•Prewhitening drastically distorts the information content of time series.•It leads to underestimation of paleo-extreme conditions, particularly droughts.•A generalized approach is presented to ...address short- and long-term persistence.
Prewhitening, the process of eliminating or reducing short-term stochastic persistence to enable detection of deterministic change, has been extensively applied to time series analysis of a range of geophysical variables. Despite the controversy around its utility, methodologies for prewhitening time series continue to be a critical feature of a variety of analyses including: trend detection of hydroclimatic variables and reconstruction of climate and/or hydrology through proxy records such as tree rings. With a focus on the latter, this paper presents a generalized approach to exploring the impact of a wide range of stochastic structures of short- and long-term persistence on the variability of hydroclimatic time series. Through this approach, we examine the impact of prewhitening on the inferred variability of time series across time scales. We document how a focus on prewhitened, residual time series can be misleading, as it can drastically distort (or remove) the structure of variability across time scales. Through examples with actual data, we show how such loss of information in prewhitened time series of tree rings (so-called “residual chronologies”) can lead to the underestimation of extreme conditions in climate and hydrology, particularly droughts, reconstructed for centuries preceding the historical period.
Recent breakthroughs in artificial intelligence (AI), and particularly in deep learning (DL), have created tremendous excitement and opportunities in the earth and environmental sciences communities. ...To leverage these new ‘data-driven’ technologies, however, one needs to understand the fundamental concepts that give rise to DL and how they differ from ‘process-based’, mechanistic modelling. This paper revisits those fundamentals and addresses 10 questions that might be posed by earth and environmental scientists, and with the aid of a real-world modelling experiment, it explains some critical, but often ignored, issues DL may face in practice. The overarching objective is to contribute to a future of AI-assisted earth and environmental sciences where AI models can (1) embrace the typically ignored knowledge base available, (2) function credibly in ‘true’ out-of-sample prediction, and (3) handle non-stationarity in earth and environmental systems. Comparing and contrasting earth and environmental problems with prominent AI applications, such as playing chess and trading in stock markets, provides critical insights for better directing future research in this field.
•DL is rooted in connectionism, hyper-flexibility, and vigorous optimization, which are alien to process-based modelling.•A knowledge base is essential to enable predictions of complex, open, partially observable, and non-stationary systems.•Bridging DL and process-based modelling is embryonic but has great potential in an age of big data and computational power.
Reservoirs significantly affect flow regimes in watershed systems by changing the magnitude and timing of streamflows. Failure to represent these effects limits the performance of hydrological and ...land-surface models (H-LSMs) in the many highly regulated basins across the globe
and limits the applicability of such models to investigate the futures of
watershed systems through scenario analysis (e.g., scenarios of climate,
land use, or reservoir regulation changes). An adequate representation of
reservoirs and their operation in an H-LSM is therefore essential for a
realistic representation of the downstream flow regime. In this paper, we
present a general parametric reservoir operation model based on piecewise-linear relationships between reservoir storage, inflow, and release to
approximate actual reservoir operations. For the identification of the model
parameters, we propose two strategies: (a) a “generalized”
parameterization that requires a relatively limited amount of data and
(b) direct calibration via multi-objective optimization when more data on
historical storage and release are available. We use data from 37 reservoir
case studies located in several regions across the globe for developing and
testing the model. We further build this reservoir operation model into the
MESH (Modélisation Environmentale-Surface et Hydrologie) modeling system, which is a large-scale H-LSM. Our results across the
case studies show that the proposed reservoir model with both
parameter-identification strategies leads to improved simulation accuracy
compared with the other widely used approaches for reservoir operation
simulation. We further show the significance of enabling MESH with this
reservoir model and discuss the interdependent effects of the simulation
accuracy of natural processes and that of reservoir operations on the overall
model performance. The reservoir operation model is generic and can be
integrated into any H-LSM.
Natural proxy records of hydroclimatic behavior, such as tree ring chronologies, are a rich source of information of past climate‐driven nonstationarities in hydrologic variables. In this study, we ...investigate tree ring chronologies that demonstrate significant correlations with streamflows, with the objective of identifying the spatiotemporal patterns and extents of nonstationarities in climate and hydrology, which are essentially representations of past “climate changes.” First and second‐order nonstationarities are of particular interest in this study. As a prerequisite, we develop a methodology to assess the consistency and credibility of a regional network of tree ring chronologies as proxies for hydrologic regime. This methodology involves a cluster analysis of available tree ring data to understand and evaluate their dependence structure, and a regional temporal‐consistency plot to assess the consistency of different chronologies over time. The major headwater tributaries of the Saskatchewan River basin (SaskRB), the main source of surface water in the Canadian Prairie Provinces, are used as the case study. Results indicate that stationarity might never have existed in the hydrology of the region, as the statistical properties of annual paleo‐hydrologic proxy records across the basin, i.e., the mean and autocorrelation structure, have consistently undergone significant changes (nonstationarities) at different points in the history of the region. The spatial pattern of the changes in the mean statistic has been variable with time, indicating a time‐varying cross‐correlation structure across the tributaries of the SaskRB. Conversely, the changes in the autocorrelation structure across the basin have been in harmony over time. The results demonstrate that the 89 year period of observational record in this region is a poor representation of the long‐term properties of the hydrologic regime, and shorter periods, e.g., 30 year periods, are by no means representative. This paper highlights the need to broaden the understanding of hydrologic characteristics in any basin beyond the limited observational records, as an improved understanding is essential for more reliable assessment and management of available water resources.
Key Points:
Significant nonstationarities exist in the mean and autocorrelation structure
Unlike the mean, autocorrelation changes consistently across space
A 30 year period is not representative of long‐term hydrologic properties
ABSTRACTThis study focuses on determining the optimal distance metric in the K-Nearest Neighbors (KNN) algorithm for spatial modelling of floods. Four distance metrics of the KNN algorithm, namely ...KNN-Manhattan, KNN-Minkowski, KNN-Euclidean, and KNN-Chebyshev, were utilized for flood susceptibility mapping (FSM) in Estahban, Iran. A spatial database comprising 509 flood occurrence points extracted from satellite images and 12 factors influencing floods was created for analysis. The particle swarm optimization (PSO) algorithm was employed for hyperparameter optimization and feature selection, considering eight influential factors as modelling inputs. The modelling results revealed that the KNN-Manhattan algorithm exhibited superior accuracy (root mean squared error (RMSE) = 0.169, mean absolute error (MAE) = 0.051, coefficient of determination (R2) = 0.884, and area under the curve (AUC) = 0.94) compared with the other algorithms for identifying flood-prone areas. The KNN-Minkowski algorithm followed closely, with an RMSE of 0.175, MAE of 0.056, R2 of 0.876, and AUC of 0.939. The KNN-Euclidean algorithm achieved an RMSE of 0.183, MAE of 0.061, R2 of 0.842, and AUC of 0.929, whereas the KNN-Chebyshev algorithm achieved an RMSE of 0.198, MAE of 0.075, R2 of 0.842, and AUC of 0.924.
Thermal pollution is an environmental impact of large dams altering the natural temperature regime of downstream river ecosystems. The present study proposes a simulation–optimization framework to ...reduce thermal pollution downstream from reservoirs and tests it on a real-world case study. This framework attempts to simultaneously minimize the environmental impacts as well as losses to reservoir objectives for water supply. A hybrid machine-learning model is applied to simulate water temperature downstream of the reservoir under various operation scenarios. This model is shown to be robust and achieves acceptable predictive accuracy. The results of simulation–optimization indicate that the reservoir could be operated in such a way that the natural temperature regime is reasonably preserved to protect downstream habitats. Doing so, however, would result in significant trade-offs for reservoir storage and water supply objectives. Such trade-offs can undermine the benefits of reservoirs and need to be carefully considered in reservoir design and operation.
Polyurethane (PU) is a very versatile material in engineering applications, whose mechanical properties can be tailored by the introduction of active fillers. The current research aims to (i) ...investigate the effect of active fillers with varying filler loads on the mechanical properties of a PU system and (ii) develop a micro-mechanical model to describe the hyperelastic behavior of (un)filled PU. Three models are taken into consideration: without strain amplification, with constant strain amplification, and with a deformation-dependent strain amplification. The measured uniaxial stress–strain data of the filled PU nanocomposites reveal clear reinforcement due to the incorporation of carbon black at 5, 10 and 20 wt%. In low concentration (1 wt%), for two different grades of carbon black and a fumed silica, it results in a reduction in the mechanical properties. The micro-mechanical model without strain amplification has a good agreement with the measured stress–strain curves at low concentrations of fillers (1 wt%). For higher filled concentrations (5–15 wt%), the micro-mechanical model with constant strain amplification leads to a better prediction performance. For samples with a larger filler volume fraction (20 wt%) and for a commercial adhesive, the model with a deformation-dependent strain amplification effect leads to the best predictions, i.e., highest R2 regarding curve fitting.
Efficient sampling strategies that scale with the size of the problem, computational budget, and users’ needs are essential for various sampling-based analyses, such as sensitivity and uncertainty ...analysis. In this study, we propose a new strategy, called Progressive Latin Hypercube Sampling (PLHS), which sequentially generates sample points while progressively preserving the distributional properties of interest (Latin hypercube properties, space-filling, etc.), as the sample size grows. Unlike Latin hypercube sampling, PLHS generates a series of smaller sub-sets (slices) such that (1) the first slice is Latin hypercube, (2) the progressive union of slices remains Latin hypercube and achieves maximum stratification in any one-dimensional projection, and as such (3) the entire sample set is Latin hypercube. The performance of PLHS is compared with benchmark sampling strategies across multiple case studies for Monte Carlo simulation, sensitivity and uncertainty analysis. Our results indicate that PLHS leads to improved efficiency, convergence, and robustness of sampling-based analyses.
•A new sequential sampling strategy called PLHS is proposed for sampling-based analysis of simulation models.•PLHS is evaluated across multiple case studies for Monte Carlo simulation, sensitivity and uncertainty analysis.•PLHS provides better performance compared with the other sampling strategies in terms of convergence rate and robustness.•PLHS can be used to monitor the performance of the associated sampling-based analysis and to avoid over- or under-sampling.
Introduction:
There is a pressing need for a holistic approach to optimize water-energy-food (WEF) resources management and to address their interlinkages with other resources due to population ...growth, socio-economic development, and climate change. However, the structural and spatial extent of the WEF system boundaries cause exponential growth in computational complexity, making exploratory data analysis crucial to obtain insight into the system’s characteristics and focus on critical components.
Methods:
This study conducts a multiscale investigation of the WEF nexus within the Canadian prairie provinces (Alberta, Saskatchewan, and Manitoba), utilizing causal-correlational analysis and the multispatial Convergence Cross Mapping (mCCM) method. Initially, we employed regression analysis to establish equations, along with their coefficients of determination (R
2
), to identify patterns among pairs of WEF sectors, gross domestic product (GDP), and greenhouse gas (GHG) emissions. Subsequently, we conducted a causal analysis between correlated pairs using the mCCM method to explore the cause-and-effect relationships between sector pairs within the Canadian prairie provinces; both individually and as a single unit over the period 1990-2020.
Results and discussion:
Results show that energy and water are the most influential sectors on GHG emissions and GDP in the prairies as a whole. Energy has a stronger influence on GHG compared to water and food sectors, while water has the strongest causal influence on the GDP of Alberta, and food and energy do so for Saskatchewan and Manitoba, respectively. The trade-offs for improving WEF nexus security strongly depend on the scale of the system under investigation, highlighting the need for careful deliberations around boundary judgment for decision-making. This study provides a better understanding of the WEF-GDP-GHG nexus in the Canadian prairies and existing interrelationships among the aforementioned sectors, helping to build more efficient WEF nexus models for further simulation and scenario analysis.
Arctic and subarctic regions are amongst the most
susceptible regions on Earth to global warming and climate change.
Understanding and predicting the impact of climate change in these regions
require ...a proper process representation of the interactions between climate,
carbon cycle, and hydrology in Earth system models. This study focuses on
land surface models (LSMs) that represent the lower boundary condition of
general circulation models (GCMs) and regional climate models (RCMs), which
simulate climate change evolution at the global and regional scales,
respectively. LSMs typically utilize a standard soil configuration with a
depth of no more than 4 m, whereas for cold, permafrost regions, field
experiments show that attention to deep soil profiles is needed to
understand and close the water and energy balances, which are tightly
coupled through the phase change. To address this gap, we design and run a
series of model experiments with a one-dimensional LSM, called CLASS
(Canadian Land Surface Scheme), as embedded in the MESH (Modélisation
Environmentale Communautaire – Surface and Hydrology) modelling system, to
(1) characterize the effect of soil profile depth under different climate
conditions and in the presence of parameter uncertainty; (2) assess the
effect of including or excluding the geothermal flux in the LSM at the
bottom of the soil column; and (3) develop a methodology for temperature
profile initialization in permafrost regions, where the system has an
extended memory, by the use of paleo-records and bootstrapping. Our study
area is in Norman Wells, Northwest Territories of Canada, where measurements
of soil temperature profiles and historical reconstructed climate data are
available. Our results demonstrate a dominant role for parameter
uncertainty, that is often neglected in LSMs. Considering such high
sensitivity to parameter values and dependency on the climate condition, we
show that a minimum depth of 20 m is essential to adequately represent
the temperature dynamics. We further show that our proposed initialization
procedure is effective and robust to uncertainty in paleo-climate
reconstructions and that more than 300 years of reconstructed climate time
series are needed for proper model initialization.
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