Climate-informed stochastic hydrological modeling: Incorporating decadal-scale variability using paleo data

A hierarchical framework for incorporating modes of climate variability into stochastic simulations of hydrological data is developed, termed the climate-informed multi-time scale stochastic (CIMSS) framework. A case study on two catchments in eastern Australia illustrates this framework. To develop an identifiable model characterizing long-term variability for the first level of the hierarchy, paleoclimate proxies, and instrumental indices describing the Interdecadal Pacific Oscillation (IPO) and the Pacific Decadal Oscillation (PDO) are analyzed. A new paleo IPO-PDO time series dating back 440 yr is produced, combining seven IPO-PDO paleo sources using an objective smoothing procedure to fit low-pass filters to individual records. The paleo data analysis indicates that wet/dry IPO-PDO states have a broad range of run lengths, with 90% between 3 and 33 yr and a mean of 15 yr. The Markov chain model, previously used to simulate oscillating wet/dry climate states, is found to underestimate the probability of wet/dry periods >5 yr, and is rejected in favor of a gamma distribution for simulating the run lengths of the wet/dry IPO-PDO states. For the second level of the hierarchy, a seasonal rainfall model is conditioned on the simulated IPO-PDO state. The model is able to replicate observed statistics such as seasonal and multiyear accumulated rainfall distributions and interannual autocorrelations. Mean seasonal rainfall in the IPO-PDO dry states is found to be 15%-28% lower than the wet state at the case study sites. In comparison, an annual lag-one autoregressive model is unable to adequately capture the observed rainfall distribution within separate IPO-PDO states. Copyright © 2011 by the American Geophysical Union.Benjamin J. Henley, Mark A. Thyer, George Kuczera and Stewart W. Frank

Climate-informed stochastic hydrological modeling: Incorporating decadal-scale variability using paleo data

A hierarchical framework for incorporating modes of climate variability into stochastic simulations of hydrological data is developed, termed the climate-informed multi-time scale stochastic (CIMSS) framework. A case study on two catchments in eastern Australia illustrates this framework. To develop an identifiable model characterizing long-term variability for the first level of the hierarchy, paleoclimate proxies, and instrumental indices describing the Interdecadal Pacific Oscillation (IPO) and the Pacific Decadal Oscillation (PDO) are analyzed. A new paleo IPO-PDO time series dating back 440 yr is produced, combining seven IPO-PDO paleo sources using an objective smoothing procedure to fit low-pass filters to individual records. The paleo data analysis indicates that wet/dry IPO-PDO states have a broad range of run lengths, with 90% between 3 and 33 yr and a mean of 15 yr. The Markov chain model, previously used to simulate oscillating wet/dry climate states, is found to underestimate the probability of wet/dry periods >5 yr, and is rejected in favor of a gamma distribution for simulating the run lengths of the wet/dry IPO-PDO states. For the second level of the hierarchy, a seasonal rainfall model is conditioned on the simulated IPO-PDO state. The model is able to replicate observed statistics such as seasonal and multiyear accumulated rainfall distributions and interannual autocorrelations. Mean seasonal rainfall in the IPO-PDO dry states is found to be 15%-28% lower than the wet state at the case study sites. In comparison, an annual lag-one autoregressive model is unable to adequately capture the observed rainfall distribution within separate IPO-PDO states. Copyright © 2011 by the American Geophysical Union.Benjamin J. Henley, Mark A. Thyer, George Kuczera and Stewart W. Frank

Short-term drought risk dynamics: the impact of multi-decadal climate variability and the water supply system properties

The impact of climate variability and climate change on the security of water supply has significant consequences for water resource planning. It is currently the subject of considerable uncertainty in Australia and around the world. Persistent drought across much of Australia and increasing water demand due to population growth has placed greater stress on water supply systems. This paper represents an alternative approach to assessing water supply system security through the use of short-term drought risks and dynamic conditional simulation techniques. A simulation framework that incorporates multi-decadal climate persistence into stochastic rainfall simulations is presented. This climate-informed multi-time scale stochastic (CIMSS) framework was compared to the widely-used AR(1) model. It was found that the CIMSS framework estimated short-term drought risks that were up to double that estimated by the AR(1) model, dependent on climate regime. However, the long-term risks showed insignificant differences between the models. Conditioning simulations on the climate regime and initial reservoir conditions induced a peak in the dynamic short-term drought risk. Thus, the concept of peak short-term conditional drought risk is introduced. A study using non-dimensional water supply system properties revealed that short-term drought risks can be significantly higher than long-term drought risks, increasing non-linearly as the system becomes more stressed. This work illustrates the value of using stochastic rainfall models that actively capture climate variability. They can be considered more informative of water supply system risks than models that rely merely on the hydrological record for their calibration. Further, it is argued that the short-term dynamic drought risk approach is a useful strategic planning tool for water authorities. It represents an advance in thinking and provides a more realistic and informative estimation of drought risk than traditional long-term approaches

Dimissioni del marittimo in regime di continuit\ue0 retribuita di lavoro: l'efficacia immediata del recesso ed il foro competente

Key Points Climate driver informed short-term drought risk methodology is introduced Risk evaluated in each time step conditioned on climate driver & initial storage Traditional approaches underestimate the severity and duration of drought risk This study proposes a methodology for quantifying the impact of climate drivers on water supply drought risk. Climate driver informed short-term drought risks are evaluated for future time steps following conditioning on the initial state of climate drivers and initial reservoir storage level. The methodology is demonstrated using a case study in eastern Australia. Simulations of future rainfall are provided by the climate-informed multitime scale stochastic (CIMSS) model, which is used to incorporate Pacific decadal variability exhibited by the Pacific Decadal Oscillation-Interdecadal Pacific Oscillation. The climate driver informed drought risks are compared to a traditional approach that evaluates long-term drought risks using a nonclimate driver informed rainfall model. The case study considers four scenarios describing a range of different climate driver initial conditions. For the PDO-IPO positive initial state scenarios, the short-term risks are found to be higher than traditional long-term risks by 20%-100%. Furthermore, the elevated short-term risks can last up to 30 years with the CIMSS model but <10 years with the traditional model. The implication of these results is that traditional approaches can significantly underestimate the severity and duration of drought risk. The case study demonstrates a practical and general approach for incorporating the influence of climate drivers and initial storage conditions into drought risk analyses, which could be adapted to other regions and climate drivers. The results prompt a recommendation to water resource planners to carefully integrate climate variability over a range of time scales into water supply system planning and operation. ©2013. American Geophysical Union. All Rights Reserved.Benjamin J. Henley, Mark A. Thyer, and George Kuczer

Virtual Hydrological Laboratories: Developing the next generation of conceptual models to support decision-making under change

As hydrological systems are pushed outside the envelope of historical experience, the ability of current hydrological models to serve as a basis for credible prediction and decision-making is increasingly challenged. Conceptual models are the most common type of surface water hydrological model used for decision making, due to suitable predictive performance, ease of use, and computational speed that facilitates scenario analysis, as well as sensitivity and uncertainty analysis. Hence, conceptual model predictions essentially represent the current “shopfront” of hydrological science as seen by practitioners. However, these models have notable limitations in their ability to resolve internal catchment processes, and subsequently capture hydrological change. New thinking is needed to confront the challenges faced by the current generation of conceptual models in dealing with a changing environment. We argue that the next generation of conceptual models should combine the parsimony of conceptual models (CMs) with our best available scientific understanding.To develop a new strategy we evaluate the principal hydrological lines of evidence (HLE), that have been used to inform process understanding and its incorporation into hydrological models, outlined as follows: (1) Lab-scale experiments, (2) Experimental catchments, (3) Paired catchments, (4) Large Sample Hydrology, and (5) a newly emerging HLE in Virtual Hydrological Laboratories. We evaluate these HLEs to determine if they support the development of new CMs that will be robust (in terms of both predictive ability and hydrological fidelity), by asking the following questions:1. Can the HLE evaluate the model for robustness on catchments of practically-relevant size?2. Can the HLE evaluate the model for robustness on a diverse range of catchment properties?3. Can the HLE evaluate the model for robustness on a wide range of processes/predictions?4. Can the HLE evaluate the model for robustness using real data?5. Can the HLE evaluate the model for robustness to “significant” change?The evaluation found that the four existing HLEs are unable to provide sufficient information to support the development of CMs that provide robust support for decision-making in the face of hydrological change across the wide range of situations of practical relevance.A newly emerging, HLE, the Virtual Hydrological Laboratory, has unique strengths that can overcome the weaknesses of existing HLEs. In particular, the ability to undertake a controlled experimental approach will facilitate and accelerate CM development because (1) In a virtual catchment all hydrological components can be observed (albeit virtually), and thus CMs can be subjected to a more comprehensive level of scrutiny than current observational datasets allow; (2) The ability to systematically change catchment characteristics will provide the ability to conduct controlled experiments that isolate the key changes in hydrological process for different catchments types - this is currently not possible with real-world experiments; and (3) The ability to systematically change climate and land cover/use characteristics will provide the opportunity to undertake hydrological change experiments and evaluate CMs on a wide range of future hydrological change scenarios that are outside the envelope of observations. This strategy provides real potential to proactively “future-proof” CMs to be able to support decision making in the face of changes that are yet to be observed.</p

The National Airborne Field Experiment Data Sets

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