Geostatistial and Geochemical Approaches to Assess

Groundwater Deterioration

EUC 2015 - Paper # 725 William Bajjali, Ph.D.

San Diego Session Title: Ground Water

Date: Wednesday, July 22, 2015 Time: 3:15 PM - 4:30 PM

Room: Room 25 A/B

The Problem • In 1960s WT of the aquifers in the area was in equilibrium (recharge = discharge)

• Several wells drilled to meet the increase demand for irrigation

• Extraction rate increased overtime and led to a

1. Discharge > Recharge

2. Regional drop in the water table

3. Deteriorate the water quality

• Water was pumped faster than it could be recharged (Overdraft condition)

• The area is characterized with high fault systems that could affect

1. Infiltration rate of PPT into the aquifer

2. Facilitating rapid infiltration of the return flow of the irrigated water

Objectives

• Use the geochemistry and geostatistical techniques in GIS environment to answer the following questions:

1. What causes the GW levels to fluctuate differently upstream & downstream of KSWTP

2. What factors affected the deterioration of water quality in the whole study region

3. Does the location of the wells have a pattern with regard to identifying hot and cold spot of the saline wells, and identifying the trend of the saline wells?

Study Area

Recharge – Discharge 1. Observation of WT convey information about relationship of PPT, infiltration, and pumping.

2. Observation well 1 showed a drop of nearly 43 meters (1 m/y between 1968 - 2012)

3. This means that

4. Extraction much faster than its recharge rate (no sustainability)

5. Aquifer is being used for present time (no sign water level was able to recover)

6. GW in the area is not a renewable resource

7. This is critical: Once aquifer pumped out

a) It would result in the end of irrigated farming

b) This might not be a big concern for the farmers

c) It is a big concern for Water Authority of Jordan

Water Level Fluctuation in Observation Well Upstream of KSWTP

Mitigation Option - Water Reclamation and Reuse

• The drop in GW level caused by over-abstraction triggers the need for mitigation options

• To lessen further decline in WL - WAJ decided to use the reclaimed water for agriculture

• KSWTP was erected in 1985 along the Zarqa River for treating wastewater

• The capacity of the plant increased over the years from 68,000 m3/day to 267,000 m3/day

• The plant treats around 72% of all the wastewater in the country.

• WAJ in 2006 modified the water standards, which allows using reclaimed water

1. For irrigation of crops that will not be eaten raw,

2. Use it for GW recharge to the aquifer not connected to domestic water supply.

KSWTP & GW • The area downstream of KSWTP started to rely on reclaimed water for irrigation

• WL started showing a rise in the wells downstream of KSWTP

• Despite huge extraction rates from wells for irrigation, GW levels increased

• The rise in water level was almost 20 cm/year after the KSWTP was built

• The rise in WL was observed in the wells that are close to the Zarqa River channel

• Observation well No. 2 recorded about a 9-m rise in WL during 1968-2012

• The rise is attributed to irrigated wastewater infiltration to GW

• The return irrigated water infiltrates back to the aquifer, acting as an artificial recharge

Observation Well Downstream of KSWTP

GW Quality Upstream KSWTP

1. Continuous pumping deteriorated the quality of water

2. TDS of some wells have increased almost 5 to 14 times above the initial level

3. Over-abstraction worsened the groundwater in three ways.

a) GW levels declined since 1968

b) TDS of GW shifted from good quality to saline water that has restricted use;

c) The drop in WL caused an increase in the cost of pumping

Downstream of KSWTP

1. TDS in the wells also increased due to two factors

a) Return flow from GW and reclaimed water

b) Infiltrated water acted as an artificial recharge

Source of Contamination • Deterioration of water quality was due to high salinity and nitrate

• Salinity increased due local irrigation

• The continued irrigation from GW caused a buildup of salinity in the soil

• Irrigation water evaporates from the soil and precipitates salts above the soil surface

• Incessant irrigation from the GW & reclaimed water would continually precipitate and simultaneously dissolve the accumulated salts from the soil surface

• The salt is flushed from the soil repeatedly by the irrigation water, causing the solute to infiltrate into the aquifer increasing the salinity of GW

• Another source come from direct river loss by leakage through the riverbed

• Urea (46 % nitrogen content) is used in the area as common chemical

G.W. Salinity

Well ID

Year 1965

Year 1989

124 275 1307 110 346 1670 115 448 4160 111 265 1862 109 291 3965 119 288 1931 121 314 1911 118 346 1950 126 292 1950

Spatial Statistics Using the Geostatistical Analyst • The Geostatistical Analyst is used to answer questions related to

1. Proximity of the wells from each other and their influence on lowering the GW levels and the increased water scarcity

2. The agriculture wells were examined to verify if they are randomly distributed, or if they adhere to a pattern, either clustered or dispersed

3. Interpolate the salinity of GW

• This work will focus on the following tools

1. Average Nearest Neighbor

2. Identify Hot & Cold Spot

3. Trend Analysis

4. Inverse Distant Weighting

Average Nearest Neighbor (ANN) • The ANN tool will detect if the features are clustered or dispersed • The tool generates two parameters: 1. Nearest Neighbor Index (I) Dr: is the average distance between each feature and its nearest neighbor of the real data and Dh is the average distance from a hypothetical data

2. Z-score: it is vital to make a decision to accept or reject the null hypothesis, and it is calculated as follow

• Each confidence level is associated with a z-score, which is simply a standard deviation

𝐼𝐼 = 𝐷𝐷𝑟𝑟𝐷𝐷ℎ

𝐷𝐷𝑟𝑟 = ∑ 𝑑𝑑𝑖𝑖𝑛𝑛𝑖𝑖=1

𝑛𝑛 𝐷𝐷ℎ = 0.5 �

𝐴𝐴𝑛𝑛

𝑍𝑍 = 𝐷𝐷𝑟𝑟 − 𝐷𝐷ℎ𝑆𝑆𝑆𝑆

𝑆𝑆𝑆𝑆 = 0.26136�𝑛𝑛2 𝐴𝐴⁄

I & Z - Score

Z-score (Standard Deviations) p-value (Probability) Confidence level

-1.65 or +1.65 0.10 0.90 -1.96 or +1.96 0.05 0.95 -2.58 or +2.58 0.01 0.99

If I < 1 Data Clustering If I > 1 Data Dispersing If I = 1 Data Randomly Distributed

Input 1. 129 wells is used 2. Area = 1,374,339,135 m2

Null Hypothesis: Wells in the study area are randomly distributed

Average Nearest Neighbor Summary Observed Mean Distance: 1108.09 Meters Expected Mean Distance: 1638.37 Meters

Nearest Neighbor Ratio: 0.67 z-score: -7.01 p-value: 0.000000

Does the location of the wells have a pattern?

Given the z-score of -7 & I ratio is 0.67

There is less than 1% possibility that the wells’ locations are distributed randomly

Therefore we reject the null hypothesis – GW wells’ locations is considered clustered

Identify Hot & Cold Spot using Cluster & Outlier Analysis

• The tool identify

1. hot and cold spots

2. features with values similar in magnitude

• The statistical tool calculates

1. Anselin Local Moran's I value

2. Z score

3. p-value

4. Code representing the cluster type for each feature.

Hot spots are found in three distinct localities 1. East of the study area (Low TDS) 2. Upstream of KSWTP in the center of study area 3. Downstream of KSWTP One well (Hand-dug) with a blue dot and it is identified as a cold spot Wells surround the cold spot well have very high salinity (TDS ranged from 2,113 to 9,067 mg/l)

Hot Spot • Red spot wells • z-score > 2 values • Bordered by similar TDS values

Cold Spot • Blue spot wells • z-score < - 2 values • Bordered by dissimilar TDS

Does the salinity of the wells reveal a trend?

• Trend surface analysis shows the general tendency of the salinity of the GW wells. • The west-east line is stronger than the north-south trend • Suggest that GW salinity pattern in the study area decreases from west to east • GW salinity downstream of KSWTP has a higher concentration in the study area

Interpolation using IDW • IDW is used to interpolate the GW salinity in the study area • Interpolation is simply defined as a process to estimate unknown values from

points with known values • In general the simplified formula for IDW is

• where, V0 is the predictable value at a point 0, Vi is the V value at control point i, Di is the distance between control point i and 0, and n is the number of known values used in the evaluation

𝑉𝑉0 = ∑ (𝑉𝑉𝑖𝑖𝐷𝐷𝑖𝑖)𝑛𝑛𝑖𝑖=1

∑ ( 1𝐷𝐷𝑖𝑖)

𝑛𝑛𝑖𝑖=1

Salinity Interpolation using IDW

• The interpolation map is classified into 5- classes • Classification is based on water-rock interaction (fresh water area is 58%, rest is brackish area) • Vertical permeability of the unsaturated zone is high in the high salinity area • This area can artificially recharged to replenish the aquifer and improve its water quality.

Class TDS (mg/l) Area (km2)

Percentage (%)

1 < 1,000 1099.33 58.66 2 1,001 – 2,000 511.35 27.29 3 2,001 – 3,000 212.44 11.34 4 3,001 – 4,000 49.09 2.61 5 > 4,000 1.76 0.09