The case of distributed rainfall and spatially adaptive modeling

2020 ◽  
Author(s):  
Ralf Loritz ◽  
Uwe Ehret ◽  
Malte Neuper ◽  
Erwin Zehe
Keyword(s):  
Spatial Distribution ◽  
Spatial Variability ◽  
Hydrological Modeling ◽  
Spatial Information ◽  
Time Variability ◽  
State Variables ◽  
Model Framework ◽  
Adaptive Modeling ◽  
Spatial Gradients ◽  
Spatially Adaptive

<p><em>How important is information about distributed precipitation when we do rainfall-runoff modeling on the catchments scale?</em></p><p>The latter is surely one of the more frequently asked research questions in hydrological modeling. Most studies tackling the issue seem thereby to agree that distributed precipitation becomes more important if the ratio of catchment size against storm size decreases or if the spatial gradients of the rainfall field increase. Furthermore, is it often highlighted that catchments are surprisingly effective in smoothing out the spatial variability of the meteorological forcing, at least, if the focus is simulation integral fluxes and average states.</p><p>However, despite these agreements there is no straightforward guidance in the hydrological literature when these thresholds have been reached and when the spatial distribution of the precipitation starts dominating. This is because the answer to the above drawn question depends on the spatial variability of system characteristics, on the system state variables as well as on the strength of the rainfall forcing and its space-time variability. As all three controls vary greatly in space and time it is challenging to identify generally valid rules when information about the distribution of rainfall becomes important for predictive modelling.</p><p>The present study aims to overcome this limitation by developing a model framework to identify periods where the spatial gradients in rainfall intensity are larger than the ability of the landscape to internally dissipate those. This newly developed spatially adaptive modeling approach, uses the spatial information content of the precipitation to control the spatial distribution of our model. The main underlying idea of this approach is to use distributed models only when they are actually needed resulting in 1) a drastic decrease in computational times as well as 2) in a more appropriate representation of a hydrological system. Our results highlight that only during a few periods throughout a hydrological year do distributed precipitation data actually matter. However, they also show that these periods are often highly relevant with respect to certain extremes and that the successful simulation of these extremes require distributed information about the forcing and state of a given system.</p>

scholarly journals Study on the effect of rainfall spatial variability on runoff modelling

2018 ◽  
Vol 20 (3) ◽  
pp. 577-587 ◽  
Author(s):  
Jun Zhang ◽  
Dawei Han ◽  
Yang Song ◽  
Qiang Dai
Keyword(s):  
Spatial Distribution ◽  
Spatial Variability ◽  
Spatial Information ◽  
Model Performance ◽  
Moran’S I ◽  
Image Size ◽  
Moran's I ◽  
Rainfall Events ◽  
Lumped Model ◽  
Distributed Models

Abstract Rainfall spatial variability was assessed to explore its influence on runoff modelling. Image size, coefficient of variation (Cv) and Moran's I were chosen to assess for rainfall spatial variability. The smaller the image size after compression, the less complex is the rainfall spatial variability. The results showed that due to the drawing procedure and varied compression methods, a large uncertainty exists for using image size to describe rainfall spatial variability. Cv quantifies the variability between different rainfall values without considering rainfall spatial distribution and Moran's I describes the spatial autocorrelation between gauges rather than the values. As both rainfall values and spatial distribution have an influence on runoff modelling, the combination of Cv and Moran's I was further explored. The results showed that the combination of Cv and Moran's I is reliable to describe rainfall spatial variability. Furthermore, with the increase of rainfall spatial variability, the hydrological model performance decreases. Moreover, it is difficult for a lumped model to cope with rainfall events assigned with complex rainfall spatial variability since spatial information is not taken into consideration (i.e. the VIC model used in this study). Therefore, it is recommended to apply distributed models that can deal with more spatial input information.

Machine Learning is Central to the Future of Hydrological Modeling

2020 ◽  
Author(s):  
Grey Nearing ◽  
Frederik Kratzert ◽  
Craig Pelissier ◽  
Daniel Klotz ◽  
Jonathan Frame ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Information Transfer ◽  
Hydrological Modeling ◽  
Spatial Information ◽  
Model Development ◽  
Time Variability ◽  
Rainfall Runoff ◽  
Catchment Scale ◽  
Ungauged Catchments

<p>This talk addresses aspects of three of the seven UPH themes: (i) time variability and change, (ii) space variability and scaling, and (iii) modeling methods. </p><p>During the community contribution phase of the 23 Unsolved Problems effort, one of the suggested questions was “Does Machine Learning have a real role in hydrological modeling?” The final UPH paper claimed that “Most hydrologists would probably agree that [extrapolating to changing conditions] will require a more process-based rather than calibration-based approach as calibrated conceptual models do not usually extrapolate well.” In this talk we will present a collection of recent experiments that demonstrate how catchment models based on deep learning can account for both temporal nonstationarity and spatial information transfer (e.g., from gauged to ungauged catchments), often achieving significantly superior predictive performance compared to other state-of-the-art (process-based) modeling strategies, while also providing interpretable results. This is due to the fact that deep learning can learn, exploit, and explain catchment and hydrologic similarity in ways and with accuracies that the community has not been able to achieve using traditional methods. </p><p>We argue that the results we have obtained motivate a path forward for hydrological modeling that centers around ‘physics-informed machine learning.’ Future model development might focus on building hybrid (AI + process-informed) models with three objectives: (i) integrating known catchment behaviors into models that are also able to learn directly from data, (ii)  building explainable deep learning models that allow us to extract scientific insights, and (iii) building hybrid models that are also able to simulate unobserved or sparsely observed variables. We argue further that while the sentiments expressed in the UPH paper about process-based modeling are common, the community currently lacks an evidence-based understanding of where and when process-based understanding is important for future predictions, and that addressing this question in a meaningful way will require true hybrids between different modeling approaches.</p><p>We will conclude by providing two fundamentally novel examples of physics-informed machine learning applied to catchment-scale and point-scale modeling: (i) conservation-constrained neural network architectures applied to rainfall-runoff processes, and (ii) integrating machine learning into existing process-based models to learn unmodeled hydrologic behaviors. We will show results from applying these strategies to the CAMELS dataset in a rainfall-runoff context, and also to FluxNet soil moisture data sets.</p>

scholarly journals The Role of Spatio-Temporal Information to Govern the COVID-19 Pandemic: A European Perspective

2021 ◽  
Vol 10 (3) ◽  
pp. 166
Author(s):  
Hartmut Müller ◽  
Marije Louwsma
Keyword(s):  
European Union ◽  
Spatial Distribution ◽  
Spatial Information ◽  
National Level ◽  
Temporal Information ◽  
The European Union ◽  
Member States ◽  
Heavy Burden ◽  
Spatio Temporal

The Covid-19 pandemic put a heavy burden on member states in the European Union. To govern the pandemic, having access to reliable geo-information is key for monitoring the spatial distribution of the outbreak over time. This study aims to analyze the role of spatio-temporal information in governing the pandemic in the European Union and its member states. The European Nomenclature of Territorial Units for Statistics (NUTS) system and selected national dashboards from member states were assessed to analyze which spatio-temporal information was used, how the information was visualized and whether this changed over the course of the pandemic. Initially, member states focused on their own jurisdiction by creating national dashboards to monitor the pandemic. Information between member states was not aligned. Producing reliable data and timeliness reporting was problematic, just like selecting indictors to monitor the spatial distribution and intensity of the outbreak. Over the course of the pandemic, with more knowledge about the virus and its characteristics, interventions of member states to govern the outbreak were better aligned at the European level. However, further integration and alignment of public health data, statistical data and spatio-temporal data could provide even better information for governments and actors involved in managing the outbreak, both at national and supra-national level. The Infrastructure for Spatial Information in Europe (INSPIRE) initiative and the NUTS system provide a framework to guide future integration and extension of existing systems.

Effect of Savanna windrow wood burning on the spatial variability of soil properties

2021 ◽  
Vol 51 ◽  
Author(s):  
Diogo Neia Eberhardt ◽  
Robélio Leandro Marchão ◽  
Pedro Rodolfo Siqueira Vendrame ◽  
Marc Corbeels ◽  
Osvaldo Guedes Filho ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Spatial Variability ◽  
Spatial Dependence ◽  
Chemical Properties ◽  
Spatial Arrangement ◽  
Nugget Effect ◽  
Tropical Savannas ◽  
Geostatistical Methods ◽  
Exchangeable Ca ◽  
Spherical Models

ABSTRACT Tropical Savannas cover an area of approximately 1.9 billion hectares around the word and are subject to regular fires every 1 to 4 years. This study aimed to evaluate the influence of burning windrow wood from Cerrado (Brazilian Savanna) deforestation on the spatial variability of soil chemical properties, in the field. The data were analysed by using geostatistical methods. The semivariograms for pH(H2O), pH(CaCl2), Ca, Mg and K were calculated according to spherical models, whereas the phosphorus showed a nugget effect. The cross semi-variograms showed correlations between pH(H2O) and pH(CaCl2) with other variables with spatial dependence (exchangeable Ca and Mg and available K). The spatial variability maps for the pH(H2O), pH(CaCl2), Ca, Mg and K concentrations also showed similar patterns of spatial variability, indicating that burning the vegetation after deforestation caused a well-defined spatial arrangement. Even after 20 years of use with agriculture, the spatial distribution of pH(H2O), pH(CaCl2), Ca, Mg and available K was affected by the wood windrow burning that took place during the initial deforestation.

scholarly journals Spatial odor discrimination in hawkmoth, Manduca sexta (L.)

2020 ◽  
Author(s):  
P. Kalyanasundaram ◽  
M. A. Willis
Keyword(s):  
Spatial Distribution ◽  
Manduca Sexta ◽  
Flight Control ◽  
Spatial Information ◽  
Egg Laying ◽  
Proboscis Extension Reflex ◽  
Flying Insects ◽  
Odor Learning ◽  
Odor Plumes ◽  
Proboscis Extension

AbstractFlying insects track turbulent odor plumes to find mates, food and egg-laying sites. To maintain contact with the plume, insects are thought to adapt their flight control according to the distribution of odor in the plume using the timing of odor onsets and intervals between odor encounters. Although timing cues are important, few studies have addressed whether insects are capable of deriving spatial information about odor distribution from bilateral comparisons between their antennae in flight. The proboscis extension reflex (PER) associative learning protocol, originally developed to study odor learning in honeybees, was modified to show hawkmoths, Manduca sexta, can discriminate between odor stimuli arriving on either antenna. We show moths discriminated the odor arrival side with an accuracy of >70%. The information about spatial distribution of odor stimuli is thus available to moths searching for odor sources, opening the possibility that they use both spatial and temporal odor information.

scholarly journals The Atmospheric Carbon and Transport (ACT) – America Mission

2021 ◽  
pp. 1-54
Author(s):  
Kenneth J. Davis ◽  
Edward V. Browell ◽  
Sha Feng ◽  
Thomas Lauvaux ◽  
Michael D. Obland ◽  
...  
Keyword(s):  
Transport Model ◽  
Atmospheric Transport ◽  
Eastern United States ◽  
State Variables ◽  
Data Set ◽  
Spatial Gradients ◽  
Research Aircraft ◽  
In Situ Sensors ◽  
Atmospheric Carbon ◽  
Model Ensembles

AbstractThe Atmospheric Carbon and Transport (ACT) – America NASA Earth Venture Suborbital Mission set out to improve regional atmospheric greenhouse gas (GHG) inversions by exploring the intersection of the strong GHG fluxes and vigorous atmospheric transport that occurs within the midlatitudes. Two research aircraft instrumented with remote and in situ sensors to measure GHG mole fractions, associated trace gases, and atmospheric state variables collected 1140.7 flight hours of research data, distributed across 305 individual aircraft sorties, coordinated within 121 research flight days, and spanning five, six-week seasonal flight campaigns in the central and eastern United States. Flights sampled 31 synoptic sequences, including fair weather and frontal conditions, at altitudes ranging from the atmospheric boundary layer to the upper free troposphere. The observations were complemented with global and regional GHG flux and transport model ensembles. We found that midlatitude weather systems contain large spatial gradients in GHG mole fractions, in patterns that were consistent as a function of season and altitude. We attribute these patterns to a combination of regional terrestrial fluxes and inflow from the continental boundaries. These observations, when segregated according to altitude and air mass, provide a variety of quantitative insights into the realism of regional CO2 and CH4 fluxes and atmospheric GHG transport realizations. The ACT-America data set and ensemble modeling methods provide benchmarks for the development of atmospheric inversion systems. As global and regional atmospheric inversions incorporate ACT-America’s findings and methods, we anticipate these systems will produce increasingly accurate and precise sub-continental GHG flux estimates.

Spatial variability of the physical-hydric properties of cohesive soils under rainfed and irrigated sugarcane cultivations

2021 ◽  
Author(s):  
Brivaldo Gomes de Almeida ◽  
Bruno Campos Mantovanelli ◽  
Thiago Rodrigo Schossler ◽  
Fernando José Freire ◽  
Edivan Rodrigues de Souza ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Spatial Variability ◽  
Structural Changes ◽  
Management Practices ◽  
Geometric Mean ◽  
Soil Layer ◽  
Multivariate Techniques ◽  
Geostatistical Approach ◽  
Physical Attributes ◽  
Soil Spatial Variability

<p>Geostatistical and multivariate techniques have been widely used to identify and characterize the soil spatial variability, as well as to detect possible relationships between soil properties and management. Besides that, these techniques provide information regarding the spatial and temporal structural changes of soils to support better decision-making processes and management practices. Although the Zona da Mata region is a reference for sugarcane production in the northeast of Brazil, only a few studies have been carried out to clarify the effects of different management on soil physical attributes by using geostatistical and multivariate techniques. Thus, the objectives of this study were: (I) to characterize the spatial distribution of soils physical attributes under rainfed and irrigated sugarcane cultivations; (II) to identify the minimum sampling for the determination of soil physical attributes; (III) to detect the effects of the different management on soil physical attributes based on the principal component analysis (PCA). The study was carried out in the agricultural area of the Carpina Sugarcane Experimental Station of the Federal Rural University of Pernambuco, 7º51’13”S, 35º14’10”W, characterized by a Typic Hapludult with sandy clay loam soil texture. The investigated plot, cultivated with sugarcane, included a rainfed and an irrigated treatment in which a sprinkler system was installed according to a 12x12m grid. The interval between consecutive watering was fixed in two days, whereas irrigation depth was calculated to replace crop evapotranspiration (ETc) and accounting for the effective precipitation of the period. Daily ETc was estimated based on crop coefficient and reference evapotranspiration (ETo) indirectly obtained through a class A evaporation pan. In both treatments, the soil spatial variability was determined according to a 56x32m grid, on 32 soil samples collected in the 0.0-0.1m soil layer, spaced 7x8m, and georeferenced with a global position system. The soil was physically characterized according to the following attributes: bulk density (BD), soil penetration resistance (SPR), macroporosity (Macro), mesoporosity (Meso), microporosity (Micro), total porosity (TP), saturated hydraulic conductivity (Ksat), gravimetric soil water content (SWCg), geometric mean diameter (GMD) and mean weight diameter (MWD). The results of the descriptive statistics showed that among the studied attributes, Ksat, SPR, and Macro presented higher CV values, equal to 63 and 69%, 35 and 40%, and 32 and 44%, under rainfed and irrigated conditions, respectively. The minimum sampling, adequate to characterize the different soil attributes, resulted in general smaller in the rainfed area, characterized by higher homogeneity. Thus, the GMD, SWCg (both with 2 points ha<sup>-1</sup>), and SPR (with 6 points ha<sup>-1</sup>) were identified as the soil physical attributes requiring the lowest sample density; on the other hand, MWD and Ksat, with 14 and 15 points ha<sup>-1</sup>, respectively, required the highest number of samples. Pearson’s correlation analysis evidenced that soil BD was the most influential physical attribute in the studied areas, with a significant and inverse effect in most of the investigated attributes. The geostatistical approach associated with the multivariate PCA provided to understand the relationships between the spatial distribution patterns associated with irrigated and rainfed management and soil physical properties.</p>

scholarly journals Vehicle Spatial Distribution and 3D Trajectory Extraction Algorithm in a Cross-Camera Traffic Scene

Sensors ◽  
2020 ◽  
Vol 20 (22) ◽  
pp. 6517
Author(s):  
Xinyao Tang ◽  
Huansheng Song ◽  
Wei Wang ◽  
Yanni Yang
Keyword(s):  
Spatial Distribution ◽  
Spatial Information ◽  
Three Dimensional ◽  
Physical Space ◽  
Extraction Methods ◽  
Geometric Constraints ◽  
Panoramic Image ◽  
Trajectory Data ◽  
Comparison Algorithms ◽  
Bounding Boxes

The three-dimensional trajectory data of vehicles have important practical meaning for traffic behavior analysis. To solve the problems of narrow visual angle in single-camera scenes and lack of continuous trajectories in 3D space by current cross-camera trajectory extraction methods, we propose an algorithm of vehicle spatial distribution and 3D trajectory extraction in this paper. First, a panoramic image of a road with spatial information is generated based on camera calibration, which is used to convert cross-camera perspectives into 3D physical space. Then, we choose YOLOv4 to obtain 2D bounding boxes of vehicles in cross-camera scenes. Based on the above information, 3D bounding boxes around vehicles are built with geometric constraints which are used to obtain projection centroids of vehicles. Finally, by calculating the spatial distribution of projection centroids in the panoramic image, 3D trajectories of vehicles are extracted. The experimental results indicate that our algorithm can effectively complete vehicle spatial distribution and 3D trajectory extraction in various traffic scenes, which outperforms other comparison algorithms.

Skeleton Network Extraction and Analysis on Bicycle Sharing Networks

2020 ◽  
Vol 16 (3) ◽  
pp. 146-167
Author(s):  
Kanokwan Malang ◽  
Shuliang Wang ◽  
Yuanyuan Lv ◽  
Aniwat Phaphuangwittayakul
Keyword(s):  
Spatial Distribution ◽  
Extraction Method ◽  
Spatial Information ◽  
Spatial Clustering ◽  
Transportation Networks ◽  
Network Size ◽  
Network Characteristics ◽  
Spatial Features

Skeleton network extraction has been adopted unevenly in transportation networks whose nodes are always represented as spatial units. In this article, the TPks skeleton network extraction method is proposed and applied to bicycle sharing networks. The method aims to reduce the network size while preserving key topologies and spatial features. The authors quantified the importance of nodes by an improved topology potential algorithm. The spatial clustering allows to detect high traffic concentrations and allocate the nodes of each cluster according to their spatial distribution. Then, the skeleton network is constructed by aggregating the most important indicated skeleton nodes. The authors examine the skeleton network characteristics and different spatial information using the original networks as a benchmark. The results show that the skeleton networks can preserve the topological and spatial information similar to the original networks while reducing their size and complexity.

scholarly journals Spatial distribution of exoplanet candidates based on Kepler and Gaia data

2020 ◽  
Vol 635 ◽  
pp. A191
Author(s):  
A. Maliuk ◽  
J. Budaj
Keyword(s):  
Spatial Distribution ◽  
Galactic Plane ◽  
Open Clusters ◽  
Field Of View ◽  
Solar Neighbourhood ◽  
Spatial Gradients ◽  
The Galaxy ◽  
Formation And Evolution ◽  
F Stars ◽  
Galactic Longitude

Context. Surveying the spatial distribution of exoplanets in the Galaxy is important for improving our understanding of planet formation and evolution. Aims. We aim to determine the spatial gradients of exoplanet occurrence in the Solar neighbourhood and in the vicinity of open clusters. Methods. We combined Kepler and Gaia DR2 data for this purpose, splitting the volume sampled by the Kepler mission into certain spatial bins. We determined an uncorrected and bias-corrected exoplanet frequency and metallicity for each bin. Results. There is a clear drop in the uncorrected exoplanet frequency with distance for F-type stars (mainly for smaller planets), a decline with increasing distance along the Galactic longitude l = 90°, and a drop with height above the Galactic plane. We find that the metallicity behaviour cannot be the reason for the drop of the exoplanet frequency around F stars with increasing distance. This might have only contributed to the drop in uncorrected exoplanet frequency with the height above the Galactic plane. We argue that the above-mentioned gradients of uncorrected exoplanet frequency are a manifestation of a single bias of undetected smaller planets around fainter stars. When we correct for observational biases, most of these gradients in exoplanet frequency become statistically insignificant. Only a slight decline of the planet occurrence with distance for F stars remains significant at the 3σ level. Apart from that, the spatial distribution of exoplanets in the Kepler field of view is compatible with a homogeneous one. At the same time, we do not find a significant change in the exoplanet frequency with increasing distance from open clusters. In terms of byproducts, we identified six exoplanet host star candidates that are members of open clusters. Four of them are in the NGC 6811 (KIC 9655005, KIC 9533489, Kepler-66, Kepler-67) and two belong to NGC 6866 (KIC 8396288, KIC 8331612). Two out of the six had already been known to be cluster members.

Sign in / Sign up

Export Citation Format

Share Document