scholarly journals Representation of spatial and temporal variability in large-domain hydrological models: Case study for a mesoscale prealpine basin

2016 ◽  
Author(s):  
Lieke Melsen ◽  
Adriaan Teuling ◽  
Paul Torfs ◽  
Massimiliano Zappa ◽  
Naoki Mizukami ◽  
...  
Keyword(s):  
Spatial Variability ◽  
Temporal Variability ◽  
Large Degree ◽  
Spatial And Temporal Variability ◽  
Hydrological Models ◽  
Infiltration Capacity ◽  
Large Domain ◽  
Modelling Studies ◽  
Temporal And Spatial ◽  
Set Up

Abstract. The transfer of parameter sets over different temporal and spatial resolutions is common practice in many large-domain hydrological modelling studies. The degree to which parameters are transferable across temporal and spatial resolutions is an indicator for how well spatial and temporal variability are represented in the models. A large degree of transferability may well indicate a poor representation of such variability in the employed models. To investigate parameter transferability over resolution in time and space we have set-up a study in which the Variable Infiltration Capacity (VIC) model for the Thur basin in Switzerland was run with four different spatial resolutions (1×1 km, 5×5 km, 10×10 km, lumped) and evaluated for three relevant temporal resolutions (hour, day, month), both applied with uniform and distributed forcing. The model was run 3,150 times using a Hierarchical Latin Hypercube Sample and the best 1 % of the runs was selected as behavioural. The overlap in behavioural sets for different spatial and temporal resolutions was used as indicator for parameter transferability. A key result from this study is that the overlap in parameter sets for different spatial resolutions was much larger than for different temporal resolutions, also when the forcing was applied in a distributed fashion. This result suggests that it is easier to transfer parameters across different spatial resolutions than across different temporal resolutions. However, the result also indicates a substantial underestimation in the spatial variability represented in the hydrological simulations, suggesting that the high spatial transferability may occur because the current generation of large-domain models have an inadequate representation of spatial variability and hydrologic connectivity. The results presented in this paper provide a strong motivation to further investigate and substantially improve the representation of spatial and temporal variability in large-domain hydrological models.

scholarly journals Representation of spatial and temporal variability in large-domain hydrological models: case study for a mesoscale pre-Alpine basin

2016 ◽  
Vol 20 (6) ◽  
pp. 2207-2226 ◽  
Author(s):  
Lieke Melsen ◽  
Adriaan Teuling ◽  
Paul Torfs ◽  
Massimiliano Zappa ◽  
Naoki Mizukami ◽  
...  
Keyword(s):  
Spatial Variability ◽  
Temporal Variability ◽  
Large Degree ◽  
Spatial And Temporal Variability ◽  
Hydrological Models ◽  
Infiltration Capacity ◽  
Large Domain ◽  
Modelling Studies ◽  
Temporal And Spatial ◽  
Set Up

Abstract. The transfer of parameter sets over different temporal and spatial resolutions is common practice in many large-domain hydrological modelling studies. The degree to which parameters are transferable across temporal and spatial resolutions is an indicator of how well spatial and temporal variability is represented in the models. A large degree of transferability may well indicate a poor representation of such variability in the employed models. To investigate parameter transferability over resolution in time and space we have set up a study in which the Variable Infiltration Capacity (VIC) model for the Thur basin in Switzerland was run with four different spatial resolutions (1 km  ×  1 km, 5 km  ×  5 km, 10 km  ×  10 km, lumped) and evaluated for three relevant temporal resolutions (hour, day, month), both applied with uniform and distributed forcing. The model was run 3150 times using the Hierarchical Latin Hypercube Sample and the best 1 % of the runs was selected as behavioural. The overlap in behavioural sets for different spatial and temporal resolutions was used as an indicator of parameter transferability. A key result from this study is that the overlap in parameter sets for different spatial resolutions was much larger than for different temporal resolutions, also when the forcing was applied in a distributed fashion. This result suggests that it is easier to transfer parameters across different spatial resolutions than across different temporal resolutions. However, the result also indicates a substantial underestimation in the spatial variability represented in the hydrological simulations, suggesting that the high spatial transferability may occur because the current generation of large-domain models has an inadequate representation of spatial variability and hydrologic connectivity. The results presented in this paper provide a strong motivation to further investigate and substantially improve the representation of spatial and temporal variability in large-domain hydrological models.

scholarly journals A new approach to model the spatial and temporal variability of recharge to karst aquifers

2012 ◽  
Vol 16 (7) ◽  
pp. 2219-2231 ◽  
Author(s):  
A. Hartmann ◽  
J. Lange ◽  
M. Weiler ◽  
Y. Arbel ◽  
N. Greenbaum
Keyword(s):  
Time Series ◽  
Spatial Variability ◽  
Temporal Variability ◽  
Model Performance ◽  
Spatial And Temporal Variability ◽  
Hydrological Models ◽  
Near Surface ◽  
New Approach ◽  
Spatially Distributed ◽  
Karst Systems

Abstract. In karst systems, near-surface dissolution of carbonate rock results in a high spatial and temporal variability of groundwater recharge. To adequately represent the dominating recharge processes in hydrological models is still a challenge, especially in data scarce regions. In this study, we developed a recharge model that is based on a conceptual model of the epikarst. It represents epikarst heterogeneity as a set of system property distributions to produce not only a single recharge time series, but a variety of time series representing the spatial recharge variability. We tested the new model with a unique set of spatially distributed flow and tracer observations in a karstic cave at Mt. Carmel, Israel. We transformed the spatial variability into statistical variables and apply an iterative calibration strategy in which more and more data was added to the calibration. Thereby, we could show that the model is only able to produce realistic results when the information about the spatial variability of the observations was included into the model calibration. We could also show that tracer information improves the model performance if data about the spatial variability is not included.

scholarly journals A hydrochemical modelling framework for combined assessment of spatial and temporal variability in stream chemistry: application to Plynlimon, Wales

2001 ◽  
Vol 5 (1) ◽  
pp. 49-58 ◽  
Author(s):  
H.J. Foster ◽  
M.J. Lees ◽  
H.S. Wheater ◽  
C. Neal ◽  
B. Reynolds
Keyword(s):  
Land Use ◽  
Land Use Change ◽  
Spatial Variability ◽  
Temporal Variability ◽  
Biological Diversity ◽  
Spatial And Temporal Variability ◽  
Stream Chemistry ◽  
Two Component ◽  
End Members

Abstract. Recent concern about the risk to biota from acidification in upland areas, due to air pollution and land-use change (such as the planting of coniferous forests), has generated a need to model catchment hydro-chemistry to assess environmental risk and define protection strategies. Previous approaches have tended to concentrate on quantifying either spatial variability at a regional scale or temporal variability at a given location. However, to protect biota from ‘acid episodes’, an assessment of both temporal and spatial variability of stream chemistry is required at a catchment scale. In addition, quantification of temporal variability needs to represent both episodic event response and long term variability caused by deposition and/or land-use change. Both spatial and temporal variability in streamwater chemistry are considered in a new modelling methodology based on application to the Plynlimon catchments, central Wales. A two-component End-Member Mixing Analysis (EMMA) is used whereby low and high flow chemistry are taken to represent ‘groundwater’ and ‘soil water’ end-members. The conventional EMMA method is extended to incorporate spatial variability in the two end-members across the catchments by quantifying the Acid Neutralisation Capacity (ANC) of each in terms of a statistical distribution. These are then input as stochastic variables to a two-component mixing model, thereby accounting for variability of ANC both spatially and temporally. The model is coupled to a long-term acidification model (MAGIC) to predict the evolution of the end members and, hence, the response to future scenarios. The results can be plotted as a function of time and space, which enables better assessment of the likely effects of pollution deposition or land-use changes in the future on the stream chemistry than current methods which use catchment average values. The model is also a useful basis for further research into linkage between hydrochemistry and intra-catchment biological diversity. Keywords: hydrochemistry, End-Member Mixing Analysis (EMMA), uplands, acidification

scholarly journals Analysis of Long-Term Solar Resource Variability Using NSRDB Version 3

2021 ◽  
Author(s):  
Manajit Sengupta ◽  
Aron Habte
Keyword(s):  
Solar Radiation ◽  
Spatial Variability ◽  
Temporal Variability ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Spatial And Temporal Variability ◽  
Cloud Optical Depth ◽  
Resource Variability ◽  
Solar Resource

<p>Understanding long-term solar resource variability is essential for planning and deployment of solar energy systems. These variabilities occur due to deterministic effects such as sun cycle and nondeterministic such as complex weather patterns. The NREL’s National Solar Radiation Database (NSRDB) provides long term solar resource data covering 1998- 2019 containing more than 2 million pixels over the Americas and gets updated on an annual basis. This dataset is satellite-based and uses a two-step physical model for it’s development. In the first step we retrieve cloud properties such as cloud mask, cloud type, cloud optical depth and effective radius. The second step uses a fast radiative transfer model to compute solar radiation.  This dataset is ideal for studying solar resource variability. For this study, NSRDB version 3 which contains data from 1998-2017 on a half hourly and 4x4 km temporal and spatial resolution was used. The study analyzed the spatial and temporal trend of solar resource of global horizontal irradiance (GHI) and direct normal irradiance (DNI) using long-term 20-years NSRDB data. The coefficient of variation (COV) was used to analyze the spatio-temporal interannual and seasonal variabilities. The spatial variability was analyzed by comparing the center pixel to neighboring pixels. The spatial variability result showed higher COV as the number of neighboring pixels increased. Similarly, the temporal variability for the NSRDB domain ranges on average from ±10% for GHI and ±20% for DNI. Furthermore, the long-term variabilities were also analyzed using the Köppen-Geiger climate classification. This assisted in the interpretation of the result by reducing the originally large number of pixels into a smaller number of groups. This presentation will provided a unique look at long-term spatial and temporal variability of solar radiation using high-resolution satellite-based datasets.</p>

scholarly journals Approaches to evaluate spatial and temporal variability of deep marine sediment characteristics under the impact of dense water formation events

2020 ◽  
Author(s):  
XAVIER DURRIEU de MADRON ◽  
MARION STABHOLZ ◽  
LARS-ERIC HEIMBÜRGER-BOAVIDA ◽  
DOMINIQUE AUBERT ◽  
PHILIPPE KERHERVÉ ◽  
...  
Keyword(s):  
Spatial Variability ◽  
Temporal Variability ◽  
Small Scale ◽  
Spatial And Temporal Variability ◽  
Geochemical Composition ◽  
Dense Water ◽  
Water Formation ◽  
Dense Water Formation ◽  
Northwestern Mediterranean Sea ◽  
The Impact

Dense shelf water cascading and open-ocean convection frequently occurs in the Gulf of Lions, northwestern Mediterranean Sea. These intense dense water formation events are capable of supplying large amounts of particulate matter as well as remobilizing and dispersing local sediments and, therefore, are thought to leave an imprint on superficial deposits. Here, we compared the spatial variability of the superficial sediment composition (grain size, organic parameters, and metals) at different scales (from decimetric to kilometric) on the continental slope and rise with the temporal variability linked to the occurrence of intense dense water formation events. The spatial and temporal variability of the geochemical composition of deep sediments was assessed using multivariate and geostatistical analysis. The results indicate that, on the outer reach of the Cap de Creus Canyon, where both processes interact, no clear relation was found between the temporal variability of the superficial sediment and the deep-water formation events, and that the small-scale spatial variability of the sediment is masking the temporal variability. Measurements across the southern slope indicate the presence of a somehow distinct geochemical signature that likely results from the influence of recurrent intense, dense water formation events as well as an unabating bottom trawling activity.

scholarly journals Environmental spatial and temporal variability and its role in non-favoured mutant dynamics

2019 ◽  
Vol 16 (157) ◽  
pp. 20180781 ◽  
Author(s):  
Suzan Farhang-Sardroodi ◽  
Amir H. Darooneh ◽  
Mohammad Kohandel ◽  
Natalia L. Komarova
Keyword(s):  
Temporal Variability ◽  
Mixed System ◽  
Fixation Time ◽  
Spatial And Temporal Variability ◽  
Fixation Probability ◽  
Wild Type ◽  
Variable Environment ◽  
Death Rates ◽  
Temporal And Spatial ◽  
Circular Graph

Understanding how environmental variability (or randomness) affects evolution is of fundamental importance for biology. The presence of temporal or spatial variability significantly affects the competition dynamics in populations, and gives rise to some counterintuitive observations. In this paper, we consider both birth–death (BD) or death–birth (DB) Moran processes, which are set up on a circular or a complete graph. We investigate spatial and temporal variability affecting division and/or death parameters. Assuming that mutant and wild-type fitness parameters are drawn from an identical distribution, we study mutant fixation probability and timing. We demonstrate that temporal and spatial types of variability possess fundamentally different properties. Under temporal randomness, in a completely mixed system, minority mutants experience (i) higher than neutral fixation probability and a higher mean conditional fixation time, if the division rates are affected by randomness and (ii) lower fixation probability and lower mean conditional fixation time if the death rates are affected. Once spatial restrictions are imposed, however, these effects completely disappear, and mutants in a circular graph experience neutral dynamics, but only for the DB update rule in case (i) and for the BD rule in case (ii) above. In contrast to this, in the case of spatially variable environment, both for BD/DB processes, both for complete/circular graph and both for division/death rates affected, minority mutants experience a higher than neutral probability of fixation. Fixation time, however, is increased by randomness on a circle, while it decreases for complete graphs under random division rates. A basic difference between temporal and spatial kinds of variability is the types of correlations that occur in the system. Under temporal randomness, mutants are spatially correlated with each other (they simply have equal fitness values at a given moment of time; the same holds for wild-types). Under spatial randomness, there are subtler, temporal correlations among mutant and wild-type cells, which manifest themselves by cells of each type ‘claiming’ better spots for themselves. Applications of this theory include cancer generation and biofilm dynamics.

scholarly journals Climatic Trends in Hail Precipitation in France: Spatial, Altitudinal, and Temporal Variability

2013 ◽  
Vol 2013 ◽  
pp. 1-10 ◽  
Author(s):  
Lucía Hermida ◽  
José Luis Sánchez ◽  
Laura López ◽  
Claude Berthet ◽  
Jean Dessens ◽  
...  
Keyword(s):  
Spatial Variability ◽  
Temporal Variability ◽  
Kendall Test ◽  
Spatial And Temporal Variability ◽  
Positive Trend ◽  
High Concentration ◽  
Entire Area ◽  
Orographic Effects ◽  
June July ◽  
Pearson Correlations

Hail precipitation is characterized by enhanced spatial and temporal variability. Association Nationale d’Etude et de Lutte contre les Fléaux Atmosphériques (ANELFA) installed hailpad networks in the Atlantic and Midi-Pyrénées regions of France. Historical data of hail variables from 1990 to 2010 were used to characterize variability. A total of 443 stations with continuous records were chosen to obtain a first approximation of areas most affected by hail. The Cressman method was selected for this purpose. It was possible to find relationships between spatial distributions of the variables, which are supported by obtained Pearson correlations. Monthly and annual trends were examined using the Mann-Kendall test for each of the total affected hailpads. There were 154 pads with a positive trend; most were located between Tarbes and Saint-Gaudens. We found 177 pads with a negative trend, which were largely south of a pine forest in Landes. The remainder of the study area showed an elevated spatial variability with no pattern, even between relatively close hailpads. A similar pattern was found in Lérida (Spain) and Southeast France. In the entire area, monthly trends were predominantly negative in June, July, and August, whereas May had a positive trend; again, however, there was no spatial pattern. There was a high concentration of hailpads with positive trend near the Pyrenees, probably owing to orographic effects, and if we apply cluster analysis with the Mann-Kendall values, the spatial variability is accentuated for stations at higher altitude.

scholarly journals Interannual climate variability data improves niche estimates in species distribution models

2021 ◽  
Author(s):  
Dirk Nikolaus Karger ◽  
Bianca Saladin ◽  
Rafael O. Wueest ◽  
Catherine H. Graham ◽  
Damaris Zurell ◽  
...  
Keyword(s):  
Climate Variability ◽  
Spatial Variability ◽  
Species Distribution ◽  
Temporal Variability ◽  
Species Distribution Models ◽  
Essential Element ◽  
Spatial And Temporal Variability ◽  
Distribution Models ◽  
Mammal Species

Aim: Climate is an essential element of species' niche estimates in many current ecological applications such as species distribution models (SDMs). Climate predictors are often used in the form of long-term mean values. Yet, climate can also be described as spatial or temporal variability for variables like temperature or precipitation. Such variability, spatial or temporal, offers additional insights into niche properties. Here, we test to what degree spatial variability and long-term temporal variability in temperature and precipitation improve SDM predictions globally. Location: Global. Time period: 1979-2013. Major taxa studies: Mammal, Amphibians, Reptiles. Methods: We use three different SDM algorithms, and a set of 833 amphibian, 779 reptile, and 2211 mammal species to quantify the effect of spatial and temporal climate variability in SDMs. All SDMs were cross-validated and accessed for their performance using the Area under the Curve (AUC) and the True Skill Statistic (TSS). Results: Mean performance of SDMs with climatic means as predictors was TSS=0.71 and AUC=0.90. The inclusion of spatial variability offers a significant gain in SDM performance (mean TSS=0.74, mean AUC=0.92), as does the inclusion of temporal variability (mean TSS=0.80, mean AUC=0.94). Including both spatial and temporal variability in SDMs shows similarly high TSS and AUC scores. Main conclusions: Accounting for temporal rather than spatial variability in climate improved the SDM prediction especially in exotherm groups such as amphibians and reptiles, while for endotermic mammals no such improvement was observed. These results indicate that more detailed information about temporal climate variability offers a highly promising avenue for improving niche estimates and calls for a new set of standard bioclimatic predictors in SDM research.

scholarly journals Representation of spatial and temporal variability of daily wind speed and of intense wind events over the Mediterranean Sea using dynamical downscaling: impact of the regional climate model configuration

2011 ◽  
Vol 11 (7) ◽  
pp. 1983-2001 ◽  
Author(s):  
M. Herrmann ◽  
S. Somot ◽  
S. Calmanti ◽  
C. Dubois ◽  
F. Sevault
Keyword(s):  
Wind Speed ◽  
Spatial Variability ◽  
Regional Climate Model ◽  
Mediterranean Sea ◽  
Temporal Variability ◽  
Climate Model ◽  
Dynamical Downscaling ◽  
Regional Climate ◽  
Spatial And Temporal Variability ◽  
Wind Events

Abstract. Atmospheric datasets coming from long term reanalyzes of low spatial resolution are used for different purposes. Wind over the sea is, for example, a major ingredient of oceanic simulations. However, the shortcomings of those datasets prevent them from being used without an adequate corrective preliminary treatment. Using a regional climate model (RCM) to perform a dynamical downscaling of those large scale reanalyzes is one of the methods used in order to produce fields that realistically reproduce atmospheric chronology and where those shortcomings are corrected. Here we assess the influence of the configuration of the RCM used in this framework on the representation of wind speed spatial and temporal variability and intense wind events on a daily timescale. Our RCM is ALADIN-Climate, the reanalysis is ERA-40, and the studied area is the Mediterranean Sea. First, the dynamical downscaling significantly reduces the underestimation of daily wind speed, in average by 9 % over the whole Mediterranean. This underestimation has been corrected both globally and locally, and for the whole wind speed spectrum. The correction is the strongest for periods and regions of strong winds. The representation of spatial variability has also been significantly improved. On the other hand, the temporal correlation between the downscaled field and the observations decreases all the more that one moves eastwards, i.e. further from the atmospheric flux entry. Nonetheless, it remains ~0.7, the downscaled dataset reproduces therefore satisfactorily the real chronology. Second, the influence of the choice of the RCM configuration has an influence one order of magnitude smaller than the improvement induced by the initial downscaling. The use of spectral nudging or of a smaller domain helps to improve the realism of the temporal chronology. Increasing the resolution very locally (both spatially and temporally) improves the representation of spatial variability, in particular in regions strongly influenced by the complex surrounding orography. The impact of the interactive air-sea coupling is negligible for the temporal scales examined here. Using two different forcing datasets induces differences on the downscaled fields that are directly related to the differences between those datasets. Our results also show that improving the physics of our RCM is still necessary to increase the realism of our simulations. Finally, the choice of the optimal configuration depends on the scientific objectives of the study for which those wind datasets are used.

scholarly journals Characteristics and controls of variability in soil moisture and groundwater in a headwater catchment

2015 ◽  
Vol 19 (4) ◽  
pp. 1767-1786 ◽  
Author(s):  
H. K. McMillan ◽  
M. S. Srinivasan
Keyword(s):  
Soil Moisture ◽  
Spatial Variability ◽  
Water Table ◽  
Temporal Variability ◽  
Potential Evapotranspiration ◽  
Spatial And Temporal Variability ◽  
Multiple Time ◽  
Runoff Generation ◽  
Dominant Type ◽  
Water Tables

Abstract. Hydrological processes, including runoff generation, depend on the distribution of water in a catchment, which varies in space and time. This paper presents experimental results from a headwater research catchment in New Zealand, where we made distributed measurements of streamflow, soil moisture and groundwater levels, sampling across a range of aspects, hillslope positions, distances from stream and depths. Our aim was to assess the controls, types and implications of spatial and temporal variability in soil moisture and groundwater tables. We found that temporal variability in soil moisture and water table is strongly controlled by the seasonal cycle in potential evapotranspiration, for both the mean and extremes of their distributions. Groundwater is a larger water storage component than soil moisture, and this general difference increases even more with increasing catchment wetness. The spatial standard deviation of both soil moisture and groundwater is larger in winter than in summer. It peaks during rainfall events due to partial saturation of the catchment, and also rises in spring as different locations dry out at different rates. The most important controls on spatial variability in storage are aspect and distance from the stream. South-facing and near-stream locations have higher water tables and showed soil moisture responses for more events. Typical hydrological models do not explicitly account for aspect, but our results suggest that it is an important factor in hillslope runoff generation. Co-measurement of soil moisture and water table level allowed us to identify relationships between the two. Locations where water tables peaked closer to the surface had consistently wetter soils and higher water tables. These wetter sites were the same across seasons. However, patterns of strong soil moisture responses to summer storms did not correspond to the wetter sites. Total catchment spatial variability is composed of multiple variability sources, and the dominant type is sensitive to those stores that are close to a threshold such as field capacity or saturation. Therefore, we classified spatial variability as "summer mode" or "winter mode". In "summer mode", variability is controlled by shallow processes, e.g. interaction of water with soils and vegetation. In "winter mode", variability is controlled by deeper processes, e.g. groundwater movement and bypass flow. Double streamflow peaks observed during some events show the direct impact of groundwater variability on runoff generation. Our results suggest that emergent catchment behaviour depends on the combination of these multiple, time varying components of storage variability.

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