Wet Spells Research Articles

Development and hydrometeorological evaluation of a new stochastic daily rainfall model: Coupling Markov chain with rainfall event model

Stochastic rainfall models have been widely used for hydrological modelling and climate change impact studies, the accuracy of which can substantially affect the reliability of water resources planning, hydraulic structure design and flood and drought risk assessment. The primary objective of this study is to develop a stochastic daily rainfall model through coupling a Markov chain model with a rainfall event model (SDRM-MCREM) to simultaneously preserve the statistical properties of rainfall time series and rainfall events. The newly developed model is applied to the Qu River basin, East China and its performance is evaluated at catchment scale. Results demonstrate that SDRM-MCREM shows a good performance in reproducing most of the rainfall time-series statistics (i.e. rainfall percentiles, average monthly and annual rainfall, inter-monthly rainfall variability and extreme rainfall) and rainfall event characteristics (i.e. distributions of wet and dry spells, occurrence frequency of different rainfall event classes, temporal rainfall patterns and their occurrence frequency in different rainfall event classes). The statistics of average runoff and extreme runoff are also well preserved by using the SDRM-MCREM simulations as input of hydrological modelling except that the interannual variability of rainfall and runoff is slightly underestimated. Moreover, SDRM-MCREM shows a great potential to be used for flood and drought risk assessment in reproducing the exceedance probabilities of high flows (e.g. annual maximum 1-day, 3-day and 5-day mean runoff) and low flows (e.g. annual minimum 7-day, 30-day and 90-day mean runoff).

Analysis of the Characteristics of Dry and Wet Spells in a Mediterranean Region

Climate change could increase the risk of future hydrological extremes over large regional areas and trigger further pressure on water resource availability. In this study, the overall characteristics of dry and wet spells during a 56-year period (1951–2006) were analysed in the Calabria region (southern Italy), considering both yearly and seasonal scales. In particular, the mean and maximum lengths of dry/wet spells, the number of dry and wet spells with different lengths, and the contribution of dry/wet spells with different lengths to the total number of dry/wet days have been investigated. The results showed similar behaviour of both dry and wet spells in the spatial distribution of the mean lengths, with a difference between the two opposite sides (Ionian and Tyrrhenian) of the region. In fact, mean values ranging between 6 and 8 days, and higher than 8 days have been detected on the Tyrrhenian and on the Ionian side, respectively. The results regarding the maximum values of the lengths show much higher values of dry spells than the wet ones, and different temporal distributions throughout the year. In particular, with respect to dry spells, in autumn the maximum lengths reached values higher than 200 days, while the longest wet spells have been identified in winter, with a maximum value of 44 consecutive days with rain. The outcomes of this study can be useful for both water resources (with respect to the dry sequences) and risk reduction (regarding the wet spells) management.

Projected changes in extreme precipitation indices from CORDEX simulations over Ethiopia, East Africa

The projection of precipitation extremes is of significant importance for the reliable management of regional water resources. Thus, this study explores the potential response of the popularly used precipitation extreme indices to the global warming for near future (2011–2040), middle future (2041–2070) and far future (2071–2100) periods, based on the COordinated Regional climate Downscaling EXperiment (CORDEX) Africa experiment outputs over Ethiopia. The results show that the wet (dry) spells were projected to significantly decrease (increase) over most parts of Ethiopia, with relatively longer (shorter) dry (wet) spells projected over the northern parts of Ethiopia. Conversely, the projected changes in extreme precipitation amount above the 95th percentile showed a substantial increase over most parts of Ethiopia, with higher values projected to be in the southern region. The result implies that extreme and heavy precipitation events are likely to be more intense, and could amplify the probability of flood risks, particularly over the southern region. The total and extreme precipitations generally show a strong positive correlation, indicating the increase in extreme precipitation is responsible for the increase in total wet-day precipitation amount in Ethiopia. In general, climate change will have high negative impacts on the dynamics of precipitation patterns over Ethiopia, which could result in more long dry seasons and shorter rainy seasons over most parts of Ethiopia. Thus, the food and water security situation in the region is highly vulnerable due to the projected changes in the intensity and frequency of climate extremes (droughts and floods). However, the increase in precipitation extremes could have the potential to enhance the dry season agricultural productivity of most of the regions in Ethiopia using efficient irrigation systems through excess water harvesting in the rainy season. This can also reduce flood occurrences and hence economic damages. In general, it is of great importance to discuss the socio-economic impacts that could result if increasing precipitation extremes, as well as the increasing (decreasing) trend of maximum length of the dry (wet) spells, continue in the future.

Simulating precipitation in the Northeast United States using a climate‐informed K‐nearest neighbour algorithm

AbstractDecadal prediction using climate models faces long‐standing challenges. While global climate models may reproduce long‐term shifts in climate due to external forcing, in the near term, they often fail to accurately simulate interannual climate variability, as well as seasonal variability, wet and dry spells, and persistence, which are essential for water resources management. We developed a new climate‐informed K‐nearest neighbour (K‐NN)‐based stochastic modelling approach to capture the long‐term trend and variability while replicating intra‐annual statistics. The climate‐informed K‐NN stochastic model utilizes historical data along with climate state information to provide improved simulations of weather for near‐term regional projections. Daily precipitation and temperature simulations are based on analogue weather days that belong to years similar to the current year's climate state. The climate‐informed K‐NN stochastic model is tested using 53 weather stations in the Northeast United States with an evident monotonic trend in annual precipitation. The model is also compared to the original K‐NN weather generator and ISIMIP‐2b GFDL general circulation model bias‐corrected output in a cross‐validation mode. Results indicate that the climate‐informed K‐NN model provides improved simulations for dry and wet regimes, and better uncertainty bounds for annual average precipitation. The model also replicates the within‐year rainfall statistics. For the 1961–1970 dry regime, the model captures annual average precipitation and the intra‐annual coefficient of variation. For the 2005–2014 wet regime, the model replicates the monotonic trend and daily persistence in precipitation. These improved modelled precipitation time series can be used for accurately simulating near‐term streamflow, which in turn can be used for short‐term water resources planning and management.

Documentary Driven Chronologies Of Rainfall Variability for Kenya, 1845-1976

The study presents the first extensive reconstruction of rainfall variability for Kenya using documentary evidence. The study used over 10,345 documentary sources to review the rainfall changes witnessed in Kenya from 1845-1976. Different published and unpublished sources including the missionary records, settlers’ diaries, explorers’ journals, governmental reports were consulted. Based on the meteorological observations for the country, the rainy years were assigned the following indices: very wet, relatively wet, normal, dry and very dry. The results shows chronologies of significant dry phases identified in the years 1849/50/51, 1868, 1875/76, 1879, 1883, 1888, 1897, and 1899, 1918/19, 1928, 1934/35, 1944, 1972 and 1975, while wet episodes occurred in 1878, 1899, 1905, 1912, 1916, 1922, and 1929 and 1961. The evidences for the major dry and wet spells and the findings are evaluated within wider historical context.

Lagged Compound Occurrence of Droughts and Pluvials Globally Over the Past Seven Decades

AbstractThe drought‐pluvial seesaw—defined as the phenomenon of pluvials (wet spells) following droughts (dry spells)—magnifies the impact of individual pluvial and drought events yet has not been systematically evaluated, especially at the global scale. We apply an event coincidence analysis to explore the aggregated seesaw behavior based on land surface model simulations for the past seven decades (1950–2016). We find that globally, about 5.9% and 7.6% of the land surface have experienced statistically significant (p<0.10) drought‐pluvial seesaw behavior during the boreal spring‐summer and fall‐winter, with an average 11.1% and 11.4% of all droughts being followed by pluvials in the following season, respectively. Although this global frequency pattern is modest and coherent changes cannot be detected at the subcontinental scale, local hot spots of drought‐pluvial seesaw have become more frequent than either droughts or pluvials alone in the last three decades, albeit with a small percentage of area coverage.

Extreme dry and wet spells face changes in their duration and timing

Dry spells are sequences of days without precipitation. They can have negative implications for societies, including water security and agriculture. For example, changes in their duration and within-year timing can pose a threat to food production and wildfire risk. Conversely, wet spells are sequences of days with precipitation above a certain threshold, and changes in their duration and within-year timing can impact agriculture, flooding or the prevalence of water-related vector-borne diseases. Here we assess changes in the duration and within-year timing of extreme dry and wet spells over 60 years (1958–2017) using a consistent global land surface precipitation dataset of 5093 rain gauge locations. The dataset allowed for detailed spatial analyses of the United States, Europe and Australia. While many locations exhibit statistically significant changes in the duration of extreme dry and wet spells, the changes in the within-year timing are less often significant. Our results show consistencies with observations and projections from state-of-the-art climate and water resources research. In addition, we provide new insights regarding trends in the timing of extreme dry and wet spells, an aspect being equally important for possible future implications of extremes in a changing climate, which has not yet received the same level of attention and is characterized by larger uncertainty.

Using the CHIRPS Dataset to Investigate Historical Changes in Precipitation Extremes in West Africa

This study aims to provide improved knowledge and evidence on current (1986–2015) climate variation based on six rainfall indices over five West African countries (Senegal, Niger, Burkina Faso, Ivory Coast, and Benin) using the Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) dataset. On average, precipitation has increased over the central Sahel and the western Sahel. This increase is associated with increase in the number of rainy days, longer wet spells and shorter dry spells. Over the Guinea Coast, the slight increase in precipitation is associated with an increase in the intensity of rainfall with a shorter duration of wet spells. However, these mean changes in precipitation are not all statistically significant and uniform within a country. While previous studies are focused on regional and sub-regional scales, this study contributes to deliver a climate information at a country level that is more relevant for decision making and for policy makers, and to document climate-related risks within a country to feed impact studies in key sectors of the development, such as agriculture and water resources.

Evaluation of historical CMIP6 model simulations of extreme precipitation over contiguous US regions

Simulated historical precipitation is evaluated for Coupled Model Intercomparison Project Phase 6 (CMIP6) models using precipitation indices defined by the Expert Team on Climate Change Detection and Indices. The model indices are evaluated against corresponding indices from the CPC unified gauge-based analyses of precipitation over seven geographical regions across the contiguous US (CONUS). The regions assessed match those in recent US National Climate Assessment Reports. To estimate observational uncertainty, precipitation indices for three other observational datasets (HadEx2, Livneh and PRISM) are evaluated against the CPC analyses. Both the moderate and extreme mean precipitation intensities are overestimated over the western CONUS and underestimated in the areas of the Central Great Plains (CGP) in most CMIP6 models tested. Most CMIP6 models overestimate the mean and variability of wet spell durations and underestimate the mean and variability of dry spell durations across the CONUS. Biases in interannual variability of most of the indices have similar patterns to those in corresponding mean biases. The median and interquartile model spreads in CMIP6 model biases are clearly smaller than those in CMIP5 model biases for wet spell durations. Multimodel medians of CMIP6 (CMIP6-MMM) and CMIP5 (CMIP5-MMM) have similar biases in climatology and variability but biases tend to be smaller in CMIP6-MMM. Depending on the index, extreme precipitation is slightly better in parts of the eastern half of the CONUS in CMIP6-MMM, otherwise, the biases in climatology and variability are similar to CMIP5-MMM. CMIP6-MMM performs better than individual models and even observational datasets in some cases. Differences between observational datasets for most indices are comparable to the CMIP6 interquartile model spread. The better-performing observational and model datasets are different in different parts of the CONUS.

A Methodology for Multiobjective Evaluation of Precipitation Products for Extreme Weather (in a Data-Scarce Environment)

AbstractIn this paper, a methodology is proposed to quantitatively evaluate precipitation products for multiple purposes. Evaluation mainly focuses on rainfall characteristics relevant to hydrological or agricultural applications: spatial distribution pattern, effect of aggregation over time, the capture of small-scale variability and seasonality, detection of dry spells and wet spells, and timing and volume of heavy rainfall events. Verification statistics were modified and metrics were reported for extreme weather performance, such as flood and drought monitoring. The analysis was performed for different rainfall categories, over regions dominated by different weather systems or with different topographical structures. The latest versions of seven commonly available, high-resolution rainfall estimates have been evaluated by the method against daily data from 16 rain gauge stations over Tanzania, during 1998–2006. They were TRMM 3B42, CHIRPS, TAMSAT, CMORPH_RAW, CMORPH_BLD, WFDEI_CRU, and CPCU. All products, except for CMORPH_BLD and CPCU, were poorly correlated to gauge data at daily time scale with correlation coefficients < 0.5. Five-day aggregation was the minimum time scale that can be used for the products to reach an accuracy better than monthly-mean of gauge data. Their performance varied across different climatic or topographical regions and different rainfall seasons. Timing of precipitation was inaccurately estimated by all products, particularly for heavy rains, with less than 40% hits. The results of the evaluation procedure allow discrimination between available products and better selection of the product to be used for a specific application, such as crop insurance or flood early warning, under particular climatic conditions.

Exploring Abrupt Alternations Between Wet and Dry Conditions on the Basis of Historical Observations and Convection‐Permitting Climate Model Simulations

AbstractExploring the dynamic evolution of the abrupt alternation between wet and dry spells in adjacent months plays a crucial role in water resources planning and agricultural development in a changing climate. The dry‐wet abrupt alternation (DWAA) has been studied based on hydrometeorological observations over the past several years. However, little effort has been made to explore DWAA from a climate projection standpoint. Furthermore, few studies have investigated potential interrelationships between DWAA and heavy rainfall. In this study, the interrelationships between DWAA events and heavy rainfall with various intensities as well as potential evapotranspiration are revealed explicitly through the convection‐permitting climate simulations for 10 climate divisions over Texas in the United States. Our findings disclose that the increasing heavy rainfall and potential evapotranspiration lead to more frequent occurrence of DWAA events over a larger spatial extent. Heavy rainfall with daily precipitation greater than 20 mm contributes most to the occurrence of DWAA. In addition, a severe phenomenon of dry‐wet‐dry alternation is projected to appear due to the increasing number of heavy rainfall and drought events as well as the deteriorated soil water holding capacity under global warming.

Exploring the predictability of within-season rainfall statistics of the Bangladesh monsoon using North American Multimodel Ensemble outputs

Improvement of rainfall forecasts on seasonal to within-season timescales is crucial for many vulnerable regions and nations, including Bangladesh. For South Asia, seasonal predictability of rainfall can be quite challenging, and Bangladesh has limited predictive skill with respect to total seasonal rainfall due to its weak relationship with ENSO variability. The relationship between total seasonal monsoon rainfall from June through September (JJAS), as simulated by the North American Multimodel Ensemble (NMME), and within-season observed rainfall statistics for Bangladesh were explored, employing 25-year cross-validations at lead times up to 3 months. The model hindcasts of total JJAS rainfall demonstrate only low-to-modest skill at predicting total observed seasonal rainfall, but a more robust predictive relationship is found for the number of observed dry and wet spells within the season, more so than with the number of extreme dry or wet days. A small ensemble of NMME models could be used to provide more valuable information to aid decision-makers than previously thought, with important implications for agricultural decision-making and climate services.

Analyses of rainfall extremes in East Africa based on observations from rain gauges and climate change simulations by CORDEX RCMs

This study derived twelve Extreme Rainfall Indices (ERIs) such as the Maximum Dry Spell (MDS) and Maximum Wet Spell (MWS) from daily rainfall observed over the period 1961–1990 at nine locations across East Africa. Capacity of six CO-ordinated Regional Climate Downscaling EXperiment (CORDEX) Africa Regional Climate Models (RCMs) driven by twenty six Climate Model Intercomparison Project phase 5 (CMIP5) General Circulation Models (GCMs) to reproduce the observed ERIs with respect to long-term mean and trends was evaluated. Four RCMs and their five driving GCMs were further analyzed with respect to ERIs. Ensemble means of the RCMs' biases in simulating trends in several ERIs were of magnitudes above 50%. On average, biases in reproducing long-term mean were smaller than those for trends in ERIs. The difference between the performances of RCMs and GCMs depended on the selected RCM–GCM pair. The ensemble means of the RCMs reproduced observed ERIs better than the individual RCMs corroborating that the use of multi-model ensembles can boost credibility of climate change simulations and projections. The RCMs performed better than their driving GCMs in reproducing MDS. The biases of both the RCMs and GCMs were smaller in reproducing the MWS than MDS. Nonetheless, in reproducing observed MWS, the ensemble mean of RCMs' biases was slightly larger than that of the driving GCMs indicating possible adding up of the uncertainties from the GCMs and RCMs. Suggested RCMs' improvements regarding aerosol impacts on rainfall include adding missing constituents (like nitrate), and refining the crudely represented components. RCMs also require high resolution description (in both space and time) of land use types, land surface covers and characteristics as well as landscape heterogeneity. The GCMs to be used as the initial and lateral boundary conditions for the RCMs require improvement in their representation of key dynamical and thermodynamical feedbacks in the Tropical Indian Ocean.

Meteorological drought lacunarity around the world and its classification

Abstract. The measure of drought duration strongly depends on the definition considered. In meteorology, dryness is habitually measured by means of fixed thresholds (e.g. 0.1 or 1 mm usually define dry spells) or climatic mean values (as is the case of the standardised precipitation index), but this also depends on the aggregation time interval considered. However, robust measurements of drought duration are required for analysing the statistical significance of possible changes. Herein we climatically classified the drought duration around the world according to its similarity to the voids of the Cantor set. Dryness time structure can be concisely measured by the n index (from the regular or irregular alternation of dry or wet spells), which is closely related to the Gini index and to a Cantor-based exponent. This enables the world’s climates to be classified into six large types based on a new measure of drought duration. To conclude, outcomes provide the ability to determine when droughts start and finish. We performed the dry-spell analysis using the full global gridded daily Multi-Source Weighted-Ensemble Precipitation (MSWEP) dataset. The MSWEP combines gauge-, satellite-, and reanalysis-based data to provide reliable precipitation estimates. The study period comprises the years 1979–2016 (total of 45 165 d), and a spatial resolution of 0.5∘, with a total of 259 197 grid points. The dataset is publicly available at https://doi.org/10.5281/zenodo.3247041 (Monjo et al., 2019).

Multi-Site Statistical Downscaling Method Using GCM-Based Monthly Data for Daily Precipitation Generation

Global Climate Models (GCMs) can provide essential meteorological data as inputs for simulating and assessing the impact of climate change on catchment hydrology. However, downscaling of GCM outputs is often required due to their coarse spatial and temporal resolution. As an effective downscaling method, stochastic weather generators can reproduce daily sequences with statistically similar statistical characteristics. Most weather generators can only simulate single-site meteorological data, which are spatially uncorrelated. Therefore, this study introduces a method for multi-site precipitation downscaling based on a combination of a single-site stochastic weather generator, CLIGEN (CLImate GENerator), and a modified shuffle procedure constrained with multi-model ensemble GCM monthly precipitation outputs. The applicability of the downscaling method is demonstrated in the Huangfuchuan Basin (arid to semi-arid climate) for a historical period (1976–2005) and a projection period (2021–2070, historical, the representative concentration path (RCP) 2.6, RCP4.5, RCP4.8 scenarios) to generate spatially correlated daily precipitation. The results show that the proposed downscaling method can accurately simulate the mean of daily, monthly and annual precipitation and the wet spell lengths, and the inter-station correlation among 10 sites in the basin. In addition, this combination method generated the projected precipitation and showed an increasing trend for future years. These findings could help us better cope with the potential risks of climate change.

Role of ocean mesoscale structures in shaping the Angola-Low pressure system and the southern Africa rainfall

Southern African climate is under the influence of both tropical and subtropical systems which result in a complex region where important interactions co-exist over a large spectrum of spatiotemporal scales. The Angola Low (AL), situated on boundary between tropical and subtropical southern Africa, has been diagnosed as a key driver of moisture distribution in the region on daily to seasonal time scales. It has been demonstrated that the AL pressure system is sensitive to the dynamics of the neighbouring oceans, but to date no study has considered the model resolution of air–sea interactions required to simulate this sensitivity. Using sensitivity experiments with a regional atmospheric model, which differ only in the mesoscale sea surface temperature (SST) forcing characteristics (either the full spectrum of SST variability or only its large-scale components are included), we first quantify the importance of SST gradients on the AL strength and variability. The results suggest that the mesoscale SST variability of the Angola–Benguela Frontal Zone (ABFZ) plays a key role in AL activity, particularly during the late summer. Synoptic-scale tropical lows, which form the AL, are automatically detected, and the results suggest more extreme events occur when the model is forced by mesoscale SSTs (everywhere and in the ABFZ area only). The rainfall resulting from those events suggests that tropical-low episodes are associated with nearly 15% of the total rain in Angola and Namibia. The link between AL dynamics and wet spells is also discussed, with the former showing a different spatial pattern as well as frequency when the ocean is fully resolved.

Deciphering the extreme rainfall scenario over Indian landmass using satellite observations, reanalysis and model forecast: Case studies

Abstract In this study, we provide a comprehensive analysis of spatio-temporal distribution of extreme rainfall of different intensities (heavy, very heavy and extremely heavy) as well as wet spell over the Indian landmass during monsoon (June–August) of 2016–2018 through satellite observations. Moreover, there were ~ 35 extreme rainfall events during the study period, which has accumulated rainfall > 120 mm hr−1 and the first 12 events were identified (having highest amount of accumulated rainfall in a day out of 35 events) for our study. The event locations were so selected where there is not any point observations [automatic weather station (AWS) or automatic rain gauge (ARG)] at the site of extreme rainfall events, not even within 50 km radius of the event site or if any AWS/ARG site is present but were unrecorded or unreported during the time of extreme rainfall event. 70% of the extreme rainfall events (convective storms) occurred in the afternoon where 75% of the events indicates > 150 mm hr−1 accumulated rainfall and the rest 25% shows the accumulation rate to be > 120 mm hr−1. The factors influencing the extreme rainfall events to occur are obtained to be increased instability parameters (convective available potential energy, K-Index, Total Total's index), meteorological, thermodynamic and dynamical parameters (upper and lower level temperature gradient difference, vertical difference of equivalent potential temperature at 850 and 500 hPa, moisture flux convergence) and decreased temperature saturation deficit. The convective growth started initiating 6–7 h before the occurrence of extreme rainfall, indicating the favourable condition for severe convection. Furthermore, if the diurnal relative humidity range is maximum and there is a sharp increase in specific humidity at a location, there is a maximum probability of extreme rainfall after 5–6 h at that location. Both the Global Forecast System (GFS) and NCMRWF Unified Model (NCUM) captured the extreme rainfall events well for the year 2018 over the Indian region.

Analysis and Modeling of Precipitation Intermittency by Compound Markov‐DARMA Models

AbstractIntermittency in precipitation is analyzed by a new class of stochastic models consisting of the product of two binary stochastic models. The ingoing models in these compound models are confined to a one‐parameter white noise model and either a first‐order or a second‐order autoregressive model, which is described by two or three parameters, respectively. The two resulting second‐order compound models have three and four parameters, respectively. Expressions of these parameters are derived, as well as equations with which to assess the parameters in the ingoing models based on the parameters in the compound models. The compound models are shown to differ considerably from the usual Markov models and discrete autoregressive moving average (DARMA) models. Daily binary precipitation time series from Copenhagen, Denmark, and Alice Springs, Australia, are analyzed by depicting the Markovian transition probabilities in probability trees with time lags of up to 5 days, resulting in 32 branches. Each tree is a kind of signature for the time series considered, but only a few of the branches, typically 3–4, are significant. Furthermore, the significant parts of the trees are rather skewed due to the intermittency of the precipitation process. This skewness makes the application of usual Markov and DARMA models less effective, whereas the new compound models better explain the observations. Based on one of the two new models, explicit but approximate expressions for the cumulative distribution (or the return period) of the durations of wet and dry spells are derived and compared to the observations from Copenhagen.

Climate change posed agricultural drought and potential of rainy season for effective agricultural water management, Kesem sub-basin, Awash Basin, Ethiopia

The Intergovernmental Panel on Climate Change concluded that climate has changed over the past century and the trend is anticipated to continue in the future. This study examines climate change posed drought and potential of rainy season rainfall in Kesem sub-basin. Rainfall variability is examined using statistical descriptors. Also, the onset and cessation dates, length of growing season, and probability of dry and wet spell are analyzed using INSTAT Plus software. On the other hand, long-term drought is analyzed using reconnaissance drought index. Rainfall variability result showed high coefficient of variance (CV) of 46.8 to 179.2% on monthly and 39.1% on annual basis. The mean duration of the main rainy season is 91 days with standard deviation of 18.5 days and CV of 20.4%. The probability of dry decade in the main rainy season is more than 50% for the 16th to 18th and 25th to 27th decades. Whereas probability occurrences of dry decades preceded by dry decades were more than 50% for decades 16th to 18th and 23th to 27th. Potential evapotranspiration showed increment of 25.9 mm from the base period for RCP 4.5 (2020) and 26.7 mm for RCP 8.5 (2020), and 55 mm for RCP4.5 (2050) and 56.8 mm for RCP8.5 (2050). Regarding the long-term drought, there were 9.7% extreme, 6.5% severe, 3.2% moderate, and 48.4% mild drought on the basis of observed data. The inter-annual variability of growing seasons, probability occurrence of dry decade, and drought during growing season are revealed. So, policymakers need to consider supplementary irrigation for crops in the area because there are high probability occurrences of dry decades.

Spatial analysis of wet spell probability over India (1971-2005) towards agricultural planning

The spatial analysis of the wet spell probability over the Indian region has been carried out using daily gridded (0.5o × 0.5o) rainfall data of 1971-2005 during the summer monsoon period, i.e. June-September. A threshold was applied to the weekly cumulative rainfall to convert the rainfall data into wet spell information. A Markov chain model was employed to estimate the initial and conditional probabilities of the wet spell for each grid and the spatio-temporal distribution of the wet spells probabilities was analyzed. The probability maps were able to capture the summer monsoon scenario over the Indian region, representing the onset, progression and withdrawal of monsoon rainfall. Higher wet spell probability was observed over the west coast and northeastern parts of India, i.e., the initial probability was maximum over these regions. However, lower probability values were observed in West Rajasthan, Gujarat and southern India. A threshold of 80% of the maximum initial probability was used to standardize the spatially-variable probability information, and a week with more than the threshold values was considered as a probable wet week. The duration of the longest probable wet spell was highest along the west coast and in northeastern India, whereas it was lowest in western and southern India. The start and duration of the longest spell of the probable wet week can be used for rainfed-agricultural planning, i.e., the start of sowing/planting, selection of crops and varieties based on their length of growing period, optimum harvesting period to avoid wet spell, etc.