Spatio-temporal variation of depth to groundwater level and its driving factors in arid and semi-arid regions of India

doi:10.1016/j.regsus.2024.100143

Abstract

Abstract:

Climate change and increasing anthropogenic activities, such as over-exploitation of groundwater, are exerting unavoidable stress on groundwater resources. This study investigated the spatio-temporal variation of depth to groundwater level (DGWL) and the impacts of climatic (precipitation, maximum temperature, and minimum temperature) and anthropogenic (gross district product (GDP), population, and net irrigated area (NIA)) variables on DGWL during 1994-2020. The study considered DGWL in 113 observation wells and piezometers located in arid western plains (Barmer and Jodhpur districts) and semi-arid eastern plains (Jaipur, Ajmer, Dausa, and Tonk districts) of Rajasthan State, India. Statistical methods were employed to examine the annual and seasonal patterns of DGWL, and the generalized additive model (GAM) was used to determine the impacts of climatic and anthropogenic variables on DGWL. During 1994-2020, except for Barmer District, where the mean annual DGWL was almost constant (around 26.50 m), all other districts exhibited increase in DGWL, with Ajmer District experiencing the most increase. The results also revealed that 36 observation wells and piezometers showed a statistically significant annual increasing trend in DGWL and 34 observation wells and piezometers exhibited a statistically significant decreasing trend in DGWL. Similarly, 32 observation wells and piezometers showed an statistically significant increasing trend and 37 observation wells and piezometers showed a statistically significant decreasing trend in winter; 33 observation wells and piezometers indicated a statistically significant increasing trend and 34 had a statistically significant decreasing trend in post-monsoon; 35 observation wells and piezometers exhibited a statistically significant increasing trend and 32 observation wells and piezometers showed a statistically significant decreasing trend in pre-monsoon; and 36 observation wells and piezometers reflected a statistically significant increasing trend and 30 observation wells and piezometers reflected a statistically significant decreasing trend in monsoon. Interestingly, most of the observation wells and piezometers with increasing trends of DGWL were located in Dausa and Jaipur districts. Furthermore, the GAM analysis revealed that climatic variables, such as precipitation, significantly affected DGWL in Barmer District, and DGWL in all other districts was influenced by anthropogenic variables, including GDP, NIA, and population. As a result, stringent regulations should be implemented to curb excessive groundwater extraction, manage agricultural water demand, initiate proactive aquifer recharge programs, and strengthen sustainable management in these water-scarce regions.

Key words: Climate change, Generalized additive model (GAM), Depth to groundwater level (DGWL), Climatic and anthropogenic variables, Arid and semi-arid regions

Suchitra PANDEY, Geetilaxmi MOHAPATRA, Rahul ARORA. Spatio-temporal variation of depth to groundwater level and its driving factors in arid and semi-arid regions of India[J]. Regional Sustainability, 2024, 5(2): 100143.

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District	Variable	Precipitation	Maximum temperature	Minimum temperature	DGWL	NIA	GDP	Population
Ajmer	Precipitation	1.00
	Maximum temperature	-0.47^*	1.00
	Minimum temperature	-0.15	0.62^*	1.00
	DGWL	-0.74^*	0.55^*	0.16	1.00
	NIA	-0.75^*	0.50^*	0.18	0.90^*	1.00
	GDP	-0.51^*	0.25	-0.23	0.84^*	0.70^*	1.00
	Population	-0.53^*	0.29	-0.26	0.11	0.64^*	0.98^*	1.00
Barmer	Precipitation	1.00
	Maximum temperature	-0.36	1.00
	Minimum temperature	-0.01	0.44^*	1.00
	DGWL	-0.60^*	0.33	-0.11	1.00
	NIA	0.04	-0.21	-0.64^*	-0.26	1.00
	GDP	-0.14	0.02	-0.52^*	-0.40	0.86^*	1.00
	Population	-0.21	0.14	-0.60^*	-0.01	0.95^*	0.86^*	1.00
Dausa	Precipitation	1.00
	Maximum temperature	-0.42^*	1.00
	Minimum temperature	-0.31	0.69^*	1.00
	DGWL	-0.17	0.13	0.22	1.00
	NIA	-0.04	-0.05	-0.27	0.42^*	1.00
	GDP	-0.18	0.25	0.14	0.97^*	0.13	1.00
	Population	-0.23	0.22	0.06	0.76^*	0.27	0.97^*	1.00
Jaipur	Precipitation	1.00
	Maximum temperature	-0.56^*	1.00
	Minimum temperature	-0.40^*	0.71^*	1.00
	DGWL	-0.23	0.29	0.30	1.00
	NIA	-0.25	0.41^*	0.44^*	-0.76^*	1.00
	GDP	-0.43^*	0.20	0.01	0.91^*	-0.47^*	1.00
	Population	-0.47^*	0.22	0.01	0.89^*	-0.54^*	0.98^*	1.00
Jodhpur	Precipitation	1.00
	Maximum temperature	-0.50^*	1.00
	Minimum temperature	-0.01	0.47^*	1.00
	DGWL	-0.31	-0.02	-0.33	1.00
	NIA	-0.35	-0.09	-0.59^*	-0.51^*	1.00
	GDP	-0.44^*	-0.08	-0.59^*	-0.64^*	0.99^*	1.00
	Population	-0.61^*	0.24	-0.53^*	-0.36	0.96^*	0.98^*	1.00
Tonk	Precipitation	1.00
	Maximum temperature	-0.36	1.00
	Minimum temperature	-0.17	0.72^*	1.00
	DGWL	-0.66^*	0.32	0.10	1.00
	NIA	-0.70^*	0.12	0.02	-0.73^*	1.00
	GDP	-0.56^*	0.27	0.10	-0.74^*	0.87^*	1.00
	Population	-0.47^*	0.19	0.02	-0.16	0.82^*	0.95^*	1.00

District	Dependent variable	Independent variable	Degree of freedom	P-value	Adjusted coefficient of determination	AIC	DE (%)
Ajmer	DGWL	Precipitation	1.00	0.00^***	0.53	-15.47	54.70
		Maximum temperature	3.48	0.02^*	0.32	-3.52	41.30
		NIA	5.05	0.00^***	0.85	-41.20	88.70
		GDP	1.00	0.00^***	0.68	-19.08	70.30
Barmer	DGWL	Precipitation	1.00	0.00^***	0.33	-1.87	36.20
Dausa	DGWL	NIA	1.00	0.03^*	0.14	7.60	17.70
		GDP	1.42	0.00^***	0.94	-53.13	94.90
		Population	2.76	0.00^***	0.96	-62.66	97.00
Tonk	DGWL	Precipitation	1.44	0.00^***	0.44	-0.78	47.00
		NIA	1.00	0.00^***	0.51	-4.74	53.70
		GDP	7.35	0.00^***	0.89	-29.04	93.20
Jaipur	DGWL	NIA	4.40	0.00^***	0.69	-15.70	74.70
		GDP	4.38	0.00^***	0.93	-61.70	95.10
		Population	4.98	0.00^***	0.94	-62.55	95.50
Jodhpur	DGWL	NIA	8.70	0.00^***	0.94	-71.74	96.60
Jodhpur	DGWL	GDP	7.01	0.00^***	0.82	-19.50	88.30

District	Model	Independent variable	Adjusted coefficient of determination	AIC	DE (%)	Degree of freedom	F-statistic	P-value	VIF)
Ajmer	Model 1	Precipitation	0.56	-16.69	59.80	1.00	17.47	0.00^***	1.30
	Model 1	Temperature	-	-	-	1.00	3.03	0.09	1.30
	Model 2	Precipitation	0.86	-43.00	87.40	1.00	5.93	0.02^*	2.32
	Model 2	NIA	-	-	-	1.88	15.28	0.00^***	2.32
	Model 3	Precipitation	0.81	-28.85	83.80	1.00	14.00	0.00^***	1.36
	Model 3	GDP	-	-	-	1.75	16.31	0.00^***	1.36
	Model 4	Temperature	0.83	-39.98	85.90	1.00	2.62	0.12	1.34
	Model 4	NIA	-	-	-	1.96	32.20	0.00^***	1.34
	Model 5	Temperature	0.78	-26.76	80.90	1.00	10.51	0.00^***	1.07
	Model 5	GDP	-	-	-	1.00	52.58	0.00^***	1.07
	Model 6	GDP	0.94	-48.61	96.00	4.21	17.70	0.00^***	2.01
	Model 6	NIA	-	-	-	1.46	8.75	0.00^**	2.01
Dausa	Model 1	NIA	0.94	-48.31	95.40	3.88	1.45	0.30	1.02
	Model 1	GDP	-	-	-	1.00	232.26	0.00^***	1.02
	Model 2	NIA	0.95	-55.2	96.00	1.00	3.63	0.07	1.08
	Model 2	Population	-	-	-	1.00	392.67	0.00^***	1.08
Tonk	Model 1	Precipitation	0.73	-16.89	77.20	2.80	5.60	0.00^***	1.98
	Model 1	NIA	-	-	-	1.00	6.06	0.02^*	1.98
	Model 2	Precipitation	0.88	-26.99	93.20	1.00	0.14	0.72	1.46
	Model 2	GDP	-	-	-	7.37	9.63	0.00^***	1.46
	Model 3	GDP	0.96	-47.90	98.30	7.05	19.92	0.00^***	4.34
	Model 3	NIA	-	-	-	2.68	4.19	0.03^*	4.34
Jaipur	Model 1	NIA	0.98	-80.77	99.00	3.24	12.87	0.00^***	1.30
	Model 1	GDP	-	-	-	4.38	65.95	0.00^***	1.30
	Model 2	NIA	0.98	-88.11	99.40	4.75	12.58	0.00^***	1.41
	Model 2	Population	-	-	-	4.08	81.82	0.00^***	1.41

District	Model	Independent variable	Basis checking results				Shapiro-Wilk normality test of GAM residuals
District	Model	Independent variable	Number of basis function	Degree of freedom	k-index	P-value	P-value
Ajmer	Model 6	GDP	9	1.46	1.75	1.00	0.47
Ajmer	Model 6	NIA	9	4.21	1.13	0.61	0.47
Dausa	Model 2	NIA	9	1.00	1.04	0.45	0.32
Dausa	Model 2	Population	9	1.00	1.24	0.79	0.32
Tonk	Model 3	GDP	9	7.05	1.13	0.61	0.28
Tonk	Model 3	NIA	9	2.68	1.66	1.00	0.28
Jaipur	Model 2	NIA	9	4.75	1.09	0.56	0.20
Jaipur	Model 2	Population	9	4.08	1.58	0.99	0.20