Characterization of climatic parameters in the perspective of irrigated agriculture in Uttar Kannada district of Karnataka, India

A rainfall and potential evapotranspiration characteristics together determine the agrometeorological regime of a region and influences decision concerning the magnitudes and timing of irrigation application. In the present study, historical rainfall and climate data pertaining to the study area, Uttar Kannada district, Karnataka, was analyzed with a view to characterizing irrigation water requirements. In addition to rainfall input, an important aspect of the water balance model is the crop evapotranspiration (ETcrop), which is the main factor in determining the irrigation schedule. ETcrop could be estimated by reference evapotranspiration (ET0) and crop coefficient. Atmospheric demand for water is represented by ‘potential evapotranspiration’ (PET) and calculated from climatic variables which is crucial for irrigation planning. It has been reported that the Penman-Monteith method gives more consistently correct ET0 estimates to other ET0 methods. While recognizing the importance of both rainfall and PET, an effective measure is known as the ‘Moisture Availability Index’ (MAI), which is computed as the ratio of 75% dependable rainfall and potential evapotranspiration. An MAI value of 1.00 indicates that dependable precipitation is equal to potential evapotranspiration. An MAI value of 0.33 or less for one month during the crop growing season is considered to be a signal of water deficit resulting reduction in crop yield. The findings of this study on MAI are used to decide the selection of the sowing period of crops so as to avoid water stress during the critical harvesting period.


Introduction
Climate is the long-term average of weather in a given location, fairly a long-time period of at least 10 years (Krishnaiah, 1998). It consists of average values of meteorological or oceanographic variables, such as air temperatures, precipitation, humidity, wind speed or ocean temperature, etc. If the climate changes over time, it can directly affect human activities by the impact on the crops that are grown, the supply of freshwater, or the mean sea level. It can also affect natural ecosystems, causing deserts to expand, wildfires to become more prevalent, or permafrost to melt. From the irrigation point of view, important characteristics of rainfall are; (i) Magnitudes of rainfall with various probabilities of occurrence.
(ii) Duration and probabilities of wet spells of rainfall.
(iii) Duration and probabilities of dry spells of rainfall.
Standard meteorological methods have been developed for regional-scale estimation of crop and irrigation water requirements for various crops using routinely available climate, soils and crop information. Estimates of evapotranspiration flux occurring from cropped land surfaces are essential in studies relating to hydrology, climate and agricultural water management. Atmospheric demand for water represented by potential evapotranspiration (PET) and calculated from climatic variables, is crucial to irrigation planning. The procedure for estimation of ET rates from agricultural crops is well established and involves as a first step in the computation of reference crop evapotranspiration (ETcrop) using regular climatologically recorded data. ETcrop could be estimated by reference evapotranspiration (ET 0 ) and crop coefficient (Doorenbos and Pruitt, 1977). It has been reported that the Penman-Monteith method gives more consistently correct ET 0 estimates compared to other ET 0 methods (Nayak and Nandagiri, 2009).

Objectives of the study
The main objectives of the study are: (i) Evaluation of rainfall characteristics for the study area. Characteristics such as dependable rainfall magnitudes and frequency of occurrence of wet and dry spells of rainfall were quantified considering historical daily rainfall observations.
(ii) Characterization of the frequency distribution of mean daily FAO Penman-Monteith reference evapotranspiration for each month and computation of effective precipitation.
(iii) Demonstration of the applicability of the concept of the Moisture Availability Index (MAI) for irrigation planning in the coastal region of Uttar Kannada district.

Evapotranspiration (ET 0 ) and Penman-Monteith (PM) Equation
As per the classification of the agro-climatic zone in Karnataka, the study area falls under Zone-10 (coastal region) which receives an annual rainfall of more than 3500 mm. Since all the climatic information required for computation of ET is not available in the area under study, climatic data of neighboring stations have been used in this study. This is permissible in areas where the same weather extends over long distances (Doorenbos and Pruitt, 1977). An IMD climate station located at Brahmavar (12° 59' N and 74° 54' E) provided historical records of daily average climatic variables required for estimation of daily mean FAO-56 Penman-Monteith reference crop evapotranspiration (ET 0 ). The recent version of the internationally accepted United Nations Food and Agriculture Organization (FAO) methodology for estimation of crop water requirement (Allen et al., 1998) where, ET 0 is reference crop ET(mm/d), R n is net radiation at crop surface (MJ/m 2 /day), G is soil heat flux density (MJ/m 2 /day), T m is mean air temperature (°C) at 2 m height, u 2 is wind speed (m/s) at 2 m height, e s is saturation vapour pressure(kpa), e a is actual vapour pressure (kpa), (e s -e a ) is saturation vapour pressure deficit (vpd) (kpa), ∆ is slope of vapour pressure versus temperature curve at temperature T (kpa/ °C) and γ is the psychometric constant (kpa/°C).
It is common practice to use mean climatic data for computing ET 0 . However, due to weather changes, ETcrop will vary from year to year and for each period within the year ( Patle et al., 2013).

Crop Evapotranspiration (ETcrop)
Paddy (rice) is one of the most common crops grown by farmers in the Uttar Kannada district in the coastal plains. Paddy cultivation is divided into two distinct periods -the wet season crop that is planted to coincide with the onset of the monsoon (May/June) and harvested by the end of the monsoon (September/October) and the dry season crop during December/January to March/April. Since the Uttar Kannada region experiences over 3500 mm of rainfall during the monsoon season, providing supplemental irrigation for the wet season crop is not necessary. Appropriate land preparation and providing field bunds ensure the capture and storage of rainfall for optimal crop growth (Nayak and Nandagiri, 2010). The dry season paddy crop receives no contribution from rainfall and therefore providing supplemental irrigation is crucial for crop growth during this period.
Crop evapotranspiration (ETcrop) is equivalent to crop water requirement and is computed by using the following equation.
Crop coefficient K c incorporates crop characteristics and averaged effects of evaporation from the soil. For normal irrigation planning and management purposes, for the development of basic irrigation schedules and for most of the hydrologic water balance studies, average crop coefficients are relevant and more convenient than the K c computed on a daily basis. (Doorenbos and Pruitt, 1977) specified K c values for paddy for different geographic locations and seasons (Table 1). Wind conditions and during the dry season, the relative humidity may be important; whereas during the dry season when the relative humidity is more than 70%, the K c values given for the wet season should be used. There are differences in the growing season according to variety; therefore, the length of the mid-season growth period will need adjustment. Local information on the length of the growing season will have to be procured from the agriculture department. K c needs to be reduced by 15-20% during initial crop stages (Doorenbos and Pruitt, 1977). Seasonal K c value of l.15 upland (wet) and 1.2 for pluvial (dry) rice is adopted in the present study for computing total irrigation requirements in further analysis.

Effective precipitation
While determining net irrigation requirements, the amount of rain that enters the soil and becomes available for crop water use must be considered. The precipitation used by the plants depends upon the rainfall intensity, soil conditions and field management practices. Several methods of estimating effective rainfall for irrigation schedules are in practice in different countries. They are based on long experience and have been found to work quite satisfactorily in the specific conditions under which they were developed (Yemenu, 2013). In the present study effective precipitation was computed using US Bureau Reclamation, Potential Evapotranspiration/Precipitation ratio method for India and USDA, SCS methods.

Total irrigation requirements
Total Irrigation Requirements (IWR) includes (i) Potential crop evapotranspiration (ET 0 * K c ) (ii) Excess water required for leaching (iii) Efficiency of project operation. (Hargreaves, 1985) assumed 60% efficiency for surface irrigation and 1000 mm of water depth for percolation, soaking and filling for rice fields. Effective precipitation (P e ) was computed using USDA, SCS method as it appeared to be a reasonable value (based on mean monthly rainfall and mean monthly consumptive use). Groundwater contribution in meeting crop water requirement was not considered.
1.6. Moisture Availability Index (MAI) (Hargreaves, 1975) found a high degree of correlation between annual surface runoff and annual sums of monthly positive values of precipitation (P) minus ET 0 . MAI = (75% probability of assured precipitation)/(ET 0 ) In the Uttar Kannada district, there is abundant rainfall in monsoon season. Hence there is no as such shortage of water for paddy crops during this period. However, dry season crop cultivated between November to March requires proper irrigation. Farmers should get prior information about starting their field preparations to avoid water stress in the later stages. Analysis of rainfall and concept of MAI gives probable weekly dates of field preparations at local level depending on climate, soil conditions and availability of water (Subhash et al., 2011). The ET 0 can be used to estimate monthly values of stream flow at a 75% probability for ungauged watersheds. A value of MAI exceeding 1.33 is an indication of the need for good, natural surface drainage (Hargreaves et al., 1985).

Rainfall analysis
It is very difficult to arrive at decisions in crop planning on the basis of monthly rainfall data since a month is a too long period. Therefore, it is ideal to analyze the rainfall data for a shorter period like a week, which will be extremely useful for crop management (Nayak and Nandagiri, 2010). Weekly totals of rainfall are computed for Bhatkal, Kumta, Gokarna, Karwar, Ankola and Honnavar station of the Uttar Kannada district from the daily rainfall data based on the standard meteorological weeks followed by Indian Meteorological Department (Fig. 1). The co-ordinates of all the selected stations are shown in (Table 2). Historical rainfall records for all the six stations were collected for the period of ten years from 2005 to 2015 to analyze various rainfall characteristics such as, (i) Mean weekly rainfall (ii) Coefficient of variation of weekly rainfall (iii) The probabilities for the weekly rainfall to exceed 1, 5, 10, 20 and 30 cm using probability analysis. (Weibull distribution was found to be the best fit for all the regions).

Frequency distribution of mean daily ET 0
ET 0 values are highest in March and the lowest in July/August months. From year to year, the monthly values show greater variation. In areas having a distinct dry and wet season, the transition month shows significant differences from year to year depending on the arrival of rains. In selecting ETcrop for project planning and design,  (Table 3). The variation of ET 0 during different weeks is shown in Fig. 2. Using frequency analysis, frequency distribution curves for mean daily ET 0 for different months are developed for Bhatkal and shown in (Fig. 3). For planning and design purposes for January month, an average ET 0 of 4.55 mm/day would be considered (Table 3). The frequency distribution of mean ET 0 for Bhatkal station has been shown in Fig. 3.    ET 0 values are lower between 24 th to 41 st weeks. Hence, irrigation requirements will also be lower during this period. Weekly ET 0 values are also used in subsequent parts for the computation of MAI (Moisture Availability Index).

Analysis of rainfall data
Analysis of average weekly rainfall data for all six stations has been carried out by collecting rainfall data from the period of 2005 to 2015. The typical distribution of weekly rainfall at Bhatkal station has been depicted in Table 5. A similar kind of analysis has been carried out for the other stations of the study region.
Predominant rainfall was observed in Bhatkal from 18 th to 45 th weeks. The coefficients of variation and standard deviation in these weeks were 158% to 164 %, and 28.3 mm to 33.22 mm respectively. The active rainy season starts from 22 nd week (Bhatkal) and the probability for the weekly rainfall to exceed 50mm is more than 40%. Rainfall is highest during 2 the 2 nd to 37 th week. The rainfall is dependable (>25mm) up to 41 st week. From here onwards, rainfall decreases considerably and is not dependable. The major paddy crop requires about 1000 mm of water for soaking, filling and percolation (Hargreaves, 1993). The analyses of rainfall data indicate that; this initial filling is met in Bhatkal in the 26 th week. This period was slightly varying in other stations i.e., it was occurring in 22 nd week at Kumta, 22 nd week at Gokarna, 23 rd week at Karwar, 22 nd week at Ankola and 23 rd week at Honnavar station.

Wet spells
As suggested by IMD, a wet day is a day with rainfall not less than 2.5 mm and a wet spell is the sequence of wet days preceded and followed by dry days. The frequency of occurrence of wet spells of different lengths during the months from March to December is computed based on 2005 to 2015 data for all the six selected stations. The frequency of occurrence of wet spells of at least 1-2, 3-5, 6-10, 11-20-and 21-31-days duration for all the six stations are given in (Tables 6-11).
The average length of a wet spell and the probability of getting wet days in percent are also given as additional information.
From (Table 13), it is seen that the average length of the wet spell is maximum at Ankola (15.203 days) and minimum at Gokarna (8.31 days) during the month of July. The average number of wet days is highest at Bhatkal and Kumta (29.11 days) and is lowest at Ankola (25.44 days) during July shown in (Table 12). The results also show that the probability of getting wet days is above 70% from June to August and below 15% during November to May. The probability of occurrence of wet days is maximum during the month of July and found to be, 92%, 82% and 89% respectively in Karwar, Ankola and Honnavar stations and 94%, 94% and 84% respectively in Bhatkal, Kumta and Gokarna stations (Table 14). It is the lowest during the month of January to March. Farmers in this region face a shortage of water if harvesting is advanced beyond the middle of March (Figs. 4-6).

Dry weather periods
Dry weather periods occur during the rainy season as a result of a break or temporary withdrawal of the monsoon. Crops are likely to experience moisture stress during the dry weather periods, if adequate moisture is not available in the soil (Majumdar, 2002). Any week receiving a rainfall less than 10 mm   is considered as a dry week (Tammets, 2010). The probabilities of occurrence of 1, 2, 3 and 4 consecutive dry weeks from 10 th week (5 th March) to 52 nd week (31 st December) have been worked out from weekly rainfall data. Table 14, shows the probabilities of the occurrence of dry weeks at Bhatkal station during the period (2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015). A similar kind of analysis has been carried out for all other study stations.  In Bhatkal and Karwar station probability of getting 2 to 3 consecutive dry weeks is greater than 50% from the 48 th week. Hence irrigation may be needed from the 48 th week onwards (2 December) for the crops of this region. Similarly, for the other stations (Kumta, Gokarna, Ankola and Honnavar) irrigation may be required from 47 th , 46 th , 46 th and 47 th week onwards respectively. The irrigation has to be continued up to 19 th to 22 nd week depending upon the rainfall. However, it was observed that farmers face shortage of water for the ripening stage at the end. of March in most of the areas in study regions which necessities to start early sowing in November.

Effective precipitation
Even though coastal Uttar Kannada district receives heavy rainfall of about 3500 mm annually, this is confined to four rainy months (June-September). Temporal variability and spatial heterogeneity make a situation of variation in the amount of water provided which definitely affects the critical crop growth stages in the later stage. These variations make the empirical estimation of effective precipitation very incorrect (Hargreaves, 1993). For purposes of calculating water requirements and agricultural benefits, a 50% probability was used after checking the ranking using gamma distributions (Hargreaves et al., 1985). Table 16, compares mean, actual (the 50% probability) and dependable (the 75% probability of assured rainfall) for the June-October rainy season at Bhatkal station for the recent 10-year record (2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015). 75% probable rainfall values are less than mean rainfall and can be used for computations. Moreover, the USBR method gives 362 mm effective precipitation for 9 months (September-May) whereas the ratio method and USDA method yields 561 mm and 311 mm respectively (excluding percolation loss in case of rice crop). For purposes of calculating water requirements, 311 mm (USDA, SCS) is used since this method gives more accurate values of effective precipitation and also recommended for soils of high infiltration (Dastane, 1975).

Total irrigation requirements
The crop water requirements for rice crop (mm/month) in the rice-cropping pattern for Bhatkal station is shown in Table 17. In the coastal region, rainwater is abundant in three months (June-August) and makes a contribution to upland irrigation (pluvial system) only. Farmers are having small landholdings (less than 1 ha of an area) for cultivation. Total irrigation water requirement for dry season crop is found to be 1700 mm and for the wet season, it is less. There is a significant contribution of rainfall to the crop water supply in the wet period. Fig. 7 depicts the variation of 75% probable assured precipitation at different stations of the study area from 17 th to 44 th week leaving zero rainfall in other weeks. Higher values of precipitation were observed from 23 rd to 40 th week. These values were significant up to 44 th week indicating that irrigation is not required up to this period. It also highlights a unimodal trend in the rainfall pattern at all the observed stations. Net (ET 0 -P 75 ) gives an indication of the required depth of irrigation necessary to achieve optimum production. For June, July and August months P 75 > ET 0, signifying potential surface runoff. But in other months ET 0 > P 75 , indicating water was required for full production in these months.

MAI values for all six regions on weekly basis
Week    Table 18 shows MAI values for all the six regions of the study area computed using Eqn. 3 on a weekly basis (Fig. 8). The highest value of MAI (Max 5.210 and Min 3.176) at all the stations was in the 28 th week (9-15 July). MAI value of 0.33 is considered as the threshold value to demarcate dry and moderate wet periods (Hargreaves et al., 1985). This index was significant up to 40 th week (except at Honnavar station)

Conclusions
The Agrometeorological regime of an area is governed by rainfall characteristics and potential evapotranspiration, which further influences the magnitude and frequency of irrigation application. Analysis of rainfall shows that higher values of precipitation were observed from 22 nd to 40 th weeks at all the stations of the study regions. These values were significant up to 43 rd week indicating that irrigation is not required during this period. For June, July and August months P75> ET 0 , signifying potential surface runoff. However, during the remaining months ET 0 > P75, indicating the necessity of additional water for full production in these months.
MAI (Moisture availability Index) values in stations under consideration are more than 1.33 from 23 nd to 38 th week indicating there is no shortage of water for agricultural activities during this period. Moreover, the shortage of water for the ripening stage in the second half of March in most of the areas in coastal regions faced by the farmers was noted. These necessities to start early sowing for paddy in the month of November. Advancing the sowing date by two to three weeks is found to be necessary, which will help the farmer to save irrigation water in the ripening stage. This preserved water can be utilized for other needs to avoid water stress in critical dry periods in April and May.
The present study also indicates that the probability of getting two to three consecutive dry weeks is greater than 50% from 47 th week onwards at all the selected stations. Hence irrigation is found to be very essential from 47 th week onwards (24 November) for the paddy crops of this region. Continuing Irrigation from 19 th to 22 nd week depending upon the beginning of monsoon rainfall is also need of the hour.