Spatio-temporal Trends of Standardized Precipitation Index for Meteorological Drought Analysis across Agroclimatic Zones of India

1. Introduction:
Drought is a normal part of climate of India and every year it affects one or the other State. Droughts, like other meteorological phenomena, have spatial and temporal characteristics that vary significantly from one region to another. The understanding of the spatio-temporal trends of meteorological drought helps in undertaking informed decisions on their preparedness and mitigation measures. Though no significant trends have been reported in the Indian Summer Monsoon Rainfall (IMSR) over long periods, the spatio-temporal trends in drought indices reveals the anomaly in rainfall across regions over different time scales which may be related to climate change induced extreme rainfall events. So, a study was carried out to compute spatio-temporal trends in Standardized Precipitation Index (SPI), an index of rainfall anomaly, using gridded monthly rainfall datasets of CRU TS 3.0 for the 1951 – 2006 period for Indian landmass and the results are reported here.

2. Methodology:
SPI is simply the difference in standardized precipitation from its mean for a specified time period divided by the standard deviation. As precipitation is typically not normally distributed for accumulation periods of 12 months or less, SPI overcomes this disadvantage by fitting an incomplete gamma distribution and then transforming it to normal distribution. Negative values of SPI due to less than normal rainfall indicate dryness while SPI less than -1 indicate drought. Delineation of homogeneous regions for climate change / trend analysis has been a debatable matter due to unwise delineation of the homogeneous regions based on a single climatic variable, mostly the isohyets. Therefore, in this study 14 Agroclimatic Zones (ACZs) of India were selected for SPI trend analysis as homogeneous regions (Fig 1) due to commonality of climatic parameters and their extremes, soil types and water resources. The results of drought trends at ACZ level also can be directly translated into plans for agricultural sectors. SPI were computed for individual months (June, July, August and September) and for the whole Indian summer monsoon duration (June-July-August-September i.e. JJAS). Mean SPIs of various ACZs for the individual months and JJAS over a 56 year period were analyzed for temporal trends using the Mann Kendall test and regional temporal trends across all ACZ together using the Regional Kendall test.

3. Results:
Significant temporal trends in monthly & JJAS SPI at 10% or lower level of significance were observed for ACZ4 (Middle Gangetic plains), ACZ5 (Upper Gangetic plains), ACZ6 (Trans Gangetic plains), ACZ7 (Eastern plateau and hills), ACZ8 (Central plateau and hills), ACZ12 (West coast) and ACZ14 (Western dry) regions. The rest of the ACZs did not show any significant trend in SPI for the 56 year study period. In ACZ4, SPI showed a consistently negative trend for JUN, JUL, AUG and JJAS, whereas in ACZ5, SPI showed a significant negative trend for JUL, AUG & JJAS. SPI showed a significant negative trend in SEP & JJAS for ACZ7, AUG & JJAS in ACZ8, JUL in ACZ12 and AUG in ACZ14. On the other hand, a significantly positive trend in SPI was observed in JUN in ACZ6, ACZ8, and ACZ12. 
The analysis of temporal trends in SPI for all ACZs taken together using the Regional Kendall test showed a significant positive trend in JUN SPI, while significant negative trends in SPI were observed for JUL, AUG and JJAS across India. No significant trend was observed for SEP SPI. The rate of increase in JUN SPI was 0.75E-02 per year, while the rate of change of SPI for JUL, AUG, & JJAS was -0.76E-02, -0.54E-02, and -0.65E-02 per year, respectively.

4. Conclusions:
Among the different ACZ of India, there was an increase in probability of meteorological drought hazard in ACZ4, ACZ5, ACZ7 and ACZ8 covering the States of Bihar, Uttar Pradesh, Madhya Pradesh, Orissa and Eastern Rajasthan. Results also point to a significant decreasing trend in rainfall in these regions for different months as well as for the JJAS monsoon period. In the dry western parts of India (Western Rajasthan and Gujarat), which are traditionally water scare regions, there is no change in probability of occurrence of meteorological drought. 
For India as a whole, there is an increase in probability of meteorological drought in the future due to decreasing trends in rainfall for the JJAS period. The months of July and August will become drier, while June will become wetter. This change in rainfall distribution towards the early period and overall drier months of July and August have an important implication for the productivity of the main crop season of India thus impacting its food security negatively

Impact of Climate Change on Northeast Monsoon System of India - Role of Siberian Teleconnection

The Northeast monsoon (NEM) precipitation has undergone a significant change in pattern over the last half century. The changing pattern of climatic variables over the moisture source (i.e. west Pacific High) and the heat sink (Siberian High) of the winter monsoon dynamics over time has caused a shift in the pattern of NEM precipitation over the southern peninsular region of India. There is no significant trend in NEM precipitation in the Indian region whereas the surface temperature has a significantly increasing trend over India during the NEM season. There is no significant trend in outgoing long wave radiation (OLR) of Siberian High (SH) while the west Pacific High (WPH) has a significantly increasing trend in OLR. Surface pressure of both the SH & WPH has no significant trend for the last 59 years (1948-2006). Surface temperature over SH & WPH has a significantly increasing trend for the last 59 years (1948-2006).There is a high correlation of NEM precipitation with El Nino & Southern Oscillation (ENSO), Indian Ocean Dipole(IOD) and OLR of WPH during the period 1970-2000. It signifies that convectional activity in the moisture source region of the NEM, warm SST in the western Indian Ocean and the ENSO have a deep bearing on the NEM precipitation during the three decades 1970-2000. The correlation of NEM precipitation with ENSO, IOD during the last period 2000-2006 has undergone changes where the NEM precipitation has shown a shift that is negatively correlated with ENSO & IOD. The change is much more in IOD than ENSO which signifies that the conventional trend of bearing of warm or cold SST of West Indian ocean on NEM precipitation has decreased during this period of 2000-2006. The correlation of NEM precipitation with the convectional activity of the moisture sink region of the NEM has been gradually decreasing since the 1970s and the moisture source of NEM has a significantly decreasing convectional activity trend. The correlation of NEM precipitation with all three variables (OLR, surface pressure & surface temperature) has shown a comparatively higher value for the heat sink regions (Siberian High) than for the moisture source region (West Pacific High) during the period 2000-2006. Thus the NEM precipitation over India has faced a deep bearing by the role of Siberian High interference

Development and validation of a multivariable risk factor questionnaire to detect oesophageal cancer in 2-week wait patients

INTRODUCTION: Oesophageal cancer is associated with poor health outcomes. Upper GI (UGI) endoscopy is the gold standard for diagnosis but is associated with patient discomfort and low yield for cancer. We used a machine learning approach to create a model which predicted oesophageal cancer based on questionnaire responses. METHODS: We used data from 2 separate prospective cross-sectional studies: the Saliva to Predict rIsk of disease using Transcriptomics and epigenetics (SPIT) study and predicting RIsk of diSease using detailed Questionnaires (RISQ) study. We recruited patients from National Health Service (NHS) suspected cancer pathways as well as patients with known cancer. We identified patient characteristics and questionnaire responses which were most associated with the development of oesophageal cancer. Using the SPIT dataset, we trained seven different machine learning models, selecting the best area under the receiver operator curve (AUC) to create our final model. We further applied a cost function to maximise cancer detection. We then independently validated the model using the RISQ dataset. RESULTS: 807 patients were included in model training and testing, split in a 70:30 ratio. 294 patients were included in model validation. The best model during training was regularised logistic regression using 17 features (median AUC: 0.81, interquartile range (IQR): 0.69-0.85). For testing and validation datasets, the model achieved an AUC of 0.71 (95% CI: 0.61-0.81) and 0.92 (95% CI: 0.88-0.96) respectively. At a set cut off, our model achieved a sensitivity of 97.6% and specificity of 59.1%. We additionally piloted the model in 12 patients with gastric cancer; 9/12 (75%) of patients were correctly classified. CONCLUSIONS: We have developed and validated a risk stratification tool using a questionnaire approach. This could aid prioritising patients at high risk of having oesophageal cancer for endoscopy. Our tool could help address endoscopic backlogs caused by the COVID-19 pandemic

Computer aided characterization of early cancer in Barrett's esophagus on i-scan magnification imaging - Multicenter international study

BACKGROUND AND AIMS: We aimed to develop a computer aided characterization system that can support the diagnosis of dysplasia in Barrett's esophagus (BE) on magnification endoscopy. METHODS: Videos were collected in high-definition magnification white light and virtual chromoendoscopy with i-scan (Pentax Hoya, Japan) imaging in patients with dysplastic/ non-dysplastic BE (NDBE) from 4 centres. We trained a neural network with a Resnet101 architecture to classify frames as dysplastic or non-dysplastic. The network was tested on three different scenarios: high-quality still images, all available video frames and a selected sequence within each video. RESULTS: 57 different patients each with videos of magnification areas of BE (34 dysplasia, 23 NDBE) were included. Performance was evaluated using a leave-one-patient-out cross-validation methodology. 60,174 (39,347 dysplasia, 20,827 NDBE) magnification video frames were used to train the network. The testing set included 49,726 iscan-3/optical enhancement magnification frames. On 350 high-quality still images the network achieved a sensitivity of 94%, specificity of 86% and Area under the ROC (AUROC) of 96%. On all 49,726 available video frames the network achieved a sensitivity of 92%, specificity of 82% and AUROC of 95%. On a selected sequence of frames per case (total of 11,471 frames) we used an exponentially weighted moving average of classifications on consecutive frames to characterize dysplasia. The network achieved a sensitivity of 92%, specificity of 84% and AUROC of 96% The mean assessment speed per frame was 0.0135 seconds (SD, + 0.006) CONCLUSION: Our network can characterize BE dysplasia with high accuracy and speed on high-quality magnification images and sequence of video frames moving it towards real time automated diagnosis

A new artificial intelligence system successfully detects and localises early neoplasia in Barrett's esophagus by using convolutional neural networks

BACKGROUND AND AIMS: Seattle protocol biopsies for Barrett's Esophagus (BE) surveillance are labour intensive with low compliance. Dysplasia detection rates vary, leading to missed lesions. This can potentially be offset with computer aided detection. We have developed convolutional neural networks (CNNs) to identify areas of dysplasia and where to target biopsy. METHODS: 119 Videos were collected in high-definition white light and optical chromoendoscopy with i-scan (Pentax Hoya, Japan) imaging in patients with dysplastic and non-dysplastic BE (NDBE). We trained an indirectly supervised CNN to classify images as dysplastic/non-dysplastic using whole video annotations to minimise selection bias and maximise accuracy. The CNN was trained using 148,936 video frames (31 dysplastic patients, 31 NDBE, two normal esophagus), validated on 25,161 images from 11 patient videos and tested on 264 iscan-1 images from 28 dysplastic and 16 NDBE patients which included expert delineations. To localise targeted biopsies/delineations, a second directly supervised CNN was generated based on expert delineations of 94 dysplastic images from 30 patients. This was tested on 86 i-scan one images from 28 dysplastic patients. FINDINGS: The indirectly supervised CNN achieved a per image sensitivity in the test set of 91%, specificity 79%, area under receiver operator curve of 93% to detect dysplasia. Per-lesion sensitivity was 100%. Mean assessment speed was 48 frames per second (fps). 97% of targeted biopsy predictions matched expert and histological assessment at 56 fps. The artificial intelligence system performed better than six endoscopists. INTERPRETATION: Our CNNs classify and localise dysplastic Barrett's Esophagus potentially supporting endoscopists during surveillance

The development of a novel endoscopic tumour model of oesophageal adenocarcinoma

Background: The incidence of oesophageal adenocarcinoma (OAC) continues to rise rapidly. There is a need for appropriate animal models of OAC to both improve our understanding of this disease and use in preclinical drug testing. Current animal models of OAC often fail to replicate the native environment within which OAC develops, and those that do require invasive surgical techniques. It was hypothesised that a less invasive and more clinically relevant animal model could be developed by implanting OAC cells into their native environment endoscopically. Such a model would allow the development of rodent endoscopy and simultaneously offer an opportunity to study OAC in a considerably less invasive manner than surgery. The aims of these thesis were to: i. Develop a method of performing upper gastrointestinal endoscopy in rats ii. Develop an approach of implanting tumour cells into the oesophageal submucosa of rats under direct endoscopic guidance iii. Assess the feasibility of growing human OAC cells in the oesophagus of immunodeficient rats after endoscopic implantation of tumour cells. Methods: Preliminary anaesthetic and endoscopic experiments were conducted to help devise a novel technique of performing rodent endoscopy. Two human cancer cell lines (HT29 and OE19) which were stably transfected to express luciferase were endoscopically implanted into the oesophageal submucosa of nude rats. Whole-body bioluminescent imaging (BLI) and endoscopy were performed at regular intervals to provide complimentary data to assess tumour development in animals. Results: A method of performing rodent gastroscopy which required endotracheal intubation to prevent rodent asphyxiation has been developed in this thesis. All implanted cancer cells were detected immediately after injection using BLI. All 3 rats injected with HT29 cells +/- Matrigel grew cancers, 2/3 of which were visible endoscopically. In comparison only 1/4 rats that were implanted with OE19 cells developed a cancer and this required Matrigel. The addition of Phorbol 12-Myristate 13-Acetate (PMA) to Matrigel led to cancers developing in 2/3 animals implanted with OE19 cells. Despite refinement of the injection needle, it was not possible to grow exophytic tumours which invaded the oesophageal lumen in any animal. Conclusion: The work in this thesis presents preliminary data pertaining to the development of a novel orthotopic tumour model for OAC. By utilising rodent gastroscopy, this model replicates the native environment in which OAC grows in a manner that confers less morbidity to animals. These findings support the original hypothesis that a less invasive and more clinically relevant animal models could be obtained by implantation of OAC cells into their native environment. The outcome of this thesis should provide a foundation for further research into developing appropriate animal models to improve our understanding of OAC

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Not AvailableRemote sensing index NDVI or its derivatives are used for agricultural drought monitoring and early warning at regional scale worldwide. Studies have shown that NDVI has lagged response to rainfall deficit. Moreover the red band used in NDVI is highly absorbed by crop canopy in comparison to short infrared which has high penetration so thus there remains a discrepancy between the levels of penetration in crop canopy. In contrast, Normalized Difference Water Index (NDWI) uses both the bands in near infrared region and is very sensitive to liquid water content of vegetation canopy and so rainfall. So this study was conducted to evaluate the sensitivity of NDWI in detecting and monitoring the agricultural drought in comparison with NDVI. In the study three indices of NDVI, NDWI5 and NDWI6 were computed using MODIS 09A1 surface reflectance product from June to October of 2002 (drought year) and 2003 (normal year) for the state of Rajasthan. NOAA Climate Prediction Centre (CPC) rainfall product was used and averaged at district level. The NDWI5 showed very strong relation with current rainfall than NDWI6 and weakest was shown by NDVI. The relation of NDVI with lagged rainfall was much better than with current rainfall. The spatial comparison of changes in NDVI and NDWI5 between the drought year (2002) and normal year (2003) for each 8 days composite showed that NDWI5 very well picks up the intensity and extent of drought. Study also showed that NDWI5 is more sensitive to agricultural drought than NDWI6. The study recommends use of NDWI5 for better early detection and monitoring of agricultural drought in operational drought management programmes.Not Availabl

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Not AvailableThe study presents a methodology to assess and map agricultural drought vulnerability during main kharif crop season at local scale and compare its intra-seasonal variations. A conceptual model of vulnerability based on variables of exposure, sensitivity, and adaptive capacity was adopted, and spatial datasets of key biophysical factors contributing to vulnerability were generated using remote sensing and GIS for Rajasthan State of India. Hazard exposure was based on frequency and intensity of gridded standardized precipitation index (SPI). Agricultural sensitivity was based on soil water holding capacity as well as on frequency and intensity of normalized difference vegetation index (NDVI)-derived trend adjusted vegetation condition index (VCITadj). Percent irrigated area was used as a measure of adaptive capacity. Agricultural drought vulnerability was derived separately for early, mid, late, and whole kharif seasons by composting rating of factors using linear weighting scheme and pairwise comparison of multi-criteria evaluation. The regions showing very low to extreme rating of hazard exposure, drought sensitivity, and agricultural vulnerability were identified at all four time scales. The results indicate that high to extreme vulnerability occurs in more than 50 % of net sown area in the state and such areas mostly occur in western, central, and southern parts. The higher vulnerability is on account of non-irrigated croplands, moderate to low water holding capacity of sandy soils, resulting in higher sensitivity, and located in regions with high probability of rainfall deficiency. The mid and late season vulnerability has been found to be much higher than that during early and whole season. Significant correlation of vulnerability rating with food grain productivity, drought recurrence period, crop area damaged in year 2009 and socioeconomic indicator of human development index (HDI) proves the general soundness of methodology. Replication of this methodology in other areas is expected to lead to better preparedness and mitigation-oriented management of droughts.IARI In-house Project Grant IARI:PHY:09:04(3

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Not AvailableImpact of elevated CO2 level (580±20 ppm) was assessed on chickpea in an open top chamber experiment during 2010-11. Elevated CO2 accelerated photosynthetic assimilation and widen the leaf C: N ratio in chickpea, due to higher grain carbohydrate assimilation under elevated CO2 condition, with dilution in grain N concentration. Although the net seed protein yield plant-1 remained unchanged. It is evident that greater partioning of photosynthate and remobilization of leaf N towards seeds are often inhibited due to sink limitation factor during pod development in chickpea, which accelerated the crop maturity under elevated CO2 exposure.ICAR - National Initiative on Climate Resilient Agriculture (NICRA) Projec