Regional Climate Model Projections Research Articles

Effect of climate change on stormwater runoff characteristics and treatment efficiencies of stormwater retention ponds: a case study from Denmark using TSS and Cu as indicator pollutants.

This study investigated the potential effect of climate changes on stormwater pollution runoff characteristics and the treatment efficiency of a stormwater retention pond in a 95 ha catchment in Denmark. An integrated dynamic stormwater runoff quality and treatment model was used to simulate two scenarios: one representing the current climate and another representing a future climate scenario with increased intensity of extreme rainfall events and longer dry weather periods. 100-year long high-resolution rainfall time series downscaled from regional climate model projections were used as input. The collected data showed that total suspended solids (TSS) and total copper (Cu) concentrations in stormwater runoff were related to flow, rainfall intensity and antecedent dry period. Extreme peak intensities resulted in high particulate concentrations and high loads but did not affect dissolved Cu concentrations. The future climate simulations showed an increased frequency of higher flows and increased total concentrations discharged from the catchment. The effect on the outlet from the pond was an increase in the total concentrations (TSS and Cu), whereas no major effect was observed on dissolved Cu concentrations. Similar results are expected for other particle bound pollutants including metals and slowly biodegradable organic substances such as PAH. Acute toxicity impacts to downstream surface waters seem to be only slightly affected. A minor increase in yearly loads of sediments and particle-bound pollutants is expected, mainly caused by large events disrupting the settling process. This may be important to consider for the many stormwater retention ponds existing in Denmark and across the world.

Incorporating Autonomous Sensors and Climate Modeling to Gain Insight into Seasonal Hydrometeorological Processes within a Tropical Glacierized Valley

Peru is facing imminent water resource issues as glaciers retreat and demand for water increases, yet limited observations and model resolution hamper understanding of hydrometerological processes on local to regional scales. Much of current global and regional climate studies neglect the meteorological forcing of lapse rates (LRs) and valley and slope wind dynamics on critical components of the Peruvian Andes' water cycle, and herein we emphasize the wet season. In 2004 and 2005 we installed an autonomous sensor network (ASN) within the glacierized Llanganuco Valley, Cordillera Blanca (9° S), consisting of discrete, cost-effective, automatic temperature loggers located along the valley axis and anchored by two automatic weather stations. Comparisons of these embedded hydrometeorological measurements from the ASN and climate modeling by dynamical downscaling using the Weather Research and Forecasting model elucidate distinct diurnal and seasonal characteristics of the mountain wind regime and LRs. Wind, temperature, humidity, and cloud simulations suggest that thermally driven up-valley and slope winds converging with easterly flow aloft enhance late afternoon and evening cloud development, which helps explain nocturnal wet season precipitation maxima measured by the ASN. Furthermore, the extreme diurnal variability of along-valley-axis LR and valley wind detected from ground observations and confirmed by dynamical downscaling demonstrate the importance of realistic scale parameterizations of the atmospheric boundary layer to improve regional climate model projections in mountainous regions.

Bias-corrected regional climate projections of extreme rainfall in south-east Australia

This study presents future changes in extreme precipitation as projected within the New South Wales and Australian Capital Territory Regional Climate Modelling (NARCliM) project’s regional climate ensemble for south-east Australia. Model performance, independence and projected future changes were considered when designing the ensemble. We applied a quantile mapping bias correction to the climate model outputs based on theoretical distribution functions, and the implications of this for the projected precipitation extremes is investigated. Precipitation extremes are quantified using several indices from the Expert Team on Climate Change Detection and Indices set of indices. The bias correction was successful in removing most of the magnitude bias in extreme precipitation but does not correct biases in the length of maximum wet and dry spells. The bias correction also had a relatively small effect on the projected future changes. Across a range of metrics, robust increases in the magnitude of precipitation extreme indices are found. While these increases are often in-line with a continuation of the trends present over the last century, they are not found to be statistically significant within the ensemble as a whole. The length of the maximum consecutive wet spell is projected to remain at present-day levels, while the length of the maximum dry spell is projected to increase into the future. The combination of longer dry spells and increases in extreme precipitation magnitude indicate an important change in the character of the precipitation time series. This could have considerable hydrological implications since changes in the sequencing of events can be just as important as changes in event magnitude for hydrological impacts.

The weather@home regional climate modelling project for Australia and New Zealand

Abstract. A new climate modelling project has been developed for regional climate simulation and the attribution of weather and climate extremes over Australia and New Zealand. The project, known as weather@home Australia–New Zealand, uses public volunteers' home computers to run a moderate-resolution global atmospheric model with a nested regional model over the Australasian region. By harnessing the aggregated computing power of home computers, weather@home is able to generate an unprecedented number of simulations of possible weather under various climate scenarios. This combination of large ensemble sizes with high spatial resolution allows extreme events to be examined with well-constrained estimates of sampling uncertainty. This paper provides an overview of the weather@home Australia–New Zealand project, including initial evaluation of the regional model performance. The model is seen to be capable of resolving many climate features that are important for the Australian and New Zealand regions, including the influence of El Niño–Southern Oscillation on driving natural climate variability. To date, 75 model simulations of the historical climate have been successfully integrated over the period 1985–2014 in a time-slice manner. In addition, multi-thousand member ensembles have also been generated for the years 2013, 2014 and 2015 under climate scenarios with and without the effect of human influences. All data generated by the project are freely available to the broader research community.

Impact of climate change on snow melt driven runoff timing over the Alpine region

We investigate the climate change impact on snowmelt-driven runoff (SDR) over the Alpine region using the output from two Med-CORDEX and two EURO-CORDEX regional climate model projections (RCP8.5 scenario) at two resolutions (12, 50 km) driven by a sub-set of the CMIP5 GCMs. Comparison with the European Water Archive observed runoff dataset (242 stations) over the Alps shows a good performance by the higher resolution models in representing present day SDR, with the lower resolution simulations being less accurate in capturing the SDR timing. In the future projections all the models show a temperature increase of up to 4° by the end of the 21st century throughout the Alps and this leads to an anticipation of SDR timing throughout the year that can span from 1 to 3 months depending on the model horizontal resolution. These timing changes are associated with changes in snow cover modulated by the complex Alpine topography. In fact, model resolution plays a critical role in regulating the magnitude, timing and spatial distribution of the response of snow cover and SDR to warming. We find that the accurate simulation of changes in runoff timing requires a high resolution representation of the Alpine topography, and can be important for water storage regulations concerning energy production, agriculture and domestic use.

Projections of future deterioration in UK river quality are hampered by climatic uncertainty under extreme conditions

ABSTRACTA modelling study was undertaken to quantify effects that the climate likely to prevail in the 2050s might have on water quality in two contrasting UK rivers. In so doing, it pinpointed the extent to which time series of climate model output, for some variables derived following bias correction, are fit for purpose when used as a basis for projecting future water quality. Working at daily time step, the method involved linking regional climate model (HadRM3-PPE) projections, Future Flows Hydrology (rainfall–runoff modelling) and the QUESTOR river network water quality model. In the River Thames, the number of days when temperature, dissolved oxygen, biochemical oxygen demand and phytoplankton exceeded undesirable values (>25°C, <6 mg L−1, >4 mg L−1 and >0.03 mg L−1, respectively) was estimated to increase by 4.1–26.7 days per year. The changes do not reflect impacts of any possible change in land use or land management. In the River Ure, smaller increases in occurrence of undesirable water quality are likely to occur in the future (by 1.0–11.5 days per year) and some scenarios suggested no change. Results from 11 scenarios of the hydroclimatic inputs revealed considerable uncertainty around the levels of change, which prompted analysis of the sensitivity of the QUESTOR model to simulations of current climate and hydrology. Hydrological model errors were deemed of less significance than those associated with the derivation and downscaling of driving climatic variables (rainfall, air temperature and solar radiation). Errors associated with incomplete understanding of river water quality interactions with the aquatic ecosystem were found likely to be more substantial than those associated with hydrology, but less than those related to climate model inputs. These errors are largely a manifestation of uncertainty concerning the extent to which phytoplankton biomass is controlled by invertebrate grazers, particularly in mid-summer; and the degree to which this varies from year to year. The quality of data from climate models for generating flows and defining driving variables at the extremes of their distributions has been highlighted as the major source of uncertainty in water quality model outputs.EDITOR A. Castellarin; ASSOCIATE EDITOR X. Fang

A simple method for Bayesian model averaging of regional climate model projections: Application to southeast Australian temperatures

AbstractRecent studies using regional climate models to make probabilistic projections break important new ground. However, they typically lack cross validation, pull the projections toward agreeing models (which can agree due to shared biases), and ignore model skill at reproducing internal variability when weighing the models. Here we conduct the first, to our knowledge, application of Bayesian model averaging (BMA) to make probabilistic projections using regional climate models (RCMs). We weigh the RCMs from the NARCliM project based on their skill at representing temperature over 12 southeast Australian regions in terms of trend, bias, and internal variability. The weights do not depend on model agreement with other models. Using the weighted ensemble, we provide probabilistic seasonal temperature projections. We cross validate the method, and demonstrate that weighted projections are well calibrated and more precise than the unweighted ones. We find considerable differences between the weighted and the unweighted projections in some cases.

Case study on the use of dynamically downscaled climate model data for assessing water security in the Lower Hunter region of the eastern seaboard of Australia

A key aim of the Eastern Seaboard Climate Change Initiative (ESCCI) is under-standing the effect of climate change on the eastern seaboard of Australia, and the implications for climate change adaptation in this area. The New South Wales (NSW) / Australian Capital Territory (ACT) Regional Climate Modelling project (NARCliM) has produced three dynamically downscaled reanalysis climate datasets along with 12 downscaled general circulation model (GCM) projections of current (1990–2009) and future climate. It is expected that the NARCliM dataset will be used for many climate change impact studies including water security assessment. Therefore, in this study we perform a case study investigation into the usefulness and limitations of using NARCliM data for water security assessment, using the Lower Hunter urban water supply system managed by Hunter Water Corporation. We compare streamflow and reservoir levels simulated using NARCliM rainfall and a gridded historical rainfall dataset (AWAP) and focus our analysis on the differences in the simulated streamflow and reservoir levels. We show that when raw (i.e. not bias-corrected) NARCliM rainfall and potential evapotranspiration (PET) data is used to simulate streamflow and reservoir storage levels, some of the NARCliM datasets produce unrealistic results when compared with the simulations using AWAP; for example, some NARCliM datasets simulate reservoirs at or near empty while the AWAP reservoir simulations rarely drop below 60%. The bias-corrected NARCliM rainfall (corrected to AWAP) produces estimates of streamflow and reservoir levels that have a closer, but still inconsistent, match with the streamflow and reservoir levels simulated using AWAP directly. The inconsistency between the simulations using bias-corrected rainfall and historical AWAP rainfall is potentially because while bias-correction reduces systematic deviations it does not fix temporal rainfall sequencing issues. Additionally, the NARCliM PET is not bias-corrected and using bias-corrected rainfall with uncorrected PET in hydrological models results in physical inconsistencies in the rainfall-PET relationship and simulated streamflow. We demonstrate that rainfall plays a large role in the streamflow simulations, while PET seems to play a large role in the reasonableness of the simulated reservoir dynamics by determining the evaporation losses from the reservoirs. The downscaled GCM datasets that simulate the greatest average PET for 1990–2009 show reservoirs often (unrealistically) near empty. This study highlights the need to assess the validity of all climate data for the applications required, with a focus on long-term statistics for reservoir modelling and ensuring realism and coherence across all projected variables.

Statistical testing of dynamically downscaled rainfall data for the Upper Hunter region, New South Wales, Australia

This study tests the statistical properties of downscaled climate data, concentrating on the rainfall which is required for hydrology predictions used in water supply reservoir simulations. The datasets used in this study have been produced by the New South Wales (NSW) / Australian Capital Territory (ACT) Regional Climate Modelling (NARCliM) project which provides a dynamically downscaled climate dataset for southeast Australia at 10 km resolution. In this paper, we present an evaluation of the downscaled NARCliM National Centers for Environmental Prediction (NCEP) / National Center for Atmospheric Research (NCAR) reanalysis simulations. The validation has been performed in the Goulburn River catchment in the Upper Hunter region of New South Wales, Australia. The analysis compared time series of the downscaled NARCliM rain-fall data with ground based measurements for selected Bureau of Meteorology rainfall stations and 5 km gridded data from the Australian Water Availability Project (AWAP). The initial testing of the rainfall was focused on autocorrelations as persistence is an important factor in hydrological and water availability analysis. Additionally, a cross-correlation analysis was performed at daily, fort-nightly, monthly and annually averaged time resolutions. The spatial variability of these statistics were calculated and plotted at the catchment scale. The auto-correlation analysis shows that the seasonal cycle in the NARCliM data is stronger than the seasonal cycle present in the ground based measurements and AWAP data. The cross-correlation analysis also shows a poor agreement between NARCliM data, and AWAP and ground based measurements. The spatial variability plots show a possible link between these discrepancies and orography at the catchment scale.

Production and use of regional climate model projections – A Swedish perspective on building climate services

We describe the process of building a climate service centred on regional climate model results from the Rossby Centre regional climate model RCA4. The climate service has as its central facility a web service provided by the Swedish Meteorological and Hydrological Institute where users can get an idea of various aspects of climate change from a suite of maps, diagrams, explaining texts and user guides. Here we present the contents of the web service and how this has been designed and developed in collaboration with users of the service in a dialogue reaching over more than a decade. We also present the ensemble of climate projections with RCA4 that provides the fundamental climate information presented at the web service. In this context, RCA4 has been used to downscale nine different coupled atmosphere-ocean general circulation models (AOGCMs) from the 5th Coupled Model Intercomparison Project (CMIP5) to 0.44° (c. 50km) horizontal resolution over Europe. Further, we investigate how this ensemble relates to the CMIP5 ensemble.We find that the iterative approach involving the users of the climate service has been successful as the service is widely used and is an important source of information for work on climate adaptation in Sweden. The RCA4 ensemble samples a large degree of the spread in the CMIP5 ensemble implying that it can be used to illustrate uncertainties and robustness in future climate change in Sweden. The results also show that RCA4 changes results compared to the underlying AOGCMs, sometimes in a systematic way.

The representation of health‐relevant heatwave characteristics in a Regional Climate Model ensemble for New South Wales and the Australian Capital Territory, Australia

ABSTRACTHeatwaves have been linked to increased rates of human mortality and morbidity. Given these adverse health impacts, it is crucial to improve our understanding of future changes in these extreme events to inform health impacts studies and adaptation planning. While this information would be most beneficial at a local scale, Global Climate Models provide projections on much coarser resolutions. Regional Climate Models, such as those used in the New South Wales/Australian Capital Territory Regional Climate Modelling (NARCliM) project, provide simulations at a finer scale more appropriate for regional assessments. This study uses NARCliM output to investigate the ability of a Regional Climate Model ensemble to represent heatwave characteristics through the Excess Heat Factor, an index that includes factors that are known to be important to the heat‐health relationship. Both uncorrected and bias‐corrected model output is evaluated against observationally‐derived heatwave characteristics for the period 1990–2009. The effect of bias‐correction on future changes in heatwave characteristics is also assessed. Overall, the simulations provided a good representation of the recent climate and bias‐correction did not greatly change simulated heatwave characteristics. Some regions were more affected by bias‐correction than others, with bias‐correction being most beneficial for coastal regions. We emphasise that these results may not apply to all indices measuring extreme heat, and demonstrate that results for an index based on a fixed absolute temperature threshold are substantially affected when bias‐correction is applied. While supporting bias‐correction, this study demonstrates that it is not necessarily required when evaluating a relative measure such as the Excess Heat Factor.

Changes of storm properties in the United States: Observations and multimodel ensemble projections

Changes in climate are likely to induce changes in precipitation characteristics including intensity, frequency, duration and patterns of events. In this paper, we evaluate the performance of multiple regional climate models (RCMs) in the North American Regional Climate Change Assessment Program (NARCCAP) to simulate storm properties including storm duration, inter-storm period, storm intensity, and within-storm patterns at eight locations in the continental US. We also investigate the future projections of them based on precipitation from NARCCAP historic runs and future runs. Results illustrate that NARCCAP RCMs are consistent with observed precipitation in the seasonal variation of storm duration and inter-storm period, but fail to simulate the magnitude. The ability to simulate the seasonal trend of average storm intensity varies among locations. Within-storm patterns from RCMs exhibit greater variability than from observed records. Comparisons between RCM-historic simulations and RCM projections indicate that there is a large variation in the future changes in storm properties. However, multi-model ensembles of the storm properties suggest that most regions of the United States will experience future changes in storm properties that includes shorter storm duration, longer inter-storm period, and larger average storm intensity.

Tailored climate indices for climate-proofing operational forestry applications in Sweden and Finland

ABSTRACT: In the light of the observed and projected regional warming trend in northern Europe, new recommendations are needed for climate-proofing the selection of forest trees seedlings and growth estimates. Drawing on recently developed climate data sets, both observations of the recent past, and scenarios for the future we present and analyse a set of climate indices tailored to be used for operational forestry applications. Data covering Sweden and Finland include two high-resolution gridded observational data sets of daily mean temperature and daily total precipitation for the period 1961-2007, and an ensemble of regional climate model (RCM) projections based on forcing data from six global scenarios representing a medium high emission scenario. The observation-based data sets are used as reference in two ways; as reference for downscaling and bias-adjustment of daily data from the regional scenarios and to highlight recent observed climate changes and how these changes are represented in the RCM ensemble. For seasonal temperature and related climate indices, there is a clear change towards warmer conditions between the two periods 1961-1990 and 1988-2007, most clearly seen in the observational data and less pronounced in the RCM ensemble. For the future period 2035-2065, 'mid-century', the medium high scenario ensemble used in this study shows substantial changes to several key indices relevant to forestry applications. (Less)

Spread of regional climate model projections: vertical structure and temporal evolution

Due to imperfections inherent to numerical climate simulations, projections of climate change need to be accompanied with an uncertainty estimate, typically derived from multi-model ensembles. Here, an analysis of regional climate model outputs spread and its temporal evolution is presented, for the European air temperatures simulated over the 21st century. To quantify the overall uncertainty, standard deviation (sd) is used and compared to median absolute deviation and inter-quartile range. The outcomes are shown for annual and seasonal means of air temperature at 2 m, 850 hPa, 700 hPa, 500 hPa and 300 hPa levels, simulated by ENSEMBLES regional climate models for eight geographical areas across Europe. Our results indicate that the estimated uncertainty of simulated air temperature changes over Europe generally increases during the 21st century, and typically decreases with height of the studied vertical level. The influence of driving global climate model on the regional simulation generally increases with height of the studied vertical level. In lower tropospheric levels, the clustering of regional climate models according to driving global climate model is stronger in winter than in summer.

Downscaling regional climate model outputs for the Caribbean using a weather generator

ABSTRACTLocally relevant scenarios of daily weather variables that represent the best knowledge of the present climate and projections of future climate change are needed by planners and managers to inform management and adaptation to climate change decisions. Information of this kind for the future is only readily available for a few developed country regions of the world. For many less‐developed regions, it is often difficult to find series of observed daily weather data to assist in planning decisions. This study applies a previously developed single‐site weather generator (WG) to the Caribbean, using examples from Belize in the west to Barbados in the east. The purpose of this development is to provide users in the region with generated sequences of possible future daily weather that they can use in a number of impact sectors. The WG is first calibrated for a number of sites across the region and the goodness of fit of the WG against the daily station observations assessed. Particular attention is focussed on the ability of the precipitation component of the WG to generate realistic extreme values for the calibration or control period. The WG is then modified using change factors (CFs) derived from regional climate model projections (control and future) to simulate future 30‐year scenarios centred on the 2020s, 2050s and 2080s. Changes between the control period and the three futures are illustrated not just by changes in average temperatures and precipitation amounts but also by a number of well‐used measures of extremes (very warm days/nights, the heaviest 5‐day precipitation total in a month, counts of the number of precipitation events above specific thresholds and the number of consecutive dry days).

Climate change effects on lowland stream flood regimes and riparian rich fen vegetation communities in Denmark

ABSTRACTThere is growing awareness that an intensification of the hydrological cycle associated with climate change in many parts of the world will have profound implications for river ecosystem structure and functions. In the present study we link an ensemble of regional climate model projections to a hydrological model with the aim to predict climate driven changes in flooding regimes in lowland riparian areas. Our specific aims were to (1) predict effects of climate change on flood frequencies and magnitudes in riparian areas by using an ensemble of six climate models and (2) combine the obtained predictions with the distribution of rich fen communities to explore whether these are likely to be subjected to increased flooding by a climate change induced increase in river runoff. We found that all regional climate models in the ensemble showed increases in mean annual runoff and that the increase continued through the two scenario periods, i.e. 2035–2065 and 2070–2099. We found concomitant increases in flood levels and flood frequencies. Flood levels and frequencies increased at sites both where the maximum water level was governed directly by river water runoff and where it was governed by river flow roughness (weed cover). We did not find evidence that the present flooding regime was an overall key factor determining the distribution of fen vegetation. However, with the predicted changes in flooding frequencies in the investigated areas we expect to see changes in species compositional patterns within the fen areas under a future climate that may affect the conservation value of these.EDITOR Z.W. Kundzewicz ASSOCIATE EDITOR T. Okruszko

Case study on the use of dynamically downscaled climate model data for assessing water security in the Lower Hunter region of the eastern seaboard of Australia

A key aim of the Eastern Seaboard Climate Change Initiative (ESCCI) is under-standing the effect of climate change on the eastern seaboard of Australia, and the implications for climate change adaptation in this area. The New South Wales (NSW) / Australian Capital Territory (ACT) Regional Climate Modelling project (NARCliM) has produced three dynamically downscaled reanalysis climate datasets along with 12 downscaled general circulation model (GCM) projections of current (1990–2009) and future climate. It is expected that the NARCliM dataset will be used for many climate change impact studies including water security assessment. Therefore, in this study we perform a case study investigation into the usefulness and limitations of using NARCliM data for water security assessment, using the Lower Hunter urban water supply system managed by Hunter Water Corporation. We compare streamflow and reservoir levels simulated using NARCliM rainfall and a gridded historical rainfall dataset (AWAP) and focus our analysis on the differences in the simulated streamflow and reservoir levels. We show that when raw (i.e. not bias-corrected) NARCliM rainfall and potential evapotranspiration (PET) data is used to simulate streamflow and reservoir storage levels, some of the NARCliM datasets produce unrealistic results when compared with the simulations using AWAP; for example, some NARCliM datasets simulate reservoirs at or near empty while the AWAP reservoir simulations rarely drop below 60%. The bias-corrected NARCliM rainfall (corrected to AWAP) produces estimates of streamflow and reservoir levels that have a closer, but still inconsistent, match with the streamflow and reservoir levels simulated using AWAP directly. The inconsistency between the simulations using bias-corrected rainfall and historical AWAP rainfall is potentially because while bias-correction reduces systematic deviations it does not fix temporal rainfall sequencing issues. Additionally, the NARCliM PET is not bias-corrected and using bias-corrected rainfall with uncorrected PET in hydrological models results in physical inconsistencies in the rainfall-PET relationship and simulated streamflow. We demonstrate that rainfall plays a large role in the streamflow simulations, while PET seems to play a large role in the reasonableness of the simulated reservoir dynamics by determining the evaporation losses from the reservoirs. The downscaled GCM datasets that simulate the greatest average PET for 1990–2009 show reservoirs often (unrealistically) near empty. This study highlights the need to assess the validity of all climate data for the applications required, with a focus on long-term statistics for reservoir modelling and ensuring realism and coherence across all projected variables.

Statistical testing of dynamically downscaled rainfall data for the Upper Hunter region, New South Wales, Australia

This study tests the statistical properties of downscaled climate data, concentrating on the rainfall which is required for hydrology predictions used in water supply reservoir simulations. The datasets used in this study have been produced by the New South Wales (NSW) / Australian Capital Territory (ACT) Regional Climate Modelling (NARCliM) project which provides a dynamically downscaled climate dataset for southeast Australia at 10 km resolution. In this paper, we present an evaluation of the downscaled NARCliM National Centers for Environmental Prediction (NCEP) / National Center for Atmospheric Research (NCAR) reanalysis simulations. The validation has been performed in the Goulburn River catchment in the Upper Hunter region of New South Wales, Australia. The analysis compared time series of the downscaled NARCliM rain-fall data with ground based measurements for selected Bureau of Meteorology rainfall stations and 5 km gridded data from the Australian Water Availability Project (AWAP). The initial testing of the rainfall was focused on autocorrelations as persistence is an important factor in hydrological and water availability analysis. Additionally, a cross-correlation analysis was performed at daily, fort-nightly, monthly and annually averaged time resolutions. The spatial variability of these statistics were calculated and plotted at the catchment scale. The auto-correlation analysis shows that the seasonal cycle in the NARCliM data is stronger than the seasonal cycle present in the ground based measurements and AWAP data. The cross-correlation analysis also shows a poor agreement between NARCliM data, and AWAP and ground based measurements. The spatial variability plots show a possible link between these discrepancies and orography at the catchment scale.

Development and Evaluation of Stochastic Rainfall Models for Urban Drought Security Assessment

Development and Evaluation of Stochastic Rainfall Models for Urban Drought Security Assessment

Predicting Changes of Rainfall Erosivity and Hillslope Erosion across New South Wales, Australia

New South Wales and Australia Capital Territory Regional Climate Modelling (NARCliM) project has produced a suite of 12 regional climate projections for south-east Australia spanning the range of likely future changes in climate. The aim of this study was to model and predict the impacts of climate change on rainfall erosivity and hillslope erosion risk across New South Wales using the NARCliM projections to assist the long-term climate change adaptation and regional planning. We developed a daily rainfall erosivity model for Southeast Australia to calculate monthly and annual rainfall erosivity values from the projected daily rainfall data for the baseline (1990-2009) and future periods (2020-2039 and 2060-2079). We produced monthly and annual hillslope erosion maps for these three periods using the Revised Universal Soil Loss Equation (RUSLE) with spatial interpolation to finer scale (100 m). Automated scripts have been developed in a geographic information system (GIS) to calculate the time-series rainfall erosivity and hillslope erosion so that the processes of large quantity climate projections are realistic, repeatable and portable. The model performance was assessed by comparing with data from Bureau of Meteorology for the baseline period and the overall coefficient of efficiency reached 0.9753 (RMSE 13.2%). Both rainfall erosivity and hillslope erosion risk are predicted to increase about 7% in the near future, and about 19% increase in the far future compared with the baseline period. The change is highly uneven in space and time, with the highest increase occurring in the Far-west in autumn. The rainfall erosivity is generally higher in summer and lower in winter, with about 10 times difference between February (highest) and July (lowest).