Water Requirement Satisfaction Index Research Articles

Satellite rainfall bias correction incorporating effects on simulated crop water requirements

ABSTRACT Satellite rainfall estimates (SRE) offer spatial-temporal rainfall representations in regions with limited ground-based gauge rainfall measurements. However, differences exist between SRE and gauge measured rainfall, which needs assessment and reduction. This study presents a method to correct errors in SRE to make their use in agro-hydrological applications and models meaningful. The main scientific objective is the determination of effective window sizes for SRE bias correction. To conclude on effective window sizes, the crop water requirement satisfaction index (WRSI) for gauged rainfall, uncorrected SRE and bias corrected SRE were estimated and propagation effects of SRE errors on respective WRSI estimates were assessed. WRSI indicates how much of the crop water needs are satisfied by rainfall. The Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) SRE was bias corrected using gauged rainfall data from 20 stations in the Lake Victoria basin of Kenya from 2012 to 2018. The results show that the error in WRSI can serve to determine effective window sizes for SRE bias correction rather than using SRE bias error itself. This proposed correction method resulted in improved estimates of WRSI.

Calibração do modelo SARRA-H para o zoneamento de risco climático do caupi na Amazônia Oriental

ABSTRACT This study aimed to calibrate and test the SARRA-H (Système d’Analyse Régionale des Risques Agroclimatologiques) crop model for cowpea, as well as conducting a climate risk zoning for this crop in a region located in Eastern Amazon, allowing the identification of locations and sowing dates that favor the production considering both the water deficit and the probability of occurrence of severe rains during the harvest period. The model was calibrated and validated with data from experiments conducted between 2013 and 2016 in the municipality of Castanhal, PA, Brazil. Low climate risk areas were defined as those that had a water requirement satisfaction index (WRSI) greater than or equal to 0.5 in the reproductive phase combined with the occurrence of rainfall below 20 mm at the harvest for, at least, 80% of the years for which planting was simulated. The model was able to simulate the water balance, growth and development of cowpea under the climate and soil conditions of the studied location with high precision and accuracy. The optimal period for sowing cowpea comprises the interval between June 5th and 25th for regions located above 2° S and between March 25th and April 15th for regions below 2º S.

Identification of Rainfall Deviation Thresholds Based on WRSI for Monitoring Water Stress in Pulses

The study was conducted to identify rainfall deviation thresholds based on water requirement satisfaction index (WRSI) in pulses for monitoring water stress due to drought during kharif season in Telangana. The districts of Medak, Warangal and Mahabubnagar with major areas under blackgram, greengram and redgram crops, respectively were considered as study area. The rainfall deviation frequency was estimated in 20 representative sites under each crop and accordingly WRSI was calculated. The rainfall and WRSI estimated were related for their dependability in the estimation of water stress occurring due to drought during kharif season in the crops under study. Based on rainfall frequency, the rainfall was deficit for blackgram and normal for greengram and redgram in most of the study period. WRSI predicted mediocre growth of blackgram and average to good growth of greengram and redgram. The total explained variation in the prediction of crop performance by WRSI index in relation to rainfall was 0.26 to 0.77 for blackgram, 0.57 to 0.96 for redgram, 0.35 to 0.94 for greengram during the study period. The rainfall deviation thresholds were identified irrespective of soil type and depth in blackgram. Whereas, in case of greengram and redgram, the thresholds varied with soil type and depth.

Probabilistic climate risk assessment in rainfed wheat yield: Copula approach using water requirement satisfaction index

The trend of population growth and the consequent need to increase agricultural production in order to provide for the necessary food is critical topic nowadays. To account for the risks and damage caused by changes in weather and climate constrains, it is important to know how these changes affect the agricultural productivity. Wheat is the world's most widely grown crop and is directly related to worldwide food security issues. As a result, it has always been of interest to researchers as a strategic crop. In this study, the correlation of the Water Requirement Satisfaction Index in six growing periods of rainfed wheat with its yield was examined and found that the highest correlation is seen over the entire growing season. Based on statistical distributions and copula functions, the sensitivity and rainfed wheat yield to the Water Requirement Satisfaction Index was examined. Clayton Copula was selected based on AIC and RMSE evaluation indices. The probability of having crop losses is around 50%, in general. The results showed that the yield risk of rainfed wheat in the Tabriz region drops to 46% under low climatic risk conditions and reaches to 98% under medium and high climate risk conditions. The results allow farmers and stakeholders to better plan and manage food security by knowing the changes in rainfed wheat yield depending on weather conditions. By using different options under different conditions such as wet events, it is also possible to work towards higher yield amounts of rainfed wheat and other rainfed crops.

Improvements and Evaluation of the Agro-Hydrologic VegET Model for Large-Area Water Budget Analysis and Drought Monitoring

We enhanced the agro-hydrologic VegET model to include snow accumulation and melt processes and the separation of runoff into surface runoff and deep drainage. Driven by global weather datasets and parameterized by land surface phenology (LSP), the enhanced VegET model was implemented in the cloud to simulate daily soil moisture (SM), actual evapotranspiration (ETa), and runoff (R) for the conterminous United States (CONUS) and the Greater Horn of Africa (GHA). Evaluation of the VegET model with independent data showed satisfactory performance, capturing the temporal variability of SM (Pearson correlation r: 0.22–0.97), snowpack (r: 0.86–0.88), ETa (r: 0.41–0.97), and spatial variability of R (r: 0.81–0.90). Absolute magnitudes showed some biases, indicating the need of calibrating the model for water budget analysis. The seasonal Landscape Water Requirement Satisfaction Index (L-WRSI) for CONUS and GHA showed realistic depictions of drought hazard extent and severity, indicating the usefulness of the L-WRSI for the convergence of an evidence toolkit used by the Famine Early Warning System Network to monitor potential food insecurity conditions in different parts of the world. Using projected weather datasets and landcover-based LSP, the VegET model can be used not only for global monitoring of drought conditions, but also for evaluating scenarios on the effect of a changing climate and land cover on agriculture and water resources.

Impact of Climate Change on Cassava Yield in Guapimirim, State of Rio de Janeiro, Southeast Brazil

Global warming has changed the climate in many parts of the world and affected ecosystems due to anthropogenic greenhouse gas (GHG) emissions that contribute to the rise in the surface temperature of the planet. Rising temperatures have important effects on agriculture, which accounts for one- third of Brazil's economy. This study assesses the impact of climate change over agriculture on cassava yield in Guapimirim city, State of Rio de Janeiro, Southeast Brazil. Bias corrected climate simulation performed from the nested model Eta-HadGEM2-ES was used to reproduce the climate data observed in the region and for climate projections under Representative Concentration Pathways (RCPs) 4.5 and 8.5 scenarios. Simulated rainfall and evapotranspiration for the period between 1961 and 1990 was compared to the observation period and, showed a correlation with R² = 0.99 and the Average Absolute Percentage Error was less than 5.0%. The effect of climate projections on water stress during crop development was estimated using the Thornthwaite-Mather (TM) soil water balance adapted for crops. Rainfall and actual evapotranspiration projections for the three thirty-year periods 2011-2040, 2041-2070, 2071-2100 served as the basis for the assessment of the Water Requirement Satisfaction Index (WRSI) and Yield Reduction (YR) for cassava crop. Projections show significant cassava yield losses around 8.6 and 9.7 ton ha-1, respectively, under RCPs 4.5 and 8.5. This approach allows exploratory analysis applied to support crop management decision-making and irrigation strategies for sustainable agriculture and to increase crop yield in the face of impacts of climate change.

An Improved Climatological Forecast Method for Projecting End-of-Season Water Requirement Satisfaction Index

Abstract A simple—yet powerful—indicator for monitoring agricultural drought is the water requirement satisfaction index (WRSI). In data-sparse, food-insecure areas, the WRSI is used to guide billions of dollars of aid every year. The WRSI uses precipitation (PPT) and reference evapotranspiration (RefET) data to estimate water availability relative to water demand experienced over the course of a growing season. If the season is in progress, to-date conditions can be combined with climatological averages to provide insight into potential end-of-season (EOS) crop performance. However, if the average is misrepresented, these forecasts can hinder early warning and delay precious humanitarian aid. While many agencies use arithmetic average climatologies as proxies for “average conditions,” little published research evaluates their effectiveness in crop-water balance models. Here, we use WRSI hindcasts of three African regions’ growing seasons, from 1981 to 2019, to assess the adequacy of the arithmetic mean climatological forecast—the Extended WRSI. We find that the Extended WRSI is positively biased, overestimating the actual EOS WRSI by 2%–23% in East, West, and southern Africa. The presented alternative combines to-date conditions with data from previous seasons to produce a series of historically realistic conclusions to the current season. The mean of these scenarios is the WRSI Outlook. In comparison with the Extended WRSI, which creates a single forecast scenario using average inputs that are not covarying, the WRSI Outlook employs an ensemble of scenarios, which more adequately capture the historical distribution of distribution of rainfall events along with the covariability between climate variables. More specifically, the impact of dry spells in individual years is included in the WRSI Outlook in a way that is smoothed over in the Extended WRSI. We find that the WRSI Outlook has a near-zero bias score and generally has a lower RMSE. In total, this paper highlights the inadequacies of the arithmetic mean climatological forecast and presents a less biased and more accurate scenario-based approach. To this end, the WRSI Outlook can improve our ability to identify agricultural drought and the concomitant need for humanitarian aid.

Maize sowing dates in the hinterland region of Northeast Brazil

Maize in Alagoas State, Northeast of Brazil, is mostly cultivated under rainfed conditions, however there are few studies evaluating the best sowing dates. The aim of this work was to assess sowing dates for the maize crop in the municipality of Arapiraca, located in a typical hinterland (Agreste) region of Alagoas State, based on the Water Requirement Satisfaction Index (WRSI). The WRSI was computed by means of a simple soil water balance model and the reference evapotranspiration was determined by the standard Penman-Monteith-FAO56 method. Daily values of air temperature, relative humidity, wind speed, sunshine duration and precipitation were measured in a conventional weather station, in the period from 1973 to 2008. The sowing dates for maize in Arapiraca with the lowest risks of water deficit lies between June 3 and June 23. Sowing maize in the beginning of April appears to be a very strategic alternative since it offers very low water deficit risk at the flowering crop stage. However, this might require water supplement at first stages of the crop due to the high-water deficit risks.

Using real-time mobile phone data to characterize the relationships between small-scale farmers’ planting dates and socio-environmental factors

Accurate and operational indicators of the start of growing season (SOS) are critical for crop modeling, famine early warning, and agricultural management in the developing world. Erroneous SOS estimates–late, or early, relative to actual planting dates–can lead to inaccurate crop production and food-availability forecasts. Adapting rainfed agriculture to climate change requires improved harmonization of planting with the onset of rains, and the rising ubiquity of mobile phones in east Africa enables real-time monitoring of this important agricultural decision. We investigate whether antecedent agro-meteorological variables and household-level attributes can be used to predict planting dates of small-scale maize producers in central Kenya. Using random forest models, we compare remote estimates of SOS with field-level survey data of actual planting dates. We compare three years of planting dates (2016–2018) for two rainy seasons (the October-to-December short rains, and the March-to-May long rains) gathered from weekly Short Message Service (SMS) mobile phone surveys. In situ data are compared to SOS from the Water Requirement Satisfaction Index (SOSWRSI) and other agro-meteorological variables from Earth observation (EO) datasets (rainfall, NDVI, and evaporative demand). The majority of farmers planted within 20 days of the SOSWRSI from 2016 to 2018. In the 2016 long rains season, many farmers reported planting late, which corresponds to drought conditions. We find that models relying solely on EO variables perform as well as models using both socio-economic and EO variables. The predictive accuracy of EO variables appears to be insensitive to differences in reference periods that were tested for deriving EO anomalies (1, 3, 5, or 10 years). As such, it would appear that farmers are either responding to short-term weather conditions (e.g., intra-seasonal variability), or longer trends than were included in this study (e.g., 25–30 years), when planting. The methodologies used in this study, weekly SMS surveys, provide an operational means for estimating farmer behaviors–information which is traditionally difficult and costly to collect.

Water requirement satisfaction index (WRSI) under south-Telangana agro-climatic conditions

Water requirement satisfaction index (WRSI) under south-Telangana agro-climatic conditions

Monitoring of water requirement satisfaction index for pearl millet

Monitoring of water requirement satisfaction index for pearl millet

Satellite rainfall bias assessment for crop growth simulation – A case study of maize growth in Kenya

In this research, the performance of satellite rainfall estimates (SREs) for crop growth simulation was investigated. Rainfall products selected were CHIRPS 2.0, CMORPH 1.0, MSWEP 2.2, and RFE 2.0. In-situ rainfall from 20 stations within the Lake Victoria basin in Kenya served as reference. Rainfall products were evaluated for onset days, rainfall depths, dry spells, and rainfall occurrence for four crop growth stages. Assessment was on a daily time step for the period 2012–2018 and on a point-to-pixel basis. Results showed that SREs exhibit large variation in timing of rainfall arrival. SREs exhibited largest interannual and spatial spreads in representing dry spell length during the flowering stage with CMORPH and CHIRPS showing best and weakest results, respectively. Bias of SREs in representing dry spells was smaller during early growth stages. Detecting rainfall occurrence by the SREs weakened as the growing season progressed. MSWEP, followed by RFE2, produced the best results in detecting rainfall events, while falsely detected rainfall was frequent in CHIRPS, particularly in later growth stages. SREs generally performed better during a wet than a dry growing season. SREs indicated less bias in rainfall depths during the early stages of crop growth but deteriorated at later stages. MSWEP and CMORPH exhibited the least and highest interannual spread in relative bias, respectively. In associating biases to severe and extreme water stress, based on crop water requirement satisfaction index, effects were more prevailing in the ripening than flowering stages. Findings of this study suggest that SREs can serve as input to crop growth modelling, but validation of SREs with rain gauge observed counterparts is essential.

Phenological Water Balance Applications for Trend Analyses and Risk Management

The overarching goal of this work is to develop and demonstrate methods that support effective agro-pastoral risk management in a changing climate. Disaster mitigation strategies, such as the Sendai Framework for Disaster Risk Reduction (SFDRR), emphasize the need to address underlying causes of disaster risk and to prevent the emergence of new risks. Such assessments can be difficult, because they require transforming changes in meteorological outcomes into sector-specific impact. While it is common to examine trends in seasonal precipitation and precipitation extremes, it is much less common to study how these trends interact with crop and pasture water needs. Here, we show that the Water Requirement (WR) component of the widely used Water Requirement Satisfaction Index (WRSI) can be used to enhance the interpretation of precipitation changes. The WR helps answer a key question: was the amount of rainfall received in a given season enough to satisfy a crop or pasture's water needs? Our first results section focuses on analyzing spatial patterns of climate change. We show how WR values can be used to translate east African rainfall declines into estimates of crop and rangeland water deficits. We also show that increases in WR, during recent droughts, has intensified aridity in arid regions. In addition, using the PWB, we also show that precipitation increases in humid areas of western east Africa have been producing increasingly frequent excessive rainfall seasons. The second portion of our paper focuses on assessing temporal outcomes for a fixed location (Kenya) to support drought-management scenario development. Kenyan rainfall is decreasing and population is increasing. How can we translate this data into actionable information? The United Nations and World Meteorological Organization advise nations to proactively plan for agro-hydrologic shocks by setting aside sufficient grain and financial resources to help buffer inevitable low-crop production years. We show how precipitation, WR, crop statistics, and population data can be used to help guide 1-in-10 and 1-in-25-year low crop yield scenarios, which could be used to guide Kenya's drought management planning and development. The first and second research components share a common objective: using the PWB to translate rainfall data into more actionable information that can inform disaster risk management and development planning.

An Agro-Pastoral Phenological Water Balance Framework for Monitoring and Predicting Growing Season Water Deficits and Drought Stress

Sharing simple ideas across a broad community of practitioners helps them to work together more effectively. For this reason, drought early warning systems spend a considerable effort on describing how hazards are detected and defined. Well-articulated definitions of drought provide a shared basis for collaboration, response planning, and impact mitigation. One very useful measure of agricultural drought stress has been the “Water Requirement Satisfaction Index” (WRSI). In this study, we develop a new, simpler metric of water requirement satisfaction, the Phenological Water Balance (PWB). We describe this metric, compare it to WRSI and yield statistics in a food-insecure region (east Africa), and show how it can be easily combined with analog-based rainfall forecasts to produce end-of-season estimates of growing season water deficits. In dry areas, the simpler PWB metric is very similar to the WRSI. In these regions, we show that the coupling between rainfall deficits and increased reference evapotranspiration amplifies the impacts of droughts. In wet areas, on the other hand, our new metric provides useful information about water excess—seasons that are so wet that they may not be conducive to good agricultural outcomes. Finally, we present a PWB-based forecast example, demonstrating how this framework can be easily used to translate assumptions about seasonal rainfall outcomes into predictions of growing season water deficits. Effective humanitarian relief efforts rely on early projections of these deficits to design and deploy appropriate targeted responses. At present, it is difficult to combine gridded satellite-gauge precipitation forecasts with climate forecasts. Our new metric helps overcome this obstacle. Future extensions could use the water requirement framework to contextualize other water supply indicators, like actual evapotranspiration values derived from satellite observations or hydrologic models.

The association between agricultural conditions and multiple dimensions of undernutrition in children 6-23 months of age in Burkina Faso

Background. The quality and quantity of food available to children affect their nutritional status, with implications for long-term health and development. In Burkina Faso, households rely on rainfed agriculture, but climate change is making crop production unreliable. We explore spatial patterns of growing season quality on dimensions of nutritional status and complementary feeding practices in children 6–23 months. Methods. The 2017 Performance Monitoring and Accountability 2020 (PMA2020) nutritional survey was spatially integrated with a contemporaneous remotely sensed drought indicator, the Water Requirement Satisfaction Index (WRSI), which captures local anomalous growing season conditions. Multi-level mixed-effects logistic regression models were estimated to explore the effects of WRSI on child mid-upper arm circumference (MUAC) score (indicating malnutrition), and two components of complementary feeding practices, adjusting for demographic and household characteristics. Results. The data set included 1,721 children. Higher WRSI values (better agricultural conditions and crop performance) were associated with 3% lower odds of malnutrition (Odds Ratio (OR) = 0.971; 95% confidence interval (CI): [0.942, 1.00]) and 7% higher odds of a child attaining minimum dietary diversity (OR = 1.07; 95% CI: [1.01, 1.14]). Undernourished mothers were significantly (p < 0.001) more likely to have an undernourished child. Minimum dietary diversity met for the child was protective against malnutrition; the association between WRSI and malnutrition persisted after adjustment. Conclusions. WRSI was associated with the child’s dietary diversity and malnutrition, highlighting the importance of seasonally and spatially varying local agricultural production and the relationship between growing season conditions and child nutritional status, with dietary diversity providing a potential mechanism for intervention.

Climate risk to peanut cultivation in Brazil across different planting seasons.

Peanuts are widely grown in Brazil because of their great importance in the domestic vegetable oil industry and the succession of sugarcane, soybean and maize crops, contributing to soil conservation and improvement in agricultural areas. Thus, the present study aimed to determine the zoning of peanuts' climatic risk by estimating the water requirement satisfaction index (WRSI) for the crop in Brazil. We used a historical series of data on average air temperature and rainfall between 1980 and 2016. Reference evapotranspiration was estimated using the method of Thornthwaite, and we subsequently calculated crop evapotranspiration and maximum evapotranspiration. Water balances for all stations were calculated using the method of Thornthwaite and Mather, with an available water capacity in the soil of 15, 30 and 45 mm. The definitions of suitable, unfit and restricted areas and the planting season were performed using the WRSI. Brazil has low climatic risk areas for growing peanuts throughout the year, except for winter. The country reveals that 88.19%, 97.93%, 99.16% and 39.25% of its area is suitable for planting peanuts on planting dates in spring, summer, autumn and winter, respectively. Brazil has a large part of the areas favorable to the planting of peanuts. The maximum availability of soil water at a depth of 15, 30 and 45 mm does not influence regions with respect to peanut growing in Brazil. The states of Piauí, Ceará and Bahia are the most unsuitable on the winter planting date, with an average WRSI of 0.22. © 2021 Society of Chemical Industry.

Impacts of Climate Variability and Drought on Surface Water Resources in Sub-Saharan Africa Using Remote Sensing: A Review

Climate variability and recurrent droughts have caused remarkable strain on water resources in most regions across the globe, with the arid and semi-arid areas being the hardest hit. The impacts have been notable on surface water resources, which are already under threat from massive abstractions due to increased demand, as well as poor conservation and unsustainable land management practices. Drought and climate variability, as well as their associated impacts on water resources, have gained increased attention in recent decades as nations seek to enhance mitigation and adaptation mechanisms. Although the use of satellite technologies has, of late, gained prominence in generating timely and spatially explicit information on drought and climate variability impacts across different regions, they are somewhat hampered by difficulties in detecting drought evolution due to its complex nature, varying scales, the magnitude of its occurrence, and inherent data gaps. Currently, a number of studies have been conducted to monitor and assess the impacts of climate variability and droughts on water resources in sub-Saharan Africa using different remotely sensed and in-situ datasets. This study therefore provides a detailed overview of the progress made in tracking droughts using remote sensing, including its relevance in monitoring climate variability and hydrological drought impacts on surface water resources in sub-Saharan Africa. The paper further discusses traditional and remote sensing methods of monitoring climate variability, hydrological drought, and water resources, tracking their application and key challenges, with a particular emphasis on sub-Saharan Africa. Additionally, characteristics and limitations of various remote sensors, as well as drought and surface water indices, namely, the Standardized Precipitation Index (SPI), Palmer Drought Severity Index (PDSI), Normalized Difference Vegetation (NDVI), Vegetation Condition Index (VCI), and Water Requirement Satisfaction Index (WRSI), Normalized Difference Water Index (NDWI), Modified Normalized Difference Water Index (MNDWI), Land Surface Water Index (LSWI+5), Modified Normalized Difference Water Index (MNDWI+5), Automated Water Extraction Index (shadow) (AWEIsh), and Automated Water Extraction Index (non-shadow) (AWEInsh), and their relevance in climate variability and drought monitoring are discussed. Additionally, key scientific research strides and knowledge gaps for further investigations are highlighted. While progress has been made in advancing the application of remote sensing in water resources, this review indicates the need for further studies on assessing drought and climate variability impacts on water resources, especially in the context of climate change and increased water demand. The results from this study suggests that Landsat-8 and Sentinel-2 satellite data are likely to be best suited to monitor climate variability, hydrological drought, and surface water bodies, due to their availability at relatively low cost, impressive spectral, spatial, and temporal characteristics. The most effective drought and water indices are SPI, PDSI, NDVI, VCI, NDWI, MNDWI, MNDWI+5, AWEIsh, and AWEInsh. Overall, the findings of this study emphasize the increasing role and potential of remote sensing in generating spatially explicit information on drought and climate variability impacts on surface water resources. However, there is a need for future studies to consider spatial data integration techniques, radar data, precipitation, cloud computing, and machine learning or artificial intelligence (AI) techniques to improve on understanding climate and drought impacts on water resources across various scales.

Evaluation and validation of TAMSAT‐ALERT soil moisture and WRSI for use in drought anticipatory action

AbstractReliable information on the likelihood of drought is of crucial importance in agricultural planning and humanitarian decision‐making. Acting based upon probabilistic forecasts of drought, rather than responding to prevailing drought conditions, has the potential to save lives, livelihoods and resources, but is accompanied by the risk of acting in vain. The suitability of a novel forecasting tool is assessed in the present paper in terms of its ability to provide skilful information of the likelihood of drought impacts on crops and pasture within a timeframe that allows for anticipatory action. The Tropical Applications of Meteorology using SATellite data—AgriculturaL Early waRning sysTem (TAMSAT‐ALERT) tool provides forecasts of seasonal mean soil moisture and the water requirement satisfaction index (WRSI). TAMSAT‐ALERT metrics were found to be strongly correlated with pasture availability and maize yield in Kenya and provided skilful forecasts early in key seasons, allowing sufficient time for preparatory actions. Incorporating TAMSAT‐ALERT forecasts in a layered approach, with actions triggered by spatiotemporally varying triggers and fundamentally informed by humanitarian actors, will provide reliable information on the likelihood of drought, ultimately mitigating food insecurity.

The use of Water Requirement Satisfaction Index for assessing agricultural drought on rain-fed maize, in the Luvuvhu River catchment, South Africa

Analysis of drought on rain-fed maize production in the Luvuvhu River Catchment was measured using the Water Requirement Satisfaction Index (WRSI). Computation of WRSI was performed using a crop water balance model in Instat+ software for a 120-day maturing maize crop using nine different planting dekads from October to December. This study made use of seven weather stations, which adequately represent climatic and geographical environments of the catchment, containing historical rainfall and temperature data from 1974 to 2015. The non-parametric Spearman’s Rank Correlation test was conducted to determine drought trends. Thereafter, probabilities and extreme widespread dry and wet agricultural seasons were analysed using STATISTICA software. Results revealed that since the 1980s, the catchment had been subjected to frequent drought conditions, with an average frequency of once every two to three seasons. Extreme drought episodes (WRSI <50) were commonly identified in the northern drier parts of the catchment as compared to the high rainfall region, whereby 50-93% of the analysed seasons were subjected to extreme drought conditions. Furthermore, there were no drought trends (ρ values close to 0) during the analysed climate period. Water Requirements Satisfaction Index (WRSI) values corresponding to more intense drought conditions were reflected during the December planting date for all stations. Moreover, at the low rainfall region, it was seen that extreme droughts occurred once in five seasons, regardless of the planting date. Thus, the study led to a recommendation of using October-November as the optimum planting date in the catchment. Furthermore, farmers located areas with high probabilities of drought during critical stages can be advised to supplement rain-fed farming with irrigation should they be located nearby to rivers.

Optimizing the Best Sowing Week by Employing Water Requirement Satisfaction Index for Maize and Sorghum for Selected Districts of Tamil Nadu

Optimizing the Best Sowing Week by Employing Water Requirement Satisfaction Index for Maize and Sorghum for Selected Districts of Tamil Nadu