Agro-Climate Challenges

## **Chapter 1**

## Temperature Based Agrometeorology Indices Variability in South Punjab, Pakistan

*Muhammad Saifullah, Muhammad Adnan, Muhammad Arshad, Muhammad Waqas and Asif Mehmood*

## **Abstract**

Climate change has a major impact on crop yield all over the world. Pakistan is one of the major affected countries by climate change. The agrometeorology indices were determined for the South Punjab region, which is a hot spot for climate change and food security. This region is rich in agriculture, but crop yield relationship is estimated with agrometeorology indices (AMI). Temperature stress (33°C), average diurnal temperature range (12°C), Average accumulative growing degree days (1303°C), phototemperature (27°C) and nyctotemperature (21°C) indices were determined for Multan. The variation in diurnal temperature was found at 0.39 for Bahawalpur region and similar variation was observed in growing degree days, which is 0.11 more than the diurnal temperature range. The extreme of these indices which influence the crop yield was found in May and June. The cropping period from sowing to harvest varied due to climate change and cause to decrease in the yield of the crop. The indices are regarded as crop performance indicators. So, policymakers and agricultural scientists should take necessary measures to mitigate such kinds of challenges.

**Keywords:** indices, South Punjab, temperature, meteorology, indices, Pakistan

## **1. Introduction**

During the last decade, climate change has raised an international attention due to concerns of negative impacts on ecosystems, agriculture, water supply and management, human welfare and regional political stability [1, 2]. The global situation is well described by the IPCC assessment report [3], which in particular cites global mean temperatures as having risen approximately by 0.74°C in the last 100 years. The majority of this warming has occurred since 1950, most likely due to increasing greenhouse gas concentrations to unprecedented levels in recent history [4]. Global climate models (GCMs) predict temperature increases of 1.1–6.4°C over 1990–2100 [3]. Due to the great influence of temperature on evapotranspiration and precipitation for soil water availability and drought events, climate change might have an

immediate, direct effect on vegetation and crop productivity and, therefore, on the related net income for farmers [1, 5, 6].

Climate change is one of the most prominent global environmental issues. During the period from 1885 to 2012, the mean global temperature has increased by 0.85°C and is predicted to increase further by 1.6–5.8°C by the end of 21st century [3]. Climate is one of the most critical limiting factors for agricultural production: frost risk during the growing period and low and irregular precipitation with high risks of drought during the cultivating period are common problems in agriculture. In recent years a change in climate has been documented in many locations throughout the world. Increasing rainfall trends were reported in Argentina [7], Australia and New Zealand [8, 9]. The minimum temperature increased almost everywhere. The maximum and mean temperature increased in northern and central Europe, over the Bulgaria [10], Canada [11], Australia, New Zealand [8], India [12]. These results support the assumptions [13] that mid-latitude regions such as the mid-western USA, southern Europe and Asia are becoming warmer and drier, whereas the lower latitudes are becoming warmer and wetter.

Developing countries are more vulnerable to such changes as they have limited resources to cope up with the disasters and agriculture plays dominant role in their national economy [14]. The northern part of Indian sub-continent has been placed under high risk zone for heat stress risks in view of future climate change scenarios [15]. Under such conditions, the sustainability of natural resources and food security for the increasing population in the region is at risk. An increase, even moderate in global temperature is expected to result in a change in frequency of extreme weather events like drought, heavy rainfall and storms [16]. Small changes in precipitation mean result in a relatively high increase in the probability of precipitation extremes [17, 18]. The same effect has been demonstrated for temperature changes [19]. Wagner [20] suggest that the frequency of extreme events is relatively more dependent on a climate change as compared to mean values of climatic parameters.

Ali et al., [21] also determined the future trends of temperature in different regions of Pakistan. Nadeem et al. [22] found the results using different climate models in south Punjab, Pakistan. The study period divided into base line period (1980–2018) and found the increase in average temperature 0.94°C for this period. According to Global Climate Models increased in temperature 3–5°C up to 2050. The yield of different crops decreased and sowing 18–25 days earlier as adaptation startgey to cope this climate change challenge. Punjab is also experiencing large fluctuations in temperature and precipitation patterns every year leading to large oscillations in agricultural productivity in the region. The unpredictable weather conditions have already started to diverse impact on productivity of wheat crop during last 5–6 years. To manage this alarming situation, there is a need to analyze the spatiotemporal variability of climatic conditions at regional scale so that viable mitigation/adaptation strategies could be developed and implemented on regional basis. Keeping in this view, the spatiotemporal climatic variability during kharif and rabi seasons has been studied for three different agroclimatic regions of South Punjab by using statistical approaches.

## **2. Materials and methods**

Daily minimum (Tmin) and maximum (Tmax) temperature from two stations in South Punjab (SP), Pakistan (**Table 1**, **Figure 1**) with the longest and complete records were used. First, monthly variations were identified in temperature series *Temperature Based Agrometeorology Indices Variability in South Punjab, Pakistan DOI: http://dx.doi.org/10.5772/intechopen.105590*


#### **Table 1.**

*Statistics of minimum and maximum temperature.*

**Figure 1.** *Location of study area.*

considering the study period. Multan and Bahawalpur stations were chosen for the time of 2012–2016. Both divisions comprised a major part of South Punjab. The region is considered very important regarding the agriculture production. Wheat and Cotton crops were dominated for the South Punjab region (**Table 1**).

## **3. Methods**

Determination of following temperature base agro metrological indices has carried out. Crops growth when the daily Tmean is above a given temperature threshold.


#### **Table 2.**

*Temperature base agro-meteorological indices.*

Different temperature base indices are used to quantify that climate change process and its effect on crop production area and yield. The temperature base agro meteorological indices are given in **Table 2**.

The maximum, minimum and average temperature of South Punjab is extreme in month of May and June (**Figure 2**). Month of July and August is identified lower as compared to extreme months due to moon soon spell.

To explore the variations of temperature during the months, the Diurnal Temperature Range (DTR) [23] is determined:

$$DTR\left(^{\circ}\text{C}\right) = T\_{\text{max}} - T\_{\text{min}} \tag{1}$$

Chilling temperature is most important during the dormancy period. It also plays key role in the growth of plants in spring season. Deciduous fruit trees demanded the accumulating chilling temperature in temperate environment.

When the daily temperature exceeds from threshold temperature. Threshold temperature can vary crop as well as species and stages of crop. Quantify the thermal

*Temperature Based Agrometeorology Indices Variability in South Punjab, Pakistan DOI: http://dx.doi.org/10.5772/intechopen.105590*

time and growing degree day used to estimate the process. It also helps for selection of crops and hybrid varieties to achieve the maximum growth at the time maturity and yield. The growing degree day is determined [24, 25]:

$$GDD = \sum\_{1}^{n} (T\_{user} - T\_b) \tag{2}$$

Where, n is the number of days in each month, Tmean is the daily average temperature computed using daily Tmin and Tmax, Tb is threshold temperature for the crop growth.

After decided the analyses of growing degree days, the heat unit is most important indices for the crops. The cumulative heat unit is calculated by summing the daily mean temperature above the base temperature [26]: The effective light temperature estimated from the given equation:

$$T\_p = T\_{\text{max}} - 0.25DTR \tag{3}$$

This index is computed for the different stages and significance for mean temperature at daytime. It is named as Phototemperature index [27]. There is also other index to compute the effect of mean temperature at full nighttime:

$$T\_u = T\_{\text{min}} + \mathbf{0.25DTR} \tag{4}$$

It is named as nyctotemperature index for given crop during the different stages. There are also identify the maximum and minimum temperature of the year highlighted for this region and alarm all the stakeholder regarding the climate change warming.

## **4. Results and discussions**

The results reveled that different agro-meteorology indices varied whole the time period for this region. The variations diurnal temperature range is more as highlighted from coefficient of variations. These variations of Diurnal temperature range observed 5% higher in Multan as compared to Bahawalpur. Growing degree day of Multan 2% higher than Bahawalpur. Temperature stress play important role for the crop growth and phenology at different stages, South Punjab region have rich in agriculture and playing key role textile industry. Textile industry raw materials produced from this region because it cottons dominant region.

The temperature above the 30°C, this index found to be similar for both stations. Phototemperature index Multan is higher 5% than Bahawalpur (**Table 3**). The variations in Nyctotemperature of Bahawalpur index lower 2% higher than Multan. The interanual variations in diuneral temperture range found extreme in month of May and June. The lowest DTR highlighted in month of October. The higher variability indicated from diuneral temperture range during the different months of 2012–2016 (**Figure 3**).

Heat stress found to be maximum in month of June for period 2012–2016 (**Figure 4**). Temperature stress of Multan was 1.5% higher than Bahawalpur for the month of June. The variations in heat stress found to be more in Multan as compared


#### **Table 3.**

*Descriptive statistics of agrometeorological indices of South Punjab.*

**Figure 3.** *Diurnal temperature range for study period.*

**Figure 4.**

*Temperature stress above threshold for study period.*

*Temperature Based Agrometeorology Indices Variability in South Punjab, Pakistan DOI: http://dx.doi.org/10.5772/intechopen.105590*

**Figure 5.** *Growing degree days indices of study area.*

to Bahawalpur. For month of April, heat stress was 32.10°C in Bahawalpur, which was 0.7°C more from Multan. Maximum variance in heat stress in month of May were noticed for Multan. Similar, minimum variance determined in month of September for Bahwalpur. The variation of heat stress determined in month of May and June for both stations. These results consistent with the findings of [28].

Accumulative growing degree days increased after the month of June. Multan found to be higher from June to December but lower during the month January to May (**Figure 5**). It can be found the matching point of both stations occurred in month of May. The accumulative growing degree day become constant in month of November and December for both stations, but Multan found to be 8% more than Bahwalpur. Minimum growing degree day ware in month of May for Bahawalpur and month January as well as March for the stations of Multan [25].

Phototemperature and Nyctotemperature index were determined for this region. May and June are critical month for temperature-based agriculture index. August and September have decreased variations due to moon soon effect. Winter seasons found to be lower regarding the temperature based agro-meteorology index. The extreme event of both index Bahawalpur is June with different amount. For Multan, phototemperature and nyctotemperture index found to maximum in month June with 18% variations. Both indices observed minimum in month of January, which is lower 71% and 57% respectively. Overall, month of May and June found to be maximum and minimum in month December and January respectively as shown in **Figures 6** and **7**.

**Figure 6.** *Phototemperature and Nyctotemperature index for Bahawalpur city.*

**Figure 7.** *Phototemperature and Nyctotemperature index for Multan city.*

## **5. Discussions**

In this study, we analyze the different agro-meteorology index, which is based on maximum and minimum temperature. Over all these indices indicate global warming decrease the average yield of wheat crop and cotton yield increase in this region. Brar and Singh [29] determined the relationship of agro-meteorology indices (AMI) and cotton yield, the experiments was conducted in north western part of India. The water scarcity was major challenge for cotton production. The crop yield were found increased with higher AMI with heavy pre-sowing irrigation and early sown crop at irrigation with four weeks. The duration to achieve the different phenological stages of wheat with different irrigation treatments for significant higher yield and growing degree days for Rajasthan, India [30]. The uneven trends in temperature indices in Serbia were found the period of 1961–2010. The temperature for growing seasons were complex pattern. The examined temperature indices were found the cooling tendency during the growing seasons and a warming tendency during the dormancy. The warming tendency for the period of 1981–2010 was detected in both seasons with similar magnitude [31]. Jan et al. [32] examined the variability in biological yield of crop with growing degree days with regression model. Fernández-Long et al. [23] investigated the trends in Argentina and agro-meteorology indices identified the potential effect of weather on crop yield and arise a challenge for management decisions. Lalić et al. [1] also highlighted the relation between agro-metrological conditions and crop yield through modeling in different countries. Capra et al. [5] determined the maximum, minimum and mean temperature variability in Italy and found to be very complex behavior with crop growth as well as production. Choudhary et al. [33] also determined the agro-meteorology indices for India and found the relation with crop. These indices identified the crop performance as indicator.

The crop yield and indices found to be closely related. The extreme temperature influence on grain yield and growth of the crop. Due to rapid GDDs shorten the span life of crop and reduce the yield. From this study, the crop yield (**Figures 8** and **9**) is reducing to due to increase in temperature as well as temperature base agro-meteorology indices, which is indicator of challenge of wheat crop in southern part of Punjab and other hand cotton crop is arising. These results were found to be consist with [34, 35]. The temperature stress, phototemperature and nyctotemperture index were also same behavior for crops as findings. Shaheen et al. [36] investigated the GDDs and estimated the shorten the length of growing seasons of crop, which ultimately affect the crop yield, which is consistent with current study. You et al. [37] determined the crop yield and temperature increase relationship for China. The increase

*Temperature Based Agrometeorology Indices Variability in South Punjab, Pakistan DOI: http://dx.doi.org/10.5772/intechopen.105590*

**Figure 8.** *Wheat yield for the study period.*

**Figure 9.** *Cotton yield for study period.*

in temperature 1°C reduce the yield 3–10% for mainland of China. The wheat yield decreased as compared to initial year, while cotton yield increase for the same period for region of South Punjab.

## **6. Conclusion**

From this study, it can be concluded that there is a necessity of management and applications of these indices to allocate inter-comparison of the significant results and to improve the understanding. The threshold temperature 30°C identified through this study. It will be helpful for developing whether calendar of wheat, crop insurance product and breeding temperature stress resistant genotype of crops for South Punjab, Pakistan. Month of May and June are more important for this region due to extreme values of temperature base index occurs in these months. These agro-meteorology indices demand to investigate it local and regional basis. There is also necessary to investigate at different time scale i.e., short, and long term. The extreme temperature impact on crop production and management should be locally assessed. Due to extreme temperature base agro-meteorology index in South Punjab, extreme weather is becoming great challenge for farmer community, which effect the livelihood of this region. It creates food security challenge for this region. Due to

extreme variations in agro-meteorology stresses, the new varities of crop is needed to introduce which are suitable with current variations in climate. Due to increase in temperature, there is also needed to cope the water scarcity challenge in this region. Therefore, economically water cheap technique should be introduced in this region. The policy maker and scientific community need to take measures to cope with this challenge.

## **Author details**

Muhammad Saifullah1 \*, Muhammad Adnan<sup>2</sup> , Muhammad Arshad3,4, Muhammad Waqas1 and Asif Mehmood5

1 Department of Agricultural Engineering, Muhammad Nawaz Shareef University of Agriculture, Multan, Pakistan

2 Institute of International Rivers and Eco-security, Yunnan University, Kunming, Yunnan, China

3 Nanjing University of Information Science and Technology, School of Atmospheric Sciences, Nanjing, China

4 Pakistan Meteorological Department, Islamabad, Pakistan

5 Key Laboratory of Water Cycle and Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research (IGSNRR), Chinese Academy of Sciences (CAS), Beijing, China

\*Address all correspondence to: saif\_2146@yahoo.com

© 2022 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

*Temperature Based Agrometeorology Indices Variability in South Punjab, Pakistan DOI: http://dx.doi.org/10.5772/intechopen.105590*

## **References**

[1] Lalić B, Eitzinger J, Thaler S, Vučetić V, Nejedlik P, Eckersten H, et al. Can agrometeorological indices of adverse weather conditions help to improve yield prediction by crop models? Atmosphere. 2014;**5**(4):1020-1041. DOI: 10.3390/atmos5041020

[2] Saifullah M, Adnan M, Zaman M, Wałęga A, Liu S, Khan MI, et al. Hydrological response of the Kunhar River basin in Pakistan to climate change and anthropogenic impactson runoff characteristics. Water. 2021;**13**(22):3163

[3] Change IC. Synthesis Report. Contribution of working groups I. II and III to the fifth assessment report of the intergovernmental panel on climate change. 2014;**151**:(10.1017)

[4] Chavas DR, Cesar Izaurralde R, Thomson AM, Gao X. Long-term climate change impacts on agricultural productivity in eastern China. Agricultural and Forest Meteorology. 2009;**149**(6-7):1118-1128

[5] Capra A, Consoli S, Scicolone B. Longterm climatic variability in Calabria and effects on drought and agrometeorological parameters. Water Resources Management. 2013;**27**(2):601-617. DOI: 10.1007/s11269-012-0204-0

[6] Moonen AC, Ercoli L, Mariotti M, Masoni A. Climate change in Italy indicated by agrometeorological indices over 122 years. Agricultural and Forest Meteorology. 2002;**111**(1):13-27. DOI: 10.1016/S0168-1923(02)00012-6

[7] Viglizzo EF, Roberto ZE, Filippin MC, Pordomingo AJ. Climate variability and Agroecological change in the central pampas of Argentina. Agriculture,

Ecosystems & Environment. 1995;**55**(1):7-16

[8] Plummer N, Salinger MJ, Nicholls N, Suppiah R, Hennessy KJ, Leighton RM, et al. Changes in climate extremes over the Australian region and New Zealand during the twentieth century. In: Weather and Climate Extremes. Dordrecht: Springer; 1999. pp. 183-202

[9] Suppiah R, Hennessy KJ. Trends in Total rainfall, heavy rain events and number of dry days in Australia, 1910-1990. International Journal of Climatology: A Journal of the Royal Meteorological Society. 1998;**18**(10):1141-1164

[10] Popova Z, Ivanova M, Pereira L, Alexandrov V, Kercheva M, Doneva K, et al. Droughts and climate change in Bulgaria: Assessing maize crop risk and irrigation requirements in relation to soil and climate region. Bulgarian Journal of Agricultural Science. 2015;**21**(1):35-53

[11] Zhang X, Vincent LA, Hogg WD, Niitsoo A. Temperature and precipitation trends in Canada during the 20th century. Atmosphere-Ocean. 2000;**38**(3):395-429. DOI: 10.1080/07055900.2000.9649654

[12] Duhan D, Pandey A, Gahalaut KPS, Pandey RP. Spatial and temporal variability in maximum, minimum and mean air temperatures at Madhya Pradesh in Central India. Comptes Rendus - Geoscience. 2013;**345**(1):3-21. DOI: 10.1016/j.crte.2012.10.016

[13] Smith B, Ludlow L, Brklacich M. Implications of global climatic warming for agriculture: A review of appraisal (reviews and analyses). Journal of Environmental Quality (EUA). 1988;**17**(4):519-527

[14] Majumder MS, Santillana M, Mekaru SR, McGinnis DP, Khan K, Brownstein JS. Utilizing nontraditional data sources for near real-time estimation of transmission dynamics during the 2015-2016 Colombian Zika virus disease outbreak. JMIR Public Health and Surveillance. 2016;**2**(1):e5814

[15] Nachtergaele F, van Velthuizen H, van Engelen V, Fischer G, Jones A, Montanarella L, et al. Harmonized World Soil Database (Version 1.2). FAO, Rome, Italy: IIASA, Laxenburg, Austria; 2012. pp. 1-50

[16] Balling Jr, Robert C, and Sherwood B Idso. "Effects of greenhouse warming on maximum summer temperatures." Agricultural and Forest Meteorology. 1990;**53**(1-2):143-147

[17] Groisman PY, Karl TR, Easterling DR, Knight RW. Changes in the probability of heavy precipitation: Important indicators of climatic change. In: Jamason PF, Hennessy KJ, Suppiah R, Page CM, Wibig J, Fortuniak K, editors. Weather and Climate Extremes. Dordrecht: Springer; 1999. pp. 243-283

[18] Waggoner A, De Biasio R, Conrad P, Bright GR, Ernst L, Ryan K, et al. Multiple spectral parameter imaging. Methods in Cell Biology. 1989;**30**:449-478

[19] Folland GB. Real Analysis: Modern Techniques and their Applications. Hoboken, New Jersey: John Wiley & Sons; 1999;**40**

[20] Wagner R. Database and expert systems applications. In: 7th International Conference, DEXA'96, Zurich, Switzerland, September 9-13, 1996. Proceedings. Berlin/Heidelberg: Springer Science & Business Media; 1996;**7**

[21] Ali S, Eum HI, Cho J, Dan L, Khan F, Dairaku K, et al. Assessment of climate extremes in future projections downscaled by multiple statistical downscaling methods over Pakistan. Atmospheric Research. 2019;**222**:114-133. DOI: 10.1016/j.atmosres.2019.02.009

[22] Nadeem M, Nazer M, Ghulam K, Fatima Z, Iqbal P, Ahmed M, et al. Application of CSM-CANEGRO model for climate change impact assessment and adaptation for sugarcane in semi-arid environment of southern Punjab, Pakistan. International Journal of Plant Production. Apr 2022. pp. 1-24. DOI: 10.1007/s42106-022-00192-6

[23] Fernández-Long ME, Müller GV, Beltrán-Przekurat A, Scarpati OE. Longterm and recent changes in temperaturebased Agroclimatic indices in Argentina. International Journal of Climatology. 2013;**33**(7):1673-1686. DOI: 10.1002/ joc.3541

[24] Kash Yapi A, Das HP. Requirement of heat unit and agrometeorological indices in selected wheat growing zones. Mausam. 1999;**january**:63-70

[25] Mcmaster GS, Wilhelm WW. Growing degree-days: One equation, two interpretations Gregory. Agricultural and Forest Meteorology. 1997;**87**(1):291-300

[26] Sattar A, Singh G, Singh SV, Mahesh Kumar P, Kumar V, Bal SK. Evaluating temperature thresholds and optimizing sowing dates of wheat in Bihar. Journal of Agrometeorology. 2020;**22**(2):158-164

[27] Zhu X, Guo R, Liu T, Kun X. Crop yield prediction based on agrometeorological indexes and remote sensing data. Remote Sensing. 2021;**13**(10):2016. DOI: 10.3390/ rs13102016

[28] Solanki NS, Devi SS, Chouhan BS, Nai G. Agrometeorological indices, heat use efficiency and productivity of wheat (Triticum Aestivum) as influenced by

*Temperature Based Agrometeorology Indices Variability in South Punjab, Pakistan DOI: http://dx.doi.org/10.5772/intechopen.105590*

dates of sowing and irrigation. Journal of Pharmacognosy and Phytochemistry. 2017;**6**(3):176-180

[29] Brar HS, Singh P. Relationship of agro-meteorological indices with cotton yield under varied pre-sowing irrigation levels, sowing dates and time of first irrigation in North-Western India. Communications in Soil Science and Plant Analysis. 2022;**53**(2):170-179. DOI: 10.1080/00103624.2021.1984513

[30] Prajapat AL, Saxena R, Sharma M, Mandeewal RL, Lal B, Didal B. Growing degree days requirement and yield of wheat cultivars as influenced by irrigation scheduling and time of sowing. International Journal of Plant & Soil Science. 2022;**February**:28-35. DOI: 10.9734/ijpss/2022/v34i330843

[31] Ruml M, Gregorić E, Matović G, Radovanović S, Počuča V. Uneven trends of temperature indices during the growing season and dormancy in Serbia. Theoretical and Applied Climatology. 2022;**147**(3-4):1277-1295. DOI: 10.1007/ s00704-021-03859-8

[32] Jan B, Anwar Bhat M, Bhat TA, Yaqoob M, Aijaz Nazir M, Bhat A, et al. Evaluation of seedling age and nutrient sources on phenology, yield and agrometeorological indices for sweet corn (Zea Mays Saccharata L.). Saudi Journal of Biological Sciences. 2022;**29**(2):735- 742. DOI: 10.1016/j.sjbs.2021.10.010

[33] Choudhary D, Raj Singh CS, Dagar AK, Singh S. Temperature based agrometeorological indices for Indian mustard under different growing environments in Western Haryana, India. International Journal of Current Microbiology and Applied Sciences. 2018;**7**(1):1025-1035. DOI: 10.20546/ ijcmas.2018.701.123

[34] Kingra PK, Setia R, Singh S, Kaur J, Kaur S, Singh SP, et al. Climatic variability and its characterisation over Punjab, India. Journal of Agrometeorology. 2017;**19**(3):246-250

[35] Premdeep ML, Niwas R, Singh M, Kumar S. Agrometeorological indices in relation to phenology of wheat. Journal of Pharmacognosy and Phytochemistry. 2019;**8**(1):2500-2503

[36] Shaheen N, Jahandad A, Goheer MA, Ahmad Q-u A. Future changes in growing degree days of wheat crop in Pakistan as simulated in CORDEX South Asia experiments. APN Science Bulletin. 2020;**10**(1):82-89. DOI: 10.30852/ sb.2020.1221

[37] You L, Rosegrant MW, Wood S, Sun D. Impact of growing season temperature on wheat productivity in China. Agricultural and Forest Meteorology. 2009;**149**(6-7): 1009-1014. DOI: 10.1016/j. agrformet.2008.12.004

## **Chapter 2**

## Evapotranspiration and Drought in Different Agricultural Zones of Bulgaria

*Valentin Kazandjiev, Veska Georgieva, Petia Malasheva and Dragomir Atanassov*

## **Abstract**

Bulgaria is located in an area with insufficient humidification and is characterized by periods of the drought of varying duration and intensity. From the last 15 to 20 years, the limiting factor in agro-meteorological conditions was the drought. Agro-meteorological drought consists of the depletion of available soil water reserves in the root zone. Ultimately, the results of droughts affect the size of yields and the quality of production. The consequences of this extreme condition from a meteorological and agro-meteorological point-of-view phenomenon can be mitigated only by expanding the irrigated areas. The aim of this work was to present the tendencies in the change of the potential evapotranspiration during the studied period 1986–2015 as a result of the climate changes and also the tendencies of the conditions for occurrence of agricultural drought in the future, and also to propose an approach where using certain indicators controls the process of accumulation and consumption of water in the soil. Such approach could find application in adapting agriculture to climate change and the updation of agro-environmental zoning relevant to climatic changes.

**Keywords:** potential evapotranspiration (ETP), soil moisture index (SMI), drought index (AI), annual and seasonal amounts of precipitations and temperatures (Σt and Σr)

## **1. Introduction**

Droughts have a significant impact on agriculture to limit crop productivity and lead to reduced yields and have caused significant economic losses in a number of areas in Europe and the world. According to research by the Institute for Environment and Sustainable Development from the Joint Research Center in Ispra, Italy, drought is one of the biggest related to meteorological disasters. Continuing over months or years, it can affect large areas and can have serious environmental, social and economic impacts. These impacts depend on the duration, severity, and spatial extent of the absence of precipitation, but also on the environment and the socio-economic vulnerability of the affected regions. Europe has rich freshwater resources, but there is a strong regional imbalance across the continent. Water scarcity, for example, is a significant problem in many European regions, particularly in semi-desert and continental climate zones.

A recent study conducted jointly by the European Commission and the Member States estimates the cost of droughts in Europe over the last 30 years at least € 100 billion. The same study estimated the economic damage from drought and heatwaves in 2003 in Central and Western Europe at more than € 12 billion. Other examples of this are the drought that developed in late 2004 in southern Portugal and Spain; the 2006 spring drought in France and the southeast of the United Kingdom and the spring 2011 drought in the enlarged parts of Western Europe, with severe economic consequences, mainly in the agricultural sector. In addition, April 2007 was the driest April according to the meteorological services of Germany, the Netherlands, and Austria, and November 2011 was the driest November in history for large parts of Europe, and Bulgaria. More facts - in the last 5 years in Bulgaria and many countries in the region there is a permanent summer drought, which usually begins in the second half of July and turns into an autumn drought by October and sometimes November.

Climate change, according to the Intergovernmental Panel on Climate Change for Europe, shows significant changes in the water balance across Europe, with an increased likelihood of summer droughts in the Mediterranean, as well as in Central and South-Eastern Europe. However, changes in the annual distribution of precipitation, as well as in energy and water balances, are likely to occur in other regions of Europe, leading to an increased likelihood of declining water levels and an increased likelihood of extreme weather events.

The European Commission published a Communication on "Meeting the Challenges of Water Scarcity and Droughts in the European Union" in December 2007, requesting a wider range of activities to adapt and mitigate the effects of drought and the changing climate in Europe. The measures requested include the development of a European Drought Observatory (EDO), the provision of consistent and timely drought information from the continental to the regional and local scales.

In order to detect, monitor, and forecast droughts on a continental scale, the Joint Research Center (JRC) of the European Commission (EC) is developing a prototype of the European Drought Observatory (EDO). A multidisciplinary set of indicators has been introduced, which is used within EDO to continuously monitor the various components of the environment potentially affected by this hazard (soil, vegetation, etc.) in order to obtain a comprehensive and up-to-date picture of the situation. Two indicators produced under EDO compare yield statistics to assess the effects of drought events on agricultural production. The test area is Spain, which is often suffered from a severe and prolonged drought. The results show that yields are significantly reduced in line with the drought events found by the indicators.

As drought is a slow-growing phenomenon affecting the whole water cycle (e.g. soil moisture, river basins, water levels in lakes, reservoirs, and groundwater) and has direct effects on vegetation, all these components must be monitored continuously throughout a long period of time. Other important aspects of adequate drought management are drought risk analysis (i.e. the likelihood of drought occurring to a certain degree, severity, and duration) and social vulnerability to drought. They are the basis of a detailed risk assessment. Finally, in the short and medium-term, forecasting the occurrence and likely development of droughts, as well as forecasting and analyzing the likely effects of climate change on drought hazards in different regions, are important to support the development of effective management plans on land.

#### *Evapotranspiration and Drought in Different Agricultural Zones of Bulgaria DOI: http://dx.doi.org/10.5772/intechopen.102391*

At the beginning of the last century, attempts began to define the concept of drought, especially agrometeorological drought, and ways to identify it. Based on the common for all types of drought - precipitation deficit, almost all indices are related to them - a different number of days without precipitation, and later they refer to the consumption of water from soil and vegetation - evapotranspiration. In 1965, Palmer published its model for determining three types of drought - meteorological (PDSI), hydrological (PDHI), and agricultural (Z-index). A historical review and analysis of the indices used in the United States for different types of drought by [1] shows that the Crop Moisture Index (CMI), the Palmer Moisture Anomaly Index, is the most widely used to study the agricultural type of drought.

In relation to significant a problem with identifying drought – duration and intensity in 2013, World Meteorological Organization (WMO) and the Global Water Partnership (GWP) started an Integrated Drought Management Programme (IDMP), Integrated Drought Management Programme. The first step of drought management is Monitoring and Early Warning Systems. The most commonly used drought indicators/indices for detecting the drought are shown in the Handbook of Drought Indicators and Indices [2]. Experience has shown that it is difficult to establish common indicators. Due to the complexity and variability of drought depending on climatic and geographical conditions, it is appropriate to work on different indicators to be included in drought monitoring and warning systems. Selecting of the indicators has to consider timely detection of drought to realize appropriate communication with users and coordination to mitigate of effect. The characteristics of climate in space and time also have to have in mind to determine drought onset and termination. One of the most important things in the selection of indicators is the availability of information.

Every year, huge financial resources are invested in agriculture to maintain a plant-friendly water and nutrition regime. Many of them are lost due to the low level or lack of scientific management of activities in every production field in our country and in most countries of the world. The losses are due to inefficient use of energy, water, fuel, and human labor, as well as data from the meteorological network. As a result, agricultural fields are an intensive source of pollutants that damage ecosystems.

## **2. Drought monitoring in Bulgaria**

The studies on the frequency of dry periods with different duration in Bulgaria have a special interest in view of the dry character of climatic conditions in the agricultural regions. An in-depth study of the conditions for the occurrence of drought and drought periods in Bulgaria during 1983–1994 was made by Kincaid and Heermann [3]. There is defined that the driest time during the year occurs most frequently in the period from August to October, and the areas with the highest number of droughts are the Seaside and Southern parts of the lowland of Tundja, Maritsa, and Struma [3, 4]. A similar trend is observed in the last 20 years - increased frequency and the dry period compared to the modern climate, especially in the Thracian Lowland and Northeastern Bulgaria [5, 6]. The most used agroclimatic indices in historical scale are Selianinov's HTK, De Martone Index, Thornthwaite Index, the balance of atmospheric humidity, aridity index, and so on. They are used for a comprehensive assessment of temperature and humidity conditions in the area under consideration. The most commonly used is aridity index because it gives an idea of the real water deficit for a certain period. Dilkov [7] found that in the period of spring, wheat growth evaporability-precipitation balance values are exceeding

200 mm in each 2 to 4 years of 10 years. More recent studies [8, 9] that the evaporability-precipitation balance in the spring growing season for the period 1971– 2000, the range between 223 mm and + 15 mm, the largest deficit of water resources is observed in some areas of the Thracian lowland and especially agricultural lands around Svilengrad, Ivailo, Plovdiv (180 mm). The values of De Martone in agricultural areas ranged from 20 to 40 - mm/°C, [10–12] which defines the terms as moderately moist, HTK Selyaninov of about 1, and wheat yields are obtained when values of the De Martone than 30 mm/°C. Comprehensive assessment of conditions in the areas of agricultural production shows that in the region of Thracian lowland and Dobroudja to obtain high yields of wheat is necessary to compensate for the water deficit, is to conduct additional irrigation at critical growth and yield formation of cultivations. The change in the deficit in the root zone of the specific soil sown with a certain crop during the current growing season differs significantly from that calculated by climatic methods, which are based on average multi-year data. Estimates of the impact of climate change during the period 1961–2000–2010 on the soil moisture in wheat cultivation on six soil types were made and results were obtained for 24 agricultural stations in the country [13].

To characterize the hydrothermal conditions for growing autumn and spring crops in the country, the Selyaninov Hydrothermal Coefficient (SHC), De Matron (AIDM), and the potential evapotranspiration on Thornthwaite (ETP) were used by processing data from 42 climatic and agrometeorological stations [10–12].

The influence of climatic changes on the atmospheric humidity, the evaporation from a free water surface, and the reference evapotranspiration on the territory of Bulgaria have been studied. The most unfavorable changes in the evaporating conditions have occurred in the Petrich-Sandanski climate region, as well as in the Central and Eastern part of the Danube Plain [10–12]. 30-year FAO reference evapotranspiration rates were obtained using the Penman-Monteith (ETo) equation for 30 agrometeorological stations. A summary is made by climatic regions [14].

The single and combined effect of the meteorological parameters of the FAO Penman-Monteith equation on the estimates for the reference evapotranspiration during different sub-periods of the potential vegetation period (PVP) related to the physiological development of crops and their sensitivity to moisture is analyzed. Onefactor and two-factor correlation analysis was conducted for 30 agrometeorological stations on the territory of agricultural production in Bulgaria [15].

Maps for the average annual spatial distribution of the atmospheric moisture balance (BAO = sum of precipitation minus reference evapotranspiration) are presented, as well as for the 30-year change of this deficit, and the areas with the most unfavorable conditions for agricultural crops are indicated. The need to update the irrigation regime of crops is justified [16, 17].

Maize in Bulgaria is a crop that suffers from water deficiency during its most sensitive phases in terms of water - sweeping-squeezing and squeezing-milk maturity. Data from the period 1971–2000 from 9 representative agrometeorological stations were processed. The climatic moisture supply of corn for grain, grown on four types of chernozems in Northern Bulgaria - typical, leached, carbonate, and degraded, is analyzed [8].

Comparative study of drought use monitoring of 3 indices is made in National Institute of meteorology and hydrology (NIMH). For detection of meteorological drought during 2009 WMO recommended the Standardized Precipitation Index (SPI) as a main meteorological index for observing and analyzing the drought conditions [18]. SPI *transform the precipitation totals for a certain period in a standard normal distribution.* In the intensity scale of SPI, the positive and negative values correlate

### *Evapotranspiration and Drought in Different Agricultural Zones of Bulgaria DOI: http://dx.doi.org/10.5772/intechopen.102391*

with the up normal rainfall and drought. McKee et al. [19] determined that the drought started at values of SPI ≤ 1, but in many cases are used different values between 0 and 1 or below 1 [20]. In many countries, including and these in Southern and South-Eastern Europe, classifications are used, including the category "normal" for SPI values in the range of 0.5.

The Soil Drought Index (SMI) developed by the HPRCC (High Plains Regional Climate Center) determines the intensity of drought by assessing the available water available to plants in the soil [21] relative to its maximum quantity for a given soil type. To calculate it, the measured soil moisture in agricultural crops is used, which allows to determine the degree of drought for a particular crop. It characterizes soil drought from normal to extreme, with the degree of drought increasing with decreasing index.

The monitoring is made with monthly data which means that the information has a diagnostic character.

Fundamental research is needed to gain knowledge about the root causes of the processes taking place in agroecosystems.

Inefficient management of activities in each field stems from the inability to easily and cheaply obtain current data on water supply (or deficit) in the soil by applying local (point) methods for measuring soil moisture and the climatic and remote methods used requiring highly qualified specialists [22]. This difficulty is related to the huge number of agricultural fields. It was found that when measuring 2 places in 10 depths for an area of 50.0 ha, the error was 79.6%. In order to reduce the error to 7.0%, it is necessary to make 50 measurements of the soil moisture profile, including 10 points in depth [23]. These results show that the representativeness of the experimental data obtained using these methods is a serious problem. Their application is limited within experimental stations, as well as for instrument calibration, regardless of the developed methodologies to reduce the number of measurements. This is confirmed by [3], who showed that for each field it is necessary to obtain representative data on water deficit over three days for the entire growing season. For example, for only one cornfield with an area of 50 ha, we need on average about 25,000 point measurements for a vegetation period lasting 150 days.

## **3. Material and methods**

Solving various global tasks, such as the improvement of irrigation systems is related to determining the agroclimatic resources for growing cereals under irrigated and non-irrigated conditions requires the processing of data from meteorological and agrometeorological observations in all parts of the country. Observations from two or three climate and agroclimatic stations were taken in some larger regional centers. Thus, the territory of the country, which consists of 28 districts, was covered with 70 stations from the meteorological and agrometeorological networks of NIMH. The location of the stations is chosen so that the distance between them is approximately 20–30 km. Such a distance between the measuring points ensures the correctness and representativeness of the field of the measured elements related to the temperature conditions and their derivative characteristics. These include the maximum, minimum, and average daily temperatures and the 24-hour amount of precipitation, and the derived characteristics include active and effective temperatures reported above a certain biological threshold. Most hydrothermal indices reflecting the conditions of moisture and the characteristics of evaporation from the soil and crops were included here, i.e. evapotranspiration.

Under these conditions, there are suspicions about the interpretation of the precipitation field, but its structure is not the subject of this study. The latter is measured with sufficient accuracy, where the error due to the slightly greater distance between the measurement points is largely compensated by the number of measurements of water reserves in soil layers to a depth of 1 m.

In addition to the temperature and precipitation to characterize the conditions of drought and drought in any part of the country, the working database included data on the average daily values of agility of water vapor, the relative humidity of 2 m in the weather cell, wind speed and the duration of sunshine.

The working database is created with the data for the average daily values of maximum (Tmax), minimum (Tmin), average daily (Tav) temperatures, the agility of water vapor (E), relative humidity (F), wind speed (w), the duration of sunshine (s), and the sum of precipitation (r) for all 70 stations, **Figure 1**.

With the mentioned data, Excel spreadsheets were formed, as a separate file was created for each station, and the data cover a 30-year measurement period - from 1986 to 2015 inclusive. In this form, the control of the data, the marking of missing data, and their recovery were carried out. Statistical data processing was also performed mean of each of the series, standard deviation, variance, standard deviation, median, mode, and type of data distribution.

In addition, a single database was created with the Excell® files. In the middle of the database are performed all calculations and selective data processing - the climatic value of meteorological elements, calculation of coefficients and indices reflecting the hydrothermal conditions and their spatial distribution throughout the country.

The methodology of the Joint Research Center in Ispra, Italy, and the General Directorate of Agriculture of the European Commission was used to characterize the drought conditions. According to her, the Aridity Index (AI) has recently been widely

*Evapotranspiration and Drought in Different Agricultural Zones of Bulgaria DOI: http://dx.doi.org/10.5772/intechopen.102391*

used to assess the conditions of drought and drought in agriculture. The calculation of the dryness index is applied by formula (1), recommended by [24], in the Methodology for identifying areas with natural constraints, described in the JRC Technical by calculating the dryness index:

$$\text{Al}\_{\text{UNEP}} = \sum \text{r/ETP},\tag{1}$$

where AI - drought index, Σr - annual amount of precipitation; and ETP - the sum of the annual potential evapotranspiration. All values of the drought index.

Potential evapotranspiration - ETR, as defined by FAO-56, is the evapotranspiration from a grass surface with a standard height of 8–15 cm of plants that are actively growing, completely shading the soil surface and not experiencing water shortages [25, 26]. In recent years, a new calibrated method for calculating potential evapotranspiration has emerged worldwide - the FAO Penman-Monteith method [27, 28].

Potential evapotranspiration (ETP) by this method is determined by the equation:

$$ET\_o = \frac{\mathbf{0}, 4\mathbf{0}\mathbf{8}\Delta (R\_n - G) + \frac{\rho \mathbf{0} \mathbf{0} U\_2 (\epsilon\_a - \epsilon\_d)}{T + 273}}{\Delta + \gamma (1 + \mathbf{0}, \mathbf{34}U\_2)}\tag{2}$$

where.

*ЕTP* potential evapotranspiration [mm d�<sup>1</sup> ],

*Rn* и *G* net radiation and heat flow in the soil [MJ m�<sup>2</sup> d�<sup>1</sup> ],

*T* the average daily air temperature в [ о С],

*Δ* water vapor pressure gradient [kPa <sup>o</sup> C�<sup>1</sup> ],

*γ* psychrometric constant [kPa <sup>o</sup> C�<sup>1</sup> ],

*ea и ed* deficit of air saturation with water vapor at standard altitude (at the level at which the meteorological measurements are performed) - 2 m [kPa],

*U2* wind speed at a reference height of 2 m [m s�<sup>1</sup> ].

Potential evapotranspiration (ETP) was used to assess the saturation of the atmosphere with water vapor, and real evapotranspiration (ETR) was used to assess the behavior of the plants concerned under certain evaporative conditions in the atmosphere.

## **4. Results and discussion**

The annual course of potential evapotranspiration in certain meteorological conditions is determined by the biological characteristics and physiological development of crops, their water needs at each stage of their development. Physical conditions have the effect of increasing or decreasing evapotranspiration, while physiological conditions associated with aging have the effect of limiting the influence of external factors and regulating the evaporation process.

The study used daily data, which were mentioned above for the period 1961–2015. The study performed the following:


emergence vegetative development and overwintering of autumn crops from October of the previous to March of the following year; 2) the period of ripening of winter cereals April-June and 3) the period of irrigation of spring crops – July-August;

• The trends of the potential evapotranspiration for the research period by representative stations and administrative regions have been obtained. The results for both the potential vegetation and the traditional period of irrigation in our country – June-August are presented.

The differences between the final and initial values of the trends representing the change in the potential evapotranspiration over the 30-year period have been calculated.

The obtained results for the potential evapotranspiration by years were averaged for the study period, the minimum and maximum values, standard deviation, coefficient of variation, steepness, and median for each point of the agricultural territory of the country were determined, **Table 1**.


#### **Table 1.**

*Statistical characteristics for 20 representative stations in northern and southern Bulgaria.*

*Evapotranspiration and Drought in Different Agricultural Zones of Bulgaria DOI: http://dx.doi.org/10.5772/intechopen.102391*

**Figure 2.**

*Average long-term values of the potential evapotranspiration by stations and periods of crop development for the period 1961–2015.*

The graphic materials and simulations cover 18 representative stations to describe in the most plausible way the conditions in the six regions into which we have divided the country.

The values of the potential evapotranspiration by stations and periods of crop development were also calculated, as already noted. During the period October– March the predominant value of ETP is above 800 mm–850 mm, higher than 900 mm is the potential evapotranspiration in Gramada, Nikolaevo, Pavlikeni, Hisar,

**Figure 3.** *Trends of potential evapotranspiration in northern Bulgaria.*

Sandanski, Svishtov, and Burgas. The lowest values of ETP were obtained in Lovetch, Borima, Dermantsi, Sevlievo, and Ivaylovgrad, a less than 800 mm, **Figure 2**.

The values of the potential evapotranspiration by stations and periods of crop development were also calculated, as already noted. During the period October– March, the predominant value of ETP is close to 800 mm.

### *Evapotranspiration and Drought in Different Agricultural Zones of Bulgaria DOI: http://dx.doi.org/10.5772/intechopen.102391*

The dynamics and the trend of change of the potential evapotranspiration in Northern and Southern Bulgaria during the investigation period are presented in **Figures 3** and **4**. It can be seen that in almost all representative stations there is a tendency to increase the potential evapotranspiration. While in the northern regions this process is clearly visible, in the southern regions there is diversity, and in the northwestern and northeastern parts of the country, the increase in ETP is well expressed, in the central regions this process oscillates around an average and only in Kanzanlak, Kyustendil, and Sofia to a slight reduction in ETP. Precise analysis shows that this is due to an increase in the amount of precipitation. These results are also confirmed by the data of the conducted significance test by the Mann-Kendall method, **Table 2**.

The values of the long-term average monthly potential evapotranspiration for 18 representative stations from Northern and Southern Bulgaria, which correspond to the transitional-Continental and transitional-Mediterranean type of climate are presented in tabular and graphical form in **Table 2** and in **Figure 5**.

**Figure 4.** *Trends of potential evapotranspiration in southern Bulgaria.*


#### **Table 2.**

*Levels of significance of the trends determined by the Mann-Kendall test for change of ETP by agricultural crops and periods of development.*

The average multi-year monthly values of the potential evapotranspiration were calculated, which are shown in **Table 3**. The highest monthly values of ETP were reported in the stations Sandanski-198 mm and Sliven-182 mm. These values were reported in July, the warmest month of the year, and the lowest values of this indicator for the same period of the year are in the Krushari – 148 and Sofia-152 mm.

The average long-term values of soil water consumption through evapotranspiration in the period 1961–2015 in Northern Bulgaria are increasing, compared to the reference period. This increase is most noticeable in the northeastern region during the autumn-winter period October–March - 117 mm,

## *Evapotranspiration and Drought in Different Agricultural Zones of Bulgaria DOI: http://dx.doi.org/10.5772/intechopen.102391*

#### **Figure 5.**

*Average multiannual values of ETP (mm) for northern and southern Bulgaria, which correspond to the transitional-continental and transitional Mediterranean types of climates and for 1986–2015.*



**Table 3***.*

*Average long-term values of potential evapotranspiration (ETP) for some representative agrometeorological stations by months for the period 1961–2015.*

after it is the northwest - 114 mm, followed by the north-central region - 102 mm. An increase in evapotranspiration is also present in the other two periods, as in the first of them the values of the increase are 27 mm–44 mm, and in the period June–August these values are 48 mm–68 mm, as the higher values refer to the northwest, and the lower ones for the northeastern region. On average for Northern Bulgaria, the increase in evapotranspiration for the entire growing season is 201 mm, **Table 3**.

Soil water consumption by evapotranspiration in Southern Bulgaria for the study period is similar and a bit lower to this in Northern Bulgaria, **Table 3**. Summarizing the results of the comparative study we should note an overall increase in potential evapotranspiration throughout the country compared to the reference period 1961– 1990. The most noticeable is the decrease of ETP during the autumn-winter period in the high valley fields of Western Bulgaria �61 mm; an Increase of ETP is observed in the southwestern region - 108 mm. During the period April–June the decrease is observed in the high valley fields of Western Bulgaria �22 mm, followed by the southcentral region - 36 mm. An increase in ETP is also observed in the period June–August, as again the highest values belong to the high valley fields in Western Bulgaria with 122 mm and the southwestern region - 59 mm. The average values of increase in soil water consumption through evapotranspiration in Southern Bulgaria in the three periods is 117 mm in the autumn-winter period; 36 mm in spring and summer and 62 mm in summer.

Obtained values of the AI index are analyzed in time and space, as a result the dry and wet years during the studied period are determined (criterion - the number of stations with AI≤0.6 for the agricultural zone of the country is more than half), and also regions in which AI≤0.6 by years (criteria - the number of years with AI ≤0.6 to be greater than or equal to 7, which is in accordance with the cited above methodology. By applying the criteria AI≤0.6 dry year; 0.6 ≤ AI≤1.0 normal year and AI≥1.0 wet year we are defined the last 30 years as dry, wet, and normal years according to the values of the drought index as follows:

Dry years are - 1985, 1986, 1988, 1989, 1990, 1992, 1993, 1994, 2000, 2001, 2006, 2008, 2013, and 2015.

Wet years are - 2002 and 2005, 2012, and 2014.

Normal years are - 1987, 1989, 1991, 1992, 1995, 1996, 1997, 1998, 2003, 2004, 2007, 2009, 2010, and 2011.

As a result of the simulations, the average multi-year dates of the beginning of the depletion of water reserves below the lower limit of optimal moisture (70% of AWC) were obtained, which requires the first irrigation of corn crops. For Northern Bulgaria, the deadlines are from May 31 to June 17, and the increase of the date is from west to east, **Table 4**.

In Southern Bulgaria, the average dates of depletion of water reserves occur one week earlier - May 24, and in the sub-Balkan fields and high valley fields of Western Bulgaria, this happens in the period June 20–26, **Table 5**.


## *Evapotranspiration and Drought in Different Agricultural Zones of Bulgaria DOI: http://dx.doi.org/10.5772/intechopen.102391*

#### **Table 4.**

*Sums of potential evapotranspiration (ETP) (mm) for character periods by agroindustrial regions of the country.*


#### **Table 5.**

*Average long-term dates of depletion of water in the soil below 70% of AWC and determination of the need for irrigation in maize-grain crops, FAO group 400–500.*

## **5. Conclusions**

This study presents part of the results related to the need for continuous monitoring of potential evapotranspiration (ETP) and monitoring of trends in its change. Within this study, the parameters of ETP for the period 1961–2015 were obtained for 72 stations from the agricultural territory of Bulgaria, and also the results for some

stations representative for the agricultural production are shown. The more important conclusions are the following:


## **Acknowledgements**

This work was supported by the Bulgarian Ministry of Education and Science under the National Research Programme "Healthy Foods for a Strong Bio-Economy and Quality of Life" approved by DCM # 577/17.08.2018.

## **Author details**

Valentin Kazandjiev\*, Veska Georgieva, Petia Malasheva and Dragomir Atanassov National Institute of Meteorology and Hydrology, Sofia, Bulgaria

\*Address all correspondence to: valentin.kazandjiev@meteo.bg

© 2022 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

*Evapotranspiration and Drought in Different Agricultural Zones of Bulgaria DOI: http://dx.doi.org/10.5772/intechopen.102391*

## **References**

[1] Keyantash J, Dracup JA. The Quantification of Drought: An Evaluation of Drought Indices. Bulletin of the American Meteorological Society. 2002;**83**:1167-1180. DOI: 10.1175/ 1520-0477(2002)083<1191: TQODAE>2.3.CO;2

[2] Available from: https://www.drough tmanagement.info/literature/GWP\_ Handbook\_of\_Drought\_Indicators\_and\_ Indices\_2016.pdf

[3] Kincaid DC, Heermann DF. Scheduling Irrigation Using a Programmable Calculation. Agricultural Research Service, USDA; 1973

[4] Knight G, Raev I, Staneva M. Drought in Bulgaria. Vol. 284. London: BAS, Routledge; 2003

[5] Alexandrov V, Radeva S, Koleva E. Utilization of SPI, PDSI, and RDI as drought indicators in South Bulgaria. 11th International Multidisciplinary Geoconference Proceedings Volume II; Albena, Bulgaria. 2011. pp. 969-976

[6] Booher LJ. Surface Irrigation. Rome: FAO; 1974

[7] Dilkov D. Moisture consumption and moisture supply of wheat in our country. Tr. IHM. 1960;**VIII**:3-48

[8] Kazandjiev V, Moteva M, Georgieva V. Climate Change, Agroclimatic Resources and Agroclimatic Zoning of agriculture in Bulgaria. Agricultural Engineering. 2010;**3**:109-116

[9] Moteva M, Kazandjiev V, Georgieva V. Assessment of the contemporary hydro-thermal conditions for crop growing in Bulgaria. Proceedings on the International Conference on "Soil Tillage and Ecology", 1–5 September; Albena, Bulgaria. 2009. pp. 134-141

[10] Moteva M, Kazandjiev V, Georgieva V. Climate change and the hydrothermal and evapotranspiration conditions in the panning regions in Bulgaria. Proceedings of 14-th International water Technology Conference, IWTC Cairo - Egypt; 2010a 12 p

[11] Moteva M, Kazandjiev V, Georgieva V. Impact of Climate Change on Soil Water Table in Sofia Region, Bulgaria. Proceedings of the International Conference on Water Observation and Information System for Decision Support, Ohrid, Macedonia, CD version: 25–29 May; 2010b

[12] Moteva M, Kazandjiev V, Georgieva V. Influence of climate change on the reference evapotranspiration in Bulgaria. Plant Science. 2010c;**2**:181-186

[13] Georgieva V, Moteva M, Kazandjiev V. Impact of Climate Change on Water Supply of Winter Wheat in Bulgaria. Agriculturae Conspectus Scientificus. 2007;**72**(1): 39-44

[14] Moteva M, Kazandjiev V, Georgieva V. Study of the reference evapotranspiration according to FAO Penman-Monteith on the territory of Bulgaria. Agricultural Machinery. 2008; **5**:26-33

[15] Kazandjiev V, Moteva M, Georgieva V. Interaction between the Meteorological Parameters in FAO 56 Penman-Monteith Equation and the Reference Evapotranspiration Estimates. Conference "Global Environmental Change–Challenges to Science and Society in Southwestern Europe" CD version; 2008

[16] Kazandjiev V, Moteva M, Georgieva V, Dimitrov P. Atmospheric water supply for the potential evapotranspiration in Bulgaria. Lviv, Ukraine, CD Version: ICID 23rd ERC 2009 "Progress in Managing Water for Food and Rural Development", 18–24 May; 2009

[17] Kazandjiev VS, Georgieva VA. In: Meena RS, editor. Chapter 4. Changes in the Agro-Climatic Conditions in Bulgaria at the End of the 20th and the Beginning of the 21st Century book Agrometeorology. BHU, India: Department of Agronomy Institute of Agricultural Sciences (BHU); 2021, 2021

[18] Hayes M, Svoboda M, Wall N, Widhalm M. The Lincoln Declaration on Drought Indices: Universal Meteorological Drought Index Recommended. Bulletin of the American Meteorological Society. 2011;**92**(4): 485-488. DOI: 10.1175/2010BAMS3103.1

[19] McKee TB, Doesken NJ, Kleist J. The Relationship of Drought Frequency and Duration to Time Scales. Boston, MA: Proceedings of the 8th Conference on Applied Climatology, 17–22 January 1993, Anaheim, CA; American Meteorological Society; 1993

[20] Available from: https://www. droughtmanagement.info/

[21] Hunt E, Hubbard K, Wilhite D, Arkebauer T, Dutcher A. The development and evaluation of a soil moisture index. International Journal Climatology. 2008;**29**:747-759. DOI: 10.1002/joc.1749

[22] Christov ID. Comparison of new computerized decision support eco-technology for precise irrigation scheduling with available products on market with similar function. Journal of Balkan Ecology. 2014;**17**(2):17-27

[23] Leonov EA, Leonova NE. Results of studying the water regime of soils and small catchments in the Don river basin. Vol. 194. Proceedings of the GGI; 1972. pp. 133-154 (in Russian)

[24] Terres J-M, Toth T, Wania A, Hagyo A, Koeble R, Nisini L. Technical report - Updated Guidelines for Applying Common Criteria to Identify Agricultural Areas with Natural Constraints. JRC; 2016

[25] Doorenbos, Pruitt. Crop water requirements. FAO Irrigation and Drainage Paper No. 24. Rome: Food and Agricultural Organization of the U.N. Rome; 1977

[26] Heim RR. A review of twentiethcentury drought indices used in United States. BAMS. 2002;**83**:1149-1165

[27] Allen RG, Smith M, Perrier A, Pereira LS. An update for the definition of reference evapotranspiration. ICID Bulletin. 1994;**43**(2):1-34

[28] Allen GR et al. Crop Evapotranspiration-Guidelines for Computing Crop Water Requirements-FAO Irrigation and Drainage Paper 56. FAO; 1998. p. 300

## **Chapter 3**
