Innovative approach applications for groundwater management at the Ajmer division

On the Earth, the vast majority of liquid freshwater is groundwater (GW) (Koutsoyiannis 2020). Across the world, GW is the main source of freshwater for irrigation and drinking (Mileham et al. 2009). As GW resource is necessary for human water supply and it is likely to become much more significant under future climatic conditions, assessment of human vulnerability in the context of the effect of climate change on GW is crucial (Doll 2009). According to climate trend analysis, since the late 19th century, the mean surface temperature (TEMP) of the planet has risen (i.e. around 0.3 to 0.6 °C) (Chen et al. 2004). IPCC reports corroborate apparent increment in the frequency and intensity of heavy precipitation, hot extremes, and heat waves, as well as an increment in precipitation amount at high latitudes and a decline in precipitation of subtropical areas (Dey et al. 2020).

Changes in regional precipitation and TEMP have crucial effects on all aspects of the hydrologic cycle (Kumar 2012). In many countries, GW is a valuable natural resource and it is an important component of the hydrological cycle, serving as the key source of water for domestic, agricultural, and industrial use (Yagbasan 2016). From the above facts, it is clearly evident that climate change has an effect on precipitation and TEMP, which are the important factors affecting GW.

In India, GW has been crucial for the development of irrigated agriculture. GW has made a major influence on rising agricultural production and productivity as well as played a vital role in attaining food security (Patle et al. 2015). Due to over-extraction and mismanagement of water resources, most Indian states, including Rajasthan, Haryana, Gujarat, Delhi, Karnataka, Punjab, Andhra Pradesh, Uttar Pradesh, and Tamil Nadu are facing GW depletion problem (Kumar et al. 2018). Hence, in India study of groundwater levels (GWLs) is very important.

As stated by Central Ground Water Board (CGWB), India (2017), around 66% of assessed units were found to have over exploitation in the Rajasthan state. Analysis of depth to water level for the pre-monsoon (PREMON) season denoted that depth to water level of more than 40 m below ground level (bgl) was observed mostly in the upper central part of the Rajasthan state covering various districts including Nagaur district (CGWB 2020). Stage of GW development in most of the blocks in districts of the Ajmer division has already exceeded 100%, which indicated that the scope of GW development is already exhausted (CGWB 2008, 2013a, 2013b, 2013c). Ajmer division consists of Ajmer, Bhilwara, Nagaur, and Tonk districts (https://haryana.pscnotes.com/prelims-notes/rajasthan-polity-prelimsnotes/divisions-and-districts-of-rajasthan/). For appropriate management of GW resources, appropriate management strategies are necessary. It is essential to know where and why these management strategies are necessary. For this, prioritization of GW blocks and identification of factors affecting declining trends in GW are essential. Thus, two innovative approaches are proposed and applied to know where and why groundwater management strategies are necessary for blocks of districts in the Ajmer division.

The first innovative approach is applied for the prioritization of blocks in the Ajmer division by employing trend analyses in corresponding seasonal GWL time series (TS). Also, first innovative approach is applied for the identification of blocks having declining (significant and non-significant) trends in seasonal GW. The second innovative approach is applied for the identification of factor(s) affecting aforesaid declining trends at blocks in the Ajmer division by performing trend analyses in corresponding rainfall (RF) and MNTEMP TS.

Because of the scientific community's attention to global climate change, trend analysis of hydrometeorological variables (such as RF, TEMP, and so on) has given great concern in recent precedent (Adarsh & Reddy 2015). TS analysis is very effectual in the usage of GW resources and management strategy planning for the development of GW resources as proved by a number of authors (Gibrilla et al. 2018).

Thus, the literature review is carried out on the topic of trend analysis in GWLs. Also, the literature review consists of studies which have identified factors affecting trends in GWLs. Several researchers have carried out studies on the topic of trend analysis in GWLs (Chen et al. 2004; Patle et al. 2015; Singh et al. 2015; Satishkumar & Rathnam 2020, etc). Also, some researchers have identified factors affecting trends in GWLs (Sishodia et al. 2016; Gibrilla et al. 2018; Singh et al. 2019; Islam et al. 2021, etc). On the basis of the literature review, following research gaps are identified: (1) none of the reviewed studies have prioritized the blocks in districts of the Ajmer division, (2) none of the reviewed studies have identified factors affecting declining trends in seasonal GW for districts in the Ajmer division, (3) majority of reviewed studies have not assessed test assumptions, pattern of data in TS for aiding trend analyses results, (4) majority of reviewed studies have not assessed statistical significance, magnitude, beginning and end of the trend and pattern/nature of trend simultaneously in GWL, RF, and MNTEMP TS (1994–2018) corresponding to four seasons for every block in districts of the Ajmer division.

All above mentioned research gaps are fulfilled in the current study by (1) applying the first innovative approach for prioritization of every block in districts of the Ajmer division by employing trend analyses in seasonal GWL TS, (2) identifying the influence of climatic (i.e. RF and MNTEMP) factors on declining seasonal GW by employing second innovative approach, (3) concurrently analyzing trends in seasonal GWL, RF and MNTEMP TS (1994–2018) of every block in districts of the Ajmer division, (4) evaluating the test assumption required for choosing suitable statistical test to assess trend significance, assessing nature or pattern of data in TS by applying innovative trend analyses (ITA) plot and smoothing curve (SMC) and using these to aid trend analyses results, (5) evaluating all aspects of the trend, i.e. statistical significance, magnitude, beginning and end of the trend and pattern of the trend concurrently for seasonal TS of every block in districts of the Ajmer division.

The GWL data corresponding to the period of 1994 to 2018 for every block in districts of the Ajmer division is downloaded from the official website of the CGWB, India (http://59.179.19.250/GWL/GWL.html?UType=R2VuZXJhbA==?UName=, accessed on 24-10-2019). For the preparation of seasonal GWL data for a block in the district of the Ajmer division, GWLs corresponding to each season of each year is averaged to get corresponding seasonal GWL TS. Missing GWL data are filled up by taking an average of the corresponding available data in a given seasonal GWL TS. In some blocks of districts in the Ajmer division, GWL data were available only for the period of 2005 to 2018, thus, for these districts, GWL data are generated by using the Thomas Fiering Model (Mujumdar 2012) corresponding to the period 1994 to 2004.

The daily RF data for every block in the Ajmer division are acquired from the website (https://app.climateengine.org/climateEngine, accessed on 15-03-2021) corresponding to the period of 1994 to 2018. MNTEMP data of every block in districts of the Ajmer division in the Rajasthan state corresponding to the period of 1994 to 2016 are acquired from the website (https://climateknowledgeportal.worldbank.org/download-data, accessed on 20-03-2021). MNTEMP data corresponding to the period of 2017 to 2018 are prepared by applying Thomas Fiering Model (Mujumdar 2012).

For appropriate management of GW resources, appropriate management strategies are necessary. It is essential to know where and why these management strategies are necessary. For this, prioritization of GW blocks and identification of factors affecting declining trends in seasonal GW are essential. Thus, in the present study, two innovative approaches are applied for the prioritization of GW blocks and identifying factors affecting declining trends in seasonal GW. Prioritization of GW blocks is performed by applying trend analysis in seasonal GWLs while identification of factors affecting declining trends in seasonal GW is performed by analyzing trends in climatic parameters, namely RF and MNTEMP. These two innovative approaches can be used anywhere in the world. Therefore, these two innovative approaches are applied at every block in districts of the Ajmer division, Rajasthan corresponding to the period 1994 to 2018. The seasons are namely post-monsoon rabi (POMRB) (January–March), PREMON (April–June), post-monsoon Kharif (POMKH) (October–December), and monsoon (July–September) (Satishkumar & Rathnam 2020).

For trend analyses, various graphical techniques and statistical tests are used in the present study. The implementation of trend detection tests is based on the assumption of observational independence. As a result, it is crucial to consider the effect of serial correlation in hydrological TS while assessing the significance of trends at local scales, because failure to do so could lead to incorrect conclusions (Khaliq et al. 2009). Non-parametric tests make an assumption of temporal independence. Calculating the autocorrelation (AC) function is one technique to quantify the extent of the dependency (correlation) (Kundzewicz & Robson 2000). Therefore, assessment of dependency is very essential.

Thus, in the current study, AC plot (Kundzewicz & Robson 2000) is employed for the evaluation of dependency of data (i.e. whether data are dependent or independent) and it is also used for the selection of suitable statistical tests for the evaluation of trend significance. For the evaluation of trend significance in seasonal GWL TS, Mann Kendall (MK) test (Patle et al. 2015; Singh et al. 2015; Sishodia et al. 2016; Satishkumar & Rathnam 2020, etc) or MK test with block bootstrapping (MKBBS) (Khaliq et al. 2009) are applied. Kundzewicz & Robson (2000) suggested a block resampling technique; block bootstrapping (BBS) is a specialized version of this approach; to incorporate the influence of serial correlation (Khaliq et al. 2009). If the data are dependent MKBBS test is utilized while if data are independent (i.e. serially uncorrelated) MK test is applied for assessing the trend significance.

The type of change, its magnitude, the time when the change occurs in the series, and the length of the series all influence the test's ability to detect the change (Kundzewicz & Robson 2000). Thus, assessment of trend magnitude is essential. Sen's slope (SS) test is the precise, unbiased and powerful test to develop the linear relationship as compared to the regression slope which means it is not much affected by gross data outliers and errors. Thus, the SS test has been extensively utilized to find the change in slope/unit time in the TS (Singh et al. 2019).

Thus, in the current study, the SS test (Patle et al. 2015; Singh et al. 2015; Sishodia et al. 2016, etc) is applied for the assessment of the trend magnitude. In hydrologic TS data, identifying the beginning time of the significant trend is of great concern. Also determining changes in trend over time is crucial in any trend detection study. The sequential MK (SQMK) test is specifically convenient for such change detection analysis (Adarsh & Reddy 2015). Thus, the SQMK test (Adarsh & Reddy 2015) is applied in the present work for the evaluation of beginning of the trend and changes in trend over time.

A change that is consistently in one direction (either always downwards or always upwards) is known as monotonic change (Kundzewicz & Robson 2000). Steadily and monotonic incrementing trends in past records lead to alteration of operation, planning, and management practices of economics, climatologists, meteorologists, atmospheric researchers, and hydrologists alike. Thus, before any future predictions, it is needed to try and identify possible monotonic trend components in any given TS (Sen 2017). Therefore, it is very essential to identify the nature or pattern of the trend (i.e. monotonic or non-monotonic) in the TS. Trends may be either monotonic or non-monotonic. By applying the ITA method, non-monotonic and monotonic trends are identified without any prior conditions of dataset size, distributions, and serial correlations (Satishkumar & Rathnam 2020). Thus, ITA plot (Sen 2017; Satishkumar & Rathnam 2020) is used in the current study for the identification of trend patterns and for supporting trend analyses results along with the use of SMC (Kundzewicz & Robson 2000). Also, some other methods can be used for trend analyses (Iliopoulou & Koutsoyiannis 2020). For assessing trend significance 5% significance level is used in the present study.

Appropriate management strategies are necessary for proper management of GW resources. It is essential to know where and why these management strategies are necessary. For this prioritization of GW blocks and identification of factors affecting declining trends in seasonal GW are essential. Thus, employing two innovative approaches at every block in districts of the Ajmer division is necessary to know where and why GW management strategies are necessary. Following criteria are employed for the prioritization of every block in districts of the Ajmer division performed by employing the first innovative approach: In a corresponding season, the block having a significant positive trend in GWLs with the highest trend magnitude is given first priority while the block having significant positive trend along with the second-highest magnitude is given second priority and so on. After a significant positive trend in GWLs, a non-significant positive trend is given priority. In a corresponding season, a non-significant positive trend in GWLs having a higher trend magnitude is given higher priority than that of a lower trend magnitude. After a non-significant positive trend in GWLs, a non-significant negative trend is given priority. After a non-significant negative trend in GWLs, a significant negative trend is given priority. In case of a negative trend, a lower magnitude of trends is given higher priority than that of a higher magnitude.

In the current work, two innovative approaches are applied at every block in districts of the Ajmer division, Rajasthan state. Two approaches are applied based on trend analyses performed in seasonal GWL, MNTEMP, and RF TS of every block in districts of the Ajmer division. For the assessment of the dependency of data and selection of suitable statistical test, an AC plot is used. For assessment of trend significance MK and MKBBS tests are used while for assessing trend magnitude SS test is used. For supporting trend analyses results, the ITA plot and SMC are used while for the assessment of beginning and end trends SQMK test is utilized. Aforesaid plots are shown only for POMRB and PREMON seasons for one block corresponding to GWL, MNTEMP, and RF TS, because of space constraints. Because of space constraints, a map showing spatial distribution of trends in GWLs, MNTEMP, and RF is shown only for the Nagaur district corresponding to the POMRB season.

Trend analyses results for POMRB, PREMON, monsoon, and POMKH seasons with prioritization of every block in districts of the Ajmer division are given in Tables 1,²³–4, respectively.

From Table 1, it can be observed that Degana, Jayal, Ladnun, Mundwa, Parbatsar, and Riyan blocks of the Nagaur district are having significant incrementing trends in GWLs (i.e. significantly declining GW) while incrementing trends in GWLs (i.e. declining GW) are found at Kuchaman, and Makrana blocks of the Nagaur district. Also, Table 1 shows that Merta, Didwana, and Nagaur blocks of the Nagaur district are having declining trends in GWLs (i.e. incrementing GW). Every block in the Nagaur district is showing an incrementing trend in MNTEMP. A significant declining trend in RF is found at the Didwana block of Nagaur district while declining trends in RF are found at Digana, Jayal, Ladnun, Mundwa, Makrana, and Nagaur blocks of the Nagaur district. Incrementing trends in RF are found at Riyan, Parbatsar, Kuchaman, and Merta blocks of the Nagaur district.

Also, Table 1 shows that the Mandal block of the Bhilwara district is having a significant incrementing trend in GWLs while incrementing trends in GWLs are found at Asind, Jahazpur, Mondalgarh, Shahapura, and Suhana blocks of the Bhilwara district. Also, Table 1 shows that Hurda, Kotri, Banera, Raipur, and Sahada blocks of the Bhilwara district are having declining trends in GWLs. Every block in the Bhilwara district is showing an incrementing trend in MNTEMP. Also, a significant declining trend in RF is found in the Mandalgarh block of the Bhilwara district while declining trends in RF are found at the Hurda, Kotri, Mandal, and Shahpura blocks of the Bhilwara district. Incrementing trends in RF are found at Jahazpur, Asind, Suwana, Banera, Raipur, and Sahada blocks of the Bhilwara district.

Also, Table 1 shows that the Nawai block of the Tonk district is having a significant incrementing trend in GWLs while incrementing trend in GWLs is found at the Uniara block of the Tonk district. Also, Table 1 shows that Deoli, Tonk, and Malpura blocks of the Tonk district are having declining trends in GWLs while a significantly declining trend in GWLs (i.e. significantly incrementing GW) is observed at the Todaraisingh block of the Tonk district. Every block in the Tonk district is showing an incrementing trend in MNTEMP. Also, a declining trend in RF is found at every block (except the Deoli block) of the Tonk district. An incrementing trend in RF is found at the Deoli block of the Tonk district.

Also, Table 1 shows that the Masuda block of the Ajmer district is having an incrementing trend in GWLs. Also, Table 1 shows that Arain, Srinagar, Pisangan, Kekri, Jawaja, and Bhinai blocks of the Ajmer district are having declining trends in GWLs. Every block in the Ajmer district is showing an incrementing trend in MNTEMP. Also, a declining RF trend is found at every block (except the Pisangan block) in the Ajmer district while incrementing trend in RF is found at the Pisangan block of the Ajmer district.

Also, Table 1 shows significantly declining GW at Jayal, Mundwa, Ladnun, Degana, Mandal, and Nawai blocks of the Ajmer division for the POMRB season may be because of the incrementing MNTEMP and declining RF. Also, Table 1 shows that significantly declining GW is found at Riyan and Parbatsar blocks of the Ajmer division for the POMRB season while MNTEMP is incrementing and RF is also incrementing. It may be because of excessive withdrawal of GW. Also, Table 1 shows declining GW at Kuchaman, Jahazpur, Asind, and Suwana blocks of the Ajmer division for the POMRB season while MNTEMP is incrementing and RF is also incrementing. It may be because of excessive withdrawal of GW. Also, Table 1 shows that declining GW is found at Makrana, Shahpura, Uniara, and Masuda blocks of the Ajmer division for the POMRB season may be because of declining RF and incrementing MNTEMP. Also, declining GW is found at the Mandalgarh block of the Ajmer division for the POMRB season may be because of significantly declining RF and incrementing MNTEMP.

From Figure 6, it can be observed that POMRB GW at Degana, Jayal, Ladnun, Mundwa, Parbatsar, and Riyan blocks of the Nagaur district is found to be significantly declining due to increment in MNTEMP and declining RF at most in the blocks of the Nagaur district.

From Table 2, it can be found that Degana, Jayal, Parbatsar, and Riyan blocks of the Nagaur district are having significant incrementing trends in GWLs while incrementing trends in GWLs are found at Ladnun, Makrana, and Mundwa blocks of the Nagaur district for the PREMON season. Also, Table 2 shows that Nagaur, Merta, and Kuchaman blocks of the Nagaur district are having declining trends in GWLs while a significantly declining trend in GWLs is found at the Didwana block of the Nagaur district. Every block in the Nagaur district is showing an incrementing trend in MNTEMP. An incrementing trend in RF is found at every block of the Nagaur district.

Also, Table 2 shows that Asind, Banera, Mandal, Raipur, Sahada, Sahpura, and Suwana blocks of the Bhilwara district are having incrementing trends in GWLs. Also, Table 2 shows that Jahazpur, Kotri, Mandalgarh, and Hurda blocks of the Bhilwara district are having declining trends in GWLs. Every block in the Bhilwara district is showing an incrementing trend in MNTEMP. Also, the declining trend in RF is found at every block (except Hurda block) of the Bhilwara district while incrementing trend in RF is found at the Hurda blocks of the Bhilwara district.

Also, Table 2 shows that the Nawai block of the Tonk district is showing a significant incrementing trend in GWLs while incrementing trend in GWLs is found at the Uniara block of the Tonk district. Also, the Tonk block of the Tonk district is showing a declining trend in GWLs while significantly declining trends in GWLs are found at Deoli, Malpura, and Todaraisingh blocks of the Tonk district. Every block in the Tonk district is showing an incrementing trend in MNTEMP. Also, declining trends in RF are found at Deoli, Todaraisingh, Tonk, and Uniara blocks of the Tonk district while incrementing trends in RF are found at Nawai and Malpura blocks of the Tonk district.

Also, Table 2 shows that the Masuda block of the Ajmer district is having incrementing trend in PREMON GWL TS while Arain, Kekri, Srinagar, Jawaja, Bhinai, and Pisangan blocks of the Ajmer district are having declining trends in GWLs. Every block in the Ajmer district is showing an incrementing trend in MNTEMP. Also, a declining trend in PREMON RF is found at the Arain block of the Ajmer district while incrementing trend in RF is found at every block (except Arain block) of the Ajmer district.

Also, Table 2 shows that significantly declining GW is found at Riyan, Jayal, Parbatsar, Degana, and Nawai blocks of the Ajmer division for the PREMON season while MNTEMP is incrementing and RF is also incrementing. It may be because of excessive withdrawal of GW. Also, declining GW is found at Mundwa, Ladnun, Makrana, and Masuda blocks of the Ajmer division for the PREMON season while MNTEMP is incrementing and RF is also incrementing. It may be because of excessive withdrawal of GW. Also, declining GW is found at Mandal, Asind, Sahada, Raipur, Banera, Suwana, Shahpura and Uniara blocks of the Ajmer division for the PREMON season may be because of incrementing MNTEMP and declining RF.

Figures 9(a)–9(c) are presenting SMC plots of GWL (a), MNTEMP (b), and RF (c), respectively for the PREMON GWL TS (1994–2018) of the Didwana block in the Nagaur district. Figure 9(a) shows a declining pattern in the GWL data, which aids the negative significant trend observed in the given TS. Figure 9(b) shows incrementing pattern in MNTEMP data, which aids the positive trend observed in the given TS. Figure 9(c) shows incrementing pattern in RF data, which aids the positive trend observed in the given TS.

From Table 3, it can be observed that Degana, Ladnun, and Parbatsar blocks of the Nagaur district are having significant incrementing trends in GWLs while incrementing trends in GWLs are found at Riyan, Jayal, Mundwa, Merta, and Makrana blocks of the Nagaur district. Also, Table 3 shows that Kuchaman, Nagaur, and Didwana blocks of the Nagaur district are having declining trends in GWLs. Every block in the Nagaur district is showing a significantly incrementing trend in MNTEMP. An incrementing trend in RF is observed at every block of the Nagaur district.

Also, the Mandal block of the Bhilwara district is showing a significant incrementing trend in GWLs while incrementing trends in GWLs are found at Raipur, Jahazpur, Asind, Banera, Suwana, Sahada, Mandalgarh, and Kotri blocks of the Bhilwara district. Also, Shahpura and Hurda blocks of the Bhilwara district are showing declining trends in GWLs. A significant incrementing trend in MNTEMP is observed at the Hurda block of the Bhilwara district while remaining blocks of the Bhilwara district are showing incrementing trends in MNTEMP. An incrementing trend in RF is observed at every block of the Bhilwara district.

Also, the Nawai block of the Tonk district is showing a significant incrementing trend in GWLs while incrementing trend in GWLs is found at the Uniara block of the Tonk district. Also, Table 3 shows that Deoli, Todaraisingh, Tonk, and Malpura blocks of the Tonk district are having declining trends in GWLs. Significant incrementing trends in MNTEMP are observed at Nawai, Uniara, Deoli, and Tonk blocks of the Tonk district while Todaraisingh and Malpura blocks of the Tonk district are showing incrementing trends in MNTEMP. Also, declining trends in RF are found at Tonk and Uniara blocks of the Tonk district while incrementing trends in RF are found at Nawai, Deoli, Todaraisingh, and Malpura blocks of the Tonk district.

Also, every block (except the Bhinai block) of the Ajmer district is showing an incrementing trend in monsoon GWLs. Also, the Bhinai block of the Ajmer district is showing a declining trend in GWLs. Significant incrementing trends in MNTEMP are observed at Masuda, Pisangan, Jawaja, Srinagar, and Bhinai blocks of the Ajmer district while Arain, Silora, and Kekri blocks of the Ajmer district are showing incrementing trends in MNTEMP. An incrementing trend in RF is found in every block of the Ajmer district.

Also, significantly declining GW is found at Degana, Ladnun, Parbatsar, and Nawai blocks of the Ajmer division for the monsoon season while MNTEMP is significantly incrementing and RF is also incrementing. It may be because of excessive withdrawal of GW. Also, significantly declining GW is found at the Mandal block of the Ajmer division for the monsoon season while MNTEMP is incrementing and RF is also incrementing. It may be because of excessive withdrawal of GW. Also, declining GW is found at Riyan, Jayal, Mundwa, Merta, Makrana, Masuda, Pisangan, Jawaja and Srinagar blocks of the Ajmer division for the monsoon season while MNTEMP is significantly incrementing and RF is also incrementing. It may be because of excessive withdrawal of GW. Also, declining GW is found at Raipur, Jahazpur, Asind, Banera, Suwana, Sahada, Mandalgarh, Kotri, Arain, and Kekri blocks of the Ajmer division for the monsoon season while MNTEMP is incrementing and RF is also incrementing. It may be because of excessive withdrawal of GW. Also, declining GW is found at the Uniara block of the Ajmer division for the monsoon season may be because of significantly incrementing MNTEMP and declining RF.

From Table 4, it can be observed that Riyan, Jayal, Ladnun, Degana, and Parbatsar blocks of the Nagaur district are having significant incrementing trends in GWLs while incrementing trends in GWLs are found at Mundwa, Kuchaman, Makrana and Merta blocks of the Nagaur district. Also, Table 4 shows that Didwana and Nagaur blocks of the Nagaur district are having declining trends in GWLs. Every block in the Nagaur district is showing a significantly incrementing trend in MNTEMP. Also, declining trends in RF are found at Jayal, Ladnun, Prabatsar, Makrana, Didwana and Kuchaman blocks of the Nagaur district while incrementing trends in RF are found at Riyan, Degana, Mundwa, Merta, and Nagaur blocks of the Nagaur district.

Also, the Mandal block of the Bhilwara district is showing a significant incrementing trend in GWLs while incrementing trends in GWLs are found at Asind, Jahazpur, Suwana, Shahpur, Banera, and Raipur blocks of the Bhilwara district. Also, Mandalgarh, Hurda, Kotri, and Sahada blocks of the Bhilwara district are showing declining trends in GWLs. Every block in the Bhilwara district is showing a significantly incrementing trend in MNTEMP. An incrementing trend in RF is observed at every block of the Bhilwara district.

Also, the Nawai block of the Tonk district is showing significant incrementing trend in GWLs while an incrementing trend in GWLs is found at the Uniara block of the Tonk district. Also, Table 4 shows that Todaisingh, Deoli and Tonk blocks of the Tonk district are showing declining trends in GWLs while a significantly declining trend in GWLs is found at the Malpura block of the Tonk district. Significant incrementing trends in MNTEMP are observed at Nawai, Todaraisingh, Deoli, Tonk, and Malpura blocks while the Uniara block is showing an incrementing trend in MNTEMP in the Tonk district. An incrementing trend in RF is observed at every block of the Tonk district.

Also, the Masuda block of the Ajmer district is showing incrementing trend in GWLs. Also, Table 4 shows that Arain, Kekri, Jawaja, Pisangan, Srinagar, and Bhinai blocks of the Ajmer district are showing declining trends in GWLs. Every block in the Ajmer district is showing a significantly incrementing trend in MNTEMP. An incrementing trend in RF is found at every block of the Ajmer district.

Also, Table 4 shows that significantly declining GW is found at Riyan, Degana, Mandal, and Nawai blocks of the Ajmer division for the POMKH season while MNTEMP is significantly incrementing and RF is also incrementing. It may be because of excessive withdrawal of GW. Also, significantly declining GW is found at Jayal, Ladnun, and Parbatsar blocks of the Ajmer division for to the POMKH season may because of significantly incrementing MNTEMP and declining RF. Also, declining GW is found at Mundwa, Merta, Asind, Jahazpur, Suwana, Shahpura, Banera, Raipur, and Masuda blocks of the Ajmer division for the POMKH season while MNTEMP is significantly incrementing and RF is also incrementing. It may be because of excessive withdrawal of GW. Also, declining GW is found at the Uniara block of the Ajmer division for the POMKH season while MNTEMP is incrementing and RF is also incrementing. It may be because of excessive withdrawal of GW. Also, declining GW is found at Kuchaman and Makrana blocks of the Ajmer division for the POMKH season may be because of declining RF and significantly incrementing MNTEMP.

For proper management of GW resources, appropriate management strategies are necessary. It is essential to know where and why management strategies are necessary. For the prioritization of GW blocks and identification of factors affecting declining trends in GW is essential. In the present study, two innovative approaches are used for the prioritization of GW blocks and for identifying factors affecting declining trends in seasonal GW. From the present study following conclusions are derived.

• Incrementing MNTEMP and declining RF at most of the blocks are found to be the reasons behind the significantly declining GW at eight blocks of the Ajmer division corresponding to the POMRB season.

• Incrementing MNTEMP at most of the blocks is found to be the reason behind the significantly declining GW at five blocks of the Ajmer division corresponding to PREMON season, although RF is incrementing.

• Significantly incrementing MNTEMP at most of the blocks and incrementing MNTEMP at very few blocks are found to be the reasons behind the significantly declining GW at five blocks of the Ajmer division corresponding to monsoon season, although RF is incrementing.

• Significantly incrementing MNTEMP at most of the blocks and declining RF at some blocks are found to be the reasons behind the significantly declining GW at seven blocks of the Ajmer division corresponding to the POMKH season.

• These innovative approaches will be useful for identification of appropriate GW management strategies needed for the proper management of GW resources.

The authors are thankful to Central Ground Water Board, India, Climate Change Knowledge Portal, and Climate Engine Website for making necessary data available, which are used in the present study. The authors are also grateful to the Centre of Excellence on ‘Water Resources and Flood Management’, Department of Civil Engineering, SV NIT, Surat.

Data cannot be made publicly available; readers should contact the corresponding author for details.

The authors declare there is no conflict.