Infrastructure Engineering - Research Publications
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ItemAssessing the degree of hydrologic stress due to climate change(Springer (part of Springer Nature), 2019-09-01)Hydrologists are commonly involved in impact, adaption and vulnerability assessments for climate change projections. This paper presents a framework for how such assessments can better differentiate between the impacts of climate change and those of natural variability, an important differentiation as it relates to the vulnerability to water availability under change. The key concept involved is to characterize “hydrologic stress” relative to the range of behaviour encountered under baseline conditions, where the degree to which climate change causes the behaviour of a system to shift outside this baseline range provides a non-dimensional measure of stress. The concept is applicable to any system that is subject to climate forcings, though the approach is applied here to a range of examples illustrative of many environmental and engineering applications. These include hydrologic systems that are dependent on the frequency of flows above or below selected thresholds, those that are dominated by storage and those which are sensitive to the sequencing of selected flow components. The analyses illustrate that systems designed or adapted to accommodate high variability are less stressed by a given magnitude of climate impacts than those operating under more uniform conditions. The metrics characterize hydrologic stress in a manner that can facilitate comparison across different regions, or across different assets within a region. Adoption of the approach requires reliance on the use of climate ensembles that represent aleatory uncertainty under both baseline and impacted conditions, and this has implications for how the outputs of climate models are provided and utilized.
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ItemHistorical developments of models for estimating evaporation using standard meteorological data(WILEY, 2016-11-01)Evaporation plays a key role in the hydrology of a catchment. World‐wide actual terrestrial evaporation is approximately two third of terrestrial precipitation. Evaporation is the focus of this study in which we describe the historical developments of models for estimating evaporation from standard meteorological data. Although Aristotle and Descartes made early contributions to understanding evaporation, Perrault is credited with having made the first experimental measurement of evaporation in about 1674 though in fact what he measured was sublimation by recording the loss of weight of a block of ice through time. In 1686, Halley carried out the first direct measurement of the evaporation of liquid water. Following a detailed set of experiments, Dalton in 1802 published an essay describing the relationship between evaporation, vapor pressure deficit, and wind speed which is the forerunner of the mass‐transfer equation to estimate open‐water evaporation. In 1921, Cummings proposed an approximate energy balance equation which in 1948 Penman combined with a mass‐transfer equation based on Dalton's work to develop the Penman equation. A key input was the Bowen ratio published in 1926. Following Penman, the next major development was by Monteith in 1965. He modified Penman's equation for a single leaf to deal with a canopy which led to the Penman–Monteith model and is the basis of the FAO56 Reference Crop model. Priestley and Taylor introduced their model in 1972, which is based on the energy term in Penman's equation, and underpins other models. The application of the Complementary Relationship to estimating regional evaporation is credited separately to Brutsaert and Stricker and to Morton. Budyko offered two important contributions. First, he developed a potential evaporation equation in which the evaporating surface temperature was estimated by iteration, whereas Penman approximated a value from the Clausius–Clapeyron equation. Budyko's second contribution is a simple relationship to estimate runoff and, in turn, mean actual evaporation. WIREs Water 2016, 3:788–818. doi: 10.1002/wat2.1172 This article is categorized under: Science of Water > Hydrological Processes Science of Water > Methods
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ItemApproximating uncertainty of annual runoff and reservoir yield using stochastic replicates of global climate model data(COPERNICUS GESELLSCHAFT MBH, 2015)Abstract. Two key sources of uncertainty in projections of future runoff for climate change impact assessments are uncertainty between global climate models (GCMs) and within a GCM. Within-GCM uncertainty is the variability in GCM output that occurs when running a scenario multiple times but each run has slightly different, but equally plausible, initial conditions. The limited number of runs available for each GCM and scenario combination within the Coupled Model Intercomparison Project phase 3 (CMIP3) and phase 5 (CMIP5) data sets, limits the assessment of within-GCM uncertainty. In this second of two companion papers, the primary aim is to present a proof-of-concept approximation of within-GCM uncertainty for monthly precipitation and temperature projections and to assess the impact of within-GCM uncertainty on modelled runoff for climate change impact assessments. A secondary aim is to assess the impact of between-GCM uncertainty on modelled runoff. Here we approximate within-GCM uncertainty by developing non-stationary stochastic replicates of GCM monthly precipitation and temperature data. These replicates are input to an off-line hydrologic model to assess the impact of within-GCM uncertainty on projected annual runoff and reservoir yield. We adopt stochastic replicates of available GCM runs to approximate within-GCM uncertainty because large ensembles, hundreds of runs, for a given GCM and scenario are unavailable, other than the Climateprediction.net data set for the Hadley Centre GCM. To date within-GCM uncertainty has received little attention in the hydrologic climate change impact literature and this analysis provides an approximation of the uncertainty in projected runoff, and reservoir yield, due to within- and between-GCM uncertainty of precipitation and temperature projections. In the companion paper, McMahon et al. (2015) sought to reduce between-GCM uncertainty by removing poorly performing GCMs, resulting in a selection of five better performing GCMs from CMIP3 for use in this paper. Here we present within- and between-GCM uncertainty results in mean annual precipitation (MAP), mean annual temperature (MAT), mean annual runoff (MAR), the standard deviation of annual precipitation (SDP), standard deviation of runoff (SDR) and reservoir yield for five CMIP3 GCMs at 17 worldwide catchments. Based on 100 stochastic replicates of each GCM run at each catchment, within-GCM uncertainty was assessed in relative form as the standard deviation expressed as a percentage of the mean of the 100 replicate values of each variable. The average relative within-GCM uncertainties from the 17 catchments and 5 GCMs for 2015–2044 (A1B) were MAP 4.2%, SDP 14.2%, MAT 0.7%, MAR 10.1% and SDR 17.6%. The Gould–Dincer Gamma (G-DG) procedure was applied to each annual runoff time series for hypothetical reservoir capacities of 1 × MAR and 3 × MAR and the average uncertainties in reservoir yield due to within-GCM uncertainty from the 17 catchments and 5 GCMs were 25.1% (1 × MAR) and 11.9% (3 × MAR). Our approximation of within-GCM uncertainty is expected to be an underestimate due to not replicating the GCM trend. However, our results indicate that within-GCM uncertainty is important when interpreting climate change impact assessments. Approximately 95% of values of MAP, SDP, MAT, MAR, SDR and reservoir yield from 1 × MAR or 3 × MAR capacity reservoirs are expected to fall within twice their respective relative uncertainty (standard deviation/mean). Within-GCM uncertainty has significant implications for interpreting climate change impact assessments that report future changes within our range of uncertainty for a given variable – these projected changes may be due solely to within-GCM uncertainty. Since within-GCM variability is amplified from precipitation to runoff and then to reservoir yield, climate change impact assessments that do not take into account within-GCM uncertainty risk providing water resources management decision makers with a sense of certainty that is unjustified.
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ItemAssessment of precipitation and temperature data from CMIP3 global climate models for hydrologic simulation(COPERNICUS GESELLSCHAFT MBH, 2015)Abstract. The objective of this paper is to identify better performing Coupled Model Intercomparison Project phase 3 (CMIP3) global climate models (GCMs) that reproduce grid-scale climatological statistics of observed precipitation and temperature for input to hydrologic simulation over global land regions. Current assessments are aimed mainly at examining the performance of GCMs from a climatology perspective and not from a hydrology standpoint. The performance of each GCM in reproducing the precipitation and temperature statistics was ranked and better performing GCMs identified for later analyses. Observed global land surface precipitation and temperature data were drawn from the Climatic Research Unit (CRU) 3.10 gridded data set and re-sampled to the resolution of each GCM for comparison. Observed and GCM-based estimates of mean and standard deviation of annual precipitation, mean annual temperature, mean monthly precipitation and temperature and Köppen–Geiger climate type were compared. The main metrics for assessing GCM performance were the Nash–Sutcliffe efficiency (NSE) index and root mean square error (RMSE) between modelled and observed long-term statistics. This information combined with a literature review of the performance of the CMIP3 models identified the following better performing GCMs from a hydrologic perspective: HadCM3 (Hadley Centre for Climate Prediction and Research), MIROCm (Model for Interdisciplinary Research on Climate) (Center for Climate System Research (The University of Tokyo), National Institute for Environmental Studies, and Frontier Research Center for Global Change), MIUB (Meteorological Institute of the University of Bonn, Meteorological Research Institute of KMA, and Model and Data group), MPI (Max Planck Institute for Meteorology) and MRI (Japan Meteorological Research Institute). The future response of these GCMs was found to be representative of the 44 GCM ensemble members which confirms that the selected GCMs are reasonably representative of the range of future GCM projections.