Flood Hazard Modelling in Upper Mandakini Basin of Uttarakhand

Floods in Himalayan region raise serious concerns regarding ongoing path of development as recent manifestations of catastrophic events establish link between climate changes and risk to anthropogenic activities in mountainous regions. Scientists predict frequent occurrence of such disasters wherein rapid glacial melting; incidents of glacial lake outburst and weather extremes may trigger floods in the Himalayan mountains. This paper examined flood risk in Upper Mandakini basin through GIS based flood simulationto highlight flood potential and risk associated with such hazard in the study area.It is observed that floods in study area display hazardous interplay of natural terrain gradient, high kinetic energy of streams, and intense rainfall. The upper sections of basin that includes Kali Ganga, Mandani Ganga, Madhyamaheshwar and Mandakini rivers shows high flood susceptibility with greatest risk in the latter. Such hazardousness is likely to be intensified by ongoing anthropogenic activities in the basin.

Weather extremes and plantation crops in the humid tropics

Monsoon rainfall across the State of Kerala was declining since last 60 years (cyclic trend of 40-60 years is also noticed with annual/monsoon rainfall) while rise in temperature is evident. Of course, rate of increase in temperature was alarming across the High ranges (where cardamom, coffee and tea are grown) due to deforestation. It is also true to some extent along the Coast (low land) due to increase in sea surface air temperature. The decade 1981-90 was the driest and warmest decade. The year 1987 was the warmest year across Kerala. The State as a whole was moving from wetness to dryness within the Humid Climate ((B4-B3 as per the Thornthwaite’s climate classification). Among weather extremes, summer drought, monsoon flood, strong wind (blows in Palghat Gap from November to February, other than cyclonic wind across the State), hailstorms, unusual rains, landslides and warming may adversely affect plantation crops’ production and its quality. Heat wave and cold waves are not relevant with reference to plantation crops under the Humid Tropics. The effect of summer drought on coconut yield was noticed in 1983, 2004 and 2013. In the case of coffee, it appears that crop matures early in recent years due to increase in temperature and climate change. The quality of coffee and black pepper, nut size in coconut and cashew are also influenced due to global warming and climate change. In the case of black pepper, the mortality rate is high in young pepper vines due to prolonged summer drought as noticed in summer 1983, 2004 and 2013. Mixed cropping/integrated farming is suggested to sustain crop income against adverse weather on long run under projected climate change scenario.

Weather extremes : A spatio-temporal perspectives

Being mainly an agricultural country the economy of India and its growth mainly depends on the vagaries of the weather and in particular the extreme weather events. India with a land of unique climatic regime due to several characteristic features, including (i) two monsoon seasons (south-west and north-east) leading to drought & flood condition, active and break cycle of monsoon and also heavy rainfall leading to flash flood and landslides, (ii) two cyclone seasons (pre and post-monsoon cyclone seasons), (iii) hot weather season characterized by severe thunderstorms, dust storms and heat waves, (iv) cold weather season characterized by violent snow storms in the Himalayan regions, cold waves and fog. The socio-economic impacts of the extreme weather events such as floods, droughts, heavy rainfall, cyclones, hail storm, thunderstorm, heat and cold waves have been increasing due to large growth of population and urbanizations, which has led to greater vulnerability. A spatio-temporal analysis of these weather extremes over India will be very helpful to understand the vulnerability potential and to improve the forecast skill and use these forecasts in minimizing the adverse impacts of such weather extremes.

Agricultural disaster management and contingency planning to meet the challenges of extreme weather events

Natural disasters of hydro-meteorological nature are playing a key role in the economic development of India. Agricultural production in India is largely dependent on the performance of summer monsoon rainfall. Apart from its spatial and temporal variability, several climatic anomalies / extremes attaining disastrous form at times were found to influence the country's agricultural production. Nature and magnitude of climate extremes that frequent India are presented with their history and region of occurrence. Droughts and floods are found to be paramount. Of late, hailstorms, cold and heat wave conditions are also exerting considerable influence on field and orchard crops. Trends in extreme events, their frequency and effects on crops are discussed. Regions in the country prone to be sensitive to the various weather extremes are presented. Management strategies and contingency planning to be adopted to cope-up the weather extremes are elucidated. Few case studies on the successful strategies adopted at the field level to cope-up extreme weather events under National Initiative on Climate Resilient Agriculture (NICRA) program are reported.

Spatiotemporal Variability of Weather Extremes Over Eastern India: Evidences of Ascertained Long Term Trend Persistence and Effective Global Climate Controls

The present study acknowledged climate variability induced periodic variation in localized extreme weather event occurrences under diverse agro eco-regions of Eastern Himalayas of India during past five decades. The widespread rise in warm nights (TN90p; 0.31-1.67 days year-1), reduced daily rainfall intensity (SDII) and changes in other weather extremes viz. temperature and precipitation extremes signified clear signals on regional atmospheric warming across eastern India. The agro-ecological regions under extended Bramhaputra valley and coastal belts of south Bengal experienced the most persistent shifts in temperature extremes, while the upper Himalayan range extended from North Bengal to Arunachal Pradesh experienced the steepest decline in average daily rainfall intensity and other absolute quantitative estimates of precipitation extremes over past five decades. Together with El Niño and La Niña events, large scale global atmospheric circulations particularly expansion of warmer Pacific Warm Pool (PWP) and changes in Atlantic Meridional Mode (AMM) contributed the periodic dynamics in weather extreme occurrences from monthly to annual time scale over eastern India. Our findings will be useful for better understanding of regional climatology, designing and successful implantation of location-specific suitable agricultural policies towards climate change adaptation in near future.

Machine learning reveals complex effects of climatic means and weather extremes on wheat yields during different plant developmental stages

Rising weather volatility poses a growing challenge to crop yields in many global breadbaskets. However, empirical evidence regarding the effects of extreme weather conditions on crop yields remains incomplete. We examine the contribution of climate and weather to winter wheat yields in Ukraine, a leading crop exporter with some of the highest yield variabilities observed globally. We used machine learning to link daily climatic data with annual winter wheat yields from 1985 to 2018. We differentiated the impacts of long-term climatic conditions (e.g., temperature) and weather extremes (e.g., heat waves) on yields during the distinct developmental stages of winter wheat. Our results suggest that climatic and weather variables alone explained 54% of the wheat yield variability at the country level. Heat waves, tropical night waves, frost, and drought conditions, particularly during the reproductive and grain filling phase, constitute key factors that compromised wheat yields in Ukraine. Assessing the impacts of weather extremes on crop yields is urgent to inform strategies that help cushion farmers against growing production risks because these extremes will likely become more frequent and intense with climate change.

‘Green podiatry’ - reducing our carbon footprints. Lessons from a sustainability panel

Background The eyes of the world will be on COP26 as it meets in Glasgow in November, 2021. Our planet is displaying weather extremes due to climate change which cannot be ignored, and which are deleterious for people’s health. Ironically, healthcare contributes to climate change, contributing approximately 5% of carbon emissions globally. Climate change due to global warming is ‘the biggest global health threat of the 21st century’. Main body The Australian Podiatry Association conference held a sustainability panel, hearing perspectives of industry and science, medicine and sport, fashion, and retail. Content unified a broad planet and human health message, which is highly relevant for podiatrists. Key themes included waste as a resource, exercise as evidence-based intervention, responsibility and circular economy recycling principles for end-of-life product (footwear) purchases, and wider ethical considerations of footwear and clothing. The Anthropocene origin of climate change requires humanity to collaborate and to live more sustainably. Innovation is essential for better energy modes, cleaner air, human health and earth care. Green Podiatry joins the concerted activity of medical and health groups within Australia. The UK’s NHS is an exemplar in this area, having already reduced healthcare emissions by 35%, and aiming for net zero by 2045, and perhaps sooner. Conclusion People are increasingly concerned about climate change, and COP26 is an important and imminent meeting for human and planet health. This commentary on Green Podiatry directs us all to lighten our carbon footprint. A final, and forthcoming commentary will outline practical ways of positively incorporating climate change communication into the clinical setting.

On the economic impact of droughts in Central Europe. The decade from 1531 to 1540 from the Polish perspective

The period from around 1450 to 1550 in Europe is extremely interesting from the perspective of research on extreme weather events. It was a period of events that strongly influenced the societies and economies of the Old Continent. So far, the literature has been more focused on Western and Northern Europe, while as regards the region of Central Europe, the greatest attention was paid to the Czech Republic or Hungary. This article revolves around the Polish lands, which experienced their greatest economic boom in the 16th century. We consider whether and how the droughts of the decade from 1531 to 1540 might have affected the country’s economic development. We analyze a number of written sources which are the product of the treasury apparatus of the time (tax registers, data from water customs, tax exemptions, inventories of land estates etc.), but also information on fluctuations in product prices in the most important cities in this part of Europe. The work not only provides a detailed account of economic data, but also attempts to reflect on the relevance of linking information on fires in urban centres in the period characterized by weather extremes.

Machine learning prediction of the Madden-Julian oscillation

The socioeconomic impact of weather extremes draws the attention of researchers to the development of novel methodologies to make more accurate weather predictions. The Madden–Julian oscillation (MJO) is the dominant mode of variability in the tropical atmosphere on sub-seasonal time scales, and can promote or enhance extreme events in both, the tropics and the extratropics. Forecasting extreme events on the sub-seasonal time scale (from 10 days to about 3 months) is very challenging due to a poor understanding of the phenomena that can increase predictability on this time scale. Here we show that two artificial neural networks (ANNs), a feed-forward neural network and a recurrent neural network, allow a very competitive MJO prediction. While our average prediction skill is about 26–27 days (which competes with that obtained with most computationally demanding state-of-the-art climate models), for some initial phases and seasons the ANNs have a prediction skill of 60 days or longer. Furthermore, we show that the ANNs have a good ability to predict the MJO phase, but the amplitude is underestimated.