Reducing uncertainty in stochastic streamflow generation and reservoir sizing by combining observed,reconstructed and projected streamflow

Reservoir sizing is one of the most important aspects of water resources engineering as the storage in a reservoir must be sufficient to supply water during extended droughts. Typically, observed streamflow is used to stochastically generate multiple realizations of streamflow to estimate the required storage based on the Sequent Peak Algorithm (SQP). The main limitation in this approach is that the parameters of the stochastic model are purely derived from the observed record (limited to less than 80 years of data) which does not have information related to prehistoric droughts. Further, reservoir sizing is typically estimated to meet future increase in water demand, and there is no guarantee that future streamflow over the planning period will be representative of past streamflow records. In this context, reconstructed streamflow records, usually estimated based on tree ring chronologies, provide better estimates of prehistoric droughts, and future streamflow records over the planning period could be obtained from general circulation models (GCMs) which provide 30 year near-term climate change projections. In this study, we developed paleo streamflow records and future streamflow records for 30 years are obtained by forcing the projected precipitation and temperature from the GCMs over a lumped watershed model. We propose combining observed, reconstructed and projected streamflows to generate synthetic streamflow records using a Bayesian framework that provides the posterior distribution of reservoir storage estimates. The performance of the Bayesian framework is compared to a traditional stochastic streamflow generation approach. Findings based on the split-sample validation show that the Bayesian approach yielded generated streamflow traces more representative of future streamflow conditions than the traditional stochastic approach thereby, reducing uncertainty on storage estimates corresponding to higher reliabilities. Potential strategies for improving future streamflow projections and its utility in reservoir sizing and capacity expansion projects are also discussed.     

A fully non-stationary linear coregionalization model for multivariate random fields

This paper introduces an extension of the traditional stationary linear coregionalization model to handle the lack of stationarity. Under the proposed model, coregionalization matrices are spatially dependent, and basic univariate spatial dependence structures are non-stationary. A parameter estimation procedure of the proposed non-stationary linear coregionalization model is developed under the local stationarity framework. The proposed estimation procedure is based on the method of moments and involves a matrix-valued local stationary variogram kernel estimator, a weighted local least squares method in combination with a kernel smoothing technique. Local parameter estimates are knitted together for prediction and simulation purposes. The proposed non-stationary multivariate spatial modeling approach is illustrated using two real bivariate data examples. Prediction performance comparison is carried out with the classical stationary multivariate spatial modeling approach. According to several criteria, the prediction performance of the proposed non-stationary multivariate spatial modeling approach appears to be significantly better.     

Stochastic synthesis approximating any process dependence and distribution

An extension of the symmetric-moving-average (SMA) scheme is presented for stochastic synthesis of a stationary process for approximating any dependence structure and marginal distribution. The extended SMA model can exactly preserve an arbitrary second-order structure as well as the high order moments of a process, thus enabling a better approximation of any type of dependence (through the second-order statistics) and marginal distribution function (through statistical moments), respectively. Interestingly, by explicitly preserving the coefficient of kurtosis, it can also simulate certain aspects of intermittency, often characterizing the geophysical processes. Several applications with alternative hypothetical marginal distributions, as well as with real world processes, such as precipitation, wind speed and grid-turbulence, highlight the scheme’s wide range of applicability in stochastic generation and Monte-Carlo analysis. Particular emphasis is given on turbulence, in an attempt to simulate in a simple way several of its characteristics regarded as puzzles.     

Predicting long-term urban growth in Beijing (China) with new factors and constraints of environmental change under integrated stochastic and fuzzy uncertainties

Numerous studies related to the simulation and prediction of urban growth to address land-use and land-cover (LULC) changes have been conducted in recent years, but very few have considered the impact of climate change, flooding impact, government relocation, corridor cities, and long-term rainfall variations simultaneously. To bridge the gap, this study predicts possible future LULC changes for 2030 and 2050 in Beijing (China), since Beijing is one of the fastest-growing megacities in the world. The proposed integrated modeling analysis covers four key scenarios to reflect the influences of different factors and constraints on LULC changes, in which cellular automata, Markov chain, and multi-criteria evaluation are fully coupled. While fuzzy membership function was used to address the uncertainty associated with the decision analysis, Markov chain, which is regarded as a stochastic process, was applied to predict future urban growth pathways. In addition, a statistical downscaling model driven by possible climate change scenarios was employed to address long-term rainfall variations in Beijing, China. This study differs from previous ones for Beijing in terms of not only the effects of climate change and flooding impact but also the newly-developed economic free trade zone in Xiong’an and the central government’s plan to relocate to the Tongzhou district. Findings indicate that there is no marked difference in LULC over the four key scenarios. Compared to the baseline LULC in 2010, the predicted results indicate that urban expansion is expected to increase more than 6 and 11% in 2030 and 2050, respectively.     

Probabilistic leak detectability assessment via state estimation in water transport networks

Leak detectability or leakage awareness refers to the capability of sensing losses from a water supply system. Several methods exist in the technical literature to tackle this problem, but only few address it with a state estimation approach. The aim of this paper is to present a new methodology that enables probabilistic assessment of the extent to which water loss could be detected using state estimation by only analysing a single hydraulic state, i.e. one time period. Significant leaks are sensed by identifying unusually high normalised state estimation residuals, which can be identified based on the largest normalised residual test. More specifically, the probability of detecting leaks is computed here by working with the multivariate distribution among measurements and estimates to take into account the noisy nature of measurements with an analytical approach rather than with sampling experiments, which are time-consuming. The methodology set out herein also provides a procedure to systematically assess the minimum leak that could be detected in different parts of the network for a specific measurement setting and operating condition. The method has been applied to a water transport network case study to show its potential and to highlight the usefulness of such a tool for practitioners. The limitations of such a methodology are also discussed, including its possible use for on-line leak detection strategies.     

Uncertainty analysis of urban sewer system using spatial simulation of radar rainfall fields: New York City case study

The goal of this study is to investigate the uncertainty of an urban sewer system’s response under various rainfall and infrastructure scenarios by applying a recently developed nonparametric copula-based simulation approach to extreme rainfall fields. The approach allows for Monte Carlo simulation of multiple variables with differing marginal distributions and arbitrary dependence structure. The independent and identically distributed daily extreme rainfall events of the corresponding urban area, extracted from nationwide high resolution radar data stage IV, are the inputs of the spatial simulator. The simulated extreme rainfall fields were used to calculate excess runoff using the Natural Resources Conservation Service’s approach. New York City is selected as a case study and the results highlight the importance of preserving the spatial dependence of rainfall fields between the grids, even for simplified hydrologic models. This study estimates the probability of combined sewer overflows under extreme rainfall events and identifies the most effective locations in New York City to install green infrastructure for detaining excess stormwater runoff. The results of this study are beneficial for planners working on stormwater management and the approach is broadly applicable because it does not rely on extensive sewer system information.     

Formation events of shoreline sand waves on a gravel beach

Kilometric-scale shoreline sand waves (KSSW) have been observed in the north-east flank of the Dungeness Cuspate Foreland (southeastern coast of the UK). They consist of two bumps separated by embayments with a 350–450-m spacing. We have analysed 36 shoreline surveys of 2-km length using the Discrete Fourier Transformation (DFT), from 2005 to 2016, and seven topographic surveys encompassing the intertidal zone, from 2010 to 2016. The data set shows two clear formation events. In order to test the role of high-angle waves on the KSSW formation, the 10-year wave series is propagated from the wave buoy located at 43 m depth up to a location in front of the undulations at 4 m depth using the SWAN wave model. The dominating SW waves arrive with a very high incidence angle (～ 80°) while the NE waves arrive almost shore normal. The ratio R, which measures the degree of dominance of high-angle waves with respect to low-angle waves, correlates well with the shoreline DFT magnitude values of the observed wavelength undulations. In particular, the highest R values coincide with the formation events. Finally, a linear stability model based on the one-line approximation is applied to the Dungeness profile and the 10-year propagated wave series. It predicts accurately the formation moments, with positive growth rates in the correct order of magnitude for wavelengths similar to the observed ones. All these results confirm that the shoreline undulations in Dungeness are self-organized and that the underlying formation mechanism is the high-angle wave instability. The two detected formation events provide a unique opportunity to validate the existing morphodynamic models that include such instability.     

Deglacial palaeoclimate at Puerto del Hambre,subantarctic Patagonia,Chile

The primary objective of this study is to further substantiate multistep climatic forcing of late‐glacial vegetation in southern South America. A secondary objective is to establish the age of deglaciation in Estrecho de Magallanes–Bahía Inútil. Pollen assemblages at 2‐cm intervals in a core of the mire at Puerto del Hambre (53°36′21″S, 70°55′53″W) provide the basis for reconstructing the vegetation and a detailed account of palaeoclimate in subantarctic Patagonia. Chronology over the 262‐cm length of core is regulated by 20 AMS radiocarbon dates between 14 455 and 10 089 ¹⁴C yr BP. Of 13 pollen assemblage zones, the earliest representing the Oldest Dryas chronozone (14 455–13 000 ¹⁴C yr BP) records impoverished steppe with decreasing frequencies and loss of southern beech (Nothofagus). Successive 100‐yr‐long episodes of grass/herbs and of heath (Empetrum/Ericaceae) before 14 000 ¹⁴C yr BP infer deglacial successional communities under a climate of increased continentality prior to the establishment of grass‐dominated steppe. The Bølling–Allerød (13 000–11 000 ¹⁴C yr BP) is characterised by mesic grassland under moderating climate that with abrupt change to heath dominance after 12 000 ¹⁴C yr BP was warmer and not as humid. At the time of the Younger Dryas (11 000–10 000 ¹⁴C yr BP), grass steppe expanded with a return of colder, more humid climate. Later, with gradual warming, communities were invaded by southern beech. The Puerto del Hambre record parallels multistep, deglacial palaeoclimatic sequences reported elsewhere in the Southern Andes and at Taylor Dome in Antarctica. Deglaciation of Estrecho de Magallanes–Bahía Inútil is dated close to 14 455 ¹⁴C yr BP, invalidating earlier dates of between 15 800 and 16 590 ¹⁴C yr BP. Copyright © 2000 John Wiley & Sons, Ltd.     

Instability in spatial error models: an application to the hypothesis of convergence in the European case

This paper focuses on the hypothesis of stability in the mechanisms of spatial dependence that are usually employed in spatial econometric models. We propose a specification strategy for which the first step is to solve a local estimation algorithm, called the Zoom estimation. The aim of this stage is to detect problems of heterogeneity in the parameters and to identify the regimes. Then we resort to a battery of formal Lagrange Multipliers to test the assumption of stability in the processes of spatial dependence. The alternative hypothesis consists of the existence of several regimes in these parameters. A small Monte Carlo serves to confirm the behaviour of this strategy in a context of finite size samples. As an illustration, we solve an application to the case of the hypothesis of convergence for the per capita income in the European regions. Our results reveal the existence of a strong Centre-Periphery dichotomy in which instability extends to all the elements (coefficients of regression as well as parameters of spatial dependence) that intervene in a classical conditional β-convergence model.     

Cultural land use and vegetation dynamics in the uplands of northern Portugal from the Middle Ages to the Modern period

In Europe, mountain landscapes have evolved in a long-term relationship with human communities and present-day landscapes reflect that ancient interaction. The present study aims to reconstruct human activity in two mountain areas in northern Portugal using palynological analysis integrated with the available regional historical, archaeological and palaeoenvironmental archives. Pollen records from two sedimentary sequences span the Medieval and Modern periods and show that mixed agriculture and livestock grazing were consistently present in both regions throughout these times. Variations in cultural indicators show that the extent of farming fluctuated throughout time, with a general increase in cultivation during the Medieval period but with contractions likely coinciding with times of social disturbance. Historical sources suggest that sociopolitical factors and population pressure were fundamental in the utilisation of upland spaces. This study did not find any convincing evidence to suggest that fire was a fundamental factor in heathland spread. We conclude that long-term occupation of the uplands was sustained by low-intensity land use throughout the Medieval to post-Medieval periods, and that the present landscape has assumed a very different character following depopulation of the mountain areas and a shift towards commercial forestry.