scholarly journals Simulating Marginal and Dependence Behaviour of Water Demand Processes at Any Fine Time Scale

Water ◽  
2019 ◽  
Vol 11 (5) ◽  
pp. 885 ◽  
Author(s):  
Panagiotis Kossieris ◽  
Ioannis Tsoukalas ◽  
Christos Makropoulos ◽  
Dragan Savic
Keyword(s):  
Time Scale ◽  
Water Demand ◽  
Spatial Scales ◽  
Stochastic Modelling ◽  
Distribution Model ◽  
Spatial And Temporal Variability ◽  
Simulation Strategy ◽  
Demand Processes ◽  
Fine Time ◽  
Dependence Properties

Uncertainty-aware design and management of urban water systems lies on the generation of synthetic series that should precisely reproduce the distributional and dependence properties of residential water demand process (i.e., significant deviation from Gaussianity, intermittent behaviour, high spatial and temporal variability and a variety of dependence structures) at various temporal and spatial scales of operational interest. This is of high importance since these properties govern the dynamics of the overall system, while prominent simulation methods, such as pulse-based schemes, address partially this issue by preserving part of the marginal behaviour of the process (e.g., low-order statistics) or neglecting the significant aspect of temporal dependence. In this work, we present a single stochastic modelling strategy, applicable at any fine time scale to explicitly preserve both the distributional and dependence properties of the process. The strategy builds upon the Nataf’s joint distribution model and particularly on the quantile mapping of an auxiliary Gaussian process, generated by a suitable linear stochastic model, to establish processes with the target marginal distribution and correlation structure. The three real-world case studies examined, reveal the efficiency (suitability) of the simulation strategy in terms of reproducing the variety of marginal and dependence properties encountered in water demand records from 1-min up to 1-h.

scholarly journals Substantial hysteresis in emergent temperature sensitivity of global wetland CH4 emissions

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Kuang-Yu Chang ◽  
William J. Riley ◽  
Sara H. Knox ◽  
Robert B. Jackson ◽  
Gavin McNicol ◽  
...  
Keyword(s):  
Temperature Sensitivity ◽  
Spatial Scales ◽  
Biogeochemical Model ◽  
Spatial And Temporal Variability ◽  
Biogeochemical Models ◽  
Model Parameterization ◽  
Machine Learning Model ◽  
Meta Analyses ◽  
The Relationship ◽  
Global Carbon

AbstractWetland methane (CH4) emissions ($${F}_{{{CH}}_{4}}$$ F C H 4 ) are important in global carbon budgets and climate change assessments. Currently, $${F}_{{{CH}}_{4}}$$ F C H 4 projections rely on prescribed static temperature sensitivity that varies among biogeochemical models. Meta-analyses have proposed a consistent $${F}_{{{CH}}_{4}}$$ F C H 4 temperature dependence across spatial scales for use in models; however, site-level studies demonstrate that $${F}_{{{CH}}_{4}}$$ F C H 4 are often controlled by factors beyond temperature. Here, we evaluate the relationship between $${F}_{{{CH}}_{4}}$$ F C H 4 and temperature using observations from the FLUXNET-CH4 database. Measurements collected across the globe show substantial seasonal hysteresis between $${F}_{{{CH}}_{4}}$$ F C H 4 and temperature, suggesting larger $${F}_{{{CH}}_{4}}$$ F C H 4 sensitivity to temperature later in the frost-free season (about 77% of site-years). Results derived from a machine-learning model and several regression models highlight the importance of representing the large spatial and temporal variability within site-years and ecosystem types. Mechanistic advancements in biogeochemical model parameterization and detailed measurements in factors modulating CH4 production are thus needed to improve global CH4 budget assessments.

scholarly journals Variability of Cloudiness over Mountain Terrain in the Western United States

2017 ◽  
Vol 18 (5) ◽  
pp. 1227-1245 ◽  
Author(s):  
Edwin Sumargo ◽  
Daniel R. Cayan
Keyword(s):  
United States ◽  
Large Scale ◽  
Spatial Scales ◽  
High Elevation ◽  
Empirical Orthogonal Functions ◽  
Western United States ◽  
Spatial And Temporal Variability ◽  
Regional Patterns ◽  
Cloud Albedo

Abstract This study investigates the spatial and temporal variability of cloudiness across mountain zones in the western United States. Daily average cloud albedo is derived from a 19-yr series (1996–2014) of half-hourly Geostationary Operational Environmental Satellite (GOES) images. During springtime when incident radiation is active in driving snowmelt–runoff processes, the magnitude of daily cloud variations can exceed 50% of long-term averages. Even when aggregated over 3-month periods, cloud albedo varies by ±10% of long-term averages in many locations. Rotated empirical orthogonal functions (REOFs) of daily cloud albedo anomalies over high-elevation regions of the western conterminous United States identify distinct regional patterns, wherein the first five REOFs account for ~67% of the total variance. REOF1 is centered over Northern California and Oregon and is pronounced between November and March. REOF2 is centered over the interior northwest and is accentuated between March and July. Each of the REOF/rotated principal components (RPC) modes associates with anomalous large-scale atmospheric circulation patterns and one or more large-scale teleconnection indices (Arctic Oscillation, Niño-3.4, and Pacific–North American), which helps to explain why anomalous cloudiness patterns take on regional spatial scales and contain substantial variability over seasonal time scales.

scholarly journals Development and Integration of Sub-Daily Flood Modelling Capability within the SWAT Model and a Comparison with XAJ Model

Water ◽  
2018 ◽  
Vol 10 (9) ◽  
pp. 1263 ◽  
Author(s):  
Dachen Li ◽  
Simin Qu ◽  
Peng Shi ◽  
Xueqiu Chen ◽  
Feng Xue ◽  
...  
Keyword(s):  
Time Scale ◽  
Flood Control ◽  
Assessment Tool ◽  
Spatial Scales ◽  
Swat Model ◽  
Flood Forecasting ◽  
Base Flow ◽  
Flood Simulation ◽  
Promising Tool ◽  
Daily Time

To date, floods have become one of the most severe natural disasters on Earth. Flood forecasting with hydrological models is an important non-engineering measure for flood control and disaster reduction. The Xin’anjiang (XAJ) model is the most widely used hydrological model in China for flood forecasting, while the Soil and Water Assessment Tool (SWAT) model is widely applied for daily and monthly simulation and has shown its potential for flood simulation. The objective of this paper is to evaluate the performance of the SWAT model in simulating floods at a sub-daily time-scale in a slightly larger basin and compare that with the XAJ model. Taking Qilijie Basin (southeast of China) as a study area, this paper developed the XAJ model and SWAT model at a sub-daily time-scale. The results showed that the XAJ model had a better performance than the sub-daily SWAT model regarding relative runoff error (RRE) but the SWAT model performed well according to relative peak discharge error (RPE) and error of occurrence time of peak flow (PTE). The SWAT model performed unsatisfactorily in simulating low flows due to the daily calculation of base flow but behaved quite well in simulating high flows. We also evaluated the effect of spatial scale on the SWAT model. The results showed that the SWAT model had a good applicability at different spatial scales. In conclusion, the sub-daily SWAT model is a promising tool for flood simulation though more improvements remain to be studied further.

The Water Gap Risk Index – a novel approach for spatially distributed and sector-specific water scarcity risk calculations in urbanized catchments

2021 ◽  
Author(s):  
Mehmet Umit Taner ◽  
Dimmie Hendiriks ◽  
Lieke Huesken ◽  
Niels Mulder ◽  
Diana Morales Irato ◽  
...  
Keyword(s):  
Water Scarcity ◽  
Water Demand ◽  
Risk Index ◽  
Spatial And Temporal Variability ◽  
The Novel ◽  
User Group ◽  
User Groups ◽  
Urban Catchments ◽  
Gap Risk ◽  
The Impact

<p>An increasing number of mega-cities, such as Cape Town, Lima, and São Paulo, are confronted with increasing droughts as well as an increase in water demand. Inevitably, this leads to increasing pressure on the available water resources and associated risks and economic impact for the water-dependent sectors (eg. drinking water supply, industry, energy production, agriculture, nature) and different user groups within the sectors (eg. low, middle- and high-income households, self-subsistence farmers, large farms). To address these problems and to develop targeted mitigation strategies, risk analyses are required that quantify the impact of water scarcity on the various sectors and users-groups in different parts of the catchment.</p><p>Here, we present the Water Gap Risk Index (WGRI) that quantifies water scarcity and its impacts on a variety of economic sectors and user groups. The WGRI provides a normalized score to reflect high spatial and temporal variability typical for urban catchments that apply to different settings and problems. Index calculation involves the combination of unmet water demand and its characteristics with socioeconomic aspects related to vulnerability and exposure. The Water Gap term quantifies water system performance over a defined time period taking into account the frequency, persistence, and severity of unmet water demand.  Vulnerability metrics provide a score for each sector and user-group separately using context-specific vulnerability indicators of each sector and user-group.</p><p>In the novel WGRI special attention is paid to the vulnerability of different water user-groups, based on their socio-economic status level (expressed in income, consumption, or other indicators) and respective water use. We consider that 1 liter of water does not have the same utility for different user groups, based on the principle of the diminishing marginal utility curve. As a result, the impact of water scarcity and mitigation measures will also play out differently for these different user groups.</p><p>The novel WGRI is being applied in the context of the WaterLOUPE approach[1], to the catchment of Sao Paolo, Lima, and Chennai.</p><p>[1] https://doi.org/10.5194/egusphere-egu2020-20505</p>

Strategies for Restoring River Ecosystems: Sources of Variability and Uncertainty in Natural and Managed Systems

2003 ◽  
Keyword(s):  
Spatial Variation ◽  
Spatial Scales ◽  
Spatial And Temporal Variability ◽  
Key Factors ◽  
Policy Makers ◽  
River Ecosystems ◽  
Spatial And Temporal Scales ◽  
Lower Productivity ◽  
Biological Attributes ◽  
Stream Biota

<em>Abstract</em>.—Productivity and biodiversity of stream and river ecosystems vary at multiple spatial and temporal scales. Spatial variation in productivity of salmonid fishes varies over two orders of magnitude worldwide and shows lesser, but still considerable, variation at the regional and watershed level. Spatial variation in production and diversity is related to variation in physical, chemical, and biological attributes of watersheds and channels. Channel constraint, gradient, and size are key factors in determining productivity and diversity. Constrained reaches generally support different species and lower productivity than lower-gradient, unconstrained channels. Variation in the condition of stream reaches is greatly influenced by disturbances. Severe disturbances fundamentally change the functional and structural properties of stream ecosystems and alter the way in which the surrounding watershed interacts with the stream. Periodic occurrence of disturbances and the process of recovery play a key role in maintaining spatial and temporal variability in stream conditions and thereby contribute to the productivity and diversity of stream biota. Land use by humans alters the frequency and characteristics of disturbances. As a result, human-altered disturbance patterns often homogenize channel conditions across a watershed rather than introducing diversity. Watershed restoration plans need to recognize the role variability and disturbance play in maintaining the productivity and diversity of stream biota. Incorporating this understanding into watershed management and restoration will require scientists, managers, and policy makers to view watersheds at much longer temporal and larger spatial scales than is currently done.

New model for acoustic waves propagating through a vortical flow

2017 ◽  
Vol 823 ◽  
pp. 658-674 ◽  
Author(s):  
Jim Thomas
Keyword(s):  
Asymptotic Analysis ◽  
Wave Field ◽  
Time Scale ◽  
Acoustic Waves ◽  
High Frequency ◽  
Spatial Scales ◽  
Amplitude Equation ◽  
Vortical Flow ◽  
Reduced Model ◽  
Scale Separation

A new amplitude equation is derived for high-frequency acoustic waves propagating through an incompressible vortical flow using multi-time-scale asymptotic analysis. The reduced model is derived without an explicit spatial-scale separation ansatz between the wave and vortical fields. As a consequence, the model is seen to capture very well the features of the wave field in the regime where the spatial scales of the wave and vortical fields are comparable, a regime for which an optimal reduced model does not seem to be available.

scholarly journals Mapping Coastal Wetland Biomass from High Resolution Unmanned Aerial Vehicle (UAV) Imagery

2019 ◽  
Vol 11 (5) ◽  
pp. 540 ◽  
Author(s):  
Cheryl Doughty ◽  
Kyle Cavanaugh
Keyword(s):  
Salt Marsh ◽  
High Resolution ◽  
Aboveground Biomass ◽  
Vegetation Index ◽  
Coastal Wetland ◽  
Spatial Scales ◽  
Biomass Estimation ◽  
Spatial And Temporal Variability ◽  
Estimation Model ◽  
Spatial And Temporal Scales

Salt marsh productivity is an important control of resiliency to sea level rise. However, our understanding of how marsh biomass and productivity vary across fine spatial and temporal scales is limited. Remote sensing provides a means for characterizing spatial and temporal variability in marsh aboveground biomass, but most satellite and airborne sensors have limited spatial and/or temporal resolution. Imagery from unmanned aerial vehicles (UAVs) can be used to address this data gap. We combined seasonal field surveys and multispectral UAV imagery collected using a DJI Matrice 100 and Micasense Rededge sensor from the Carpinteria Salt Marsh Reserve in California, USA to develop a method for high-resolution mapping of aboveground saltmarsh biomass. UAV imagery was used to test a suite of vegetation indices in their ability to predict aboveground biomass (AGB). The normalized difference vegetation index (NDVI) provided the strongest correlation to aboveground biomass for each season and when seasonal data were pooled, though seasonal models (e.g., spring, r2 = 0.67; RMSE = 344 g m−2) were more robust than the annual model (r2 = 0.36; RMSE = 496 g m−2). The NDVI aboveground biomass estimation model (AGB = 2428.2 × NDVI + 120.1) was then used to create maps of biomass for each season. Total site-wide aboveground biomass ranged from 147 Mg to 205 Mg and was highest in the spring, with an average of 1222.9 g m−2. Analysis of spatial patterns in AGB demonstrated that AGB was highest in intermediate elevations that ranged from 1.6–1.8 m NAVD88. This UAV-based approach can be used aid the investigation of biomass dynamics in wetlands across a range of spatial scales.

scholarly journals A conditional disaggregation algorithm for generating fine time-scale rainfall data in a warmer climate

2013 ◽  
Vol 479 ◽  
pp. 86-99 ◽  
Author(s):  
Seth Westra ◽  
Jason P. Evans ◽  
Rajeshwar Mehrotra ◽  
Ashish Sharma
Keyword(s):  
Time Scale ◽  
Rainfall Data ◽  
Fine Time

scholarly journals Arctic sea ice thickness loss determined using subsurface, aircraft, and satellite observations

2015 ◽  
Vol 9 (1) ◽  
pp. 269-283 ◽  
Author(s):  
R. Lindsay ◽  
A. Schweiger
Keyword(s):  
Sea Ice ◽  
Large Scale ◽  
Spatial Scales ◽  
Arctic Basin ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Ice Thickness ◽  
Spatial And Temporal Variability ◽  
Sampling Errors ◽  
Sea Ice Thickness

Abstract. Sea ice thickness is a fundamental climate state variable that provides an integrated measure of changes in the high-latitude energy balance. However, observations of mean ice thickness have been sparse in time and space, making the construction of observation-based time series difficult. Moreover, different groups use a variety of methods and processing procedures to measure ice thickness, and each observational source likely has different and poorly characterized measurement and sampling errors. Observational sources used in this study include upward-looking sonars mounted on submarines or moorings, electromagnetic sensors on helicopters or aircraft, and lidar or radar altimeters on airplanes or satellites. Here we use a curve-fitting approach to determine the large-scale spatial and temporal variability of the ice thickness as well as the mean differences between the observation systems, using over 3000 estimates of the ice thickness. The thickness estimates are measured over spatial scales of approximately 50 km or time scales of 1 month, and the primary time period analyzed is 2000–2012 when the modern mix of observations is available. Good agreement is found between five of the systems, within 0.15 m, while systematic differences of up to 0.5 m are found for three others compared to the five. The trend in annual mean ice thickness over the Arctic Basin is −0.58 ± 0.07 m decade−1 over the period 2000–2012. Applying our method to the period 1975–2012 for the central Arctic Basin where we have sufficient data (the SCICEX box), we find that the annual mean ice thickness has decreased from 3.59 m in 1975 to 1.25 m in 2012, a 65% reduction. This is nearly double the 36% decline reported by an earlier study. These results provide additional direct observational evidence of substantial sea ice losses found in model analyses.

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