North Gaza. 3. Evaluate the existing stormwater drainage system in North Gaz using integrated GIS environment and SewerGEMs model. 4. Propose inventory/mitigation measures for the vulnerable areas based on the flood vulnerability mapping.

1.4 Brief Methodology

The methodology followed in this study is presented in Figure 1-1, which includes data collection, Analytic Hierarchy Process, manipulation of collected data in a GIS environment to produce vulnerability map and validation for the modeling outputs. In order to achieve the main objectives of this study, the following steps were performed:

1) Problem definition and study area specific information (spatial Data). 2) Desk review, field visits if needed, data collection and review of all related documents, existing flooding zones and stormwater and wastewater facilities. 3) Prepare Analytic Hierarchy Process (AHP), a multi-criteria analysis method, to assign weight and rank alternatives. 4) Prepare a flood vulnerability map for the specific area that addresses all recognized deficiencies. 5) Catchment area delineation through modeling the existing topography of the study area Using the ArcGIS environment. 6) Evaluate the existing stormwater system and determine if areas fail to meet the existing level of service requirements for the stormwater using hydraulic model SewerGEMs.

Figure 1-1: Brief Flow Chart Methodology

1.5 General Framework

This study is intended to include the following structure in order to reach up the aforementioned goal and objectives: Chapter 1 Introduction. This chapter describes the general framework of what is going to be done in this research, the research objectives, and problem definition.

Chapter 2 Literature review. This chapter is a review on stormwater computational methods and vulnerability assessment, which describes MCA techniques in general and AHP method in particular. It also includes the concept of this research and the applied model in addition to previous related study in this context. Chapter 3 Study area. This chapter describes the general and main data of the North Gaza governorate which was chosen as the targeted area for this research. Chapter 4 Methodology. This chapter discusses the detailed methodology used for assessing the flood vulnerability assessment and mapping using ArcGIS, as well as introducing the SewerGEMs modeling methodology. It also introduces the concept of AHP methodology, relative measurements, pairwise comparison, scaling and building up the hierarchy. Chapter 5 Results and discussion. This chapter highlighted the findings of the applied methodology described in Chapter 4. The discussion explores the outputs of assessing the flood vulnerability using AHP method and investigate the different responses to the vulnerability criteria and alternatives. Chapter 6 Conclusion and Recommendation. This final chapter addresses the raised conclusion and draws recommendations.

Chapter 2 Literature Review

2 Chapter 2 Literature Review

Chapter 2 Literature Review

2.1 Introduction

This chapter has briefly illustrated the state-of-the-art of the main parts of this research. The first part defines the stormwater, demonstrates the main factors that would affect the runoff volume and presents methods for runoff computation. The second part discusses the concept of the multi-criteria analysis (MCA) and the main steps followed using MCA, applications, categories, the comparison between the main popular categories, and why we choose the Analytical hierarchy method (AHP) for flood vulnerability assessment. The third part, outlines the AHP method, steps, and previous related studies while the last part reviews the flood vulnerability assessment concept and the related studies that devote the use of AHP method in combination with ArcGIS in determining and mapping the flood vulnerability areas.

2.2 Stormwater Runoff

Stormwater runoff is defined as the accumulated surface flow which occurs as a result of precipitation whether in the form of rainfall or melted snow that does not soak into the ground or evaporate but flows along the surface of the ground as runoff (Parikh et. al, 2005).

2.3 Factors Affecting Runoff

Many factors would affect the stormwater runoff which in turns affects surface and groundwater availability of the area which is briefly described as following:

2.3.1 Land Cover and Land-Use The characteristics of runoff are affected to a large extent to the Land Cover and Land Use (LCLU). In General, Arnold et. al. (1993) stated that various uses of lands affect both the quality and quantity of the stormwater runoff and its occurrence in the drainage area as the following: 1. Small quantities of runoff, as well as few pollutants, take place in natural spaces with open vegetated areas

2. Accelerated erosion and flooding issues caused by larger volumes of runoff basically occur in the developed areas. 3. Residential districts, industries and businesses areas can result in wide enough quantities of pollutants in the runoff

Arnold et al. (1993) stated that the impervious surfaces such as roofs, roads, parking lots and compacted soil would increase the quantity of stormwater runoff. As the water infiltration into the soil decrease due to the impervious surface and land development, the runoff volume increases and becomes a big issue. Figure 2-1 illustrates that the runoff quantity is at least five times higher in the highly urbanized areas. Compared with open areas or natural ground cover the annual volume of stormwater runoff could be increased by 2 to 6 times its pre-development rate according to the imperviousness degree of the watershed. This would result in a proportional reduction in the amount of groundwater recharge (Schueler, 1995).

Figure 2-1: land use and surface runoff (FISRWG, 1998)

2.3.2 Soil Type Soil type serves a pivotal function in the infiltration process during the hydrological cycle. The ability of the soil to provide high infiltration performance depends on disturbance, compaction, and degradation of soils (Bauwens W., 2016).

The infiltration capacity of the soil is defined by the rate at which the water may infiltrate through the soil, under a sufficient water supply condition. The infiltration capacity depends on a different parameter such as (Bauwens W., 2016)

1. The soil type and compaction;

2. The initial moisture content;

3. The surface cover and soil structure;

4. The viscosity of the water;

5. The depth of the water on the soil surface.

For the initial and final infiltration capacity of soils, the data are presented in Table 2-1 and Table 2-2of the table may be used. Table 2-1: The initial infiltration capacity rate of dry soils (Bauwens, 2016)

Infiltration rate (mm/hr) Soil type Min. Max. Sand 127 254 Loam 76 152 Clay 25 50

Table 2-2: The final infiltration capacity rate of soils (Bauwens, 2016)

Soil type Infiltration rate (mm/hr). Deep sand & loess; structured loam 7.5 – 11 Shallow sand & loess; less structured loam 3.8 – 7.5 Heavy loam 1.2 – 3.8 Heavy clay; swelling soils 0 – 1.2

2.4 Stormwater Runoff Computations

To estimate the quantity of stormwater runoff, a variety of hydrologic calculation were used. Two methods are discussed below which are classified as more acceptable to determine runoff volume.

2.4.1 Method 1: SCS Dimensionless Unit Hydrograph method The SCS Unit Hydrograph Method is one of the appropriate methods for drainage areas 80 hectares (0.8 Km2) or greater. In this study, the unit hydrograph method, spatially the Soil Conservation Service (SCS) Dimensionless Unit Hydrograph method is the basis for computing runoff. The adopted SCS method depends on dimensionless rainfall temporal patterns called type curves for four different regions in the US. Those curves are in the form of percentage mass (cumulative) curves based on 24-hr rainfall of the desired frequency. In order to make this method fit to North Gaza governorate, usually a single precipitation depth of desired frequency should be known, in that, the SCS type curve is rescaled by multiplying the curve with the precipitation depth to get the modified time distribution. Figure 2-2 shows the SCS 24-hour storm distribution type curve.

Figure 2-2: SCS 24-hour storm distribution type curves (Eshtawi, 2018)

To calculate the SCS Dimensionless Unit Hydrograph method, there are two parameters needed (Eshtawi, 2018):

3 I. Peak Discharge (m /s), Qp

= 0.278 × × 𝑃𝑃𝑒𝑒𝑒𝑒𝑒𝑒 𝑄𝑄𝑝𝑝 𝐴𝐴 𝑇𝑇𝑝𝑝 2-1 Where Qp: Peak discharge (m3/s).

Peff: total effective rainfall (m). A: Watershed area (ha.)

Tp: Peak time (hr.) The effective rainfall is usually equal to the difference between total rainfall and actual evapotranspiration. At ground level, water from effective rainfall is split into two fractions: surface run-off and infiltration.

II. Peak Time

= + 2 𝐷𝐷 𝑇𝑇 𝑝𝑝 𝑇𝑇𝑙𝑙 2-2 Where

Tp: time to peak (hr)

D: rainfall duration (hr)

TL: lag time (hr)

0.7 1000  2.587 × L0.8 ×  − 9  CN  T = L 1900 × H 0.5 2-3 Where L: Hydraulic watershed length (m), L= 110 A0.6 A: Watershed area (ha) H: Average watershed land slope (%) CN: Hydrologic area-weighted curve number

In the SCS method the combination of land use, hydrologic soil group was represented using a developed index called the SCS runoff curve number (CN). The Empirical analyses proposed that CN is a function of soil type, land cover, and antecedent moisture conditions. There are different common tables regarding CN but, in this study Table 2-3, Table 2-4 and Table 2-5 were used.

Table 2-3: Characteristics of soils assigned to soil groups (USDA NRCS, 1986)

Group A: Deep sand; deep loess; aggregated silts

Group B: Shallow loess; sandy loam

Group C: Clay loams; shallow sandy loam; soils low in organic content; soils usually high in clay

Group D: Soils that swell significantly when; heavy plastic clays; certain saline soils

Table 2-4: Runoff curve number for different urban characteristics (SCS CN method) (USDA NRCS, 1986)

Curve Numbers for Hydrologic Soil Land Use Description Group A B C D Fully developed urban areas (vegetation established) Lawns, open spaces, parks, golf courses, cemeteries, etc.

Good condition: grass cover on 75% or more of the area 39 61 74 80

Fair condition: grass cover on 50% to 75% of the area 49 69 79 84

Poor condition: grass cover on 50% or less of the area 68 79 86 89 Paved parking lots, roofs, driveways, etc. 98 98 98 98 Streets and roads Paved with curbs and storm sewers 98 98 98 98 Gravel 76 85 89 91 Dirt 72 85 87 89 Paved with open ditches 83 89 92 93 Average % Impervious Commercial and business areas 85 89 92 94 95 Industrial districts 72 81 88 91 93 Row houses, townhouses, and residential homes with hot 65 77 85 90 92 sizes 1/8 acre or less Residential average lot size 1/4 acre 38 61 75 83 87 1/3 acre 30 57 72 81 86 1/2 acre 25 54 70 80 85 1 acre 20 51 68 9 84 2 acres 12 46 65 77 82 Developing urban areas (no vegetation established) Newly graded area 77 86 91 94 Western desert urban areas Natural desert landscaping (previous area only) 63 77 85 88 Artificial desert landscaping 96 96 96 96

Table 2-5: Runoff curve number for different urban characteristics (SCS CN method) (continued)

Curve Numbers for Hydrologic Land Use Description Treatment or practice Hydrologic Soil Group condition A B C D Cultivated agricultural land Straight row or bare soil 77 86 91 94 Fallow Conservation tillage Poor 76 85 90 93 Conservation tillage Good 74 83 88 90 Row crops Straight row Poor 72 81 88 91 Straight row Good 67 78 85 89 Conservation tillage Poor 71 80 87 90 Conservation tillage Good 64 75 82 85 Contoured Poor 70 79 84 88 Contoured Good 65 75 82 86 Contoured Poor 69 78 83 87 Conservation tillage Good 64 74 81 85 Contoured and terraces Poor 66 74 80 82 Contoured and terraces Good 62 71 78 81 Contoured and terraces Poor 65 73 79 81 and conservation tillage Good 61 70 77 80

2.4.2 Method 2: Rational Method The rational method is another simple technique that was developed by Kuichling in 1889. This method was suggested to estimate the design discharge from small watersheds limited to a few tens of acres (Thompson, 2006).

It is based on a simple equation that relates the potential of producing the watershed runoff, the average rainfall intensity for a given period of time (the time of concentration), and the drainage area (Thompson, 2006). The formula is: Q = CIA

2-4 Where Q: design discharge (m3/s),

C: runoff coefficient (dimensionless),

I: design rainfall intensity (m/s), and

A: watershed drainage area (m2).

I. Runoff Coefficient

The runoff coefficient, C, is a dimensionless ratio presents the expected amount of precipitation converted to runoff and generated by specific watershed has given the average rainfall intensity (Thompson, 2006). Values are tabulated in Table 2-6. Table 2-6: General runoff coefficients for the rational method (Thompson, 2006)

Description Runoff Coefficient Business Downtown Areas 0.70–0.95 Neighborhood Areas 0.50–0.70 Residential Single-family 0.30–0.50 Multi-family detached 0.40–0.60 Multi-family attached 0.60–0.75 Residential suburban 0.25–0.40 Apartments 0.50–0.70 Parks, cemeteries 0.10–0.25 Playgrounds 0.20–0.35 Railroad yards 0.20–0.40 Unimproved areas 0.10–0.30 Drives and walks 0.75–0.85 Roofs 0.75–0.95 Streets Asphalt 0.70–0.95 Concrete 0.80–0.95 Brick 0.70–0.85 Lawns; sandy soils Flat, 2% slopes 0.05–0.10 Average, 2%–7% slopes 0.10–0.15 Steep, 7% slopes 0.15–0.20

Lawns; heavy soils Flat, 2% slopes 0.13–0.17 Average, 2%–7% slopes 0.18–0.22 Steep, 7% slopes 0.25–0.35

II. Storm Intensity

The storm intensity, I, is a function of geographic location and return period (frequency). It is well known that for a given storm duration, the shorter the return period, the lower the rainfall intensity. Moreover, the higher the average rainfall intensity, the shorter the length of the storm. The three components, storm duration, storm return period and storm intensity were gathered and presented by curve called the duration frequency curves, or IDF curves as shown in Figure 2-3 (Thompson, 2006).

Figure 2-3: Rainfall intensity-duration-frequency (National Weather Service, 1961) Usually, the rainfall intensity selected according to the design rainfall duration and return period. The design rainfall duration is equal to or greater than the time of concentration for the considered watershed, while the frequency is variable based on a statistical record. Table 2-7 shows the intensity-duration relationship for various hydrological periods of Gaza city.

Table 2-7: Intensity duration relationship for various hydrological periods of Gaza City (Sogreah, et. al., 1999).

Return Period: 2 years - a: 4.06 - b: -0.636 Period 5 15 30 1 h 2 h 3 h 6 h 12 h 18 h 24 h Pj = min min min P24h×0.875 Rainfall 7.3 10.9 14.0 18.0 23.2 26.9 34.6 44.5 51.6 57.3 50.0 Return Period: 5 years - a: 6.18 - b: 0.649 Period 5 15 30 1 h 2 h 3 h 6 h 12 h 18 h 24 h Pj = min min min P24h×0.875 Rainfall 10.9 16.0 20.4 26.0 33.2 38.2 48.8 62.2 71.7 79.4 69.0 Return Period: 10 years - a: 7.95 - b: 0.660 Period 5 15 30 1 h 2 h 3 h 6 h 12 h 18 h 24 h Pj = min min min P24h×0.875 Rainfall 13.7 20.0 25.3 32.0 40.5 46.5 58.8 74.4 85.5 94.2 82.0 Return Period: 20 years - a: 9.39 - b: 0.665 Period 5 15 30 1 h 2 h 3 h 6 h 12 h 18 h 24 h Pj = min min min P24h×0.875 Rainfall 16.1 23.3 29.3 37.0 46.7 53.5 67.5 85.1 97.5 107.3 94.0 Return Period: 50 years - a: 11.89 - b: 0.675 Period 5 15 30 1 h 2 h 3 h 6 h 12 h 18 h 24 h Pj = min min min P24h×0.875 Rainfall 20.1 28.7 35.9 45.0 56.4 64.3 80.5 100.9 155.1 126.4 111.0 Return Period: 100 years - a: 13.60 - b: 0.682 Period 5 15 30 1 h 2 h 3 h 6 h 12 h 18 h 24 h Pj = min min min P24h×0.875 Rainfall 22.7 32.2 40.1 50.0 62.3 70.9 88.4 110.2 125.4 137.4 120.0

The derived intensity duration equation is given as:

I = aT b 2-5

Where

I: Rainfall intensity (mm/min)

T: The duration time (min) a, b: Constants related to the selected returned period

III. Time of Concentration

The time of concentration is the travel time of runoff from the upstream to the downstream and represents the best shape of the watershed. The runoff reaches the peak when the entire drainage area is completely contributed to each other. In that, the time of concentration is t the required time to flow water from the most remote point in the watershed to the outlet point. It is also used to develop the maximum runoff rate (Sogreah, et al., 1999). The Kirpich formula will be suitable to be used in determining the concentration-time for overland runoff flows in the Gaza Strip, which is:

= 1.15 (52 ( ) . ) 𝐿𝐿 𝑇𝑇𝑐𝑐 0 38 𝐻𝐻 2-6 Where

Tc: Concentration time in minutes

L: Longest path of the drainage area in meter

H: Difference in elevation between the most remote point and the outlet in meters

2.5 Multicriteria Analysis Method

Experts in the field of decision-making defined the multi-criteria analysis (MCA) as a good technique used to integrate and combine all technical information with the stakeholder’s advice to support specific decisions (Linkov and Moberg, 2011).

In other words, Multi-criteria analysis is one such method that can not only aggregate the variegated views of conflicting stakeholders but can also weight criteria with different units, scales, and meanings against each other effectively (Linkov and Moberg, 2011).

For complex multi-criteria problems which include qualitative and/or quantitative aspects, Mendoza et al. (1999) proposed the MCA as a reliable and efficient tool for making a decision.

Generally, MCA can estimate different adaption options among a large number of criteria, and various set of objectives of the decision makers. Each criterion has a pertinent weighting which may highly result in an overall outcome for every considered option.

MCA emphasizes on few aspects including the consistency of the decision-makers figure, on providing objectives and criteria, on calculating adjacent weights, and to some extent on judging the performance of every alternate according to every criterion. The most outstanding option with a high enough score is the option to be selected among all options. (CYPADAPT, 2013).

Furthermore, the multi-criteria analysis offers an assessment of various options when data is not available or there is a need to consider cultural and ecological criteria which are difficult to quantify (UNFCCC, 2000).

Moreover, the efficiency of the MCA analysis results is affected by a various factor including the uncertainty of the information related to the selected criteria and the weight given to criteria and agreed by stakeholders. In that, and in order to verify the results consistency, sensitivity analysis must be used (UNFCCC, 2000).

In all MCA methods of multi options, there are five steps to be taken illustrated in Figure 2-4 and summarized as following (UNFCCC, 2000): 1. First and foremost, the objective of adaption has to be agreed on and to distinguish different possible options. 2. Agree on the decision-making criteria which can widely help each involved participant in the assessment approach to own the necessary common knowledge and understanding. 3. Compare and contrast each adaption option performance against the chosen criteria. If scores of the different criteria vary in units, standardization has to be taken into consideration. This allows efficient comparison of the each picked criteria. 4. Set priorities through assigning weights to each criterion. 5. By the end, rank the options in order to calculate, by multiplication of the scores, a total score of each selected option.

The results of the MCA lead to results in ranked order of options and an appreciation of the strengths and weaknesses of all the attributes of each of the options.

Figure 2-4: Steps of Multi-criteria analysis 2.6 Categorization of the MCA methods

There are wide MCA techniques and methods according to what the growing body of literature suggests. The number of those techniques is constantly rising and the reasons for such increases are several such as the following (CYPADAPT 2013)

1. The diversity of multiple decision types, which fit the wide circumstances of MCA.

2. The flexibility of time to proceed related analysis may vary.

3. The quantity and nature of helpful data to cultivate the analysis may vary

4. The diversity of analytical skills of the ones supporting the decision and,

5. The wide useful demands, requirements, and administrative culture of organizations.

The classification and categorization of the MCA techniques and methods are set according to the initial hypotheses and assumptions, the input data type, the adapted convenient technique of analysis and the output conclusion.

Taking into consideration the variety of type of decision model, a certain classification of the MCA methods is presented in Figure 2-5 (Pokharel and Chandrashekar, 1998; Hooman Mostofi Camare,2011; Ramanathan and Ganesh, 1995).

Figure 2-5: Multicriteria methods (Haralamopoulos, et.al., 2006)

In the next paragraphs an overview of multicriteria analysis methods. The main families of methodologies include (CYPADAPT, 2013):

1. Outranking methods

Outranking notion proposed by Roy (1968). Basically, the main idea suggests that Alternative Ai outranks Aj if Ai performs at least good enough as Aj on a large scale of the criteria (concordance condition), while its worse performance is still agreeable on other scales of the criteria (non-discordance condition). For each pair, alternatives are specified. These pairwise outranking estimations are put together resulting in a partial or total ranking whether one alternative overtops another. Those are few examples of outranking methods Elimination Et ChoixTraduisant la Reality (ELECTRE) family (Roy and Vincke, 1981; Vincke,1992), the Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE) I and II methods (Brans and Vincke, 1986), and REGIE Method Analysis (Nijkamp et al., 1990)

2. Value or utility function-based methods

The utility or value function process proposes the transition of criterion maps to a common standard or popular scale. Utility or value displays a number that is attached to a potential decision result (or level of attribute). Each result has a value or utility. This utility or value task transforms the different levels for an advantage to a utility or value

22 scores. Potential decision results are relative to a scale which reflects the related choices of the decision makers.

The function of utility or data relates worth, generally on a scale basis of 0 to 1, to the involved attributes. This is the way in which the function submits an expression of a standardized scale worth of every value of a range of attribute data. Examples of value or utility function-based techniques are the Multi-Attribute Utility Theory (MAUT) (Keeney and Raiffa, 1976), the Simple Multi-Attribute Rated Technique (SMART) (von Winterfeldt and Edwards, 1986), the Analytic Hierarchy Process (AHP) (Saaty, 1980), and the most elementary multicriteria technique, the Simple Additive Weighting (SAW).

3. Other methods

There are many others, some of which have a record of application, but many others which have not advanced significantly beyond the conceptual phase. Categories that have not been explicitly discussed but which are referred to in the MCA literature include methods based on Rough Sets, or on Ideal Points and several methods that are heavily dependent on interactive development, using specially constructed computer packages like Novel Approach to Imprecise Assessment and Decision Environment (NAIADE) (Munda, 1995), Flag Model (Nijkamp and Vreeker, 2000), Stochastic Multiobjective Acceptability Analysis (SMAA) (Lahdelma et al., 1998).

2.7 Comparison among MCA methods

Over the last decade, all major MCA techniques (utility based and outranking) has been significantly used. Below is a general description of the main two techniques widely used. While Table 2-8 presents a brief comparison between the MCA methods. In the following - There is a group of value or utility function-based approached using pairwise comparisons of criteria such as the Analytic Hierarchy Process (AHP) (Saaty, 1980), and its extension the Analytic Network Process (ANP). These two processes may work, by the employment of matrix algebra (while involving either eigenvalue-based or identical calculations methods) with incomplete or inconsistently clashing inputs, in order to come out with weights, overall scores, and a measure of correspondence (Ishizaka and Lusti, 2006). Every alternative is given a score by AHP, like other MCA methods and processes. Theoretically, at some points and cases, there’s a potential for alternatives to transfer demand

relying on the way other parts of the problem are constructed. In that case, such scores cannot be explicated totally like the way MAUT scores are actually interpreted. - In case a linear MAUT sample is applied, conversion, as used in SMART properly, submits the same outcome as MAUT even if we consider that MAUT allows larger flexible ability in preference trade-off functions. Both scale and significance are reflected by weights in MAUT. Scales in SMART are converted to a general common basis, therefore, weights, in this case, reflect significance. Unlike what is mostly demanded of MAUT derived approaches, Smart doesn’t demand or request preference of indifference judgment between picked alternatives. (Edward and Barron, 1994).

Table 2-8: MCA methods comparison (CYPADAPT, 2013).

Methods Advantages Disadvantages ELECTRE - Has the ability to fulfill a comparison of - Can only find the most powerful the alternatives. alternative, not the preferable - More dependable than other sensitive alternative. methods. - The outcomes generated in a system of - Both qualitative and quantitative binary outstanding relations among the judgments can be dealt with. picked alternatives and that system isn’t necessarily complete

- It is mostly a sophisticated decision- making approach which demands a wide diversity of initial data.

PROMETHEE - Can supply, with respectively positive - It can hardly provide a particular and negative outranking flows, overall approach depending to which the ranking of the various picked weights might be specified. alternatives. - in case of an important number of - It interprets the technique of how any criteria almost up to seven, it might be picked alternative can be outranked by very complicated for the decision any other alternative yielded and figures to acquire an explicit vision of displayed for the evaluation process. the problem. Also, it will be definitely - It involves enough flexible ability to hard to estimate the outcomes, and this permit analysis and the foundation of is because it doesn’t provide a the most elevated permitted deviations structuring potentiality (Macharis et al., from the original weights through 2004a). vulnerability. (Brans, 1996)

Methods Advantages Disadvantages AHP - It involves a large potential application - The uncertainty of the approach might of the AHP to the weighting of fuzzy highly increase as a result of the carried criteria along with other firm criteria out pairwise comparisons which may throughout ratio scaled and scoring. become so many. (Machairs et al, 2004) - Throughout simply paired comparison - AHP has been criticized for its judgments, it is definitely reasonable to dependability based on the decision join the lesser to the greater. makers’ choices and beliefs. - Solid and simple - Has enough flexibility to handle both qualitative and quantitative judgments (Macharis et al., 2004). - A consistency test, which is able to screen out incoherent judgments, is applied. - It permits emphasizing the quality and adequate efficiency of the conclusive decision (Pohekar and Ramachandran, 2004). MAUT - The quality of outcomes is of better - It is considered to be a more knowledge and understanding of the sophisticated method problem for decision figures - It would not be easily the preferred (parameters, criteria, etc). example for energy planning issues - It is mostly a preferred method as is resolution. helps decision makers to obtain a better

understanding of the objective of the issue, the sub-objectives, the weights and scores, and the vulnerability analysis. SMART - Straightforward, utility-based approach. - Least popular method than others, and - The more adapted and preferred more sophisticated problems. approach for simplified decision- making issues. - It treats together qualitative and quantitative data

Depending on the previously mentioned number of important pros and advantages of the AHP technique, it is, therefore, the method selected at this project. It is, for instance, a relatively straightforward technique for decision makers. The fact it also involves a

25 collection of pairwise comparison data particularly in the subjective issues and cases which is a good aspect attracting decision makers.

2.8 Analytical Hierarchy Process (AHP)

The analytic hierarchy process (AHP) is a method of measurement through pairwise comparisons and relies on the judgments of experts to derive priority scale organizing and analyzing complex decisions, based on mathematics and psychology (Saaty, 2008). AHP was considered as one of the most popular and widely used methods for multiple criteria decision-making problems. Since Thomas L. Saaty in the 1980s developed this tool it has been extensively studied and refined since then.

AHP is considered an effectively and properly applied process in cases where criteria can be structured into a hierarchy by classifying the issue or problem’s features and characteristics into sub-criteria. (Saaty, 2008)

The experts generally involve the decision makers to properly construct and build up the problem along with the whole decision-making processes. It is worth mentioning that specific distinguishing feature of AHP method which is allowing when assigning the weights, a hierarchical framework of the criteria, which by some means supplies users with greater concentration on particular criteria and sub-criteria. AHP approach has theoretically a more significant task rather than prescribing the right decision which is guiding decision figures to find the one that is the most appropriate for their needs, which also help them with a better understanding of the problem. If it says anything, it implies that AHP is a genuine decision-making method depending on the authentic capability of those who are entitled to make up critical decisions (Ouma et.al 2014).

In the AHP technique, to prioritize the selected criteria according to their relevant importance to each other and their ability to fulfill the objectives, a pairwise comparison method is used (Saaty, 2008). AHP provides an effective quantitative decision-making tool to deal with complex and unstructured problems.

AHP provides an efficient, simple and more sophisticated determination for the criteria selection, criteria weighting, and analysis (Bojovic et al., 2008). Once the framework of AHP has constructed the experts and participants can state giving their priorities regarding all of its levels. Figure 2-6 presents the general framework of the AHP modified from Zahedi (Zahedi,1986).

Figure 2-6: General structure of the analytical hierarchy process (AHP) for multi- criteria decision making (Zahedi, 1986)

Increasing levels to 4 or more than 4 may depend on the complexity of the elements. Figure 2-7 shows a hierarchical structure for a four-level problem developed from (Sadiq et al,2004).

Figure 2-7: Hierarchical structure for a four-level problem (Sadiq et al,2004)

Three steps are involved in the Analytical Hierarchy Process method (Saaty, 1980):

1) Calculating the criteria weights.

2) Computing option scores matrix

3) Ranking the options.

The following section describes in details of each step considering the m evaluation criteria, while n potions supposed to be evaluated.

Figure 2-8 shows the AHP three main steps.

Figure 2-8: Analytical Hierarchy Process steps

1) Calculating the weights of criteria

The AHP approach starts creating a pairwise comparison matrix A in an attempt to calculate the weights for the various criteria. The matrix A is an m×m real matrix, where m is considered the number of estimation criteria taken into consideration. Each entry ajk of the matrix A symbolizes the significance of the jth criterion related to the kth criterion. th th in case ajk > 1, the j criterion would be more significant than the k criterion. On the th other hand, if ajk < 1 then the result would be that the j criterion is less important than the kth criterion. While in case the entry ajk is 1, then both criteria have equal significance.

Both entries ajk and akj satisfy the following constraint:

ajk.akj =1 2-7

Obviously, ajj = 1 for all j. The adjacent significance between both criteria is measured relating on a numerical scale from 1 to 9, as displayed in Table 2 9. There is also a potential to allocate intermediate values, which actually do not match, to a precise interpretation. Table 2-9: Table of relative scores (Saaty, 1980).

Value of ajk Interpretation 1 J and k are equally important 3 J is slightly more important than k 5 J is more important than k 7 J is strongly more important than k 9 J is absolutely more important than k

Another Nine-point intensity of importance scale presented in Table 2-10, modified from (Schoenherr, 2018) was also used because nine objects are the largest number an individual can compare simultaneously and consistently. Table 2-10: Nine-point intensity of importance scale, modified from (Schoenherr, 2008).

Intensity of Definition Description importance 1 Equally important Two factors contribute equally to the objective. Moderately more Experience and judgment slightly favor one over the 3 important other Strongly more Experience and judgment slightly favor one over the 5 important other Very strong more Experience and judgment very strongly favor one over 7 important the other. Its importance is demonstrated in practice. Extremely more The evidence favoring one over the other is of the highest 9 important possible validity. 2,4,6,8 Intermediate values When compromise is needed

Once the matrix A is built, it is possible to derive from A the normalized pairwise comparison matrix A norm by making equal to 1 the sum of the entries on each column, i.e. each entry ajk of the matrix A norm is computed as

= 𝑎𝑎𝑗𝑗𝑗𝑗 𝑎𝑎𝑗𝑗𝑗𝑗 𝑚𝑚 ∑𝑖𝑖=1 𝑎𝑎𝑗𝑗𝑗𝑗 2-8 Finally, the criteria weight vector w (that is an m-dimensional column vector) is built by averaging the entries on each row of A norm, i.e.

= 𝑚𝑚 ∑𝑖𝑖=1 𝑎𝑎𝑗𝑗𝑗𝑗 𝑤𝑤𝑗𝑗 2-9 𝑚𝑚 2) Calculating the option scores matrix

The matrix of option scores is an n×m real matrix S. Each entry Sij of S symbolizes the score of the ith option with respect to the jth criterion. In order to derive such scores, a pairwise comparison matrix B(j) is first structured for each of the m criteria, j=1..., m. The matrix B(j) is an n×n real matrix, where n is the number of options estimated. Each entry

(j) (j) th th b ih of the matrix B represents the evaluation of the i option compared to the h option with respect to the jth criterion. (j) th th (j) If b ih >1, then the i option is better than the h option, while if b ih <1 then the ith option is worse than the hth option. If two options are evaluated as equivalent with th (j) (j) (j) respect to the j criterion, then the entry b ih is 1. The entries b ih and b hi satisfy the following constraint: th th (j) (j) The i option outranks the h option in case b ih >1, on the other hand, if b ih <1 th th (j) then the i option is worse than the h option. Finally, the entry b ih is 1 in case both th (j) options are estimated as even an equivalent in respect of the j criterion. The entries b ih (j) and b hi satisfy the following constraint: Identical to the scale displayed in Table 2-9, an estimation scale may be applied by the decision maker in an attempt to transfer his pairwise estimations into numbers.

(j) (j) b ih . b hi =1 2-10

(j) and b ii for all i. An evaluation scale similar to the one introduced in Table 2-9 may be used to translate the decision maker’s pairwise evaluations into numbers. The AHP applies the same two-step process explained for the pairwise comparison matrix A to every matrix B(j). In another word, it classifies each entry by the sum of the entries in the same column, then it averages the entries on each row, finally acquiring the score vectors s(j), j=1..., m. The vector s(j) consists of the scores of the estimated options with respect to the jth criterion. Finally, the score matrix S is obtained as S = [ s(1) ... s(m) ] 2-11 i.e. the jth column of S corresponds to s(j)

3) Ranking the options The AHP acquires a vector v of global scores throughout multiplying S and w, i.e. the moment the weight vector w and the score matrix S have been finally calculated and computed.

v = S · w 2-12

The ith entry vi of v represents the global score allocated by the AHP to the ith choice. The option ranking finally achieved when ordering the global scores in declining order as the final step

2.9 Weighted linear combination method

The weighted linear combination (WLC) can be formalized by means of the multi- criteria analysis (MCA) problem (Pereira et al., 1993, Malczewski 1996). In general, the WLC model is one of the most widely used resolution rules on GIS. The primary reason for its popularity is that the method is very easy to implement within the GIS environment using map algebra operations and cartographic modeling. The method is also easy-to-understanding and intuitively appealing to decision makers (Tomlin 1990, Berry 1993) WLC decision rule. Formally, the decision rule evaluates each alternative, ai, by the following value function:

( ) = ( ) =

𝑉𝑉 𝑥𝑥𝑖𝑖 � 𝑤𝑤𝑗𝑗𝑣𝑣𝑖𝑖 𝑥𝑥𝑖𝑖 � 𝑤𝑤𝑗𝑗𝑟𝑟𝑖𝑖𝑖𝑖 𝑗𝑗 𝑗𝑗 2-13

Where wj is a normalized weight, such that = 1, ( ) is the value function for the j-th attribute, xi = (xi1, xx2…, xi), and rij ∑is𝑤𝑤 the𝑖𝑖 attribute𝑣𝑣𝑗𝑗 𝑥𝑥𝑖𝑖 transformed into the comparable scale. The weights represent the relative importance of the attributes. The most preferred alternative is selected by identifying the maximum value of V(xi) for i=1, 2…, m. WLC can be operationalized using any GIS system having overlay capabilities. The overlay techniques allow the attribute map layers (input maps) to be aggregated in order to determine the composite map layer (output map) the methods can be implemented in both raster and vector GIS environments (ERSI 1995, Chrisman 1996).

2.10 Flood Risk Assessment

The flood risk assessment is a combination of hazard and vulnerability dimensions, both have a different level of impact on the society and environment (Sharman et. al., 2018). European Centre of Technological Safety,(2008) defined hazard "like “a threatening event, or the probability of occurrence of a potentially damaging phenomenon

31 within a given time period and area. It also stated that the occurring damage of hazardous event depends on the elements exposed to risks such as the population, economic activities, public services and infrastructures, building and civil works. While Vulnerability was defined for each hazard according to the resulting impacts as the susceptibility to degradation or damage from adverse factors or influences” (Regional Vulnerability Assessment of the United States Environmental Protection Agency,2000). Regarding UNISDR (2009), vulnerability refers to non-directly measured conditions such as the social, physical, environmental, and economic conditions, which increase the sensitivity of the elements to the impact of hazards. In other words, flood hazard becomes a disaster if it affects the vulnerable community or system. In that, understanding the flood hazard and vulnerability is essential to manage flood risk. (Reilly,2009) Many researchers successfully seek to understand flood risk assessment. (Scheuer et al., 2011) worked on the flood risk assessment by sitting a comparison between the risk (without coping capacities) and the risk (with coping capacities) considering integrated social, economic and ecological dimensions. The MCA spatial application of flood risk assessment is relatively new and there are few examples exist. With reference to case studies reported by (Brouwer and van Ek, 2004; Janssen et al., 2003; Penning-Rowsell et al., 2003; Socher et al., 2006 and Bana et al., 2004), the MCA flood risk assessment focuses primarily on the assessment of flood mitigation rather than risk mapping (Meyer et al., 2009). Cowen (1988) stated that GIS could be effectively used as a decision support tool to address problems with spatial reference data. On the other hand, Malczewski (2006), pointed out that multi-criteria analysis collects information from a geographical area and considers decision-maker preferences to reach the decision-making process.

2.11 Flood Vulnerability assessment

Since vulnerability is not directly measurable, several methods of estimating it has been suggested - including the curves of damage (Merz et al., 2010; Papathoma-Köhle, 2016), vulnerability curves (Ozturk et al., 2015; Tsubaki et al. 2016) and vulnerability indicators (Cutter et al., 2003; Roy and Blaschke, 2013). Each of the damage and vulnerability curves constructs a specific type and focuses on the physical vulnerability

32 of the structures to a particular risk, thus neglecting social vulnerability and adaptive capacity of the population (Koks et al., 2015). Since AHP is easy to understand and use and do not require detailed data such as damage and vulnerability curves, widespread vulnerability indicators have been published to assess social vulnerability (Fekete, 2009; Frigerio and de Amicis, 2016), socioeconomic vulnerability (Kienberger et al., 2009), as well as the combination of multiple dimensions of vulnerability (Roy and Blaschke, 2013; Vojinovic et al., 2016).

2.12 Previous related studies

A multi-criteria flood vulnerability modeling in combination with GIS and hydraulic models were used and a relationship between risk and criteria and their relative importance were presented at this study. In that, some previous related studies have been reviewed and Summarized as following. Elsheikh et al. (2015) have ranked the potential location of flood risk in Terengganu Malaysia using multi-criteria analysis (MCA) integrated with GIS. They considered the drainage network, basin slope, the annual rainfall and type of soil as a causative factor for flooding. Appropriate weights had been derived for each criterion to compute the priority weights using AHP. A final flood potential map was derived from the MCA, as shown in Figure 2-9, and compared with the original flood map of Terengganu which classified based on the flood vulnerability rank as following a) Highly vulnerable to flooding (Class 4). b) Moderately vulnerable to flooding (Class 3). c) Less vulnerable to flooding (Class 2). d) No flooding area (Class 1).

Figure 2-9: Flood potential map (Elsheikh et al., 2015) Another simple approach for urban flood hazard assessment was presented by Fernandez et. al. (2010) using MCA techniques in combination with GIS support. They stated that GIS provides powerful tools that can deal with a large amount of data to be used in multi-criteria decision analysis. A simple weighted linear combination was applied to combines the main five parameters causes a flood in Tucumán Province, Argentina, distance to the drainage channels, groundwater table depths, topography (heights and slopes) and urban land use. Weights and rates values were assigned according to the relative of each parameter. Meyer et. al., (2009), also applied GIS-based multicriteria flood risk using two different multi-criteria decision rules, which was used to assess the flood risk at the Mulde river, Germany. Annual average damage aggregated environmental risk, annual average affected population and probability of social hot spots of being affected were chosen as the main evaluation criteria. Flood risk was presented using the following equation Risk =Probability × Consequence 2-14 Hazarika et. al, (2016) have assessed the flood hazard, vulnerability, and risk of Dhemaji district in India using indicator-based method taking in consideration stakeholder

34 knowledge and multi-criteria evaluation in the GIS. They stated that vulnerability indicators are more important than hazard indicators while calculating flood risk. Yahya et. al., (2008) presented an example application on the integration of Multi- criteria Analysis (MCA) with GIS for flood vulnerability analysis in Hadejia Jama’are River Basin in Nigeria. Boolean and WLC approach in GIS and two methods in MCA, pairwise comparison method (Analytical Hierarchy Process-AHP) and Ranking Method, were applied to provide more accurate and flexible decisions to evaluate the effective factors. He stated that the main causative factors for flooding in the drainage area are basin slope, annual rainfall, drainage network, land cover and the soil type. At the end of the study, a flood vulnerability map was created to assess flood risk as shown in Figure 2-10.

Figure 2-10: Map of flood-vulnerable areas in Hadejia Jama’are River Basin (Yahia, 2008) De Brito et. al. (2018), presented a participatory multi-criteria analysis (MCA) for assessing flood vulnerability considering the relationships between vulnerability criteria. The preferences of each participant regarding the criteria importance were spatially modeled through the AHP and the analytical network process (ANP) multi-criteria methods. Both AHP and ANP proved to be effective for flood vulnerability assessment Delphi technique was used to select the vulnerability criteria in their study. The selected criteria were presented in Table 2-11.

Table 2-11: Selected criteria and metrics used to measure them (De Brito et. al., 2018)

Criteria Consensus Metric A person under 12 years Yes Person Km -2 A person over 60 years Yes Person Km -2 Person with disabilities Yes Person Km -2 Monthly per capita income No USD Households with improper building material Yes Percentage Households with accumulated garbage Yes Percentage Households with open sewage Yes Percentage -2 Disaster prevention institutions Yes Inst. Km -2 Evacuation drills and training Yes Drills. Km

Distance to shelters Yes Meters -2 Health care facilities Yes Facilities Km AHP hierarchical tree was constructed as shown in Figure 2-11. A reciprocal pairwise matrix was constructed by comparing the criteria and assigning a relative importance value to its relation according to a nine-point scale Table 2-9. The weights and consistency ratio (CR) were calculated once the comparisons were done. The Assigned weights of criteria were presented as shown in Table 2-12.

Figure 2-11: AHP hierarchical tree (De Brito et. al., 2018)

Table 2-12: Group criteria weights and their respective standard deviation (SD) and interquartile range (IQR). An IQR of 20% or less indicates consensus, 20-30 % indicates moderate divergence, 30-40% significant divergence, and < 40% strong divergence. (De Brito et. al., 2018)

AHP results Sun- index AHP weight Criteria Weight SD IQR Social 30.64 Person under 12 years 6.80 4.47 10.20 vulnerability A person over 60 years 6.64 4.17 17.68 Person with disabilities 9.39 9.97 23.03 Monthly per capita income 7.81 10.69 52.87 Structural 28.68 Households with improper building 14.61 9.54 34.39 vulnerability material Households with accumulated 6.97 7.17 28.01 garbage Households with open sewage 7.10 9.40 22.48 Coping 40.67 Disaster prevention institutions 10.80 9.91 25.52 capacity Evacuation drills and training 14.17 11.87 36.79 Distance to shelters 6.42 5.23 7.32 Health care facilities 9.23 7.63 19.10

As the selected criteria were standardized the data using value functions. The values of proposed criteria were transformed into a gradual scale from 0 which represents no vulnerability to 1 that represents complete vulnerability, then the flood vulnerability map was driven by summation of the multiplied standardized criteria by the derived weights. (De Brito et. al.,2018) stated that the AHP method theoretically is simple and easy to use. However, decomposition of the problem into a hierarchy with sub-criteria (e.g. economic) is required. Furthermore, one of the main hypotheses of AHP that the criteria should be independent. The following case study illustrates the application of an integrated analytical hierarchy with GIS analysis techniques in vulnerability assessment in the case of Eldoret, Kenya (Ouma et al., 2014). For this case study, a three-level structure was adopted to consider physical, socioeconomic factors Figure 2-12.

Figure 2-12: A three- levels hierarchical structure of the characteristics of the parameters that represent urban flood vulnerability (Ouma,2014)

To presents the significance of each factor in the producing flood hazard as compared to the other criteria, the standardized raster layers are weighed by an eigenvector. The results of the pairwise comparison and ranking of the criterion are presented in Table 2-13.

Table 2-13: Ranking of urban flood casing criteria to obtain the pairwise comparison matrix (Ouma,2014)

Comparison Matrix Criteria Rainfall Drainage Elevation Slope Soil Land use Rainfall 2 1 1 1 2 2 3 2 Drainage 2 1 1 1 2 2 3 2 Elevation 1 1 3 1 1 1 3 3 2 2 4 Slop 1 2 2 1 1 4 4 3 Soil 1 1 1 1 1 1 2 2 3 4 Land-use 1 1 1 1 1 1 2 2 3 4 Total 1 1 1 1 6 6 4 3 13 13 2 2 3 4

The pairwise comparison matrix, as well as the factor maps, were used in weight and ranking computation step. Weight values symbolize the preferences, which are firm numbers ranging from zero to one. A larger weight value of the factors symbolizes more preferences or more influence than others. Flood sensitivity map was acquired using a weighted linear integration (WLC) overlay of the resolution factors according to the equation. 1 = 𝑛𝑛

𝑊𝑊𝑊𝑊𝑊𝑊 � 𝐷𝐷𝑖𝑖𝑊𝑊𝑖𝑖 𝑛𝑛 𝑖𝑖=1 2-15 Where WLC = linear combination; Di = decision parameter; Wi = AHP weight; n = numbers of parameters. Nearly a fifth of the overall municipal district is considered subject to a potential high or very high flood risks and hazards according to the outcomes. Such areas and districts are the ones close to rivers and in general constructed at low elevations in the paved areas. Four-fifth of the study area, on the other hand, was subject to flood risks and damages between very low to moderate level. Another Case study in Paschim Medinipur district in India was presented, to evaluate and assess flood the vulnerability risk using the AHP method and the weighted linear combination. Flood zones were delineated and a composite vulnerability index map was prepared which both were accomplished with the Physical Vulnerability Index, Social Vulnerability Index, and Coping Capacity Index (Dandapat, et.al.,2017). The concept of Risk was using three factors- hazard, exposure, and Vulnerability which can be expressed as: Risk = hazard × exposure × vulnerability 2-16 Another concept that could be used to express risk is the combination of hazard and vulnerability. Risk = hazard × vulnerability 2-17 A hierarchical decision model is designed so that the top level displays the decision's overall goal. The criteria are presented at the upper level of the model then each of them is further divided into sub-criteria. In terms of their relative importance, each pair of criteria or sub-criteria element is compared using a 9-point as shown in Table 2-10.

The weight of each criterion (priority) is calculated from a matrix of a paired comparison by dividing the cell value by the summation of the column. The consistency of the comparisons is evaluated by calculating a Consistency Ratio (CR). CR = Consistency Index/ Random Index 2-18 The comparisons are considered consistent the acceptable CR is equal to or less than 0.1, otherwise it would be rejected. Vulnerability analysis using decision hierarchy model and Pairwise comparison matrix for the indicators of the Physical, social and coping capacity criteria of the targeted area were determined individually, for example, decision hierarchy model for social vulnerability assessment include demographic, socio-economic and infrastructure lifeline criteria are divided into sub-criteria as shown in Figure 2-13.

Figure 2-13: Decision hierarchy model for social vulnerability assessment (Dandapat, et.al.,2017).

Gathering the vulnerability and coping capacity maps using the WLC method, A flood risk map was derived as shown in Figure 2-14 by multiplying the hazard score and composite vulnerability map. Results show that areas near to water bodies or close to the river with elevation less than five meters assigned as a higher vulnerable to flooding. Furthermore, zones present a high level of poverty, a large number of houses with a high population density also classified as highly vulnerable areas.

However, they realized that communities experienced frequently flooding incidents exhibit a high coping capacity rather than communities regularly did not experience any flooding incidents.

Figure 2-14: Flood risk map (Dandapat, et.al.,2017).

Furthermore, Matoriet, et.al. (2014) presents combining GIS-based AHP model as spatial forecasting tools that attempt to provide a flood forecasting map for flood hazard assessment rainfall, geology, slope gradient, land use, soil type, drainage density, temperature, etc. are generally considered as influencing flood factors. Each factor’s weight was calculated and allocated to the factors previously illustrated in the GIS environment. AHP method was applied in generating the relative weights depending on the Saaty’s scale of influence 1 to 9. After that, it is combined into a GIS system to cultivate the flood-sensitive regions. The combination of factors was carried out in a GIS using the Weighted Linear Combination (WLC) method in accordance with the computed weights. Pairwise comparisons of the factors were carried out through a number of experts of MCA. The responses of the experts were computed in Table 2-14 while Table 2-15 present the normalization of relative weights of criteria.

Table 2-14: A matrix of pair-wise comparison of five criteria for the AHP process

(Matoriet, et.al.,2014)

RF GL ST S LU RF 1.00 4.92 3.41 2.30 3.61 GL 0.20 1.00 2.05 0.39 0.48 ST 0.29 0.49 1.00 0.70 0.46 S 0.44 2.58 1.42 1.00 0.60 LU 0.28 2.08 2.17 1.66 1.00 SUM 2.21 11.07 10.05 6.05 6.15 Where, RF = Rainfall; GL = Geology; ST = Soil type; S = Slope; LU = Land use.

Table 2-15: Determined normalization of relative criterion weights (Matoriet, et.al.,2014)

RF GL ST S LU Weight RF 0.45 0.44 0.34 0.38 0.44 0.41 GL 0.09 0.09 0.20 0.06 0.11 0.11 ST 0.13 0.04 0.10 0.12 0.09 0.096 S 0.20 0.23 0.14 0.17 0.17 0.182 LU 0.13 0.19 0.22 0.27 0.19 0.2 SUM 1.00 1.00 1.00 1.00 1.00

By the end of their studies, they reported that GIS-based analytical analysis was a successful method of simulation of flood areas which in term will help decision makers and planners to assess damages and losses caused during and post-flooding disasters.

Table 2-16 summarizes the above-mentioned previous studies in the same field of application.

Table 2-16: Summary of previous studies

Authors Year Country Objective

De Brito et. al. 2018 German A participatory multi-criteria analysis (MCA) for assessing flood vulnerability

Dandapat, et.al 2017 Paschim Medinipur Evaluate and assess flood the vulnerability risk district in India using the AHP method and the weighted linear combination.

Hazarika et. al 2016 Dhemaji district in Assessed the flood hazard, vulnerability, and India risk using the indicator-based method

Authors Year Country Objective

(Ouma, 2014 Eldoret, Kenya Application of an integrated analytical hierarchy with GIS analysis techniques in vulnerability assessment

Matoriet, et.al. 2014 A flood forecasting map and hazard assessment using combining GIS-based AHP model as spatial forecasting tools that attempt to provide

Elsheikh et al. 2015 Terengganu Malaysia Flood risk Malaysia using multi-criteria analysis (MCA) integrated with GIS.

Fernandez et. 2010 Tucumán Province, Flood hazard assessment using MCA techniques al. Argentina in combination with GIS support.

Meyer et. al. 2009 Mulde river, Germany. GIS-based multicriteria flood risk

Yahya (2008) Hadejia Jama’are River Integration of Multi-criteria Analysis (MCA) Basin in Nigeria. with GIS for flood vulnerability analysis

Chapter 3 Study Area

3 Chapter 3 Study Area

Chapter 3 Study Area

3.1 General Information

Since the vulnerability is site-specific, the municipalities of North Gaza governorate (62 km2), which represents more than 17 percent of the total area of the Gaza Strip, were selected as a case study. In 2016, the total population was approximately 368,978 capita (18 % of the Palestinian population) Table 3-1 shows the North Gaza population for each municipality in 2016. This put North Gaza as one of the densest governorates with a density of 6.1 cap/dun (PCBS, 2016). North Gaza is composed of four municipalities as shown in Figure 3-1. These municipalities are Jabalia, Beit Lahia, Beit Hanon, and Um Al Nasser. North Gaza includes five main urban clusters (Jabalia, Jabalia camp, Beit Lahia, Beit Hanoun, Sheikh Zayed Residential Neighborhood) and several rural communities (Um Al-Nasser village, Ezbet Beit Hanoun – Al Alami Project - Tel Zaatar - Bir Al-Nuja). In general, Figure 3-2 below present the North Gaza governorate municipalities and their district.

Figure 3-1: North governorate location map Table 3-1: Population of North Gaza municipalities (2016)

SN Municipality Population % 1 Jabalia 230,159 61.00 2 Beit Lahia 89,949 24.00 3 Beit Hanoun 53,094 14.00 4 Um AL Nasser 3,923 1.00

Figure 3-2: North Gaza districts North Gaza is bordered by the occupied Palestinian territories on the northern and eastern side and by Gaza city on the south and the Mediterranean Sea at the west. In North Gaza governorate the topography slope in the city inclines towards the east, where the land elevation is up 80 meters above MSL, decreasing to the west of the governorate and to sea level at the coastal. Figure 3-3 shows the contour lines of the North Gaza governorate. Figure 3-4 presents the general DEM map from NASA for Gaza Strip.

Figure 3-3: Topographic contour lines for North Gaza governorate

Figure 3-4: NASA DEM map (CMWU, 2017)

3.2 Hydrology

In the study area, the groundwater aquifer consists mainly of sand and gravel and sandstone (Kurkar) intercalated by clay and silt. A hard and non-productive layer of clay and marl with low permeability (Sakia Formation) has a thickness of about 800-1000 m situated below the coastal aquifer at a depth of about 100 m in the northern governorate. The transmissivity values of the upper 20-30m saturated part interval of the aquifer are ranging between 700 and 5,000 m2/d. The corresponding values of hydraulic conductivity (K) are within a relatively narrow range, 20-80 m/d. Based on lithology and

47 information from previous studies, the specific yield of the unconfined coastal aquifer is in the 0.15-0.3 range (CMWU,2016). Depth to the water level in the northern governorate varies between few meters in the low land area along the shoreline to about 60 m towards the eastern parts. A cone of depression had occurred in the northern governorate with water level depth of -5 m below sea level based on the water level records of monitoring wells in 2014 (CMWU,2016).

3.3 Climate

Climate is the statistics of weather over long periods of time. It is measured by assessing the patterns of variation in temperature, humidity, wind, evaporation, precipitation, and other meteorological variables in a given region over long periods of time (Shepherd et. al., 2005, Planton, 2013).

3.3.1 Temperature North Gaza has a typical Eastern Mediterranean climate with hot dry summers and mild winters. The temperature gradually changes during the year. The main findings of the time series indicate that the daily mean of air temperature ranges between 12°C and 25°C. The temperature gradually reaches its maximum in August (summer) and its minimum in January (winter); the average daily maximum temperature ranges from about 18°C in January to 31 °C in August while the average daily minimum temperature for January is about 7.0 °C and 20 °C for August (Weather statistics for North Gaza,2018).

3.3.2 Humidity Daily relative humidity fluctuates between 60% and 85% in the daytime in the summer and between 60% and 80% respectively in winter (CMWU, 2016).

3.3.3 Wind At summer time, sea breeze blows all day long while the land breeze blows only at night. At noon, wind speed value reaches its peak and starts to decrease at night. Whereas in winter wind stream blows mostly from the Southwest. The average wind speed reaches 15.12 km/hr. In summer wind stream blows roughly only at precise hours. The average wind speed in summer can reach 14.04 km/hr. daily coming from the Northwest direction. On the other hand, in winter a maximum hourly wind speed of 18 m/s have been observed (CMWU,2011).

3.3.4 Evaporation In Gaza Strip, evapotranspiration measures based on 25 established records, mark that the strip has possible evapotranspiration of almost 1291 mm/yr. The highest evaporation rate was observed and measured during July and August, the hottest months in the strip with an evaporation rate of nearly 138 mm. whereas the minimum evaporation rate happens in January with a rate of 63 mm. Figure 3-5 shows the average monthly rainfall and evaporation in the Gaza strip between 1980/2005 (Sirhan, 2014).

Figure 3-5: Average monthly rainfall and evaporation in Gaza Strip between 1980/2005.

3.3.5 Rainfall The main source for the natural recharge of Gaza coastal aquifer is rainfall. Gaza strip has 12 manual rainfall stations distributed through different governorates as shown in Figure 3-6. Three stations in the Northern area: Beit Lahia, Beit Hanoun and Jablia stations, four stations in Gaza city: Shati, Remal, Moghraga and south Gaza stations, two in the middle area: Nussirate and Deir al-Balah stations and three in the Southern area: Khanyounes, Khuzaa and Rafah stations. (MOA, 2015). Data from these stations are collected on a daily basis. The ministry of agriculture operates those stations. The average amount of regional precipitation in the Gaza Strip is computed using Thiessen Method. In Thiessen method, the rainfall recorded at each station is given weight on the basis of an area closest to the station. The method mainly divides the catchment area into Thiessen polygons depend on the locations of the rainfall station as shown in Figure 3-6. Figure 3-7 shows the average annual rainfall rates of Gaza Strip for the time

49 period 1976-2017, while Figure 3-8 shows the average annual rainfall rates in the North governorate rain station for the time period 1976-2017.

Figure 3-6: Average precipitation from the year 1976 to 2017 over the Gaza Strip by Thiessen Method

Figure 3-7: Annual rainfall for Gaza Strip between 1976 and 2017 (MOA, 2017)

Figure 3-8: Average annual rainfall for North Gaza governorate rain stations between 1976 and 2017 (MOA, 2017)

Rainfall quantity is varied from 413 mm/y in the North, 386 mm/y in Gaza, 336 in the middle governorate, to about 254 mm/y in the south over the last 40 years. Table 3-2 shows the rainfall stations locations and the average and total rainfall for each area while Table 3-3 shows rainfall values of Northern area, Gaza city area, middle Governorate, and Southern area stations for years 1985, 1990, 1995, 2000, 2005,2010, 2015, 2016 and 2017. The northern governorate always has more rainfall precipitation than the middle and southern parts. In addition to that, for the same station, there are significant variations from year to year. MOA (2017) defined two seasons, the wet season and the dry season. The first starts from October to April with almost all rainfall, while the second starts from

May to September. The annual average rainfall in the northern governorate is about 360 mm for the last three years 2015, 2016 and 2017.

Table 3-2: Rainfall stations location, and average and total values (mm/yr) (MOA, 2017)

Average Total Station (X) (Y) Thiessen Station Name rainfall rainfall No. Coordinate Coordinate Area (km2) (mm) (mm) 1 Beit-Hanoun 106420.00 105740.00 407 214 29.00 2 Beit-Lahia 99750.00 108280.00 413 249 14.25 3 Jabalia 99850.00 105100.00 420 233 15.50 4 Shati 97474.78 105428.23 376 207 2.25 5 Gaza-City 97140.00 103300.00 373 241 13.00 6 Tuffah 100500.00 101700.00 408 248 23.25 7 Gaza-South 95380.00 98000.00 347 276 35.00 8 Nusseirat 91950.00 94080.00 344 172 29.50 9 D-Balah 88550.00 91600.00 317 219 38.50 10 Khanyunis 84240.00 83880.00 285 167 83.50 11 Khuzaa 83700.00 76350.00 248 171 42.50 12 Rafah 79060.00 75940.00 229 149 38.75 Total 344 2546 365

Table 3-3: Average rainfall values in selected years (mm/yr), (MOA, 2017)

Year/ Avg. 85/86 90/91 00/01 05/06 10/11 14/15 15/16 16/17 Location Annual Beit-Hanoun 215.5 442 497.5 368.9 234.2 663.0 302 214 407 Beit-Lahia 258 435.5 490.4 363.9 243.9 593.6 317 249 413 Jabalia ------540 345.4 276.0 615.5 281 233 420 Shati 232 446.8 478.9 317.2 258.0 539.8 206 207 376 Gaza-City 228.5 365.6 511.9 322.4 293.6 611.2 303 241 373 Tuffah ------533.4 363.5 285.5 480.7 267 248 408 Gaza-South ------563.6 274.4 276.4 442.6 175 276 347 Nusseirat 204.5 370.7 558.3 295.0 263.0 532.2 319 172 344 D-Balah 240 324.6 550.5 257.0 244.0 427.0 233 219 317 Khanyunis 301.2 348.6 381 270.5 200.1 418.0 190 167 285 Khuzaa ------284.3 214.0 145.5 405.0 172 171 248 Rafah 150.2 241.5 308 203.0 117.0 352.0 157 149 229 Total Annual rainfall 1829.9 2975.3 5697.8 3595.2 2837.2 6080.6 2922 2546 344 (mm/yr)

3.4 Soil

Gaza Strip is privileged with its rich soil, that is basically composed of major six types of soil: loess soil, dark brown/reddish brown, sandy loess soil, loessial sandy soil, sandy loess soil over loess and sandy regosol (PEPA, 1996). Sand dunes that lie along the coastal side of the Gaza Strip are the main soil type in the Strip. The thickness of the dunes ranging between 2 m to nearly 50 m and expands up to 4-5 km in the north and south regions, and less at the core of the strip. Furthermore, the loess soil which thickness reaches up to 25-30 m is located around the Wadi. While dark brown soil which is clay is situated in the northeastern parts of the strip. Characteristics and classification of the diverse types of soil in the Gaza Strip are summed up in Table 3-4.

Table 3-4: Classification and characteristics of the different soil types in the Gaza strip (MOPIC, 1997; Goris and Samain, 2001).

Local Location Description Texture classification Loam Loess soil Between the Loess soils sedimented in Sandy loam Gaza Pleistocene until Holocene Series. (6% clay, silt city and the Wadi The grain size of loess fluctuates 34%, sand Gaza, along with from 0.002 to0.068 mm. Loess has 58%) the mid of Gaza been transported by winds and Strip sedimented in loose form in the upper part, and in a hard form in the lower part of the layers. They are brownish-yellow colored often with an accumulation of lime concretions in the subsoil and containing 8 – 12 % calcium carbonate. Clay Dark brown Beit Hanoun, These alluvial soils are usually dark Sandy clay /reddish Wadi Gaza, and brown to reddish in color, with a loam (25% brown east of Kahn well-developed structure. At some clay, 13% silt, Younis depth, lime concretions can be 62% sand) found. The calcium carbonate content can be around 15–20% Sandy Sandy loess Deir-Balah, This is a transitional soil, Sandy clay Loam Soil Abasan, and east characterized by a rather uniform, loam (23% of Khan Younis lighter texture. Apparently,

Local Location Description Texture classification windblown sands have been mixed clay, 21% silt, with loessial deposits. 56% sand) Sandy Loessial It is found in the Forms a transitional zone between The top layer clay sandy center, southern the sandy soil and the loess soil, is sandy loam loam Soil part of the strip, usually with a calcareous loamy (14% clay, and east of Gaza sandy texture and a deep uniform 20% silt, 66% and North area pale brown soil profile. sand). The lower profile is loam (21% clay, 30% silt, 49% sand) Loamy Sandy loess It is found east of It is loess or loessial soils which Sandy loam Sand soil over loess Rafah and Khan have been covered by a 20 to 50 cm (17.5% clay, Younis, along thick layer of sand dune 16.5% silt, with the coast of 66% sand) Rafah and North area Sand Sandy regosol It is found along Soil without a marked profile. The The top layer the coast of Gaza texture in the top meters is usually is loamy sand strip uniform and consists of medium to (9% clay, 4% coarse quartz sand with a very low silt, 87% water holding capacity. The soils sand). are moderately calcareous, very low Deeper profile matter and chemically poor, but is sand (7.5% physically suitable for intensive clay, 0% silt, horticulture in greenhouses. In the 92.5% deeper subsurface occasionally sand) loam or clay loam layers of alluvial found.

A soil map was prepared using 198 soil sample collected and added to the ArcGIS program then interpolated using Proximity Polygons method which creates Thiessen polygon or proximal zones for each single point input feature. These zones represent a full area where any location within the zone is closer to its associated input point than to any other input points. Figure 3-9 shows the soil map for the North Gaza governorate.

Figure 3-9: Classification of soil map for the North Gaza Strip

3.5 Land use

Land in Gaza Strip considered one of the basic natural resources, and its use varies from agricultural areas, governmental areas, and built-up areas. The land use and land cover (LULC) map of Gaza strip were derived by the analysis of the satellite image and integration of ArcGIS/ArcMap program for the years 2004 and 2010 which illustrate a comparison of LULC total surface and percentages for the two years as shown in Figure 3-10. While Table 3-5 presents a comparison of the calculated LULC total surface and percentages for the two years.

Figure 3-10: Land use land cover for the Gaza Strip in the year 2004 and 2010 (CLIMP, 2012)

Table 3-5: Comparison of calculated LULC total surface area and percentages for 2004, 2010 (CLIMP, 2012).

Classification Area 2004 (ha) % Area 2004 Area 2010 (ha) % Area 2010 Natural vegetation 2424 6.6 1265 3.5 Built-up Areas 8370 22.8 9151 25.0 Sand 4263 11.6 3540 9.7

Mixed 39.7 14543 11330 30.9 Agriculture Horticulture 2363 6.4 1775 4.8 Citrus Orchards 3182 8.7 3646 9.9 Greenhouses 1365 3.7 814 2.2 Rainfed 152 0.5 336 0.9 Agriculture Olive Orchards 4806 13.1 Total 36662 100.0 36662 100.0

Regarding the previous figures and table, it was obvious that in 2004, the agricultural area covers the highest portion with about 65.6%, while, in 2010 the percentage of the agricultural land was slightly decreased to reach 65.3 % of the total area.

Most of the agricultural lands are located in the eastern parts of Gaza, where the population density of the population is lower. Regarding the urban built-up areas, which represent the second most important land uses, they cover 22.8% of the land uses in 2004 and 25% in 2010. Which means that built up areas percentage increased from 2004 to 2010 by 2.2%, this rate of increase is matched by a decrease in agriculture and sand rates in 2010. On the other hand, the regional plan of Gaza Governorates shows that the land is scarce and the pressure on it for all kind of uses; urban, industrial, and agricultural uses as shown in Figure 3-11 and it is expected that future expansion will be for the built up use only (MoP, 2005).

Figure 3-11: Regional plan for Gaza Governorate 2005-2020, (MoP, 2005)

The northern governorate is characterized by water availability and flourishing of agriculture. Most of the agricultural areas are distributed along the borders. However, highly populated areas are found in the middle especially at Jabalia. Some industrial areas concentrated at the eastern parts of the governorate. Figure 3-12 shows the land use of the northern governorate, including the existing build-up areas. Some areas are expected to reach saturation, where any increase in the population will not affect the population density in the area, on the other hand, it is expected that some of the agricultural areas, will be converted to built-up areas, especially for those in the western parts of the governorate.

Figure 3-12: Northern governorate land use map for 2016 The total built-up area represents about 17.3 % from the total area and it is located at different elevations from the sea level with varies slope directions, where the agricultural area represents an area of about 43.4% and distributed into different soil types (see Figure 3-12). The open area represents 39.3% of the total governorate area.

3.6 Existing stormwater drainage system in North Gaza Governorate.

Parts of stormwater networks were constructed in various location of the North Gaza municipalities, while almost the western part has no stormwater infrastructure. The runoff

58 in the western part infiltrates naturally to the groundwater as it is characterized by sandy soil cover. According to PWA (2011) and North Gaza municipalities the existing stormwater systems in Northern Gaza (Beit Hanoun, Beit Lahia, Jabalia and Um Al Nasser) are as following:

a) Beit Hanoun: Storm drainage normally takes place in the streets and drained to Wadi Beit Hanoun. A new stormwater network has been built in roads constructed in different areas. Stormwater drainage problem has been reported at Al Ajoz street.

b) Beit Lahia: Storm drainage takes normally place in the streets. PWA prepared a plan for the stormwater collection and disposal schemes for the urban communities in Beit-Lahia. There are two stormwater infiltration basins that have been identified in the area and have been designed. The basins are:

- Al Manshiah close to the sewage pumping station (Manshiah site).

- Al Jammia (Jammia site).

c) Jabalia: Stormwater run-off in Jabalia town is a less problem than that of Jabalia Camp. Jabalia Camp is located in inland catchments with no natural drainage towards the sea. Most stormwater runoff takes place in the streets; UNRWA and PWA have implemented some drains in the Camp. PWA upgraded Abu Rashid pond to store up to 30,000 m3 with improved infiltration capacity and pumping the collected stormwater for groundwater recharge in the dunes close to Beit Lahia WWTP. In the case of Jabalia town, there are some local stormwater run-off problems reported in areas in the southeast part of Jabalia. Stormwater run-off takes place on the streets. PWA prepared a plan for the stormwater collection and disposal schemes for the urban communities in the Jabalia. There are 3 stormwater infiltration basins that have been identified in the area and have been designed. The basins are:

- East of Al Saftawi neighborhood (Nazla site).

- West of Al Awda road at Khalaf land (Khalaf site).

- At the vacancy of old cemetery (Cemetery site)

d) Um Al Nasser: Storm drainage takes normally place in the street while others drained by a new stormwater network built in roads and connected to 5000m3 Dunnum stormwater basin. Stormwater drainage problems have been reported at Shamas, Besan, and Al Ghazalat area. Figure 3-13 and Figure 3-14 illustrates the existing stormwater infiltration basins and the existing stormwater network respectively in the Northern Governorate based on the investigation and interviews with the key municipalities' staff. Many mixing points between wastewater and stormwater have been emphasized. This confirms the hypothesis of not having a complete separate stormwater collection system. However, the stormwater records during the rainy seasons always revealed lack in the capacity of the stormwater system, which always leads to an increase in wastewater records, not vice versa. This led us to deeply believes that all the mixing point is not having any major effects on the stormwater drainage system, except for decreasing pressure on the stormwater and in sometimes preventing flooding in some areas, while in the other hand it will increase pressure in the wastewater networks and facilities.

Figure 3-13: Stormwater infiltration basins in Northern governorate

Figure 3-14: Existing Stormwater network at the Northern Governorate

3.7 Existing wastewater drainage system in North Gaza Governorate

The existing wastewater collection and pumping system was constructed since 1998. Till this moment, fragmented upgrading and new sewage infrastructure were constructed. The sewage system includes two categories of pipes: gravity sewers (152 km) and force mains (38 km) as shown in Figure 3-15 and Figure 3-16. The gravity pipelines size range from 8 to 24 inches in diameter while the pressurized force mains size range from 8 to 26 inches in diameter. The collected wastewater is transported or pumped to Biet Lahia wastewater treatment plant (PWA, 2016).

Figure 3-15: Existing wastewater networks in the northern governorate (PWA, 2017)

Figure 3-16:The existing wastewater pumping system in the northern governorate (PWA,2017)

North Gaza collection system includes 17 main pumping stations distributed as shown in Figure 3-17 one of them, Alzaitoun in Biet Hanoun, still in the operation phase.

Table 3-6 summarizes the location, flow rate and head of each pumping stations. (PWA, 2016).

Table 3-6: Data of wastewater pumping stations (PWA, 2016)

Facility No. of Max. Pump Power Pump Design Flow Head at Location Name Pumps (kW/pump) Curve Rate (m3/hr) Manifold (m) Beit Alsekka 3 54 Yes 250 50 Hanoun Beit Industrial 2 78 No 220 50 Hanoun Beit Alsultan 2 37 No 150 40 Hanoun Um Um 2 27 No 200 20 Alnassir Alnassir Almashroa Beit Lahia 2 54 Yes 300 50 Alsalateen Beit Lahia 2 26 No 220 25 Aslan Beit Lahia 2 54 Yes 300 40 Almanshia Beit Lahia 4 54 Yes 300 38 Alhatabia Beit Lahia 2 75 No 226 46 Amer Beit Lahia 2 125 No 500 40 Abu Jabalia 4 85 Yes 800 40 Rashed Mahader Jabalia 3 40 No 38 40 Hawaber Jabalia 3 78 No 400 40 Tal Al Jabalia 3 78 No 400 40 Zaatar Alalami Jabalia 2 54 Yes 550 40 TPS Beit Lahia 2 No 1300 40

The catchment area for each pumping station was also updated based on the updated topographic map. The produced map for the catchment area is shown in Figure 3-17.

Figure 3-17: Wastewater pumping station and their service area (PWA, 2016) Since some of the pumping station is designed to pump the collected wastewater into other pumping stations, then, the new operational scheme was illustrated in Figure 3-18 (PWA, 2016).

Figure 3-18: Routes of a pumping station in North Gaza (PWA, 2016)

4 Chapter4 Methodology

Chapter 4

Methodology

Chapter 4 Methodology

4.1 Introduction

Flooding risk consists of hazard and vulnerability. Hazards can be defined as threatening events or the probability of occurrence of a potentially damaging phenomenon within a given time period and area. While the vulnerability is defined as the kind of elements at risk and the degree of damage caused by threatening events. The elements at risk are most likely the population, buildings and civil engineering structures, economic activities, public services and infrastructure (Barroca et.al., 2006). The flood vulnerability analysis applied in this study consists of two basic phases.

1. Developing the AHP model 2. Assessment of existing stormwater drainage infrastructure. This chapter introduces the concept of the AHP method, relative measurements, pairwise comparison, scaling and building up the hierarchy. It also discusses the detailed methodology used for assessing the flood vulnerability and mapping using ArcGIS, as well as introducing the SewerGEMs modeling process. Figure 4-1 shows the methodology of this study. This chapter comprises a detailed description of the research methodology including the used models.

Figure 4-1: Research Project Methodology

4.2 Developing the AHP – Flood vulnerability mapping Flood vulnerability is the most vital element of risk. It determines whether exposure to a hazard constitutes a risk that may actually result in a disaster. It refers to the physical, social, economic, and environmental conditions, (UNISDR, 2009). Since the vulnerability is not directly measurable, vulnerability indicators have been proposed to estimate it, which is not focusing only on the physical vulnerability, it also concentrates on the ability of a society to anticipate, cope with, and get over from a disaster to assess floods potential impacts. AHP integrated with GIS environment and SewerGEMs modeling software was applied to identify vulnerable areas to flood and to assess the existing drainage system. The evaluation and assessment procedures consist of the following steps: 4.2.1 Building the AHP Model

A model based on the AHP approach was promoted in this step. The model involves the establishment of the hierarchy by determining the main objects and the elements of the hierarchy.

In this research project, flood vulnerability estimation model was identified to be the linear composition (WLC) of the product among the relative weight of every selected criterion and their ranking in an attempt to divide the targeted region from the least to the most sensitive. It also determines the overall relative weights of the alternatives using the WLC of the product from the relative weight of the main criterion and the relative weight of the alternative for that criterion. Furthermore, the developed Analytic Hierarchy Process (AHP) was divided into several phases, which are as follow:

4.2.1.1 Setting goal/objectives At this phase, the hierarchy model had its overall goal and objectives. The main criteria, as well as possible alternatives, were divided into several levels and then organized into a structure that refers to the main goal. In order to test the set of objectives, different issues should be taken into account (Bodily, 1985), namely completed objectives, minimum redundant and ambiguity, and measurable elements.

4.2.1.2 Structuring the hierarchy Once the main goal developed, the main hierarchy was structured. The overall goal was set at the top of the hierarchy. However, the lower levels interpret main and sub-main elements of the upper hierarchy level. Simple hierarchy levels for a flood hazard sample

68 that was taken into consideration in this research for the North governorate of the Gaza Strip are set below.

- Level 1 (the top level): overall goal. - Level 2 (the second level): main vulnerability criteria. - Level 3 (the third level): sub- main vulnerability criteria. - Level 4 (the lowest level): alternative actions.

4.2.1.3 Setting the priorities After building the hierarchy structure, the weights of each component, which reflects the priorities of each, were calculated based on the judgment of experts. Five key elements were considered in structuring priorities development.

 Identification of relevant experts

Experts were defined as people who have an academic background or professional experience in a field. Experts from universities, governmental organizations, NGOs, municipalities, and private companies have engaged in the development of vulnerability criteria weighting.

 Pairwise comparison

A pairwise matrix was carried out by comparing the criteria and assigning a relative importance value to its relation according to a nine-point scale as shown previously in Table 2-9. The pairwise comparison was conducted from the top level below the first one down to the lowest level. As displayed in, the component was organized into a comparison matrix. Table 4-1, the component was organized into a comparison matrix.

Table 4-1: Sample of Comparison matrix

A B C D E F

1 Xi/Xj X1 X2 X3 X4 Xn

2 X1 1 X1/X2 X1/Xn

3 X2 X2/X1 1 4 … 1 1

5 Xn Xn/X1 1

Priority questions

To make the pairwise comparisons, questions were set to manage the respondents correctly taking inconsideration language and terms used for judgment so they are clear for everybody. This process also would help to keep judgments consistent and accurate. The main goal of the leading questions is to elicit respondents’ point of view of each pair of criteria as far as which one is more probable or more imperative than the other.

- Level 2

At this level, no priority questions were used. The overall weights of the main criteria were calculated through the summation of sub-criteria weighs of each one.

- Level 3

The questions at this level are used to determine the sub-main vulnerability criteria impact the flood in North Gaza city. For Example, at level 3 nine comparisons were derived due to that there are nine criteria on it. An example question is stated below:

“With respect to producing flood vulnerability map, which is more likely to increase the vulnerability during the flood, poverty/culture or the population density ”?

- Level 4

Level 4 was addressed the judgment of an expert on the alternatives’ significance, which is presented in this model. In hierarchy level 4, every component had identical comparison conduct, where there are four alternative comparisons that were carried out for each criterion. For more illustration, an example question is stated below:

“Which is more significant with respect to social sensitivity, relocation or developing integrated stormwater management”?

 Criteria normalization

To calculate the normalized values for each criterion and alternative in their respective matrices, the value for each cell was divided by its column total. This process produces a column total of 1 for each criterion and alternative. The ranking values were normalized by dividing the R-value by the Maximum value of the criterion ranking scale as shown in the following equation:

= 𝑅𝑅𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝑅𝑅𝑛𝑛𝑛𝑛𝑛𝑛 𝑅𝑅𝑚𝑚𝑚𝑚𝑚𝑚 4-1 For example:

If the maximum rank was 3, therefore, we got 1/3 =0.33 for the minimum, 2/3 = 0.67 for intermediate and 3/3= 1 for maximum.

 Derivation of vulnerability criteria weights

As the vulnerability criteria have a different level of importance, assessing the criteria weights is an essential step. AHP was used as a multi-criteria method to elicit experts’ preferences about criteria weights.

Once the comparisons are accomplished, the criteria weights were acquired by the leading eigenvector of the matrix. This means on every row, the criteria being averaged (Saaty, 1980). In respect of the overall goal, the outcome values give the relative weights of the criteria, and with respect to the criteria, it will give the adjacent weights of the alternatives. Then the best option was chosen by the decision makers; depending on the total relative weights of the alternatives.

4.2.1.4 Verifying the Consistency of Judgments The consistency is defined by the relation between the entries of the evaluation matrix. These steps are necessary to determine the consistency of the evaluation by calculating the consistency ratio before a decision is made.

The consistency was identified by the link between the entries of the estimation matrix. Such steps are very significant to mark the coherence and consistency of the estimation throughout computing the rate of consistency before taking a decision by the decision makers.

In order to calculate the consistency ratio, we will get back to the original matrix that contain the original set of pairwise not the one we normalized then multiplies that with the weight vector calculated which was the average of the row of the normalized matrix and that will give us a new vector called Ws vector as shown in the following equation.

{Ws }= [C] {W}

4-2

Where C: the original matrix W: the weight Then to get the consistency vector some numerical measures were done , 1 Consistency vector = {W }. W s � � 4-3 Where the average of the elements of the consistency vector will give us λ max which is called also the eigenvalue of the matrix system. Then consistency index CI can be calculated using the following equation.

CI = (λ max -n) / (n -1)

4-4 Where n: the number of criteria in the matrix

The consistency ratio (CR), is used to deduce whether or not the expert’s resolutions and judgments were adequately consistent. As interpreted in equation 4-5, it is computed and calculated as the ratio of the CI, and the random index (RI). The values of RI are tabulated in Table 2.

CR = CI / RI

4-5 Where The random index (RI) is given by the following table

Table 4-2: Random Index (RI) used to compute consistency ration (CR) (Satty, 2008)

N 1 2 3 4 5 6 7 8 9 10 Random Index RI) 0 0 0.58 0.9 1.12 1.24 1.32 1.41 1.45 1.49

(Saaty, 1980) proposes that the judgments may be too incoherent and inconsistent to be reliable in case the ratio surpasses 0.1. However, a CR underneath 0.1% or 10% is acceptable. The three-step procedure is taken into consideration when the estimation and judgment are inconsistent (Saaty, 2008):

1. Determine which evaluation is the most inconsistent in the decision matrix;

2. Set a range of values the inconsistent estimation can be changed in order to decline the related inconsistency; and 3. Request reconsideration of the judgment to a “reasonable value” from the decision maker.

4.2.1.5 Alternative priorities A comparison matrix for the types of alternative action to deal with a specific type of vulnerability towards flood will be created using the same steps previously followed in setting priorities and verifying the consistency.

While there are four alternatives, the adaption of anyone will be affected by the vulnerability criteria. In that, the main formula to calculate the flood vulnerability alternative was identified as following:

= ×

𝑙𝑙2 𝐿𝐿3 𝑉𝑉 � 𝑃𝑃 𝐴𝐴 4-6

Where the PL2 is the probability/weight of the flood vulnerability criteria presented in level 2 in the hierarchy and AL3 is the probability of importance of alternative in level 3 in the hierarchy. By the end of this step, each type of alternative action will be priorities according to the respondents and with reference to each type of criteria.

4.2.1.6 Vulnerability criteria ranking Once the weights of each criterion are determined, the criteria will be ranked according to their influence to flood events in North Gaza which are described in Table 4-3. The table shows the range of criteria which contribute to the ranking values.

Table 4-3: Flood Vulnerability Ranking for the case study area

Vulnerability Criteria Range Ranking decision Very High 5 High 4 Poverty/culture Medium 3 Low 2 Very Low 1 250-3000 1 Population Density 3000-7500 2 (cap/Km2) 7500-15,000 3

Vulnerability Criteria Range Ranking decision 15,000-25,000 4 25,000-75,000 5 Very High 1 High 2 Coping Medium 3 Low 4 Very Low 5 High Resistance 1 Structural Medium Resistance 2 Low Resistance 3 Sand 1 Sandy Loam 2 Soil Type Sandy clay 3 Clay Loam 4 Clay 5 Agriculture and open area 1 Residential Land Use 2 Industrial/Wastewater treatment 3 site High reliability 1 Drainage Medium reliability 2 Low reliability 3 <1 5 1-3 4 Slope (%) 3-5 3 5-10 2 >10 1 376 1 407 2 Rainfall (mm) 408 3 413 4 420 5

4.2.1.7 Flood vulnerability Mapping To produce a flood vulnerability map, weighted linear combination (WLC) overlay of the main criteria is used. The result is a map showing the most and least vulnerable area in North Gaza governorate.

In this step, ArcGIS10.1 was used to create nine vulnerable maps of criteria using existing spatial data. The vulnerable criteria are described below: 1. Coping map. 2. Social map (population density, poverty, and culture). 3. Structural map. 4. Physical (soil type, land used, drainage, slope, rainfall). To create the vulnerability criteria maps, IDW interpolation technique was applied for the available data. In the current research data of 1269 topography points has been obtained from local surveyors in North Gaza combined with DEM map of NASA. A topography map has been produced using a GIS program by making interpolation for these points using inverse distance weight (IDW). IDW interpolation implements the assumption that things that are close to one another are more alike than those that are farther apart. IDW used the measured values surrounding the prediction location to predict a value for any unmeasured location. Another model was also used to create the slope map is the SLOPE module of ArcGIS based on the raster Digital elevation map of North Gaza municipalities. Each of these maps was standardized according to the range described in Table 4-3. Also, a weighting map for each one was prepared with reference to the previously determined weights. The derived weights and rates maps were multiplied for each vulnerable criterion according to equation 4-7 in order to enhance its importance. Thus, the final overall vulnerability map was derived using the sum of the overlay of the seven map layers as shown in the following equation. The higher values describe the higher vulnerability = + + +

𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉 𝑚𝑚𝑚𝑚𝑚𝑚 𝐶𝐶𝑤𝑤𝐶𝐶𝑟𝑟 𝑆𝑆𝑤𝑤𝑆𝑆𝑟𝑟 𝑅𝑅𝑤𝑤𝑅𝑅𝑟𝑟 𝑃𝑃𝑤𝑤𝑃𝑃𝑟𝑟 4-7 Where C: Coping vulnerability criteria S: Social vulnerability criteria R: Structural vulnerability criteria P: Physical vulnerability criteria

75 w, r: the subscripts are the rating and weighting values respectively assigned to each criterion. Figure 4-2 disputes example of the process of deriving the final flood vulnerability map for the physical vulnerability criteria.

Figure 4-2: Flood vulnerability map derivation process To provide coherent and reliable vulnerability mapping of the existing stormwater networks, drainage assessment using integrated GIS technique and SewerGEMs was used. The following section describes step by step, in details, how to develop the proposed drainage vulnerability map from the general assessment of the existing stormwater network system.

4.3 Assessment of existing stormwater drainage infrastructure

GIS is an effective technique in extracting the flood inundation and to demarcate the flood hazard-prone areas in the North Gaza. Moreover, the mapping of insufficient storm infrastructure using SewerGEMs will identify the areas that are most vulnerable to flooding and estimate the number of people affected by floods in a particular area. In that, Archydro “GIS extension”, coupled with Digital Elevation Model (DEM), was used to estimate urban catchment flow then examine the capacity of the existing storm network. By the end of this phase vulnerability, mapping for drainage/networks system was developed and used as input data for the AHP analysis method.

4.3.1 Data collection and Desk review During this study, many documents have been reviewed, including the stormwater infiltration plan for Gaza governorates, 2011, Master Plan for Sewerage and Stormwater Drainage in the Gaza Governorates,2000, sewage/wastewater master plan for the northern governorate,2016 and many other documents, previous projects, reports, and studies. In addition to collecting and examining available data relevant to the study area characteristics including recent elevation data, soil map, land use, rainfall. Interviews with the key municipality staff have also been conducted to fill in some gaps related to the existing stormwater networks, stormwater collection, and infiltration basins and common flooding points. At this stage, the lack of available information, relevant to the stormwater sector at the Northern Governorate, form a great challenge. Although many required data have been gathered, some data was still missing as follows: 1. Exact topographic data It was replaced using recently available elevation points for North Gaza Strip. The missing areas estimated by comparing the elevation data at the given points with DEM elevation and integrate them especially for unbuilt areas. Interpolation was applied for all the points to produce new contour map for the northern governorate. 2. Stormwater system data (the existing stormwater network at Bite Lahia, invert levels, pipe diameters) The relevant data was gathered from the municipal key informants then re-build spatial data of each one. Some of the existing stormwater collection systems were recently developed; thus, the available stormwater networks are not to a large extent and most of the time the municipalities drain the stormwater through the available wastewater networks. The output of this review provided identification of the gaps and needs for the stormwater sector in the Northern Governorate, taking into account national requirements, sector potentials and limitations, and the actual need of information for a sound stormwater sector analysis for future development and evaluation. 4.3.2 Data Base Development A GIS application is used for managing, producing, analyzing and combining spatial data. The data needed in this study are produced from collected or existing data by using

77 different kinds of spatial functions and analysis. The data required for this study was acquired from Municipalities, Ministry of Agriculture (MoA), Ministry of local government (MoLG), Universities and private companies. Raw data from municipalities about the existing stormwater networks and their diameters size, infiltration basins and their capacity and flooding areas. Most of the data was in a DWG. format some of them have been developed with municipal key informants. Pre-processing to manipulate and transform raw data using ArcGIS to make it easily accessible. Many discrete tasks such as loading data or data ingestion, data fusion, data cleansing, data augmentation, and data delivery were performed in this task. 4.3.3 Selection Evaluation of Criteria The selection of criteria that has spatial reference is an important step in determining the primary effective factors. This step is a crucial step and would affect the results of the evaluation process. To avoid complexity, a minimal number of criteria were selected to build up a GIS-based flood risk map and to show the spatial distribution of flood risks. The criteria used in this study due to their relevance to the study area, are listed below: 1. Rainfall (Precipitation). 2. Elevation of the study area. 3. Stormwater networks. 4. Soil type. 5. Land Use. 4.3.4 Creation of data Layer and Attributes All the spatial data created in different layer converted into a compatible GIS format, and then the attributes tables created for each particular layer using ArcGIS. Geo-databases were developed based on different raw data considering some reports and excel records that could support spatial analysis process, Arc hydro model, and other processes were developed. Table 4-4 shows the collected raw data and the development prosses used.

Table 4-4: Collected Data

Collected Data Attribute Format Output

Rainfall station X, Y coordinate Excel (.xlsx) Exported by Arc GIS. Average rainfall for Thiessen Polygons to estimate each one the catchment area for each rainfall station. Output: Rainfall Station map illustrate the catchment area (Thiessen Polygons)

Soil Type X, Y coordinate Excel (.xlsx) Soil type was coded from 1-10 Soil type Thiessen Polygons proximity method was processed.

Output: Soil map of the Gaza Strip

Land Use Agriculture area Shapefiles The land use shapefiles were Industrial area extracted to fit the study are (North Gaza) Wadi Output: North Gaza Land Use Tourism

Built up area

Planned build up area

Recent Areal photo for AutoCAD Coordinated Areal Photo for Gaza Strip (2015) Gaza Strip

Stormwater networks in Diameter and Length AutoCAD Stormwater network in the four North Gaza municipalities was imported using ArcGIS.

Attribute table was modified to contain the length and diameter of the Stormwater lines Output: Stormwater network map in North Gaza

Stormwater Level, Basins size AutoCAD Stormwater basins in the four collection/infiltration municipalities were imported basins in North Gaza using ArcGIS.

Collected Data Attribute Format Output

Attribute table was modified to contain the level and the size of the Stormwater basins

Output: Stormwater basins map in North Gaza

Stormwater flooding area Level AutoCAD Stormwater flooding zones in in North Gaza the four municipalities were imported using ArcGIS. Attribute table was modified to contain the level of the flooding areas. Output: Stormwater flooding map in North Gaza

The most populated area Feature Name, Shapefile The populated area shapefile (Point) in Gaza Strip (2014) Population Place was extracted to fit the study are (North Gaza)

Output: North Gaza most populated area

Wastewater network in Name Shapefile Output: North Gaza wastewater (Line) the north Length network

Wastewater pumping Area name Shapefile Output: North Gaza main (Point) station Governorate pumping station

Municipality

X, Y and Z coordinate

Flow (m3/hr) Pump Power Pump type

Generator Tank capacity

Pump head

Mixing point Address Shapefile Output: North Gaza main (Point) X, Y coordinate mixing point between stormwater and wastewater.

4.3.5 GIS process There are several stages to prepare this data for the GIS environment. ArcGIS was used for the purpose of manipulating and processing data. The estimation of surface runoff is an essential step to evaluate the required stormwater facilities. Considering the delineated catchments as well as the intersected land use, soil and topography, the catchments flow were determined using (Archydro) under the GIS environment. The detailed process is stipulated in the following:

4.3.5.1 Catchment area delineation This curtail process is the first step to quantify the stormwater quantities accumulating at different depression areas or ponds as well as directing to the sea or outside boundaries. ArcGIS was used as a processing environment with ArcHydro extension. The recently available elevation map for North area was used to build a digital elevation model (DEM) taking into account an available DEM (30 m resolution) by ASTER Global Digital Elevation Model (NASA) to be informed about the ground elevations in bordered areas. The detailed process is illustrated as the following

1. Exporting the carried-out elevation map to ArcGIS considering “Palestine Grid 1923” as a geographical coordinate combining some points from DEM (30 m resolution) by ASTER Global Digital Elevation Model (NASA) in the non- covered areas. 2. Reviewing ASTGTM2_N31E034_dem from “NASA” and investigating its intersection with the Gaza Governorates. 3. Investigating the potential catchments that intersect North Gaza governorate and locate outside North Gaza boundary. 4. Starting the catchments delineation process based on the new DEM as shown in Figure 4-3. Using Arc hydro, the catchment area of the North Gaza area and its main parameters have been defined. The model primary inputs were the DEM map, North Gaza map location, stormwater basins, and flooding points. Several maps were developed as internal products between DEM map developing to the final delineated catchment map such as flow direction, flow accumulation, stream definition, drainage line, catchment boundary, watershed point. Figure 4-3 presents a flowchart of the model.

Figure 4-3: Catchment Area Delineation process.

4.3.5.2 Outcomes Validation Validation of flood model outcomes including potential flooding zones is important in order to check whether the results of the flooding point are sufficiently accurate to make confident decisions. On 19 and 20 September 2018 key informant meetings were held to discuss the results of the catchments delineation as well as its drainage direction. The meetings were held separately for each municipality. On those occasions, municipal experts were briefed on the results of the model and discussed the validation of the potential flooding zones.

Findings and recommendations from those meetings were identified toward the catchment areas delineation, the classification of the catchments whether they include stormwater and wastewater network or not, the mixing point between stormwater and wastewater that would cause flooding as a result of the insufficient capacity of the wastewater network to accommodate the additional stormwater quantity. 4.3.6 Developing a hydraulic-hydrologic model Bentley SewerGEMs V8i model working under ArcGIS was proposed for this task to estimate the stormwater flows for the main watershed intersecting with North Gaza governorate boundary. Whereas, the Soil Conservation Service (SCS) Dimensionless Unit Hydrograph method was used to estimate the peak flows regarding urbanized catchments areas. The response of catchments to urban development was measured in terms of changes in the flow regime and groundwater recharge. Accordingly, the realistic scenarios of urban area expansion within north governorate were investigated to quantify the corresponding changes of surface runoff and drainage lines flow, which was considered in the evaluation of required stormwater facilities. The following steps illustrate the development of the hydraulic-hydrologic model using SewerGEMs for the study area:

4.3.6.1 Develop the storm event data The SCS 24-Hour Storm Distributions (Time-depth relation) was used to develop a storm event. SCS type curves are in the form of percentage mass (cumulative) curves based on 24-hr rainfall of the desired frequency. Curve type II was used in this project which is suitable for semi-arid regions such as the Gaza Strip. Figure 2-2 depicts these curves.

4.3.6.2 Rainfall Intensity The rainfall intensity was selected on the basis of design rainfall duration and frequency of occurrence. Where the design duration is equal to or larger than the time of concentration for the drainage area under consideration while the design frequency of occurrence is a statistical variable. At this study, the design duration was established to be 24 hours while the frequency was selected to be 5 years.

4.3.6.3 Catchment Building Based on the catchment zones developed using Arc-hydro under the ArcGIS environment, catchments, as well as the catchments inlets, were built in SewerGEMs model. The following data were required for each catchment: - The ID of the outflow element - Runoff method - Loss method - Unit hydrograph method - Time of concentration method TC with the relevant hydraulic depth, slope and curve number CN (based on the developed DEM map) - SCS CN - SCS unit hydrograph method The following data were required for each catchment inlet: - Inlet type - Inlet location (on Grade or in Sag) - Ground elevation - Invert elevation - Structure type - Diameter

4.3.6.4 Select the Soil Conservation Service Curve Number (SCS CN) The CN is a function of three factors: soil group, the cover complex, and antecedent moisture conditions. In that, the soil texture was intersected with the targeted sub-watersheds. Catchments were classified according to the soil type and land use then CN were selected using Table 2-3, Table 2-4 and Table 2-5.

4.3.6.5 Intersection with the existing stormwater system The developed catchments were intersected with the existing stormwater pipelines to link the developed hydrologic model with the hydraulic system, i.e., the inlets were connected to pre-defined manholes conveying the accumulated surface runoff from catchments to pipelines whereupon to stormwater ponds.

The following data were required for stormwater network pipelines: - Pipe Diameter. - Pipe material.

- Ground elevation. - Invert elevation. The following data were required for each stormwater pond: - Area (scaled). - Volume type. - Elevation area (Top and bottom elevation, Area for each elevation, percent void space). 4.3.7 Intervention procedure for the vulnerable areas

At this stage, an intervention strategy was proposed for each vulnerable area, according to the main causes of flood risk. This step will comprise: 1. Evaluation of the existing stormwater infrastructure. 2. Identifying the needed new infrastructure (stormwater facilities including stormwater pipes and ponds) to meet future needs; 3. List of all possible alignments and gradients for the proposed mains, connection and disconnection works. 4. Recommended system improvements. 5. Maps showing improvement components and service areas.

Chapter 5

Results and Discussion

5 Chapter 5 Results and Discussion

Chapter 5 Results and Discussion

5.1 Introduction

This chapter discusses the findings of the applied methodology described in Chapter 4. At this chapter, in order to achieve the objective of assessing the flood vulnerability in North Gaza governorate, criteria that might be involved were identified, categorized and organized using AHP model to investigate the different responses to the vulnerability criteria and alternatives.

The discussion explores the delineation of catchments areas using ArcGIS and includes analysis to investigate the resilience of the wastewater facilities to adapt with the extra stormwater load during the rainy seasons.

The chapter also highlights the concept and development of the hydraulic SewerGEMs model which was developed based on the findings from ArcGIS, data collection related to the stormwater facilities and interviews. PART 01: Developing the AHP method In order to analyze flood vulnerability, all factors might involve organized into a hierarchy system to develop an approach based on experience and knowledge. This approach could give a better understanding and be more useful for the vulnerability assessment as illustrated in the following.

5.2 Building the AHP model

At this step, the AHP model’s results were discussed for measuring the flood vulnerability, the model adopted the general definition of probability. So, it incorporated the criteria probability components and alternative components. The alternative was also provided in a hierarchy structure to demonstrate the best one to deal with the flooding cases taking into consideration the vulnerability criteria.

5.2.1 Hierarchy main goal The main goal is mapping the flood vulnerability in North Gaza that can be used to demonstrate the probability of vulnerability criteria as well as the probability of alternative that can be used for reducing the flood risk.

5.2.2 Analytical Hierarchy structures The hierarchy model for the flood vulnerability assessment for North Gaza is divided into four levels as described below and shown in Figure 5-1.

Level 1: Flood vulnerability mapping.

Level 2: Flood vulnerability main criteria.

After reviewing the literature review, the factors that determine the flood hazard were selected according to the conditions and the available data in North Gaza governorate. The main four criteria were social vulnerability, coping vulnerability, structural vulnerability, and physical criteria.

Level 3: Flood vulnerability Sub-main criteria.

The sub-criteria were more specifically to describe the meaning of “the flood vulnerability map” in the upper level of the hierarchy. The sub-criteria were as following: 1. Social vulnerability: General poverty and cultural characteristics and population density. 2. Coping vulnerability: Evacuation training, health care facilities, shelters, and disaster prevention. 3. Structural vulnerability: Householder with improper safety measures. 4. Physical criteria: Land use, drainage network, soil type, rainfall and slope (elevation).

Level 4: Alternative Actions

This level is divided into four types of alternative actions for dealing with a flood in North Gaza governorate in our study option can be as follows:

1. Relocation (temporary or permanent); which means either permanently relocate or move the whole citizens from the flooding area into free flood areas and prevent the settlement within the flooding zones or temporary relocation of the citizens during the seasons of the heavy storms and resettlement them when it’s safe. 2. Early warning systems; improve or develop a flood detection system that can detect the flood hazard in its early stage either using manual tools or by routine human observation. 3. Integrated stormwater management; reduce or minimize the flooding using stormwater basins, improvement of the drainage system, improve the household.

4. Do nothing; leaving everything with doing anything.

Figure 5-1: AHP Hierarchy model for flood vulnerability in North Gaza 5.2.3 Pairwise comparison At this study, the flood vulnerability model was developed using the AHP technique, where each element is compared to other elements at the same level. Where pairwise comparison reduces the complexity of the problem by using the single comparison between elements using the 1-9 fundamental scale see Table 2-9. At the end of this step, a priority value to each element with reference to other elements was assigned. Thus, it will help the decision makers to understand the connections between the vulnerability elements and the alternatives actions to reduce flood. Using the guiding questions, the comparison was stated from the top to the bottom of the hierarchy model. Fifteen field experts were asked to give their judgment related to the significance of flood criteria.

Using the sample question level 3

Based on your experience related to the flood vulnerability in North Gaza, which is more likely to increase the vulnerability during the flood, poverty/culture vulnerability or the population density vulnerability. The question was varied according to the type of vulnerability criteria compared. By the end, the results will be translated in a comparison matrix as shown in Table 5-1.

Table 5-1: Comparison Matrix level 3

Item Description Poverty /culture Population Coping Slope Land Rainfall Soil Type Drainage Householder structure density Use Poverty/culture 1.00 0.33 1.00 0.20 0.14 0.11 0.14 0.11 0.20 Population density 3.00 1.00 1.00 0.20 0.20 0.14 0.14 0.11 0.20 Coping 1.00 1.00 1.00 0.33 0.20 0.14 0.14 0.11 0.33 Slope 5.00 5.00 3.00 1.00 0.33 0.33 1.00 0.20 0.33 Land use 7.00 5.00 5.00 3.00 1.00 0.33 1.00 0.20 5.00 Rainfall 9.00 7.00 7.00 3.00 3.00 1.00 3.00 1.00, 5.00 Soil Type 7.00 7.00 7.00 1.00 1.00 0.33 1.00 0.20 3.00 Drainage 9.00 9.00 9.00 5.00 5.00 1.00 5.00 1.00 3.00 Houshold Structure 5.00 5.00 3.00 3.00 0.20 0.20 0.33 0.33 1.00 Sum 47.00 40.33 37.00 16.73 11.08 3.60 11.76 3.27 18.07

Sample question level 4

“Based on your experience related to the flood vulnerability in North Gaza governorate, which is more effective, relocation or developing integrated stormwater management. Results are presented in Table 5-2.

Table 5-2: Matrix comparison level 4 (Social)

Alternatives/ Social Relocation Early warning ISWM Do Nothing Relocation 1.00 3.00 0.33 9.00 Early warning 0.33 1.00 0.11 3.00 ISWM 3.00 9.00 1.00 7.00 Do Nothing 0.11 0.33 0.14 1.00 Sum 4.44 13.33 1.59 20.00

5.2.4 Column summary At this stage will calculate the contribution of each criterion to the overall flood vulnerability based on expert’s opinion which in turn will present the relative priority. Table 5-3 presents the summary values in each column for criteria comparison. While Table 5-4, Table 5-5, Table 5-6 and Table 5-7 present the values summary in each column for alternatives comparison.

Table 5-3: Column summary of matrix level 3

Item Description Poverty /culture Population Coping Slope Land Rainfall Soil Type Drainage Householder structure density Use Poverty/culture 1.00 1.47 1.02 0.34 0.69 0.60 0.80 0.19 0.89 Population density 0.68 1.00 1.36 0.62 0.77 1.20 1.04 0.38 0.79 Coping 0.98 0.74 1.00 0.43 0.76 0.37 0.52 0.19 0.42 Slope 2.90 1.60 2.32 1.00 2.77 1.20 3.08 0.98 2.32 Land use 1.44 1.31 1.32 0.36 1.00 0.61 2.02 0.39 1.80 Rainfall 1.66 0.83 2.67 0.83 1.65 1.00 3.27 0.80 2.32 Soil Type 1.25 0.96 1.91 0.32 0.49 0.31 1.00 0.32 1.28 Drainage 5.35 2.65 5.13 1.02 2.59 1.25 3.09 1.00 2.87 Houshold Structure 1.13 1.27 2.37 0.43 0.56 0.43 0.78 0.35 1.00 Sum of column 16.40 11.83 19.10 5.36 11.27 6.97 15.60 4.60 13.68

The values in the table are the average of all expert’s opinion

Table 5-4: Column summary of matrix level 4 (Social)

Item Description Relocation Early warning ISWM Do Nothing Relocation 1.00 1.14 0.67 5.50 Early warning 0.88 1.00 0.50 4.63 ISWM 1.50 2.00 1.00 6.78 Do Nothing 0.18 0.22 0.15 1.00 Sum 3.56 4.36 2.31 17.90 Table 5-5: Column summary of matrix level 4 (Coping)

Item Description Relocation Early warning ISWM Do Nothing Relocation 1.00 1.00 0.80 5.71 Early warning 1.00 1.00 0.67 5.20 ISWM 1.25 1.50 1.00 7.14 Do Nothing 0.18 0.19 0.14 1.00 Sum 3.43 3.69 2.61 19.06 Table 5-6: Column summary of matrix level 4 (Structural)

Item Description Relocation Early warning ISWM Do Nothing Relocation 1.00 1.67 0.50 4.50 Early warning 0.60 1.00 0.25 4.40 ISWM 2.00 4.00 1.00 7.78 Do Nothing 0.22 0.23 0.13 1.00 Sum 3.82 6.89 1.88 17.68 Table 5-7: Column summary of matrix level 4 (Physical)

Item Description Relocation Early warning ISWM Do Nothing Relocation 1.00 0.67 0.17 4.20 Early warning 1.50 1.00 0.25 5.14 ISWM 6.00 4.00 1.00 8.11 Do Nothing 0.24 0.19 0.12 1.00 Sum 8.74 5.86 1.54 18.45

5.2.5 Normalization and weighting At this stage normalization by dividing the matrix values in each column by the total of each column. Then weighting to specify the relative priorities were calculated by averaging each row of the normalized matrix. Results of normalization are presented in Table 5-8, Table 5-9, Table 5-10, Table 5-11 and Table 5-12 while Table 5-13 summarizes overall priority rank of the vulnerability elements. Figure 5-2 illustrate overall priority rank for main and sub-main vulnerability criteria.

Table 5-8: Normalization and relative priorities calculation matrix level 3

Item Description Poverty /culture Population Coping Slope Land Rainfall Soil Drainage Householder Weight density Use Type structure Poverty/culture 0.06 0.12 0.05 0.06 0.06 0.09 0.05 0.04 0.06 6.74% Population density 0.04 0.08 0.07 0.12 0.07 0.17 0.07 0.08 0.06 8.44% Coping 0.06 0.06 0.05 0.08 0.07 0.05 0.03 0.04 0.03 5.36% Slope 0.18 0.14 0.12 0.19 0.25 0.17 0.20 0.21 0.17 18.00% Land use 0.09 0.11 0.07 0.07 0.09 0.09 0.13 0.08 0.13 9.51% Rainfall 0.10 0.07 0.14 0.15 0.15 0.14 0.21 0.17 0.17 14.55% Soil Type 0.08 0.08 0.10 0.06 0.04 0.04 0.06 0.07 0.09 7.04% Drainage 0.33 0.22 0.27 0.19 0.23 0.18 0.20 0.22 0.21 22.69% Houshold Structure 0.07 0.11 0.12 0.08 0.05 0.06 0.05 0.08 0.07 7.67%

Sample of calculations

Poverty/Culture against Population density = 1.47/11.83 = 0.12 (from Table 5-3)

Weight of Poverty/Culture = (0.06+0.12+0.05+0.06+0.06+0.09+0.05+0.04+0.06) /9 = 6.74 %

Figure 5-2: Relative priorities level 3 Table 5-9: Normalization and relative priorities calculation matrix level 3 (Social)

Item Description Relocation Early warning ISWM Do Nothing Weighting Relocation 0.28 0.26 0.29 0.31 28.5% Early warning 0.25 0.23 0.22 0.26 23.7% ISWM 0.42 0.46 0.43 0.38 42.3% Do Nothing 0.05 0.05 0.06 0.06 5.5% Table 5-10: Normalization and relative priorities calculation matrix level 3 (Coping)

Item Description Relocation Early warning ISWM Do Nothing Weighting Relocation 0.29 0.27 0.31 0.30 29.2% Early warning 0.29 0.27 0.26 0.27 27.3% ISWM 0.36 0.41 0.38 0.37 38.2% Do Nothing 0.05 0.05 0.05 0.05 5.2% Table 5-11: Normalization and relative priorities calculation matrix level 3 (Structural)

Item Description Relocation Early warning ISWM Do Nothing Weighting Relocation 0.26 0.24 0.27 0.25 25.6% Early warning 0.16 0.15 0.13 0.25 17.1% ISWM 0.52 0.58 0.53 0.44 51.9% Do Nothing 0.06 0.03 0.07 0.06 5.4% Table 5-12: Normalization and relative priorities calculation matrix level 3 (Physical)

Item Description Relocation Early warning ISWM Do Nothing Weighting Relocation 0.11 0.11 0.11 0.23 14.1% Early warning 0.17 0.17 0.16 0.28 19.6% ISWM 0.69 0.68 0.65 0.44 61.5% Do Nothing 0.03 0.03 0.08 0.05 4.9% Overall summary of each level element of vulnerability element is shown in Table 5-13 and illustrated in Figure 5-3.

Table 5-13: The priority rank of each vulnerability element in the hierarchy

Vulnerability Vulnerability Weight Weight Criteria Criteria Level 2 Social Population 8.44% 15.18% density Poverty/ culture 6.74% Coping 5.36% Coping 5.36% Structural Household 7.67% 7.67% structure Physical Slope 18% Land use 9.51% 71.79% Rainfall 14.55% Soil Type 7.04% Drainage 22.69% Social Coping Structural Physical Total Level 3 Relocation 28.5% 29.2% 25.6% 14.1% 18.0% Early warning 23.7% 27.3% 17.1% 19.6% 20.4% Integrated stormwater 42.3% 38.2% 51.9% 61.5% 56.6% management Do Nothing 5.5% 5.2% 5.4% 4.9% 5.0%

Sample of calculations

Total for relocation = 28.5 ×15.18 + 29.2 ×5.36+25.6 ×7.67+ 14.1× 71.79 = 18%

Figure 5-3: Relative priorities level 4 According to the criteria weight found for North Gaza, it was obviously noticed that physical criteria play the main role in raising the vulnerability of the area to flooding. While the second criteria that have a major contribution to increasing the

95 vulnerability toward flooding were the structure of the building. On the other hand, social and coping criteria were having less effect compared to other criteria.

Table 5-13 shows that the preferable alternative with 55.8% of the overall priority is the integrated stormwater management. The other two alternatives (Relocation and early a warning) have slightly different from each other and were prioritized next to the first alternative. That means both alternatives can be chosen as an alternative in dealing with flooding. Early a warning is considered to be slightly more important than relocation. However, relocation in North Gaza is a big issue because it would be costly, but during heavy storms, some areas become highly threatened by flood due to low topography, low infiltration, bad infrastructure, and improper buildings, so relocation become a better alternative. On the other hand, do nothing alternative was ranked as the lowest priority.

5.2.6 Consistency of judgments At this stage, the consistency ratio derived by multiplying the original matrix with the weight vector. Then averaging each row of the produced matrix. Results of the consistency calculations for both levels (level 3 and Level4) are presented in Table 5-14, Table 5-15, Table 5-16, Table 5-17 and Table 5-18. It was clear that the consistency ratio of the judgment for both levels were less than 10% which is acceptable.

Table 5-14: Consistency calculation matrix level 3

Poverty Population Coping Slope Land Rainfall Soil Drainage Householder SUM SUM/Weight density Use Type Network Poverty 0.07 0.12 0.05 0.06 0.07 0.09 0.06 0.04 0.07 0.63 9.31 Population density 0.05 0.08 0.07 0.11 0.07 0.17 0.07 0.09 0.06 0.78 9.26 Coping 0.07 0.06 0.05 0.08 0.07 0.05 0.04 0.04 0.03 0.50 9.32 Slope 0.20 0.14 0.12 0.18 0.26 0.18 0.22 0.22 0.18 1.69 9.40 Land Use 0.10 0.11 0.07 0.06 0.10 0.09 0.14 0.09 0.14 0.89 9.41 Rainfall 0.11 0.07 0.14 0.15 0.16 0.15 0.23 0.18 0.18 1.37 9.40 Soil Type 0.08 0.08 0.10 0.06 0.05 0.04 0.07 0.07 0.10 0.66 9.37 Drainage Network 0.36 0.22 0.28 0.18 0.25 0.18 0.22 0.23 0.22 2.13 9.41 Householder 0.08 0.11 0.13 0.08 0.05 0.06 0.05 0.08 0.08 0.71 9.31 lambda 9.355 max CI 0.044 CR 0.03 Sample of calculations

Poverty/Culture against Population density = 1.47× 11.83 = 0.12

Sum = 0.07+0.12+0.05+0.06+0.07+0.09+0.06+0.04+0.07 =0.63

Sum/Weight = 0.63/6.74 = 9.31

/ Lambda max = = 84.19/9 = 9.355 𝑠𝑠𝑠𝑠𝑠𝑠 𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤ℎ𝑡𝑡 CI = (lambda max∑ – 𝐶𝐶𝐶𝐶N)𝐶𝐶𝐶𝐶𝐶𝐶 / (N-1) = (9.355-9)/ (9-1) =0.044

CR = CI /Cr (from Table 4-2) = 0.044/1.45 = 0.03

Table 5-15: Consistency calculation matrix level 4 (Social)

Relocation Early warning ISWM Do Nothing SUM SUM/Weight Relocation 0.28 0.27 0.28 0.30 1.14 4.01 Early 0.25 0.24 0.21 0.25 0.95 4.01 warning ISWM 0.43 0.47 0.42 0.37 1.70 4.02 Do Nothing 0.05 0.05 0.06 0.06 0.22 4.00 lambda 4.010 max

CI 0.003 CR 0.004 Table 5-16: Consistency calculation matrix level 4 (Coping)

Relocation Early warning ISWM Do Nothing SUM SUM/Weight Relocation 0.29 0.27 0.31 0.30 1.17 4.00 Early 0.29 0.27 0.25 0.27 1.09 4.00 warning ISWM 0.37 0.41 0.38 0.37 1.53 4.00 Do Nothing 0.05 0.05 0.05 0.05 0.21 4.00 lambda 4.003 max

CI 0.001 CR 0.001 Table 5-17: Consistency calculation matrix level 4 (Structural)

Relocation Early warning ISWM Do Nothing SUM SUM/Weight Relocation 0.26 0.29 0.26 0.24 1.04 4.08 Early 0.15 0.17 0.13 0.24 0.69 4.05 warning ISWM 0.51 0.68 0.52 0.42 2.14 4.11 Do Nothing 0.06 0.04 0.07 0.05 0.22 4.01 lambda 4.061 max

CI 0.020 CR 0.02 Table 5-18: Consistency calculation matrix level 4 (Physical)

Do Relocation Early warning ISWM SUM SUM/Weight Nothing Relocation 0.14 0.13 0.10 0.20 0.58 4.10 Early 0.21 0.20 0.15 0.25 0.81 4.14 warning ISWM 0.85 0.78 0.61 0.39 2.64 4.29 Do Nothing 0.03 0.04 0.08 0.05 0.20 4.03 lambda 4.142 max

CI 0.047 CR 0.05

5.2.7 Flood vulnerability Mapping This section presents criteria and their sub-criteria as a factor in developing AHP- GIS flood vulnerability map. It also presents the choice of sub-criteria used in the vulnerability analysis and their classification into classes and degree of importance Table 4-3. The criteria were chosen for this step according to their importance in causing a flood in the targeted area. The factors considered are Poverty/culture, population density, coping, household structure, slope, land use, soil type, drainage/network, rainfall.

5.2.7.1 Ranking of flood criteria At this step, each criterion was ranked assuming a scale among the type and importance of the criteria indicating 1 the lowest important and the highest value is the most important. The maps were produced based on the district of the municipality as shown in Figure 3-2.

I. Slope Slope plays an important role in influencing the flow direction and water accumulation which in turn cause flooding. The high gradient slope areas are low vulnerable compared to areas with low gradient slope. As the high slope area prevent water from accumulation and causing a flood in contrast with the low slope areas. The slope map in this study was produced based on the DEM map and using the slope generation tool in ArcGIS as flowing:

Preparation of the DEM map To identify the depression zones in the North Governorate, the special analyst toolbox, IDW interpolation, was used to determine and describe the physical characteristic of the ground’s surface. Importing DEM from “NASA” and investigating its intersection with the Gaza Governorates, as shown in Figure 3-4, was also used to enhance data elevation points in the bordered uninhabited areas.

Modified elevation points were imported in the GIS environment as a prelude to using it in the interpolation process Figure 5-4. A digital elevation map for North Gaza was the output of this process presented in Figure 5-5.

Figure 5-4: Available ground surface Elevation Points for north Gaza governorate

Figure 5-5: The derived DEM based on the elevation map and NASA map The derived map was also classified into five classes as shown in Figure 5-6. The higher class was assigned lower rank and the higher ranks were assigned to the low slope classes. By the end of this process, the classification map was smoothed and reclassified into three

100 categories to simplify the output map as shown in Figure 5-7. In general, the study area locates in a moderately steep slope. However, some areas located in a depression between two very steep slope area, which plays a dominant role in causing a flood.

(a) (b)

Figure 5-6: Raster map of a) Slope map b) reclassified Slope map

(c)

Figure 5-7: Raster map of simplifying slope classes.

II. Soil type Soil type and the characteristic is the second dominant factor affecting flood causing events. Sandy soils classified as porous media with a high infiltration rate and lower runoff.

101

On the other hand, the clay soils are classified as less porous and higher runoff rate. The sandy soils were assigned lower rank than clay soils had. For the study area, using the soil data presented in Figure 3-9, the soil was classified accordingly into five categories: Sandy, sandy loam, sandy clay, clay loam, and clay. The classification map was ranked and converted into raster as shown in Figure 5-8. It was obvious that the east part of the study area s the most vulnerable according to the soil type.

(a) (b)

Figure 5-8: a) Vector map of soil type; b) Raster map of reclassified soil type

III. Rainfall Floods caused by heavy rainfall. Runoff is related to land use and soil type. At this study mean annual rainfall from 1976-2017 presented in Figure 3-8 was considered and interpolated using Theisen polygon which was categorized into five categories. The resulting map was reclassified into five classes from 1 the least rainfall to 5 the highest rainfall. Figure 5-9 presents the results of the Theisen polygon interpolation and reclassification of rainfall in North Gaza.

102

(a) (b)

Figure 5-9: a) Vector map of rainfall data; b) Raster map of reclassified rainfall data

IV. Network /Drainage reliability Drainage is another controlling criterion in flooding events. Existing of the drainage indicates the ability of the area to be drained during the storm events. The areas with adequate drainage system were ranked 1 as the highest reliable one, the moderate drainage system which is classified as not completely sufficient to drain the runoff during the storm events was ranked 2, while the ones with no drainage system were ranked as the lowest reliable with a ranked of 3. The drainage map was derived from the network’s maps of the municipalities while the reliability of the networks was classified through meetings with the municipal key informants and through the general assessment of the stormwater network using SewerGEMs. The results of the assessment were illustrated in the flowing section. Figure 5-10 presents the results of the drainage reliability and reclassification of the drainage map.

103

(a) (b)

Figure 5-10: a) Vector map of network reliability; b) Raster map of reclassified network reliability

V. Land Use The land-use of the study area is important because it reflects the infiltration capacity of the area. Based on the land use map exhibited in Figure 3-12. The runoff of stormwater in the cultivated area is less than the built-up areas. The land use map was prepared based on the district zones and categorized into five classes then reclassified into three ranks for simplification and converted to raster as shown in Figure 5-11. The areas were ranked according to the capacity to increase the runoff from 1-3. The highest rank was given for the areas with high runoff rate and cause contamination in case of flooding (Industrial, wastewater treatment plant) while the lowest ranked was given for the area with low runoff rate (agricultural). Some areas were closed to the current built-up area were predicted to become built up area, in that it was considered to be ranked for 2.

104

(a) (b)

Figure 5-11: a) Vector map of land use; b) Raster map of reclassified land use

VI. Population density Using the population information of each district from PSCB, 2018 the population density map was derived. It was overlaid with the district map to find the ratio of the population to the total area of the district. The population density was calculated using the field calculator based on the following equation

= /

𝐷𝐷 𝑃𝑃 𝐴𝐴 5-1 D: Population density (Cap/Km2)

P: District population (Capita)

A: Total area of the district (Km2)

The higher the population density, the higher the area vulnerable to flood. The result population map was ranked and converted to raster. Results of the population density data analysis and rank are shown in Figure 5-12.

105

(a) (b)

Figure 5-12: a) Vector map of population density; b) Raster map of reclassified population density

VII. Poverty/Culture The social criteria are one of the main factors that enhance vulnerability during the flooding events. In this study, it will involve the general cultural characteristic of each district and the socioeconomic status. Data required in this regard was collected through municipalities. The poverty characteristics of the districts were given according to the general lifestyle of the citizens, conditions of the buildings and the ability of people to pay bills, while the culture of each district was classified considering the general behavior of citizens and how far cooperation of them extent with municipalities during flooding events. By the end of this step, the socially vulnerable map was overlaid district map of each municipality and was classified socially into five categories from very high to very low through municipal key informants. Figure 5-13 shows the social classification and ranking of the study area.

106

(a) (b)

Figure 5-13: a) Vector map of social; b) Raster map of reclassified social

VIII. Coping Coping capacity of the area includes receiving the residence evacuation training, existence of shelters and health care facilities and previous experience in dealing with a similar flooding event. The coping classification for each district focuses on the ability of citizens to deal immediately with flooding and evacuation if needed, if they experienced similar events before, if they have any kind of shelters, they can use in emergency situations, schools for example and if they have any health care centers nearby, they can easily reach. Thus, the coping map was also prepared with reference to the field key informants judgment in the municipality then overlaid with discrete. The areas having a maximum coping value was assigned lower rank while the areas having less coping value were assigned higher rank. The results were presented in Figure 5-14.

107

(a) (b)

Figure 5-14: a) Vector map of coping; b) Raster map of reclassified coping

IX. Household Structure Building material and condition is one of the dominant factors contribute to raising the vulnerability of the area towards flooding. In this study household structure of North, Gaza districts were classified considering the buildings material, level of entrance reference to street level and the existence of drainage system. In that, districts with general proper buildings materials, conditions and level higher than the street level got lower ranks while districts with improper buildings materials, conditions and have the same or lower level of the street was ranked lower. Relevant information was collected from the judgment of the field key informant of the municipalities. The outputs were classified into three categories then converted to raster as shown in Figure 5-15.

108

(a) (b)

Figure 5-15: a) Vector map of the household structure; b) Raster map of the reclassified household structure

5.2.7.2 Weighing of Criteria In this step, criteria maps were produced using the weight values of each criterion form the pairwise comparison. The weight value represents the priorities which are a value between zero and one as shown in Table 5-8. The higher weight value means more priority and more likely to affect the vulnerability of the study area. Figure 5-16 shows the weighting map of the nine sub-main criteria.

(a) (b)

109

(e) (f)

(g) (h)

(i)

Figure 5-16: Raster map of sub-criteria weights

110

5.2.7.3 Normalization of Reclassified maps The reclassified maps of the vulnerable criteria were presented by different measurement scales, which need to be transformed into common units. The criteria were recast into gradual ranging from 0 (no vulnerability) to 1 (full vulnerability) using equation 4-1. Figure 5-17, Figure 5-18, Figure 5-19, Figure 5-20 and Figure 5-21 show the normalization maps of the sub-criteria.

(a) (b)

Figure 5-17: Raster maps of a) Population density normalization, b) Poverty/culture normalization

111

(a) (b)

Figure 5-18: Raster maps of a) Household structure normalization, b) Coping normalization

(a) (b)

Figure 5-19: Raster maps of a) Network normalization, b) Slope normalization

112

(a) (b)

Figure 5-20: Raster maps of a) Rainfall normalization, b) Land Use normalization

Figure 5-21: Raster maps of Soil type normalization

5.2.7.4 Weighting and Rating of input criteria Using weighted linear combination overly of criteria according to equation 4-7, the ranking maps of each criterion was multiplied with its weight map using raster calculator in ArcGIS. Figure 5-22 showed the results of population density and poverty multiplication maps and Figure 5-23 showed the overall social vulnerability map. Figure 5-24 represents 113

the overall coping and household structure vulnerability maps. Figure 5-25, Figure 5-26, Figure 5-27 below showed the result of weighting and ranking multiplication maps of physical criteria while Figure 5-28 presents the overall physical vulnerability map. Table 5-19 presents a summary of the flood vulnerability criteria, their respective weights and ranks according to their influence on increasing and decreasing the flood vulnerability.

(a) (b)

Figure 5-22: Raster map of weight and rate a) Social; b) Population density; c) Summation map of population density and poverty/culture map

Figure 5-23: Summation map of social criteria (population density and poverty/culture)

114

(a) (b)

Figure 5-24: Raster map of weight and rate a) Coping; b) Household structure

(a) (b)

Figure 5-25: Raster map of weight and rate a) Network; b) Slope

115

(b) (a)

Figure 5-26: Raster map of weight and rate a) Rainfall; b)

(e)

Figure 5-27: Raster map of weight and rate of Soil Type

116

Figure 5-28: Summation map of physical criteria (network, slope, rainfall, land use and soil type)

Table 5-19: Summary of weight and rate of flood vulnerability criteria

Vulnerability Weight Vulnerability Sub-Weight Ranking Range Criteria % Sub-Criteria % decision Very High 1 High 2 Poverty/culture 6.74 Medium 3 Low 4 Very Low 5 Social 15.18 250-3000 1 Population 3000-7500 2

Density 7500-15,000 3 8.44 (cap/Km2) 15,000-25,000 4 25,000-75,000 5 Very High 1 High 2 Coping 5.36 Coping 5.36 Medium 3 Low 4 Very Low 5

117

Vulnerability Weight Vulnerability Sub-Weight Ranking Range Criteria % Sub-Criteria % decision High Resistance 1 Household Household 7.67 7.67 Medium Resistance 2 structure Structural Low Resistance 3 Sand 1 Sandy Loam 2 Soil Type 7.04 Sandy clay 3 Clay Loam 4 Clay 5 Agriculture 1 Residential Land Use 9.51 2 Industrial/Wastewater 3 treatment site High reliability 1 Drainage 22.69 Medium reliability 2 Physical 71.79 Low reliability 3 <1 5 3-1 4 Slope 18 3-5 3 5-10 2 >10 1 376 1 407 2 Rainfall 14.55 408 3 413 4 420 5

5.2.7.5 Flood vulnerability Mapping for North Gaza To produce the flood vulnerability map, the summation of the overlay maps derived from the multiplication of weights and ranks for all vulnerability criteria (social…. Coping ….). The result is a flood vulnerability map showing the most and least vulnerable areas to flooding within the North Gaza governorate.

The resulting map shows that 44% of North Gaza is expected to expose to high and very high flood risk with overall vulnerability value greater than 60. Those areas are located in a low elevation within a populated area, low infiltration soil type and close to the 118 stormwater ponds, while 22% of the study area falls within the moderate flood vulnerability category. Only 33% of North Gaza are among the least vulnerable to flooding with overall vulnerability value of less than 44. However, almost 70% of North Gaza population inhabited within the highest vulnerable area. Table 5-20 present the percent area and population of each flood vulnerability category and Table 5-21 shows each district categorization. Most of the areas lack the appropriate drainage system and the existing part of the drainage system is undersized for draining combined storm and wastewater. By the end and as a result of the multi-criteria method a single map from all the analyzed maps presents the output of flood vulnerability prediction map as shown in Figure 5-29.

Figure 5-29: Flood vulnerability map of North Gaza governorate

119

Table 5-20: Area percent of each flood vulnerability category

Item Category Area (km2) % Area %Population Very Low 36.34 -44.82 8 19.00 9 Low 44.83-52.5 6 14.71 8 Medium 52.51-60.17 9 22.55 14 High 60.18-69.26 11 27.31 31 Very High 69.27-87.84 7 16.43 38 42 100.00 100 Table 5-21: District flood vulnerability categorization

No. District Category No. District Category 1 Izba 75 V. High 21 Al Manshia 60 Medium 2 Sikka 68 High 22 Shikh Zayed 62 High 3 Namra 73 V. High 23 Qawasma 61 High 4 Zayton 65 High 24 Bir Al Naaja 72 V. High 5 Sinaaia 53 Medium 25 Al Saif 42 V. Low 6 Basel Naem 68 High 26 Al Israa 42 V.Low 7 Masryeen 62 High 27 Al Salateen 43 V. Low 8 Amal 59 Medium 28 Center of the town 56 Medium 9 Nazaz 56 Medium 29 Karama 41 Very Low 10 Old Town 88 V. High 30 Ebad Al Rahman 48 Low 11 Abd Al Daym 53 Medium 31 Zahor 75 V. High 12 Qrman 67 High 32 Nahda 78 V. High 13 Farta 62 High 33 Rawda 54 Medium 14 Banat 60 Medium 34 Nuzha 62 High 15 Qatbania 53 Medium 35 Jabalia Camp 78 V. High 16 Um Al Nasser 82 V. High 36 Old Town 67 High 17 Al Shyma’a 68 V. High 37 Salam 48 Low 18 Attatra 42 Very Low 38 Al Nour 45 Low 19 Al Amal 70 V. High 39 Tal Al Zaatar 62 High 20 Al Mashroa’a 65 High 40 East Region 56 Medium

120

PART 02: Assessment of the existing stormwater drainage system At this part the assessment results of the stormwater drainage infrastructure will be presented as follows:

5.3 Catchments Area delineation

To identify the depression zones in the North Governorate, the A digital elevation map for North Gaza presented in Figure 5-5 was used as a base map for this process.

5.3.1 Investigate watersheds intersect to North Governorate This step was enhanced by reviewing previous related study conducted by Eshtawi et.al, 2017, using NASA DEM map (30 m DEM). It shows the main storm watersheds that intersect with Gaza strip. In the North, most of the streamlines were drained outside the boundaries except for the east part which was flowing to the inside, but it was bounded by a reservoir form the Israeli side as shown in Figure 5-30. In that, North Gaza has no potential watersheds intersect with and locates out of the boundaries.

Figure 5-30: Main watersheds of the Gaza Strip 5.3.2 Arc Hydro Model Following the Arc Hydro model process as shown in the previous chapter primary outputs were produced such as flow direction, flow accumulation, stream definition, drainage line, catchment boundary, watershed point as shown in Figure 5-31.

121

Fill sink Flow Direction

Flow Accumulation Stream segmentation

Drainage line Processing Drainage Point processing

Catchment Polygon processing Adjoint Catchment Processing

Figure 5-31: Some intermediate results for Arc Hydro Model

122

5.3.3 Identify the main watershed for North Gaza Using Arch hydro model in the GIS environment, the catchments area in North Gaza governorate were merged and recognized as a five main watershed as shown in Figure 5-32.

1. Beach region: sub-catchments flow discharges to the beach. 2. Middle region: most urbanized catchments comprising different depression areas. 3. Southern region: streams flow directed mostly to the south (Gaza governorate) and most drained to Al Shiekh Ridwan pond 4. North region: sub-catchments flow discharges out bordered to North Gaza 5. East region: streams flow drained mostly to the east.

Figure 5-32: The delineated catchments including five main watersheds 5.3.4 Verification Delineated catchments areas were verified in two steps as follows:

Step1: Verification of the catchment’s boundaries At this step, the flow directions and developed zones for the main catchments were verified considering: the delineated sub-catchments, the depression areas, the main roads,

123 and available ponds as shown in Figure 5-33 and Figure 5-34. The sub-catchments were modified, merged, and clipped using GIS processes.

Figure 5-33: The delineated catchments intersection with the built-up area

Figure 5-34: The main delineated catchment and its flow zones including flood areas

124

Step 2: Verification of the catchments with the Key informant

On a micro scale level, the boundary of the middle catchment was verified with municipality key informant. considering the built-up areas and the main roads, a number of sub-catchment areas were modified to match the reality. Findings and recommendation were presented from the key informants illustrated Figure 5-35 and briefly described as following:

1. Qebaa (District No. 33) sub-catchment area was discharged by wastewater network to Al Baqqara wastewater pumping station 2. Al Dakhilia (Part of district No. 33, 34, 36, 37 and 38) sub-catchment totally discharged through wastewater collection system which in turn pumped by Mahather pump station to Abu Rashid pumping station. In heavy storms, the pumping station becomes not capable to discharge the additional quantities. Then, the excess quantities calibrated in the flood area at Al Dakhilia st. 3. Hawaber (District No. 31, and part of district 27) wastewater pumping station receives additional stormwater quantities from the existing stormwater pipeline extended at Al Bahar street. The pipeline was considered to collect the stormwater only accumulated in the street area. In a heavy storm, the pump station fails to discharge the received quantities. As a result, the flood point created near the civil defense. 4. Othman (Part of district No. 33 and 34) sub-catchment area discharged using gullies directly connected to wastewater network which is pumped to NWWTP by Aslan pumping station. 5. Studio Abu sultan (District No. 34, and part of district 35) sub-catchment has a stormwater pipeline discharge to Timraz area which in turn discharge to Abu Rashid stormwater pond. 6. Timraz (District No. 35, and part of district 34) sub-catchment area has totally separated stormwater system which is drained to Abu Rashid pond. 7. Khalaf pond (Part of district No. 20, and 39) sub-catchment area is drained by surface runoff as it has neither the stormwater network nor connected to existing wastewater network.

125

8. Overflow gravity pipeline from Khalaf pond to Abu Rashid pond is used in the peak periods or long storm events. In flooding events, Tal Al-Zaatar pumping station near to Khalaf pond is used to discharge the stormwater directly to the NWWTP. 9. Al Alami (District No. 20) sub-catchment area directly discharged to Abu Rashed pond using box culvert. 10. Abu Rashid pond receives stormwater from Al Alami (Al Sahwa) sub-catchment, overflow from Tal Al-Zaatar pumping station, Temraz (receives form Tal Al-Sultan sub-catchment), and Al Fakhora area. 11. Abu Rashed has two pumping stations, one for stormwater and another for wastewater totally separated. Both pump the inflow to the NWWTP stormwater lagoon and wastewater lagoons respectively.

Figure 5-36 and Figure 5-37 present the final sub-catchments modification for the main urbanized area considering the flooding areas, built-up areas, and main roads. As a result, the sub-catchments were zoned as shown in Figure 5-38 and the area of each one was calculated using the calculate geometry function in GIS as indicated in Figure 5-36 and summarized in Table 5-23.

Figure 5-35: Findings and recommendation during the verification

126

Figure 5-36: The main urbanized catchment including flood areas

Figure 5-37: The main urbanized catchment intersects with land use and streets and its

flow zones

127

Figure 5-38: Zoning of the main urbanized catchment

5.4 The resilience of wastewater networks.

At this step, the sub-catchments were classified according to the presence of stormwater and wastewater networks as shown in Table 5-22. It was clear that more than 50% of the targeted areas adopt the combined system to drain the stormwater using the existing wastewater networks and that leads to creating new flooding zones as a result of insufficient capacity to enlarge the extra amount of stormwater drained by sanitation system. In that, wastewater network facilities should be re-evaluated to accommodate the existing situation or propose an integrated stormwater system totally separated from the wastewater network, which it seems to be more applicable.

Figure 5-39 presents the existing stormwater and wastewater network and the classification of the sub-catchments. While Table 5-23 summarized the sub-catchment areas names, areas, municipality name and their classification according to be served by stormwater and sanitation systems or not.

128

Table 5-22: Classification of sub-catchment areas according to the existing stormwater and wastewater network

Classification Abbreviation Description

Wastewater Network (WWN) Has a wastewater network only and stormwater drained by surface runoff Combined Waste and Stormwater Combined W & S WN Has wastewater and network stormwater network but one drained to another. Combined Wastewater Network Combined WWN Has no stormwater network, otherwise has some stormwater gullies connected to wastewater networks Separated waste and stormwater Separated S &W WN Has totally two separated networks network one for the storm and the other for the wastewater Neither storm nor wastewater NoN Has neither storm nor network wastewater network.

Figure 5-39: Classification of the sub-catchment areas according to the existing stormwater and wastewater network

129

Table 5-23: Summary of the sub-catchment zoning, area, and classification

Zone Catchment Area Sub-Catchment Name Municipality Classification No. (Km2) 1 Abu Afif Um Al Nasser WWN 0.052 2 Abu Garad Um Al Nasser NON 0.429 3 Abu Gazal Pond Um Al Nasser NON 0.134 4 Hatabia Bite Lahia Combined WWN 0.185 5 Zarandah Bite Lahia Combined WWN 0.050 6 Fadous Bite Lahia WWN 1.647 7 Al-Abbas Bite Lahia Combined WWN 0.154 8 Ghaben Well Bite Lahia Combined WWN 0.079 9 Al Sandid Bite Lahia Combined WWN 2.185 10 Tal Al Dahab -Aslan Pond Bite Lahia Combined WWN 1.869 11 Al Manshia Bite Lahia Combined WWN 0.617 Separated W&S 12 Sirdah Jabalia 0.109 WN 13 Al Faloja St. Bite Lahia Combined WWN 0.186 14 Qarmot - Tal Al Zaatar Bite Lahia Combined WWN 0.080 Separated W and S 15 Al Shikh Zayied Bite Lahia 0.535 WN Abu Saada- Indonesian 16 Bite Lahia Combined WWN 0.159 Hospital 17 Al Alami -AlSahwa Bite Lahia WWN 0.122 18 Masjed Othman Bi Affan Jabalia Combined WWN 0.294 19 Al Khulafaa Jabalia Combined WWN 0.662 20 Hawaber Pump Jabalia WWN 1.290 21 Ahmed Yasi St. Jabalia Combined WWN 0.332 Separated W and S 22 Timraz- Jabalia Camp Jabalia 0.387 WN Combined W and 23 Studio Sultan - Abu Al Aish Jabalia 0.718 S WN 24 Al Dakhilia St. Jabalia Combined WWN 1.968 25 Qebaa Jabalia Combined WWN 0.209 26 Hammouda Jabalia SWN 4.974 Separated W and S 27 Al Ajouza Biet Hanon 0.502 WN Separated W and S 28 Shaikh Zayied - Pond 2 Bite Lahia 0.551 WN 29 Mashroaa - Pond 1 Bite Lahia Combined WWN 0.757

130

Zone Catchment Area Sub-Catchment Name Municipality Classification No. (Km2) Separated W and S 30 Abu Rashid Pond Jabalia 0.547 WN 31 Khalaf Pond Jabalia WWN 0.369 32 Nazla Pond Jabalia Combined WWN 1.812 Separated W and S 33 Um Al Nasser Pond Um Al Nasser 0.422 WN Due to the mixing between waste and stormwater and the large overlap between both networks, it was necessary to re-evaluate the existing wastewater facilities especially the pumping station and if could accommodate with the overload quantities received during the rainy season. According to PWA report, wastewater master plan, 2017, North Gaza has 17 pumping stations distributed as shown in Figure 3-17 all of them in operation except for Azzaytoon pumping station. The service area for each pumping station was also updated based on the updated topographic map. The produced map for the catchment area is also shown in Figure 3-17.

Some of the pumping stations receive additional quantities from other surrounding pumps, for example, Hawaber pumping station receives waste from Amer and Aslan wastewater pumping stations, while Asslateen pumps the wastewater toward Al Manshia pumping station and Abu Rashid pumping station receives wastewater form Mahather pumping station. The routes of the pumping station system schemed in Figure 3-18.

On the middle catchment microscale level, most urbanized area with depression zones was only considered to specify the number of pumping stations that would be overloaded by additional stormwater quantities and their catchments. In that, two main layers, areas with combined wastewater network and catchments of wastewater pumping stations, in arc GIS were clipped to match the middle catchment as shown in Figure 5-40 and Figure 5-41 then both were overly together, as shown in Figure 5-42, to create a composite map for the overlapped areas using intersect function that includes combined geometry and attributes as shown in Figure 5-43 and Figure 5-44.

131

Figure 5-40: Areas with combined WWN in the main urbanized catchment and intersect with wastewater pumping station service area

Figure 5-41: The service area of the wastewater pumping station

132

Figure 5-42: Overlay the areas with combined WWN layer and catchments of the pumping station layer

Figure 5-43: Intersect of the combined WWN and catchments of the pumping station

133

Figure 5-44: Stormwater catchment added to the wastewater pumping

As a result, it was clear that a number of wastewaters pumping stations will be overloaded by additional quantities of stormwater. Table 5-24 lists the pumping station and stormwater catchment that could be discharged to existing wastewater networks and in turn to the pumping stations.

The conversion rainfall intensity coefficient:

The rainfall intensity for 24 hr period (daily rainfall depth) for the Gaza city = 79.4 mm

Average annual rainfalls in Beit Lahia/Average annual rainfall in Gaza= 413/373=1.11

So, the rainfall intensity for 24 hr period (daily rainfall depth) for North Governorate = 79.4 × 1.11= 88.13 mm.

Runoff coefficient for built up area 0.6 (CMWU, 2016)

Runoff coefficient for an agricultural area and open area 0.25 (CMWU,2016)

The land use of middle catchment was intersected with the stormwater catchments added to the wastewater pumping station as shown in Figure 5-45.

134

Figure 5-45: Land Use of the stormwater catchments added to the wastewater pumping station According to the measured storm quantities presented in PWA (2016) report for North Gaza wastewater master plan, the expected amount of stormwater that would reach to NGWWTP was assumed to be 25% of the calculated catchments runoff, since the current wastewater network does not accommodate with the additional stormwater quantities. In that the expected additional storm quantities were calculated as shown in Table 5-24.

Table 5-24: Strom catchment area added to the wastewater pumping station

Stormwater Build Up Area Agricultural area Q Q Pump Name Catchment (Km2) (Km2) (Km2) (m3/d) (m3/hr.) Aslan 0.90 0.3 0.6 7,576 474 Al Manshia 0.45 0.2 0.25 3,908 244 Al Mashroa 0.70 0.6 0.1 8,193 512 Al Hatabia 0.19 0.1 0.09 1,455 91 Assalateen 3.22 0.9 2.32 24,463 1529 Hawaber 1.97 1.5 0.47 22,349 1397 Mahather 1.44 0.8 0.64 14,055 878 Tal Al Zaatar 0.44 0.4 0.04 5,486 343 Abu Rashid 1.64 1.2 0.44 18,237 1140 Total stormwater quantities pumped to NGWWTP 105,722 6,608

135

5.5 Developing the hydrologic model 5.5.1 Rainfall Intensity At this study, the average rainfall rate in mm/hr was calculated using the following table which shows the intensity-duration relationship for 5 years returns periods in Gaza city reference to Table 2-7.

Return Period: 5 years 5 15 30 1 2 3 6 12 18 24 Period min min min h h h h h h h Rainfall 10.9 16.0 20.4 26.0 33.2 38.2 48.8 62.2 71.7 79.4 Using the rainfall intensity for North Governorate 88.13 mm, the cumulative SCS 24-hour storm distribution for North Gaza governorate was developed using SewerGEMs as figured below in Figure 5-46.

Figure 5-46: Cumulative SCS 24-Hour Storm Distribution for North Gaza

5.5.2 Catchment building Based on the catchment zones developed using Arc-hydro under the ArcGIS environment, catchments, as well as the catchments inlets, were built in SewerGEMS model to calculate the runoff quantities of each catchment using SCS method. Figure 5-47 exhibits these features. Table 5-25 summarizes the runoff quantity for each catchment.

136

STORM WATER POND

DEPRESSION ZONES

FLOW LINES

Figure 5-47: Catchments and inlets in SewerGEMs model Table 5-25: Summary of the Catchments flow, volume, SCS number and Time of concentration

Volume (Total Time of Concentration Label Flow (Max.) (m3/s) SCS CN Runoff) (m3) (hours) CM-01 1.27 3,683.10 92 0.204 CM-02 1.75 10,580.00 68 0.707 CM-03 0.33 2,746.70 65 1.009 CM-04 0.32 3,753 65 1.636 CM-05 0.08 1,018 65 1.775 CM-06 6.55 62,673 77 1.554 CM-11 0.72 9,560 61 1.736 CM-07 0.61 5,832 77 1.515 CM-08 0.91 3,049 77 0.308 137

Volume (Total Time of Concentration Label Flow (Max.) (m3/s) SCS CN Runoff) (m3) (hours) CM-29 7.03 29,340 77 0.464 CM-09 5.72 53,240 68 1.29 CM-10 7.86 71,292 77 1.424 CM-13 1.96 7,224 77 0.371 CM-14 0.79 3,106 77 0.43 CM-18 0.34 4,563 61 1.773 CM-16 1.23 6,116 77 0.607 CM-31 3.04 14,270 77 0.567 CM-15 8.15 19,622 77 0.083 CM-19 4.1 25,537 77 0.852 CM-30 2.57 20,948 77 1.225 CM-22 2.32 14,849 77 0.885 CM-21 1.66 12,724 77 1.137 CM-12 0.82 4,235 77 0.642 CM-25 1.25 8,026 77 0.886 CM-27 6.52 35,137 92 0.709 CM-20 5.41 49,168 77 1.422 CM-26 10.32 236,018 84 5.361 CM-32 7.07 68,915 77 1.57 CM-17A 0.39 2,806 77 1.03 CM-17B 0.65 1,891 77 0.229 CM-33B 1.18 9,260 75 1.15 CM-33A 1.65 9,178 87 0.749 CM-23B 2.44 15,762 77 0.897 CM-23A 1.61 11,883 77 1.081 CM-24B 9.24 82,746 92 1.445 CM-24A 2.15 29,016 77 2.437 Total 110.01 949,779.10

5.5.3 Intersection with the existing stormwater system The developed catchments were an intersection with the existing stormwater pipelines to link the developed hydrologic model with the hydraulic system, i.e., the inlets were connected to pre-defined manholes conveying the accumulated surface runoff from

138

catchments to pipelines whereupon to the collection or infiltration ponds. Figure 5-48 and Figure 5-49 show the intersection between catchments, inlets, stormwater pipelines, main flow direction, and infiltration ponds.

Figure 5-48: Catchments intersect with the existing stormwater system

Figure 5-49: Catchments, inlets, stormwater pipelines, the main flow direction, and stormwater ponds 139

5.5.4 Soil and land use The soil texture that intersects with the targeted sub-watershed was varied from sand in most sub-watersheds to clay in some areas. This type of soil was classified as “hydrologic group A for sand and hydrologic group B for clay”. North Gaza governorate can be classified as a high density-urban area, so that CN could be assumed to be 61 for the low density-urban areas to 92 areas currently have high density-urban. Some built-up areas have currently medium density-urban area; however, the simulation process considered that all urban areas will be a high density-urban area, so, it was assumed to have medium to a high density (CN =65 to 83). Figure 5-50 shows the land use of the middle catchments and Figure 5-51 shows the combined soil type and land use of the middle catchments.

Figure 5-50: Land use of the middle catchment

140

Figure 5-51: Combined map of Soil type and Land use of the middle catchment

5.6 Modeling the current situation

Taking into account pipes dimensions and ground and invert levels, the hydrologic- hydraulic model was run to simulate the current situation. The result investigates whether pipes have ever been surcharged. Figure 5-52 depicts these results showing the stormwater pipelines in Al Ajoza Street (Bite Hanon) are undersized. Also, stormwater pipelines directed from Hammoda area (FP 26) up to Wadi bite Hanon are also undersized in addition to pipelines directed down from Al Dakhelia area (FP 24) to Al Shikh Radwan pond. Inlet pipes for each of Abu Rashid pond, Nazla in Jabalia, Bite Lahia 01, Bite Lahia 02 ponds and Um Al Nasser pond was also estimated to be undersized.

Around 27 depression zones were investigated. Most of the depression zones are not serviced by stormwater pipelines, however, some of them are connected to the wastewater pipeline. All of the depression zones were investigated to have an outlet pipe not less than 1 m and in some areas like Tal Al dahab (FP 10) in Aslan area, Hammouda Area and Al Dakhilia the outlet pipe should be not less than 2 m.

141

Flooding depth was investigated to be in the range between 0.74 m in Studio Sultan -Abu Al Aish (FP 23), and 2 m depth in most of the depression zones. In Hammouda Area flooding depth can reach 2.57 m.

A Aslan area

D C

Hamouda area

Al Dakhilia

Figure 5-52: Simulated stormwater pipelines in SewerGEMs A

Um Al Nasser Pond

Figure 5-53: Simulated stormwater pipelines for Um Al Nasser Pond

142

Bite Lahia 01

B Figure 5-54: Simulated stormwater pipelines for Bite Lahia 01 Pond

Bite Lahia 02 C

Khalaf pond

Abu Rashid

Figure 5-55: Simulated stormwater pipelines for Bite Lahia 02, Khalaf and Abu Rashid Ponds

143

Al Nazla pond

Figure 5-56: Simulated stormwater pipelines for Al Nazla Pond The storage capacity of the ponds was also estimated and compared to the total volume of the runoff simulated by the model which showed a shortage in the capacity of all the ponds in north Gaza except for Abu Rashid pond. Table 5-26 summarizes the model output related to the capacity of the stormwater ponds.

Table 5-26: Summary of ponds capacity of the system

Storage Flow (Max.) Volume (Total Overflow Pond 3 3 3 (Maximum) (m³) (m /s) Runoff) (m ) volume (m ) Um Al Nasser 10,644.10 2.83 18,438.6 -7,795 Bite lahia 2 3,652.20 8.15 19,622.60 -15,970 Bite lahia 1 12,303.10 7.03 29,340.40 -17,037 Khalaf 8,125 3.04 14,270.80 -6,146 Al Nazla 14,490 7.07 68,915.6 -54,426 Abu Rashid 47,520 5.93 40,495.50 7,025 Abu Rashid pond receives the overflow of Khalaf pond in that the total volume Aburashed may receive reaches to 46,641 m3 still within Abu Rashid capacity volume.

5.6.1.1 Intersect the flood vulnerability map and assessment of drainage Intersect the flood vulnerability map with the results of stormwater drainage assessment showed some areas are over surged and others were not. Interestedly the areas within the highest vulnerability range showed overflooding in the drainage system of the 144

area. In that, the intervention procedure to mitigate the vulnerability were required. Figure 5-57 presents the flood vulnerability map in combination with the assessment results of the stormwater drainage system.

Figure 5-57: Overflowing drainage parts with respect to flood vulnerability map

5.6.1.2 Intervention procedure At this part number of the intervention procedure was proposed for each district in North Gaza governorate as following:

1. Prepare integrated stormwater management (ISWM) and contingency plan (CP). It includes extending the stormwater system to cover all depression zones and redesign and build for the undersized existing parts. It also includes householding improvement and raising awareness of residents and evacuation training in the heavy storm events.

This intervention would be more necessary and effective in the following areas where they classified to have the most vulnerable area with regards to the four criteria:

- Izbit bite Hanon, Namera district and the Old town of Bite Hanon. - Um Al Nasser districts. - Al Shyma’a district, Al Amal district and Bir Al Naaja and in Bite Lahia. - Al Zahor district, Al Nahda district, the old town and Jabalia camp in Jabalia.

145

2. Improve the stormwater network

This option focuses on the area have low resilience in drainage networks through on upgrade the under-size pipelines. It includes the following areas

- Center of Bite Lahia, Al Manshia, Al Masroa’a, Al Shikh Zayed at Bite Lahia. - Al Nuzha , Al Rawda at Jabalia. - Al Sikka, Al Zaytoun, Nimra, Al Amal, Qerman and Banat at Bite Hanon. 3. Raising awareness and evacuation training for residents

This intervention will be implemented where only social and coping vulnerability is found. It will be used for the west and east districts that would not be affected by any physical or structural vulnerability.

4. Do Nothing will be at areas where the vulnerability in its lowest level and has not any kind of flooding zones.

Figure 5-58 and Figure 5-59 show the flood intervention procedure and priority in order to mitigate the flood risk to its minimum level.

Figure 5-58: Flooding intervention procedure

146

Figure 5-59: Flooding intervention priority

5.6.1.3 The interaction between intervention priority and SewerGEMs outputs Overlaying the existing stormwater network with intervention priority showed matches in the priority intervention and SewerGEMs model. It was clear that the parts of the drainage system which classified over surged was also ranked at the first level or the second level of intervention priority. Figure 5-60 presented the overly result of AHP and SewerGEMs.

Figure 5-60: Overlay AHP and SewerGEMs result 147

Chapter 6 Conclusions and

Recommendations

148

Chapter 6 Conclusions and Recommendations

5.7 Conclusion

The results of this study show that integration between AHP and ArcGIS techniques was an important tool for mapping the flood vulnerability. A flood vulnerability map was derived using multi-criteria that combine social, coping, household structure and physical factors. The derived map can help the decision maker rabidly to assess and evaluate the flooding impacts in North Gaza Governorate. In addition, the consistency ratio of judgment during the AHP process was calculated to be less than 10%, thus the use of multi-criteria analysis tool in integration with GIS was useful for mapping the vulnerability and predicting the possibilities of each criteria affecting the vulnerability and available alternatives. In overall, the final map shows the categories of flood vulnerability zonation based on AHP-GIS approaches.

It was obviously clear that expert consensus about being the physical is the main factors that increase the vulnerability of the area with a relative weight of 71.79% while they were also consistent that the best alternative to mitigate the flooding impacts over the residents is the integrated stormwater management plan.

Results of vulnerability mapping illustrate that almost 45% of North Gaza is identified as extremely vulnerable to flooding risks and classified as high and very high- risk areas. It also showed that 70% of North Gaza population are among the most vulnerable areas.

On the other hand, the GIS model was sufficient tool to measure the flood catchment areas in North Gaza governorate basically using the topographic information of the North. By verifying the model with the municipal key informant, the model was able to obtain the exact catchment areas taking into consideration the direction of flow lines, built-up areas, and streets.

To measure the stormwater amount and predict the flooding in each zone as well as testing the hydraulic performance of the existing stormwater network SewerGEMs model was used. The combination between ArcGIS and SewerGEMs was important to specify the

149 flooding points location and the amount of flooding which in turn might be important to take the appropriate precautions to reduce flood risk. The hydraulic performance of the stormwater system showed that 40% of the storm pipelines is not sufficient to accommodate the expected stormwater quantities and capacities of stormwater ponds are inadequate. In that, intervention procedures and priorities were chosen according to the degree and type of vulnerability for each specific area.

Finally, flood vulnerability assessment requires detailed information on conditions of the study area field, geologic and hydrologic information, statics, ... etc. so that results can be more comprehensive to indicated the impact of the flood on the study area.

5.8 Recommendations

1) Prioritizing integrated stormwater management in North Gaza governorate especially in the highly vulnerable areas that had been identified in this thesis. This will be used as a mitigation measures to flooding since this will allow free flow of runoff to drain through the drainage system and prevent cause flooding through extending the stormwater system to cover depression areas, upgrading the existing system and built up new stormwater basins. 2) Manage the developing highly vulnerable areas at least in conjunction with proper infrastructure is built and a basin management plan is formulated. 3) Reassessing the wastewater drainage system of the North governorate at the impact of additional stormwater quantities that drained through the combined drainage system. 4) Raising awareness of the residents would reduce the vulnerability of the area when they have the right training to deal with such events. 5) Developing a GIS database of the area at a large extent would help to continual vulnerability mapping with a higher degree accuracy.

150

5.9 Future Development

Future research related to wastewater system reliability is needed. Especially to test the pumps performance during various flooding events. This would help to lead better flood management.

Also, future research related to developing an integrated stormwater management plan can be built upon the result of this research which in turn would help to develop a contingency plan for the flooded areas in North Gaza governorate.

151

6 References

References

152

The References

Antonie, J., Fischer, G., and Makowski, M., 1997. Multiple criteria land use analysis. Applied Mathematics and Computation. Vol.85 pp.195-215. Arnold A., Line E., Coffey W., and Spooner J., (1993). Stormwater Management Guidance Manual. North Carolina: Division of Environmental Management. Arthur Neslen, (2018, March 21). Flooding and heavy rains rise 50% worldwide in a decade. The Guardian, Retrieved from https://www.theguardian.com. Aurora .M, 2003. GIS and Multi-Criteria Decision Making to Determine Marketability of Pay Pond Businesses in West Virginia, West Virginia University, Division of Forestry. Bana E, Costa CA, Da Silva PA, Nunes Correia F. 2004. Multi-criteria evaluation of flood control measures: the case of Ribeira do Livramento. Water Resour Manag. 18(21):263–283. Bauwens, W. (2016). Surface Water Hydrology. Vrije Universiteit Brussel: Belgium. Belton S. and Stewart T.S., 2002. Multiple Criteria Decision Analysis. An Integrated Approach, Kluwer Academic Publishers, Massachusetts. Berry J K 1993 Cartographic modeling: The analytical capabilities of GIS. In: Goodchild M, Parks Band Steyaert L (eds) Environmental Modeling with GIS. Oxford, Oxford University Press: 58± 74. Brans J.P., Vincke Ph. And Mareschal B., 1986, “How to select and how to rank projects: The PROMETHEE method”, European Journal of Operational Research 24, pp. 228-238. Brouwer R, van Ek R. 2004. Integrated ecological, economic and social impact assessment of alternative flood control policies in the Netherlands. Ecol Econ. 50(1–2):1–21. Bojovic, N.; Milenkovic, M. The best rail fleet mix problem. Int. J. Oper. Res. 2008, 8, 77–87. Carver S J 1991 Integrating multi-criteria evaluation with geographical information systems. International Journal of Geographical Information Systems 5: 321±339. Centre for Research on the Epidemiology of Disasters. EM-DAT—The International Disaster Database. ([available at http://www.emdat.be/database], 2015). Chrisman N R 1996 Exploring Geographic Information Systems. New York, John Wiley, and Sons, Inc. 153

CMWU, CMWU Annual Report on Water Status in the Gaza Strip, 2011. Ciurean, R. L., Schröter, D., and Glade, T.: Conceptual frameworks of vulnerability assessments for natural disasters reduction, in Approaches to Disaster Management - Examining the Implications of Hazards, Emergencies, and Disasters, InTech, 3–32, 2013. Cowen D. 1988. GIS versus CAD versus DBMS: what are the differences? Photogramm Eng Remote Sensing. 54:1551– 1555. Cutter, S. L., Boruff, B. J., and Shirley, W. L.: Social vulnerability to environmental hazards, Soc. Sci. Quart., 84, 242–261. CYPADAPT, Report on the literature review on the state-of-the-art multi-criteria analysis tools used for the development of adaptation plans worldwide. Deliverable 4.1. 2013. Dandapat K·, Panda G., Flood vulnerability analysis and risk assessment using the analytical hierarchy process. Model. Earth Syst. Environ. DOI 10.1007/s40808-017-0388-7. David B. (2006). The Rational Method_ Thompson. Civil Engineering Department Texas Tech University Draft: 20 September 2006). De Brito, M. M., and Evers, M.and Almoradie A: Participatory flood vulnerability assessment; a multi-criteria approach, Hydrol Earth Syst. Sci., 22, 373-390, 2018. Department for Communities and Local Government: London, 2009, Multi-criteria analysis: a manual. Doocy, S., Daniels, A., Murray, S. & Kirsch, T. D. The Human Impact of Floods: a Historical Review of Events 1980-2009 and Systematic Literature Review. PLoS Curr., doi: 10.1371/currents.dis.f4deb457904936b07c09daa98ee8171a (2013). Eastman J R, Kyem P A K, Toledano J, and Jin W 1993 GIS and Decision Making. Geneva,UNITAR. Edwards W., Barron F.H., 1994, Smarts and Smarter: Improved Simple Methods for Multi- Attribute Utility Measurement. Organizational Behaviour and Human Decision Processes 60, pp 306-325. Elsheikh RFA, Ouerghi S, Elhag AR. 2015. Flood risk map based on GIS, and multi-criteria techniques (case study Terengganu Malaysia). J Geogr Inform Syst. 07(04):348.

154

Environmental Systems Research Institute (ESRI) 1995 Understanding GIS: The ARC/INFO Method. Cambridge, UK, Geoinformation International. Eshtawi T., Evers M., and Tischbein B., 2016, Quantifying the impact of urban area expansion on groundwater recharge and surface runoff. Hydrological Sciences Journal. ISSN: 0262-6667 (Print) 2150-3435 (Online) Journal homepage: http://www.tandfonline.com/loi/thsj20. Eshtawi T., 2018. Hydrology and Sanitary Engineering, in a PowerPoint presentation [PowerPoint slides]. Fekete, A.: Validation of a social vulnerability index in context to river-floods in Germany, Nat. Hazards Earth Syst. Sci., 9, 393– 403. Fernandez, D.S. and Lutz, M.A. (2010) Urban Flood Hazard Zoning in Tucumán Province, Argentina, Using GIS and Multicriteria Decision Analysis. Engineering Geology, 111, 90-98. Fernandez, D.S. and Lutz, M.A. (2010) Urban Flood Hazard Zoning in Tucumán Province, Argentina, Using GIS and Multicriteria Decision Analysis. Engineering Geology, 111, 90-98. Figueira J. and Roy B., 2002. Determining the weights of criteria in the ELECTRE type methods with a revised Simos’ procedure. European Journal of Operational Research 139, pp 317-326. Forman E, Gass S. The Analytic Hierarchy Process: An Exposition. Oper Res 2001;49:469– 86. Frigerio, I. and de Amicis, M.: Mapping social vulnerability to natural hazards in Italy: a suitable tool for risk mitigation strategies, Environ. Sci. Policy, 63, 187–196. FISRWG. (1998). Hunter, C.J. 1991. Better Trout Habitat. A Guide to Stream, Restoration, and Management. Montana Land Reliance, Island Press Washington, DC. Hupp, CR. And Bryan, BA. 1989. A dendrogeomorphic approach to flash-flood. Godfrey, A., Ciurean, R. L., van Westen, C. J., Kingma, N. C., and Glade, T.: Assessing the vulnerability of buildings to hydrometeorological hazards using an expert-based

155

approach – an application in Nehoiu Valley, Romania, Int. J. Disast. Risk Re., 13, 229– 241. Hazarika N, Barman D, Das AK, Sarma AK, Borah SB. 2016. Assessing and mapping flood hazard, vulnerability and risk in the upper Brahmaputra River valley using stakeholders’ knowledge and multicriteria evaluation (MCE). J Flood Risk Management: doi:10.1111/ jfr3.12237. Heywood I, Oliver J and Tomlinson S 1995 Building an exploratory multi-criterion modeling environment for spatial decision support. In Fisher P (ed) Innovations in GIS 2. London, Taylor, and Francis: 127±36. Hooman Mostofi Camare, Ottawa, Canada, 2011. Thesis: Multicriteria decision evaluation of adaptation strategies for vulnerable coastal communities Hopkins L 1977 Methods for generating land suitability maps: a comparative evaluation. Journal for American Institute of Planners 34: 19±29. House of Water and Environment, final report, Setting-up Groundwater Protection Plan of the Coastal Aquifer of the Gaza Strip,2010. Huang I.B. Keisler J., Linkov J. (2011). Multi-criteria decision analysis in environmental sciences: Ten years of applications and trends. The science of the Total Environment, Vol. 409. Issue 19. Pages 3578–3594. United States., Janssen R, Herwijnen M, Beinat E. 2003. Definite—case studies and user manual. Amsterdam: Vrije Universiteit Amsterdam/IVM. Jenni Neste and Timo P. Karjalainen (2013) The use of multi-criteria decision analysis in Environmental Impact Assessment Joerin, F., The´riault, M., Musy, A., 2001. Using GIS and outranking multicriteria analysis for land-use suitability assessment. International Journal of Geographical Information Science 15 (2), 153–174. Kablan M.M., 2004, “Decision support for energy conservation promotion: an analytic hierarchy process approach”, Energy Policy 32, 1151-1158. Kappes, M. S., Papathoma-Köhle, M., and Keiler, M.: Assessing physical vulnerability for multi-hazards using an indicator-based methodology, Appl. Geogr., 32, 577–590.

156

Keeney, R., and Raiffa, H. 1976. The decision with multiple objectives: Preferences and value tradeoffs. New York: John Wiley & Sons. Keeney R.L. and Raiffa H., 1976, Decisions with multiple objectives. New York: Cambridge University Press. Kienberger, S., Lang, S., and Zeil, P.: Spatial vulnerability units – expert-based spatial modeling of socio-economic vulnerability in the Salzach catchment, Austria, Nat. Hazards Earth Syst. Sci., 9, 767–778, https://doi.org/10.5194/nhess-9-767-2009, 2009. Koks, E. E., Jongman, B., Husby, T. G., and Botzen, W. J. W.: Combining hazard, exposure and social vulnerability to provide lessons for flood risk management, Environ. Sci. Policy, 47, 42– 52. Lahdelma, R., Hokkanen, J., and Salminen, P. 1998. SMAA—Stochastic Multiobjective Acceptability Analysis. European Journal of Operational Research 106:137–143. Lin, H., Wan, Q., Li, X., Chen, J. and Kong, Y., 1997. GIS-based multicriteria evaluation for investment environment. Environment and Planning B: Planning and Design, v: 24, pp: 403- 414. Linkov I., Varghese A., Jamil S., Seager T.P., Kiker G., Bridges T., 2004, “Multi-criteria decision analysis: a framework for structuring remedial decisions at contaminated sites”, Comparative Risk Assessment and Environmental Decision Making, pp.15-54, Kluwer Academic Publishers. Macharis C., Springael J., De Brucker K., Verbeke A., 2004, PROMETHEE and AHP: The design of operational synergies in the multicriteria analysis. Strengthening PROMETHEE with ideas of AHP. European Journal of Operational Research 153, pp 307–317. Malczewski J. 2006. GIS-based multicriteria decision analysis: a survey of the literature. Int J Geogr Inf Sci. 20(7):703– 726. Malczewski, J., and Ogryczak, W., 1996, The multiple criteria location problem: Preference- based techniques and interactive decision support. Environment and Planning A, 28, 69–98. Malczewski J 1999 GIS and Multicriteria Decision Analysis. New York, John Wiley, and Sons.

157

Matori A., Lawal D., Yusof K., Hishim A., Balogun A., (2014) Spatial Analytic Hierarchy Process Model for Flood Forecasting: An Integrated Approach. 7th IGRSM International Remote Sensing &GIS Conference and Exhibition. IOP conf. Series: Earth and Environmental Science 20, 012029. Mendoza GA, Macoun P, Prabhu R, Sukadri D, Purnomo H, Hartanto H. 1999. Guidelines for applying multi-criteria analysis to the assessment of criteria and indicators. Vol. 9. Bogor: CIFOR. Merz, B., Kreibich, H., Schwarze, R., and Thieken, A.: Review article “Assessment of economic flood damage”, Nat. Hazards Earth Syst. Sci., 10, 1697–1724. Meyer V, Scheuer S, Haase D. 2009. A multicriteria approach for flood risk mapping exemplified at the Mulderiver, Germany. Nat Hazards. 48(1):17–39. Munda, G. 1995. Multicriteria evaluation in a fuzzy environment. Theory and applications. In Ecol.Econ. Physica. Heidelberg: Verlag. Musungu K, Motala S, Smit J. 2012. Using multi-criteria evaluation and GIS for flood risk analysis in informal settlements of Cape Town: the case of Graveyard Pond. South African J Geomatics. 1(1):92–108. Nijkamp, P., Rietvelt, P., and Voogd, H. 1990. Multi-criteria evaluation in physical planning. Amsterdam: North-Holland. Nijkamp, P., and Vreeker, R. 2000. Sustainability assessment of development scenarios: Methodology and application to Thailand. Ecological Economics 33:7–27. Ouma Y., and Tateishi R., 2014. Urban Flood Vulnerability and Risk Mapping Using Integrated Multi-Parametric AHP and GIS: Methodological Overview and Case Study Assessment. Water. ISSN 2073-4441. www.mdpi.com/journal/water Ozturk, U., Tarakegn, Y. A., Longoni, L., Brambilla, D., Papini, M., and Jensen, J.: A simplified early-warning system for imminent landslide prediction based on failure index fragility curves developed through numerical analysis, Geomat. Nat. Haz. Risk, 5705, 1–20.

158

Papathoma-Köhle, M.: Vulnerability curves vs. vulnerability indicators: application of an indicator-based methodology for debris-flow hazards, Nat. Hazards Earth Syst. Sci., 16, 1771–1790, https://doi.org/10.5194/nhess-16-1771-2016, 2016. Parikh, P. et. al. (2005). Application of market mechanisms and incentives to reduce stormwater runoff: An integrated hydrologic, economic and legal approach. Environmental Science & Policy 8. 133-144. Pereira JMC and Duckstein L 1993 A multiple criteria decision-making approach to GIS-based land suitability evaluation. International Journal of Geographical Information Systems 7: 407±24 Planton, Serge (France; editor) (2013). "Annex III. Glossary: IPCC – Intergovernmental Panel on Climate Change" (PDF). IPCC Fifth Assessment Report. p. 1450. Archived from the original (PDF) on 2016-05-24. Retrieved 25 July 2016. Pohekar S. D., Ramachandran M., 2004, “Application of multi-criteria decision making to sustainable energy planning – A review”, Renewable and Sustainable Energy Reviews 8, pp. 365-381. PWA. (2011). Assessment of stormwater harvesting practices. Gaza: Palestinian Water Authority PWA. (2011). The Gaza Emergency Technical Assistance Programme (GETAP) on Water Supply to the Gaza strip, The Comparative Study of Options for an Additional Supply of Water for the Gaza strip (CSO-G), The Updated Final Report. Gaza strip: Palestinian Water Authority. PWA. (2017). Consultancy Services for Design of Water and Wastewater Projects Raaijmakers R, Krywkow J, van derVeen A. 2008. Flood risk perceptions and spatial multi- criteria analysis: exploratory research for hazard mitigation. Nat Hazards. 46(3):307– 322. Reilly, B.: Disaster and Human History: Case Studies in Nature, Society, and Catastrophe, McFarland, Jefferson, 2009. Roy, B., 1968, "Classement et choix en présence de points de vue multiple (la méthode electre)., RAIRO, 2, 57-75.

159

Roy, B., and Vincke, Ph. 1981. Multicriteria analysis: Survey and new directions. European Journal of Operational Research 8:207–218. Roy, D. C., and Blaschke, T.: Spatial vulnerability assessment of floods in the coastal regions of Bangladesh, Geomat. Nat. Haz. Risk, 5705, 1–24, Saaty, T.L., 1980. “The Analytic Hierarchy Process.” McGraw-Hill, New York. Saaty TL. Decision making with the analytic hierarchy process. Int J Serv Sci 2008;1:83-98. Sadiq, R.; Kleiner, Y.; Rajani, B. Aggregative risk analysis for water quality failure in distribution networks. J. Water Supply Res. Technol. AQUA 2004, 53, 241–261. Sajikumar N. and Remya R.S., (2014). Impact of land cover and land use change on runoff characteristics. Journal of Environmental Management, Volume 161, 15 September 2015, Pages 460-468 Scheuer S, Haase D, Meyer V. 2011. Exploring multicriteria flood vulnerability by integrating economic, social and ecological dimensions of flood risk and coping capacity: from a starting point view towards an endpoint view of vulnerability. Nat Hazards. 58(2):731– 751. Shepherd, Dr. J. Marshall; Shindell, Drew; O'Carroll, Cynthia M. (1 February 2005). "What's the Difference Between Weather and Climate?". NASA. Retrieved 13 November 2015. Shivaprasad Sharma SV, Parth sarathi Roy, Chakravarthi Vand Srinivasa Rao G. 2018. Flood risk Assessment Using multi-criteria analysis; a case study from Kopili River Basin, Assamm India. Geomatics Natural Hazard and Risk. Vol, 9, No 1, 79-93 Salminen P., Hokkanen J., Lahdelma R., 1998, Comparing multicriteria methods in the context of environmental problems. European Journal of Operational Research 104, pp 485- 496. Schoenherr, T.; Tummala, R.; Harrison, T.P. Assessing supply chain risks with the analytic hierarchy process: Providing decision support for the offshoring decision by a US manufacturing company. J. Purch. Supply Manag. 2008, 14, 100–111. Schueler, T.R. (1995). Site Planning for Urban stream protection. Metro. Washington Council of Governments. Washington, DC. 232 p.

160

Sheppard, S. R. J., and Meitner, M.: Using multi-criteria analysis and visualization for sustainable forest management planning with stakeholder groups, Forest. Ecol. Manag., 207, 171–187. Socher M, Sieber HU, Muller G, Wundrak P. 2006. Verfahren zur landesweiten Priorisierung von Hochwasserschutzmaßnahmen in Sachsen. Hydrologie und Wasserbewirtschaftung. 50(3). Sogereah, B., and Team (1999), “Master Plan for Sewage and Stormwater Drainage in the Gaza Governorates”, Final Report, Palestinian National Authority. Tomlin C D 1990 Geographical Information Systems and Cartographic Modeling. Englewood Cliffs, NJ, Prentice-Hall. Tsubaki, R., Bricker, J. D., Ichii, K., and Kawahara, Y.: Development of fragility curves for railway embankment and ballast scour due to overtopping flood flow, Nat. Hazards Earth Syst. Sci., 16, 2455–2472. UNISDR: Terminology on Disaster Risk Reduction, UNISDR, Geneva, 2009. United Nations Framework Convention on Climate Change (UNFCCC). 2000. Methods and Tools to Evaluate Impacts and Adaptation: Information on Impacts and Adaptation Assessment Methods (Progress Report, Note by the Secretariat). Subsidiary Body for Scientific and Technological Advice, Twelfth Session, Bonn, 12-16 June 2000. Internet article available. United States Department of Agriculture Natural Resource Conservation Service (USDA NRCS). 1986. Urban Hydrology for Small Watersheds. Technical Release No. 55, Washington, D.C.: USDA. UN Office for Disaster Risk Reduction. (2019, Jan 24). 2018: Extreme weather events affected 60m people. Received from www.unisdr.org Vaidya OS, Kumar S. Analytic hierarchy process: An overview of applications. Eur J Oper Res 2006;169:1–29. Vargas L.G., 1990. An overview of the analytic hierarchy process and its applications. Von Winterfeldt, D., and Edwards, W. 1986. Decision analysis and behavioral research. Cambridge, MA: Cambridge University Press.

161

Vojinovic, Z., Hammond, M., Golub, D., Hirunsalee, S., Weesakul, S., Meesuk, V., Medina, N., Sanchez, A., Kumara, S., and Abbott, M.: Holistic approach to flood risk assessment in areas with cultural heritage: a practical application in Ayutthaya, Thailand, Nat. Hazards, 81, 589–616. Wang J.J., Y.-Y. Jing, C.-F. Zhang, J.-H. Zhao, 2009. Review of multi-criteria decision analysis aid in sustainable energy decision-making. Renewable and Sustainable Energy Reviews 13, pp 2263–2278. Weather statistics for North Gaza, Gaza Strip (Palestine). Retrieved (2018, December 15) from https://www.yr.no/place/Palestine/Gaza_Strip/North_Gaza/statistics.html Yahaya S., Ahmed N., and Abdalla R., Multicriteria Analysis for Flood Vulnerable Areas In Hadejia-Jama’are River Basin, Nigeria, Asprs 2008 Annual Conference Portland, Oregon Zahedi, F. The analytic hierarchy process: A survey of the method and its applications. Interfaces 1986, 16, 96–108. Zopounidis C. and Doumpos M., 2002, Multi-criteria Decision Aid in Financial Decision Making: Methodologies and Literature Review. Journal of Multi-Criteria Decision Analysis 11, pp 167–186.

162

7 Appendices

Appendices

163

Appendix 1

List of Experts

No. Name of Expert Sector

1 Dr. Mazen Abu Tayef I Islamic University of Gaza

Private Sector

2 Dr. Thaer Abu Shbak Al Azhar University

3 Dr. Khalid Qahman Environmental Quality Authority

4 Dr. Fahid Rabah Islamic University of Gaza

Private Sector -GVC consult

5 Dr. Younis Mogheirz Islamic University of Gaza

Private Sector- GVC consult

6 Dr. Hussam Al Najjar Islamic University of Gaza

7 Dr. Sami Hamdan Palestinian Water Authority

8 Dr. Abd Al Majeed Nassar Islamic University of Gaza

9 Dr. Mohammed Al Aila Private Sector- Al Madina Consult

10 Dr. Ahmed Abu Foul Islamic University of Gaza

Private Sector- Infra

11 Dr. Tamer Eshtawi University college of applied science

Private Sector- EMCC

12 Eng. Rifat Diab Private Sector- EMCC

13 Eng. Salah Taha Private Sector – RAI consult

14 Eng. Alaa Hassan Private Sector- Al Madina Consult

164

Appendix 2

AHP Questionnaire

Flood Vulnerability Assessment Using Multi-Criteria Approach: A Case Study from North Gaza

Name: ...…………………………………………………………………………………… Position…………………………………………………………………………………….

The objective of this questioner

The object of this questioner is to determine the expert’s opinion in the field of water resource management about the probability of vulnerable criteria.

The Structure of the hierarchy The structure of the hierarchy consists of the top of the hierarchy is the overall goal, while lower levels will describe important components of the upper hierarchy level. Below are simple hierarchy levels as a flood vulnerability model in that constructed for North Gaza governorate.

Pairwise comparisons: Flood Vulnerability Sub-criteria (Level 2) This level comprises the main criteria sub-components as following:

165

1. Social vulnerability. Poverty, culture and population density 2. Coping vulnerability. Evacuation training, health care facilities, shelters. 3. Structural vulnerability. Householder with improper safety measures, 4. Physical criteria. Land use, rainfall, drainage network, soil type, slope.

Sample question level 2 Based on your experience related to the flood vulnerability in North Gaza, with respect to reducing the flood risk which is more likely to increase the vulnerability during the flood, Poverty/culture or the Population Density.

Poverty/Culture Population Density

For example, if Poverty/culture is Extreme important than Population Density then the value is equal to 9, if Population Density is Extreme important than Poverty/culture then the value should be 1/9.

166

Item Item Number 1 2 3 4 5 6 7 8 9 No. Cooping Structure Social Vulnerability Physical Vulnerability Vulnerability Vulnerability Householder Poverty/ Population Land Soil Drainage Item Description Cooping Slope Rainfall with improper Culture Density use type network safety measures 1 Poverty/ Culture 1.00 2 Population Density 1.00 3 Cooping 1.00 4 Slope 1.00 5 Land use 1.00 6 Rainfall 1.00 7 Soil type 1.00 8 Drainage network 1.00 Householder with 9 improper safety 1.00 measures Sum

167

Pairwise comparisons: Alternative/options actions (Level 3) This level is divided into three types of alternative actions for dealing with a flood in North Gaza governorate in our study option can be as follows: 1. Alternative 1: Relocation (temporary or permanent); which means either permanently relocate or move the whole citizens from the flooding area into free flood areas and prevent the settlement within the flooding zones or temporary relocation of the citizens during the seasons of the heavy storms and resettlement them when it’s safe. 2. Alternative 2: Early warning systems; improve or develop a flood detection system that can detect the flood hazard in its early stage either using manual tools or by routine human observation. 3. Alternative 3: Integrated stormwater management; reduce or minimize the flooding using stormwater basins, improvement of the drainage system, improve the household 4. Alternative 4: Do nothing

Sample question level 3 “Based on your experience related to the flood vulnerability in North Gaza, with respect to main flood vulnerability which is more important, relocation or developing integrated stormwater management.

Level 3 Social vulnerability 1 2 3 4 Item Number Item Description Early Do Relocation ISWM warning nothing 1 Relocation 1.00 2 Early warning 1.00 3 ISWM 1.00 4 Do nothing 1.00 Sum Level 3 Coping vulnerability 1 2 3 4 Item Number Item Description Early Do Relocation ISWM warning nothing 1 Relocation 1.00 2 Early warning 1.00 3 ISWM 1.00 4 Do nothing 1.00 Sum

168

Level 3 Structural vulnerability 1 2 3 4 Item Number Item Description Early Do Relocation ISWM warning nothing 1 Relocation 1.00 2 Early warning 1.00 3 ISWM 1.00 4 Do nothing 1.00 Sum Level 3 Physical criteria 1 2 3 4 Item Number Item Description Early Do Relocation ISWM warning nothing 1 Relocation 1.00 2 Early warning 1.00 3 ISWM 1.00 4 Do nothing 1.00 Sum

169

Appendix 3

AHP Calculations

Dr. Mazen Abu Tayef

CR Value = 0.090 OK Pairwise comparisons ONLY ENTER in Item Number Item Number 1 2 3 4 5 6 7 8 9 10 Item Descriptions Item Description Poverty Population denCoping Slope Land Use Rainfall Soil Type Drainage NetwHouseholder 1 Poverty 1.00 3.00 0.20 0.11 0.20 0.11 0.33 0.20 0.11 2 Population density 0.33 1.00 0.20 0.11 0.20 0.11 0.33 0.20 0.11 3 Coping 5.00 5.00 1.00 0.14 0.33 0.20 1.00 0.33 0.20 4 Slope 9.00 9.00 7.00 1.00 3.00 0.33 7.00 3.00 3.00 5 Land Use 5.00 5.00 3.00 0.33 1.00 1.00 3.00 1.00 0.20 6 Rainfall 9.00 9.00 5.00 3.00 1.00 1.00 7.00 3.00 0.33 7 Soil Type 3.00 3.00 1.00 0.14 0.33 0.14 1.00 0.20 0.14 8 Drainage Network 5.00 5.00 3.00 0.33 1.00 0.33 5.00 1.00 0.33 9 Householder 9.00 9.00 5.00 0.33 5.00 3.00 7.00 3.00 1.00 10 1.00 Sum 46.33 49.00 25.40 5.51 12.07 6.23 31.67 11.93 5.43

STANDARDIZED MATRIX Poverty Population denCoping Slope Land Use Rainfall Soil Type Drainage NetwHouseholder Weight 1 Poverty 0.02 0.06 0.01 0.02 0.02 0.02 0.01 0.02 0.02 2.1% 2 Population density 0.01 0.02 0.01 0.02 0.02 0.02 0.01 0.02 0.02 1.5% 3 Coping 0.11 0.10 0.04 0.03 0.03 0.03 0.03 0.03 0.04 4.8% 4 Slope 0.19 0.18 0.28 0.18 0.25 0.05 0.22 0.25 0.55 24.0% 5 Land Use 0.11 0.10 0.12 0.06 0.08 0.16 0.09 0.08 0.04 9.4% 6 Rainfall 0.19 0.18 0.20 0.54 0.08 0.16 0.22 0.25 0.06 21.1% 7 Soil Type 0.06 0.06 0.04 0.03 0.03 0.02 0.03 0.02 0.03 3.5% 8 Drainage Network 0.11 0.10 0.12 0.06 0.08 0.05 0.16 0.08 0.06 9.2% 9 Householder 0.19 0.18 0.20 0.06 0.41 0.48 0.22 0.25 0.18 24.3% 10

CI and CR worksheet Poverty Population denCoping Slope Land Use Rainfall Soil Type Drainage NetwHouseholder SUM SUM/Weight 1 Poverty 0.02 0.05 0.01 0.03 0.02 0.02 0.01 0.02 0.03 0.20 9.47 2 Population density 0.01 0.02 0.01 0.03 0.02 0.02 0.01 0.02 0.03 0.16 10.33 3 Coping 0.11 0.08 0.05 0.03 0.03 0.04 0.04 0.03 0.05 0.45 9.47 4 Slope 0.19 0.14 0.34 0.24 0.28 0.07 0.25 0.28 0.73 2.51 10.45 5 Land Use 0.11 0.08 0.14 0.08 0.09 0.21 0.11 0.09 0.05 0.96 10.18 6 Rainfall 0.19 0.14 0.24 0.72 0.09 0.21 0.25 0.28 0.08 2.20 10.44 7 Soil Type 0.06 0.05 0.05 0.03 0.03 0.03 0.04 0.02 0.03 0.34 9.73 8 Drainage Network 0.11 0.08 0.14 0.08 0.09 0.07 0.18 0.09 0.08 0.92 10.01 9 Householder 0.19 0.14 0.24 0.08 0.47 0.63 0.25 0.28 0.24 2.52 10.36 10

count 9.00 lambda max 10.049 CI 0.131 CR 0.09 constant 1.45

170

Dr. Thaer Abu Shbak

CR Value = 0.098 OK Pairwise comparisons ONLY ENTER in Item Number Item Number 1 2 3 4 5 6 7 8 9 10 Item Descriptions Item Description Poverty Population denCoping Slope Land Use Rainfall Soil Type Drainage NetwHouseholder 1 Poverty 1.00 0.33 0.33 0.11 0.33 0.14 0.33 0.14 1.00 2 Population density 3.00 1.00 0.33 0.14 0.33 0.14 0.33 0.14 0.33 3 Coping 3.00 3.00 1.00 0.14 0.33 0.14 0.33 0.20 0.33 4 Slope 9.00 7.00 7.00 1.00 3.00 3.00 3.00 1.00 3.00 5 Land Use 3.00 3.00 3.00 0.33 1.00 0.33 3.00 0.20 0.33 6 Rainfall 7.00 7.00 7.00 0.33 3.00 1.00 3.00 0.33 1.00 7 Soil Type 3.00 3.00 3.00 0.33 0.33 0.33 1.00 0.33 1.00 8 Drainage Network 7.00 7.00 5.00 1.00 5.00 3.00 3.00 1.00 1.00 9 Householder 1.00 3.00 3.00 0.33 3.00 1.00 1.00 1.00 1.00 10 1.00 Sum 37.00 34.33 29.67 3.73 16.33 9.10 15.00 4.35 9.00

STANDARDIZED MATRIX Poverty Population denCoping Slope Land Use Rainfall Soil Type Drainage NetwHouseholder Weight 1 Poverty 0.03 0.01 0.01 0.03 0.02 0.02 0.02 0.03 0.11 3.1% 2 Population density 0.08 0.03 0.01 0.04 0.02 0.02 0.02 0.03 0.04 3.2% 3 Coping 0.08 0.09 0.03 0.04 0.02 0.02 0.02 0.05 0.04 4.2% 4 Slope 0.24 0.20 0.24 0.27 0.18 0.33 0.20 0.23 0.33 24.8% 5 Land Use 0.08 0.09 0.10 0.09 0.06 0.04 0.20 0.05 0.04 8.2% 6 Rainfall 0.19 0.20 0.24 0.09 0.18 0.11 0.20 0.08 0.11 15.6% 7 Soil Type 0.08 0.09 0.10 0.09 0.02 0.04 0.07 0.08 0.11 7.4% 8 Drainage Network 0.19 0.20 0.17 0.27 0.31 0.33 0.20 0.23 0.11 22.3% 9 Householder 0.03 0.09 0.10 0.09 0.18 0.11 0.07 0.23 0.11 11.2% 10

CI and CR worksheet Poverty Population denCoping Slope Land Use Rainfall Soil Type Drainage NetwHouseholder SUM SUM/Weight 1 Poverty 0.03 0.01 0.01 0.03 0.03 0.02 0.02 0.03 0.11 0.30 9.69 2 Population density 0.09 0.03 0.01 0.04 0.03 0.02 0.02 0.03 0.04 0.32 9.95 3 Coping 0.09 0.10 0.04 0.04 0.03 0.02 0.02 0.04 0.04 0.42 9.98 4 Slope 0.28 0.22 0.30 0.25 0.25 0.47 0.22 0.22 0.34 2.54 10.28 5 Land Use 0.09 0.10 0.13 0.08 0.08 0.05 0.22 0.04 0.04 0.84 10.20 6 Rainfall 0.22 0.22 0.30 0.08 0.25 0.16 0.22 0.07 0.11 1.63 10.50 7 Soil Type 0.09 0.10 0.13 0.08 0.03 0.05 0.07 0.07 0.11 0.74 9.92 8 Drainage Network 0.22 0.22 0.21 0.25 0.41 0.47 0.22 0.22 0.11 2.34 10.48 9 Householder 0.03 0.10 0.13 0.08 0.25 0.16 0.07 0.22 0.11 1.15 10.27 10

count 9.00 lambda max 10.141 CI 0.143 CR 0.10 constant 1.45

171

Dr. Khalid Qahman

CR Value = 0.094 OK Pairwise comparisons ONLY ENTER in Item Number Item Number 1 2 3 4 5 6 7 8 9 10 Item Descriptions Item Description Poverty Population denCoping Slope Land Use Rainfall Soil Type Drainage NetwHouseholder 1 Poverty 1.00 3.00 0.33 0.11 0.33 0.14 0.20 0.14 1.00 2 Population density 0.33 1.00 0.20 0.14 0.20 0.11 0.20 0.14 0.14 3 Coping 3.00 5.00 1.00 0.20 1.00 0.14 0.33 0.33 0.33 4 Slope 9.00 7.00 5.00 1.00 7.00 3.00 5.00 3.00 5.00 5 Land Use 3.00 5.00 1.00 0.14 1.00 0.20 1.00 0.20 0.33 6 Rainfall 7.00 9.00 7.00 0.33 5.00 1.00 5.00 1.00 3.00 7 Soil Type 5.00 5.00 3.00 0.20 1.00 0.20 1.00 0.20 0.33 8 Drainage Network 7.00 7.00 3.00 0.33 5.00 1.00 5.00 1.00 1.00 9 Householder 1.00 7.00 3.00 0.20 3.00 0.33 3.00 1.00 1.00 10 1.00 Sum 36.33 49.00 23.53 2.66 23.53 6.13 20.73 7.02 12.14

STANDARDIZED MATRIX Poverty Population denCoping Slope Land Use Rainfall Soil Type Drainage NetwHouseholder Weight 1 Poverty 0.03 0.06 0.01 0.04 0.01 0.02 0.01 0.02 0.08 3.3% 2 Population density 0.01 0.02 0.01 0.05 0.01 0.02 0.01 0.02 0.01 1.8% 3 Coping 0.08 0.10 0.04 0.08 0.04 0.02 0.02 0.05 0.03 5.1% 4 Slope 0.25 0.14 0.21 0.38 0.30 0.49 0.24 0.43 0.41 31.6% 5 Land Use 0.08 0.10 0.04 0.05 0.04 0.03 0.05 0.03 0.03 5.1% 6 Rainfall 0.19 0.18 0.30 0.13 0.21 0.16 0.24 0.14 0.25 20.1% 7 Soil Type 0.14 0.10 0.13 0.08 0.04 0.03 0.05 0.03 0.03 6.9% 8 Drainage Network 0.19 0.14 0.13 0.13 0.21 0.16 0.24 0.14 0.08 15.9% 9 Householder 0.03 0.14 0.13 0.08 0.13 0.05 0.14 0.14 0.08 10.3% 10

CI and CR worksheet Poverty Population denCoping Slope Land Use Rainfall Soil Type Drainage NetwHouseholder SUM SUM/Weight 1 Poverty 0.03 0.05 0.02 0.04 0.02 0.03 0.01 0.02 0.10 0.32 9.88 2 Population density 0.01 0.02 0.01 0.05 0.01 0.02 0.01 0.02 0.01 0.17 9.43 3 Coping 0.10 0.09 0.05 0.06 0.05 0.03 0.02 0.05 0.03 0.49 9.63 4 Slope 0.29 0.12 0.26 0.32 0.36 0.60 0.35 0.48 0.51 3.29 10.39 5 Land Use 0.10 0.09 0.05 0.05 0.05 0.04 0.07 0.03 0.03 0.51 9.97 6 Rainfall 0.23 0.16 0.36 0.11 0.26 0.20 0.35 0.16 0.31 2.12 10.57 7 Soil Type 0.16 0.09 0.15 0.06 0.05 0.04 0.07 0.03 0.03 0.70 10.06 8 Drainage Network 0.23 0.12 0.15 0.11 0.26 0.20 0.35 0.16 0.10 1.67 10.54 9 Householder 0.03 0.12 0.15 0.06 0.15 0.07 0.21 0.16 0.10 1.06 10.34 10

count 9.00 lambda max 10.091 CI 0.136 CR 0.09 constant 1.45

172

Eng. Alaa Hassan

CR Value = 0.089 OK Pairwise comparisons ONLY ENTER in Item Number Item Number 1 2 3 4 5 6 7 8 9 10 Item Descriptions Item Description Poverty Population denCoping Slope Land Use Rainfall Soil Type Drainage NetwHouseholder 1 Poverty 1.00 0.14 1.00 0.11 0.11 0.20 0.33 0.11 0.20 2 Population density 7.00 1.00 5.00 0.20 1.00 7.00 3.00 1.00 1.00 3 Coping 1.00 0.20 1.00 0.11 0.11 0.33 0.30 0.14 0.20 4 Slope 9.00 5.00 9.00 1.00 5.00 5.00 5.00 3.00 3.00 5 Land Use 9.00 1.00 9.00 0.20 1.00 1.00 3.00 0.33 0.14 6 Rainfall 5.00 0.14 3.00 0.20 1.00 1.00 1.00 0.14 0.33 7 Soil Type 3.00 0.33 3.33 0.20 0.33 1.00 1.00 0.20 0.33 8 Drainage Network 9.00 1.00 7.00 0.33 3.00 7.00 5.00 1.00 1.00 9 Householder 5.00 1.00 5.00 0.33 7.00 3.00 3.00 1.00 1.00 10 1.00 Sum 49.00 9.82 43.33 2.69 18.56 25.53 21.63 6.93 7.21

STANDARDIZED MATRIX Poverty Population denCoping Slope Land Use Rainfall Soil Type Drainage NetwHouseholder Weight 1 Poverty 0.02 0.01 0.02 0.04 0.01 0.01 0.02 0.02 0.03 1.9% 2 Population density 0.14 0.10 0.12 0.07 0.05 0.27 0.14 0.14 0.14 13.2% 3 Coping 0.02 0.02 0.02 0.04 0.01 0.01 0.01 0.02 0.03 2.1% 4 Slope 0.18 0.51 0.21 0.37 0.27 0.20 0.23 0.43 0.42 31.3% 5 Land Use 0.18 0.10 0.21 0.07 0.05 0.04 0.14 0.05 0.02 9.6% 6 Rainfall 0.10 0.01 0.07 0.07 0.05 0.04 0.05 0.02 0.05 5.2% 7 Soil Type 0.06 0.03 0.08 0.07 0.02 0.04 0.05 0.03 0.05 4.7% 8 Drainage Network 0.18 0.10 0.16 0.12 0.16 0.27 0.23 0.14 0.14 16.9% 9 Householder 0.10 0.10 0.12 0.12 0.38 0.12 0.14 0.14 0.14 15.1% 10

CI and CR worksheet Poverty Population denCoping Slope Land Use Rainfall Soil Type Drainage NetwHouseholder SUM SUM/Weight 1 Poverty 0.02 0.02 0.02 0.03 0.01 0.01 0.02 0.02 0.03 0.18 9.36 2 Population density 0.13 0.13 0.10 0.06 0.10 0.36 0.14 0.17 0.15 1.35 10.28 3 Coping 0.02 0.03 0.02 0.03 0.01 0.02 0.01 0.02 0.03 0.20 9.53 4 Slope 0.17 0.66 0.19 0.31 0.48 0.26 0.24 0.51 0.45 3.27 10.43 5 Land Use 0.17 0.13 0.19 0.06 0.10 0.05 0.14 0.06 0.02 0.92 9.56 6 Rainfall 0.10 0.02 0.06 0.06 0.10 0.05 0.05 0.02 0.05 0.51 9.83 7 Soil Type 0.06 0.04 0.07 0.06 0.03 0.05 0.05 0.03 0.05 0.45 9.49 8 Drainage Network 0.17 0.13 0.15 0.10 0.29 0.36 0.24 0.17 0.15 1.76 10.42 9 Householder 0.10 0.13 0.10 0.10 0.67 0.16 0.14 0.17 0.15 1.73 11.43 10

count 9.00 lambda max 10.037 CI 0.130 CR 0.09 constant 1.45

173

Dr. Fahid Rabah

CR Value = 0.093 OK Pairwise comparisons ONLY ENTER in Item Number Item Number 1 2 3 4 5 6 7 8 9 10 Item Descriptions Item Description Poverty Population denCoping Slope Land Use Rainfall Soil Type Drainage NetwHouseholder 1 Poverty 1.00 0.20 0.33 0.20 0.14 0.11 0.14 0.11 0.20 2 Population density 5.00 1.00 0.33 0.33 0.20 0.14 0.14 0.11 0.20 3 Coping 3.00 3.00 1.00 0.33 0.20 0.14 0.20 0.14 0.33 4 Slope 5.00 3.00 3.00 1.00 0.20 0.20 0.20 0.20 0.33 5 Land Use 7.00 5.00 5.00 5.00 1.00 1.00 1.00 0.20 5.00 6 Rainfall 9.00 7.00 7.00 5.00 1.00 1.00 3.00 1.00 3.00 7 Soil Type 7.00 7.00 5.00 5.00 1.00 0.33 1.00 0.20 3.00 8 Drainage Network 9.00 9.00 7.00 5.00 5.00 1.00 5.00 1.00 3.00 9 Householder 5.00 5.00 3.00 3.00 0.20 0.33 0.33 0.33 1.00 10 1.00 Sum 51.00 40.20 31.67 24.87 8.94 4.26 11.02 3.30 16.07

STANDARDIZED MATRIX Poverty Population denCoping Slope Land Use Rainfall Soil Type Drainage NetwHouseholder Weight 1 Poverty 0.02 0.00 0.01 0.01 0.02 0.03 0.01 0.03 0.01 1.6% 2 Population density 0.10 0.02 0.01 0.01 0.02 0.03 0.01 0.03 0.01 2.9% 3 Coping 0.06 0.07 0.03 0.01 0.02 0.03 0.02 0.04 0.02 3.5% 4 Slope 0.10 0.07 0.09 0.04 0.02 0.05 0.02 0.06 0.02 5.3% 5 Land Use 0.14 0.12 0.16 0.20 0.11 0.23 0.09 0.06 0.31 15.9% 6 Rainfall 0.18 0.17 0.22 0.20 0.11 0.23 0.27 0.30 0.19 20.9% 7 Soil Type 0.14 0.17 0.16 0.20 0.11 0.08 0.09 0.06 0.19 13.3% 8 Drainage Network 0.18 0.22 0.22 0.20 0.56 0.23 0.45 0.30 0.19 28.4% 9 Householder 0.10 0.12 0.09 0.12 0.02 0.08 0.03 0.10 0.06 8.1% 10

CI and CR worksheet Poverty Population denCoping Slope Land Use Rainfall Soil Type Drainage NetwHouseholder SUM SUM/Weight 1 Poverty 0.02 0.01 0.01 0.01 0.02 0.02 0.02 0.03 0.02 0.16 9.79 2 Population density 0.08 0.03 0.01 0.02 0.03 0.03 0.02 0.03 0.02 0.27 9.18 3 Coping 0.05 0.09 0.04 0.02 0.03 0.03 0.03 0.04 0.03 0.34 9.79 4 Slope 0.08 0.09 0.11 0.05 0.03 0.04 0.03 0.06 0.03 0.51 9.64 5 Land Use 0.11 0.15 0.18 0.26 0.16 0.21 0.13 0.06 0.41 1.66 10.47 6 Rainfall 0.14 0.20 0.25 0.26 0.16 0.21 0.40 0.28 0.24 2.15 10.31 7 Soil Type 0.11 0.20 0.18 0.26 0.16 0.07 0.13 0.06 0.24 1.42 10.66 8 Drainage Network 0.14 0.26 0.25 0.26 0.79 0.21 0.67 0.28 0.24 3.11 10.95 9 Householder 0.08 0.15 0.11 0.16 0.03 0.07 0.04 0.09 0.08 0.81 9.98 10

count 9.00 lambda max 10.084 CI 0.136 CR 0.09 constant 1.45

174

Dr. Ahmed Abu Foul

CR Value = 0.093 OK Pairwise comparisons ONLY ENTER in Item Number Item Number 1 2 3 4 5 6 7 8 9 10 Item Descriptions Item Description Poverty Population denCoping Slope Land Use Rainfall Soil Type Drainage Network Householder 1 Poverty 1.00 0.33 5.00 3.00 7.00 7.00 9.00 1.00 7.00 2 Population density 3.00 1.00 7.00 7.00 7.00 9.00 9.00 3.00 5.00 3 Coping 0.20 0.14 1.00 0.20 0.20 0.20 0.33 0.20 1.00 4 Slope 0.33 0.14 5.00 1.00 1.00 1.00 5.00 1.00 3.00 5 Land Use 0.14 0.14 5.00 1.00 1.00 1.00 5.00 0.33 1.00 6 Rainfall 0.14 0.11 5.00 1.00 1.00 1.00 5.00 1.00 1.00 7 Soil Type 0.11 0.11 3.00 0.20 0.20 0.20 1.00 0.33 0.33 8 Drainage Network 1.00 0.33 5.00 1.00 3.00 1.00 3.00 1.00 3.00 9 Householder 0.14 0.20 1.00 0.33 1.00 1.00 3.00 0.33 1.00 10 1.00 Sum 6.07 2.52 37.00 14.73 21.40 21.40 40.33 8.20 22.33

STANDARDIZED MATRIX Poverty Population denCoping Slope Land Use Rainfall Soil Type Drainage Network Householder Weight 1 Poverty 0.16 0.13 0.14 0.20 0.33 0.33 0.22 0.12 0.31 21.7% 2 Population density 0.49 0.40 0.19 0.48 0.33 0.42 0.22 0.37 0.22 34.6% 3 Coping 0.03 0.06 0.03 0.01 0.01 0.01 0.01 0.02 0.04 2.5% 4 Slope 0.05 0.06 0.14 0.07 0.05 0.05 0.12 0.12 0.13 8.8% 5 Land Use 0.02 0.06 0.14 0.07 0.05 0.05 0.12 0.04 0.04 6.5% 6 Rainfall 0.02 0.04 0.14 0.07 0.05 0.05 0.12 0.12 0.04 7.3% 7 Soil Type 0.02 0.04 0.08 0.01 0.01 0.01 0.02 0.04 0.01 2.8% 8 Drainage Network 0.16 0.13 0.14 0.07 0.14 0.05 0.07 0.12 0.13 11.3% 9 Householder 0.02 0.08 0.03 0.02 0.05 0.05 0.07 0.04 0.04 4.5% 10

CI and CR worksheet Poverty Population denCoping Slope Land Use Rainfall Soil Type Drainage Network Householder SUM SUM/Weight 1 Poverty 0.22 0.12 0.13 0.26 0.46 0.51 0.26 0.11 0.32 2.37 10.95 2 Population density 0.65 0.35 0.18 0.61 0.46 0.65 0.26 0.34 0.23 3.72 10.73 3 Coping 0.04 0.05 0.03 0.02 0.01 0.01 0.01 0.02 0.05 0.24 9.55 4 Slope 0.07 0.05 0.13 0.09 0.07 0.07 0.14 0.11 0.14 0.86 9.86 5 Land Use 0.03 0.05 0.13 0.09 0.07 0.07 0.14 0.04 0.05 0.66 10.08 6 Rainfall 0.03 0.04 0.13 0.09 0.07 0.07 0.14 0.11 0.05 0.72 9.91 7 Soil Type 0.02 0.04 0.08 0.02 0.01 0.01 0.03 0.04 0.02 0.26 9.29 8 Drainage Network 0.22 0.12 0.13 0.09 0.20 0.07 0.09 0.11 0.14 1.15 10.15 9 Householder 0.03 0.07 0.03 0.03 0.07 0.07 0.09 0.04 0.05 0.46 10.21 10

count 9.00 lambda max 10.081 CI 0.135 CR 0.09 constant 1.45

175

Dr. Younis Moghaier

CR Value = 0.094 OK Pairwise comparisons ONLY ENTER in Item Number Item Number 1 2 3 4 5 6 7 8 9 10 Item Descriptions Item Description Poverty Population denCoping Slope Land Use Rainfall Soil Type Drainage Network Householder 1 Poverty 1.00 3.00 1.00 0.20 0.20 0.20 0.14 0.14 1.00 2 Population density 0.33 1.00 0.20 0.20 0.20 0.20 0.14 0.14 0.20 3 Coping 1.00 5.00 1.00 0.20 0.20 0.20 0.20 0.14 1.00 4 Slope 5.00 5.00 5.00 1.00 3.00 0.20 1.00 0.20 3.00 5 Land Use 5.00 5.00 5.00 0.33 1.00 0.20 1.00 0.20 3.00 6 Rainfall 5.00 5.00 5.00 5.00 5.00 1.00 3.00 1.00 5.00 7 Soil Type 7.00 7.00 5.00 1.00 1.00 0.33 1.00 0.20 3.00 8 Drainage Network 7.00 7.00 7.00 5.00 5.00 1.00 5.00 1.00 7.00 9 Householder 1.00 5.00 1.00 0.33 0.33 0.20 0.33 0.14 1.00 10 1.00 Sum 32.33 43.00 30.20 13.27 15.93 3.53 11.82 3.17 24.20

STANDARDIZED MATRIX Poverty Population denCoping Slope Land Use Rainfall Soil Type Drainage Network Householder Weight 1 Poverty 0.03 0.07 0.03 0.02 0.01 0.06 0.01 0.05 0.04 3.5% 2 Population density 0.01 0.02 0.01 0.02 0.01 0.06 0.01 0.05 0.01 2.1% 3 Coping 0.03 0.12 0.03 0.02 0.01 0.06 0.02 0.05 0.04 4.1% 4 Slope 0.15 0.12 0.17 0.08 0.19 0.06 0.08 0.06 0.12 11.4% 5 Land Use 0.15 0.12 0.17 0.03 0.06 0.06 0.08 0.06 0.12 9.5% 6 Rainfall 0.15 0.12 0.17 0.38 0.31 0.28 0.25 0.32 0.21 24.3% 7 Soil Type 0.22 0.16 0.17 0.08 0.06 0.09 0.08 0.06 0.12 11.7% 8 Drainage Network 0.22 0.16 0.23 0.38 0.31 0.28 0.42 0.32 0.29 29.0% 9 Householder 0.03 0.12 0.03 0.03 0.02 0.06 0.03 0.05 0.04 4.4% 10

CI and CR worksheet Poverty Population denCoping Slope Land Use Rainfall Soil Type Drainage Network Householder SUM SUM/Weight 1 Poverty 0.04 0.06 0.04 0.02 0.02 0.05 0.02 0.04 0.04 0.33 9.44 2 Population density 0.01 0.02 0.01 0.02 0.02 0.05 0.02 0.04 0.01 0.20 9.40 3 Coping 0.04 0.11 0.04 0.02 0.02 0.05 0.02 0.04 0.04 0.38 9.32 4 Slope 0.18 0.11 0.20 0.11 0.28 0.05 0.12 0.06 0.13 1.24 10.85 5 Land Use 0.18 0.11 0.20 0.04 0.09 0.05 0.12 0.06 0.13 0.97 10.28 6 Rainfall 0.18 0.11 0.20 0.57 0.47 0.24 0.35 0.29 0.22 2.63 10.85 7 Soil Type 0.25 0.15 0.20 0.11 0.09 0.08 0.12 0.06 0.13 1.20 10.25 8 Drainage Network 0.25 0.15 0.29 0.57 0.47 0.24 0.58 0.29 0.31 3.15 10.85 9 Householder 0.04 0.11 0.04 0.04 0.03 0.05 0.04 0.04 0.04 0.42 9.60 10

count 9.00 lambda max 10.095 CI 0.137 CR 0.09 constant 1.45

176

Dr. Hussam Al Najjar

CR Value = 0.086 OK Pairwise comparisons ONLY ENTER in Item Number Item Number 1 2 3 4 5 6 7 8 9 10 Item Descriptions Item Description Poverty Population denCoping Slope Land Use Rainfall Soil Type Drainage Network Householder 1 Poverty 1.00 3.00 3.00 0.20 0.14 0.14 0.20 0.11 0.33 2 Population density 0.33 1.00 3.00 0.14 0.33 0.14 0.20 0.11 0.33 3 Coping 0.33 0.33 1.00 0.20 0.14 0.14 0.20 0.14 0.33 4 Slope 5.00 7.00 5.00 1.00 1.00 0.33 1.00 0.20 3.00 5 Land Use 7.00 3.00 7.00 1.00 1.00 0.33 0.33 0.20 3.00 6 Rainfall 7.00 7.00 7.00 3.00 3.00 1.00 5.00 0.33 5.00 7 Soil Type 5.00 5.00 5.00 1.00 3.00 0.20 1.00 0.33 3.00 8 Drainage Network 9.00 9.00 7.00 5.00 5.00 3.00 3.00 1.00 5.00 9 Householder 3.00 3.00 3.00 0.33 0.33 0.20 0.33 0.20 1.00 10 1.00 Sum 37.67 38.33 41.00 11.88 13.95 5.50 11.27 2.63 21.00

STANDARDIZED MATRIX Poverty Population denCoping Slope Land Use Rainfall Soil Type Drainage Network Householder Weight 1 Poverty 0.03 0.08 0.07 0.02 0.01 0.03 0.02 0.04 0.02 3.4% 2 Population density 0.01 0.03 0.07 0.01 0.02 0.03 0.02 0.04 0.02 2.7% 3 Coping 0.01 0.01 0.02 0.02 0.01 0.03 0.02 0.05 0.02 2.0% 4 Slope 0.13 0.18 0.12 0.08 0.07 0.06 0.09 0.08 0.14 10.7% 5 Land Use 0.19 0.08 0.17 0.08 0.07 0.06 0.03 0.08 0.14 10.0% 6 Rainfall 0.19 0.18 0.17 0.25 0.22 0.18 0.44 0.13 0.24 22.2% 7 Soil Type 0.13 0.13 0.12 0.08 0.22 0.04 0.09 0.13 0.14 12.0% 8 Drainage Network 0.24 0.23 0.17 0.42 0.36 0.55 0.27 0.38 0.24 31.7% 9 Householder 0.08 0.08 0.07 0.03 0.02 0.04 0.03 0.08 0.05 5.3% 10

CI and CR worksheet Poverty Population denCoping Slope Land Use Rainfall Soil Type Drainage Network Householder SUM SUM/Weight 1 Poverty 0.03 0.08 0.06 0.02 0.01 0.03 0.02 0.04 0.02 0.32 9.42 2 Population density 0.01 0.03 0.06 0.02 0.03 0.03 0.02 0.04 0.02 0.26 9.40 3 Coping 0.01 0.01 0.02 0.02 0.01 0.03 0.02 0.05 0.02 0.19 9.59 4 Slope 0.17 0.19 0.10 0.11 0.10 0.07 0.12 0.06 0.16 1.08 10.16 5 Land Use 0.24 0.08 0.14 0.11 0.10 0.07 0.04 0.06 0.16 1.00 10.05 6 Rainfall 0.24 0.19 0.14 0.32 0.30 0.22 0.60 0.11 0.26 2.38 10.73 7 Soil Type 0.17 0.14 0.10 0.11 0.30 0.04 0.12 0.11 0.16 1.24 10.37 8 Drainage Network 0.31 0.25 0.14 0.53 0.50 0.67 0.36 0.32 0.26 3.33 10.51 9 Householder 0.10 0.08 0.06 0.04 0.03 0.04 0.04 0.06 0.05 0.51 9.80 10

count 9.00 lambda max 10.002 CI 0.125 CR 0.09 constant 1.45

177

Dr. Sami Hamdan

CR Value = 0.094 OK Pairwise comparisons ONLY ENTER in Item Number Item Number 1 2 3 4 5 6 7 8 9 10 Item Descriptions Item Description Poverty Population denCoping Slope Land Use Rainfall Soil Type Drainage Network Householder 1 Poverty 1.00 3.00 1.00 0.11 0.14 0.14 0.20 0.11 0.33 2 Population density 0.33 1.00 0.33 0.11 0.11 0.14 0.33 0.11 0.33 3 Coping 1.00 3.00 1.00 0.11 0.14 0.14 0.33 0.11 0.33 4 Slope 9.00 9.00 9.00 1.00 3.00 1.00 7.00 0.20 5.00 5 Land Use 7.00 9.00 7.00 0.33 1.00 0.33 5.00 0.20 5.00 6 Rainfall 7.00 7.00 7.00 1.00 3.00 1.00 5.00 0.33 7.00 7 Soil Type 5.00 3.00 3.00 0.14 0.20 0.20 1.00 0.11 3.00 8 Drainage Network 9.00 9.00 9.00 5.00 5.00 3.00 9.00 1.00 9.00 9 Householder 3.00 3.00 3.00 0.20 0.20 0.14 0.33 0.11 1.00 10 1.00 Sum 42.33 47.00 40.33 8.01 12.80 6.10 28.20 2.29 31.00

STANDARDIZED MATRIX Poverty Population denCoping Slope Land Use Rainfall Soil Type Drainage Network Householder Weight 1 Poverty 0.02 0.06 0.02 0.01 0.01 0.02 0.01 0.05 0.01 2.5% 2 Population density 0.01 0.02 0.01 0.01 0.01 0.02 0.01 0.05 0.01 1.7% 3 Coping 0.02 0.06 0.02 0.01 0.01 0.02 0.01 0.05 0.01 2.6% 4 Slope 0.21 0.19 0.22 0.12 0.23 0.16 0.25 0.09 0.16 18.3% 5 Land Use 0.17 0.19 0.17 0.04 0.08 0.05 0.18 0.09 0.16 12.6% 6 Rainfall 0.17 0.15 0.17 0.12 0.23 0.16 0.18 0.15 0.23 17.3% 7 Soil Type 0.12 0.06 0.07 0.02 0.02 0.03 0.04 0.05 0.10 5.6% 8 Drainage Network 0.21 0.19 0.22 0.62 0.39 0.49 0.32 0.44 0.29 35.3% 9 Householder 0.07 0.06 0.07 0.02 0.02 0.02 0.01 0.05 0.03 4.1% 10

CI and CR worksheet Poverty Population denCoping Slope Land Use Rainfall Soil Type Drainage Network Householder SUM SUM/Weight 1 Poverty 0.03 0.05 0.03 0.02 0.02 0.02 0.01 0.04 0.01 0.23 9.10 2 Population density 0.01 0.02 0.01 0.02 0.01 0.02 0.02 0.04 0.01 0.16 9.59 3 Coping 0.03 0.05 0.03 0.02 0.02 0.02 0.02 0.04 0.01 0.24 9.20 4 Slope 0.23 0.15 0.23 0.18 0.38 0.17 0.39 0.07 0.20 2.01 10.99 5 Land Use 0.18 0.15 0.18 0.06 0.13 0.06 0.28 0.07 0.20 1.31 10.42 6 Rainfall 0.18 0.12 0.18 0.18 0.38 0.17 0.28 0.12 0.28 1.89 10.92 7 Soil Type 0.13 0.05 0.08 0.03 0.03 0.03 0.06 0.04 0.12 0.56 9.98 8 Drainage Network 0.23 0.15 0.23 0.92 0.63 0.52 0.50 0.35 0.37 3.90 11.03 9 Householder 0.08 0.05 0.08 0.04 0.03 0.02 0.02 0.04 0.04 0.39 9.59 10

count 9.00 lambda max 10.091 CI 0.136 CR 0.09 constant 1.45

178

Dr. Mohammed Al Aila

CR Value = 0.095 OK Pairwise comparisons ONLY ENTER in Item Number Item Number 1 2 3 4 5 6 7 8 9 10 Item Descriptions Item Description Poverty Population denCoping Slope Land Use Rainfall Soil Type Drainage Network Householder 1 Poverty 1.00 0.33 0.20 0.33 0.33 0.14 0.33 0.14 0.20 2 Population density 3.00 1.00 0.20 0.33 0.33 0.33 1.00 0.14 0.33 3 Coping 5.00 5.00 1.00 3.00 3.00 3.00 3.00 0.33 0.33 4 Slope 3.00 3.00 0.33 1.00 3.00 0.33 3.00 0.20 0.20 5 Land Use 3.00 3.00 0.33 0.33 1.00 0.20 1.00 0.20 0.20 6 Rainfall 7.00 3.00 0.33 3.00 5.00 1.00 5.00 0.20 0.20 7 Soil Type 3.00 1.00 0.33 0.33 1.00 0.20 1.00 0.20 0.20 8 Drainage Network 7.00 7.00 3.00 5.00 5.00 5.00 5.00 1.00 3.00 9 Householder 5.00 3.00 3.00 5.00 5.00 5.00 5.00 0.33 1.00 10 1.00 Sum 37.00 26.33 8.73 18.33 23.67 15.21 24.33 2.75 5.67

STANDARDIZED MATRIX Poverty Population denCoping Slope Land Use Rainfall Soil Type Drainage Network Householder Weight 1 Poverty 0.03 0.01 0.02 0.02 0.01 0.01 0.01 0.05 0.04 2.3% 2 Population density 0.08 0.04 0.02 0.02 0.01 0.02 0.04 0.05 0.06 3.9% 3 Coping 0.14 0.19 0.11 0.16 0.13 0.20 0.12 0.12 0.06 13.7% 4 Slope 0.08 0.11 0.04 0.05 0.13 0.02 0.12 0.07 0.04 7.4% 5 Land Use 0.08 0.11 0.04 0.02 0.04 0.01 0.04 0.07 0.04 5.1% 6 Rainfall 0.19 0.11 0.04 0.16 0.21 0.07 0.21 0.07 0.04 12.2% 7 Soil Type 0.08 0.04 0.04 0.02 0.04 0.01 0.04 0.07 0.04 4.2% 8 Drainage Network 0.19 0.27 0.34 0.27 0.21 0.33 0.21 0.36 0.53 30.1% 9 Householder 0.14 0.11 0.34 0.27 0.21 0.33 0.21 0.12 0.18 21.2% 10

CI and CR worksheet Poverty Population denCoping Slope Land Use Rainfall Soil Type Drainage Network Householder SUM SUM/Weight 1 Poverty 0.02 0.01 0.03 0.02 0.02 0.02 0.01 0.04 0.04 0.22 9.72 2 Population density 0.07 0.04 0.03 0.02 0.02 0.04 0.04 0.04 0.07 0.37 9.63 3 Coping 0.11 0.19 0.14 0.22 0.15 0.37 0.13 0.10 0.07 1.48 10.84 4 Slope 0.07 0.12 0.05 0.07 0.15 0.04 0.13 0.06 0.04 0.73 9.78 5 Land Use 0.07 0.12 0.05 0.02 0.05 0.02 0.04 0.06 0.04 0.47 9.37 6 Rainfall 0.16 0.12 0.05 0.22 0.25 0.12 0.21 0.06 0.04 1.23 10.12 7 Soil Type 0.07 0.04 0.05 0.02 0.05 0.02 0.04 0.06 0.04 0.40 9.41 8 Drainage Network 0.16 0.27 0.41 0.37 0.25 0.61 0.21 0.30 0.64 3.22 10.70 9 Householder 0.11 0.12 0.41 0.37 0.25 0.61 0.21 0.10 0.21 2.40 11.30 10

count 9.00 lambda max 10.097 CI 0.137 CR 0.09 constant 1.45

179

Dr. Abd Al Majeed Nassar

CR Value = 0.093 OK Pairwise comparisons ONLY ENTER in Item Number Item Number 1 2 3 4 5 6 7 8 9 10 Item Descriptions Item Description Poverty Population denCoping Slope Land Use Rainfall Soil Type Drainage Network Householder 1 Poverty 1.00 1.00 0.33 0.11 0.11 0.11 0.11 0.11 0.33 2 Population density 1.00 1.00 1.00 0.11 0.11 0.11 0.11 0.11 0.20 3 Coping 3.00 1.00 1.00 0.14 0.14 0.14 0.14 0.14 0.20 4 Slope 9.00 9.00 7.00 1.00 5.00 1.00 1.00 1.00 0.33 5 Land Use 9.00 9.00 7.00 0.20 1.00 1.00 1.00 1.00 0.33 6 Rainfall 9.00 9.00 7.00 1.00 1.00 1.00 1.00 1.00 0.33 7 Soil Type 9.00 9.00 7.00 1.00 1.00 1.00 1.00 1.00 0.33 8 Drainage Network 9.00 9.00 7.00 1.00 1.00 1.00 1.00 1.00 0.33 9 Householder 3.00 5.00 5.00 3.00 3.00 3.00 3.00 3.00 1.00 10 1.00 Sum 53.00 53.00 42.33 7.57 12.37 8.37 8.37 8.37 3.40

STANDARDIZED MATRIX Poverty Population denCoping Slope Land Use Rainfall Soil Type Drainage Network Householder Weight 1 Poverty 0.02 0.02 0.01 0.01 0.01 0.01 0.01 0.01 0.10 2.3% 2 Population density 0.02 0.02 0.02 0.01 0.01 0.01 0.01 0.01 0.06 2.0% 3 Coping 0.06 0.02 0.02 0.02 0.01 0.02 0.02 0.02 0.06 2.7% 4 Slope 0.17 0.17 0.17 0.13 0.40 0.12 0.12 0.12 0.10 16.6% 5 Land Use 0.17 0.17 0.17 0.03 0.08 0.12 0.12 0.12 0.10 11.9% 6 Rainfall 0.17 0.17 0.17 0.13 0.08 0.12 0.12 0.12 0.10 13.1% 7 Soil Type 0.17 0.17 0.17 0.13 0.08 0.12 0.12 0.12 0.10 13.1% 8 Drainage Network 0.17 0.17 0.17 0.13 0.08 0.12 0.12 0.12 0.10 13.1% 9 Householder 0.06 0.09 0.12 0.40 0.24 0.36 0.36 0.36 0.29 25.3% 10

CI and CR worksheet Poverty Population denCoping Slope Land Use Rainfall Soil Type Drainage Network Householder SUM SUM/Weight 1 Poverty 0.02 0.02 0.01 0.02 0.01 0.01 0.01 0.01 0.08 0.21 9.20 2 Population density 0.02 0.02 0.03 0.02 0.01 0.01 0.01 0.01 0.05 0.20 9.60 3 Coping 0.07 0.02 0.03 0.02 0.02 0.02 0.02 0.02 0.05 0.26 9.89 4 Slope 0.21 0.18 0.19 0.17 0.59 0.13 0.13 0.13 0.08 1.81 10.89 5 Land Use 0.21 0.18 0.19 0.03 0.12 0.13 0.13 0.13 0.08 1.21 10.15 6 Rainfall 0.21 0.18 0.19 0.17 0.12 0.13 0.13 0.13 0.08 1.34 10.25 7 Soil Type 0.21 0.18 0.19 0.17 0.12 0.13 0.13 0.13 0.08 1.34 10.25 8 Drainage Network 0.21 0.18 0.19 0.17 0.12 0.13 0.13 0.13 0.08 1.34 10.25 9 Householder 0.07 0.10 0.13 0.50 0.36 0.39 0.39 0.39 0.25 2.59 10.22 10

count 9.00 lambda max 10.080 CI 0.135 CR 0.09 constant 1.45

180

Eng. Rifat Diab

CR Value = 0.047 OK Pairwise comparisons ONLY ENTER in Item Number Item Number 1 2 3 4 5 6 7 8 9 10 Item Descriptions Item Description Poverty Population denCoping Slope Land Use Rainfall Soil Type Drainage Network Householder 1 Poverty 1.00 1.00 1.00 0.11 0.11 0.11 0.11 0.11 0.20 2 Population density 1.00 1.00 1.00 0.11 0.11 0.11 0.11 0.11 0.20 3 Coping 1.00 1.00 1.00 0.14 0.14 0.14 0.14 0.14 0.20 4 Slope 9.00 9.00 7.00 1.00 1.00 1.00 1.00 1.00 0.33 5 Land Use 9.00 9.00 7.00 1.00 1.00 1.00 1.00 1.00 0.33 6 Rainfall 9.00 9.00 7.00 1.00 1.00 1.00 1.00 1.00 0.33 7 Soil Type 9.00 9.00 7.00 1.00 1.00 1.00 1.00 1.00 0.33 8 Drainage Network 9.00 9.00 7.00 1.00 1.00 1.00 1.00 1.00 0.33 9 Householder 5.00 5.00 5.00 3.00 3.00 3.00 3.00 3.00 1.00 10 1.00 Sum 53.00 53.00 43.00 8.37 8.37 8.37 8.37 8.37 3.27

STANDARDIZED MATRIX Poverty Population denCoping Slope Land Use Rainfall Soil Type Drainage Network Householder Weight 1 Poverty 0.02 0.02 0.02 0.01 0.01 0.01 0.01 0.01 0.06 2.1% 2 Population density 0.02 0.02 0.02 0.01 0.01 0.01 0.01 0.01 0.06 2.1% 3 Coping 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.06 2.3% 4 Slope 0.17 0.17 0.16 0.12 0.12 0.12 0.12 0.12 0.10 13.4% 5 Land Use 0.17 0.17 0.16 0.12 0.12 0.12 0.12 0.12 0.10 13.4% 6 Rainfall 0.17 0.17 0.16 0.12 0.12 0.12 0.12 0.12 0.10 13.4% 7 Soil Type 0.17 0.17 0.16 0.12 0.12 0.12 0.12 0.12 0.10 13.4% 8 Drainage Network 0.17 0.17 0.16 0.12 0.12 0.12 0.12 0.12 0.10 13.4% 9 Householder 0.09 0.09 0.12 0.36 0.36 0.36 0.36 0.36 0.31 26.7% 10

CI and CR worksheet Poverty Population denCoping Slope Land Use Rainfall Soil Type Drainage Network Householder SUM SUM/Weight 1 Poverty 0.02 0.02 0.02 0.01 0.01 0.01 0.01 0.01 0.05 0.19 9.19 2 Population density 0.02 0.02 0.02 0.01 0.01 0.01 0.01 0.01 0.05 0.19 9.19 3 Coping 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.05 0.21 9.27 4 Slope 0.19 0.19 0.16 0.13 0.13 0.13 0.13 0.13 0.09 1.30 9.70 5 Land Use 0.19 0.19 0.16 0.13 0.13 0.13 0.13 0.13 0.09 1.30 9.70 6 Rainfall 0.19 0.19 0.16 0.13 0.13 0.13 0.13 0.13 0.09 1.30 9.70 7 Soil Type 0.19 0.19 0.16 0.13 0.13 0.13 0.13 0.13 0.09 1.30 9.70 8 Drainage Network 0.19 0.19 0.16 0.13 0.13 0.13 0.13 0.13 0.09 1.30 9.70 9 Householder 0.10 0.10 0.12 0.40 0.40 0.40 0.40 0.40 0.27 2.60 9.72 10

count 9.00 lambda max 9.541 CI 0.068 CR 0.05 constant 1.45

181

Eng. Salah Taha

CR Value = 0.099 OK Pairwise comparisons ONLY ENTER in Item Number Item Number 1 2 3 4 5 6 7 8 9 10 Item Descriptions Item Description Poverty Population denCoping Slope Land Use Rainfall Soil Type Drainage NetwHouseholder 1 Poverty 1.00 3.00 0.20 0.14 1.00 0.20 0.20 0.14 0.20 2 Population density 0.33 1.00 0.20 0.11 1.00 0.11 0.33 0.11 0.20 3 Coping 5.00 5.00 1.00 1.00 5.00 0.33 1.00 0.33 0.20 4 Slope 7.00 9.00 1.00 1.00 3.00 0.33 3.00 0.20 0.33 5 Land Use 1.00 1.00 0.20 0.33 1.00 0.14 1.00 0.20 0.14 6 Rainfall 5.00 9.00 3.00 3.00 7.00 1.00 1.00 0.33 0.20 7 Soil Type 5.00 3.00 1.00 0.33 1.00 1.00 1.00 0.14 0.20 8 Drainage Network 7.00 9.00 3.00 5.00 5.00 3.00 7.00 1.00 1.00 9 Householder 5.00 5.00 5.00 3.00 7.00 5.00 5.00 1.00 1.00 10 1.00 Sum 36.33 45.00 14.60 13.92 31.00 11.12 19.53 3.46 3.48

STANDARDIZED MATRIX Poverty Population denCoping Slope Land Use Rainfall Soil Type Drainage NetwHouseholder Weight 1 Poverty 0.03 0.07 0.01 0.01 0.03 0.02 0.01 0.04 0.06 3.1% 2 Population density 0.01 0.02 0.01 0.01 0.03 0.01 0.02 0.03 0.06 2.2% 3 Coping 0.14 0.11 0.07 0.07 0.16 0.03 0.05 0.10 0.06 8.7% 4 Slope 0.19 0.20 0.07 0.07 0.10 0.03 0.15 0.06 0.10 10.7% 5 Land Use 0.03 0.02 0.01 0.02 0.03 0.01 0.05 0.06 0.04 3.1% 6 Rainfall 0.14 0.20 0.21 0.22 0.23 0.09 0.05 0.10 0.06 14.2% 7 Soil Type 0.14 0.07 0.07 0.02 0.03 0.09 0.05 0.04 0.06 6.3% 8 Drainage Network 0.19 0.20 0.21 0.36 0.16 0.27 0.36 0.29 0.29 25.8% 9 Householder 0.14 0.11 0.34 0.22 0.23 0.45 0.26 0.29 0.29 25.7% 10

CI and CR worksheet Poverty Population denCoping Slope Land Use Rainfall Soil Type Drainage NetwHouseholder SUM SUM/Weight 1 Poverty 0.03 0.07 0.02 0.02 0.03 0.03 0.01 0.04 0.05 0.29 9.46 2 Population density 0.01 0.02 0.02 0.01 0.03 0.02 0.02 0.03 0.05 0.21 9.38 3 Coping 0.15 0.11 0.09 0.11 0.16 0.05 0.06 0.09 0.05 0.87 9.93 4 Slope 0.22 0.20 0.09 0.11 0.09 0.05 0.19 0.05 0.09 1.08 10.06 5 Land Use 0.03 0.02 0.02 0.04 0.03 0.02 0.06 0.05 0.04 0.31 9.87 6 Rainfall 0.15 0.20 0.26 0.32 0.22 0.14 0.06 0.09 0.05 1.50 10.57 7 Soil Type 0.15 0.07 0.09 0.04 0.03 0.14 0.06 0.04 0.05 0.67 10.59 8 Drainage Network 0.22 0.20 0.26 0.54 0.16 0.43 0.44 0.26 0.26 2.76 10.68 9 Householder 0.15 0.11 0.44 0.32 0.22 0.71 0.32 0.26 0.26 2.79 10.84 10

count 9.00 lambda max 10.154 CI 0.144 CR 0.10 constant 1.45

182

Dr. Tamer Eshtawi

CR Value = 0.092 OK Pairwise comparisons ONLY ENTER in Item Number Item Number 1 2 3 4 5 6 7 8 9 10 Item Descriptions Item Description Poverty Population denCoping Slope Land Use Rainfall Soil Type Drainage NetwHouseholder 1 Poverty 1.00 0.33 0.33 0.11 0.11 0.14 0.20 0.11 1.00 2 Population density 3.00 1.00 0.33 0.11 0.14 0.20 0.20 0.11 3.00 3 Coping 3.00 3.00 1.00 0.20 0.20 0.20 0.20 0.11 1.00 4 Slope 9.00 9.00 5.00 1.00 3.00 1.00 3.00 0.33 5.00 5 Land Use 9.00 7.00 5.00 0.33 1.00 1.00 3.00 0.33 3.00 6 Rainfall 7.00 5.00 5.00 1.00 1.00 1.00 1.00 0.33 3.00 7 Soil Type 5.00 5.00 5.00 0.33 0.33 1.00 1.00 0.20 1.00 8 Drainage Network 9.00 9.00 9.00 3.00 3.00 3.00 5.00 1.00 5.00 9 Householder 1.00 0.33 1.00 0.20 0.33 0.33 1.00 0.20 1.00 10 1.00 Sum 47.00 39.67 31.67 6.29 9.12 7.88 14.60 2.73 23.00

STANDARDIZED MATRIX Poverty Population denCoping Slope Land Use Rainfall Soil Type Drainage NetwHouseholder Weight 1 Poverty 0.02 0.01 0.01 0.02 0.01 0.02 0.01 0.04 0.04 2.1% 2 Population density 0.06 0.03 0.01 0.02 0.02 0.03 0.01 0.04 0.13 3.8% 3 Coping 0.06 0.08 0.03 0.03 0.02 0.03 0.01 0.04 0.04 3.9% 4 Slope 0.19 0.23 0.16 0.16 0.33 0.13 0.21 0.12 0.22 19.3% 5 Land Use 0.19 0.18 0.16 0.05 0.11 0.13 0.21 0.12 0.13 14.1% 6 Rainfall 0.15 0.13 0.16 0.16 0.11 0.13 0.07 0.12 0.13 12.8% 7 Soil Type 0.11 0.13 0.16 0.05 0.04 0.13 0.07 0.07 0.04 8.8% 8 Drainage Network 0.19 0.23 0.28 0.48 0.33 0.38 0.34 0.37 0.22 31.3% 9 Householder 0.02 0.01 0.03 0.03 0.04 0.04 0.07 0.07 0.04 4.0% 10

CI and CR worksheet Poverty Population denCoping Slope Land Use Rainfall Soil Type Drainage NetwHouseholder SUM SUM/Weight 1 Poverty 0.02 0.01 0.01 0.02 0.02 0.02 0.02 0.03 0.04 0.19 9.37 2 Population density 0.06 0.04 0.01 0.02 0.02 0.03 0.02 0.03 0.12 0.35 9.22 3 Coping 0.06 0.11 0.04 0.04 0.03 0.03 0.02 0.03 0.04 0.40 10.33 4 Slope 0.19 0.34 0.19 0.19 0.42 0.13 0.26 0.10 0.20 2.03 10.55 5 Land Use 0.19 0.27 0.19 0.06 0.14 0.13 0.26 0.10 0.12 1.47 10.37 6 Rainfall 0.14 0.19 0.19 0.19 0.14 0.13 0.09 0.10 0.12 1.30 10.19 7 Soil Type 0.10 0.19 0.19 0.06 0.05 0.13 0.09 0.06 0.04 0.92 10.42 8 Drainage Network 0.19 0.34 0.35 0.58 0.42 0.38 0.44 0.31 0.20 3.21 10.28 9 Householder 0.02 0.01 0.04 0.04 0.05 0.04 0.09 0.06 0.04 0.39 9.84 10

count 9.00 lambda max 10.064 CI 0.133 CR 0.09 constant 1.45

183

CR Value = 0.078 OK Pairwise comparisons ONLY ENTER in Item Number Item Number 1 2 3 4 5 6 7 8 9 10 Item Descriptions Item Description Poverty Population denCoping Slope Land Use Rainfall Soil Type Drainage Network Householder 1 Poverty 1.00 0.33 1.00 0.20 0.14 0.11 0.14 0.11 0.20 2 Population density 3.00 1.00 1.00 0.20 0.20 0.14 0.14 0.11 0.20 3 Coping 1.00 1.00 1.00 0.33 0.20 0.14 0.14 0.11 0.33 4 Slope 5.00 5.00 3.00 1.00 0.33 0.33 1.00 0.20 0.33 5 Land Use 7.00 5.00 5.00 3.00 1.00 0.33 1.00 0.20 5.00 6 Rainfall 9.00 7.00 7.00 3.00 3.00 1.00 3.00 1.00 5.00 7 Soil Type 7.00 7.00 7.00 1.00 1.00 0.33 1.00 0.20 3.00 8 Drainage Network 9.00 9.00 9.00 5.00 5.00 1.00 5.00 1.00 3.00 9 Householder 5.00 5.00 3.00 3.00 0.20 0.20 0.33 0.33 1.00 10 1.00 Sum 47.00 40.33 37.00 16.73 11.08 3.60 11.76 3.27 18.07

STANDARDIZED MATRIX Poverty Population denCoping Slope Land Use Rainfall Soil Type Drainage Network Householder Weight 1 Poverty 0.02 0.01 0.03 0.01 0.01 0.03 0.01 0.03 0.01 1.9% 2 Population density 0.06 0.02 0.03 0.01 0.02 0.04 0.01 0.03 0.01 2.7% 3 Coping 0.02 0.02 0.03 0.02 0.02 0.04 0.01 0.03 0.02 2.4% 4 Slope 0.11 0.12 0.08 0.06 0.03 0.09 0.09 0.06 0.02 7.3% 5 Land Use 0.15 0.12 0.14 0.18 0.09 0.09 0.09 0.06 0.28 13.3% 6 Rainfall 0.19 0.17 0.19 0.18 0.27 0.28 0.26 0.31 0.28 23.6% 7 Soil Type 0.15 0.17 0.19 0.06 0.09 0.09 0.09 0.06 0.17 11.9% 8 Drainage Network 0.19 0.22 0.24 0.30 0.45 0.28 0.43 0.31 0.17 28.7% 9 Householder 0.11 0.12 0.08 0.18 0.02 0.06 0.03 0.10 0.06 8.3% 10

CI and CR worksheet Poverty Population denCoping Slope Land Use Rainfall Soil Type Drainage Network Householder SUM SUM/Weight 1 Poverty 0.02 0.01 0.02 0.01 0.02 0.03 0.02 0.03 0.02 0.18 9.40 2 Population density 0.06 0.03 0.02 0.01 0.03 0.03 0.02 0.03 0.02 0.25 9.19 3 Coping 0.02 0.03 0.02 0.02 0.03 0.03 0.02 0.03 0.03 0.23 9.65 4 Slope 0.09 0.13 0.07 0.07 0.04 0.08 0.12 0.06 0.03 0.70 9.57 5 Land Use 0.13 0.13 0.12 0.22 0.13 0.08 0.12 0.06 0.42 1.41 10.63 6 Rainfall 0.17 0.19 0.17 0.22 0.40 0.24 0.36 0.29 0.42 2.44 10.35 7 Soil Type 0.13 0.19 0.17 0.07 0.13 0.08 0.12 0.06 0.25 1.20 10.11 8 Drainage Network 0.17 0.24 0.22 0.37 0.66 0.24 0.59 0.29 0.25 3.02 10.53 9 Householder 0.09 0.13 0.07 0.22 0.03 0.05 0.04 0.10 0.08 0.81 9.75 10

count 9.00 lambda max 9.908 CI 0.113 CR 0.08 constant 1.45

184

Average

CR Value = 0.031 OK Pairwise comparisons ONLY ENTER in Item Number Item Number 1 2 3 4 5 6 7 8 9 10 Item Descriptions Item Description Poverty Population denCoping Slope Land Use Rainfall Soil Type Drainage Network Householder 1 Poverty 1.00 1.47 1.02 0.34 0.69 0.60 0.80 0.19 0.89 2 Population density 0.68 1.00 1.36 0.62 0.77 1.20 1.04 0.38 0.79 3 Coping 0.98 0.74 1.00 0.43 0.76 0.37 0.52 0.19 0.42 4 Slope 2.90 1.60 2.32 1.00 2.77 1.20 3.08 0.98 2.32 5 Land Use 1.44 1.31 1.32 0.36 1.00 0.61 2.02 0.39 1.80 6 Rainfall 1.66 0.83 2.67 0.83 1.65 1.00 3.27 0.80 2.32 7 Soil Type 1.25 0.96 1.91 0.32 0.49 0.31 1.00 0.32 1.28 8 Drainage Network 5.35 2.65 5.13 1.02 2.59 1.25 3.09 1.00 2.87 9 Householder 1.13 1.27 2.37 0.43 0.56 0.43 0.78 0.35 1.00 10 1.00 Sum 16.40 11.83 19.10 5.36 11.27 6.97 15.60 4.60 13.68

STANDARDIZED MATRIX Poverty Population denCoping Slope Land Use Rainfall Soil Type Drainage Network Householder Weight 1 Poverty 0.06 0.12 0.05 0.06 0.06 0.09 0.05 0.04 0.06 6.74% 2 Population density 0.04 0.08 0.07 0.12 0.07 0.17 0.07 0.08 0.06 8.44% 3 Coping 0.06 0.06 0.05 0.08 0.07 0.05 0.03 0.04 0.03 5.36% 4 Slope 0.18 0.14 0.12 0.19 0.25 0.17 0.20 0.21 0.17 18.00% 5 Land Use 0.09 0.11 0.07 0.07 0.09 0.09 0.13 0.08 0.13 9.51% 6 Rainfall 0.10 0.07 0.14 0.15 0.15 0.14 0.21 0.17 0.17 14.55% 7 Soil Type 0.08 0.08 0.10 0.06 0.04 0.04 0.06 0.07 0.09 7.04% 8 Drainage Network 0.33 0.22 0.27 0.19 0.23 0.18 0.20 0.22 0.21 22.69% 9 Householder 0.07 0.11 0.12 0.08 0.05 0.06 0.05 0.08 0.07 7.67% 10 100.0%

CI and CR worksheet Poverty Population denCoping Slope Land Use Rainfall Soil Type Drainage Network Householder SUM SUM/Weight 1 Poverty 0.07 0.12 0.05 0.06 0.07 0.09 0.06 0.04 0.07 0.63 9.31 2 Population density 0.05 0.08 0.07 0.11 0.07 0.17 0.07 0.09 0.06 0.78 9.26 3 Coping 0.07 0.06 0.05 0.08 0.07 0.05 0.04 0.04 0.03 0.50 9.32 4 Slope 0.20 0.14 0.12 0.18 0.26 0.18 0.22 0.22 0.18 1.69 9.40 5 Land Use 0.10 0.11 0.07 0.06 0.10 0.09 0.14 0.09 0.14 0.89 9.41 6 Rainfall 0.11 0.07 0.14 0.15 0.16 0.15 0.23 0.18 0.18 1.37 9.40 7 Soil Type 0.08 0.08 0.10 0.06 0.05 0.04 0.07 0.07 0.10 0.66 9.37 8 Drainage Network 0.36 0.22 0.28 0.18 0.25 0.18 0.22 0.23 0.22 2.13 9.41 9 Householder 0.08 0.11 0.13 0.08 0.05 0.06 0.05 0.08 0.08 0.71 9.31 10

count 9.00 lambda max 9.355 CI 0.044 CR 0.03 constant 1.45

185

Appendix 4

Maps

Catchment and Inlets in SewerGEMs model

186

Catchments intersect with existing stormwater system

187

Simulated stormwater pipelines in SewerGEMs

188