Diagnosis and Monitoring Of Drought Using Regional Climate Model Over Bangladesh
Introduction
Preamble
Drought is a prolonged, continuous period of dry weather along with abnormal insufficient rainfall. It occurs when evaporation and transpiration exceed the amount of precipitation for a reasonable period. Drought causes the earth to parch and a considerable hydrologic (water) imbalance resulting water shortages, Wells to dry, depletion of groundwater and soil moisture, stream flow reduction, crops to wither leading to crop failure and scarcity in fodder for livestock. Drought is a major natural hazard faced by communities directly dependent on rainfall for drinking water, crop production, and rearing of animals. Since ancient times droughts have far-reaching effects on mankind. Large land areas often suffer damages from dust storms and fire. Drought could be the reason for migration of early human communities. It has long been considered to be a natural hazard responsible for us and downs of many civilisations of the world. It is the slow onset natural disaster, which commences before anybody notices it and by the time it is noticeable it is too late. Unlike other natural disasters, it starts unnoticed and develops cumulatively, thus its impact is not immediately observable by naked eye or ground data. It may be the most devastating, yet least understood of all weather phenomenon. Drought can erupt in a matter of months, or it can gradually creep up on an unsuspecting society over several seasons. Drought is rarely forecasted with any skill, and goes unobserved by the public until Impacts from the drought have already occurred. Inevitably, officials charged with mitigating those impacts want to know how a current drought measures up historically to other droughts in terms of intensity, areal coverage, and duration. Additionally, these three factors differ in relative time and space scales from drought to drought.
Meteorological drought is a short lived, recurring natural disaster, which originate from the lack of precipitation and can bring significant economic losses [Pal et al., 2000]. It is not possible to avoid meteorological droughts, but it can be monitored, and their adverse impacts can be alleviated [Gommes, 1994]. The success of the drought prediction depends on how well it is defined and identified.
Bangladesh is one of the most seriously affcteded countries suffering from meteorological disasters such as droughts in pre- and post-monsoon seasons and floods in the summer monsoon, tropical cyclones and meso-scale disturbances. Additionally, agriculture, power generation and industrial production subtantially depend upon precipitation (Devkota, 2006). In view of the critical influnence of large inter-annual variability of precipitation on agricultural and industrial production, seasonal prediction of drought becomes very important for policy making efforts [Giorgi et al., 1996]. Beside the other natural disasters, Bangladesh face drought situation. Now-a-days food security is an important issue in the World. Because drought is intimately related with food security, therefore, its diagnosis and monitoring is essential to carry out too. The diagnosis of drought is also important for the utilization of drought projection using climate modeling facilities for the stakeholders and planners of a country.
1.2 Drought: Definitions
Droughts have no universal definition. As drought definitions are region specific, reflecting differences in climate characteristics as well as incorporating different physical, biological and socio-economic variables, it is usually difficult to transfer definitions derived for one region to another. However some of the common definitions of drought can be noted as under:
The Director of Common Wealth Bureau of Meteorology in 1965 suggested a broad definition of drought as “severe water shortage”.
Definition given by Palmer states that “Drought is an interval of time, generally of the order of months of years in duration, during which the actual moisture supply at a given place rather consistently falls short of the climatically expected or climatically appropriate moisture supply (Palmer, 1965)”.
According to Mc Mohan and Diaz Arena (1982), “Drought is a period of abnormally dry weather sufficiently for the lack of precipitation to cause a serious hydrological imbalance and carries connotations of a moisture deficiency with respect to man’s usage of water”.
Another definition given by Flag is worth mentioning “ Drought is a period of rainfall deficiency, extending over months to year of such a nature that crops and pasturage for stock are seriously affected, if not completely burnt up and destroyed, water supplies are seriously depleted or dried up and sheep and cattle perish”.
According to Hangman (1984), “Drought is considered by many to be the most complex but least understood of all natural hazards affecting more people than any other hazard.”
According to National Drought Policy Commission, “Apersistent and abnormalmoisture deficiency having adverse impacts on vegetation, animals, and people”.
According to The Convention to Combat Desertification (CCD), “drought” means the naturally occurring phenomenon that exists when precipitation has been significantly below normal recorded levels, causing serious hydrological imbalances that adversely affect land resources production systems.
A drought is a complex phenomenon that can be defined from several perspectives. Wilhite and Glantz ((2000) categorize drought definitions into conceptual (definitions formulated in general terms) and operational. Conceptual definitions formulated in general terms; help people understand the concept of drought but these normally do not provide quantitative answers. Operational definitions on the other hand help identify the drought beginning, end and degree of severity.
By studying the above definitions it can be understood that drought is mainly concerned with the shortage of water which in turn affects availability of food and fodder thereby leading to displacement and loss to economies as a whole.
Classification of Drought
Drought can be classified in three main ways:
• Meteorological drought: related to rainfall amounts
• Hydrological drought: determined by water levels in reservoir
• Agricultural drought: related to the availability of water for crops.
Meteorological Drought
Meteorological drought is generally defined by comparing the rainfall in a particular place and at a particular time with the average rainfall for that place. The definition is, therefore, specific to a particular location. Meteorological drought leads to a depletion of soil moisture and this almost always has an impact on crop production. When we define drought this way, we only consider the reduction in rainfall amounts and don’t take into account the effects of the lack of water on water resevoirs, human needs or on agriculture.
Meteorologically drought can be classified into three types: permanent drought – characterised by arid climate; seasonal drought – caused by irregularities in recognised rainy and dry seasons; and contingent drought – caused by irregular rainfall. In Bangladesh, the last two types are more prevalent.
1.3.2 Hydrological Drought
Hydrological drought is associated with the effect of low rainfall on water levels in rivers, reservoirs, lakes and aquifers. Hydrological droughts usually are noticed some time after meteorological droughts. First precipitation decreases and, some time after that, water levels in rivers and lakes drops.
Hydrological drought affects uses which depend on the water levels. Changes in water levels affect ecosystems, hydro electrical power production and recreational, industrial and urban water use.
1.3.3 Agricultural drought
Agricultural drought occurs when there is not enough water available for a particular crop to grow at a particular time. This drought doesn’t depend only in the amount of rainfall, but also on the correct use of that water. Imagine a period of low rainfall where water is used carelessly for irrigation and other purposes. Under these circumstances, the effect of the drought becomes more pronounced than it was before.
Agricultural drought is typically seen after meteorological drought (when rainfall decreases) but before a hydrological drought (when the water level in rivers, lakes and reservoirs decreases).
It is important to mention that the effects of droughts are different in irrigated and non-irrigated agriculture. In regions which rely on irrigation, the impacts of short lived agricultural droughts are usually lower than in regions where crops are not irrigated. Irrigated agriculture relies on stocks of water so if it doesn’t rain, these crops still get the water they need (until the reservoirs run dry). However, in non-irrigated agriculture crops depend directly on the rain as their water source. If it doesn’t rain, the crops don’t get the water they need to survive.
1.4 Drought in Bangladesh
Bangladesh extends from 20o34’N to 26o38’N latitude and from 88001’E to 92041’E longitude. Climatically, the country belongs to sub-tropical region where monsoon weather prevails throughout the year. The average temperature of the country ranges from 17 to 20.6oC during winter and 26.9 to 3l.1oC during summer. Four distinct seasons can be recognized in Bangladesh from climatic point of view: (i) the winter season from December to February, (ii) the pre-monsoon season from March to May, (iii) the monsoon season which lasts from June to September and (iv) Post-Monsoon season lasting from October and November. The average annual rainfall of the country varies from 1329 mm in the northwest to 4338 mm in the northeast (Shahid et a1., 2005). The gradient of rainfall from west to east is approximately 9 mm/km. The western part of Bangladesh experiences an average areal rainfall of approximately 2044 mm, which is much lower than other parts of the country. The rainfall is also very much seasonal, almost 66% of rainfall occurs during monsoon. In summer, the hottest days experience temperatures of about 45oC in the North-Western region. Again in the winter the temperature even falls at 5oC in some places.
On the global level, impact of natural hazards and disasters are staggering. In Bangladesh, the major natural hazards are also in line with global patterns. In the context of global warming, most of the climate models project a decrease in precipitation in dry season and an increase during monsoon in south Asia (Christensen et a1., 2007). This will cause a cruel combination of more extreme floods and droughts in this region. According to the report of National Drought
Fig 2.1.1 Frequency of drought occurrences all over the World by country during 1974 to 2003 shows the drought situation all over the world.
Source: CRED International Disaster Database EM-DAT.
Mitigation Center (2006) Bangladesh has already shown an increased frequency of droughts in recent years. Concern among scientists has grown on changes of precipitation and frequent occurrence of droughts both in Bangladesh. Therefore, study on drought hazards especially drought monitoring and projection are essential for implementing mitigation to reduce drought impact in Bangladesh.
After 1971 Bangladesh has experienced droughts of major magnitude in 1973, 1978, 1979, 1981, 1982, 1989, 1992, 1994, and 1995. Although droughts are not always continuous in any area, they do occur sometimes in the low rainfall zones of the country. As listed above, Bangladesh experienced consecutive droughts in 1978-1979, 1981- 1982, and 1994-1995. The droughts of 1994-95 in the northwestern districts of Bangladesh led to a shortfall of rice production of 3.5 million tons (Paul, 1995).
Literature Review
2.1 Review of the previous work
In Bangladesh, a number of studies have been carried out on the impact of droughts on agriculture (Jabbar et al., 1982; Karim et al., 1990; Jabbar, 1990; Saleh et a1., 2000; Mazid et al., 2005), food production (Ahmed and Bemard, 1989; Ericksen et al.,1993), land degradation (Rasheed,1998; Karim and lqbal, 2001; Government of Bangladesh, 2005), economy (Erickson et al., 1993; World Bank Bangladesh, 2000), and society (Erickson et al., 1993; Paul, 1998) in Bangladesh. WARPO-EGIC (1996) prepared maps of winter and pre-monsoon drought prone areas of Bangladesh using the agro ecological zones database and land resources inventory map. Up to date so far, No standard drought index method has been used for the assessment of projection of droughts in Bangladesh using Regional Climate Model simulated output data.
Jabbar et al. (1982), emphasized the combined role of drought hazard and vulnerability in defining risk. Standardized precipitation index method in a GIS environment was used to map the spatial extents of drought hazards in different time steps. The key social and physical factors that define drought vulnerability in the context of Bangladesh were identified and corresponding thematic maps in district level are prepared. Composite drought vulnerability map was developed through the integration of those thematic maps. The risk was computed as the product of the hazard and vulnerability. The result showed that droughts pose highest risk to the northern and north-western districts of Bangladesh.
Ministry of environment and Forests, Government of the Peoples Republic of Bangladesh, Dhaka (2001), produced a notional report on “Implementation of United Nations Convention to Combat Desertification”. On this report drought was calculated considering the distribution of rainfall and evapotranspiration regimes and the drought condition in the country, it was proposed that the regions fulfilling the following conditions may comprise dry regions in Bangladesh. The conditions are: (i) annual rainfall ® should be less than 2000 mm; (ii) dry season (November – May) Excess Evapotranspiration (ETo-R) should be more than 400 mm and (iii) dry season R/ ETo ratio value should be less than 0.65.
With this assumption made and applied on the rainfall and evapotranspiration data available for the agro ecological zones of Bangladesh, the Northwest, Southwest and North central zones can be considered as dry region of the country. It was further proposed that the dry regions may be divided into two sub regions on the basis of severity of dryness as below:
Table 2.1.1 Severity of Dry Regions
Severity Conditions/ Criteria Defined
Moderate Annual Rainfall ® less than 1600 mm
Dry season (November-May) Excess Evapotranspiration (ETo- R) – more than 400 mm
Dry season (November-May) R/ ETo ratio value less than 0.4
Slight Annual Rainfall ® 1600-2000 mm
Dry season (November-May) Excess Evapotranspiration (ETo- R ) – 200-400 mm
Dry season (November-May) R/ ETo ratio value 0.4 – 0.65
Non dry Annual Rainfall ® more than2000 mm
Dry season (November-May) Excess Evapotranspiration (ETo- R ) – less than 200 mm
Dry season (November-May) R/ ETo ratio value more than 0.65
Considering the rainfall and evapotranspiration data available in WARPO a map was prepared to show the severity of dryness in the dry regions. A map of the dry regions of Bangladesh had been prepared. The Rivers and Estuary hydrological region; the coastal region and the Sunderbans are considered as non-dry. The dry zones are extended over an area of 6.442 M ha. The extents of the dry zones are given in the following table:
Table 2.1.2 Extent of Dry Zones of Bangladesh
Dry Zones No. of Thanas occupied Area covered (M ha) Total land (%)
Moderate 64 2.015 14.37
Slight 163 4.427 31.56
Nondry 263 7.585 54.07
Two critical dry periods were distinguished (Karim et al., 1990), Rabi and pre-Kharif drought (January – May), due to: (i) the cumulative effect of dry days; (ii) higher temperatures during pre-Kharif (> 40 degrees Celsius in March-May); and (iii) low soil moisture availability. This drought affects all the Rabi crops, such as HYV Boro, Aus, wheat, pulses and potatoes especially where irrigation possibilities are limited. It also affects sugarcane production. Kharif droughts in the period June/July to October, created by sub-humid and dry conditions in the highland and medium highland areas of the country (in addition to the west/northwest also the Madhupur tract is drought prone). Shortage of rainfall affects the critical reproductive stages of transplanted Aman crops in December, reducing its yield, particularly in those areas with low soil moisture holding capacity.
Considering the Agro ecological Zones (AEZ) database and land resources inventory map at 1:1,000,000 scale, BARC has identified and mapped drought prone areas of Bangladesh for Rabi and Pre-Kharif seasons (WARPO- EGIS, 1996). Recently BARC has reviewed this concept and produced three different maps for Rabi, Pre-Kharif and Kharif seasons (BARC, 2001). The drought maps had been revised by BARC to produce three maps for Rabi, Pre-Kharif and Kharif seasons. The drought severity classes were defined; Moderate, severe and Very severe related to the yield losses of 15-20%, 20-35%, 35-45%, and 45-70% respectively for different crops (Karim and Iqbal, 2001).
The northwestern part is prone to drought mainly because of rainfall variability in the pre-monsoon and the postmonsoon periods. Inadequate pre-monsoon showers, a delay in the onset of the rainy season or an early departure of the monsoon may create drought conditions in Bangladesh, and adversely affect crop output. Since it puts severe strain on the land potential. It acts as a catalyst of land degradation through reduced soil moisture and water retention, increased soil erosion, decline in soil organic contents and overexploitation of sparse vegetation. Human interventions in the form of land abuse and mismanagement have exacerbated these actions during the spells of periodic droughts. An analysis of the relative effects of flood and drought on rice production between 1969-70 and 1983-84 shows that drought is more devastating than floods to aggregate production (World Bank, 2000).
2.2 Meteorological Drought indices
The impact of drought on society and agriculture is a real issue but it is not easily quantified. Reliable indices to detect the spatial and temporal dimensions of drought occurrences and its intensity are necessary to assess the impact and also for decision-making and crop research priorities for alleviation.
Several indices have been developed for quantification of drought based on the type of drought. Palmer Drought Severity Index developed by Palmer (1965), is the most widely used drought index based on the demand and supply concept of water balance equation. Palmer (1968) derived another index, the Crop Moisture Index (CMI) by modifying PDSI to find out the severity of agricultural drought. Hydrological droughts characterized by low precipitation, lowering groundwater level. The National Drought Mitigation Center is using a newer index, the Standardized Precipitation Index, to monitor moisture supply conditions. Distinguishing traits of this index are that it identifies emerging droughts months sooner than the Palmer Index and that it is computed on various time scales. Most water supply planners find it useful to consult one or more indices before making a decision. A brief review of these indices is given below:
The percent of normal is a simple calculation well suited to the needs of TV weathercasters and general audiences. It is Quite effective for comparing a single region or season though, it could be misunderstood, as normal is a mathematical construct that does not necessarily correspond with what we expect the weather to be. The percent of normal precipitation is one of the simplest measurements of rainfall for a location. Analyses using the percent of normal are very effective when used for a single region or a single season. Percent of normal is also easily misunderstood and gives different indications of conditions, depending on the location and season. It is calculated by dividing actual precipitation by normal precipitation—typically considered to be a 30-year mean—and multiplying by 100%. This can be calculated for a variety of time scales. Usually these time scales range from a single month to a group of months representing a particular season, to an annual or water year. Normal precipitation for a specific location is considered to be 100%.
One of the disadvantages of using the percent of normal precipitation is that the mean, or average, precipitation is often not the same as the median precipitation, which is the value exceeded by 50% of the precipitation occurrences in a long-term climate record. The reason for this is that precipitation on monthly or seasonal scales does not have a normal distribution. Use of the percent of normal comparison implies a normal distribution where the mean and median are considered to be the same. An example of the confusion this could create can be illustrated by the long-term precipitation record in Melbourne, Australia, for the month of January. The median January precipitation is 36.0 mm (1.4 in.), meaning that in half the years less than 36.0 mm is recorded, and in half the years more than 36.0 mm is recorded. However, a monthly January total of 36.0 mm would be only 75% of normal when compared to the mean, which is often considered to be quite dry. Because of the variety in the precipitation records over time and location, there is no way to determine the frequency of the departures from normal or compare different locations. This makes it difficult to link a value of a departure with a specific impact occurring as a result of the departure, inhibiting attempts to mitigate the risks of drought based on the departures from normal and form a plan of response.
2.2 .2 Standardized Precipitation Index (SPI)
The SPI is an index based on the probability of precipitation for any time scale.
Many drought planners appreciate the SPI’s versatility. The SPI can be computed for different time scales, can provide early warning of drought and help assess drought severity, and is less complex than the Palmer. Developed by T.B. McKee, N.J. Doesken, and J. Kleist, Colorado State University, 1993. The understanding that a deficit of precipitation has different impacts on groundwater, reservoir storage, soil moisture, snowpack, and streamflow led McKee, Doesken, and Kleist to develop the Standardized Precipitation Index (SPI) in 1993. The SPI was designed to quantify the precipitation deficit for multiple time scales. These time scales reflect the impact of drought on the availability of the different water resources. Soil moisture conditions respond to precipitation anomalies on a relatively short scale. Groundwater, streamflow, and reservoir storage reflect the longer-term precipitation anomalies. For these reasons, McKee et al. (1993) originally calculated the SPI for 3–, 6–,12–, 24–, and 48–month time scales.
The SPI calculation for any location is based on the long-term precipitation record for a desired period. This long-term record is fitted to a probability distribution, which is then transformed into a normal distribution so that the mean SPI for the location and desired period is zero (Edwards and McKee, 1997). Positive SPI values indicate greater than median precipitation, and negative values indicate less than median precipitation. Because the SPI is normalized, wetter and drier climates can be represented in the same way, and wet periods can also be monitored using the SPI.
McKee et al. (1993) used the classification system shown in the SPI values table to define drought intensities resulting from the SPI. McKee et al. (1993) also defined the criteria for a drought event for any of the time scales. A drought event occurs any time the SPI is continuously negative and reaches an intensity of -1.0 or less. The event ends when the SPI becomes positive. Each drought event, therefore, has a duration defined by its beginning and end, and an intensity for each month that the event continues. The positive sum of the SPI for all the months within a drought event can be termed the drought’s “magnitude”. Mathematical derivation of SPI is discussed in the chapter 4.
2.2 .3 Palmer Drought Severity Index (PDSI)
The Palmer is a soil moisture algorithm calibrated for relatively homogeneous regions. Many U.S. government agencies and states rely on the Palmer to trigger drought relief programs. It is the first comprehensive drought index developed in the United States. Palmer values may lag emerging droughts by several months; less well suited for mountainous land or areas of frequent climatic extremes; complex—has an unspecified, built-in time scale that can be misleading. W.C. Palmer developed an index to measure the departure of the moisture supply (Palmer, 1965). Palmer based his index on the supply-and-demand concept of the water balance equation, taking into account more than just the precipitation deficit at specific locations. The objective of the Palmer Drought Severity Index (PDSI), as this index is now called, was to provide measurements of moisture conditions that were standardized so that comparisons using the index could be made between locations and between months (Palmer 1965). The PDSI is a meteorological drought index, and it responds to weather conditions that have been abnormally dry or abnormally wet. When conditions change from dry to normal or wet, for example, the drought measured by the PDSI ends without taking into account streamflow, lake and reservoir levels, and other longer-term hydrologic impacts (Karl and Knight, 1985). The PDSI is calculated based on precipitation and temperature data, as well as the local Available Water Content (AWC) of the soil. From the inputs, all the basic terms of the water balance equation can be determined, including evapotranspiration, soil recharge, runoff, and moisture loss from the surface layer. Human impacts on the water balance, such as irrigation, are not considered. Complete descriptions of the equations can be found in the original study by Palmer (1965) and in the more recent analysis by Alley (1984).
2.2 .4 Crop Moisture Index (CMI)
The Crop Moisture Index (CMI) uses a meteorological approach to monitor week-to-week crop conditions. It was developed by Palmer (1968) from procedures within the calculation of the PDSI. Whereas the PDSI monitors long-term meteorological wet and dry spells, the CMI was designed to evaluate short-term moisture conditions across major crop-producing regions. It is based on the mean temperature and total precipitation for each week within a climate division, as well as the CMI value from the previous week.
2.2 .5 Surface Water Supply Index
The Surface Water Supply Index (SWSI) was developed by Shafer and Dezman (1982) to complement the Palmer Index for moisture conditions across the state of Colorado. The Palmer Index is basically a soil moisture algorithm calibrated for relatively homogeneous regions, but it is not designed for large topographic variations across a region and it does not account for snow accumulation and subsequent runoff. Shafer and Dezman designed the SWSI to be an indicator of surface water conditions and described the index as “mountain water dependent”, in which mountain snowpack is a major component.
The objective of the SWSI was to incorporate both hydrological and climatological features into a single index value resembling the Palmer Index for each major river basin in the state of Colorado (Shafer and Dezman 1982). These values would be standardized to allow comparisons between basins. Four inputs are required within the SWSI: snowpack, streamflow, precipitation, and reservoir storage. Because it is dependent on the season, the SWSI is computed with only snowpack, precipitation, and reservoir storage in the winter. During the summer months, streamflow replaces snowpack as a component within the SWSI equation.
2.2 .6 Reclamation Drought Index
The Reclamation Drought Index (RDI) was recently developed as a tool for defining drought severity and duration, and for predicting the onset and end of periods of drought. The impetus to devise the RDI came from the Reclamation States Drought Assistance Act of 1988, which allows states to seek assistance from the Bureau of Reclamation to mitigate the effects of drought.
Like the SWSI, the RDI is calculated at a river basin level, and it incorporates the supply components of precipitation, snowpack, streamflow, and reservoir levels. The RDI differs from the SWSI in that it builds a temperature-based demand component and duration into the index. The RDI is adaptable to each particular region and its main strength is its ability to account for both climate and water supply factors.
Oklahoma has developed its own version of the RDI and plans to use the index as one tool within the monitoring system designated in the state’s drought plan. The RDI values and severity designations are similar to the SPI, PDSI, and SWSI.
2.3 Objectives of the present study
The aim of the present study is to characterize the spatial and temporal pattern of drought hazards, identify the possibility of utilizing climate model outputs in drought diagnosis and monitoring in Bangladesh.
Regional Climate Model (RegCM) successfully forecasts climatic parameters with some discrepancy between real & model data. It is essential to do some validation of the RegCM model outputs with the ground based data; rain-gauge and surface air temperature to adopt the RegCM for this region [Liu et al., 1996]. The studies on prediction of drought over Bangladesh exhibits a large spatial and temporal variability [Islam, M. N, 2005]. This study will allow us to identify the drought conditions for a large coverage using observed data in Bangladesh. The same will be obtained from RegCM outputs. This will allow to obtain the model efficiency in detection of drought over the country. Once the model data is found useful in drought diagnosis in Bangladesh then it may be useful in projection of drought events which are helpful for planners and decision makers.
Regional Climate Model (RegCM)
Introduction
Climate models are numerical representations of the fundamental equations that describe the behaviour of the climate system and the interactions across its components: Atmosphere, ocean, cryosphere, biosphere and chemosphere. It is a huge computer codes based on fundamental mathematical equations of motion, thermodynamics and radiative transfer. These equations govern:
Flow of air and water
Exchange of heat, water & momentum between atmosphere and earth.
Release of latent heat by condensation during the formulation of clouds and raindrops.
Absorption of sunshine and emission of thermal radiation (infra-red)
The equations of climate model are:
Horizontal equation of motion
; (Conservation of momentum)
b. Hydrostatic assumption
(Conservation of water)
c. Continuity equation
(Conservation of mass)
d. Conservation of energy
(1st law of thermodynamics)
e. Equation of state for gas
The idea that limited area models (LAMs) could be used for regional studies was originally proposed by Dickinson et al. (1989) and Giorgi (1990). This idea was based on the concept of one-way nesting, in which large scale meteorological fields from General Circulation Model (GCM) runs provide initial and time dependent meteorological lateral boundary conditions (LBCs) for high resolution Regional Climate Model (RCM) simulations, with no feedback from the RCM to the driving GCM.
The first generation NCAR RegCM was built upon the NCAR-Pennsylvania State University (PSU) Mesoscale Model version 4 (MM4) in the late 1980s (Dickinson et al., 1989; Giorgi, 1989). For application of the MM4 to climate studies, a number of physics parameterizations were replaced, mostly in the areas of radiative transfer and land surface physics, which led to the first generation RegCM (Dickinson et al., 1989; Giorgi, 1990). The first generation RegCM included the Biosphere-Atmosphere Transfer Scheme, BATS (Dickinson et al., 1986) for surface process representation, the radiative transfer scheme of the Community Climate Model version 1 (CCM1), a medium resolution local planetary boundary layer scheme, the Kuo-type cumulus convection scheme of (Anthes, 1977) , and the explicit moisture scheme of (Hsie et al., 1984). Changes in the model physics include a large-scale cloud and precipitation scheme which accounts for the subgrid-scale variability of clouds (Pal et al., 2000), new parameterizations for ocean surface fluxes (Zeng et al) 1998), and a cumulus convection scheme (Emanuel, 1991; Emanuel and Zivkovic-Rothman, 1999). Also new in the model is a mosaic-type parameterization of subgrid-scale heterogeneity in topography and land use (Giorgi et al., 2003b). Other improvements in RegCM3 involve the input data. Lastly, improvements in the user-friendliness of the model have been made. New scripts have been included which make running the programs easier. Also, a new website has been developed where users can freely download the entire RegCM system, as well as all of the input data necessary for a simulation.
The RegCM modeling system has four components: Terrain, ICBC, RegCM, and Postprocessor. Terrain and ICBC are the two components of RegCM preprocessor. Terrestrial variables (including elevation, land use and sea surface temperature) and three-dimensional isobaric meteorological data are horizontally interpolated from a latitude-longitude mesh to a high-resolution domain on either a Rotated (and Normal) Mercator, Lambert Conformal, or Polar Stereographic projection. Vertical interpolation from pressure levels to the ??coordinate system of RegCM is also performed.
3.2 Physics
3.2.1 Radiation Scheme
RegCM3 uses the radiation scheme of the NCAR CCM3, which is described in Kiehl et al. (1996). Briefly, the solar component, which accounts for the effect of O3, H2O, CO2, and O2, follows the d-Eddington approximation of Kiehl et al. (1996). It includes 18 spectral intervals from 0.2 to 5 ?m. The cloud scattering and absorption parameterization follow that of Slingo (1989), whereby the optical properties of the cloud droplets (extinction optical depth, single scattering albedo, and asymmetry parameter) are expressed in terms of the cloud liquid water content and an effective droplet radius.
3.2.2 Land Surface Model
The surface physics are performed using Biosphere-Atmosphere Transfer Scheme version 1e (BATS1e) which is described in detail by Dickinson et al. (1993). BATS is a surface package designed to describe the role of vegetation and interactive soil moisture in modifying the surface-atmosphere exchanges of momentum, energy, and water vapour. The model has a vegetation layer, a snow layer, a surface soil layer 10 cm thick, or root zone layer 1-2 m thick, and a third deep soil layer 3 m thick. Prognostic equations are solved for the soil layer temperatures using a generalization of the force-restore method of Deardoff (1978).
3.2.3 Planetary Boundary Layer Scheme
The planetary boundary layer scheme, developed by Holtslag et al. (1990), is based on a nonlocal diffusion concept that takes into account countergradient fluxes resulting from large-scale eddies in an unstable, well-mixed atmosphere.
3.2.4 Convective Precipitation Schemes
Convective precipitation is computed using one of three schemes: (1) Modified-Kuo scheme (Anthes, 1977); (2) Grell scheme (Grell, 1993); and (3) MIT-Emanuel scheme (Emanuel, 1991; Emanuel and Zivkovic-Rothman, 1999). In addition, the Grell parameterization is implemented using one of two closure assumptions: (1) the Arakawa and Schubert closure Grell et al. (1994) and (2) the Fritsch and Chappell closure (Fritsch and Chappell, 1980) hereafter referred to as AS74 and FC80, respectively.
3.3 Pre-Processing and simulation
Before performing a regional climate simulation there are two pre-processing steps that need to be completed. The first step involves defining the domain and grid interval, and interpolating the landuse and elevation data to the model grid. The second step is to generate the files used for the initial and boundary conditions during the simulation. The input data necessary to run the model can be downloaded from the PWC website at the following URL:
http://www.ictp.trieste.it/pubregcm/RegCM3.
The regcm.x script will compile and execute the model. Compile the source code and start the simulation. Running the model generates the following monthly output files,
Atmospheric model output (see Table 3.1.1.): ATM.YYYYMMDDHH
Land surface model output (see Table 3.1.2.): SRF.YYYYMMDDHH
Radiation model output : RAD.YYYYMMDDHH
3.4 Post-processing
The model generates three output files every month
• ATM.YYYYMMDDHH from the atmospheric model (see Table 3.1.1 for list of variables)
• SRF.YYYYMMDDHH from the land surface model (see Table 3.1.2 for list of variables)
• RAD.YYYYMMDDHH from the radiation model.
The RegCM postprocessor converts these model output files to new output files of averaged variables in commonly used formats GrADS (Grid Analysis Display System). This will need to modify the postproc.in file in working directory to specify how to average the variables (daily, monthly, etc.) and the file format. Then run the postproc.x script which will compile and execute the program.
Table 3.1.1. List of output variables from atmosphere.
| Variables | Description |
| u | Eastward wind (m s?1) |
| w | Omega (hPa) p-velocity |
| qv | Water vapour Mixing ratio (g kg-1) |
| v | Northward wind (m s-1) |
| tpr | Total precipitation (mm) |
| t | Temperature (K) |
| tgb | Lower soil layer temp (K) |
| qc | Cloud water mixing ratio (g kg?1) |
| smt | Total soil water (mm) |
| psa | Surface pressure (Pa) |
| rno | Base flow (mm day?1) |
Table 3.1.2. List of output variables from surface model.
| Variables | Description |
| u10m | Anemometer eastward wind (ms-1) |
| v10m | Anemometer northward wind (ms-1) |
| uvdrag | Surface drag stress |
| tgb | Ground temperature (K) |
| tlef | Foliage temperature (K) |
| t2m | Anemometer temperature (K) |
| q2m | Anemometer specific humidity kg kg-1 |
| ssw | Top layer soil moisture (mm) |
| rsw | Root layer soil moisture (mm) |
| tpr | Total precipitation (mm day-1) |
| evp | Evapotranspiration (mm day-1) |
| runoff | Surface runoff (mm day-1) |
| scv | Snow water equivalent (mm) |
| sena | Sensible heat (W m-2) |
| flw | Net longwave (W m-2) |
| fsw | Net solar absorbed (W m-2) |
| flwd | Downward longwave (W m-2) |
| sina | Solar incident (W m-2) |
| prcv | Convective precipitation (mm day-1) |
| psb | Surface pressure (Pa) |
| zpbl | PBL height (m) |
| tgmax | maximum ground temperature (K) |
| tgmin | minimum ground temperature (K) |
| t2max | maximum 2m temperature (K) |
| t2min | minimum 2m temperature (K) |
| w10max | maximum 10m wind speed (m s-1) |
| psmin | minimum surface pressure (hPa) |
Standardized Precipitation Index
McKee et al. (1993) developed the Standardized Precipitation Index (SPI) for the purpose of defining and monitoring drought. Among others, the Colorado Climate Center, the Western Regional Climate Center, and the National Drought Mitigation Center use the SPI to monitor current states of drought in the United States. The nature of the SPI allows an analyst to determine the rarity of a drought or an anomalously wet event at a particular time scale for any location in the world that has a precipitation record.
Thom (1966) found the gamma distribution to fit climatological precipitation time series well. The gamma distribution is defined by its frequency or probability density function:
for x>0 (4.1)
Where:
? >0 ? is a shape parameter (4.2)
? >0 ? is a scale parameter (4.3)
x >0 x is a precipitation amount (4.4)
?(?) is the gamma function (4.5)
Computation of the SPI involves fitting a gamma probability density function to a given frequency distribution of precipitation totals for a station. The alpha and beta parameters of the gamma probability density function are estimated for each station, for each time scale of interest (3 months, 12 months, 48 months, etc.), and for each month of the year. From Thom (1966), the maximum likelihood solutions are used to optimally estimate ? and ?:
(4.6)
(4.7)
Where:
(4.8)
n = number of precipitation observations (4.9)
The resulting parameters are then used to find the cumulative probability of an observed precipitation event for the given month and time scale for the station in question. The cumulative probability is given by:
(4.10)
Letting , this equation becomes the incomplete gamma function :
(4.11)
Since the gamma function is undefined for x=0 and a precipitation distribution may contain zeros, the cumulative probability becomes:
(4.12)
where q is the probability of a zero. If m is the number of zeros in a precipitation time series, Thom (1966) states that q can be estimated by m/n. Thom (1966) uses tables of the incomplete gamma function to determine the cumulative probability G(x). McKee et al. (1993) use an analytic method along with suggested software code from Press et al. (1988) to determine the cumulative probability.
The cumulative probability, H(x), is then transformed to the standard normal random variable Z with mean zero and variance of one, which is the value of the SPI. This is an equiprobability transformation which Panofsky and Brier (1958) state has the essential feature of transforming a variate from one distribution (ie. gamma) to a variate with a distribution of prescribed form (ie. standard normal) such that the probability of being less than a given value of the variate shall be the same as the probability of being less than the corresponding value of the transformed variate. This method is illustrated in fig. 4.1. In this Fig, a 3 month precipitation amount (January through March) is converted to a SPI value with mean of zero and variance of one. The left side of fig 4.1. contains a broken line with horizontal hash marks that designate actual values of 3 month precipitation amounts (x-axis) for Fort Collins, Colorado for the months of January through March for the period 1911
Fig.4.1. Example of equiprobability transformation from fitted gamma distribution to the standard normal distribution.
through 1995. The broken line also denotes the empirical cumulative probability distribution (y-axis) for the period of record. The empirical cumulative probabilities were found optimally as suggested by Panofsky and Brier (1958) where the precipitation data is sorted in increasing order of magnitude so that the kth value is k-1 values from the lowest and where n is the sample size:
empirical cumulative probability (4.13)
The smooth curve on the left hand side of Fig 4.1 denotes the cumulative probability distribution of the fitted gamma distribution to the precipitation data. The smooth curve on the right hand side of Fig 4.2 denotes the cumulative probability distribution of the standard normal random variable Z using the same cumulative probability scale of the empirical distribution and the fitted gamma distribution on the left hand side of the Fig. The standard normal variable Z (or the SPI value) is denoted on the x-axis on the right hand side of the Fig. Hence, this Fig can be used to transform a given 3 month (January through March) precipitation observation from Fort Collins, Colorado to a SPI value. For example, to find the SPI value for a 2 inch precipitation observation, simply go vertically upwards from the 2 inch mark on the x-axis on the left hand side of Fig 4.2 until the fitted gamma cumulative probability distribution curve is intersected. Then go horizontally (maintaining equal cumulative probability) to the right until the curve of the standard normal cumulative probability distribution is intersected. Then proceed vertically downward to the x-axis on the right hand side of Fig 3.2 in order to determine the SPI value. In this case, the SPI value is approximately +0.3.
Since it would be cumbersome to produce these types of Figs for all stations at all time scales and for each month of the year, the Z or SPI value is more easily obtained computationally using an approximation provided by Abramowitz and Stegun (1965) that converts cumulative probability to the standard normal random variable Z:
for (4.14)
for (4.14)
Where:
for (3.17)
for (3.18)
=2.515517
=0.802853
=0.010328
=1.432788
=0.189269
=0.001308
Conceptually, the SPI represents a z-score, or the number of standard deviations above or below that an event is from the mean. However, this is not exactly true for short time scales since the original precipitation distribution is skewed. Nevertheless, fig. 4.2 shows that during the base period for which the gamma parameters are estimated, the SPI will have a standard normal distribution with an expected value of zero and a variance of one. Katz and Glantz (1986) state that requiring an index to have a fixed expected value and variance is desirable in order to make comparisons of index values among different stations and regions meaningful.
Additionally, no matter the location or time scale, the SPI represents a cumulative probability in relation to the base period for which the gamma parameters were estimated. Table 4.1 is a table of SPI and its corresponding cumulative probability.
An analyst with a time series of monthly precipitation data for a location can calculate the SPI for any month in the record for the previous i months where i=1, 2, 3, …, 12, …, 24, …, 48, … depending upon the time scale of interest. Hence, the SPI can be computed for an observation of a 3 month total of precipitation as well as a 48 month total of precipitation. For this study, a 1, 3 and 6 month SPI are used for a short-term or seasonal drought index, a 9, 12 and 24 month SPI are used for an longe-term drought index. Therefore, the SPI for a month/year in the period of record is dependent upon the time scale.
Fig. 4.2. Standard normal distribution with the SPI having a mean of zero and a variance of one.
Table 4.1. : SPI and Corresponding Cumulative Probability in Relation to the Base Period.
| SPI | Cumulative Probability |
| -3.0 | 0.0014 |
| -2.5 | 0.0062 |
| -2.0 | 0.0228 |
| -1.5 | 0.0668 |
| -1.0 | 0.1587 |
| -.05 | 0.3085 |
| 0.0 | 0.5000 |
| +0.5 | 0.6915 |
| +1.0 | 0.8413 |
| +1.5 | 0.9332 |
| +2.0 | 0.9772 |
| +2.5 | 0.9938 |
| +3.0 | 0.9986 |
Data used and Methodology
5.1 Data used for analysis
In order to diagnosis and monitor drought in Bangladesh, rainfall collected by Bangladesh Meteorological Department (BMD) for 27 stations over Bangladesh is utilized during 1961 to 1990. Regional Climate Model (RegCM) with Grell Arakawa and Schubert Scheme (GAS)
output rainfall are extracted at rain-gauge locations of BMD during 1961 to 1990.
Fig. 5.1.1. Map of Bangladesh with the name of observation sites, observation location, elevation (in m) and detailed analyzed stations (circle).
Record of drought event of Bangladesh obtained from ‘Agricultural Statistics Year book of Bangladesh’ published by Bangladesh Bureau of Statistics (BBS) archive from 1975 to 1990. Chronology of major drought events in Bangladesh is also used from International Disaster database (EM-DAT) archive during 1971-1990. There was no information regarding drought in Bangladesh before the year 1971.
Station names over Bangladesh are shown above the station location (plus mark, Fig.5.1.1) with the elevation (below plus mark, Fig. 5.1.1). The symbol circle is used with plus marks to indicate the stations which are used in detail study. These stations are namely Rangpur, Dhaka, Khulna, Cox’s Bazar and Sylhet.
To analyze the drought condition of whole country, 27 BMD stations are divided into four regions according to topography and the record of rainfall by BMD (Fig. 5.2.1.) named (a) Central Region- Chandpur, Comilla, Dhaka, Faridpur, Feni, Mymensingh, Tangail, (b) North Region- Bogra, Dinajpur, Rajshahi, Rangpur, (c) South-West Region- Barisal, Hitiya, Jessore, Khepupara, Khulna, Mcourt, Patuakhali and (d) Eastern Region- Chittagong, Cox’s bazar, Kutubdia, Rangamati, Sandip, Sitakundu, Srimangal, Sylhet, and Teknaf.
Fig. 5.1.2. Deviation of rainfall (mm/day) for 27 observed BMD station from 50 years average (6.049 mm/d) of all over Bangladesh.
5.2 Analysis procedure
There are several indices that measure how much precipitation for a given period of time has deviated from historically established norms. Although none of the major indices is inherently superior to the rest in all circumstances, some indices are better suited than others for certain uses. For example, the Palmer Drought Severity Index (PDSI) has been widely used by the U.S. Department of Agriculture to determine when to grant emergency drought assistance, but the PDSI is better when working with large areas of uniform topography. This procedure may not much effective for the small area like the country Bangladesh. The National Drought Mitigation Center of USA is using the Standardized Precipitation Index (SPI) to monitor moisture supply conditions. Distinguishing traits of this index are that it identifies emerging droughts months sooner than the Palmer Index and that it is computed on various time scales.
In order to investigate the spatial and temporal extents and severity of drought occurrence in the study area, Standardized Precipitation Index (Mckee et al., 1993) is used. SPI is a widely used drought index based on the of precipitation on multiple time scales. It has been demonstrated by several researches (McKee et al., 1995; Guttman, 1998, 1999; Hayes et al., 1999) that the SPI is a good tool for detecting and monitoring the drought events. This study computes SPI during 1961-1990 from the observation (Rain-gauge) monthly rainfall data and RegCM model simulated rainfall data.
The Standardized Precipitation Index (SPI) based on the probability of precipitation, computed the characteristics of drought in Bangladesh for multiple time scales; 1, 3, 6, 9, 12, 24 months time steps. These time scales reflect the impact of drought on the availability of the different water resources. Particular drought year is detected from the time series analysis. The range of SPI in different drought categories is shown in Table 5.2.1. The negative values of SPI are considered as dry and positive values for wet periods. The SPI is calculated for the same observed station locations and duration using RegCM output rainfall data. Statistical scores are also calculated to obtain the efficiency-index of RegCM in drought diagnosis. SPI detected drought cases in the particular region of Bangladesh for both observed and model data are also verified using historical record of drought from BBS and EM-DAT data archive.
Table 5.2.1. Drought categories defined for SPI values.
| SPI value | Drought category |
| 0 to -0.99 | Near normal or mild drought |
| -1.00 to -1.49 | Moderate drought |
| -1.50 to -1.99 | Severe drought |
| -2.00 and less | Extreme drought |
Historical records of Drought in Bangladesh
6.1 Bangladesh Bureau of Statistics Archive
Record of drought event of Bangladesh obtained from ‘Agricultural Statistics Year book of Bangladesh’ published by Bangladesh Bureau of Statistics (BBS) from 1975 to 1990. Drought affected area, time period, length of drought and damage has given below by table in detail. According to BBS data, in the year 1984, 1985 and 1986 most of the places of the country experienced moderate drought at pre-monsoon season (February to April). These prolong drought hindered crop production of all over the country. There was severe drought occurred in 1980, 1981 and 1982 in North, Central and western part of Bangladesh. In 1979 drought, 43 per cent of area and 48.93 per cent of population affected in Bangladesh.
Table 6.1.1 Drought affected area, drought spell obtained from BBS for year 1975-1976.
| Year | Affected Area | Drought time Period | Length (Month) |
| 1975-76 | Dinajpur | Dec-May | 6 |
| Rangpur | Dec-April | 5 | |
| Rajshahi | Nov-May | 7 | |
| Bogra | Oct-April | 7 | |
| Kishoregonj | Dec-April | 5 | |
| Mymensingh | Dec-April | 5 | |
| Tangail | Dec-April | 5 | |
| Dhaka | Oct -Feb |