Improving hydrological and vegetation modelling using regional model calibration schemes together with remote sensing data

Remotely sensed data are widely used for estimating hydrological variables, such as land surface soil moisture, land surface evapotranspiration and catchment runoff because they provide temporally dynamic and spatially explicit information on land surface characteristics. Passive microwave observations have been used to infer surface soil moisture information because they are not affected by cloud cover and there is a physical relationship relating emissions to soil water. Remote sensing vegetation cover types and leaf area index time series data have been used as inputs into distributed, semi-distributed and lumped hydrological models (Liu et al., 2007). This paper investigates the potential to improve runoff, soil moisture and vegetation dynamics predictions in ungauged catchments using a land surface hydrological model, AWRA-L, together with remotely sensed leaf area index measurements from NOAA-AVHRR and surface soil moisture measurements from TRMM-TMI. The study is conducted in 579 unregulated catchments across Australia. The AWRA-L model was regionally calibrated (i.e. a single set of parameters optimised) for half the catchments in four experiments: (1) against daily recorded streamflow data (Exp1); (2) against daily recorded streamflow together with monthly NOAAAVHRR leaf area index data (Exp2); (3) against daily recorded streamflow together with daily TRMM-TMI soil moisture data (Exp3); and (4) against all three data sets (Exp4). Next, the four optimised parameter sets obtained from the four regional calibration schemes were applied to the remaining half of the catchments for validation to evaluate the modelling skills for daily runoff and soil moisture predictions in independent catchments. This validation gives an indication of the abilities of the different calibration schemes to provide predictions in ungauged or poorly gauged catchments. The results here show that (1) it is technically feasible (i.e. use of advanced scientific computing, such as CSIRO GPU cluster) to use regional model calibration for hydrological modelling for continental Australia; (2) the incorporation of remotely sensed data into the calibration objective function marginally improves the daily runoff estimates, but noticeably improves the leaf area index and soil moisture estimates in the validation catchments; (3) the biggest benefit comes from Exp4 calibrating against recorded runoff and remotely sensed leaf area index and soil moisture observations. This study is being extended to investigate regional calibration over hydroclimate regions (rather than across the whole of Australia) and in a gridded modelling application to better use the spatial remotely sensed data and to represent rainfall gradients within catchments. It is likely that this, together with adaptation of surface hydrological models to make better use of remotely sensed data, will improve runoff estimates across large regions and the impact of climate and land use changes on runoff. It is noted that the global optimiser, the genetic algorithm toolbox built in MATLAB® did not found global optimum for the regional model calibration scheme one. Nevertheless, this should not noticeably impact the comparison results between the four regional calibration schemes in the validation catchments. This is an ongoing study. It needs to re-configure the optimiser to for obtaining better regional model calibrations

An assessment of modelling capacity to identify the impacts of climate variability on catchment hydrology

The aim is to investigate the consistency or variability of catchment response over time and space and evaluate the predictive error caused by the impacts of climate variability on streamflow. For this purpose, both data- and top-down model-based analyses of the dynamic relation between rainfall and runoff for selected sub-catchments have been undertaken. Data analysis techniques (e.g. trend analysis, deconvolution and baseflow filtering) were used to assess the temporal and spatial variation in the hydrologic response characteristics for each site. The lumped conceptual rainfall-runoff model IHACRES CMD (Catchment Moisture Deficit) version is applied to the sub-catchments to assess the adequacy of the model response in representing the impact of weather patterns on streamflow. Several performance criteria have been used to evaluate the performance of the model in each calibration period using a multi-criteria approach. The IHACRES-3S (3 Storage) model is applied to assess low flow behaviour and capture the timing in the switch between baseflow and no flow periods. Rainfall-runoff model performance characteristics of each sub-catchment are quite related to their incident rainfall regime. Sub-catchments which are located in a lower rainfall regime show poor to average model performance. The reduction in performance in R2 is due to the poor fitting to the peaks for both large and small streamflow events, with the model underestimating the highest flow peaks, and overestimating smaller peaks. Further work will be needed to assess observed data reliability and improve model performance in order to separate the impacts of climate variations and land use change on hydrological response. An appropriate model structure having a variable partitioning between quick and slow flow components is under consideration and techniques are being used to identify problematic periods and events with high error in the observational data

Seasonal streamflow forecasts to improve management of water resources: 3. Issues in assembling an adequate set of Australian historical streamflow data for forecasting

The aim of the national Rainman Streamflow Project is to aid water management in Australia by: (1) working with primary producers and water agencies to assess the value of streamflow/runoff forecasts, (2) developing methods to forecast streamflows and runoff, (3) assembling a national streamflow and runoff data set for use in the AUSTRALIAN RAINMAN computer software package, and (4) building a communications program to facilitate adoption of improved practices. This paper reports on issues encountered in assembling an adequate set of historical streamflow and runoff data suitable for seasonal forecasting. These include negotiating access to data held by state water agencies, assembling the data in a standard format, devising a system of national quality codes, assembling metadata, and extending monthly records from unimpaired streams by rainfall-runoff modelling

Towards model adequacy for identifying the impacts of climate and land use on catchment hydrology

Land use and climate change/variability can have a major impact on catchment hydrology and these impacts can be strongly interrelated. This paper is part of a study that has an ultimate objective to isolate the impacts of land use change on streamflow from those of climate variation in the upper Murrumbidgee catchment, south-eastern Australia. The aim is to investigate the consistency or variability of catchment response and model parameters and performance over time and space. Subcatchments located in the upper part of the Queanbeyan (at Tinderry), Cotter (Gingera), Goodradigbee (Brindabella), Molonglo (Burbong) and Orroral (Crossing) Rivers with daily rainfall-discharge data for 40 years or more and minimal impact from dams have been selected for the study. As often occurs in non-experimental catchments, the land use in these catchments is closely correlated with climate differences, making a classic paired catchment study impossible. Instead, future research will focus on determining under what conditions the model performs well, and investigating whether a land use signal can be detected in the resulting subset of the data. For this purpose, both data- and model-based analyses of the dynamic relation between rainfall and runoff for these subcatchments are presented. Prior to performing the analyses, the rainfall data from selected stations have been checked and corrected to reduce the impact of errors in the areal rainfall estimates. Data analysis techniques (e.g. trend analysis, deconvolution and baseflow filtering) are used to assess the temporal and spatial variation in the hydrologic response characteristics for each site. The lumped conceptual rainfallrunoff model IHACRES CMD (Catchment Moisture Deficit) version is applied to the subcatchments to assess the adequacy of the model response in representing the impact of weather patterns on streamflow. A number of performance criteria have been used to evaluate the performance of the model in each calibration period using a multi-criteria approach. Data-based analysis shows that there has been a significantly stronger decline in streamflow compared to rainfall in all subcatchments after 1990. This decreasing trend in streamflow was more prominent at Burbong and Tinderry, with these subcatchments having lower and more variable storage capacity (the combined surface and sub-surface storage capacity of the catchment inferred from the data-base analysis) than the other subcatchments. The model-based analysis revealed that Tinderry required at least a 6 year length of record to stabilize performance statistics and yield parameter consistency in calibration. Subsequently, an 8 year calibration period was used for all subcatchments. Gingera and Brindabella showed good model performance for all performance indicators, while Tinderry, Burbong and Orroral Crossing showed poor to average model performance in R2, R2ln and bias. The reduction in performance in R2 for Tinderry and Burbong subcatchments was due to the poor fitting to the peaks for both large and small streamflow events, with the model underestimating the highest flow peaks, and overestimating smaller peaks. This suggests that the effective rainfall and/or the quick flow volume for large events are underestimated in the IHACRES model for these catchments. Further work will be needed to improve model performance for Tinderry and Burbong subcatchments in order to separate the impacts of climate variations and land use change on hydrological response. An appropriate model structure having a variable partitioning between quick and slow flow components is under consideration. Different drivers of the variability in the partitioning are being explored, including relating the slow flow volume to effective rainfall depth, seasons and rainfall depth. In addition, modifications to the CMD module of IHACRES are being investigated to improve the estimation of the effective rainfall for large streamflow events

Seasonal streamflow forecasts to improve management of water resources: 5. Major issues and future directions in Australia

The paper reports on major issues encountered in two related projects aimed at improved management of water resources through use of seasonal forecasting of streamflow, and discusses future directions to improve water resource management in Australia. The national Rainman Streamflow Project set out to aid water management in Australia by: (1) working with primary producers and water agencies to assess the value of streamflow/runoff forecasts, (2) developing methods to forecast streamflows and runoff, (3) assembling a national streamflow and runoff data set for use in the AUSTRALIAN RAINMAN computer software package, and (4) building a communications program to facilitate adoption of improved practices. Further information about these tasks may be found in the associated papers at this conference. A related Murray Darling Basin Project examined in detail the economic benefits of using seasonal forecasting of streamflow for irrigated cotton production in the impacted Border Rivers catchment of Queensland / New South Wales. The Streamflow Project showed that empowering people to analyse streamflow and runoff data using AUSTRALIAN RAINMAN was an effective way to improve their water management. However, long-term reliable records of streamflow with local relevance were needed for seasonal forecasting. Data shortages could be largely overcome by use of models to extend short records and to separate out climatic effects from human impacts, provided the modelling results were made available by water agencies. Forecasting often involved integrating information about rainfall, streamflow and climate. There was considerable dependency at present on workshops to raise awareness, provide a basic background in climatology, and build self-reliance with computer software. There were major synergies when irrigators, water agencies and scientists worked together, for example in documenting potential financial benefits. Seasonal climate forecasting has a lot to offer water users (including irrigators, water agencies, environmentalists and government). This paper considers that the main issues of seasonal climate forecasting to improve management of water resources are: obtaining locally relevant data including modelled data from impacted catchments; extending records to the limit of rainfall by modelling; implementing and improving new forecasting tools; integrating forecasts with local rules to assess water allocation; forecasting of overland flows; assessing the value of forecasting (economic, environmental, managerial [demand/supply], water trading); balancing needs of different water use groups where there is potential conflict; transparency of forecast methods; and communication and education about these issues. An holistic approach to land and water issues involving climate variability is essential for future progress