Fuzzy modelling of acid mine drainage environments using geochemical, ecological and mineralogical indicators

Fuzzy logic was applied to model acid mine drainage (AMD) and to obtain a classification index of the environmental impact in a contaminated riverine system. The data set used to develop this fuzzy model (a fuzzy classifier) concerns an abandoned mine in Northern Portugal— Valdarcas mining site. Here, distinctive drainage environments (spatial patterns) can be observed based on the AMD formed in the sulphide-rich waste-dumps. Such environments were established, as the effluent flows through the mining area, using several kinds of indicators. These are physical–chemical, ecological and mineralogical parameters, being expressed in a quantitative or qualitative basis. The fuzzy classifier proposed in this paper is a min– max fuzzy inference system, representing the spatial behaviour of those indicators, using the AMD environments as patterns. As they represent different levels (classes) of contamination, the fuzzy classifier can be used as a tool, allowing a more reasonable approach, compared with classical models, to characterize the environmental impact caused by AMD. In a general way it can be applied to other sites where sulphide-rich waste-dumps are promoting the pollution of superficial water through the generation of AMD

Resiliency Estimates for Irrigation Systems

In this paper, the criteria and methodologies proposed by Hashimoto et al (1982) and Fiering (1982), for estimating resiliency are applied to an irrigation system consisting of a single reservoir serving multiple crops. The resiliency is related to the soil moisture depletion. The failure index is determined based on the irrigation deficit occurring in a period. Partial failures are considered by defining a demand factor. The methodology is demonstrated for a case study in India

Reforms for Quality Improvement in R&D in Water Sector

This paper presents perceptions of the author on assessment of quality of research in the water sector in the country, identifies areas where improvements are necessary and provides a list of reforms to ensure a quality improvement in R&D in the water sector

Implications of climate change for sustainable water resources management in India

This paper presents an overview of the current water resources scenario in India, and recent work carried out in India to assess the climate change impact on hydrology and water resources. Issues that need to be addressed with respect to climate change/variability in sustainable water resources planning and management are discussed

A conditional random field-based downscaling method for assessment of climate change impact on multisite daily precipitation in the Mahanadi basin

Downscaling to station-scale hydrologic variables from large-scale atmospheric variables simulated by general circulation models (GCMs) is usually necessary to assess the hydrologic impact of climate change. This work presents CRF-downscaling, a new probabilistic downscaling method that represents the daily precipitation sequence as a conditional random field (CRF). The conditional distribution of the precipitation sequence at a site, given the daily atmospheric (large-scale) variable sequence, is modeled as a linear chain CRF. CRFs do not make assumptions on independence of observations, which gives them flexibility in using high-dimensional feature vectors. Maximum likelihood parameter estimation for the model is performed using limited memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) optimization. Maximum a posteriori estimation is used to determine the most likely precipitation sequence for a given set of atmospheric input variables using the Viterbi algorithm. Direct classification of dry/wet days as well as precipitation amount is achieved within a single modeling framework. The model is used to project the future cumulative distribution function of precipitation. Uncertainty in precipitation prediction is addressed through a modified Viterbi algorithm that predicts the n most likely sequences. The model is applied for downscaling monsoon (June-September) daily precipitation at eight sites in the Mahanadi basin in Orissa, India, using the MIROC3.2 medium-resolution GCM. The predicted distributions at all sites show an increase in the number of wet days, and also an increase in wet day precipitation amounts. A comparison of current and future predicted probability density functions for daily precipitation shows a change in shape of the density function with decreasing probability of lower precipitation and increasing probability of higher precipitation

PERFORMANCE EVALUATION OF AN IRRIGATION SYSTEM UNDER SOME OPTIMAL OPERATING POLICIES

Three indicators are used to study the performance of a single purpose irrigation reservoir in Kamataka, India. The three indicators are reliability, resiliency and a productivity index. The performance of the reservoir is evaluated when it is operated with optimal operating policies over a sufficiently long period of time. Three different optimal operating policies are derived, having increasing mathematical complexity, using stochastic dynamic programming (SDP). Two of the three policies, Policy II and Policy III, incorporate a detailed soil moisture dynamics model as an integral part of the SDP. Policy III considers, in addition, an optimal allocation of water among the irrigated crops when there is competition for water. The reservoir releases are simulated under each optimal operating policy using synthetically generated inflows, and a comparison of the system performances is made

Modeling GCM and scenario uncertainty using a possibilistic approach: Application to the Mahanadi River, India

Climate change impact assessment on water resources with downscaled General Circulation Model (GCM) simulation output is characterized by uncertainty due to incomplete knowledge about the underlying geophysical processes of global change ( GCM uncertainties) and due to uncertain future scenarios ( scenario uncertainties). Disagreement between different GCMs and scenarios in regional climate change impact studies indicates that overreliance on a single GCM with a scenario could lead to inappropriate planning and adaptation responses. This paper focuses on modeling GCM and scenario uncertainty using possibility theory in projecting streamflow of Mahanadi river, at Hirakud, India. A downscaling method based on fuzzy clustering and Relevance Vector Machine ( RVM) is applied to project monsoon streamflow from three GCMs with two green house emission scenarios. Possibilities are assigned to all the GCMs with scenarios based on their performance in modeling the streamflow of the recent past ( 1991 - 2005), when there are signals of climate forcing. The possibilities associated with different GCMs and scenarios are used as weights in computing the possibilistic mean of the CDFs projected for three standard time slices 2020s, 2050s, and 2080s. The result shows that the value of streamflow at which the CDF reaches 1 reduces with time, which shows the reduction in probability of occurrence of extreme high flow events in future. Historic record of monsoon streamflow of Mahanadi river also shows similar decreasing trend, which may be due to the effect of high surface warming. Reduction in Mahandai streamflow is likely to pose a major challenge for water resources engineers in meeting water demands in future

A comparison of three methods for downscaling daily precipitation in the Punjab region

Many downscaling techniques have been developed in the past few years for projection of station-scale hydrological variables from large-scale atmospheric variables simulated by general circulation models (GCMs) to assess the hydrological impacts of climate change. This article compares the performances of three downscaling methods, viz. conditional random field (CRF), K-nearest neighbour (KNN) and support vector machine (SVM) methods in downscaling precipitation in the Punjab region of India, belonging to the monsoon regime. The CRF model is a recently developed method for downscaling hydrological variables in a probabilistic framework, while the SVM model is a popular machine learning tool useful in terms of its ability to generalize and capture nonlinear relationships between predictors and predictand. The KNN model is an analogue-type method that queries days similar to a given feature vector from the training data and classifies future days by random sampling from a weighted set of K closest training examples. The models are applied for downscaling monsoon (June to September) daily precipitation at six locations in Punjab. Model performances with respect to reproduction of various statistics such as dry and wet spell length distributions, daily rainfall distribution, and intersite correlations are examined. It is found that the CRF and KNN models perform slightly better than the SVM model in reproducing most daily rainfall statistics. These models are then used to project future precipitation at the six locations. Output from the Canadian global climate model (CGCM3) GCM for three scenarios, viz. A1B, A2, and B1 is used for projection of future precipitation. The projections show a change in probability density functions of daily rainfall amount and changes in the wet and dry spell distributions of daily precipitation