Evaluation of precipitation in observations, reanalysis and climate models over the Himalayas from an agricultural point of view.

Abstract:

The Himalayas support the agricultural and water resource needs of over 2 billion people. According to some reports the impacts of climate change in the region may be catastrophic so, there is an urgent need for better understanding the impact of climate change on this region, especially on agricultural production, which is the primary livelihood of the population. However, the region is known for its unique landscape and complicated biophysical and socioeconomic systems. This complexity is a source of difficulty for obtaining both reliable observations and realistic predictions of future climate in the region. The ultimate aim of our inter-disciplinary research project is to couple disparate climate and agro-ecosystem models to understand how future changes in precipitation may impact agriculture in the Himalayas. The project is divided into two parts:

1. Quantify the response of precipitation to climate change in state-of-the-art climate model simulations,
2. Evaluate the impact of projected future changes in precipitation on agricultural production with an agro-ecosystem model.

For the first part of the project we evaluated the ability of climate models to represent the present climate of this region. Climate model output was compared against precipitation observations from ground stations, satellites, and atmospheric reanalyses to document the biases of the models and evaluate objectively the future changes in precipitation. After evaluating the biases of our models, climate model predictions of precipitation changes over the Himalayas were quantified. The models clearly show a significant increase in precipitation over a large part of the region, especially during the monsoon season and the season preceding the monsoon.

The second component of the study seeks to introduce these precipitation changes into an agro-ecosystem model. This model, that contains multiple dynamic variables with regulate-able stochasticity and will allow us to visualize probable impacts of variable precipitation on selected crops.