Description:
Using a key station approach, statistical downscaling of monthly general circulation model outputs to monthly precipitation, evaporation, minimum temperature and maximum temperature at 17 observation stations located in Victoria, Australia was performed. Using the observations of each predictand, over the period 1950-2010, correlations among all stations were computed. For each predictand, the station which showed the highest number of correlations above 0.80 with other stations was selected as the first key station. The stations that were highly correlated with that key station were considered as the member stations of the first cluster. By employing this same procedure on the remaining stations, the next key station was found. This procedure was performed until all stations were segregated into clusters. Thereafter, using the observations of each predictand, regression equations (inter-station regression relationships) were developed between the key stations and the member stations for each calendar month. The downscaling models at the key stations were developed using reanalysis data as inputs to them. The outputs of HadCM3 pertaining to A2 emission scenario were introduced to these downscaling models to produce projections of the predictands over the period 2000-2099. Then the outputs of these downscaling models were introduced to the inter-station regression relationships to produce projections of predictands at all member stations.
Description:
This article is the first of two companion articles providing details of the development of two separate models for statistically downscaling monthly precipitation. The first model was developed with National Centers for Environmental Prediction/National Center for Atmospheric Research (/) reanalysis outputs and the second model was built using the outputs of Hadley Centre Coupled Model version 3 (). Both models were based on the multi‐linear regression () technique and were built for a precipitation station located in Victoria, Australia. Probable predictors were selected based on the past literature and hydrology. Potential predictors were selected for each calendar month separately from the / reanalysis data, considering the correlations that they maintained with observed precipitation. Based on the strength of the correlations, these potential predictors were introduced to the downscaling model until its performance in validation, in terms of Nash–Sutcliffe Efficiency (), was maximized. In this manner, for each calendar month, the final sets of potential predictors and the best downscaling models with / reanalysis data were identified. The 20th century climate experiment data corresponding to these final sets of potential predictors were used to calibrate and validate the second model. In calibration and validation, the model developed with / reanalysis data displayed of 0.74 and 0.70, respectively. The model built with outputs showed of 0.44 and 0.17 during the calibration and validation periods, respectively. Both models tended to under‐predict high precipitation values and over‐predict near‐zero precipitation values, during both calibration and validation. However, this prediction characteristic was more pronounced by the model developed with outputs. A graphical comparison of observed precipitation, the precipitation reproduced by the two downscaling models and the raw precipitation output of , showed that there is large bias in the precipitation output of . This indicated the need of a bias‐correction, which is detailed in the second companion article.
Description:
This article is the first of two companion articles providing details of the development of two separate models for statistically downscaling monthly precipitation. The first model was developed with National Centers for Environmental Prediction/National Center for Atmospheric Research (/) reanalysis outputs and the second model was built using the outputs of Hadley Centre Coupled Model version 3 (). Both models were based on the multi‐linear regression () technique and were built for a precipitation station located in Victoria, Australia. Probable predictors were selected based on the past literature and hydrology. Potential predictors were selected for each calendar month separately from the / reanalysis data, considering the correlations that they maintained with observed precipitation. Based on the strength of the correlations, these potential predictors were introduced to the downscaling model until its performance in validation, in terms of Nash–Sutcliffe Efficiency (), was maximized. In this manner, for each calendar month, the final sets of potential predictors and the best downscaling models with / reanalysis data were identified. The 20th century climate experiment data corresponding to these final sets of potential predictors were used to calibrate and validate the second model. In calibration and validation, the model developed with / reanalysis data displayed of 0.74 and 0.70, respectively. The model built with outputs showed of 0.44 and 0.17 during the calibration and validation periods, respectively. Both models tended to under‐predict high precipitation values and over‐predict near‐zero precipitation values, during both calibration and validation. However, this prediction characteristic was more pronounced by the model developed with outputs. A graphical comparison of observed precipitation, the precipitation reproduced by the two downscaling models and the raw precipitation output of , showed that there is large bias in the precipitation output of . This indicated the need of a bias‐correction, which is detailed in the second companion article.
Description:
General Circulation Models (GCMs), based on the laws of physics, are regarded as the best tools available for the prediction of global climate, hundreds of years into future. However, the coarse spatial resolutions of present day GCMs do not allow the direct use of their outputs in hydrologic studies at the catchment level. The gap between coarse resolution GCM outputs and fine resolution hydroclimatic data needed at the catchment level, is bridged either by dynamic or statistical downscaling. Statistical downscaling has gained popularity owing to its simplicity and low computational cost. Although statistical downscaling possesses these advantages, there are shortcomings associated with both, the methods and also the GCM outputs used as the main input to the downscaling models. The aim of this paper is to discuss some of the issues associated with statistical downscaling of GCM outputs to hydroclimatic variables at the catchment scale. The following issues are discussed in detail in the paper: (1)outputs from GCMs offer a limited degree of certainty, due to the lack of theoretical robustness and incomplete understanding of various atmospheric processes; (2) the presence of a number of Greenhouse Gas emission scenarios with equal likelihood of occurrence leads to scenario uncertainty; (3) the incorporation of various climate indices, such as Southern Oscillation Index or Indian Ocean Dipole, may seem to be a way of improving the results of a downscaling model, but the unavailability of climate indices for the future and the presence of variants of indices due to different definitions and calculation procedures limit their use; and (4)outputs of downscaling studies can also vary with the statistical downscaling technique employed. Although statistical downscaling faces above issues, still it is regarded as a potential method for predicting future catchment hydroclimatology, under changing climate. However, the outputs of statistical downscaling studies should be used sensibly in any catchment scale studies.
Description:
Victoria suffered a severe drought over the period 1998-2007, when the annual average precipitation plunged by about 13% from the long term average. Precipitation is directly related to the availability of water resources in a catchment. Therefore it is useful to predict precipitation, particularly in light of any future climate change, which will help in the management of water resources at the catchment level. General Circulation Models (GCMs) are considered to be the most advanced tools available for simulating the future climate. Due to the coarse spatial resolution, however, GCM outputs cannot be used directly at the catchment scale. To overcome this problem statistical and dynamic downscaling techniques have been developed. Downscaling techniques link the coarse GCM outputs to catchment scale hydroclimatic variables. The present research has focussed on statistically downscaling monthly NCEP/NCAR reanalysis outputs to monthly precipitation at the catchment level. A precipitation station in the operational area of the Grampians Wimmera Mallee Water Corporation (GWMWater) in northwestern Victoria was considered as the case study. Multi-linear regression was used in the development of the downscaling models. This research employed separate downscaling models for each calendar month, with the intention of better capturing the seasonal variations of precipitation. A set of probable predictors were selected following the past literature and hydrology. Data for the probable predictors and precipitation were split into three 20 year time slices; 1950-1969, 1970-1989 and 1990-2010. The probable predictors which displayed the best statistically significant correlations consistently with precipitation over the three time slices and the whole period of the study were selected as potential predictors, for each calendar month. These potential predictors were introduced to the downscaling model one at a time based on the strength of the correlation, over the whole period of the study, until the model performance, in terms of Nash-Sutcliffe Efficiency (NSE), was maximised. This approach ensured the identification of the best potential predictor for each calendar month. In calibration and validation, the model displayed good performances with NSEs of 0.74 and 0.70 respectively. In calibration, the average precipitation was perfectly reproduced by the model and in validation it was slightly over-predicted. However, both in calibration and validation, the model tended to under-predict high precipitations and over-predict near-zero precipitations. A graphical comparison of observed precipitation, downscaling model reproduced precipitation and the Hadley Centre Coupled Model version 3 GCM (HadCM 3) simulated raw precipitation output, revealed that there is large bias in the HadCM 3 precipitation outputs. Therefore, before producing any future precipitation projections with the downscaling model, a bias correction to GCM outputs is prescribed.
Description:
Two statistical downscaling models were developed for downscaling monthly General Circulation Model (GCM) outputs to precipitation at a site in north-western Victoria, Australia. The first downscaling model was calibrated and validated with the National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) reanalysis outputs over the periods of 1950–1989 and 1990–2010 respectively. The projections of precipitation into the future were produced by introducing the outputs of HadCM3, ECHAM5, GFDL2.0 and GFDL2.1, pertaining to A2 and B1 greenhouse gas emission scenarios to this downscaling model. In this model, the input data used in the development and future projections are not homogeneous, as they originate from two different sources. As a solution to this issue, the second downscaling model was developed and precipitation projections into the future were produced with a homogeneous set of inputs. To produce a homogeneous set of inputs to this model, regression relationships were formulated between the NCEP/NCAR reanalysis outputs and the twentieth-century climate experiment outputs corresponding to the variables used in the first downscaling model obtained from the ensemble consisting of HadCM3, ECHAM5 and GFDL2.0. The outputs of these relationships pertaining to the periods of 1950–1989 and 1990–1999 were used for the calibration and validation of this downscaling model respectively. Using the outputs of HadCM3, ECHAM5 and GFDL2.0 pertaining to A2 and B1 emission scenarios on these relationships, inputs for the second downscaling model pertaining to the period of 2000–2099 were generated. The first downscaling model withNCEP/NCARreanalysis outputs showed a highNash–Sutcliffe Efficiency (NSE) of 0.75 over the period 1950–1999. When this downscaling model was run with the twentieth-century climate experiment outputs of HadCM3, ECHAM5, GFDL2.0 and GFDL2.1, it exhibited limited performances over the period 1950–1999, which was indicated by relatively lowNSEs of−0.62,−2.54,−0.40 and−0.48 respectively. The second downscaling model displayed an NSE of 0.35 over the period 1950–1999.
Description:
Abstract—Many water supply systems in Australia are currently undergoing significant reconfiguration due to reductions in long term average rainfall and resulting low inflows to water supply reservoirs since the second half of the 20th century. When water supply systems undergo change, it is necessary to develop new operating rules, which should consider climate, because the climate change is likely to further reduce inflows. In addition, water resource systems are increasingly intended to be operated to meet complex and multiple objectives representing social, economic, environmental and sustainability criteria. This is further complicated by conflicting preferences on these objectives from diverse stakeholders. This paper describes a methodology to develop optimum operating rules for complex multi-reservoir systems undergoing significant change, considering all of the above issues. The methodology is demonstrated using the Grampians water supply system in northwest Victoria, Australia. Initial work conducted on the project is also presented in this paper.
Description:
Since the latter half of the 20th century, many regions of Australia experienced a drop in average rainfall, causing low inflows to reservoirs. Until the recent heavy rainfalls of late 2010 and early 2011, Victoria suffered a severe drought commencing 1997. This resulted in a reduction of annual average inflows to Melbourne's main water supply reservoirs of about 38%, during the period 1997-2008. The Grampians Wimmera Mallee Water (GWMWater) supply system in north-western Victoria also experienced a drop in annual inflows to its reservoirs of about 75%, from the long term average since 1997. Already being the driest inhabited continent in the world, this drop in inflows to reservoirs was of significant concern to water managers across much of Australia. Such a significant deviation from the long term average highlights the importance of being able to reliably predict streamflows considering the likely future climate change and variability, which will ultimately aid in future planning of the water supply systems. General Circulation Models (GCMs) are the most advanced tools available for the simulation of future climate. However, the coarse spatial resolution of GCMs does not allow for hydroclimatic predictions at the catchment scale. Indeed, they are incapable of producing outputs at the fine spatial resolution needed for most hydrological studies. To address this issue, downscaling methods have been developed, which link coarse resolution GCM outputs to surface hydroclimatic variables at finer resolutions. Downscaling techniques are broadly classified as either dynamic or statistical. The computation cost associated with dynamic downscaling methods is much higher than that of statistical downscaling. Another major drawback of dynamic downscaling models is their high complexity. The aim of the present study was to develop a model capable of statistically downscaling monthly GCM outputs to catchment scale monthly streamflows, accounting for the climate change. The current study investigated only the calibration and validation of the abovementioned statistical downscaling model. This was demonstrated through a case study applied to the GWMWater supply system in north-western Victoria, Australia. It is a large scale complex multi-reservoir system that is operated to meet a range of economic, social, and environmental interests. Support Vector Machine (SVM), a statistical downscaling technique, was used in the current streamflow downscaling exercise. The selection of SVM for downscaling was based on its better capability in capturing complex non-linear relationships between GCM outputs and catchment level variables, than artificial neural networks (ANN) and multi-linear regression (MLR), as observed in the past studies. National Center for Environmental Predictions/National Center for Atmospheric Research (NCEP/NCAR) reanalysis data and observed streamflow data, over the study area, were used for the calibration and verification of the downscaling models. The model calibration (1950-1989) and validation (1990-2010) were performed on each calendar month separately and later results were aggregated to produce the time series of prediction. It was found that, the model was able to produce better predictions over the summer and winter months than in autumn and spring. The model tended to over predict the peaks of streamflows particularly after the 1997 drought in Victoria. It was further observed that the NCEP/NCAR reanalysis variables used in the study did not show a clear change corresponding to the drop in streamflow observed after 1997. The problems associated with the method over the recent severe drought have revealed important information to enable improvements for future model work. Downscaling streamflows from the GCMs skips complex hydrologic modelling, saves time and effort in predicting streamflows. Also, the current work in downscaling streamflows from GCM outputs is believed to be the first in Australia. The present research employed downscaling models based on the 12 calendar months enabling a better capture of streamflow characteristics, unlike the models based on seasons used in the past studies.