An effective three-step algorithm for multi-site generation of stochastic weekly hydrological time series

Ilich, Nesa

doi:10.1080/02626667.2013.822643

Cited by 19 publications

(13 citation statements)

References 25 publications

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“…Spatio-temporal modelling of precipitation fields, for example, may be performed using a technique based on phase randomization. However, it must be noted that due to the large number of zero 25 observations (specifically with fine temporal resolution) the normal score transformation can become non-unique. In this case, additional efforts are needed to preserve the spatial structure of precipitation.…”

Section: Resultsmentioning

confidence: 99%

“…This distribution will be used for the back transformation in Step 7, and permits extreme values going beyond the empirical distribution to be obtained. It has four parameters and its cumulative 25 distribution function is expressed as where ξ is the location parameter, α is the scale parameter which must be positive, and k and h are the shape parameters.…”

Section: Stochastic Streamflow Simulationmentioning

confidence: 99%

“…To do so, we used the daily values in a 30-day window around The method is extended to the simulation of stochastic streamflow time series at multiple sites. To model the cross-correlation 25 between sites, the phase randomization performed in Step 4 of the procedure is performed in the same way for all the stations in the dataset (Prichard and Theiler, 1994). In contrast, the parameters of the monthly Kappa distributions and the power spectrum are calculated for each individual site separately.…”

Section: Stochastic Streamflow Simulationmentioning

confidence: 99%

“…Many different approaches have been proposed for the stochastic simulation of streamflow time series, each able to fulfill 25 some but usually not all of the desired properties listed above. One commonly used approach is the use of a synthetic weather generator in combination with a rainfall-runoff model (Pender et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

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Technical note: Stochastic simulation of streamflow time series using phase randomization

Brunner¹,

Bàrdossy²,

Furrer³

2019

Preprint

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Abstract. Stochastically generated streamflow time series are widely used in water resource planning and management. Such series represent sets of plausible yet unobserved streamflow realizations which should reproduce the main characteristics of observed data. These characteristics include the distribution of daily streamflow values and their temporal correlation as expressed by short- and long-range dependence. Existing streamflow generation approaches have mainly focused on the time domain, even though simulation in the frequency domain provides good properties. These properties comprise the simulation of both short- and long-range dependence, as well as extension to multiple sites. Simulation in the frequency domain is based on the randomization of the phases of the Fourier transformation. We here combine phase randomization simulation with a flexible, four-parameter Kappa distribution, which allows for the extrapolation to yet unobserved low and high flows. The simulation approach consists of seven steps: 1) fitting the theoretical Kappa distribution, 2) normalization and deseasonalization, 3) Fourier transformation, 4) random phase generation, 5) inverse Fourier transformation, 6) back transformation, and 7) simulation. The simulation approach is applicable both to individual and multiple sites. It was applied to and validated on a set of four catchments in Switzerland. Our results show that the stochastic streamflow generator based on phase randomization produces realistic streamflow time series with respect to distributional properties and temporal correlation. However, cross-correlation among sites was in some cases found to be underestimated. The approach can be recommended as a flexible tool for various applications such as the dimensioning of reservoirs or the assessment of drought persistence.

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Section: Resultsmentioning

confidence: 99%

Section: Stochastic Streamflow Simulationmentioning

confidence: 99%

Section: Stochastic Streamflow Simulationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Technical note: Stochastic simulation of streamflow time series using phase randomization

Brunner¹,

Bàrdossy²,

Furrer³

2019

Preprint

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“…Stochastic hydrology has been an active area of research since the early 1970s, evolving in the last three decades as a special branch of the earth sciences that combines hydrology and statistical methods for the purpose of generating randomized times series that can closely represent natural hydrologic processes. The use of stochastic time series as alternative inputs includes the evaluation of reservoir operating rules, drought management, water quality studies, and the design of hydraulic structures (Ilich, ; Ilich & Despotovic, ). Recent applications in Alberta (Canada) include real‐time reservoir operating rules (Ilich, ), optimizing river basin allocation (Ilich, ), and river basin management (Ilich et al, ).…”

Section: Introductionmentioning

confidence: 99%

Nine Hundred Years of Weekly Streamflows: Stochastic Downscaling of Ensemble Tree‐Ring Reconstructions

Sauchyn

Ilich²

2017

Water Resources Research

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We combined the methods and advantages of stochastic hydrology and paleohydrology to estimate 900 years of weekly flows for the North and South Saskatchewan Rivers at Edmonton and Medicine Hat, Alberta, respectively. Regression models of water‐year streamflow were constructed using historical naturalized flow data and a pool of 196 tree‐ring (earlywood, latewood, and annual) ring‐width chronologies from 76 sites. The tree‐ring models accounted for up to 80% of the interannual variability in historical naturalized flows. We developed a new algorithm for generating stochastic time series of weekly flows constrained by the statistical properties of both the historical record and proxy streamflow data, and by the necessary condition that weekly flows correlate between the end of a year and the start of the next. A second innovation, enabled by the density of our tree‐ring network, is to derive the paleohydrology from an ensemble of 100 statistically significant reconstructions at each gauge. Using paleoclimatic data to generate long series of weekly flow estimates augments the short historical record with an expanded range of hydrologic variability, including sequences of wet and dry years of greater length and severity. This unique hydrometric time series will enable evaluation of the reliability of current water supply and management systems given the range of hydroclimatic variability and extremes contained in the stochastic paleohydrology. It also could inform evaluation of the uncertainty in climate model projections, given that internal hydroclimatic variability is the dominant source of uncertainty.

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Challenges in modeling and predicting floods and droughts: A review

et al. 2021

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Predictions of floods, droughts, and fast drought‐flood transitions are required at different time scales to develop management strategies targeted at minimizing negative societal and economic impacts. Forecasts at daily and seasonal scale are vital for early warning, estimation of event frequency for hydraulic design, and long‐term projections for developing adaptation strategies to future conditions. All three types of predictions—forecasts, frequency estimates, and projections—typically treat droughts and floods independently, even though both types of extremes can be studied using related approaches and have similar challenges. In this review, we (a) identify challenges common to drought and flood prediction and their joint assessment and (b) discuss tractable approaches to tackle these challenges. We group challenges related to flood and drought prediction into four interrelated categories: data, process understanding, modeling and prediction, and human–water interactions. Data‐related challenges include data availability and event definition. Process‐related challenges include the multivariate and spatial characteristics of extremes, non‐stationarities, and future changes in extremes. Modeling challenges arise in frequency analysis, stochastic, hydrological, earth system, and hydraulic modeling. Challenges with respect to human–water interactions lie in establishing links to impacts, representing human–water interactions, and science communication. We discuss potential ways of tackling these challenges including exploiting new data sources, studying droughts and floods in a joint framework, studying societal influences and compounding drivers, developing continuous stochastic models or non‐stationary models, and obtaining stakeholder feedback. Tackling one or several of these challenges will improve flood and drought predictions and help to minimize the negative impacts of extreme events. This article is categorized under: Science of Water > Science of Water

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An effective three-step algorithm for multi-site generation of stochastic weekly hydrological time series

Cited by 19 publications

References 25 publications

Technical note: Stochastic simulation of streamflow time series using phase randomization

Technical note: Stochastic simulation of streamflow time series using phase randomization

Nine Hundred Years of Weekly Streamflows: Stochastic Downscaling of Ensemble Tree‐Ring Reconstructions

Challenges in modeling and predicting floods and droughts: A review

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