Contents Agroclimatic Analysis...3

Contents Agroclimatic Analysis...3 Introduction...3 Purpose and scope...3 1.Collection of data...4 a. Identification of locations...4 b. Sources of weather data...4 c. Determining minimum weather data requirement...5 d. Collection of daily weather data...5 2. Database development...6 a. Digitization of data (MS-Excel format)...6 b. Bringing all data in to common units...6 Practical exercise Daily weather data conversion in to common units...7 c. Scrutiny and quality checking...12 Practical exercise Data quality checking...14 Practical exercise Data quality checking with normals...20 d. Converting daily weather data into weekly, monthly, seasonal and annual values along with CV and normals...25 i) Purpose and objectives...25 Objectives...25 ii) Methodology...25 iii) Definition of key words...25 Practical exercise Daily weather data in to weekly weather data...26 Practical exercise: Daily weather data into monthly weather data...31 Practical exercise: Daily weather data in to annual weather data...36 Practical exercise: Daily weather data in to annual weather...41 Practical exercise: Daily weather data in to daily, weekly, seasonal and annual normal weather data...46 3.Rainfall analysis...51.a. Number of rainy days along with Coefficient of Variation...51 i) Purpose and objectives...51 ii) Methodology...51 iii) Definition of key words...51 Practical exercise Rainy day analysis...52 b. Initial and conditional probabilities and probability for consecutive wet and dry weeks...56 i) Purpose and objectives...56 ii) Methodology...57 iii) Definition of key words...57 Practical exercises - Initial and conditional probabilities...57 Practical exercises Probability of consecutive wet and dry weeks...63

4. Water balance and LGP analysis...69 i) Purpose and objectives...69 a. Water balance analysis...69 b. Computation of Potential Evapotranspiration (PET)...69 c. Computation of indices like Aridity index (Ia), Humidity Index (Ih), Moisture Index (Im) and MAI...69 d. Components like water surplus, water deficit, Actual Evapotranspiration (AET)...69 e. Beginning and end of growing period...70 f. Length of growing period analysis...70 ii) Methodology...70 iii) Definition of key words...70 Practical exercises...72 5. Drought analysis...76 a. Classification of droughts...76 b. Analysis of meteorological and agricultural drought...76 i). Purpose and objectives...76 Objectives:...77 ii). Methodology...77 iii). Definition of key words...77 Practical exercises - Meteorological drought analysis...78 Practical exercise: Agriculture drought analysis...83 6. Climate variability and change...89 a. Trend analysis of climatic parameters...89 i). Purpose and objectives...89 ii). Methodology...90 iii). Definition of key words...90 Practical exercises...91 b. Extreme event analysis (Rainfall and temperature)...96 i). Purpose and objectives...96 ii). Methodology...96 Practical exercises Heavy rainfall analysis...97 Practical exercises Maximum temperature analysis... 101 Practical exercises Minimum temperature analysis... 106 Practical Exercise for batch Processing (To process more than one input file at a time)... 111 References... 116

Agroclimatic Analysis Introduction Climate is the primary important factor for agricultural production. Concerning the potential effects of climatic change on agriculture has motivated important change of research. The research on climate change concentrates on possible physical effects of climatic change on agriculture, such as changes in crop and livestock yields as well as the economic consequences of these potential yield changes Agroclimatological analysis is used to study about climatic characteristics and crop performance of a particular region and also to know the climatic variability/climate change and its impact on Agriculture. In order to achieve maximum and sustainable crop production from available farm resources, it is essential to have proper knowledge of the agro climatic resources of the location/region. Therefore, a thorough understanding of the climatic conditions would help in determining the suitable agricultural management practices for taking advantage of the favorable weather condition and avoiding or minimizing risks due to adverse weather conditions. Purpose and scope These training modules on agroclimatic analysis will help to study location-specific and spatial based agroclimatic resource characterization especially for the semiarid regions of the world. Agroclimatic information is necessary in enhancing crop productivity through better agricultural planning including land use planning, water resources availability, crop suitability, pests and disease management and also in weather based agro advisories. This particular training is aimed to develop skills of technical persons/scientists towards agroclimatic analysis, which helps in efficient utilization of available resources, and also to develop the resources for sustainable agricultural growth.

1.Collection of data a. Identification of locations Location identification mainly depends on the objective of a particular research study or project. With respect to climate change project, identification of location refers to identification of the areas/locations to collect the secondary weather data. Depending upon the objectives of the project, macro level data can be collected which includes country / region / state level locations and micro level data at district or tehsil/ mandal level locations. b. Sources of weather data Finding out the sources, for reliable, long-term weather data is the further step after identification of location. Each country has premier meteorological organization to record the weather parameters through its network stations across the nation. In India, India Meteorological Department (IMD), New Delhi is responsible government organization to record meteorological observation and to forecast the weather for different activities viz., transport, agriculture, tourism, water resources and defence. The IMD has National Data Centre at Pune to collect the meteorological data recorded at various part of India and supply quality controlled data on demand on nominal charges. In addition to this, other organizations like state agriculture department, Central Water commission, Central/state government directorate of economics and statistics and Central institutes and State Agricultural Universities under Indian Council of Agriculture Research have their own meteorological / raingauge stations to record the weather variables. Long series of rainfall and temperature data are available for major stations with IMD and for different meteorological sub-divisions with research institutes website like Indian Institute of Tropical Meteorology (IITM) and Indian Agricultural Statistical research Institute (IASRI). At international level, rainfall and temperature data are accessible in different websites like National Oceanography and Atmospheric Administration (NOAA), World Meteorological Organization (WMO) and National Data Center.

c. Determining minimum weather data requirement To characterize a place/region long-term weather data is basic requirement. Different type of analysis needs different weather elements either single or multiple weather parameters. For example, rainfall analysis/drought analysis (meteorological & agricultural) and rainfall trend analysis require only rainfall data. In case of water balance analysis, seven weather parameters viz., temperature (maximum and minimum), relative humidity (morning and evening), wind speed, sunshine hours and evaporation are necessary to compute potential evapotarnspiration (PET). Thus, it is desirable to collect all weather parameters (minimum data set for agroclimatic analysis) at a time. The minimum data set for agroclimatic analysis includes (i) rainfall (ii) temperature (maximum and minimum) (iii) relative humidity (morning and evening) (iv) wind speed (v) sunshine hours and (vi) evaporation. In addition to these weather parameters, coordinates of place (Latitude, Longitude and Altitude), soil information like permanent wilting point, field capacity of the soil (maximum water holding capacity) are also needed for agro climatic analysis. d. Collection of daily weather data The basis for the agroclimatic analysis emerges from daily weather data. Weekly / monthly / seasonal / annual format of weather data is necessary for acquire knowledge about distribution of different weather parameters over different weeks, months and season. On the other hand, daily data is useful to find extreme weather events like heavy / very heavy rainfall recorded in 24 hrs and highest maximum / lowest minimum temperature. In the case of temperature, certain temperature thresholds have a great role in crop growth and development. Extreme high and extreme low temperatures indicate possible losses to agriculture. Relative humidity is important to determine the dryness or wetness of a certain place. It is important to analyze it, together with the temperature, in forecasting the occurrence of diseases, and the growth and development of pests and insects. Knowledge of the wind speed and direction is also necessary for planning sensitive spray application and for the design of wind protection structures. Extreme winds cause mechanical damage to crops (e.g., lodging or leaf damage) and forests (wind throw). In the case of missing data in a weather data series, daily normal values of weather data are essential to fill the missing data.

2. Database development a. Digitization of data (MS-Excel format) The weather data collected from different sources might have been prepared in different formats. It may be stored in text file / word file / excel file / comma separated value file (csv format) / specialized software programmes like MS-Access / NetCDF (network common data form) and also in hard copies such as research reports / bulletins. It is inevitable to bring basic weather data into one common format preferably in excel. Handling and analysing the data in this format is easy and conversion to other formats from excel is also trouble-free job. Hence, preparation of basic weather data in excel format is preferable for further use in different agro-climatic analysis. b. Bringing all data in to common units Weather data available in different countries are (WMO recognized countries) presently following common units (SI units). For instance, units for the following eight basic weather parameters (Tmax, Tmin, RH1, RH2, Wind speed, Evaporation sun shine hours and and rainfall) are furnished in the following table. Parameter Temperature (Max and Min) Degree Celsius ( C) Relative Humidity (RH1 and RH 2) Percentage (%) Unit Rainfall Wind speed Sunshine Evaporation Millimeter (mm) Kilometer per hour (kmph) Hours (hrs) Millimeter (mm) However, in early days (may be before 1950s) units for measuring weather parameters were not similar in different countries. The different units followed for the same weather parameters are presented in the following table.

Parameter Temperature (Max and Min) Rainfall Wind speed Evaporation Unit Degree Celsius ( C), Degree Fahrenheit ( F), Kelvin (k), Millimeter (mm), Centimeter (cm), Inches (in), Foot (ft) Kilometer per hour (kmph), Miles per hour (mph), Meter per second (mps), Knots (kts), Foot per second (fps) Millimeter (mm), Centimeter (cm), Inches (in), Foot (ft) Application program in Visual Basic (DACON) has been developed to convert the different unit systems in to standard unit system i.e SI system. Help option is provided in program to prepare the input file for running this DACON program. Practical exercise Daily weather data conversion in to common units Step 1: Double click on weather cock icon Double click on wc icon to get weathercock

Step 2: Weathercock program window Step 3: Selecting Daily weather data conversion in to common units Select option Daily weather data conversion in to common units

Step 4: Click Enter for dacon program window Click Enter to get dacon program window Step 5: Click Browse for input folder Click Browse to go to input file folder

Step 6: Selecting input file Select csv files (.csv) by clicking down arrow key Select input file and click open Step 7: Processing input file Input file format Click Process button

Step 8: End of the process After successful process End of Job window appear Click OK Step 9a: Viewing output file by clicking View button Output file format Click view to see output file

Step 9b: Viewing output file Output file will be saved in input file folder by default Output file in input file folder c. Scrutiny and quality checking It is very important that all agro-meteorological data be carefully scrutinized with respect to its typographical errors and unusual values, both at the observing station and at regional or national centres by subsequent automatic processing of computers. Doing analysis with wrong data could lead to unreliable results. Therefore, it is mandatory to check the quality of data before proceeding for further analysis. Possible errors such as monitoring stations relocations, changes in instrumentation, changes of the surroundings, instrumental inaccuracies, typographical errors etc., make data unrepresentative of the actual climate variation and may lead to misinterpretations. Consequently, it is an important task to assess the homogeneity of long climate records before they can be

reliably used for further analysis. Every measurement must be checked if the value is unreasonable and it should be corrected. The possible errors while entering data are 1. The data may be typed like 12.8.0 or 12..8 or 12.8. instead of 12.8 2. Data may be typed as a non-numeric symbols (space, _, -, +), alphabetical letters in between data values. Nevertheless in the case of temperature (especially minimum temperature) (minus) is exempted due to freezing temperature is possible during winter season. For ascertaining the quality of observed data, normal daily data is needed if any corrections are required. The history of local conditions, instrumentation, operation, data processing and other factors relevant to the observed data is also useful to do quality checking. Doubtful data can also be compared with nearby station values. Application program in Visual Basic (daqua) has been developed to check the quality of daily data with daily possible ranges. The following table illustrates basic quality control value for different weather parameters and this particular quality check program trace the errors wherever data values exceeds their threshold values. Parameter Basic quality control value Max.T Less than 10 C and more than 50 C Min.T Less than 0 C and more than 30 C Max.T Min.T Less than 5 C RH1 and RH2 Less than 10 and more than 100% RH1 RH2 Less than or equal to 0% Rainfall Less than 0 and more than 200 mm Wind speed Less than 0 and more than 20 km/h Sunshine Less than 0 and more than 12 hours Evaporation Less than 0 and more than 10 mm Another application program also in Visual Basic (daquan) has been developed to check the quality of daily weather data with daily normal weather data. The following table

illustrates quality control value for different weather parameters and this particular quality check program trace the values wherever daily values exceed their normal values. Parameter Quality control value Max. T ± 3 C Min. T ± 3 C RH1 and RH2 ± 10% Rainfall Half to two-times the normal Wind speed ± 3 km/h Sunshine ± 3 hours Evaporation ± 3 mm Practical exercise Data quality checking Step 1: Double click on weather cock icon Double click on wc icon to get weathercock window

Step 2: Weathercock program window Step 3: Selecting Data quality checking option Select Data quality checking option

Step 4: Click Enter for Data quality checking program window Click Enter to get daqua program window Step 5: Click Browse for input folder Click Browse to go to input file folder

Step 6: Selecting input file Select csv files (.csv) by clicking down arrow menu Select input file and click open Step 7: Processing input file Click

Step 8: End of the process After successful process End of Job window appear Click OK

Step 9a: Viewing output file by clicking View button Click view to see output file Step 9b: Viewing output file Output file will be saved in input file folder by default Output file in input folder

Practical exercise Data quality checking with normals Step 1: Double click on weather cock icon Double click on wc icon to get weathercock window Step 2: Weathercock program window

Step 3: Selecting Data quality checking with normals option Select Data quality checking with normals option Step 4: Click Enter for Data quality checking with normals program window Click Enter to get daquan program window

Step 5: Click Browse for input folder Click Browse to go to input file folder Step 6: Selecting input file Select csv files (.csv) by clicking down arrow key Select input file and click open

Step 7: Processing input file Click Process button Step 8: End of the process After successful process End of Job window appear Click OK

Step 9a: Viewing output file by clicking View button Click view to see output file Step 9b: Viewing output file Output file will be saved in input file folder by default Output file in input folder

d. Converting daily weather data into weekly, monthly, seasonal and annual values along with CV and normals i) Purpose and objectives Daily data of weather parameters is base for all agroclimatic analysis. Though it is base, it is necessary to convert these data in to weekly / monthly / seasonal / annual format in order to understand distribution of different weather parameters over different periods (weeks / months / season / annual). These tools (weeks / months / season / annual) are very useful to characterize the region in relation to weather. Objectives Conversion of daily weather parameters in to weekly, monthly, seasonal and annual formats To understand normal distribution of different weather parameters To understand variability of different weather parameters ii) Methodology Daily data of Tmax, Tmin, relative humidity (morning & evening), wind speed and sunshine hours are converted in to weekly / monthly / seasonal / annual averages. In the case of rainfall and evaporation, it is converted in to total of weekly / monthly / seasonal / annual. iii) Definition of key words Weather: It is the day-to-day state of the atmosphere, and its short-term (minutes to weeks) variation. Climate: Climate is commonly defined as the weather averaged over a long period of time or otherwise it is defined as statistical weather information that describes the variation of weather at a given place for a specified interval. Climatological Normal: Averages of climatological data computed for the consecutive periods of 30 years. (For example: 1st Jan 1901 to 31st December 1930 and 1St January 1931 to 31st December 1960).

Practical exercise Daily weather data in to weekly weather data Step 1: Double click on weather cock icon Double click on wc icon to get weathercock window Step 2: Weathercock program window

Step 3: Selecting Daily to weekly conversion of daily weather data option Select Daily to weekly conversion of daily weather data option Step 4: Click Enter for dawe program window Click Enter to get dawe program window

Step 5: Click Browse for input folder Click Browse to go to input file folder Step 6: Selecting input file Select csv files (.csv) by clicking down arrow key Select input file and click open

Step 7: Processing input file Click Process button Step 8: End of the process After successful process End of Job window appear Click OK

Step 9a: Viewing output file by clicking View button Click view to see output file Step 9b: Viewing output file Output file will be saved in input file folder by default Output file in input folder

Practical exercise: Daily weather data into monthly weather data Step 1: Double click on weather cock icon Double click on wc icon to get weathercock window Step 2: Weathercock program window

Step 3: Selecting Daily to monthly conversion of daily data option Select Daily to monthly conversion of daily weather data option Step 4: Click Process for damo daily to monthly program window Click Process to get damo program window

Step 5: Click Browse for input folder Click Browse to go to input file folder Step 6: Selecting input file Select csv files (.csv) by clicking down arrow key Select input file and click open

Step 7: Processing input file Click Process button Step 8: End of the process After successful process End of Job window appear Click OK

Step 9a: Viewing output file by clicking View button Click view to see output file Step 9b: Viewing output file Output file will be saved in input file folder by default Output file in input file

Practical exercise: Daily weather data in to annual weather data Step 1: Double click on weathercock icon Double click on wc icon to get weathercock window Step 2: Weathercock program window

Step 3: Selecting Daily to annual conversion option Select Daily to annual conversion of daily weather data option Step 4: Click Enter for daan daily to annual program window Click Enter to get daan program window

Step 5: Click Browse for input folder Click Browse to go to input file folder Step 6: Selecting input file Select csv files (.csv) by clicking down arrow key Select input file and click open

Step 7: Processing input file Click Process button Step 8: End of the process After successful process End of Job window appear Click OK

Step 9a: Viewing output file by clicking View button Click view to see output file Step 9b: Viewing output file Output file will be saved in input Output file in input file folder

Practical exercise: Daily weather data in to annual weather Step 1: Double click on weathercock icon Double click on wc icon to get weathercock window Step 2: Weathercock program window

Step 3: Selecting Daily to annual conversion option Select Daily to annual conversion of daily weather data option Step 4: Click Enter for daan daily to annual program window Click Enter to get daan program window

Step 5: Click Browse for input folder Click Browse to go to input file folder Step 6: Selecting input file Select csv files (.csv) by clicking down arrow key Select input file and click open

Step 7: Processing input file Step 7: Processing input file Click Process button Step 8: End of the process After successful process End of Job window appear Click OK

Step 9a: Viewing output file by clicking View Click view to see output file Step 9b: Viewing output file Output file will be saved in input file folder by default Output file in input file folder

Practical exercise: Daily weather data in to daily, weekly, seasonal and annual normal weather data Step 1: Double click on weathercock icon Double click on wc icon to get weathercock window Step 2: Weathercock program window

Step 3: Selecting Normals option Select Normals option Step 4: Click Enter for Normals program window norm al Click Enter to get Normals program window

Step 5: Click Browse for input folder Step 6: Selecting input file Select csv files (.csv) by clicking down arrow key Select input file and click open norm

Step 7: Processing input file Click Process button Step 8: End of process norma After successful process End of Job window appear Click OK

Step 9: Viewing output files Output files saved in the input file folder by default Output files (4) are saved in input file folder

3.Rainfall analysis.a. Number of rainy days along with Coefficient of Variation i) Purpose and objectives The number of rainy day analysis gives an idea on rainy days in a week / month / season /annual. Information of rainy days of a place over a period of time determine the need and design both for rainwater harvesting and structure to recharge groundwater aquifers. With the help of number of rainy days planners may plan cropping pattren/cropping systems. ii) Methodology Number of rainy days is calculated by adding the number of rainy days (rainfall amount equal or more than 2.5 mm) for a specified period i.e., week / month / season / annual. iii) Definition of key words Rainy day: A day with rainfall amount equal or more than 2.5 mm considered as a rainy day according to India Meteorological Department for Indian region. Standard deviation: The standard deviation is derived by taking the square root of the sum of the difference for each value from the mean, squared, divided by the number of values minus. Coefficient of Variation: The ratio of the standard deviation of a distribution and to its arithmetic mean. Coefficient of Variation = Standard deviation / mean *100

Practical exercise Rainy day analysis Step 1: Double click on weathercock icon Double click on wc icon to get weathercock window Step 2: Weathercock program window

Step 3: Selecting Rainyday analysis option Select Rainyday analysis option Step 4: Click Enter for rainyday analysis program window Click Enter to get radaan program window

Step 5: Click Browse for input folder Click Browse to go to input file folder Step 6: Selecting input file Select csv files (.csv) by clicking down arrow key Select input file and click open

Step 7: Processing input file Click Process button Step 8: End of process After successful process End of Job window appear Click OK

Step 9: Viewing output file Output files (7) saved in input file folder b. Initial and conditional probabilities and probability for consecutive wet and dry weeks i) Purpose and objectives Agricultural operations are determined by the certain amount of rainfall received in a period. There are specific amounts of rainfall required for the activities like land preparation, sowing and for various agricultural activities. Hence, estimation of probabilities with respect to a given amount of rainfall is useful for rainfed agricultural planning especially in semiarid region. Initial probability rainfall analysis will give percentage probability to get certain amount of rainfall in a given week. Probability of wet week is denoted as P(W) and dry week as P(D). Conditional probability rainfall analysis will give the percentage probability for wet week followed by wet week [P(W/W)], wet week followed by dry week [P(W/D)], dry week followed by dry week [P(D/D)] and dry week followed by wet week [P(D/W)]. It is also important to find out percentage probability of consecutive wet weeks (2W, 3W, 4W) and consecutive dry weeks (2D, 3D, 4D).

Objectives: To find percentage probability for getting certain amount of rainfall (for example 20 mm) for a particular week To find percentage probability for wet week followed by wet week or dry week for a particular week To find percentage probability for consecutive wet weeks (2W, 3W, 4W) and consecutive dry weeks (2D, 3D, 4D) ii) Methodology Several techniques are in use to work out wet and dry spells.the initial and conditional probababilities as per the first order Markov chain model is widely used world over to understand the crop growing seasons based on dry and wet spells. iii) Definition of key words Initial probability: It is the probability of receiving a certain amount of rainfall in a given week. Conditional probability: It is the probability of getting a next week as a wet week, given the condition that the current week is also a wet week. Consecutive wet and dry weeks: It is the probability of getting two or three or four weeks as a wet week consecutively for a given amount of rainfall. The probability for getting consecutive dry weeks refers to probability for getting less than the given amount of rainfall consecutively for two/three/four weeks. Practical exercises - Initial and conditional probabilities Step 1: Double click on weathercock icon Double click on wc icon to get weathercock window

Step 2: Weathercock program window Step 3: Selecting Initial and conditional Probabilities of rainfall Select Initial and conditional Probabilities of rainfall option

Step 4: Click Enter for initial and conditional probabilities of rainfall Click Enter for incopro program Step 5: Click Browse for input folder Click Browse to go to input file folder

Step 6: Selecting input file Select csv files (.csv) by clicking down arrow key Select input file and click open Step 7: Filling up required information Fill the columns of starting and ending week, starting and ending year and limits

Step 8: Processing input file Click Process button Step 9: End of the process After successful process End of Job window Click OK

Step 10a: Viewing output file by clicking View button Click view to see output file Step 10b: Viewing output file Output file will be saved in input file folder by default Output file in input file folder

Practical exercises Probability of consecutive wet and dry weeks Step 1: Double click on weathercock icon Double click on wc icon to get weathercock window Step 2: Weathercock program window

Step 3: Selecting Probability of consecutive wet and dry weeks option Select option Probability of consecutive wet and dry weeks Step 4: Click Enter for PRODRYWETWEK program window Click Enter to get PRODRYWETWEK program

Step 5: Click Browse for input folder Click Browse to go to input file folder Step 6: Selecting input file Select csv files (.csv) by clicking down arrow key Select input file and click open

Step 7: Filling up required information Fill the columns of starting and ending week, starting and ending year and limits Step 8: Processing input file Click Process button

Step 9: End of the process After successful process End of Job Click OK Step 10a: Viewing output file by clicking View button Click view to see output file

Step 10b: Viewing output file Output file will be saved in input file folder by default Output file in input file folder

4. Water balance and LGP analysis i) Purpose and objectives a. Water balance analysis Availability of water in right quantity and in the right time and its management with suitable agronomic practices are essential for good crop growth, development and yield. To assess water availability to crops, soil moisture is to be taken into account and the net water balance through soil moisture can be estimated using the water balance technique. The concepts of PET (Potential evapotransipiration) and water balance have been extensively applied to studies such as climatic classification, aridity, humidity and drought. b. Computation of Potential Evapotranspiration (PET) Information on PET for a location on a short timescale has great importance in agricultural water management. Many empirical methods are available viz., Thornthwaite (1948), Blaney and Criddle (1950), Hargreaves and Christiansen (1973) and others. Guidelines were developed and published in the FAO Irrigation and Drainage Paper No 24 Crop Water Requirements (Doorenbos and Pruitt 1977) to compute ETo using several methods. FAO Penman Monteith method is recommended as the sole standard method. FAO Penman-Monteith method is selected as the method by which the evepotranspiration of the reference surface (ETo) can be unambiguously determined, and this method provides consistent ETo values in all regions and climates. c. Computation of indices like Aridity index (Ia), Humidity Index (Ih), Moisture Index (Im) and MAI These indices are output of water balance analysis. The indices viz., Aridity index (Ia), Humidity Index (Ih), Moisture Index (Im) are useful in climatic classification and to find climatic type of a particular place. Moisture Adequacy Index (MAI) provides a good indication of the moisture status of the soil in relation to the water-need, high values of the index signifying good moisture availability and vice versa. d. Components like water surplus, water deficit, Actual Evapotranspiration (AET) Water surplus (WS) and water deficit (WD) occur in different seasons at most places and both are significant in water balance studies. The information about when the period of water surplus and deficit occurring in a season or year is helpful to find ideal period for

starting of crop season and stages, which may fall in deficit period. It also helps in flood and drought analysis. e. Beginning and end of growing period At the start of rainy season, seed germination and initial crop growth depend on the amount and distribution of rainfall. The beginning and end of growing period is identified based on IMA values. The growing season begins when the IMA is above 50% consecutively for at least three weeks. The end of season is identified when the IMA falls below 25% for four consecutive weeks. f. Length of growing period analysis The length of growing period or the moisture availability period is an important parameter to assess the suitability of climate for agricultural production. It is a key parameter, which helps in assessing climatic suitability to go for particular cropping system. For example, monocropping with short duration pulses can be adopted for areas with 75 days of LGP and monocropping with short to medium duration crops for areas with 75-140 days LGP and double cropping can be adopted for areas with more than 180 days LGP. ii) Methodology Potential evapotranspiration: FAO Penman-Monteith method Water balance analysis: Thornthwaite water balance method iii) Definition of key words Potential evapotranspiration (PET): It is defined as the maximum quantity of water, which is transpired and evaporated by a uniform cover of short dense grass (Reference crop) when the water supply is not limited. Reference crop: A hypothetical reference crop with an assumed crop height of 0.12m, a fixed surface resistance of 70s/m and an albedo of 0.23.

Water balance: It refers to the climatic balance obtained, by comparing the rainfall as income with evapotarnspiration as loss or expenditure, soil being a medium for storing water during periods of excess rainfall and utilizing or releasing moisture during periods of deficit precipitation. Water surplus: It is the excess amount of water remaining after the evaporation needs of the soil have been met (i.e., when actual evapotranspiration equals potential evapotranspiration) and soil storage has been returned to the water holding capacity level. Water deficit: It is the amount by which the available moisture fails to meet the demand for water and is computed by subtracting the potential evapotranspiration from the actual evapotranspiration for the period of interest. Actual Evapotranspiration: It is the actual amount of water lost to the atmosphere by evaporation and transpiration under existing conditions of moisture availability. Aridity Index Ia (%) = Water deficit / PET * 100 Humidity Index Ih (%) = Water surplus / PET * 100 Moisture Index Im (%) = Ih Ia Moisture Adequacy Index MAI (%) = AET / PET *100

Practical exercises Step 1: Input file format Step 2: Double click on PENWBLGP icon Double click on PENWBLGP icon to get program

Step 3: PENWBLGP FORTRAN program window Step 4: Enter input file name with extension (Input file name should be equal or less than 8 characters excluding extension name)

Step 5: Enter water balance output file name with extension (output file name should be equal or less than 8 characters excluding extension name) Step 6: Enter LGP output file name with extension (output file name should be equal or less than 8 characters excluding extension name

Step 7: Water balance output file format Step 8: Length of growing period (LGP) output file format

5. Drought analysis a. Classification of droughts Drought is a normal, recurrent climatic feature that occurs in virtually around the world causing huge loss for the farming community. Drought is universally acknowledged as a phenomenon associated with deficiency of rainfall. There is no single definition, which is acceptable universally. Droughts occur at random and there is no periodicity in its occurrence and cannot be predicted in advance. In semiarid stations, the occurrence of rainfall is seasonal and is known more for its variability with respect to space and time. Drought is characterized by moisture deficit resulting either from i) Below normal rainfall ii) erratic rainfall distribution iii) higher water need iv) a combination of all the three factors. Wilhite and Glantz (1985) analysed more than 150 such definitions of drought and thus broadly grouped these into four categories and explained below. Meteorological drought: A period of prolonged dry weather condition due to below normal rainfall. Agricultural drought: Agricultural impacts caused due to short-term precipitation shortages, temperature anomaly that causes increased evapotransipration and soil water deficits that could adversely affect crop production. Hydrological drought: Effect of precipitation shortfall on surface or sub-surface water sources like rivers, reservoirs and groundwater. Socio-economic drought: The socio-economic effect of meteorological, agricultural and hydrological drought in relation to supply and demand of the society. b. Analysis of meteorological and agricultural drought i). Purpose and objectives In agroclimatic analysis, meteorological and agricultural drought study is important. The frequencies of occurrence of different type of meteorological droughts (mild, moderate

and severe) over a period of year would give insight for vulnerability of a particular location/region to drought on annual basis. Agricultural drought analysis would give idea about susceptibility of a region to drought on seasonal basis, i.e., main crop growing season. Objectives: To understand the categories of drought To identify vulnerable areas to different types of droughts / drought prone regions Probability of occurrence of different types of droughts for a region ii). Methodology Meteorological Drought- According to India Meteorological Department 3 types: based on rainfall deficit from normal Mild : 0-25% Moderate : 26-50% Severe : > 50% Agricultural drought according to National Commission on Agriculture, 1976 kharif - At least four consecutive weeks receiving less than half of the normal rainfall (> 5 mm) Rabi - Six such consecutive weeks iii). Definition of key words Normal rainfall: Average rainfall for a location/region over a period of years (preferable 30 years). Deficit rainfall: Any negative deviation of rainfall from normal Meteorological drought: As per India Meteorological Department (IMD) meteorological drought is a situation when the deficiency of rainfall of a region is 25 per cent or more of the long-term average (LTA) of that region for a given period. The drought is considered

"moderate", if the deficiency is between 26 and 50 per cent, and severe" if it is more than 50 per cent. Agricultural drought: It is defined as a period of four consecutive weeks (of severe meteorological drought) with a rainfall deficiency of more than 50 per cent of the LTA or with a weekly rainfall of 5 cm or less during the period from mid-may to mid- October (the Kharif season) when 80 per cent of the country s total crop is planted, or six such consecutive weeks during the rest of the year. Practical exercises - Meteorological drought analysis Step 1: Double click on weathercock icon Double click on wc icon to get weathercock window Step 2: Weathercock program window

Step 3: Select Meteorological Drought option Select Meteorological drought option Step 4: Click Enter for Meteorological drought program window Click Enter to get Meteorological Drought program window

Step 5: Click Browse for input folder Click Browse for input file folder Step 6: Selecting input file Select csv files (.csv) by clicking down arrow key Select input file and click open

Step 7: Processing input file Click Process button Step 8: End of process After successful process End of Job Click OK

Step 8a: Viewing output file by clicking View button Click View to see output file Step 8b: Viewing output file Output file will be saved in input file folder by default Output file in input file

Step 8b: Viewing output file Output file will be saved in input file folder by default Output file in input file folder Practical exercise: Agriculture drought analysis Step 1: Double click on weathercock icon Double click on wc icon to get weathercock window

Step 2: Weathercock program window Step 3: Select Agricultural Drought option Select Agricultural drought option

Step 4: Click Enter for Agricultural Drought window Click Enter to get Agricultural Drought program window Step 5: Click Browse for input folder Click Browse to go to input file folder

Step 6: Selecting input file Select csv files (.csv) by clicking down arrow key Select input file and click open Step 7: Processing input file Click Process button

Step 8: End of process After successful process End of Job Click OK Step 9a: Viewing output file by clicking View Click View to see output file

Step 9b: Viewing output file Output file will be saved in input file folder by default Output file in input file folder

6. Climate variability and change a. Trend analysis of climatic parameters i). Purpose and objectives Warming of the climate system is undeniable, as is now apparent from observations of increases in global average air and ocean temperatures, widespread melting of snow and ice and rising global average sea level. Most of the observed increase in global average temperatures since the mid-20th Century is very likely due to increase in anthropogenic GHG concentrations. IPCC reported that the impact of climate change is severe in lower latitudes, especially in seasonally dry and tropical regions, where crop productivity is projected to decrease for even small local temperature increases (1 to 2 C), which would increase the risk of hunger (medium confidence). Decreases in precipitation are predicted by more than 90% of climate model simulations by the end of the 21st century for the northern and southern sub-tropics (IPCC, 2007a). Increases in precipitation extremes are also very likely in the major agricultural production areas in Southern and Eastern Asia, in East Australia and in Northern Europe (Christensen et al., 2007). However, agricultural productivity can also be increased, costs reduced and impending crop shortfalls mitigated or avoided through the judicious use of information and knowledge about climate and weather, including early warning and agro meteorological advisories. The productivity of a region in a particular farming operation may be increased by the reduction of many kinds of losses resulting from unfavorable climate and weather, and also by the more rational use of labor and equipment. TREND is designed to facilitate statistical testing for trend, change and randomness in hydrological and other time series data. TREND has 12 statistical tests, based on the WMO/UNESCO Expert Workshop on Trend / Change Detection. The Trend software program can be downloaded from the TREND homepage www.toolkit.net.au/trend. TREND requires a continuous time series as input data in comma separated value file (.csv file). TREND displays as an output the value of the test statistic, the critical values of the test statistic at 0.01 (90 % significant level), 0.05 (95 % significant level) and 0.1

(90 % significant level), and a statement of the test result, for all the statistical tests selected by the user. ii). Methodology Twelve statistical tests both parametric and non-parametric tests are being used in TREND software. The statistical tests in TREND are relatively easy to understand and the user can gain a good appreciation. iii). Definition of key words Climate change: According to IPCC, it refers to a change in the state of the climate that can be identified (e.g. using statistical tests) by changes in the mean and/or the variability of its properties, and that persists for an extended period, typically decades or longer. Climate variability: It refers to variations in the mean state and other statistics (such as standard deviations, the occurrence of extremes, etc.) of the climate on all spatial and temporal scales beyond that of individual weather events. Variability may be due to natural internal processes within the climate system (internal variability), or to variations in natural or anthropogenic external forcing (external variability) Greenhouse gases: They are those gaseous constituents of the atmosphere, both natural and anthropogenic, that absorb and emit radiation at specific wavelengths within the spectrum of thermal infrared radiation emitted by the Earth s surface, the atmosphere itself, and by clouds. This property causes the greenhouse effect. Water vapour (H2O), carbon dioxide (CO2), nitrous oxide (N2O), methane (CH4) and ozone (O3) are the primary greenhouse gases in the Earth s atmosphere.

Practical exercises Step 1 Step 2: Go to Start Programs Toolkit - Trend

Step 3: Click Select option to go input file folder Click on Select and Data Step 4: Selecting input file Select station file and click Open and Next button

Step 5: Click on NEXT button Step 6: Click on NEXT button

Step 7: Click on SAVE button Step 8: Saving output file

Step 9: Output file view

b. Extreme event analysis (Rainfall and temperature) i). Purpose and objectives The weather outlook is of great help to agriculture operations. Certain rainfall amount and temperature thresholds have a great influence on crop production. Advises are given to the agricultural community about extreme events like heavy rainfall and high and low temperature event would help to reduce losses to agriculture. ii). Methodology Rainfall: Frequency and total rainfall amount for the rainfall categories 25-50,50-75, 75-100 mm and more than 100 mm occurred during four seasons (winter, summer, Southwest and Northeast) and year wise also. Highest rainfall occurred in a year with date (on which date it occurred) and amount of rainfall. Temperature: v Month-wise number of days which registered =>40 C, =>41 C up to =49 C and for the year and also grand total v Date on which, different categories of temperature recorded (for example, 40-41 C, 41-42 C up to 48-49 C) v Month-wise number of days which registered <=10 C, <=7 C, <=5 C, <=2 C, <=0 C and for the year and also grand total v Date on which, different categories of temperature observed (for example, 7-10 C, 5-7 C, 2-5 C, 0-2 C and less than 0 C) iii). Definition of key words: Extreme weather event: Extreme weather includes weather phenomena that are at the extremes of the historical distribution, especially severe or unseasonable weather.

Extreme climate event: When a pattern of extreme weather persists for some time, such as a season, it may be classed as an extreme climate event, especially if it yields an average or total that is itself extreme (e.g., drought or heavy rainfall over a season). Practical exercises Heavy rainfall analysis Step 1: Double click on weather cock icon Double click on wc icon to get weathercock window Step 2: Weathercock program window

Step 3: Selecting Heavy rainfall analysis option Select Heavy Rainfall Analysis Step 4: Click Enter for Heavy Rainfall Analysis window Click Enter to get Heavy Rainfall Analysis Program window

Step 5: Click Browse for input folder folder Click Browse to go to input file folder Step 6: Selecting input file Select csv files (.csv) by clicking down arrow key Select input file and click open

Step 7: Click Process button to process the input file Click Process button Step 8: End of process After successful process End of Job window appear Click OK

Step 9: Viewing output file Output file will be saved in input file folder by default Output files (3) saved in input file folder Practical exercises Maximum temperature analysis Step 1: Double click on weather cock icon Double click on wc icon to get weathercock window

Step 2: Weathercock program window Step 3: Selecting Maximum temperature analysis option Select Maximum temperature analysis option

Step 4: Click Process for Maximum temperature analysis program window Click Enter to get matean program window Step 5: Click Browse for input folder Click Browse to go to input file folder

Step 6: Selecting input file Select csv files (.csv) by clicking down arrow key Select input file and click open Step 7: Processing input file Click Process button

Step 8: End of the process After successful process End of Job window appear Click OK Step 9: Viewing output file Output file will be saved in input file folder by default Output files (2) saved in input file folder

Practical exercises Minimum temperature analysis Step 1: Double click on weather cock icon Double click on wc icon to get weathercock window Step 2: Weathercock program window

Step 3: Selecting Minimum temperature analysis option Select minimum temperature analysis Step 4: Click Process for Minimum temperature analysis program window Click Enter to get mitean program window

Step 5: Click Browse for input folder Click Browse to go to input file folder Step 6: Selecting input file Select csv files (.csv) by clicking down arrow Select input file and click open

Step 7: Processing input file Click Process button Step 8: End of the process After successful process End of Job window appear Click OK

Step 9: Viewing output file Output file will be saved in input file folder by default Output files (2) saved in input file folder

Practical Exercise for batch Processing (To process more than one input file at a time) Step 1: Double click on weather cock icon Double click on wc icon to get weathercock window Step 2: Weathercock program window

Step 3: Selecting Daily to weekly conversion of daily weather data conversion option Select Daily to weekly conversion of daily weather data option Step 4: Click Enter for dawe program window Click Enter to get dawe program window

Step 5: Click Browse for input folder Click Browse to go to input file folder Step 6: Selecting input file Select csv files (.csv) by clicking down arrow key

Step 7: Selecting batch option Click Batch button & select one (first) input file and click Open Step 8: Processing input file Click Process button

Step 9: End of process After successful process End of Job window appear Click OK Step 10: Viewing output files Output files saved in the input file folder by default

References 1. Thornthwaite, C. W. 1948. An approach toward a rational classification of climate. Geographic Review 38:55-94 2. Blaney, H.F. & Criddle, W.P. 1950 Determining water requirements in irrigated areas from climatological and irrigation data. U S Department of Agriculture, Soil Conservation Series (SCS) TP-96, 48 3. Christensen, J. E., and Harhreaves G.H, 1973 Irrigation Requirements from Evaporation, Inter-national Commission on Irrigation and Drainage, Seventh Congress, 25.569-23.596 4. Doorenbos. J.. Pruitt, W.O., 1977. Crop water requirements, irrigation and Drainage Paper No. 24, (rev.) FAO, Rome, 5. Penman-Monteith method 1977 "Crop Evapotranspiration - Guidelines for computing crop water requirements, Paper No. 56, (http://www.fao.org/docrep/x0490e/x0490e00.htm) 6. Wilhite, D.A. and M.H. Glantz. 1985. Understanding the Drought Phenomenon: The Role of Definitions. Water International 10:111-120. 7. National Commission on Agriculture. 1976. Rainfall and Cropping Patterns;Vol. XIV, Government of India, Ministry of Agriculture, New Delhi. 8. The Fourth Assessment Report-AR4 2007, IPCC - Intergovernmental Panel on Climate Change, Working Groups Report.(www.ipcc.ch/ -) 9. Christiansen JE et al 2007. Estimating pan evapotranspiration from climatic data. Irrigation and drainage special conference. Las Vegas, NV, USA, pp. 993-231