Gridded Rainfall Research Articles

A study of rainfall thresholds for landslides in Badung Regency using satellite-derived rainfall grid datasets

Integrating field rainfall data with satellite data improves data accuracy and overcomes rainfall data limitations for rain thresholds. Integration can involve field rainfall data, satellite rainfall data, or a different satellite dataset. Merging these rainfall data sources provides more spatial coverage of satellite data. To determine how well rainfall thresholds predict rainfall-triggered landslides, the threshold model must be validated. This study will evaluate satellite rainfall data before and after integration in developing a rainfall threshold model for landslide prediction in Badung Regency. To do so, the study used a cumulative rainfall threshold over 3, 7, 15, and 30 days and two rainfall satellite products (integrated merged multi-satellite retrievals (IMERG) and precipitation estimation from remotely sensed information using artificial neural networks (PERSIANN)). Median, first, and third quartiles were used to set thresholds. The area under the curve (AUC) was calculated to validate rainfall threshold outcomes using receiver operating characteristic (ROC) curves. Analysis showed that integrating satellite rainfall data into the rainfall threshold model for landslide prediction yields better results than other methods. An AUC value of 0.903 (90.3%) for the 30-day cumulative rainfall thresholds supports this claim. This model could be a good input for a landslide early warning system in Badung Regency.

영향한계강우량을 고려한 삼척시 호우특보 기준에 관한 연구

Heavy rain has a short duration of occurrence, causing many casualties and property damage. However, the heavy rain warning criteria are a single warning criterion that does not reflect local characteristics. Therefore, this study aims to study and propose heavy rain warning criteria for Samcheok City that consider the threshold rainfall to supplement the uniform decision-making system for heavy rain disasters. By analyzing the basic statistical values of the threshold rainfall grid for each advisory area in Samcheok City, the criteria for heavy rain warning for each advisory area were selected in four levels, and the applicability was examined by applying it to past rainfall events. As a result, this study visibly forecasted the expected risk level that changes depending on the rainfall event by advisory area, and categorized the advisories and warnings of heavy rain warning from the KMA were categorized into Warning and Danger levels. The results of this study are expected to be useful in securing more golden time for responding to heavy rain disasters and preparing efficient measures against heavy rain disasters.

An efficient 2-D flood inundation modelling based on a data-driven approach

Study regionThe plain areas of Yilan County in Taiwan. Study focusThe present study employed hourly precipitation and inundation simulation data from a 5-km resolution HiRAM-WRF (High-Resolution Atmospheric Model-Weather Research and Forecasting) downscaled model and a 40-m resolution SOBEK model to construct a data-driven two-dimensional flood inundation model (D2DFIM) based on the support vector regression (SVR) approach. New hydrological insights for the regionThe dataset comprised 149 rainfall events and corresponding inundation events. We extracted the inundated and interpolated rainfall grid points distributed across Yilan County, Taiwan, from the SOBEK and HiRAM-WRF models. The D2DFIM (data-driven two-dimensional flood inundation model) utilized the parallel computing approach in the training phase, saving 90% of the elapsed time. The comparisons between the data-driven and physical-based models indicate that the D2DFIM could perform better in flood simulation if the input features comprised previous inundation depth and the last three time steps of rainfall information. Regarding the minimal required number of training samples, we found almost no significant difference in the flooding simulation once the training samples equalled or exceeded 50. Additionally, the elapsed time of D2DFIM is 9–10 times faster than that of SOBEK. We validated the well-developed D2DFIM using three historical inundated events induced by typhoons in Yilan County; the flood extents derived from the D2DFIM agreed well with the survey results.

Land Cover Trajectories and Their Impacts on Rainfall-Triggered Landslide Occurrence in a Cultivated Mountainous Region of Western Japan

Land cover changes in mountainous regions are potential precursors to landslide disasters. However, the effects of past long-term land cover changes on the characteristics of recent landslides remains underexplored. We studied land cover evolution over a 56-year period on Omishima Island in western Japan to examine the spatial relations of landslides in the July 2018 storm event based on rainfall, land cover trajectories, and topography. We generated land cover maps for 1962, 1981, and 2018 by aerial photo interpretation. We also identified 512 new landslides. Based on 47-year precipitation records, we estimated the return periods of 1- to 264-h rainfalls during the storm using the generalised extreme value (GEV) distributions. Return periods showed wide variation when the derived GEV distributions were applied to 1-km grid rainfall distributions. Despite such pronounced spatial variations in rainfall, we did not observe a clear correlation between rainfall intensity and landslide distribution. In contrast, land cover trajectories had a pronounced effect on landslide occurrence. Landslides were more concentrated on slopes that experienced land cover changes after 1962. A comparison of slopes on farmland developed between 1962 and 1981 (mainly citrus orchards) indicated that landslide density and area ratio were significantly lower on slopes that had reverted to forests than on those remaining as farmland. However, the values of the reforested slopes exceeded those of forests and farmlands that remained since before 1962. Our geospatial analysis revealed that even if the field had shifted to forests, the effects of reduced slope stability due to orchard development had remained for at least 37 years. This suggested that the impacts of converting forests to orchards last longer than harvesting in managed plantation forests.

Evaluasi Data Curah Hujan Satelit TRMM Dan GPM Terhadap Data Curah Hujan Observasi Di Kalimantan Tengah

Rainfall is an indispensable component of hydrological analysis. However, the observational rainfall data has problems such as incomplete and shorter data. Rainfall measurements using high resolution and wide area coverage satellites provide a potential alternative for various hydrological analyses. But the measurement of rainfall using satellites is not necessarily accurate due to the difference in resolution of the observational rainfall data, especially in Central Kalimantan Province. This study aims to determine the accuracy of satellite rainfall data based on observational rain data and evaluate it. Evaluation is done by looking for the relationship between the two-rainfall data using a regression model. The results showed that the TRMM total grid rainfall data (R2 = 0.5233) had a higher correlation to the observational rainfall data compared to the GPM rainfall data (R2 = 0.3909). Furthermore, a discharge analysis was carried out in one of the watersheds in Central Kalimantan, namely the Kahayan watershed using the Nakayasu Hydrograph Synthesis Unit method with a certain return period with satellite rainfall data input which has a fairly good accuracy based on the results of the analysis. The estimated discharge with return periods of 2, 5, 10, 25, and 50 years for TRMM rainfall data before correction are 1976.74m3/s, 2165.66 m3/s, 2239.19 m3/s, 2313.63 m3/s, 2375.69 m3/s respectively, and for the estimated discharge after correction are 2549.35 m3/s, 2670.59 m3/s, 2736.42 m3/s, 2801.37 m3/s, 2854.82 m3/s.

Annual soil loss assessment of smallholder rubber growing lands in the Kalutara District, Sri Lanka using the RUSLE model in GIS

Soil loss resulting from various land management practices in traditional rubber-growing areas of Sri Lanka has been a major issue of concern and one of the factors responsible for declining rubber land productivity. There is no or limited information on the spatial variability of soil loss from rubber lands in Sri Lanka's traditional rubber growing areas. This constraint has had a significant impact on the effective management of soil conservation in rubber-growing lands. This study focuses on assessing the soil loss from the smallholder rubber-growing lands in the Kalutara District using remotely sensed satellite image-based Digital Elevation Model (DEM), rainfall grid data, and prepared soil maps with ground-level surveys by Natural Resource Management Centre (NRMC) Sri Lanka. The factors including rainfall, topography, land area and the crop-specific coefficient for the rubber-growing lands in the study area were analyzed using the Revised Universal Soil Loss Equation (RUSLE) and Geographic Information System (GIS). The study revealed that about 30% of smallholder rubber-growing lands fall under the risk to severe risk categories of soil loss while about 60% of rubber lands are under the low-risk category. About 8,500 ha of smallholder rubber lands can be categorized as risk to severe risk for soil loss whilst about 15,000 ha are under the low-risk category for soil loss. The findings of this study are useful in the implementation of an effective soil conservation management plan and has the potential in applying this methodological approach in other areas of Sri Lanka for various crops.

A spatio-temporal analysis of changing trends in rainfall patter: A case study of Kutch District

Changing trends captivate millions of analytical minds. It grabs much when the impact on human lives and properties is involved. The utmost concern is changing weather patterns, which led to a worse impact on the amount and distribution of the precipitation. Essentially, the challenge in several nations is to reach the whole population with adequate water per day. Even a country like India, which has thousands of rivers, streams, lakes, ponds, etc. unable to reach the whole population, as it depends on the uneven event, the Monsoon for its >80% annual rain during monsoon months (June-September). The Nation’s westernmost district, Kutch, which holds the title for the largest area-wise district in India, has been bearing from changing trends in rainfall for a long time. The rainfall distribution within the district is uneven and has experienced contrasting extreme events like drought and flood within consecutive years. The present study attempts to analyse the Spatio-temporal rainfall pattern for 58 years from 1961 to 2018 for the Kutch district using the IMD4 long-term daily gridded (High Spatial Resolution, 0.25° x 0.25°) rainfall dataset. In addition, the Mann-Kendall (MK) test and Sen’s slope method were performed to detect the trends and the magnitude of change over the studied period, respectively. The results indicate high rainfall variability during monsoon months in the north and northwest, while relatively moderate and low in the south and west parts of the district, respectively, and show an upward trend in monsoon rainfall at a 5% significance level in all the Taluka of the Kutch district.

The Impact of Rainfall on Soil Moisture Variability in Four Homogeneous Rainfall Zones of India during Strong, Weak, and Normal Indian Summer Monsoons

This observational study mainly examines the impact of rainfall on Indian soil moisture (SM) variability in four homogeneous rainfall zones (i.e., central India (CI), northwest India (NWI), south peninsula India (SPIN), and northeast India (NEI)) as defined by India Meteorological Department (IMD) during strong, weak, and normal Indian summer monsoons (ISMs), which are determined regionally for each homogeneous rainfall zone separately. This study uses the daily gridded (0.25° × 0.25°) rainfall data set provided by IMD and the daily gridded (0.25° × 0.25°) SM combined product version 06.1 from European Space Agency Climate Change Initiative (ESA CCI) over the period 1992–2020. Results reveal that monthly and seasonal mean SM in NWI, CI, and SPIN are overall higher during strong than during weak ISMs. The daily SM and its dependence on rainfall appear to be region-locked in space and phase-locked in time: Strong correlation and large response to rainfall generally occur in most parts of SPIN and NWI during June (June–July) of strong (weak) ISMs where SM values are relatively small; Weak correlation and small response generally occur in CI and NEI in July-September (August–September) of strong (weak) ISMs where SM values are relatively large. The phase-locked feature is associated with the features of ISMs. The region-locked feature is presumably associated with the local features, such as soil and vegetation types and/or environmental conditions. Both region-locked and phase-locked features cause regional distinct features in SM persistence.

HOTRUNZ: an open-access 1 km resolution monthly 1910–2019 time series of interpolated temperature and rainfall grids with associated uncertainty for New Zealand

Abstract. Long time series of temperature and rainfall grids are fundamental to understanding how these environmental variables affect environmental or ecological patterns and processes such as plant distributions, plant and animal phenology, wildfires, and hydrology. Ideally such temperature and rainfall grids are openly available and associated with uncertainties so that data-quality issues are transparent to users. We present a History of Open Temperature and Rainfall with Uncertainty in New Zealand (HOTRUNZ) that uses climatological aided natural neighbour interpolation to provide monthly 1 km resolution grids of total rainfall, mean air temperature, mean daily maximum air temperature, and mean daily minimum air temperature across New Zealand from 1910 to 2019. HOTRUNZ matches the best available temporal extent and spatial resolution of any open-access temperature and rainfall grids that include New Zealand and is unique in providing associated spatial uncertainty in the variables' units. The HOTRUNZ grids capture the dynamic spatial and temporal nature of monthly temperature and rainfall and the uncertainties associated with the interpolation. We also demonstrate how to quantify and visualise temporal trends across New Zealand that recognise the temporal and spatial variation in uncertainties in the HOTRUNZ data. The HOTRUNZ data are openly available at https://doi.org/10.7931/zmvz-xf30 (Etherington et al., 2021).

An update of the spatial and temporal variability of rainfall erosivity (R-factor) for the main agricultural production zones of Austria

Rainfall erosivity is one of the key parameters influencing the degree of soil erosion. Due to the high spatiotemporal variability of rainfall erosivity and the influence of a changing climate it is crucial to use spatially well-distributed and temporally current rainfall data. Rainfall erosivity in Austria has been estimated by previous studies with varying rainfall data amounts. This study aimed to create an updated R-factor map for Austria and its main agricultural production zones based on a larger number of rainfall stations and a recent time series. As well as, compare R-factors from previous studies to identify differences in erosivity estimation. Rainfall data from 171 stations throughout Austria were gap-filled and corrected to improve data quality. Rainfall erosivity was calculated for 1995–2015 for the vegetation period and annually and used to establish two linear regressions describing rainfall erosivity as a function of mean rainfall amount. The regressions were applied to the 1 km2 daily rainfall grids from the SPARTACUS dataset to create the spatially distributed rainfall erosivity maps. Differences in the temporal and spatial distribution of rainfall erosivity, erosion index and erosivity density between the main agricultural production zones showed areas at risk of soil erosion and timing of vulnerability. The highest rainfall erosivities were found in the agriculturally important eastern regions of Austria during the summer months. Compared to previous studies, considerable differences in local R-factor estimation were found. The significantly larger number of rainfall stations and an updated time series increased the representativeness of rainfall erosivity estimation in Austria, which can contribute to a more precise soil erosion risk assessment.

Assessment of heavy rainfall forecasts over the southern Vietnam by using WRF-ARW with different physical parameterization schemes

This research aims to investigate heavy rainfall forecasting over southern Vietnam (SVN) by using the Weather Research and Forecasting with the Advanced Research dynamical solver (WRF-ARW) model. The selection of 34 heavy rainfall events from 2012 to 2016 over SVN during the active phase of the Southeastern Asian summer monsoon was studied. Sixty different WRF-ARW configurations are determined from three cumulus schemes (Kain–Fritsch with three different types of trigger mechanisms, Grell–Devenyi and Betts– Miller–Janjic), three planetary boundary schemes (Yonsei University, Mellor–Yamada–Janjic and Bougeault-Lacarrère) and four cloud microphysics schemes (Lin, Thompson and two WRF microphysics schemes with five and six moments). Typical skill scores are calculated based on the surface observation data and spatial verifications are carried out using the fraction skill score (FSS) and grid rainfall observations from the satellite-based GSMaP product. It is clear that the most sensitive to heavy rainfall forecasts were cumulus scheme selections, especially at thresholds below 50 mm/24 h. The configurations using the Kain–Fritsch scheme have wet bias issues. However, overall, in terms of a high probability of detection (POD) and high threat scores (CSI), the most skillful configuration was the use of the Kain–Fritsch scheme with the third trigger mechanism in combination with local diffusion planetary boundary schemes.

Evaluation and Bias correction of Tropical Applications of Meteorology using SATellite (TAMSAT) daily rainfall estimates over the data-scarce region of Southern Ethiopia

Precipitation data is an intrinsic parameter of rainfall-runoff simulation. since it is strongly hooked into the accuracy of the spatial and temporal representation of the precipitation. In regions where rainfall stations are scarce, additional data sources could also be needed. Satellite platforms have provided a satisfactory alternative because of their global coverage. Tropical Applications of Meteorology using SATellite (TAMSAT) is one such data source provided by the research group of the University of Reading, UK. This dataset provides rainfall estimates across Africa which captures the rainfall pattern in GenaleDawa River basin (southern Ethiopia) but with a magnitude bias of over-estimation and under-estimations. In this study, magnitude bias correction of the TAMSAT dataset with a linear scaling technique resulted in a rainfall grid of the area with ~4 km spatial resolution of a 35 year (1983–2017) daily rainfall dataset. Intercomparison between Satellite rainfall product and observed data were done using point to grid method selecting nine representative metrological stations.The average correlation R is 0.45 and the average NS is 0.028 and the Categorical index POD, FAR, FB and HSS, values were 0.49,0.4,0.84, and 0.4) before bias correction. After bias correction, these values were improved to the average value of R=0.87 and NS =0.764 and POD, FAR, FB, and HSS of (0.71,0.22, 0.92, and 0.66 respectively

Precipitation Data Retrieval and Quality Assurance from Different Data Sources for the Namoi Catchment in Australia

Within the Horizon 2020 Project WaterSENSE a modular approach was developed to provide different stakeholders with the required precipitation information. An operational high-quality rainfall grid was set up for the Namoi catchment in Australia based on rain gauge adjusted radar data. Data availability and processing considerations make it necessary to explore alternative precipitation approaches. The gauge adjusted radar data will serve as a benchmark for the alternative precipitation data. The two well established satellite-based precipitation datasets IMERG and GSMaP will be analyzed with the temporal and spatial requirements of the applications envisioned in WaterSENSE in mind. While first results appear promising, these datasets will need further refinements to meet the criteria of WaterSENSE, especially with respect to the spatial resolution. Inferring information from soil moisture-derived from EO observations to increase the spatial detail of the existing satellite-based datasets is a promising approach that will be investigated along with other alternatives.

Trend Analysis of Rainfall Using Gridded Data over a Region of Southern Italy

Climate change is affecting all regions worldwide. Globally, polar ice shields are melting and the sea is rising. Moreover, some regions are facing more common extreme weather events and rainfall, while others are experiencing more extreme heat waves and droughts, causing changes in mean renewable water supplies e.g., precipitation and runoff. In this work, in order to detect possible rainfall trends in the Calabria region (southern Italy), a gridded database has been obtained from a rainfall data set of 129 monthly series collected for the period 1951–2016. In particular, the Inverse Distance Weighed was applied to build 603 rainfall grid series with a spatial resolution of 5 km × 5 km and, for each grid point, the monthly, seasonal and annual rainfall series were analyzed with the Mann–Kendall non-parametric test and the Theil–Sen estimator. Results showed a decreasing trend for the annual and winter–autumn rainfall and an increasing trend for the summer one.

Assessing Smallholder Farmers’ Perception on Climate Variability in Relation to Climatological Evidence: A Case Study of Benguene in the Sudanian Zone of Mali

Sudanian zone is one of the regions in West Africa most confronted with high climate variability, challenging agricultural sustainability and rural livelihoods. Despite multiple research efforts devoted to exploring how farmers in this region perceive climate variability and how perceptions relate to adaptive responses, much remains to be done. This study examined farmers’ perception of climate variability compared with scientific evidence in the terroir of Benguene, between 1983 and 2018. Data was collected from twenty-nine (29) farmland heads (representing 49% of the total farmland heads in the village) through a survey conducted in October 2019. Monthly temperature data from ground measurement and daily rainfall data extracted from the gridded African Rainfall Climatology version 2 (ARC-2) time series was used. Descriptive statistic was used to analyse survey data. Climate data analysis included Mann Kendall trend analysis and computation of growing season parameters (Onset, Duration and Cessation) and its trend. In the end, a conformity index was used to compare farmers’ perceptions to climatological evidence. Conformity graph showed that apart from temperature, the farmers have low, to a weak perception of the other variables used to assess their perception. Results showed that many smallholder farmers have observed a rise in temperature (90%), delay onset (93%) and early cessation (93%) of rainfall. The analysis of the climate data showed high variability in the climate during the study period as observed in the increasing trends in temperature (p-value of 0.0007) and in the rainfall (p-value of 0.0001). Concerning the season parameters, the results show increasingly early-onset (June 1st ± 22 days) increasingly late cessation (October 09th ± 14 days) and consequently a long duration of the rainy season (130 ± 27 days). Conversely, farmers thought the opposite. These results imply the urgent need to increase smallholder farmers awareness of climate variability and change by providing climate information for better adaptation

RN-Net: A Deep Learning Approach to 0-2 Hour Rainfall Nowcasting Based on Radar and Automatic Weather Station Data.

Precipitation has an important impact on people’s daily life and disaster prevention and mitigation. However, it is difficult to provide more accurate results for rainfall nowcasting due to spin-up problems in numerical weather prediction models. Furthermore, existing rainfall nowcasting methods based on machine learning and deep learning cannot provide large-area rainfall nowcasting with high spatiotemporal resolution. This paper proposes a dual-input dual-encoder recurrent neural network, namely Rainfall Nowcasting Network (RN-Net), to solve this problem. It takes the past grid rainfall data interpolated by automatic weather stations and doppler radar mosaic data as input data, and then forecasts the grid rainfall data for the next 2 h. We conduct experiments on the Southeastern China dataset. With a threshold of 0.25 mm, the RN-Net’s rainfall nowcasting threat scores have reached 0.523, 0.503, and 0.435 within 0.5 h, 1 h, and 2 h. Compared with the Weather Research and Forecasting model rainfall nowcasting, the threat scores have been increased by nearly four times, three times, and three times, respectively.

Evaluation of the most proper management scale on water use efficiency and water productivity: A case study of the Heihe River Basin, China

Increasing agricultural water use efficiency (WUE) and water productivity (WP) is imperative to alleviate water scarcity and achieve sustainable agriculture in inland river basins. The confusion on the use of the two indicators at demarcated scales in this issue has been addressed. Analysis of the literature, however, lacks evaluation of the most proper management scale. The Heihe River Basin (HRB) in China was selected as the study area and WUE and WP were calculated spatially at the field crop, irrigation district, and county scales using the remote sensing-based evapotranspiration (ET), grid rainfall, land use, and statistical data. In addition, the most proper management scale was firstly evaluated by comparing WUE and WP based on statistical properties and spatial distributions. The results show that higher WUE and WP were observed at the field crop scale. The differences between WUE and WP are significant at field crop scale but the gap narrows with increasing spatial scale. Spatial analysis reveals that WP was higher in the plain oasis area and lower in the mountainous and desert oasis area. Among three scales, the WUE and WP are relatively lower and the variances of them are relatively larger at the irrigation district scale. WUE ranges from 0.1 to 0.85 (mean 0.34) and WP varies from 0.08 kg/m3 to 0.8 kg/m3 (mean 0.48 kg/m3) across irrigation districts in the whole basin. The large differences indicate the presence of a tremendous management gap among irrigation districts. We thus declare that mesoscale views are more suitable and irrigation district scale is the most proper management scale for resolving water resource utilization problems in arid river basins. Further, agricultural water management at the irrigation district scale should target at improving irrigation technology and adjusting crop structure.

Advanced Rainfall Trend Analysis of 117 Years over West Coast Plain and Hill Agro-Climatic Region of India

In this paper, the rainfall trend of the West Coast Plain and Hill Agro-Climatic Region is analyzed for 117 years (1901–2017). This region is a globally recognized biodiversity hotspot and known for one of the highest rainfall receiving regions in India. Rainfall grid dataset is used for the analysis of rainfall trends on monthly, seasonal, and decadal time scales. Modified Mann–Kendall’s test, Linear Regression, Innovative Trend Analysis, Sen’s Slope test, Weibull’s Recurrence Interval, Pearson’s Coefficient of Skewness, Consecutive Disparity Index, Kurtosis, and some other important statistical techniques are employed for trend analysis. Results indicate that the rainfall trend is significant in January, July, August, September as well as the Winter season. Among all the significant trends, January and July showed a decreasing rainfall trend. July has the highest contribution (30%) among all the obtained monotonic trend to annual rainfall and coincidentally has the highest trend magnitude. August and September months with a combined contribution of 30% to annual rainfall, show an increasing monotonic trend with high magnitude whereas Winter season shows a monotonic decreasing rainfall trend with comparatively low magnitudes. Decadal analysis along with the study of recurrence interval of excess and deficit years helps to understand the decadal rhythm of trend and the magnitude of extreme monthly and seasonal events. Skewness reveals that rainfall dataset of all the periodic results is right-skewed and the recurrence interval also supports the skewness results. Sharply decreasing rainfall in July and rising rainfall in August and September is predictive of the impact on agriculture, biodiversity and indicates the rainfall regime shift in the region.

Evaluation of the impact of climate change on the characteristics of drought in Sahel Region of Nigeria: 1971–2060

ABSTRACT The impact of climate change on the agricultural and water resources sectors cannot be overemphasized since West Africa is prone to drought as a result of high variability in rainfall and temperature. In this study, an assessment on the impact of climate change in the Sahel Region of Nigeria (SRN) on drought was carried out between 1971 and 2060. Gridded temperature and rainfall datasets from Climate Research Unit (CRU) and seven Global Climate Models (GCMs) using regional climate model (RCA-4) under Representative Concentration Pathway (RCP) 8.5 scenario were used. Standardized Precipitation and Evapotranspiration Index (SPEI) and the Standardized Precipitation Index (SPI) at 3- and 12-month time steps were used for drought characterization. The RCA-4 ensemble of the 7 GCMs was able to pattern the seasonal cycle of the maximum and minimum temperatures of SRN well as most of the climatic features were captured as indicated by the CRU observation. Furthermore, the results indicated by the RCA-4 ensemble median of SPEI-3 and SPEI-12 show more drought intensity changes than SPI-3 and SPI-12 as the twenty-first century progresses. An awareness that can help policymakers know the impact climate change may have on the agricultural and hydrological sectors of SRN was created.

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.