Statistical Relationship Between Drought and Groundwater Characteristics in Al-Alam District

Authors:
  • Maryam Firas Hassan , Department of Geography, College of Education for Humanities, Tikrit University, Iraq
  • Muhammad Atiya Saleh , Department of Geography, College of Education for Humanities, Tikrit University, Iraq

Article Information:

Published:May 11, 2026
Article Type:Original Research
Pages:3134 - 3144
Received:April 14, 2026
Accepted:May 3, 2026

Abstract:

Drought is an important climatic and environmental phenomenon that directly affects water resources, especially groundwater. A decline in rainfall, rising temperatures, and increased evaporation rates lead to a reduction in the natural recharge of groundwater reservoirs, which in turn affects both its quantity and quality. Drought also influences the physical and chemical features of groundwater as a result of increasing salts and dissolved elements concentration, as well as reducing groundwater recharge rates. Chapter three addresses the concept of drought and the methods which used to measure it through several climatic and hydrological indicators, such as the Reconnaissance Drought Index (RDI) and the Standardized Precipitation Index (SPI). These indicators rely on the relationship between rainfall and evaporation to determine drought intensity and duration. Groundwater is one of the most important water resources in the study area, especially in arid and semi-arid regions such as Al-Alam district, where the population and agricultural activities depend on it as a supplementary or alternative source to surface water. The quality features of groundwater are affected by several factors, the most important of which are geological formations, groundwater depth, the residence time of water in the aquifer, and climatic conditions, particularly drought, which leads to increased salt concentration and higher chemical properties such as pH. Therefore, studying the statistical relationship between drought and some chemical properties of groundwater helps in understanding how climatic conditions affect groundwater quality in the study area.

Keywords:

Groundwater geological formations climatic conditions

Article :

INTRODUCTION:

Multiple linear regression is one of the most important statistical methods used in environmental and hydrogeological studies. It aims to measure the combined effect of several independent variables on a single dependent variable and to identify the nature of the relationships between them in a precise quantitative manner. This method is also used to locate the variables that have the greatest influence on the studied phenomenon and to measure the contribution of each variable in explaining variance in the dependent variable, in addition to testing the statistical significance and explanatory power of the model [1].

 In this study, multiple linear regression were used to assess how much the chemical components of groundwater affect drought, and to pinpoint the elements most strongly linked and influential. This involved feeding all the chemical elements into a single statistical model, which helps reveal each element's true impact after accounting for statistical interactions between variables.



 

Material And Methods:

The Study's Problem:

Al-Alam District has witnessed a steady decline in its water resources due to repeated drought waves, which has negatively affected both the available water quantities and quality. The sharp shortages in water supply have made it tough to meet residents' needs and agricultural demands, while also degrading water quality with rising salinity and pollutants. This situation raises a core question:

1-      How have repeated drought waves affected the quantitative changes in water resources (surface and groundwater) in Al-Alam District?

2-      What are the impacts of these quantitative and qualitative changes in water resources on agricultural activities and residents' livelihoods?

3-      What are the alternatives or strategies could mitigate drought effects and ensure the sustainability of water resources in the district?

 Study Hypotheses:

1-      There's a positive relationship between repeated drought waves and decreases in available surface and groundwater resources in Al-Alam District.

2-      Drought's impact on water resources varies over time and space, depending on the intensity of climate waves and differences in natural conditions across the district's areas.

3-      The quantitative and qualitative changes in water resources have negatively affected agricultural activities and residents' living conditions, through reduced crop production and increased burdens of accessing water.

 

Importance of the Study

  The importance of this study stems from several scientific and practical angles, which can be outlined as follows:

·        It contributes in expanding academic knowledge on the relationship between climate phenomena (drought) and water resources through quantitative and qualitative analysis of water in an area that's among the most vulnerable to drought waves in Iraq.

·        The study offers a practical model that can be applied in similar research linking climate change to water resource management, enriching the body of geographical and hydrological literature.

·        It provides a scientific framework for understanding the temporal and spatial variations in drought's impact on water resources, paving the way for comparative studies with other areas inside and outside Iraq.

 Study objectives

The study aims to achieve the following:

·        Analyzing quantitative changes in surface and groundwater resources in Al-Alam District and measuring their correlation with the frequency of drought waves.

·        Monitoring qualitative changes in water in terms of salinity, chemical and physical pollutants, and suitability for drinking and agriculture.

·        Identifying temporal and spatial variations in the impact of drought on water resources within Al-Alam District.

 Location of the Study Area

 Astronomically, the study area lies between latitudes 34°30'00" to 35°05'00" North and longitudes 43°30'10" to 44°55'00" East. Geographically, it is bordered to the east and northeast by the Hamrin Hills, and to the west and northwest by the Tigris River. Administratively, it falls within Salah al-Din Governorate in the northeastern section, bordered to the northeast by Kirkuk Governorate, to the south by Al-Dour District, and to the west and northwest by the Tigris River—as shown in Map (1-1).

Map (1-1) Location of the study area

Source: According to administrative map of Iraq at scale: 1:100000, by using Arc Map10.8 program

4.1  Statistical Analysis of the Relationship Between Drought and Groundwater Characteristics by Using Pearson's Correlation Coefficient

Introduction:


 Quantitative analysis has become a cornerstone of geographical studies, especially with the massive developments in digital tools and modern statistical software. This shift represents a major scientific breakthrough in handling environmental data and interpreting spatial and temporal relationships between different phenomena. Statistical packages and Geographic Information Systems (GIS) have made it easier to process huge datasets, turning them into reliable, precise results that help explain natural and environmental processes. There is doubt, one of the standout developments in contemporary geographical thought is the quantitative approach—using cutting-edge statistical methods to analyze the interplay between natural environmental elements and human activities. It uncovers patterns of influence and interaction between them with scientific precision.

 

Statistical analysis allows to measure the strength and direction of the relationship between drought indicators and various chemical elements, revealing the elements most affected by drought-related environmental changes. This helps explain the nature of groundwater quality deterioration and quantitatively identify the dominant hydrochemical patterns in the study area[1]. According to what mentioned, this section aims to analyze the statistical relationship between drought indicators and groundwater chemical elements using descriptive and inferential statistical methods in particular correlation coefficients and multiple linear regression in order to uncover the nature of the link between drought intensity and hydrochemical changes in groundwater. Key statistical indicators were relied upon, such as the multiple correlation coefficient (R), the coefficient of determination (R²), the F-test, and the statistical significance level (Sig), to evaluate the strength and direction of the relationship, identify the chemical elements most impacted by drought, test the significance of the statistical models, and assess their ability to explain variations in groundwater chemical properties. This section also seeks to provide a scientific interpretation of the results in light of prevailing climatic and hydrogeological characteristics, as well as the impact of drought on hydrochemical processes related to groundwater, thereby bridging numerical results with environmental and hydrological explanations. This analysis represents a crucial step toward understanding the chemical response of groundwater to drought, identifying the elements most sensitive to environmental and climatic changes, and contributing to the assessment of groundwater quality status and its vulnerability to climate shifts in the study area [2].

In order to create a comprehensive statistical model capturing the linear relationship between the drought variable and groundwater chemical elements, the model could be divided into two main parts. The first part consists of equations of multiple linear regression which explains the mathematical nature of the relationship between variables. The second section includes a set of statistical tests used to measure the model’s efficiency and reliability, such as the multiple correlation coefficient (R), the coefficient of determination (R²), the F-test, and the level of statistical significance (Sig). It also examines the significance of the independent variables and their ability to explain changes in the chemical properties of groundwater under drought conditions, as follows:

 4.1.1 Multiple Linear Regression

Regression: is a statistical method enable us to build a model for estimating the relationship between one (quantitative dependent variable) and one (quantitative independent variable), or several quantitative independent variables. When the relationship involves one independent variable, it is called simple regression; when it involves several independent variables, it is called (multiple regression), Multiple linear regression is not just a single technique, but rather a set of methods used to identify the relationship between a continuous dependent variable and a number of independent variables, which are usually also continuous, the linear equation in multiple linear regression is as follows[3]:

 Y = a + b1X1 + b2X2 + ……… + e

where:

[y] = the dependent variable

[a] = intercept or constant value

[b_1] = the regression coefficient of [y] with respect to the first independent variable

[b_2] = the regression coefficient of [y] with respect to the second independent variable

[x_1] = the first independent variable

[x_2] = the second independent variable

Multiple linear regression can be used when the following conditions are available:

1-      The relationship between the independent variables and the dependent variable is linear.

2-      The data for the independent and dependent variables are normally distributed.

After obtaining the linear regression results, the linear regression must subject to a group of statistical linear regression to determine whether these transactions are acceptable from the linear regression perspective in other words they are statistically significant [4]. To estimate the validity of the linear regression symmetry in interpreting the relationship between the linear regression and the independent and dependent variables, we will present a group of several statistical tests as follows:

 

1- Multiple correlation Coefficient (R)

This is the coefficient that measures the strength of the relationship between two or more variables, mentioning that the linear regression coefficient here does not rely on linearity because these relationships are not uniformly linear across all points within the regression line model  [5]. Pearson's correlation coefficient is described by the linear regression formula[6]:

 

1- Adjust interpretation coefficient (R²)

This test is used to measure the explanatory power of the estimated model through determining the percentage of variables in independent variable (Y) which explained by regression model, it ranges from 0 to 1, where the explanatory power of the model increasing by when its values increased and vice versa [7].

1- Overall significance of regression test or F-Test:

This test aims to identifying whether the explanatory variables (xi-xn) have a significant impact on the dependent variable (Y). If the calculated [F] value exceeds the tabled critical value at the desired significance level and critical degrees (V2 = n-k) (Vi = k -1), we reject the null hypothesis. This means the regression is statistically significant I and not all regression coefficients are zero. Conversely, if (F* < F), we accept the null hypothesis, indicating that the explanatory variables do not explain changes in [Y]. In other words, there's no relationship between [Y] and the explanatory variables[8]  .



[1]) World Health Organization (WHO), Climate change and health, Geneva,World Health Organization. 2021

[2] ) Fathi Abdul Aziz Abu Radi, Introduction to Quantitative Methods in Geography. Dar Al-Ma'rifa Al-Jami'iyya for Printing and Publishing, Alexandria, Egypt, 2000, p. 7.

[3] ) Mark Tranmer, Mark Elliot، Multiple Linear Regression, cathie marsh center for census and survey research, India,2001,p21.

[4] ) Farid Khalil Al-Ja'looni, "The Method of Multiple Linear Regression Analysis in Studying the Most Important Economic, Social, and Demographic Variables Affecting the Total Fertility Rate," Journal of Damascus University for Economic and Legal Sciences, Vol. 24, No. 2, 2008, p. 241.

 

[5] ) Sami Aziz Abbas Al-Utbi and Muhammad Yusuf Hajem Ilahiti, Scientific Research Methodology, Baghdad, 2011, pp. 172–173.

[6] ) Salem bin Saeed Al-Qahtani, Research Methods in the Behavioral Sciences, Dar Al-Masira for Publishing and Distribution, Amman, 2012, pp. 278–280.

[7] ) Nu'man Shahada, The Researcher on Forest Work Using the Brain, United Arab Emirates University, 2nd ed., Dar al-Safa for Publishing and Distribution, Amman, 2002, p. 383.

[8] ) Sami Aziz Abbas, A Quantitative Economic Study of the Demand for Chemical Fertilizers for Some Field Crops in Iraq, Master's thesis, College of Agriculture, University of Baghdad, 1981, p. 67

 

Result:

Database Used in the Study

The study adopted statistical analysis to reveal the nature of the relationship between drought indices derived from satellite imagery and the chemical elements in groundwater, as determined through laboratory analysis. First, well locations were represented as a (Point Feature Class) in ArcGIS by using their geographic coordinates. These points were then overlaid onto a drought map showing the spatial distribution of drought values extracted from the NDVI vegetation index and the VCI index for the same year the water samples were analyzed. To extract the drought value for each well, we used the "Extract Values to Points" tool from ArcGIS's Spatial Analyst tools. This tool pulls the raster cell value corresponding to each well point and adds it to the point layer's attribute table. As a result, every groundwater sample now had a numerical value representing the drought severity at its geographic location, enabling various statistical analyses between drought values and groundwater chemical elements as shown in Table (1).

 Next, Pearson's correlation coefficient was used to measure the strength and direction of the relationship between the drought variable and various chemical elements. This method is one of the most common in environmental and hydrochemical studies because it reveals whether the relationship is positive or negative, while also indicating its strength and statistical significance. Analysis results were used to pinpoint the chemical elements most affected by drought, and to reveal the nature of the hydrogeochemical response in groundwater under the impact of vegetation cover degradation and increasing drought severity 

Table (1) Drought valve and chemical elements of groundwater value in Al-Alam district

Cl

ECC

TDS1

Na

SO_

pH

Ca

Mg

K

HCO3

NO3

PO4

Drought value

86.5

2240

445

205

1819

7.2

326

198

6.9

350

94

2.2

0.158

94.8

3485

1095

232

1680

7.3

317

138

10.1

360

70

2.1

0.265

103.6

780

842

216

1973

7.2

412

89

3.2

370

53

2

0.084

112.9

4130

356

194

2097

7.5

451

165

4.5

380

90

1.9

0.272

120.4

2890

690

168

1850

6.7

305

191

2.6

85

57

0.6

0.171

127.5

3290

985

181

3336

7.3

473

72

8.3

390

74

1.8

0.211

138.7

1725

1295

194

1620

7.4

198

86

1.8

92

72

0.7

0.131

145.2

1175

598

169

1241

7.2

318

148

0.6

400

122

1.7

0.073

156.9

4100

942

221

2136

7.2

128

145

3.4

110

94

0.8

0.311

163.8

3940

768

295

1728

8.1

501

126

2.1

410

56

1.6

0.378

172.3

950

1390

157

1789

7.8

134

220

0.9

125

63

0.9

0.056

181.4

2590

1155

273

1349

7.3

486

93

13.4

420

67

1.5

0.278

189.6

2680

842

182

1340

7.1

175

135

4.7

138

82

1

0.194

197.9

860

1178

248

1167

7.7

571

184

5.7

430

81

1.4

0.151

205.8

3460

1001

205

2016

7.6

186

97

2.1

145

52

0.7

0.27

214.6

4410

742

225

1190

7.6

489

82

1.2

440

59

1.3

0.352

218.4

2150

721

176

1539

8.3

205

180

6.3

152

119

0.6

0.164

229.7

3725

556

214

2314

8.5

221

78

1.2

160

67

0.8

0.303

232.1

3065

518

210

1680

7.3

533

154

7.8

95

96

1.2

0.259

241.2

1250

487

189

986

7.4

268

162

3.9

168

84

0.9

0.128

248.7

1525

495

237

2304

7.8

418

111

3.9

105

75

1.1

0.196

253.9

3017

990

163

1538

7.3

82

124

5.1

175

59

1.1

0.216

263.4

3720

372

221

1938

8.2

512

104

0.4

115

48

1

0.321

266.5

1860

1089

147

1638

6.8

165

101

0.7

182

72

1.2

0.132

276.8

2395

902

198

2193

7.2

317

212

6.2

125

91

0.9

0.22

279.1

4320

978

238

2131

7.7

118

200

2.8

190

90

1

0.38

288.9

905

665

186

2339

7.3

396

130

9.9

135

79

0.8

0.119

291.8

2595

934

171

1763

8.1

149

140

4.2

198

47

0.9

0.209

301.3

4155

898

172

2155

7.6

532

92

2.8

145

117

0.7

0.31

304.6

720

889

199

2014

7.5

235

90

1.5

205

74

0.8

0.126

313.6

2815

1160

259

2349

7.4

497

172

4

155

61

0.6

0.319

317.3

3985

1058

184

1635

7.6

402

168

7.6

212

126

0.7

0.317

326.8

1720

542

243

1819

7.3

561

75

14.1

165

66

0.8

0.239

329.9

2810

632

156

1300

7.4

152

73

3.1

220

69

1.1

0.219

339.1

3990

836

218

1567

7.6

439

151

1.5

175

84

1

0.358

342.6

1640

348

209

1070

7.7

178

149

0.4

228

82

1.3

0.205

351.6

3100

402

203

1178

7.4

482

120

5

185

64

1.2

0.292

355.4

4525

1215

192

1441

7.5

154

128

2.3

235

55

1.4

0.37

364.9

1865

685

189

1459

8.5

615

99

8.6

195

93

1.4

0.205

368.1

2385

764

226

840

7.8

512

95

5.8

242

97

1.2

0.276

377.2

4520

1078

266

1337

7.1

598

140

0.7

205

72

1.6

0.452

381.7

3095

428

178

1201

8.6

258

186

1.9

250

75

1

0.275

389.7

2700

495

251

3124

7.4

391

214

3.4

215

56

1.8

0.327

394.2

870

1102

165

2734

7.4

163

84

8.4

258

65

0.9

0.128

402.4

1980

965

229

2044

7.6

455

130

6.6

225

89

2

0.265

407.9

3550

805

241

1382

6.9

421

156

3.6

265

85

1.5

0.376

415.8

3465

340

214

1387

7.2

446

95

11.2

235

75

2.2

0.346

421.5

2140

365

203

1736

7.4

447

114

0.6

272

120

1.6

0.254

428.6

2160

792

197

941

8.3

548

175

2

245

123

2.4

0.251

435.2

4660

1168

187

1900

7.3

232

106

2.7

280

57

1.4

0.398

441.1

4300

468

178

2124

7.3

479

76

4.7

255

58

2.3

0.369

448.8

2930

692

172

2834

8.1

365

215

4.9

288

70

1.3

0.28

454.7

1255

245

206

2730

7.6

471

158

15.3

265

69

2.1

0.213

462.3

980

1090

259

1746

7.2

201

132

1.1

295

91

1.2

0.252

467.2

2890

612

234

1322

7.4

505

120

1.1

275

86

1.9

0.346

475.9

3810

1086

231

1348

7.4

239

94

6.8

302

62

1.1

0.403

480.8

3705

318

219

2206

7.9

498

99

5.9

285

71

1.7

0.386

489.4

2260

915

196

2485

7.6

196

175

3.3

310

79

1

0.276

494.6

2105

305

201

2623

6.7

446

194

9.1

295

81

1.5

0.273

503.1

4475

1115

181

2746

7.9

452

76

0.2

318

114

1.6

0.402

508.9

4620

428

285

668

8.6

521

135

0.5

305

93

1.3

0.518

516.7

1990

1170

168

1740

8.8

338

154

5.5

325

66

1.7

0.239

522.4

1985

265

262

801

8.1

439

88

3.1

315

65

1.1

0.336

530.2

3225

738

215

2894

7.3

189

121

9.2

332

80

1.8

0.367

536.7

3220

762

247

973

7.8

514

162

7.4

325

84

0.9

0.402

543.8

1410

522

244

1049

7.2

371

100

2.4

340

50

1.5

0.289

550.1

1500

589

231

819

7.1

536

71

12.8

335

69

0.7

0.283

557.4

3680

1195

207

2844

7.5

274

194

1.7

348

92

1.4

0.396

564.8

4050

310

215

789

7.6

552

147

2.3

345

95

0.6

0.429

571

2520

856

193

2588

8

188

88

4.1

355

76

1.3

0.314

579.3

2750

435

193

691

7.3

386

123

4.2

355

58

0.8

0.331

584.6

905

412

176

1489

7.4

256

147

6

362

124

1.2

0.201

593.7

950

1012

222

606

7.1

492

92

16

365

77

1

0.253

598.2

4150

1015

228

1337

7.6

245

219

0.8

370

53

1.1

0.459

608.4

3350

945

254

560

8.2

487

181

1.4

375

89

1.2

0.439

611.7

2795

625

219

1783

8.2

176

138

3.5

378

71

1.9

0.37

623.9

1875

395

239

500

7.7

501

80

4.6

385

61

1.4

0.337

625.3

1065

348

201

1979

7.8

279

98

7.1

385

88

2

0.249

638.9

3420

1138

188

2247

7.6

173

182

2.9

392

62

1.8

0.386

650.9

4580

702

227

1765

9

468

152

1.9

410

91

1.6

0.501

652.5

2310

892

170

2847

7.9

142

79

1.3

400

95

1.7

0.303

666.1

4605

1135

252

1438

8.4

158

165

10.4

408

68

1.6

0.533

672.8

2645

545

172

1988

7.2

489

109

7.3

295

74

1.8

0.333

679.6

1765

1075

235

1994

7.5

326

127

4.6

415

84

1.5

0.344

693.2

3995

785

211

1344

7.7

351

103

2

422

121

1.4

0.462

705.3

3920

385

201

2450

7.5

476

102

0.8

175

83

2

0.451

706.8

2665

318

197

1004

8.3

217

205

5.9

430

56

1.3

0.37

720.4

820

1008

183

1120

7.9

258

143

8.7

438

73

2.1

0.245

734

4215

825

262

1794

8.2

126

92

1.6

445

91

2.2

0.54

739.6

1210

274

193

1328

7.4

573

208

5.4

260

67

2.2

0.286

747.5

3030

465

246

1604

7.3

389

170

3.8

120

46

2

0.455

761.1

1120

1186

223

950

7.5

312

74

6.5

130

79

1.9

0.317

774.7

3745

748

206

1018

7.3

342

150

0.5

140

116

1.8

0.467

788.3

2450

998

191

1219

7.4

405

130

2.2

150

66

1.7

0.376

801.9

1580

1205

174

1862

7.9

301

97

11.8

160

82

1.6

0.309

815.4

4085

698

229

1920

8.7

283

189

4.3

170

53

1.5

0.524

829

2895

945

217

1363

7.6

295

86

7.9

180

94

2.3

0.442

842.6

895

1140

198

2506

7.5

452

158

1.4

190

75

2.4

0.304

856.2

4345

772

185

1584

7.3

508

118

3

200

127

2.2

0.508

869.8

3195

915

271

1680

7.2

291

108

5.2

210

60

2.1

0.529

883.3

1015

1025

254

1901

8

328

117

0.9

220

67

2

0.38

896.9

3615

548

236

2342

7.6

371

98

2.6

230

86

1.9

0.528

910.5

2735

928

214

1556

7.5

392

189

9.6

240

64

1.8

0.455

924.1

645

336

199

2880

7.6

346

84

6.1

250

96

1.7

0.315

937.7

4265

1120

208

2100

7.4

504

142

1

260

73

2.5

0.552

951.2

2965

905

224

2243

7.3

379

218

4.8

270

49

2.4

0.492

964.8

1090

648

190

1478

7

362

133

12.6

280

92

2.3

0.346

978.4

3875

1105

177

1517

7.2

251

96

3.7

290

77

2.2

0.509

992

2125

1152

163

2246

7.8

482

178

0.3

300

118

2.1

0.391

1005.6

4680

815

286

2248

7.6

519

77

5.4

310

57

2

0.678

1019.1

2530

482

268

1718

8.4

401

160

7.2

320

68

1.9

0.531

1032.7

1380

1068

241

1382

7.9

389

122

2.5

330

83

1.8

0.437

1046.3

4010

708

219

1603

7.6

468

100

1.8

340

62

2.3

0.581

Source: according to digital matching between drought and chemical elements of groundwater

1- The relationship between drought and chloride element (CL)

Chloride is one of the most sensitive chemical elements to drought conditions in groundwater. As a conservative ion, it doesn't easily precipitate or undergo complex reactions in the aquifer, so changes in its concentration directly reflect shifts in water balance and climate, especially evaporation and reduced recharge. During intense droughts, recharge drops and evaporation ramps up, concentrating dissolved salts and driving up chloride levels. That's why hydrochemical studies often use chloride as a key indicator of water quality decline and rising salinity in arid and semi-arid areas[1]. After applying statistical correlation analysis, the results appear in Table (2).

 

Table 2: Results of Correlation and Simple Linear Regression Between Drought and Chloride (Cl)

R

Adjusted R²

F

Sig

Beta

Std. Error

The element

0.714

0.51

0.506

115.62

0

0.714

0.128

CL

Source: Researcher's work using SPSS v.28 statistical package

The table shows a strong positive relationship between drought and chloride (Cl) in groundwater. The correlation coefficient (R) hit 0.714, pointing to a solid link between the two which means higher drought intensity goes hand-in-hand with rising chloride concentrations in the study's wells. It also reveals that drought explains about (51%) of the variation in chloride levels, a solid percentage that highlights its major influence compared to other factors affecting groundwater chemistry.

 

   (F) test value of (115.62) with a significance level (Sig = 0.000), confirms the model's high reliability in explaining the relationship between drought and chloride concentration, the standardized regression coefficient (Beta = 0.714) shows a clear positive effect to chloride, in other words, more severe drought leads to higher chloride concentrations in the groundwater.

 

  This is scientifically explained by the fact that drought conditions reduce water recharge and increase evaporation rates, leading to higher concentrations of dissolved salts, especially chloride, which is a conservative ion with low precipitation and chemical reactivity. As a result, it responds quickly to climatic and hydrological changes, particularly in arid and semi-arid environments.

 

1- The Relationship Between Drought and Electrical Conductivity (EC)

Electrical conductivity (EC) is one of the key hydrochemical indicators used to assess groundwater quality, as it directly reflects the amount of dissolved salts and ions in the water. EC values are heavily influenced by climatic conditions, especially drought, because lower water recharge and higher evaporation rates boost dissolved salt concentrations, which in turn raises EC levels. So EC is widely used in environmental studies as a critical indicator of declining water quality and rising salinity under drought [2]. By applying simple linear correlation and regression analysis between drought and EC, the statistical results shown in Table (3).

 

Table (3): Results of Simple Linear Correlation and Regression Between Drought and Electrical Conductivity

R

Adjusted R²

F

Sig

Beta

Std. Error

The element

0.667

0.445

0.44

89.14

0

0.667

0.141

ECC

Source: Researcher's work using SPSS v.28 statistical package

The results in Table (3) reveal a relatively strong positive relationship between drought and electrical conductivity (EC), with the correlation coefficient (R) reaching 0.667. This means that as drought intensity increases, EC values in groundwater rise accordingly. The analysis also shows that drought explains about 44.5% of the variation in EC values which is a clear sign of drought impact on saline properties of groundwater.

 

  (F) test value and statistical significance level (Sig = 0.000) confirm the statistical significance of the relationship and the efficiency of the model used. Scientifically, this means that increased drought leads to higher concentrations of ions and dissolved salts due to reduced water recharge and heightened evaporation. This directly boosts electrical conductivity, which serves as a general indicator of dissolved salts in groundwater.

 

1- The Relationship Between Drought and Sodium (Na)

Sodium is a key chemical element in hydrogeochemical studies, as it's one of the main dissolved ions in groundwater and is clearly affected by climatic and hydrological changes, especially in arid and semi-arid environments. Drought reduces water recharge and ramps up evaporation rates, which concentrates dissolved salts in aquifer reservoirs and raises sodium levels. Elevated sodium can also tie into ion exchange processes and the dissolution of saline minerals, making it a critical factor in assessing drought's impact on groundwater quality[3] .

By applying simple linear correlation and regression analysis between drought and sodium (Na) yielded the statistical results shown in Table (4).

Table (4): Results of Simple Linear Correlation and Regression Between Drought and Sodium (Na)

R

Adjusted R²

F

Sig

Beta

Std. Error

The element

0.487

0.237

0.23

34.52

0

0.487

0.173

Na

  Source: Researcher's work using SPSS v.28 statistical package

   The table results shows a moderate positive relationship between drought and sodium (Na), with the correlation coefficient (R) at 0.487. This indicates that greater drought intensity correlates with relatively higher sodium concentrations in groundwater. The results also reveal that drought explains about (23.7%) of variation in sodium concentrations and it is a ratio that clearly signals the impact of drought. However, it's less pronounced compared to chloride and electrical conductivity.

 

(F) test value and statistical significance level (Sig = 0.000) confirm a statistically meaningful relationship between these variables. This explains that drought conditions boost dissolved salt concentrations through evaporation and reduced water recharge. Sodium levels may also rise due to ion exchange processes and the dissolution of sodium-bearing minerals, driving up its presence in groundwater.

 

5- The Relationship Between Drought and Phosphate (PO4)

Phosphate is one of those chemical elements that indirectly ties into environmental and climatic conditions. Its concentration in groundwater is influenced by factors like agricultural activity, chemical fertilizer use, and hydrological changes linked to water recharge and drought. During drought, lower water volumes and higher evaporation rates concentrate certain dissolved elements including phosphate due to weaker dilution and a relative buildup of chemicals in the groundwater[4] . By applying linear correlation and regression analysis between drought and phosphate (PO4), the results shown in Table (5).

 

Table 5: Results of Simple Linear Correlation and Regression Between Drought and Phosphate (PO4)

R

Adjusted R²

F

Sig

Beta

Std. Error

The element

0.414

0.171

0.164

22.91

0

0.414

0.186

PO4

Source: Researcher's work using SPSS v.28 statistical package

Table (5) results shows a moderate positive relationship between drought and phosphate (PO4), with the correlation coefficient (R) at (0.414) , which indicate  that as drought intensifies, phosphate concentrations in groundwater tend to rise relatively.As the results showed that drought explains (17.1 %) of the variation in phosphate concentrations , a percentage  that points to a real drought effect, though it's still smaller compared to elements directly tied to salinity.

(F) test value and statistical significance level (Sig = 0.000) confirms the meaningful statistical link between the two variables. This can be explained as drought conditions reduces water volumes, boosting the relative concentration of dissolved elements. Plus, phosphates might get a lift from agricultural activities and chemical fertilizers, which ramp up their levels in groundwater in certain areas.

 

1- The relationship Between Drought and Hydrogen Ion Concentration (pH)

pH, or hydrogen ion concentration, is a core chemical property of groundwater. It shows whether the water leans acidic or basic, influenced by factors like rock types, geological formations, dissolved salts, and ion exchange processes in the aquifer. Drought can indirectly nudge pH values too through higher evaporation rates and weak water recharge [5]. And by using simple linear correlation and regression analysis between drought and pH, the results in table (6) appeared:

 

Table (6): Results of Simple Linear Correlation and Regression Between Drought and Hydrogen Ion Element (pH)

R

Adjusted R²

F

Sig

Beta

Std. Error

The element

0.218

0.048

0.039

5.53

0.021

0.218

0.211

PH

 Source: Researcher's work using SPSS v.28 statistical package.

  The results of the table shows a weak positive correlation between drought and pH, with the correlation coefficient (R) at (0.218). This indicate that drought has a limited impact on groundwater pH compared to other chemical elements. Indeed, drought explains just (4.8%) of the variation in pH values, indicating that the element is influenced more by other factors than by drought, such as the nature of the rocks and chemical interactions within the groundwater.

 

Despite the weak relationship, the statistical significance level indicates an acceptable level of statistical meaningfulness for the model. This can be explained by the fact that drought increasing leads indirectly to a slight change in the balance of dissolved ions, which affects the acidity and alkalinity values of the water. However, this effect remains limited due to the regulatory and balancing nature of the hydrogen ion (pH) in groundwater systems.

6- The Relationship Between Drought and Bicarbonate (HCO)

 Bicarbonate is one of the main ions in groundwater, directly linked to the dissolution of carbonate rocks and geochemical interactions between the water and geological formations. Bicarbonate concentrations are affected by several factors, most notably the amount of carbon dioxide, the solubility of carbonate minerals, and prevailing climatic and hydrological conditions. Drought can also cause a relative change in bicarbonate concentrations due to reduced water recharge and increased relative concentration of dissolved elements in the groundwater. By applying simple linear correlation and regression between drought and bicarbonate (HCO), the statistical results shown in Table (7) were obtained:

 Table (7): Results of Simple Linear Correlation and Regression Between Drought and Bicarbonate (HCO 3)

R

Adjusted R²

F

Sig

Beta

Std. Error

The element

0.203

0.041

0.032

4.79

0.031

0.203

0.214

HCO3

Source: Researcher's work using SPSS v.28 statistical package

 The results in Table (7) show a weak positive relationship between drought and bicarbonate (HCO), with the correlation coefficient (R) reaching 0.203. This suggests that drought's impact on bicarbonate concentrations is relatively limited compared to elements directly tied to salinity. The results also indicate that drought explains only about (4.1%) of the variation in bicarbonate concentrations, pointing to the fact that this element is strongly influenced by geological factors and chemical interactions inside the aquifer. In spite of the weak relationship, the results of the statistical significance test indicate an acceptable level of statistical significance for the model. This can be explained by the fact that drought indirectly contributes to higher bicarbonate concentrations due to reduced water dilution and increased relative concentrations of dissolved elements. However, the nature of bicarbonates tied to chemical equilibrium and the dissolution of carbonate rocks makes their response to drought less pronounced compared to other salinity elements.

 

7- The Relationship Between Drought and Calcium (Ca)

Calcium is one of the major chemical elements in groundwater, directly linked to the dissolution processes of limestone, gypsum, and geological formations containing carbonate minerals, their concentrations are influenced by climatic and hydrological factors, especially in arid and semi-arid environments and drought leads to reduced water recharge and higher evaporation rates, which can increase the relative concentration of certain dissolved ions, including calcium in groundwater [6]. By applying simple linear correlation and regression between drought and calcium (Ca), the statistical results shown in Table (8) appeared.

 

Table (8): Results of Simple Linear Correlation and Regression Between Drought and Calcium (Ca)

R

Adjusted R²

F

Sig

Beta

Std. Error

The element

0.188

0.035

0.027

4.08

0.046

0.188

0.219

Ca

Source: Researcher's work using SPSS v.28 statistical package.

The results in table (8) show a weak positive relationship between drought and calcium (Ca), with the correlation coefficient (R) reaching (0.188) indicating that drought's impact on calcium concentrations is relatively limited. The analysis also reveals that drought explains about (3.5%) of the variance in calcium levels is a low percentage that reflects how this element is more influenced by other geological and chemical factors than by direct drought effects.

(F) test and statistical significance level further confirm an acceptable significance statistical of the relationship though it was relatively weak. This makes sense because calcium is primarily tied to dissolution processes of carbonate and gypsum rocks within the aquifer means its response to drought is less pronounced compared to elements directly tied to rising salinity and evaporation.

[1] ) Todd, D. K., & Mays, L. W., Groundwater Hydrology, 3rd ed., John Wiley & Sons, New York, 2005, p. 389.

[2] ) Freeze, R. A., & Cherry, J. A., Groundwater, Prentice Hall, New Jersey, 1979, p. 104.

[3] ) Hem, J. D., Study and Interpretation of the Chemical Characteristics of Natural Water, U.S. Geological Survey Water-Supply Paper, 1985, p. 89.

[4] ) Appelo, C. A. J., & Postma, D., Geochemistry, Groundwater and Pollution, 2nd ed., CRC Press, London, 2005, p. 153.

[5] ) Drever, J. I., The Geochemistry of Natural Waters, 3rd ed., Prentice Hall, New Jersey, 1997, p. 45.

[6] ) Todd, D. K., & Mays, L. W., Groundwater Hydrology, 3rd ed., John Wiley & Sons, New York, 2005, p. 401.

 

CONCLUSION :

The study reached the following key findings after achieving its objectives:

1-      Pearson correlation analysis revealed the nature of the relationship between drought intensity (X) and electrical conductivity values (EC) (Y) over 2013 and 2024. The sum of the products of deviations was (4.4187), the sum of squared drought deviations was (0.00076), and the sum of squared EC deviations was (25,665.78), this led to a correlation coefficient of r = -1, reflecting a perfect inverse relationship between the variables which means a drop in the drought index.

2-      The absence of sanitary sewage networks in the area, coupled with residents using cesspits to dispose of human waste, allows these wastes to seep into the ground and reach the groundwater. This heightens contamination, especially in the floodplain where groundwater sits close to the surface.

3-      Producing a groundwater pollution vulnerability map using the (DRASTIC) method.

4-      Using of kriging interpolation methods available in ArcGIS software.

Recommendations:

1-      Farmers should test the chemical and physical properties of groundwater before planting, matching them to soil type and crops to maximize yields while minimizing soil damage and water waste.

2-      Drill monitoring wells to track any changes that could impact aquifer use.

3-      Implement the planned Nayef irrigation project in the study area to ease pressure on groundwater and boost its recharge.

4-      Reducing pollution by regulating the discharge of agricultural and industrial waste

BIBLIOGRAPHY:

1.      Abdul Aziz Abu Radi, Introduction to Quantitative Methods in Geography. Dar Al-Ma'rifa Al-Jami'iyya for Printing and Publishing, Alexandria, Egypt, 2000, p. 7.

2.      Appelo, C. A. J., & Postma, D., Geochemistry, Groundwater and Pollution, 2nd ed., CRC Press, London, 2005, p. 153.

3.      Drever, J. I., The Geochemistry of Natural Waters, 3rd ed., Prentice Hall, New Jersey, 1997, p. 45.

4.      Farid Khalil Al-Ja'looni, "The Method of Multiple Linear Regression Analysis in Studying the Most Important Economic, Social, and Demographic Variables Affecting the Total Fertility Rate," Journal of Damascus University for Economic and Legal Sciences, Vol. 24, No. 2, 2008, p. 241.

5.      Fathi Abdul Aziz Abu Radi, Introduction to Quantitative Methods in Geography. Dar Al-Ma'rifa Al-Jami'iyya for Printing and Publishing, Alexandria, Egypt, 2000, p. 7.

6.      Freeze, R. A., & Cherry, J. A., Groundwater, Prentice Hall, New Jersey, 1979, р. 104.

7.      Hair, J. F. et al., Multivariate Data Analysis, 7th ed., Pearson Education, London, 2010. p. 162. World Health Organization (WHO), Climate change and health, Geneva, World Health Organization. 2021.

8.      Hem, J. D., Study and Interpretation of the Chemical Characteristics of Natural Water, U.S. Geological Survey Water-Supply Paper, 1985, p. 89.

9.      Mark Tranmer, Mark Elliot. Multiple Linear Regression, cathie marsh center for census and survey research, India, 2001, p21.

10.   Nu'man Shahada, Quantitative Methods in Geography Using Computers, 2nd ed., UAE University, Dar Al-Safa for Publishing and Distribution, Amman, 2002, p. 383.

11.   Salem bin Saeed Al-Qahtani, Research Methods in the Behavioral Sciences, Dar Al-Masira for Publishing and Distribution, Amman, 2012, pp. 278–280.

12.   Sami Aziz Abbas Al-Utbi and Muhammad Yusuf Hajem Ilahiti, Scientific Research Methodology, Baghdad, 2011, pp. 172–173.

13.   Todd, D. K., & Mays, L. W., Groundwater Hydrology, 3rd ed., John Wiley & Sons, New York, 2005, p. 389.