Open Access Digital Repository of Ministry of Earth Sciences, Government of India

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

Raje, D and Mujumdar, PP (2011) A comparison of three methods for downscaling daily precipitation in the Punjab region. Hydrological Processes, 25 (23). pp. 3575-3589.

Full text not available from this repository. (Request a copy)

Abstract

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.

Item Type: Article
Additional Information: Copyright of this article belongs to John Wiley & Sons.
Uncontrolled Keywords: Atmospheric variables; Comparison; Conditional random field; Daily rainfall; Daily rainfall amounts; Down-scaling; Downscaling methods; Dry and wet; General circulation model; Given features; Global climate model; Hydrological impacts; Hydrological variables; Intersite correlation; K nearest neighbours (k-NN); Length distributions; Machine-learning; Model performance; Monsoon; Monsoon regimes; Non-linear relationships; Probabilistic framework; Punjab; Random sampling; SVM model; Training data; Training example; Weighted set; Wet and dry, Atmospheric thermodynamics; Climate change; Computer simulation; Image retrieval; Image segmentation; Probability density function; Probability distributions; Rain, Climate models, climate modeling; downscaling; global climate; hydrology; monsoon; numerical model; pattern recognition; precipitation assessment; probability density function; rainfall; statistical analysis, India; Punjab India
Subjects: Meteorology and Climatology
Depositing User: IITM Library
Date Deposited: 27 Oct 2014 07:30
Last Modified: 27 Oct 2014 07:30
URI: http://moeseprints.incois.gov.in/id/eprint/644

Actions (login required)

View Item View Item