Forecasting rain for groundnut farmers�how good is good enough

: We illustrate how climatological information about adverse weather events and meteorological forecasts (when available) can be used to decide between alternative strategies so as to maximize the long-term average returns for rainfed groundnut in semi-arid parts of Karnataka, We show that until the skill of the forecast, i.e. probability of an adverse event occurring when it is forecast, is above a certain threshold, the forecast has no impact on the optimum strategy, This threshold is determined by the loss in yield due to the adverse weather event and the cost of the mitigatory measures, For the specific case of groundnut, it is found that while for combating some pests/diseases, climatological information is adequate, for others a forecast of sufficient skill would have a significant impact on the productivity.

Use of Data Driven Techniques for Short Lead Time Streamflow Forecasting in Mahanadi basin

Streamflow forecasts at daily time scale are necessary for effective management of water resources systems. Typical applications include flood control, water quality management, water supply to multiple stakeholders, hydropower and irrigation systems. Conventionally physically based conceptual models and data-driven models are used for forecasting streamflows. Conceptual models require detailed understanding of physical processes governing the system being modeled. Major constraints in developing effective conceptual models are sparse hydrometric gauge network and short historical records that limit our understanding of physical processes. On the other hand, data-driven models rely solely on previous hydrological and meteorological data without directly taking into account the underlying physical processes. Among various data driven models Auto Regressive Integrated Moving Average (ARIMA), Artificial Neural Networks (ANNs) are most widely used techniques. The present study assesses performance of ARIMA and ANNs methods in arriving at one-to seven-day ahead forecast of daily streamflows at Basantpur streamgauge site that is situated at upstream of Hirakud Dam in Mahanadi river basin, India. The ANNs considered include Feed-Forward back propagation Neural Network (FFNN) and Radial Basis Neural Network (RBNN). Daily streamflow forecasts at Basantpur site find use in management of water from Hirakud reservoir. (C) 2015 The Authors. Published by Elsevier B.V.

Probabilistic Models for Forecasting Earthquakes in the Northeast Region of India

Northeast India and its adjoining areas are characterized by very high seismic activity. According to the Indian seismic code, the region falls under seismic zone V, which represents the highest seismic-hazard level in the country. This region has experienced a number of great earthquakes, such as the Assam (1950) and Shillong (1897) earthquakes, that caused huge devastation in the entire northeast and adjacent areas by flooding, landslides, liquefaction, and damage to roads and buildings. In this study, an attempt has been made to find the probability of occurrence of a major earthquake (M-w > 6) in this region using an updated earthquake catalog collected from different sources. Thereafter, dividing the catalog into six different seismic regions based on different tectonic features and seismogenic factors, the probability of occurrences was estimated using three models: the lognormal, Weibull, and gamma distributions. We calculated the logarithmic probability of the likelihood function (ln L) for all six regions and the entire northeast for all three stochastic models. A higher value of ln L suggests a better model, and a lower value shows a worse model. The results show different model suits for different seismic zones, but the majority follows lognormal, which is better for forecasting magnitude size. According to the results, Weibull shows the highest conditional probabilities among the three models for small as well as large elapsed time T and time intervals t, whereas the lognormal model shows the lowest and the gamma model shows intermediate probabilities. Only for elapsed time T = 0, the lognormal model shows the highest conditional probabilities among the three models at a smaller time interval (t = 3-15 yrs). The opposite result is observed at larger time intervals (t = 15-25 yrs), which show the highest probabilities for the Weibull model. However, based on this study, the IndoBurma Range and Eastern Himalaya show a high probability of occurrence in the 5 yr period 2012-2017 with >90% probability.

Testing the Forecasting Performance of Ibex 35 Option-implied Risk-neutral Densities

Published also as: Documento de Trabajo Banco de España 0504/2005.

A review of travel time estimation and forecasting for advanced traveler information systems

Providing on line travel time information to commuters has become an important issue for Advanced Traveler Information Systems and Route Guidance Systems in the past years, due to the increasing traffic volume and congestion in the road networks. Travel time is one of the most useful traffic variables because it is more intuitive than other traffic variables such as flow, occupancy or density, and is useful for travelers in decision making. The aim of this paper is to present a global view of the literature on the modeling of travel time, introducing crucial concepts and giving a thorough classification of the existing tech- niques. Most of the attention will focus on travel time estimation and travel time prediction, which are generally not presented together. The main goals of these models, the study areas and methodologies used to carry out these tasks will be further explored and categorized.

Forecasting Climate Impacts on Future Production of Commercially Exploited Fish and Shellfish

Foreword [pdf, < 0.1 MB] Acknowledgements PHASE 1 [pdf, 0.2 MB] Summary of the PICES/NPRB Workshop on Forecasting Climate Impacts on Future Production of Commercially Exploited Fish and Shellfish (July 19–20, 2007, Seattle, U.S.A.) Background Links to Other Programs Workshop Format Session I. Status of climate change scenarios in the PICES region Session II. What are the expected impacts of climate change on regional oceanography and what are some scenarios for these drivers for the next 10 years? Session III. Recruitment forecasting Session IV. What models are out there? How is climate linked to the model? Session V. Assumptions regarding future fishing scenarios and enhancement activities Session VI Where do we go from here? References Appendix 1.1 List of Participants PHASE 2 [pdf, 0.7 MB] Summary of the PICES/NPRB Workshop on Forecasting Climate Impacts on Future Production of Commercially Exploited Fish and Shellfish (October 30, 2007, Victoria, Canada) Background Workshop Agenda Forecast Feasibility Format of Information Modeling Approaches Coupled bio-physical models Stock assessment projection models Comparative approaches Similarities in Data Requests Opportunities for Coordination with Other PICES Groups and International Efforts BACKGROUND REPORTS PREPARED FOR THE PHASE 2 WORKSHOP Northern California Current (U.S.) groundfish production by Melissa Haltuch Changes in sablefish (Anoplopoma fimbria) recruitment in relation to oceanographic conditions by Michael J. Schirripa Northern California Current (British Columbia) Pacific cod (Gadus macrocephalus) production by Caihong Fu and Richard Beamish Northern California Current (British Columbia) sablefish (Anoplopoma fimbria) production by Richard Beamish Northern California Current (British Columbia) pink (Oncorhynchus gorbuscha) and chum (O. keta) salmon production by Richard Beamish Northern California Current (British Columbia) ocean shrimp (Pandalus jordani) production by Caihong Fu Alaska salmon production by Anne Hollowed U.S. walleye pollock (Theragra chalcogramma) production in the eastern Bering Sea and Gulf of Alaska by Kevin Bailey and Anne Hollowed U.S. groundfish production in the eastern Bering Sea by Tom Wilderbuer U.S. crab production in the eastern Bering Sea by Gordon H. Kruse Forecasting Japanese commercially exploited species by Shin-ichi Ito, Kazuaki Tadokoro and Yasuhiro Yamanka Russian fish production in the Japan/East Sea by Yury Zuenko, Vladimir Nuzhdin and Natalia Dolganova Chum salmon (Oncorhynchus keta) production in Korea by Sukyung Kang, Suam Kim and Hyunju Seo Jack mackerel (Trachurus japonicus) production in Korea by Jae Bong Lee and Chang-Ik Zhang Chub mackerel (Scomber japonicus) production in Korea by Jae Bong Lee, Sukyung Kang, Suam Kim, Chang-Ik Zhang and Jin Yeong Kim References Appendix 2.1 List of Participants PHASE 3 [pdf, < 0.1 MB] Summary of the PICES Workshop on Linking Global Climate Model Output to (a) Trends in Commercial Species Productivity and (b) Changes in Broader Biological Communities in the World’s Oceans (May 18, 2008, Gijón, Spain) Appendix 3.1 List of Participants Appendix 3.2 Workshop Agenda (Document contains 101 pages)

Extreme events and ecological forecasting

Almost all extreme events lasting less than several weeks that significantly impact ecosystems are weather related. This review examines the response of estuarine systems to intense short-term perturbations caused by major weather events such as hurricanes. Current knowledge concerning these effects is limited to relatively few studies where hurricanes and storms impacted estuaries with established environmental monitoring programs. Freshwater inputs associated with these storms were found to initially result in increased primary productivity. When hydrographic conditions are favorable, bacterial consumption of organic matter produced by the phytoplankton blooms and deposited during the initial runoff event can contribute to significant oxygen deficits during subsequent warmer periods. Salinity stress and habitat destruction associated with freshwater inputs, as well as anoxia, adversely affect benthic populations and fish. In contrast, mobile invertebrate species such as shrimp, which have a short life cycle and the ability to migrate during the runoff event, initially benefit from the increased primary productivity and decreased abundance of fish predators. Events studied so far indicate that estuaries rebound in one to three years following major short-term perturbations. However, repeated storm events without sufficient recovery time may cause a fundamental shift in ecosystem structure (Scavia et al. 2002). This is a scenario consistent with the predicted increase in hurricanes for the east coast of the United States. More work on the response of individual species to these stresses is needed so management of commercial resources can be adjusted to allow sufficient recovery time for affected populations.