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Characterizing Meteorological Droughts in Nepal: A Comparative Analysis of Standardized Precipitation Index and Rainfall Anomaly Index
Characterizing Meteorological Droughts in Nepal: A Comparative Analysis of Standardized...
Aryal, Anil;Maharjan, Manisha;Talchabhadel, Rocky;Thapa, Bhesh Raj
Article Characterizing Meteorological Droughts in Nepal: A Comparative Analysis of Standardized Precipitation Index and Rainfall Anomaly Index 1 , 2 3 4 Anil Aryal *, Manisha Maharjan , Rocky Talchabhadel and Bhesh Raj Thapa Interdisciplinary Center for River Basin Environment (ICRE), University of Yamanashi, Kofu 400-8510, Japan Independent Researcher, Kofu 400-0008, Japan; firstname.lastname@example.org Texas A&M AgriLife Research, Texas A&M University, El Paso, TX 79927, USA; email@example.com Department of Civil Engineering, Universal Engineering and Science College, Lalitpur 44700, Nepal; firstname.lastname@example.org * Correspondence: email@example.com Abstract: Drought is an environmental disaster related to the extremes (on a drier side) in hydromete- orology. The precipitation amount modulates drought in Nepalese river basins. It is vital for efﬁcient water resources management to quantify and understand drought. This paper aims to characterize the droughts in Nepal based on standard precipitation index (SPI) and rainfall anomaly index (RAI) using daily precipitation data and assess their impacts on annual crop yields. We used 41 years (1975–2015) of daily precipitation data to compute monthly means and then the drought indices, namely, SPI and RAI, at 123 stations across Nepal. Results showed that the northwest and eastern regions experienced drought compared to other regions, although the severity and duration were shorter. For stations 101 and 308, we found extreme drought events after 2005 for SPI-1, SPI-3, and Citation: Aryal, A.; Maharjan, M.; SPI-6. However, for SPI-6, extreme drought was also observed in 1989 and 1994 at both stations. The Talchabhadel, R.; Thapa, B.R. year 1992 was one of the severest drought years for the western and northwest regions, where the Characterizing Meteorological severity crossed more than 2.0 for all SPI months. Similar to SPI, RAI also revealed a similar degree Droughts in Nepal: A Comparative of drought in the country. RAI showed that the eastern region depicted a higher degree of severity of Analysis of Standardized drought compared to other areas beyond 2004. The lesser severity is also seen in the far west part Precipitation Index and Rainfall beyond 2005. The results showed that SPI and RAI could equally be used to analyze drought severity. Anomaly Index. Earth 2022, 3, More frequent drought incidents have been observed after 2010 at all the considered precipitation 409–432. https://doi.org/10.3390/ stations. With the increase in the drought severity, the crop yield (such as paddy, maize, barley, millet, earth3010025 and wheat) has been directly impacted. These results might be signiﬁcant for planning water resource Academic Editor: Hossein Bonakdari and irrigation water management systems. Received: 30 January 2022 Accepted: 2 March 2022 Keywords: crop yield; drought; rainfall anomaly index; standardized precipitation index; water Published: 4 March 2022 resource management Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional afﬁl- 1. Introduction iations. Drought is a recurrent natural hazard caused by precipitation deﬁciency resulting in damage to crops, water resources, economies, and human lives  over an extended space-time domain. Drought has a signiﬁcant impact on various aspects of the global Copyright: © 2022 by the authors. ecosystem [2–5]. The drought phenomena can be meteorological, hydrological, agricultural, Licensee MDPI, Basel, Switzerland. or/and socio-economic [6–8]. Meteorological drought is deﬁned as the lack of precipitation This article is an open access article over a region for a more extended period, and hydrological drought is related to the low distributed under the terms and water supply in streams, reservoirs, and groundwater levels . Agricultural drought conditions of the Creative Commons usually refers to declining soil moisture and consequent crop failure without reference to Attribution (CC BY) license (https:// surface water resources. In addition, socioeconomic drought is associated with the inability creativecommons.org/licenses/by/ of water resources systems to meet water demands [10,11]. A signiﬁcant relationship 4.0/). Earth 2022, 3, 409–432. https://doi.org/10.3390/earth3010025 https://www.mdpi.com/journal/earth Earth 2022, 3 410 between meteorological drought and other droughts could be found . Propagation of the meteorological drought to other drought forms is characterized by essential features such as pooling, attenuation, lag, and lengthening [13,14], emphasizing its importance. The spatial variation of drought can vary from global to local scales . A drought may last from a month to a decade , affecting the ecosystem balance . In addition, drought might affect various socio-economic sectors, including agriculture, hydropower, inland navigation, and even tourism . Thus, it is evident to characterize the spatiotem- poral variations in drought for effective water resources management. The quantitative analysis of drought can be done by estimating different drought indices, such as the Palmer drought severity index (PDSI), standardized precipitation index (SPI), rainfall anomaly index (RAI), crop water stress index (CWSI), Palmer hydrological drought index (PHDI), streamﬂow drought index (SDI), normalized difference water index (NDWI), vegetation condition index (VCI), and normalized difference vegetation index (NDVI) [6,19–21]. These indices have been used to assess the duration and intensity of extreme precipitation at different temporal scales [22,23]. Extreme precipitation events such as heavy and low rainfall are analyzed based on the daily rainfall events. In contrast, long-term drought events are assessed based on monthly and annual rainfall to ensure water scarcity. Drought indices, speciﬁcally, SPI  and RAI , are simple to use yet robust on performance. In addition, they only need rainfall data. SPI has been widely applied in various studies globally and regionally [26–30]. SPI has numerous advantages over other drought indices , including the data required for temporal and spatial characteristics on application . The SPI has also been used to analyze projection results [33,34], whereas RAI receives less attention . Several studies have been carried out to check SPI and RAI [36–38]. A comparison of model results with the observed data can be made using standardized precipitation anomalies . Compared to SPI, RAI poses a higher degree of transparency and is less complicated in mathematical computation . RAI can also be computed at identical time scales to SPI with a similar degree of robustness. Drought in Nepal is mostly characterized by meteorological events caused by late monsoon onset or/and inadequate rainfall distribution . Such poor rainfall distribution has affected both agricultural and non-agricultural sectors. Importantly, agriculture is the country’s critical economic activity. The yield of the major crops decreased to 56,000 metric tons in 2013 compared to 727,460 metric tons in 1982 [40,41]. The drought of winter 2008–2009 affected most of the country’s districts, resulting in a signiﬁcant crop yield reduction . Drought in Nepal has equally affected the non-agricultural sectors, such as human health  and migration of the males searching for jobs . Adhikari et al.  further reported that Nepal received several meteorological droughts during 1970–1990, and winter droughts during 2002–2004 attributed to the reduction in precipitation by 50%. Between 2005 and 2010, the country’s hilly region had dry spells and wet monsoon, reducing the crop yield . Nepal receives most of (about 80%) its rainfall in the monsoon season. The rainfall vari- ability plays a key indicator in the country’s dry and wet conditions [43,44]. Approximately 60% of Nepal’s irrigable land depends on monsoon rainfall. An insufﬁcient amount of meteorological, hydrological, and water resource infrastructure makes it more challenging to deal with droughts. Irregularity in monsoon seasons has also acted as a catalyst for low crop yield. In addition, the soil moisture index (SMI) plays a crucial role in the planning of the irrigation facilities at the ﬁeld level . All droughts are mainly related to meteo- rological and soil moisture conditions, affecting irrigation facilities’ planning at the ﬁeld level to achieve optimum crop production. Thus, characterizing the drought condition in spatial and temporal constraints can provide a sound understanding of different droughts and their relationship in an agricultural country like Nepal. Few studies on meteorological drought are conducted in Nepal using SPI on either a catchment or regional scale, for instance, Sigdel and Ikeda  and Dahal et al. . The studies in the past were conducted either at the local level only or have used limited meteorological data in both time and space. The current study aims to complement past studies and fulﬁll these research gaps. Further, Earth 2022, 3 411 the current study also tests the suitability of RAI and SPI to characterize the drought events in the case of Nepal; it is the ﬁrst of this kind of study in the country. In this context, spatio-temporal characterization of drought using different techniques is essential for crop planning, irrigation scheduling, irrigation infrastructure development, and planning of irrigation facilities. That information helps the policymakers, farmers, and researchers for early planning of the crop calendar and associated irrigation scheduling in that area. Furthermore, proper planning could increase crop production, retain the rural area population, and motivate them to engage in agriculture. Hence, this study uses long-term rainfall data to characterize the drought using SPI and RAI techniques across Nepal. 2. Materials and Methods 2.1. Study Area The study area lies between 26 12’ N and 30 27’ N latitudes and 80 04’ E and 88 12’ E longitudes. The country covers 147,516 km , ranging from 60 m in the south to 8848 m above mean sea level in the north, thereby demonstrating high climate variability. The country is divided into seven provinces: (1) Province 1, (2) Madhesh, (3) Bagmati, (4) Gandaki, (5) Lumbini, (6) Karnali, and (7) Sudurpaschim, extending from east to west (Figure 1). The country is rich in water resources reﬂecting the importance of water-intensive services such as agriculture production, inland navigation, and hydropower production. Almost two-thirds of the population is engaged in agriculture, although it is in a low development stage with low competitiveness and low productivity . One of the signiﬁcant enabling environments for enhancing agricultural productivity is irrigation facilities like improved seeds, fertilizers, innovative tools, and farm mechanization techniques. In Nepal, approximately 40% of the total agricultural areas are irrigated, and the rest of the land’s agricultural activities solely depends on the rainfall. Karnali, Narayani, and Koshi are the major rivers that ﬂow within the country with high river ﬂow and potential for navigation. The country also poses medium-sized rivers such as Bagmati, Babai, Rapti, Kamala, Kankai, etc., which act as a source of surface water for irrigation and hydropower generation. The country has six seasons: spring (mid-March–mid-May, Basanta), summer (mid-May–mid-July, Grishma), rainy (mid-July–mid-September, Barsha), autumn (mid- September–mid-November, Sharad), pre-winter (mid-November–mid-January, Hemanta), and winter (mid-January–mid-March, Shishir), with spatial and temporal variability on the amount of the rain received. The rainy (Barsha) season receives almost 80% of the rainfall, leaving the remaining seasons as moderate and lesser rainfall seasons. In the Tarai (southern Nepal), summer temperatures exceed 37 C and are higher in some areas, and winter temperatures range from 7 C to 23 C in Tarai. In mountainous regions, hills, and valleys, summers are temperate, whereas winter temperatures can plummet below zero. 2.2. Data We collected the required rainfall data from the Department of Hydrology and Me- teorology (DHM), Kathmandu, Nepal, at a daily time scale for 41 years (1975–2015). We then ﬁlled in the missing daily rainfall data using an average station approach, one of the popular methods used in the Himalayan region. The daily data are then converted to a monthly time scale for calculating the SPI values in R-programming language. Similarly, we obtained the annual crop data from the Ministry of Agricultural Devel- opment for 1980–2011. In Nepal, cereal crops such as maize, paddy, millet, barley, and others are prepotent. Among these, paddy is water intensive and depends on rainfall and irrigation infrastructures. Paddy is grown all across the country but is more produced in the Tarai (southern belt) and hilly regions (northern belt). The Tarai region is also con- sidered the food basket of the country. The temporal change in the rainfall pattern alters rice production in Tarai. Millet and wheat require a temperate and tropical climate for cultivation. The crops like maize and millet are grown in Tarai and Hilly regions during dry seasons. We chose the crops of paddy, maize, wheat, and barley for the analysis as they Earth 2022, 3 412 are grown widely across the country throughout the year. The crop yield of any region Earth 2022, 3, 4 is more dependent on the amount of rainfall, land use and management practices, and irrigation systems. Figure 1. Spatial distribution of mean annual rainfall across different provinces of Nepal for 1975– Figure 1. Spatial distribution of mean annual rainfall across different provinces of Nepal for 2015. The inset shows the meteorological (rainfall) stations used for analysis. 1975–2015. The inset shows the meteorological (rainfall) stations used for analysis. 2.2. Data 2.3. Methods We collected the required rainfall data from the Department of Hydrology and Me- We show the overall methodological framework that characterizes the meteorological teorology (DHM), Kathmandu, Nepal, at a daily time scale for 41 years (1975–2015). We drought of Nepal in Figure 2. We analyzed the impact of meteorological droughts on then filled in the missing daily rainfall data using an average station approach, one of the different major crops such as paddy, maize, barley, and millet at various districts in Nepal. popular methods used in the Himalayan region. The daily data are then converted to a The detailed description and calculation method of the approaches used in the current study monthly aretime described scale for c in subsequent alculating the sections. SPI values in R-programming language. Similarly, we obtained the annual crop data from the Ministry of Agricultural Devel- 2.3.1. Standardized Precipitation Index (SPI) opment for 1980–2011. In Nepal, cereal crops such as maize, paddy, millet, barley, and others are prepotent. Among these, paddy is water intensive and depends on rainfall and Standardized precipitation index (SPI) is a broadly used drought index to characterize irrigation infrastructures. Paddy is grown all across the country but is more produced in meteorological drought and was developed by Mckee et al. . The SPI application the Tarai (southern belt) and hilly regions (northern belt). The Tarai region is also consid- is not limited to drought only; itis also used for frequency analysis and climate impact ered the food basket of the country. The temporal change in the rainfall pattern alters rice studies. As per the World Meteorological Organization (WMO) recommendation, SPI is a production in Tarai. Millet and wheat require a temperate and tropical climate for culti- meteorological drought index in which precipitation is the primary inﬂuencing climatic vation. The crops like maize and millet are grown in Tarai and Hilly regions during dry parameter . The SPI quantiﬁes observed precipitation as a standardized departure from seasons. We chose the crops of paddy, maize, wheat, and barley for the analysis as they a selected probability distribution function that models the raw precipitation data. The raw are grown widely across the country throughout the year. The crop yield of any region is precipitation data are typically ﬁtted to a gamma or a Pearson type III distribution and then more dependent on the amount of rainfall, land use and management practices, and irri- gation systems. Earth 2022, 3 413 Earth 2022, 3, 5 transformed to a normal distribution. Therefore, the SPI values can be interpreted as the number of standard deviations the observed anomaly deviates from the long-term mean. It 2.3. Methods uses monthly precipitation aggregates at various time scales (1, 3, 6, 12, 18, and 24 months, We show the overall methodological framework that characterizes the meteorological etc.). The SPI value ranges from 2.00 (dry) to +2.00 (wet). As a result, the index shows drought of Nepal in Figure 2. We analyzed the impact of meteorological droughts on dif- a different degree of severity, as represented in Table 1. The calculation is based on the ferent major crops such as paddy, maize, barley, and millet at various districts in Nepal. gamma distribution. We used the SPEI package in R-programming software to calculate The detailed description and calculation method of the approaches used in the current SPI at various periods . study are described in subsequent sections. Meteorological Drought Standardized Precipitation Index (SPI), Standardized Precipitation Evapotranspiration Index (SPEI), Rainfall Anomaly Index (RAI), Aridity Anomaly Index (AAI) Standardized Precipitation Index (SPI) R-Programing Language Rainfall Anomaly Index (RAI) Microsoft Excel Standardized Precipitation Index (SPI) Gamma Distribution Data required Rainfall Anomaly Index (RAI) Anomaly based classification Precipitation Crops: Temporal (1975–2015) Rice, Maize, Barley, Millet Spatial (Whole Nepal) Figure 2. An overall methodological framework to access the meteorological drought characteristics Figure 2. An overall methodological framework to access the meteorological drought characteristics in Nepal using two drought indices. in Nepal using two drought indices. 2.3.1. Standardized Precipitation Index (SPI) Table 1. Degree of severity based on standardized precipitation index (SPI). Standardized precipitation index (SPI) is a broadly used drought index to character- Category Values Category Values ize meteorological drought and was developed by Mckee et al. . The SPI application is not limited to drought only; itis also used for frequency analysis and climate impact Extreme wet 2.00 Moderate drought 1.0~ 1.49 studies Sever . As p e er wet the World Meteo 1.50~1.99 rological Organizati Sever on e (W drMO) ought recommen datio 2.00~ n, S 1.50 PI is a Moderate wet 1.0~1.49 Extreme drought 2.00 meteorological drought index in which precipitation is the primary influencing climatic Near normal 0.99~0.99 parameter . The SPI quantifies observed precipitation as a standardized departure from a selected probability distribution function that models the raw precipitation data. 2.3.2. SPI Calculation The raw precipitation data are typically fitted to a gamma or a Pearson type III distribu- tion and then transformed to a normal distribution. Therefore, the SPI values can be inter- We computed the SPIs for both space and time using the method developed by preted as the number of standard deviations the observed anomaly deviates from the Mckee et al. . The method uses monthly precipitation aggregates at various time scales long-term mean. It uses monthly precipitation aggregates at various time scales (1, 3, 6, (1, 3, 6, 12, 18, and 24 months, etc.). For an instance, to calculate SPI for a 3-month time 12, 18, and 24 months, etc.). The SPI value ranges from −2.00 (dry) to +2.00 (wet). As a scale, the precipitation accumulated from month j 2 to month j is summed and attributed result, the index shows a different degree of severity, as represented in Table 1. The cal- to month j. At this time scale, the ﬁrst two months of the data time series are missing. Then, culation is based on the gamma distribution. We used the SPEI package in R-program- the normalization procedure is conducted, in which an appropriate probability density ming software to calculate SPI at various periods . function is ﬁrst ﬁtted to the long-term time series of aggregated precipitation. Finally, the ﬁtted function is used to calculate the cumulative distribution of the data points, which are Table 1. Degree of severity based on standardized precipitation index (SPI). ﬁnally transformed into standardized normal variates. This procedure is repeated for all needed time scales. The maximum-likelihood (ML) estimation method  was used to ﬁt Category Values Category Values Extreme wet ≥2.00 Moderate drought −1.0~−1.49 Indices Indices Distribution Process Used Available Impact Software Earth 2022, 3 414 gamma probability distribution functions to each time series. The detail of the computation is given by Equations (1)–(5) as follows: The probability density function of the gamma distribution is deﬁned as a 1 x/b g(x) = x e , for x > 0 (1) b G(a) where x > 0 is the amount of precipitation, a > 0 and b > 0 are the shape and scale parameters, respectively, and G(a) is the gamma function. Detailed explanations on the gamma distribution can be found in Lloyd-Hughes and Saunders  and Guttman . To ﬁt the distribution parameters, a and b are estimated from the sample data using the approximation for ML deﬁned by Thom  which are given by: 1 4 A µ ˆ = 1 + 1 + and (2) 4 A 3 b = x/µ ˆ where x is mean precipitation and A is given by A = ln(x) n å ln(x). For a given month and time scale, the cumulative probability G(x) of an observed amount of precipitation is given by a ˆ x/b G(x) = x e dx (3) ˆa ˆ ˆ 0 b G(a) Let us consider t = x/b, the expression is reduced to the incomplete gamma function as follows: a ˆ