Conrad Wasko's research while affiliated with University of Melbourne and other places

Publications (18)

Climate change coupled with the current trend in rapid urbanization is increasing the risk of flooding that can cause loss of life and damage to property. Adapting to climate change impacts and flood mitigation has become a life critical factor as well as a severe challenge. Green infrastructure and low impact development methods are common approaches that are increasingly used to address stormwater management in developed environments. The analysis and results of this study show that AMC/IC does impact flood response even in urban developed catchments and that it can significantly impact flood responses during storm events. We show that considering AMC/ICs coupled with changes in seasonal rainfall patterns that are projected for warmer climates in the future can modulate some of the increases in flood risk due to climate change. The prevailing thought is that Antecedent Moisture Conditions (AMCs) have little to no relevance in urban hydrology, particularly in relation to climate change. However, current trends in use of stormwater management methods that depend on local storage and infiltration is increasingly making AMC or Initial Conditions (IC) a factor in urban flooding. Despite this trend there is little available literature that discuss the aspect of AMC/IC that can have an implication on urban flood management. Here, we move towards filling this gap in current literature related to impacts of AMCs in flooding of developed areas by focusing on how possible changes in seasonal rainfall patterns in a warming climate might impact AMC and starting conditions in stormwater Best Management Practices (SWBMPs), and how those changed initial conditions impact flood risk in developed areas. Using a comprehensive hydrologic/hydraulic model of an urban/developed catchment and continuous simulation, we demonstrate the importance of accurately accounting for initial conditions in flood assessments. We consider average summer temperatures based on approximately 70 years of historic data to select warm and cold years. We compare the model results between the warm and cold years as a proxy to look at trends related to a future warming climate.
Anthropogenic climate change is increasing extreme rainfall as a result of an increased water-holding capacity of the atmosphere due to higher temperatures. However, observed rainfall-temperature scaling relationships often differ from the theorized increases in moisture-holding capacity. This discrepancy is most evident in the tropics, where higher surface temperatures show a marked decrease in extreme rainfall intensity despite observed increases in extreme rainfall. Here we use atmospheric moisture measurements from the National Aeronautics and Space Administration's Atmospheric Infrared Sounder with surface data to investigate the tropical rainfall-temperature scaling relationship. We show rainfall intensity scales positively with integrated water vapor in all regions. Further, integrated water vapor does not consistently scale positively with surface air temperature and its dependence on background temperature offers a physical explanation for the apparent negative scaling. We conclude that the inconsistent relationship between surface air temperature and moisture is the reason for the “apparent” negative scaling consistently found in the tropics.
Despite evidence of increasing precipitation extremes, corresponding evidence for increases in flooding remains elusive. If anything, flood magnitudes are decreasing despite widespread claims by the climate community that if precipitation extremes increase, floods must also. In this commentary we suggest reasons why increases in extreme rainfall are not resulting in corresponding increases in flooding. Among the possible mechanisms responsible, we identify decreases in antecedent soil moisture, decreasing storm extent, and decreases in snowmelt. We argue that understanding the link between changes in precipitation and changes in flooding is a grand challenge for the hydrologic community and is deserving of increased attention.
The spatial extent and organization of extreme storm events has important practical implications for flood forecasting. Recently, conflicting evidence has been found on the observed changes of storm spatial extent with increasing temperatures. To further investigate this question, a regional climate model assessment is presented for the Greater Sydney region, in Australia. Two regional climate models were considered: the first a convection-resolving simulation at 2-km resolution, the second a resolution of 10 km with three different convection parameterizations. Both the 2- and the 10-km resolutions that used the Betts-Miller-Janjic convective scheme simulate decreasing storm spatial extent with increasing temperatures for 1-hr duration precipitation events, consistent with the observation-based study in Australia. However, other observed relationships of extreme rainfall with increasing temperature were not well represented by the models. Improved methods for considering storm organization are required to better understand potential future changes.
The effects of climate change are causing more frequent extreme rainfall events and an increased risk of flooding in developed areas. Quantifying this increased risk is of critical importance for the protection of life and property as well as for infrastructure planning and design. The updated National Oceanic and Atmospheric Administration (NOAA) Atlas 14 intensity–duration–frequency (IDF) relationships and temporal patterns are widely used in hydrologic and hydraulic modeling for design and planning in the United States. Current literature shows that rising temperatures as a result of climate change will result in an intensification of rainfall. These impacts are not explicitly included in the NOAA temporal patterns, which can have consequences on the design and planning of adaptation and flood mitigation measures. In addition there is a lack of detailed hydraulic modeling when assessing climate change impacts on flooding. The study presented in this paper uses a comprehensive hydrologic and hydraulic model of a fully developed urban/suburban catchment to explore two primary questions related to climate change impacts on flood risk. (1) How do climate change effects on storm temporal patterns and rainfall volumes impact flooding in a developed complex watershed? (2) Is the storm temporal pattern as critical as the total volume of rainfall when evaluating urban flood risk? We use the NOAA Atlas 14 temporal patterns, along with the expected increase in temperature for the RCP8.5 scenario for 2081–2100, to project temporal patterns and rainfall volumes to reflect future climatic change. The model results show that different rainfall patterns cause variability in flood depths during a storm event. The changes in the projected temporal patterns alone increase the risk of flood magnitude up to 35 %, with the cumulative impacts of temperature rise on temporal patterns and the storm volume increasing flood risk from 10 to 170 %. The results also show that regional storage facilities are sensitive to rainfall patterns that are loaded in the latter part of the storm duration, while extremely intense short-duration storms will cause flooding at all locations. This study shows that changes in temporal patterns will have a significant impact on urban/suburban flooding and need to be carefully considered and adjusted to account for climate change when used for the design and planning of future storm water systems.
There is overwhelming consensus that the intensity of heavy precipitation events is increasing in a warming world. It is generally expected such increases will translate to a corresponding increase in flooding. Here, using global data sets for non-urban catchments, we investigate the sensitivity of extreme daily precipitation and streamflow to changes in daily temperature. We find little evidence to suggest that increases in heavy rainfall events at higher temperatures result in similar increases in streamflow, with most regions throughout the world showing decreased streamflow with higher temperatures. To understand why this is the case, we assess the impact of the size of the catchment and the rarity of the event. As the precipitation event becomes more extreme and the catchment size becomes smaller, characteristics such as the initial moisture in the catchment become less relevant, leading to a more consistent response of precipitation and streamflow extremes to temperature increase. Our results indicate that only in the most extreme cases, for smaller catchments, do increases in precipitation at higher temperatures correspond to increases in streamflow.
Continuous rainfall sequences are often used as inputs in hydrologic modeling, particularly where a probabilistic assessment is required. Continuous rainfall sequences provide a means for accounting of all aspects of rainfall that produce flooding, for example, not just the design rainfall event but also the rainfall prior to the extreme rainfall event. With the advent of climate change, higher temperatures have been associated with changes in rainfall, in particular intensifying rainfall extremes with less uniform temporal patterns. Given these demonstrated changes to extreme rainfall with temperature rise, there is a need to modify continuous rainfall generators to account for current and likely future changes in temperature. In this work we propose a novel method for simulating continuous rainfall sequences for a future warmer climate by conditioning parameters on their historical sensitivity with temperature. To demonstrate the proposed technique we use a one-dimensional Neyman-Scott Rectangular Pulses model at two locations across Australia. The statistics used in the parameter estimation are conditioned on their historical sensitivity to average monthly temperature to simulate rainfall for a change in temperature. The results are validated by comparing the simulated rainfall against observations originating from differing temperatures and it is shown that the model captures the relative difference in the mean monthly rainfall and monthly maxima. Encouraged by these results we simulate rainfall for higher temperatures and capture expected changes to annual maxima and design temporal patterns for a warmer climate. While we demonstrate our methodology in the simulation of sub-daily rainfall using a specific model, the approach presented here can be applied to all weather generation schemes for projection in a warmer climate.
The dependence between extreme rainfall and temperature is used to understand climatic relationships, constrain model predictions and evaluate future changes to rainfall. Understanding this dependence, however, is limited by the fact that many areas worldwide lack gauged data, particularly at short time scales. The advent of remote sensing allows a new insight into this dependence quasi-globally. Here, we address whether remotely sensed daily rainfall and temperature can be used in ungauged areas to understand extreme rainfall scaling with temperature. Using the multi-sensor Tropical Rainfall Measuring Mission 3B42 (v7) rainfall product and remotely sensed air temperature we examine the spatial homogeneity in remotely sensed rainfall scaling with temperature and demonstrate that it replicates the spatial variation in the scaling observed in ground data. Finally, changes to duration and percentile are examined showing that the scaling response is climatologically sensitive.
Floodplain managers require accurate and reliable information quantifying flood flow behaviour to support effective land use planning and flood emergency planning. Two dimensional numerical models, typically solving the shallow water approximation of the Navier-Stokes equations, have become a de-facto standard for predicting design flow behaviour. In urban floodplains, the built environment can have a profound influence on the passage and distribution of floodwaters. Obstacles such as buildings, fences and walls can block and redistribute flows overriding the gradient of the topography and locally increasing the flood hazard. The development and application of numerical models for urban floodplains is open to considerable interpretation and numerous modelling techniques have been proposed to represent the buildings and other obstacles to flow. Here, a comprehensive dataset for an urban overland flowpath is developed to help practitioners assess numerical model performance. A physical model of the Morgan-Selwyn floodway in Merewether, Newcastle, Australia was developed and validated against the historical extreme June 2007 “Pasha Bulker” storm. Detailed measurements of the flow behaviour were then collected. The comprehensive dataset of the physical model topography, flood flow boundary conditions as well as detailed measurements of flow depth and velocity are freely available to practitioners who wish to further investigate the dataset or apply the dataset in their numerical modelling.
Extreme precipitation intensity is expected to increase in proportion to the water-holding capacity of the atmosphere. However, increases beyond this expectation have been observed, implying that changes in storm dynamics may be occurring alongside changes in moisture availability. Such changes imply shifts in the spatial organization of storms, and we test this by analyzing present-day sensitivities between storm spatial organization and near-surface atmospheric temperature. We show that both the total precipitation depth and the peak precipitation intensity increases with temperature, while the storm's spatial extent decreases. This suggests that storm cells intensify at warmer temperatures, with a greater total amount of moisture in the storm, as well as a redistribution of moisture toward the storm center. The results have significant implications for the severity of flooding, as precipitation may become both more intense and spatially concentrated in a warming climate.
Low-frequency variability, in the form of the El Niño-Southern Oscillation, plays a key role in shaping local weather systems. However, current continuous stochastic rainfall models do not account for this variability in their simulations. Here a modified Random Pulse Bartlett Lewis stochastic generation model is presented for continuous rainfall simulation exhibiting low-frequency variability. Termed the Hierarchical Random Bartlett Lewis Model (HRBLM), the model features a hierarchical structure to represent a range of rainfall characteristics associated with the El Niño-Southern Oscillation with parameters conditioned to vary as functions of relevant climatic states. Long observational records of near-continuous rainfall at various locations in Australia are used to formulate and evaluate the model. The results indicate clear benefits of using the hierarchical climate-dependent structure proposed. In addition to accurately representing the wet spells characteristics and observed low-frequency variability, the model replicates the interannual variability of the antecedent rainfall preceding the extremes, which is known to be of considerable importance in design flood estimation applications.
Predicting future precipitation extremes is difficult and therefore many studies have used the historical relationship between precipitation intensity and temperature to consider what might occur in a future warmer climate. In general extreme precipitation intensity is expected to increase as temperatures increase. However, in tropical areas it has been observed that, for higher temperatures, lower precipitation intensities occur, contradicting the expected relationship. This has been thought to be due to limits in moisture availability. In this work we show that the negative scaling found in previous studies may be a result of the analysis methods. By conditioning the precipitation intensity and temperature relationship on storm duration we demonstrate that positive scaling of precipitation intensity with temperature in tropical regions of Australia is possible. We argue that methods for estimating scaling relationships should be modified to include storm duration.
The mechanisms that cause changes in precipitation, as well as the resulting storm dynamics, under potential future warming remain debated. Measured sensitivities of precipitation to temperature variations in the present climate have been used to constrain model predictions, debate precipitation mechanisms and speculate on future changes to precipitation and flooding. Here, we analyse data sets of precipitation measurements at 6-min resolution from 79 locations throughout Australia, covering a broad range of climate zones, along with sub-daily temperature measurements of varying resolution. We investigate the relationship between temporal patterns of precipitation intensity within storm bursts and temperature variations in the present climate by calculating the scaling of the precipitation fractions within each storm burst. We find that in the present climate, a less uniform temporal pattern of precipitation-more intense peak precipitation and weaker precipitation during less intense times-is found at higher temperatures, regardless of the climatic region and season. We suggest invigorating storm dynamics could be associated with the warming temperatures expected over the course of the twenty-first century, which could lead to increases in the magnitude and frequency of short-duration floods.
The consensus in the scientific community is that the intensity of extreme precipitation will increase in a warmer climate. However, as there is limited observational evidence to this effect, there is a growing body of research which focuses on directly investigating the relationship between temperature and precipitation. This is currently performed by binning precipitation data in temperature bins and then investigating the trend in the precipitation percentiles in each bin with temperature. In this paper we highlight limitations in the binning approach and present quantile regression as an alternative to the above process. Quantile regression allows estimation of this scaling directly and, unlike binning, is unbiased with sample size. Moreover, quantile regression presents a natural framework for investigation into other factors (covariates) that may be affecting the nature of the scaling relationship. Results using sub-daily rainfall data for Australia show the efficacy of the proposed quantile regression method, as well as the presence of season indicators as significant covariates that affect the scaling relationship of precipitation with temperature. A general increase in the scaling coefficient in winter versus summer is observed.
[1] This paper presents a combinatorial approach for improving spatial predictions. First, copulas are used to interpolate a spatially distributed point rainfall field to a uniform spatial grid. It is observed that results vary substantially depending on the parameters chosen for interpolation leading to the hypothesis that it may be advantageous to estimate copula parameters locally or to combine local and global copula predictions. It is found that by modifying the method of forecast combinations, prediction errors in the spatial interpolation of rainfall can be reduced. Although this method of combining predictions is applied in the context of rainfall interpolation using local and global copula predictions, it can be used on other spatial variables and interpolation methods.
The 11-year solar cycle is used as the basis of imparting variability in solar forcing in a General Circulation Model. Using different amplitude solar forcing it was found that the resulting global atmospheric moisture content exhibited a high power in its wavelet spectrum at the 11-year frequency. To identify long term climatic trends, the Earth's atmosphere was conceptualized as a storage reservoir filling and depleting the atmospheric moisture with time. A high required storage implied that the climate of that region exhibits larger climate variations leading to a larger moisture scarcity or drought. It was found that regions that had relatively small storage requirements experienced an increase in storage required due to the 11-year solar cycle, while regions with relatively large storage requirements experiencing a decrease in storage. It is concluded that the 11-year solar cycle may act to reduce sustained variations in moisture availability across significant climatic regions.

Citations (270)