Cause, conseguenze e strategie di mitigazione Proponiamo il primo di una serie di articoli in cui affronteremo l’attuale problema dei mutamenti climatici. Presentiamo il documento redatto, votato e pubblicato dall’Ipcc - Comitato intergovernativo sui cambiamenti climatici - che illustra la sintesi delle ricerche svolte su questo tema rilevante.

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Climate Change 2007: The Physical Science Basis.

Prefaces Acknowledgements 1. Mountains and their climatological study 2. Geographical controls of mountain meteorological elements 3. Circulation systems related to orography 4. Climatic characteristics of mountains 5. Regional case studies 6. Mountain bioclimatology 7. Changes in mountain climates Appendix General index Author index.

Mountain Weather and Climate

Over the past 15 years, artificial neural networks (ANNs) have been used increasingly for prediction and forecasting in water resources and environmental engineering. However, despite this high level of research activity, methods for developing ANN models are not yet well established. In this paper, the steps in the development of ANN models are outlined and taxonomies of approaches are introduced for each of these steps. In order to obtain a snapshot of current practice, ANN development methods are assessed based on these taxonomies for 210 journal papers that were published from 1999 to 2007 and focus on the prediction of water resource variables in river systems. The results obtained indicate that the vast majority of studies focus on flow prediction, with very few applications to water quality. Methods used for determining model inputs, appropriate data subsets and the best model structure are generally obtained in an ad-hoc fashion and require further attention. Although multilayer perceptrons are still the most popular model architecture, other model architectures are also used extensively. In relation to model calibration, gradient based methods are used almost exclusively. In conclusion, despite a significant amount of research activity on the use of ANNs for prediction and forecasting of water resources variables in river systems, little of this is focused on methodological issues. Consequently, there is still a need for the development of robust ANN model development approaches.

Review: Methods used for the development of neural networks for the prediction of water resource variables in river systems: Current status and future directions

Hydrology does not yet possess a generally agreed upon catchment classification system. Such a classification framework should provide a mapping of landscape form and hydro-climatic conditions on catchment function (including partition, storage, and release of water), while explicitly accounting for uncertainty and for variability at multiple temporal and spatial scales. This framework would provide an organizing principle, create a common language, guide modeling and measurement efforts, and provide constraints on predictions in ungauged basins, as well as on estimates of environmental change impacts. In this article, we (i) review existing approaches to define hydrologic similarity and to catchment classification; (ii) discuss outstanding components or characteristics that should be included in a classification scheme; and (iii) provide a basic framework for catchment classification as a starting point for further analysis. Possible metrics to describe form, hydro-climate, and function are suggested and discussed. We close the discussion with a list of requirements for the classification framework and open questions that require addressing in order to fully implement it. Open questions include: How can we best represent characteristics of form and hydro-climatic conditions? How does this representation change with spatial and temporal scale? What functions (partition, storage, and release) are relevant at what spatial and temporal scale? At what scale do internal structure and heterogeneity become important and need to be considered?

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Catchment Classification and Hydrologic Similarity

This study aims to model the joint drought duration and severity distribution using two-dimensional copulas. The method of inference function for margins (IFM method) is employed to construct copulas. Two separate maximum likelihood estimations of univariate marginal distributions are performed first, then followed by a maximization of the bivariate likelihood as a function of the dependence parameters. The drought duration and severity are assumed to be exponential and gamma distributions, respectively. Several copulas are tested to determine the best data fitted copula. Droughts, defined using the Standardized Precipitation Index (SPI), of Wushantou (Taiwan) are employed as an example to illustrate the proposed methodology. The copula fitting results for drought duration and severity are quite satisfactory. The bivariate drought analyses, including the joint probabilities and bivariate return periods, based on the derived copula-based joint distribution are also investigated to demonstrate the advantages of bivariate modeling of droughts.

Fitting Drought Duration and Severity with Two-Dimensional Copulas

The present study attempts to explore changes in rainfall pattern in the Indian Himalayas during 20th century using 80-year data from 30 rain gauge stations maintained by the India Meteorological Department (IMD). Modified Mann–Kendall test (MMK) was applied to detect trend, and Pettitt–Mann–Whitney (PMW) test was employed to detect possible shift. Implication of the change is discussed in terms of percentage change over mean. The results show that the most probable year of change in annual as well as monsoon rainfall in the region is 1964. There was an increasing trend upto 1964 (corroborating with all India and nearby plains), followed by a decreasing trend in 1965–1980 (exclusive to this region). In the entire region, changes are most conspicuous over the Shivaliks and the southern part of the Lesser Himalayas. Copyright © 2008 Royal Meteorological Society

Analysis of historical changes in rainfall in the Indian Himalayas

Improving real time flood forecasting using fuzzy inference system

L'impact du changement climatique devrait avoir des effets variables dans et entre les pays. Une information au sujet de ce changement est necessaire aux echelles globale, regionale et de bassin, par rapport a une multiplicite d'enjeux. Nous avons cherche a identifier les tendances dans les series de temperature de 125 stations reparties en Inde, dans son ensemble. Le test non-parametrique de Mann-Kendall a ete applique pour detecter les tendances monotones dans les series de temperatures moyennes annuelles et saisonnieres. Trois variables associees a la temperature, c'est-a-dire la moyenne, le maximum moyen et le minimum moyen, ont ete considerees et analysees en annuel et en saisonnier. Chaque annee a ete divisee en quatre saisons principales: l'hiver, la pre-mousson, la mousson et la post-mousson. Les pourcentages de significativite de tendance obtenus pour chaque grandeur et chaque saison sont presentes. Les anomalies sont identifiees, et il apparait que la temperature moyenne annuelle, la temperature maximale moyenne et la temperature minimale moyenne ont augmente respectivement de 0.42, 0.92 et 0.09°C (100 ans) -1 . Du point de vue regional, les stations du sud et de l'ouest de l'Inde montrent des tendances croissantes a hauteur de 1.06 et 0.36°C (100 ans) -1 , respectivement, tandis que les stations des plaines du nord de l'Inde presentent une tendance decroissante de -0.38°C (100 ans) -1 . La temperature moyenne saisonniere a augmente de 0.94°C (100 ans) -1 pour la saison de post-mousson et de 1.1°C (100 ans) -1 pour l'hiver.

Evaluation of temperature trends over India

The impact of climate change is projected to have different effects within and between countries. Information about such change is required at global, regional and basin scales for a variety of purposes. An investigation was carried out to identify trends in temperature time series of 125 stations distributed over the whole of India. The non-parametric Mann-Kendall test was applied to detect monotonic trends in annual average and seasonal temperatures. Three variables related to temperature, viz. mean, mean maximum and mean minimum, were considered for analysis on both an annual and a seasonal basis. Each year was divided into four principal seasons, viz. winter, pre-monsoon, monsoon and post-monsoon. The percentages of significant trends obtained for each parameter in the different seasons are presented. Temperature anomalies are plotted, and it is observed that annual mean temperature, mean maximum temperature and mean minimum temperature have increased at the rate of 0.42, 0.92 and 0....

Evaluation of temperature trends over India / Evaluation de tendances de température en Inde

Continuous rainfall data in grid format are required to run models for hydrological and agricultural research as well as water resources planning and management. The present work attempts to prepare a normal annual rainfall map in Himalayan region of India lying in Uttarakhand state at 1 km spatial resolution which currently is not available. In the region, India Meteorological Department maintains observatories/raingauge stations and data from 44 stations were used in this study. A comparative analysis of interpolation techniques like Inverse Distance Weighted, Polynomial, Splines, Ordinary Kriging and Universal Kriging shows that Universal Kriging with hole-effect model and natural logarithmic transformation with constant trend having Root Mean Square Error (RMSE) of 328.7 is the best choice. This is followed by Ordinary Kriging (RMSE 329.1), Splines (RMSE 392.4), Inverse Distance Weighted (RMSE 409.8) and Polynomial Interpolation (RMSE 418.5). Cross validation of the results shows the largest over prediction at Tehri rainfall station (62.5%) and largest under prediction at Nainital station (−36.5%). Physiographic zone wise, the least errors occur in the plains and the largest in the Great Himalayas. The spatial average rainfalls are 1,472 mm for Terai/Bhabar, 1,782 mm for the Shivalik ranges, 1,591 mm for the Lesser Himalayas and 1,635 mm for the Great Himalayan region. The mean areal rainfall in the region is 1,608 mm.

N. K. Goel

Papers

Analysis of historical changes in rainfall in the Indian Himalayas

Improving real time flood forecasting using fuzzy inference system

Evaluation of temperature trends over India

Evaluation of temperature trends over India / Evaluation de tendances de température en Inde

Spatial Distribution of Rainfall in Indian Himalayas – A Case Study of Uttarakhand Region