Effect of baseline meteorological data selection on hydrological modelling of climate change scenarios

This study evaluates how differences in hydrological model parameterisation resulting from the choice of gridded global precipitation data sets and reference evapotranspiration (ETo) equations affects simulated climate change impacts, using the north western Himalayan Beas river catchment as a case study. Six combinations of baseline precipitation data (the Tropical Rainfall Measuring Mission (TRMM) and the Asian Precipitation – Highly Resolved Observational Data Integration Towards Evaluation of Water Resources (APHRODITE)) and Reference Evapotranspiration equations of differing complexity and data requirements (Penman-Monteith, Hargreaves –Samani and Priestley – Taylor) were used in the calibration of the HySim model. Although the six validated hydrological models had similar historical model performance (Nash–Sutcliffe model efficiency coefficient (NSE) from 0.64-0.70), impact response surfaces derived using a scenario neutral approach demonstrated significant deviations in the models’ responses to changes in future annual precipitation and temperature. For example, the change in Q10 varies between -6.5 % to -11.5% in the driest and coolest climate change simulation and +79% to +118% in the wettest and hottest climate change simulation among the six models. The results demonstrate that the baseline meteorological data choices made in model construction significantly condition the magnitude of simulated hydrological impacts of climate change, with important implications for impact study design.NER

Hydrological and sedimentation implications of landscape changes in a Himalayan catchment due to bioenergy cropping

There is a global effort to focus on the development of bioenergy and energy cropping, due to the generally increasing demand for crude oil, high oil price volatility and climate change mitigation challenges. Second generation energy cropping is expected to increase greatly in India as the Government of India has recently approved a national policy of 20 % biofuel blending by 2017; furthermore, the country’s biomass based power generation potential is estimated as around ∼24GW and large investments are expected in coming years to increase installed capacity. In this study, we have modelled the environmental influences (e.g.: hydrology and sediment) of scenarios of increased biodiesel cropping (Jatropha curcas) using the Soil and Water Assessment Tool (SWAT) in a northern Indian river basin. SWAT has been applied to the River Beas basin, using daily Tropical Rainfall Measuring Mission (TRMM) precipitation and NCEP Climate Forecast System Reanalysis (CFSR) meteorological data to simulate the river regime and crop yields. We have applied Sequential Uncertainty Fitting Ver. 2 (SUFI-2) to quantify the parameter uncertainty of the stream [U+FB02]ow modelling. The model evaluation statistics for daily river flows at the Jwalamukhi and Pong gauges show good agreement with measured flows (Nash Sutcliffe efficiency of 0.70 and PBIAS of 7.54 %). The study has applied two land use change scenarios of (1) increased bioenergy cropping in marginal (grazing) lands in the lower and middle regions of catchment (2) increased bioenergy cropping in low yielding areas of row crops in the lower and middle regions of the catchment. The presentation will describe the improved understanding of the hydrological, erosion and sediment delivery and food production impacts arising from the introduction of a new cropping variety to a marginal area; and illustrate the potential prospects of bioenergy production in Himalayan valleys

Numerical solution of the two-phase tumour growth model with moving boundary

A novel numerical technique has been proposed to solve a two-phase tumour growth model in one spatial dimension without needing to account for the boundary dynamics explicitly. The equivalence to the standard definition of a weak solution is proved. The method is tested against equations with analytically known solutions, to illustrate the advantages over the existing techniques. The tumour growth model is solved using the new procedure and showed to be consistent with results available in the literature.Comment: 11 pages, 3 figures, CTAC 2018 conference proceedings (submitted to ANZIAM J

Strong bounded variation estimates for the multi-dimensional finite volume approximation of scalar conservation laws and application to a tumour growth model

A uniform bounded variation estimate for finite volume approximations of the nonlinear scalar conservation law $\partial_t \alpha + \mathrm{div}(\boldsymbol{u}f(\alpha)) = 0$ in two and three spatial dimensions with an initial data of bounded variation is established. We assume that the divergence of the velocity $\mathrm{div}(\boldsymbol{u})$ is of bounded variation instead of the classical assumption that $\mathrm{div}(\boldsymbol{u})$ is zero. The finite volume schemes analysed in this article are set on nonuniform Cartesian grids. A uniform bounded variation estimate for finite volume solutions of the conservation law $\partial_t \alpha + \mathrm{div}(\boldsymbol{F}(t,\boldsymbol{x},\alpha)) = 0$, where $\mathrm{div}_{\boldsymbol{x}}\boldsymbol{F} \not=0$ on nonuniform Cartesian grids is also proved. Such an estimate provides compactness for finite volume approximations in $L^p$ spaces, which is essential to prove the existence of a solution for a partial differential equation with nonlinear terms in $\alpha$, when the uniqueness of the solution is not available. This application is demonstrated by establishing the existence of a weak solution for a model that describes the evolution of initial stages of breast cancer proposed by S. J. Franks et al. [14]. The model consists of four coupled variables: tumour cell concentration, tumour cell velocity--pressure, and nutrient concentration, which are governed by a hyperbolic conservation law, viscous Stokes system, and Poisson equation, respectively. Results from numerical tests are provided and they complement theoretical findings.Comment: 35 pages, 7 figures, 13 Table

Probabilistic modeling of flood characterizations with parametric and minimum information pair-copula model

This paper highlights the usefulness of the minimum information and parametric pair-copula construction (PCC) to model the joint distribution of flood event properties. Both of these models outperform other standard multivariate copula in modeling multivariate flood data that exhibiting complex patterns of dependence, particularly in the tails. In particular, the minimum information pair-copula model shows greater flexibility and produces better approximation of the joint probability density and corresponding measures have capability for effective hazard assessments. The study demonstrates that any multivariate density can be approximated to any degree of desired precision using minimum information pair-copula model and can be practically used for probabilistic flood hazard assessment

Investigating Deuterium destruction in BBN with Felsenkeller accelerator

openThe Big Bang Nucleosynthesis (BBN) began as the universe cooled below 0.1 MeV, about 3 seconds after the Big Bang, creating the first light elements in the universe. The primordial deuterium formed in this process is highly sensitive to the baryon density of the early universe and is therefore an excellent indicator of the same. Direct observations of the Cosmic Microwave Background (CMB) by PLANCK have constrained the baryon density at high precision (< 1%). The observations of primordial clouds provide an independent approach to constrain the cosmological parameters, but this requires accurate knowledge about the reaction rates affecting the primordial deuterium abundance. In this thesis, the focus is on the 2H(p,γ)3He reaction which is the dominant reaction that destroys the primordial deuterium. Previous studies of the reaction by the LUNA collaboration at LNGS in the BBN energy range (30 to 260 keV) found the S factor at high precision (≈ 1%). A follow-up experiment at HZDR in the higher energy range (265 to 1094 keV) found a 10% discrepancy with the LUNA S factor fit. The new 2H(p,γ)3He campaign described in this thesis aims to confirm the findings of LUNA as well as the constrain the existing tension in the S factor between these two previous measurements using an independent setup. The experiment is performed in the energy range 300 to 800 keV (lab energy) using a proton beam, solid deuterated targets and a High Purity Germanium (HPGe) detector setup. The photons produced in the reaction (Q = 5.493 MeV) are measured by the HPGe detectors, which are placed at different angles around the target chamber to facilitate a study of the angular distribution of the cross-section as well. In this thesis, all the steps performed for the measurement of the preliminary S factor for the 2H(p,γ)3He reaction are described, starting from the detector characterization, target analysis and finally the analysis of the 2H(p,γ)3He spectra to arrive at the S factor of the reaction. The preliminary results are shown, in comparison with the LUNA and HZDR measurements and the angular distribution is also discussed. Keywords: BBN, CMB, S factor, FelsenkellerThe Big Bang Nucleosynthesis (BBN) began as the universe cooled below 0.1 MeV, about 3 seconds after the Big Bang, creating the first light elements in the universe. The primordial deuterium formed in this process is highly sensitive to the baryon density of the early universe and is therefore an excellent indicator of the same. Direct observations of the Cosmic Microwave Background (CMB) by PLANCK have constrained the baryon density at high precision (< 1%). The observations of primordial clouds provide an independent approach to constrain the cosmological parameters, but this requires accurate knowledge about the reaction rates affecting the primordial deuterium abundance. In this thesis, the focus is on the 2H(p,γ)3He reaction which is the dominant reaction that destroys the primordial deuterium. Previous studies of the reaction by the LUNA collaboration at LNGS in the BBN energy range (30 to 260 keV) found the S factor at high precision (≈ 1%). A follow-up experiment at HZDR in the higher energy range (265 to 1094 keV) found a 10% discrepancy with the LUNA S factor fit. The new 2H(p,γ)3He campaign described in this thesis aims to confirm the findings of LUNA as well as the constrain the existing tension in the S factor between these two previous measurements using an independent setup. The experiment is performed in the energy range 300 to 800 keV (lab energy) using a proton beam, solid deuterated targets and a High Purity Germanium (HPGe) detector setup. The photons produced in the reaction (Q = 5.493 MeV) are measured by the HPGe detectors, which are placed at different angles around the target chamber to facilitate a study of the angular distribution of the cross-section as well. In this thesis, all the steps performed for the measurement of the preliminary S factor for the 2H(p,γ)3He reaction are described, starting from the detector characterization, target analysis and finally the analysis of the 2H(p,γ)3He spectra to arrive at the S factor of the reaction. The preliminary results are shown, in comparison with the LUNA and HZDR measurements and the angular distribution is also discussed. Keywords: BBN, CMB, S factor, Felsenkelle

Effect of Hedging-Integrated Rule Curves on the Performance of the Pong Reservoir (India) During Scenario-Neutral Climate Change Perturbations

This study has evaluated the effects of improved, hedging-integrated reservoir rule curves on the current and climate-change-perturbed future performances of the Pong reservoir, India. The Pong reservoir was formed by impounding the snow- and glacial-dominated Beas River in Himachal Pradesh. Simulated historic and climate-change runoff series by the HYSIM rainfall-runoff model formed the basis of the analysis. The climate perturbations used delta changes in temperature (from 0° to +2 °C) and rainfall (from −10 to +10 % of annual rainfall). Reservoir simulations were then carried out, forced with the simulated runoff scenarios, guided by rule curves derived by a coupled sequent peak algorithm and genetic algorithms optimiser. Reservoir performance was summarised in terms of reliability, resilience, vulnerability and sustainability. The results show that the historic vulnerability reduced from 61 % (no hedging) to 20 % (with hedging), i.e., better than the 25 % vulnerability often assumed tolerable for most water consumers. Climate change perturbations in the rainfall produced the expected outcomes for the runoff, with higher rainfall resulting in more runoff inflow and vice-versa. Reduced runoff caused the vulnerability to worsen to 66 % without hedging; this was improved to 26 % with hedging. The fact that improved operational practices involving hedging can effectively eliminate the impacts of water shortage caused by climate change is a significant outcome of this study

Effect of baseline snowpack assumptions in the HySIM model in predicting future hydrological behavior of a Himalayan catchment

Glaciers and snowpacks influence streamflow by altering the volume and timing of discharge. Without reliable data on baseline snow and ice volumes, properties and behaviour, initializing hydrological models for climate impact assessment is challenging. Two contrasting HySIM model builds were calibrated and validated against observed discharge data (2000–2008) assuming that snowmelt of the baseline permanent snowpack reserves in the high-elevation sub-catchment are either constrained (snowmelt is limited to the seasonal snow accumulation) or unconstrained (snowmelt is only energy-limited). We then applied both models within a scenario-neutral framework to develop impact response surface of hydrological response to future changes in annual temperature and precipitation. Both models had similar baseline model performance (NSE of 0.69–0.70 in calibration and 0.64–0.66 in validation), but the impact response surfaces differ in the magnitude and (for some combinations) direction of model response to climate change at low (Q10) and high (Q90) daily flows. The implications of historical data inadequacies in snowpack characterization for assessing the impacts of climate change and the associated timing of hydrological tipping points are discussed