GIS-remote sensing-based village-level hydrological balance approach for agricultural water planning

Ensuring food security through the increase in food production can be realised by converting agricultural fallow lands into cultivable ones and assuring irrigations in three crop seasons in all agricultural lands. That can be done through a village (i.e., mouza) level water management planning through rainwater harvesting. This needs step by step procedures based on hydrologic balance for providing the best way of management of water resources to secure precious agricultural lands from man-made degradation. This study was conducted at a mouza Gohalura in the Red and Laterite Zone of West Bengal, India. In that village major crops grown were ‘Aman’ rice (A practices of Rice cultivation by transplanting in rainy season and harvested in early winter season) during the rainy (i.e., monsoon) season; Groundnut during Rabi under both rainfed and irrigated farming situations and ‘Boro’ rice (a practice of rice cultivation by transplanting in late winter and harvesting in early summer) in summer. The major problems in crop production in that village were some rainfed agricultural lands with erratic and uncertain rainfall of which about 26 percent (i.e., 464 mm) received during non-monsoon period (15 October to 7 June), and a high infiltration rate of soil. Shortfall in annual water balance of 248.13 mm could be managed through existing river lift irrigation from the adjoining river Dulung, a tributary of the Subarnarekha River. Application of GIS and remote sensing were useful in land use land cover classification, creation of a digital elevation model of the village and calculation of areas under individual classes of land. Creating and renovating water harvesting ponds in the mouza would facilitate multipurpose benefits for the farming community including agriculture in three crop seasons in all cultivable lands through such rational water management planning in that village. Following such village-wise water balance approach another 16.37 ha (i.e., agricultural fallow out of the total agricultural land area of 40.23 ha) i.e., about 69 per cent increase in irrigated area could be made possible. Such methodologies could be projected for other areas, and that could be followed in other areas also.


Introduction Introduction Introduction Introduction
Interaction of agricultural water management for ensuring food security, nutrition and health, and their combined outcomes has become an insatiable question for solving present-days' intense problems of water, food and health security. So, necessarily the questions arise on (i) increasing agricultural water security towards improving food and nutrition securities, (ii) advancing methodologies and data acquisition for joint monitoring of water and food security interventions and (iii) finding out policy for agricultural water management to meet projected changes in diets and water security. Such works necessarily require an advancing state of science and knowledge to integrate research into policy, development, and practice. Despite the substantial progress in health infrastructures in the present times, the overall conditions are that one in three people do not have access to safe drinking water and two out of five people do not have a basic hand-washing facility with soap and water (WHO, 2019). Such situations still prevail mostly rural areas for billions of people lacking the basic services of safe clean water and sanitation and inadequate funding. For that reason, UN SDG (2021) Goal 6 has been listed to ensure access to water and sanitation for all populations. Anticipating almost forty percent shortfall in global freshwater resources by 2030 combined with increasing population, requires attention towards the global water crisis and, thereby apprehending growing challenge of water scarcity. The UN General Assembly has undertaken activities for mobilizing water management under the programme of Water Action Decade 2018 -2028 on 22 March 2018 . In the meantime, UN SDG (2020) Goal 2 towards zero hunger has been necessarily enumerated towards increasing food production for achieving Sustainable Development Goal (SDG) No. 2.4 of Zero Hunger of the United Nations by 2030 with a single Target Indicator (2.4.1) of the proportion of agricultural area under productive and sustainable agriculture.

Water in health care
In the health care facilities, hundreds of millions of people face an increased risk of infection even now due to a lack of basic necessities including water (WHO, 2015). To improve water, sanitation, and hygiene (WASH) services in health care WHO and UNICEF, co-leading the implementation of a global road map built from country-led initiatives, have identified eight practical steps including actions for improving infrastructure, maintenance, and engaging communities (WHO and UNICEF, 2019). Generating infrastructure for more water availability through rain water harvesting at the village level requires more engagement of man-power in this regard towards the creation of greater leadership and accountability within/among community members as a whole. Consequently, four pillars of the science-policy interface, are identified, through Agropolis International (2021), like (i) generating actionable knowledge, (ii) articulating models, knowledge and, place-based innovation, (iii) connecting expertise mechanisms towards sustainable development, and (iv) strengthening scientific cooperation. Thus, the bonding of science and policy requires research on agricultural water management to focus on improving the production and efficiency and environmental impact of water use both in rainfed and irrigated systems as well. Agricultural water management is a key intervention towards shaping nutrition and health outcomes and conserved water is the potential accessory of infrastructure for health facility/facilities.

Rationality in agricultural water use
Water is a free gift of nature, and it is an essential resource for sustaining life and environment. Water is the key to human life as well as food security and nutrition. Good quality and enough water are needed for agricultural production and processing of food. Out of about 70 percent of all water withdrawals globally for agriculture purpose, 40 percent of irrigation uses groundwater sources (Rockström et al., 2014). Climate change affects the seasonal patterns of precipitations, with impacts on agriculture (HLPE, 2015). Water security is a broad term commonly used to describe innumerable water challenges (Bigas, 2013). Due to the prevalence of severity of water issues worldwide, many interconnections between water and nutrition and the global nutrition community or both are of present interest. Global water crises of insufficient, excessive, and/or polluted water have aggravated in recent times due to climate change. Sustaining such troubles for with increasing numbers of populations is mostly occurring as a challenge in developing countries (OECD, 2015;WEF, 2020). According to WHO and UNICEF, globally 4 percent of the urban populations and 18 percent of the rural populations (47 percent of them in Sub Saharan Africa) are still lacking access to improved drinking water sources and 25 percent of the populations are unable to access to improved or shared sanitation (HLPE, 2015). On the other hand, ensuring food for all population in 2050, global food production is required to be increased by 50 percent between 2012 to mid-century (FAO, 2017). In the developing countries, water demand will continue to rise, it represents the largest share of total water demand which is expected to grow three times further in this period. According to the World Water Development Report (UNESCO, 2018), climate change and land cover change can affect or moderate future agricultural water demand by increasing (e.g., through more intense precipitation) or reducing average water discharge (through an increase in evapotranspiration). Higher temperatures have increased snowmelt, while the expansion of agricultural areas and low water holding capacity, have increased surface runoff which causing recurrent flood (i.e., excess water) and drought (i.e., deficit water) situations at watershed level (Dieguez and Paruelo, 2017;Van der Esch et al., 2017;Sonneveld et al., 2018).
Planning for village-level rainwater harvesting: Water scarcity is already prevalent in many regions of the world and it is now becoming more widespread and prominent. The available water resources are under pressure due to faster industrialisation, urbanization and day by day increase in water demand by ever growing population. In India, the main source of water is rainfall during the monsoon season. The rainfall is mainly confined to the months from June to September. But it is not regular and erratic with respect to both time and place. Now a day's drought and floods are unavoidable recurrent natural hazards in different parts of the country. As the requirement for agricultural production is expected to rise steeply by 2025, India must concentrate on managing increased area under irrigation and improving the productivity of both land and water to meet the needs of the population (Hussain, 2006). The water demand is increasing day by day due to several factors including an increase in population growth for which water has become a scarce resource. For India, being powerful in agriculture, the use of the critical resource of water is still lacking well management for ensuring food security through increased productivity in agriculture. Conservation of water resources is urgently required to be done during the rainy season. Water management has always been practised in our communities since ancient times, but today this must be done on a priority basis (Deshmukh, 2006). This can be realised through planning for water management at mouza (village) level based on hydrological balance (Panda, 2005;Saren, 2019).
Rainwater harvesting for ensuring food production and health care in rural areas Considering the troubles in the global water and health crisis (UN SDG 6), population growth, as well as ensuring zero hunger (UN SDG 2), the present studies have considered four primary topics; (i) farm-level water management in a village basis, (ii) crop water relations and crop yields, (iii) irrigation and drainage in cultivated areas and (iv) rainwater harvesting and crop water management in rainfed areas of red and lateritic tracts of Jhargram district in West Bengal state in India. For increasing food production, improvement in cropping intensity could be ensured through the present studies. That could be achieved through village-level crop water management planning based on hydrological balance approach for rainwater harvesting, and thereby, increasing provision for irrigation for crop cultivation. Thus, that would be helpful towards maintaining ecosystems and strengthening capacity of community for adaptation to climate change, drought, flood and for progressively improving land, soil quality and human health. Crop production on red and laterite soil under rainfed condition is low and unstable, because of erratic and uncertain rainfall. That soil has a low water holding capacity with high infiltration. The lateritic zone is an undulating tract of high rises and low valleys. The soil is generally formed of poor rooting conditions occasionally with gravels, stone and morum. Those marginal lands are not able to sustain arable crops particularly during the drought years. Planning water harvesting in farm ponds on a village (i.e., mouza, meaning conglomerated administrative recorded land holdings of the concerned community within a single boundary) basis is suitable for farmers for the purpose of construction of such ponds. But those farm ponds would be needed to be built-up on the basis of hydrological balance approach. Because, water conserved in those ponds would be sufficient for one or two other crops after monsoon and /or one or two irrigations for sustaining crops even during dry spells within monsoon period. This is the suitable way for minimising misuse of land resources and securing precious agricultural land from man-made degradation (Annon, 2007;Nazem, 2011;Ghosh and Indu, 2019). Again, major and / or medium and minor irrigation projects in the command areas have also reached potential capacities. To ensure sustainable crop production through excavation of mouza wise farm ponds and managing conserved rainwater needs step by step methods for collecting data based on the hydrologic balance equation (Murty, 1985;Panda and De, 1989;Panda et al., 1990;Singh et al., 1990;Tideman, 2000;Panda, 2005;Saren, 2019). Village wise farm ponds are suitable for climate resilient agriculture and have shown a great role in enhancing the survivability of many species and conservation of the ecosystem for which a new policy direction has been chalked out for global pond conservation (Bastakoti et al., 2016;Phadke et al., 2018;Partelow et al., 2018;Hill et al., 2018;Lewis-Phillips et al., 2019). Present work on village-level rainwater harvesting involving GIS-remote sensing In this paper, planning of irrigation from village (i.e., mouza) based rainwater harvesting ponds is detailed through procurement and estimations of the components of water balance equation (i.e., meteorological, geo-hydrological and crop water related data) for the concerned locality. Application of GISremote sensing and on-field monitoring of data for water balance would be essential for planning of irrigation. Thus, this approach would be helpful for increased water storage in rainwater harvesting ponds. This hydrological planning would be beneficial for mitigation of water scarcity during non-monsoon months and occasional dry spells during monsoon period for securing the production of crops and as well as ensuring increased cropping intensity for more food production in the village (i.e., mouza).

Study area
The study has been carried out in the village Mouza Gohalura, J. That village is about 19.14 km away in the south-western direction from the district Headquarter, Jhargram (22.4550°N Lat., 86.9974°E Long.) and 43.41 km in the west-southern direction from the erstwhile Paschim Medinipur District Headquarter, Medinipur (22.4257°N Lat., 87.3199°E Long.). Cultivable Land Utilization in the mouza can be classified under rainfed and irrigated areas in kharif (i.e., rainy), rabi (i.e., winter) and summer seasons crops (Table 1).
There is about 1250 population in 250 families with 60 percent of families engaged in agriculture, and ten families are solely dependent on agriculture (Saren, 2019). As per 'The Watershed Atlas of India' (AISLUS, 1984) the area is within the Water Resource Region 4 (Rivers falling into the Bay of Bengal, other than the Ganga and Brahmaputra System), Mahanadi to Ganges Basin (H), Subarnarekha Catchment (4H3), Sub Catchment -Lower Subarnarekha up to confluence with Kharkai (4H3A), Dulung Watershed (4H3A3).
Agroclimatic, the area is within the Red and Laterite Zone (Ghosh, 1991) in West Bengal (Sen Gupta et al., 2001). Agriculture in the mouza is largely dependent on River Lift Irrigation RLI (20 ha, 2018-19) from the adjoining Dulung River, one of the (left bank) tributaries of the Subarnarekha and the rest quantum of agricultural land (8 ha) is rainfed. Apart from those about 4.59 ha are waste lands, which can also be taken under cultivation. For those rainfed and waste lands rainwater harvesting is required. Due to climatic anomalies in some years when RLI cannot afford supplying irrigation to some plots, farm ponds may supply lifesaving irrigation during kharif season also. Three major crops are grown in the mouza aman or kharif rice (A practices of Rice cultivation by transplanting in rainy season and harvested in early winter season), groundnut as rabi crop and boro rice (a practice of rice cultivation by transplanting in late winter and harvesting in early summer). Both aman rice and rabi groundnut are raised under rainfed and irrigated farming situations (Saren, 2019).  Data sources In the present study, both the primary and secondary data were collected from various sources. Primary data were extracted from a Sentinel -2 satellite image of the study area for the year of 2020 downloaded from USGS Earth Explorer (Table 2) to explore the areas under different types of lands and cropping pattern in Mouza Gohalura under Jhargram district. SRTM 1 Arc-Second Global satellite image of that village was also downloaded from USGS Earth Explorer, (2020). The secondary data, official Mouza map were collected from village Panchayet office under Gopiballavpur -II Block and Land Use Land Cover (LULC) information of satellite imagery for the year of 2020 was collected from Google Earth Pro, (2020) platform.
Sentinel -2 satellite image consists of 13 spectral bands (USGS EROS Archive -Sentinel-2, 2020), with different wave lengths, out of which Bands 2 to 4 and 8 with 10-meters spatial resolution, were selected for creating a composite image and obtaining a better spectral reflectance in this experiment.

Image processing
The satellite imageries of 2020 were geometrically corrected to the Datum WGS84 and projected coordinate system UTM zone N45 (Bhunia et al., 2012). Some image enhancement techniques were applied to fix atmospheric error correction and obtain high quality images. The different bands of Sentinel -2 imageries having 10-m resolution were selected for layers stacking to get composite imagery of entire study area using ERDAS IMAGINE software (Dolui et al., 2014). After the creation of AOI layer, a sub-setting was applied to extract the study area from the composite imagery. Supervised classification, based on Maximum Likelihood (parametric rule) algorithm, was performed to classify the pre-processed image into different training sites of each land use class for the year 2020. Then the signatures of infinite numbers, based on their Digital Number (DN) values and spectral information of individual classes or categories, were collected. Then merging of those signatures were done and classified as per different land classes viz. water body, dense forest, open land/ sand dune, agricultural fallow, agriculture land/ crop land. Then areas (ha) of all individual classes were calculated from the classified Sentinel -2 imagery of mouza Gohalura under Jhargram district.  Bhunia et al., 2012).

Accuracy assessment of classified image
After completing the image classification process, accuracy assessment of the classified image was done through ERDAS Imagine (2015) software accuracy assessment tool to create 70 random points of pixels from resulting mouza image and checking their labels against classes determined from reference data. The results were expressed in tabular form recognized as the Error Matrix, previously presented by (Congalton, 1991;Bhunia et al., 2012). The two different measures viz. user's and producer's accuracy could be derived from the values of Error Matrix as generated from the ERDAS Imagine software. Finally, accuracy of resulting map was checked on the basis of Kappa coefficient (Hudson and Ramm, 1987;Congalton and Green, 1999).

Analysis of hydrological balance Collection of meteorological data
The mouza is about 12 km distant from Asui-Dharampur River gauge -Rain gauge station on the Subarnarekha River. Daily rainfall data for the period of 10 years (2009 to 2018) were collected for analysing Onset of Effective Monsoon (OEM), End of Effective Monsoon (EEM) and Critical Dry Spells (CDS) in the study area (Raman, 1974;Ashok Raj, 1979). The normal monthly (maximum and minimum) air temperature data of Medinipur town (43.41 km from the study area) were used to estimate Potential Evapotranspiration (PET) of the study area (India Water Portal, 2019). Such distance may be taken as granted because in original works of C.W. Thornthwaite (1948), distance between any two stations was not more than 40 miles (about 64.34 km) (Doerr, 1961).

Analysis of daily rainfall data and monsoon
Analysis of daily rainfall data (2009 to 2018) was performed to find out Onset of Effective Monsoon (OEM) based on the criterion of 7-days' spell satisfying, the total rain not less than (5e + 10) mm (Ashok Raj, 1979). Critical Dry spell (CDS) is the length of period in days of the gap between the end of one OEM with beginning of another 7-days' spell. When the above criterion was not continued in the season, the onset of last CDS was considered as EEM. Total amount of rainfall during monsoon period in a year is calculated on days between the OEM to EEM. Mean of the dates of OEMs and EEMs of 10 years (2009 to 2018) was found out for crop planning based on amount of rainwater and crop evapotranspiration, estimated on the basis of normal mean monthly air temperature data.
Analysis of daily air temperature data and measurement of ET in the field From normal mean monthly maximum and minimum air temperature data PET in the area was calculated through Thornthwaite formula (Thornthwaite, 1948;Doerr, 1961). Consumptive use of water for crops were estimated using Blaney-Criddle formula as shown in Eqn. 1 (Blaney and Criddle, 1962)   Measuring percolation loss of water in the crop field Percolation loss of stored water from crop field was measured through Inverse auger hole method ( Figure 3) (Ojha et al., 2017).
Estimating water requirement for special needs The soil samples from five categories (based on water retention capacities of surface soils as experienced by the farmers themselves) of crop fields ware collected for analysis the pH and ECe (Ghosh et al., 1983) for ascertaining any special water requirements in the field, like draining out excess salts from root zone depth. Figure 3. Figure 3. Figure 3. Figure 3. Inverse auger hole to determine permeability in field at Gohalura Mouza (Based on Ojha et al., 2017) Estimating rainfall recharge Mechanical analysis of soil samples was done following the method described by (Piper, 1966). Rainfall recharge to groundwater was estimated by following Datta et al. (1973) formula: P=0.4×R×e^(-0.046C) (2) where: P = rainfall recharge (cm); R = annual rainfall (cm); C = clay percentage of topsoil.

Estimation of hydrological or water balance
For rabi and summer crops the required amount of irrigation water was calculated on the basis of water balance approach (Michael, 1978) (Figure 4 and Table 3A). The total area under Water Bodies (pond + river) was 4.27 ha which was slightly lower than 1/5 th (4.772 ha) of total agriculture land (23.86 ha). While area of stored water pond (0.75 ha), represented about 3.14 percent of total agriculture land of that village. The identified Error Matrix of classified image, developed from the supervised classification technique, showed 88.57 percent overall accuracy with overall Kappa (K^) statistics value of 0.80 (Table 3B).    The Digital Elevation Model (DEM) analysis showed the general land slope from north eastern to south western direction in the mouza. Most of the low lands were found in eastern sides whereas the western portion was with highly elevated lands. The low and medium lands are more than two thirds of total area of Mouza Gohalura ( Figure 5, Figure 6, and Table 4).       Analysis of monthly air temperature Based on Thornthwaite formula (Thornthwaite, 1948;Xu and Singh, 2001), Potential Evapotranspiration was estimated using normal monthly air temperature data (Table 5).
It was estimated that the annual total PET for Gohalura mouza was 1867.60 mm. Minimum and maximum PET values were for the months of January (4.29 mm for the month with 1.38 mm/day) and March (28.35 mm for the month with 9.15 mm/day) respectively (Table 5).
Based on Blaney Criddle formula (Blaney and Criddle, 1962;Michael, 1978) monthly consumptive use of water for crops grown in mouza was computed using normal monthly air temperature data (Tables 6A and  6B). It was found that minimum (5.12) and maximum (8.05) monthly consumptive use factors were for the months of December and May respectively (Table 6A). Minimum consumptive use of 254.51 mm water was for Groundnut crop (November to January; 90 days duration) and maximum consumptive use of 864.36 mm irrigation water was for Aman rice (July -November; 135 days duration), while Boro rice (February -May; 120 days duration) with irrigation water requirement of 694.94 mm was in between those two crops (Table  6B).  Michael (1978): Table 7.8, p.524]; † Table 6A Analysis of rainfall From analysis of ten years (2009)(2010)(2011)(2012)(2013)(2014)(2015)(2016)(2017)(2018) daily rainfall data mean annual rainfall was computed to be 1789.02 mm, with about 1325 mm during monsoon period (8 June to 14 October) and the rest amount of about 464 mm during non-monsoon period of 15 October to 7 June (Table 7).  , 1973 1973 1973 1973, , , , formula) formula) formula) formula)   (Table 5) Based on Datta et al. (1973) formula the rainfall recharge values using average clay content of surface soils of five types of agricultural lands (Figure 7, Tables 8A and B) during monsoon and non-monsoon periods were 518.71 mm and 181.65 mm respectively with a total 700.36 mm in a year (Table 8A).  Table 8A in Mouza -Gohalura, Gopiballavpur-II Block, Jhargram Table 8A. Table 8A. Table 8A.  Percolation loss of rainfall and stored water in pond While measuring infiltration capacity of soil during field studies at Gohalura mouza through Inverse Auger Hole method (Ojha et al., 2017), it was found that the rates of infiltration varied from 5.86 to 14.66 cm/h in five types of agricultural lands (Figure 7; Table 8A). Those results did not conform with the rainfall recharge by Datta et al. (1973) formula (Table 8A). Results of infiltration capacity of soil reflected the unsaturated soil conditions during measurements in field studies. Those infiltration data did not reflect field conditions regarding percolation loss of water from rainwater harvesting ponds, as found from old tank (Lal Bandh) with percolation rate of 2.98 mm/day (Table 9). From measuring the lowering of water level from old tank (Lal Bandh) in the mouza it could be estimated that rate of percolation loss from that tank was 2.98 mm/day which gave an annual estimated total percolation loss of 1087.70 mm constituting 387.40 mm and 700.30 mm losses during monsoon and nonmonsoon periods respectively (Table 9). This rate of percolation loss of water from old tank in the mouza reflected better actual conditions of percolation of water from rainwater harvesting ponds throughout the year, then the rainfall recharge estimated through Datta et al. (1973) formula (Table 9). Table 9  Table 9  Table 9  Table 9. . . . Comparison of groundwater recharge from agricultural lands (Datta et al., 1973, formula Water for special needs From the analysis of soil (Piper, 1966;Jackson, 1973;Ghosh et al., 1983) it was revealed that soil of the mouza is nearly neutral regarding pH (slightly acidic to normal) and with normal Electrical Conductivity of saturation extract (ECe) of 1:2 soil: water saturation, having good drainage condition with silty loam to silty clay loam soil in the mouza (Table 8A). Those results revealed that there were no requirements of water for special needs for draining out of salt deposited in soil or water needed to dissolve lime to be applied in neutralising acidity in soil. The infiltration rate of water depends on clay content of soil. The higher the clay (%) content the lower would be the infiltration rate of water through soil. After the analysis it was found that both clay content and infiltration rate were higher in B and D types of land followed by A, C and E types of land in the village ( Figure  7 and Table 8A). That result revealed that the farmers followed deep tillage operation before cultivation of crop, which helped to break the clay hard pan on the surface layer (0-15 cm) of soil and, thereby, helping water movement below the surface layer with quite high (5.86 -14.66 cm/h) rates of infiltration.
Water balance for rainwater harvesting and planning crop water management For increasing area under cultivation, agriculture fallow (16.37 ha) might be considered with the present agriculture (23.86 ha) land covered under irrigation facility and the total area of which would be (16.37 ha + 23.86 ha) i.e., 40.23 ha. Therefore, for these present studies total cultivable land was considered as 40.23 ha (Tables 3A and 8B). So, for yearlong cultivation of three crops the required area of water body (as propounded by Panda, 2005;Saren, 2019) would need to be 1/5 th of 40.23 ha (Table 3A) i.e., 8.046 ha. So, following that water balance approach another 16.37 ha (i.e., agricultural fallow out of the total agricultural land area of 40.23 ha as shown in Table 3A) i.e., about 69 per cent increase in irrigated area could be made possible.
Considering rainfall, PET by Thornthwaite formula and rainfall recharge by Datta et al. (1973) formula water balances for monsoon and non-monsoon periods were computed to be 41.04 mm and (-) 820.00 mm respectively, summing up to be (-) 778.96 mm annually for the mouza (Table 7; Figure 8). PET reflected maximum (i.e., potential) capacity of climatic condition for evapotranspiration. So, consumptive use of water for crops would be of help for estimating water balance in the mouza (Table 10).  (Table 7); Annual PET = 1867.60 mm (Tables 5 and 7) Considering rainfall received, consumptive use of crops cultivated in the mouza and percolation loss of water from old tank (Lal Bandh) in the mouza water balance was computed to be with a shortfall of 248.13 mm and that was computed without considering water requirement for Aman rice crop; because rainfall recharge and water uptake by Aman crop happened simultaneously and most of the growing periods of Aman rice were covered during rainy days (Table 10).  Table 7  Table 7  Table 7  Table 7; ** Table 6B  Table 6B  Table 6B  Table 6B; *** Table 9  Table 9  Table 9  Table 9 Conclusion Conclusion Conclusion Conclusion i) Application of GIS was useful in land use land cover classification, analysis of digital elevation model of the village and calculation of areas under individual classes of land.
ii) From analysing the planning for village level rain water harvesting based on water balance, it may be concluded that PET reflected the maximum (i.e., potential) capacity of climatic condition for evapotranspiration. So, the consumptive use of water for crops would help in for estimating water balance in the mouza. From the analysis of mean monthly air temperature data, it was found that consumptive use of Groundnut crop was minimum compared to Aman rice and Boro rice. From analysis of ten years' daily rainfall data (2009 to 2018), mean annual rainfall was computed to be about 26 percent (464 mm) during nonmonsoon period (15 October to 7 June).
iii) A total amount of 700.36 mm rainfall recharge in a year was estimated. About 181.65 mm rainfall recharge occurred during non-monsoon periods. Laboratory analysis of soil samples of the mouza has revealed that there were no requirements of water for special needs for draining out of salt deposited in soil or water needed to dissolve lime needed to be applied in acidic soil. The rate of percolation loss from old tank (Lal Bandh) in the mouza was 2.98 mm/day which gave an annual estimated total recharge of 1087.70 mm including about 35 per cent (387.40 mm) and about 65 percent (700.30 mm) losses during monsoon and nonmonsoon periods respectively.
iv) The rate of percolation loss of water through old tank in the mouza reflected better actual conditions of percolation of water from water harvesting ponds throughout the year, than the rainfall recharge computed through Datta et al. (1973) formula for estimating water balance in the mouza. v) Considering the received rainfall, the consumptive use of crops cultivated in the mouza and percolation loss of water from old tank (Lal Bandh), water balance was computed to be with a shortfall of 248.13 mm and that was done without considering water requirement for Aman rice crop, grown during rainy season. That shortfall could be managed depending on the existing river available in the mouza using lift irrigation system, which provides water to all available lands for agriculture purposes in the mouza.
vi) Following such village-wise water balance approach another 16.37 ha (i.e., agricultural fallow out of the total agricultural land area of 40.23 ha) i.e., about 69 percent increase in irrigated area could be made possible Such water management planning would be helpful for increasing food production and creating facilities for multipurpose benefits of ponds for farming community in the village of Gohalura mouza in Gopiballavpur-II Block under Jhargram District in Red and Laterite Zone of West Bengal. Such methodologies for villagewise agricultural water management planning based on water balance approach of could be projected for other areas also. All authors read and approved the final manuscript.