AL CoE
Alabama 

Center of Excellence

Dr. John Valentine, Director

Dr. Kenneth Heck, Deputy Director

Dorothy Byron, Program Manager
AL-CoE: Who we are

Housed at Dauphin Island Sea Lab our 

mission is to provide results from innovative, 

forward- looking research conducted on 

areas of coastal concern to interested 

members of government, academic 

community, and the public.
AL-CoE: Where we work
❖ Improve core infrastructure for 

experimentation and ocean 

observation

❖ Upgrades to the Alabama Real-Time 

Coastal Observation System (ARCOS) 

(www.arcos.disl.edu)

❖ State of the Art Wetlab Facility for 

multi-stressor experiments

❖ Innovated Monitoring Approaches

Multi-layered Approach: 

Updating Scientific Capacity
AL-CoE: Timeline

 2019 2020 2021 2022 2023 2024

DISL named 

COE by AGCRC 

& ADCNR

ALCoE 

Established

Hurricane Sally 

impacts 

Alabama Coast

COVID Stay at 

home orders 

begin

RFP 1 

Released

RFP 1 Projects 

Begin

RFP 2 

Released

RFP 1 Awards 

Announced

Coming soon: 

RFP 2 Awards 

Announced

RFP 2 Projects 

scheduled to 

begin

Multi-stressor 

Wetlab online

ARCOS Updates 

Complete
Fund research investigating on the effects of multiple 

stressors, influenced by our changing climate, as they 

affect the natural resources of the 

northcentral Gulf of Mexico

Competitive Grant Program: 

RFP1 Focus
❖ Using MS/AL Sea Grant Technical Review Panel 

approach

❖ Fund research focused on coastal vulnerability, resilience and 

sustainability - looking at the past and current conditions to 

inform predictions for the future (RFP1)

❖ Fund ‘proof of concept’ research that pushes the limits of 

current research technologies (RFP2)

Multi-layered Approach: 

Grant Program
• Amount of funding distributed = ~$4.4 million 

• Average project cost: $435,000

• Total number of awards: 10

Competitive Grant Program: 

RFP1
RFP1:

Distribution of Funds
Projects funded

 Groundwater quality/quantity

 Citizen Science project using domestic well owners to 

collect data to understand groundwater quality

 Impacts of Sea Level Rise on Aquifer condition and coastal 

resilience

 Modeling ecosystem health, water resources and social 

resilience using a holistic platform that integrates multi-scale 

observations, machine learning and systems modeling using 

the past 30 years to predict the next 30 years.
Projects funded

 Oyster fisheries under changing climate, specifically ocean 

acidification and warming

 Changes in quality and quantity of food source 

 Changes in oyster growth, survival and energetic trade offs

 Impacts of Sea Level Rise on the Condition and Function of 

Tidal Freshwater Forested Wetlands of the Mobile-Tensaw 

Delta
 Living Shorelines & Nature-based Barriers Sustainability 

with Sea Level Rise

 Impacts of coastal warming on manatee distributional patterns 

and habitat use

 Physical Determinants of Hypoxia on the Alabama Shelf

Projects funded
Fund projects that use emerging technologies to improve 

our efforts in support of integrated research, developing 

predictions, forecasting change and improving the 

affordability of data collection and monitoring in coastal 

Alabama.

Competitive Grant Program: 

RFP2 Focus
• Amount of funding available = ~$1.5 million 

• Average project cost: $150,000

• Total number of awards: up to 10

Competitive Grant Program: 

RFP2
Featured Speakers

Dr. Yong Zhang

University of Alabama

Geological Sciences

Dr. Wanyun “Abby” Shao

University of Alabama

Department of Geography

Dr. Ann Ojeda

Auburn University

Geosciences
Thank you

This project was paid for [in part] with federal funding from the Department of the Treasury under the Resources and Ecosystems 

Sustainability, Tourist Opportunities, and Revived Economies of the Gulf Coast States Act of 2012 (RESTORE Act). The statements, 

findings, conclusions, and recommendations are those of the author(s) and do not necessarily reflect the views of the Department 

of the Treasury or ADCNR
Modeling Alabama's Groundwater Sustainability and 

Vulnerability: Connecting Past and Future

Yong Zhang (University of Alabama)

Chaloemporn Ponprasit (UA student)

Hossein Gholizadeh (UA student)

Olaoluwa Oluwaniyi (UA student)

Bahareh Karimidermani (UA student)

Co-PIs: Geoffrey Tick (UA)

Natasha Dimova (UA)

Erkan Nane (Auburn)

Ponprasit, C., Zhang, Y., Gu, X., Goodliffe, A.M. and Sun, H., 2023. Assessing vulnerability of regionalscale aquifer-aquitard systems in East Gulf Coastal Plain of Alabama by developing groundwater flow and 

transport models. Water, 15(10), 1937. https://doi.org/10.3390/w15101937
Goal: Groundwater Sustainability & Vulnerability

Objective 1: Southern Alabama groundwater sustainability

 Model 1: GMS/MODFLOW

 Model 2: Machine Learning Model (Neural Hydrology Network)

 Model 3: Statistical Model (WTF & Wavelet Analysis)

Objective 2: Evolution of Alabama coastal groundwater quality 

 Model 4: Seawater Intrusion Model (HGS)

 Model 5: Groundwater Quality Model (LSTM vs WRTDS)

Objective 3: Southern Alabama groundwater vulnerability

 Model 6: Backward Particle Tracking Model (from ADE to fADE)

 Model 7: Vulnerability (Aquifers vs. Vadose Zone)
EGCP in Alabama with 

41 observation wells and 

74 pumping wells (a). 

The 3-d geology model 

for EGCP is shown in 

(b). 

East Gulf Coastal Plain (EGCP), Alabama: 6 

cross-sections from 48 boreholes (a). Cross 

section A-A' (b). Cross section F'-F (c).

 Model 1: Groundwater Flow (GMS)

Ponprasit, Zhang, Gu, Goodliffe, and Sun [Water, 2023]

The reference groundwater head contour 

map during 10/2020 (a) vs. the simulated 

groundwater head contour map using 

MODFLOW (b) (units: feet)
21 wells in 4 zones

 Surface water using the Neural Hydrology Network

Gholizadeh, Zhang, Frame, Gu, and Green [Science of the Total Environment, 2023]

 Model 2: Machine Learning Model (Neural Hydrology Network/LSTM)

Surface water LSTM inputs

Precipitation

Solar radiation

Maximum temperature

Minimum temperature

Vapor pressure

Mean basin elevation

Mean basin slope Basin area

Forest fraction

Maximum leaf area index (LAI)

Minimum LAI

LAI difference

Maximum green vegetation 

fraction (GVF)

Minimum GVF

Soil depth

Soil porosity

Soil conductivity

Maximum water content

Sand fraction

Silt fraction

Clay fraction 

Carbonate rocks fraction

Permeability

Mean potential 

evapotranspiration (PET)

Aridity

Snow fraction

Rank (top 10)

Surface water LSTM

1. Precipitation

2. Permeability

3. Maximum temperature

4. Solar radiation

5. Vapor pressure

6. Maximum water content

7. Minimum temperature

8. Soil depth

9. Carbonate rocks fraction

10. Forest fraction
GW-LSTM

Groundwater hydrograph

GW-LSTM inputs

Precipitation

Temperature

Soil depth

Porosity

Maximum water content

Leaf area index (LAI)

Mean potential

Evapotranspiration (PET)

Sand fraction

Silt fraction

Clay fraction

Carbonate rocks fraction

Hydraulic conductivity

Rank (top 5)

GW-LSTM

1. Precipitation

2. Hydraulic conductivity

3. Soil depth

4. Maximum water content

5. Leaf area index (LAI)
Impact of climate 

change on 

groundwater head 

fluctuations: GWLSTM
 Model 3: Statistical Model (WTF)

Oluwaniyi, Zhang, Gholizadeh, Li, Gu, Sun, & Lu [Sustainability, 2023]

Alabama Groundwater Storage Change U.S. Drought (https://droughtmonitor.unl.edu)

2000 2001 2002 2003 2004 2005

2006 2007 2008 2009 2010 2011

2012 2013 2014 2015 2016 2017

2018 2019 2020 2021 2022 2023

2000 2001 2002 2003 2004 2005

2006 2007 2008 2009 2010 2011

2012 2013 2014 2015 2016 2017

2018 2019 2020 2021
Trend Analysis

-200

-100

0

100

200

300

1/1/00 12/31/01 12/31/03 12/30/05 12/30/07 12/29/09 12/29/11 12/28/13 12/28/15 12/27/17 12/27/19 Monthly GWSC or Precipitation (mm)

GWSC GWSC Trend Precipitation Precipitation trend

North well: 125687

-200

-100

0

100

200

300

1/1/00 12/31/01 12/31/03 12/30/05 12/30/07 12/29/09 12/29/11 12/28/13 12/28/15 12/27/17 12/27/19 Monthly GWSC or Precipitation (mm)

GWSC GWSC Trend Precipitation Precipitation trend

-200

-100

0

100

200

300

1/1/00 12/31/01 12/31/03 12/30/05 12/30/07 12/29/09 12/29/11 12/28/13 12/28/15 12/27/17 12/27/19 Monthly GWSC or Precipitation (mm)

GWSC GWSC Trend Precipitation Precipitation trend

South well: 92197

-200

-100

0

100

200

300

1/1/00 12/31/01 12/31/03 12/30/05 12/30/07 12/29/09 12/29/11 12/28/13 12/28/15 12/27/17 12/27/19 Monthly GWSC or Precipitation (mm)

GWSC GWSC Trend Precipitation Precipitation trend

GRACE

Middle well: 119684

-200

-100

0

100

200

300

Jan-00 Dec-01 Dec-03 Dec-05 Dec-07 Dec-09 Dec-11 Dec-13 Dec-15 Dec-17 Dec-19 GWSC Trend

-200

-100

0

100

200

300

Jan-00 Dec-01 Dec-03 Dec-05 Dec-07 Dec-09 Dec-11 Dec-13 Dec-15 Dec-17 Dec-19

Precipitation trend

-200

-100

0

100

200

300

Jan-00 Dec-01 Dec-03 Dec-05 Dec-07 Dec-09 Dec-11 Dec-13 Dec-15 Dec-17 Dec-19 GWSC Trend

-200

-100

0

100

200

300

Jan-00 Dec-01 Dec-03 Dec-05 Dec-07 Dec-09 Dec-11 Dec-13 Dec-15 Dec-17 Dec-19

Precipitation trend
 Model 4: Seawater Intrusion Model (HGS)
 Model 5: Groundwater Quality Model (LSTM vs WRTDS)

Nitrate concentration: calculated by the LSTM model (red line) vs. the observed data (symbols) at Tombigbee River and a well nearby.
 Model 6: Backward Particle Tracking Model (from ADE to fADE)

డ ఏ೘

ᇲ ஼೘

డ௧ + β డം,ഊ ఏ೘஼೘డ௧ം,ഊ = −

డ

డ௫ θ௠VC௠ + eି఑௫ డഀషభడ௫ഀషభ θ௠D

డ ௘ഉೣ஼೘

డ௫ − θ௠R௠

௜ డ஼೘

డ௧

డ ఏ೔೘஼೔೘

೔

డ௧ = f t ∗

డ ఏ೘஼೘

೔

డ௧ + f t θ௠C௠,଴

௜ − θ௜௠C௜௠,଴

௜ − θ௜௠R௜௠௜ డ஼೔೘

೔

డ௧

C௠

௜ x, t = 0 = C௠,଴

௜ x

C௜௠

௜ x, t = 0 = C௜௠,଴௜ x

dଵθ௠VC௠

௜ − dଶeି఑௫ డഀషమ

డ௫ഀషమ θ௠D డడ௫ e఑௫ C௠

௜

ቚ௫ୀ௅

= gଵ t

dଷθ௠VC௠

௜ − dସeି఑௫ డഀషమ

డ௫ഀషమ θ௠D డడ௫ e఑௫ C௠

௜

ቚ௫ୀோ

= gଶ t

Zhang [Water Resources Research, 2022]

Zhang, Brusseau, Neupauer, and Wei [Environmental Science & Technology, 2022]

Zhang [Journal of Hazardous Materials Advances, 2023]

Zhang, Fogg, Sun, Reeves, Neupauer, and Wei [Hydrology and Earth System Sciences, 2023]
 Model 7: Vulnerability (Aquifer & Soil)

Aquifer Vulnerability: (a) Groundwater age. (b) Residence time (c) 

Total travel time at 300 (top) and 700 (bottom) ft below GW table.

24 wells of 14C samples 

from Solder (2020) (red 

dots) and 4 wells of 36Cl 

samples from Penny and 

Lee (2003) (black squares) 

(a). Comparison between 

the isotopic-dated ages and 

MODPATH (units: year) 

(b).

Ponprasit, Zhang, Gu, Goodliffe, and Sun [Water, 2023]
Acknowledgement

This project was paid for in part with federal funding from the Department

of the Treasury under the Resources and Ecosystems Sustainability, Tourist

Opportunities, and Revived Economies of the Gulf Coast States Act of

2012 (RESTORE Act). The statements, findings, conclusions, and

recommendations are those of the authors and do not necessarily reflect the

views of the Department of the Treasury or ADCNR.
EXPLORING DIFFERENT APPROACHES 

TO MEASURING COMMUNITY 

VULNERABILITY IN MOBILE BAY

PRESENTER: WANYUN SHAO, PH.D

ASSOCIATE PROFESSOR OF GEOGRAPHY

UNIVERSITY OF ALABAMA

1
THE SPATIOTEMPORAL PATTERNS 

OF COMMUNITY VULNERABILITY 

IN MOBILE BAY FROM 2000-2020

AUTHORS: *HEMAL DEY, WANYUN SHAO, SHUFEN PAN, HANQIN TIAN 

*STUDENT COAUTHOR

2
BACKGROUND

• THE COASTAL REGION IS CONFRONTED WITH HEIGHTENED RISKS POSED BY CLIMATE CHANGE (IPCC 

2022)

• MOBILE RIVER BASIN (MRB) PROVIDES CRITICAL ECOSYSTEM SERVICES TO THE COUNTRY

• THE COASTAL ECOSYSTEM OF MRB AND ITS ADJACENT COASTS ARE VULNERABLE TO ENVIRONMENTAL 

STRESSORS INDUCED BY LOCAL HUMAN ACTIVITIES AND GLOBAL CLIMATE CHANGE. 

• COMMUNITY VULNERABILITY REFERS TO THE SUSCEPTIBILITY OF A COMMUNITY TO THE DAMAGING 

EFFECTS OF A HAZARD (TATE, 2012)

• EFFECTIVE MEASURE OF COMMUNITY VULNERABILITY IS COMPLICATED DUE TO THE COMPLEXITY OF THE 

SELECTION AND WEIGHTING OF THE INDICATOR (BIRKMANN ET AL. 2013; CUTTER ET AL. 2008; 2012).

3
FIG. 1: STUDY AREA MAP (A) ADMINISTRATIVE MAP OF MOBILE BAY (B) ELEVATION MAP OF 

MOBILE BAY (C) LOCATION OF STUDY AREA AND ALABAMA STATE IN CONTEXT OF CONUS4
FIG. 2: METHODOLOGICAL FLOWCHART OF THIS STUDY

5
TABLE 1: SOCIAL VULNERABILITY COMPONENT SUMMARY

6
FIG. 2: SPATIAL DISTRIBUTION OF SOCIAL VULNERABILITY IN MOBILE BAY

7
FIG. 3 (A) SOCIALLY VULNERABLE HOTSPOTS (B) SOCIAL VULNERABILITY CLUSTERS 

8
FIG. 4 SANKEY DIAGRAM OF LULC CHANGES IN MOBILE BAY FROM 2001 TO 2019

9
FIG. 5 THE LINK BETWEEN LULC CHANGES AND SOCIAL VULNERABILITY CHANGES

10
EXPLORING THE INFLUENCE OF STAKEHOLDERS' OPINIONS 

ON SELECTING AND WEIGHTING SOCIAL VULNERABILITY 

INDICATORS IN FLOOD RISK MANAGEMENT

AUTHORS: *MD MUNJURUL HAQUE, WANYUN SHAO, *HEMAL DEY

*STUDENT COAUTHOR

11
BACKGROUND

• THE PROCESS OF SELECTING AND ASSIGNING WEIGHTS TO INDICATORS IN DISASTER

MANAGEMENT VARIES AMONG STAKEHOLDERS AND HAS A DIRECT IMPACT ON THE

EFFECTIVENESS OF MEASURES (RECKNER AND TIER, 2023).

12
RESEARCH QUESTION

• HOW DO STAKEHOLDERS' OPINIONS INFLUENCE THE SELECTION AND WEIGHTING OF

COMMUNITY VULNERABILITY INDICATORS?

13
SURVEY DESIGN

• THE QUALTRICS SURVEY PLATFORM WAS USED TO COLLECT PRIMARY DATA FOR THIS STUDY.

• THE PURPOSIVE SAMPLING TECHNIQUE WAS DEPLOYED AS THE TARGETED RESPONDENTS WERE 

EMERGENCY MANAGERS, NGO STAFF, RESEARCHERS, METEOROLOGISTS, ECOSYSTEM MANAGERS. 

• THE TARGETED RESPONDENTS HAVE THE MOST UPDATED AND COMPETING KNOWLEDGE REGARDING 

FLOOD MANAGEMENT IN THE SELECTED AREA. 

• A TOTAL OF 47 RESPONSES WERE RECORDED FROM FEBRUARY TO MAY 2023.

14
CONSIDERATION OF SOCIAL VULNERABILITY DURING 

DECISION MAKING

15
SOCIAL VULNERABILITY INDICATORS WITH THEIR IMPORTANCE

16
VULNERABILITY MAPPING FACTORS

17
BLOCK GROUP-LEVEL 

VULNERABILITY MAPPING

18
FINDINGS

• A TOTAL OF 90% OF RESPONDENTS STRONGLY AGREED THAT INDICATOR-BASED VULNERABILITY

ASSESSMENT MIGHT BE AN EFFECTIVE FLOOD MANAGEMENT TOOL.

• 88% OF STAKEHOLDERS PRIMARILY HIGHLIGHTED PAST HISTORIC FLOOD EXPERIENCES AND

GEOGRAPHIC LOCATION AS THE MOST CRUCIAL. THIS UNDERSCORES THEIR MEANINGFUL IMPACT ON

INDICATOR SELECTION.

• GEOGRAPHIC LOCATION AND PAST HISTORIC EVENTS WERE THE MOST IMPORTANT SOCIAL

VULNERABILITY INDICATORS.

19
FUTURE RESEARCH

• COMPARATIVE STUDIES ACROSS REGIONS OR COMMUNITIES MAY OFFER INSIGHTS INTO DIVERSE STAKEHOLDER

OPINIONS, ENABLING TAILORED APPROACHES TO FLOOD MANAGEMENT.

• LONG-TERM RESILIENCE.

• HOW DO POLICIES AND GOVERNANCE FRAMEWORKS AFFECT STAKEHOLDER INVOLVEMENT AND DECISIONMAKING? WHAT ARE THE BEST WAYS TO INCORPORATE STAKEHOLDER OPINIONS INTO POLICY DEVELOPMENT?

• INTERDISCIPLINARY RESEARCH CAN HELP US TO GAIN A HOLISTIC UNDERSTANDING OF HOW STAKEHOLDER

OPINIONS INFLUENCE FLOOD RISK MANAGEMENT.

20
REFERENCES

BIRKMANN, J., CARDONA, O. D., CARREÑO, M. L., BARBAT, A. H., PELLING, M., SCHNEIDERBAUER, S., ... & WELLE, T. (2013).

FRAMING VULNERABILITY, RISK AND SOCIETAL RESPONSES: THE MOVE FRAMEWORK. NATURAL HAZARDS, 67, 193-211. 3

CUTTER, S. L., BARNES, L., BERRY, M., BURTON, C., EVANS, E., TATE, E., & WEBB, J. (2008). A PLACE-BASED MODEL FOR

UNDERSTANDING COMMUNITY RESILIENCE TO NATURAL DISASTERS. GLOBAL ENVIRONMENTAL CHANGE, 18(4), 598-6

CUTTER, S. L., BORUFF, B. J., & SHIRLEY, W. L. (2012). SOCIAL VULNERABILITY TO ENVIRONMENTAL HAZARDS. IN HAZARDS

VULNERABILITY AND ENVIRONMENTAL JUSTICE (PP. 143-160).

IPCC, (2022). SUMMARY FOR POLICYMAKERS [H.-O. PÖRTNER, D.C. ROBERTS, E.S. POLOCZANSKA, K. MINTENBECK, M. TIGNOR, A.

ALEGRÍA, M. CRAIG, S. LANGSDORF, S. LÖSCHKE, V. MÖLLER, A. OKEM (EDS.)]. IN: CLIMATE CHANGE 2022: IMPACTS, ADAPTATION

AND VULNERABILITY. CONTRIBUTION OF WORKING GROUP II TO THE SIXTH ASSESSMENT REPORT OF THE INTERGOVERNMENTAL

PANEL ON CLIMATE CHANGE, H.-O. PÖRTNER, D.C. ROBERTS, M. TIGNOR, E.S. POLOCZANSKA, K. MINTENBECK, A. ALEGRÍA, M.

CRAIG, S. LANGSDORF, S. LÖSCHKE, V. MÖLLER, A. OKEM, B. RAMA (EDS.). CAMBRIDGE UNIVERSITY PRESS, CAMBRIDGE, UK AND

NEW YORK, NY, USA, PP. 3–33, DOI:10.1017/9781009325844.001.

RECKNER, M., & TIEN, I. (2023). COMMUNITY-SCALE SPATIAL MAPPING TO PRIORITIZE GREEN AND GREY INFRASTRUCTURE

LOCATIONS TO INCREASE FLOOD RESILIENCE. SUSTAINABLE AND RESILIENT INFRASTRUCTURE, 8(SUP1), 289-310. 21
THANK YOU!

• IF YOU HAVE ANY QUESTIONS, PLEASE CONTACT DR. WANYUN SHAO AT WSHAO1@UA.EDU

• ACKNOWLEDGEMENTS

• THIS RESEARCH WAS SUPPORTED BY THE ALABAMA CENTER OF EXCELLENCE

22