Oportunidades de mejora (lecciones aprendidas)

Climate change influences the availability of water resources in a catchment. The recent droughts (e.g. Millennium drought 1997-2010), most likely caused or influenced by climate change, led to a large drop in inflows to many of the water supply reservoirs in Australia. Melbourne’s water supply reservoirs, reservoirs of the Grampian Wimmera Mallee Water Corporation (GWMWater) in north-western Victoria, and reservoirs which serve Perth in south-west of Western Australia are good examples of reservoir systems affected. Projection of climate into the future accounting for likely climate change allows for water resource managers to better consider the planning and operation of these reservoirs in the future. As an example, the knowledge of the extreme precipitation is useful in the effective management of floods and droughts which involves design and construction of dams, reservoirs and flood levees. Furthermore, precipitation projections produced into the future aids in the determination of future availability of water in a catchment. Hence it is useful in determining the sustainable allocation of water and sharing resources for various purposes such as consumptive use, recreation and waterway health. Furthermore, the hydroclimatic projections produced in this study accounting for climate change, will feed into a parallel study which is focused on the development of a set of new operating rules for the management of water resources in the water supply systems of GWMWater.

In a conventional downscaling model, calibration and validation (development) are performed using some form of reanalysis outputs as inputs to the model. Then the projections of catchment scale climate into future are produced by using the outputs of a different GCM on the above developed downscaling model. The reanalysis data used in the development phase of the downscaling model are outputs of a GCM quality controlled and corrected against observations. The outputs of the GCM used for the projection of climate into future are not corrected or quality controlled as they refer to the future climate. Therefore there is a difference in quality of the inputs used to the downscaling model in its development and future projection phases. In such case, since the inputs to the downscaling model are obtained from two different sources with

Chapter 1: Introduction

Sachindra, D.A: Catchment Scale Downscaling of Hydroclimatic Variables from General Circulation Model Outputs 21  different degrees of accuracy, these inputs are not homogeneous. In this study, two potential solutions for the above issue were investigated. As the first potential solution to the issue of non-homogeneity in inputs to a downscaling model, a statistical downscaling model was developed using the outputs of a GCM in view of using the outputs of the same GCM pertaining to future for producing projections of catchment scale climate into future. According to the knowledge of the author, there are no records in the published literature on the development of a downscaling model using GCM outputs as inputs prior to this study, possibly owing to the limited performances of such downscaling model. As the second potential solution, another statistical downscaling model was developed and the projections of catchment scale climate into future were produced using multi-model ensemble outputs derived from the outputs of a set of different GCMs as inputs to the model.

Furthermore, owing to differences in the structure, different GCMs tend to simulate the climate of the future differently. This phenomenon also influences the downscaling models, causing them to produce catchment scale projections of climate which vary with the GCM used in providing inputs to the downscaling model. As a solution to this issue, multi-model ensemble techniques are used in combining the outputs of different GCMs into a single projection. In this study, a downscaling model (the latter model described previously) was developed with multi-model ensemble outputs derived from the outputs of a set of different GCMs. The projections of catchment scale climate into the future were produced by introducing the multi-model ensemble outputs generated from the outputs of the same set of GCMs pertaining to the future. Hence this approach allows using the outputs of different GCM on a downscaling model for producing a single prediction at the point of interest in the catchment.

In a statistical downscaling study which involves downscaling at multiple stations, it is important to preserve the cross-correlation structure among the stations in the study area for a certain predictand and also among different predictands. However, the majority of the techniques currently used in multi-station and multi-station multivariate downscaling exercises are quite complex. As a solution to the above issue, in the current

Chapter 1: Introduction

Sachindra, D.A: Catchment Scale Downscaling of Hydroclimatic Variables from General Circulation Model Outputs 22  study relatively simple yet effective approaches for multi-station and multi-station multivariate downscaling were developed. Furthermore, in this study downscaling GCM outputs directly to streamflows was investigated. This investigation was performed as downscaling GCM outputs directly to streamflows enables the making of a quick estimate of streamflows and avoids the need of a hydrologic model. Also, this study is the first in Australia to use statistical downscaling for the prediction of catchment streamflows, and is one of the very few studies across the world.