Optimizing the location of weather monitoring stations using estimation uncertainty

In this article, we address the problem of planning a network of weather monitoring stations observing average air temperature (AAT). Assuming the network planning scenario as a location problem, an optimization model and an operative methodology are proposed. The model uses the geostatistical uncertainty of estimation and the indicator formalism to consider in the location process a variable demand surface, depending on the spatial arrangement of the stations. This surface is also used to express a spatial representativeness value for each element in the network. It is then possible to locate such a network using optimization techniques, such as the used methods of simulated annealing (SA) and construction heuristics. This new approach was applied in the optimization of the Portuguese network of weather stations monitoring the AAT variable. In this case study, scenarios of reduction in the number of stations were generated and analysed: the uncertainty of estimation was computed, interpreted and applied to model the varying demand surface that is used in the optimization process. Along with the determination of spatial representativeness value of individual stations, SA was used to detect redundancies on the existing network and establish the base for its expansion. Using a greedy algorithm, a new network for monitoring average temperature in the selected study area is proposed and its effectiveness is compared with the current distribution of stations. For this proposed network distribution maps of the uncertainty of estimation and the temperature distribution were created. Copyright (c) 2011 Royal Meteorological Societyinfo:eu-repo/semantics/publishedVersio

The roughness of Martian topography: A metre-scale fractal analysis of six selected areas

Available online 23 May 2022Studies of the roughness of natural surfaces (landscapes) provide useful information for planetary geology. This paper covers the mapping and analysis of the spatial variability of the surface roughness of Martian topography at a high spatial resolution (metre-scale). The methodology provides new images of the Martian surface texture at the metre-scale that can assist in the interpretation of geological events, processes and formations. It can also assist in geological mapping and in the evaluation of sites that merit further exploration. Digital elevation models, generated by stereo-pair HiRISE images, of six different terrains (aeolian, volcanic, hydrated, cratered, reticulate and sublimated) were used to characterize the metre-scale terrain roughness of representative test sites on Mars. Surface roughness was evaluated by using the local fractal dimension and the results show that the mean of the local fractal dimension ranges from 2.17 in reticulate terrain to 2.71 in sublimated terrain in the southern polar cap. The roughness of the sublimated terrain is significantly higher than the roughness of typical terrains on Earth. Basically, the roughness of the Martian terrain at the metre-scale depends on the rugosity of the landscape, which can be quantified as the number of metric-scale closed depressions and mounds present on the terrain. The information provided by the spatial variability patterns of metre-scale roughness maps provides a significant resource for local planetary geology research at high resolution scale.E. Pardo-Igúzquiza, P.A. Dow

The Many Forms of Co-kriging: A Diversity of Multivariate Spatial Estimators

OnlinePublIn this expository review paper, we show that co-kriging, a widely used geostatistical multivariate optimal linear estimator, has a diverse range of extensions that we have collected and illustrated to show the potential of this spatial interpolator. In the context of spatial stochastic processes, this paper covers scenarios including increasing the spatial resolution of a spatial variable (downscaling), solving inverse problems, estimating directional derivatives, and spatial interpolation taking boundary conditions into account. All these spatial interpolators are optimal linear estimators in the sense of being unbiased and minimising the variance of the estimation error.Peter A. Dowd, Eulogio Pardo-Igúzquiz

Automatic detection and delineation of karst terrain depressions and its application in geomorphological mapping and morphometric analysis

Digital elevation models (DEM) are digital representations of topography that are especially suitable for numerical terrain analysis in earth sciences and engineering. One of the main quantitative uses of DEM is the automatic delineation of flow networks and watersheds in hydrology and geomorphology. In these applications (using both low-resolution and precision DEM) depressions hinder the inference of pathways and a lot of work has been done in designing algorithms that remove them so as to generate depression-free digital elevation models with no interruptions to flow. There are, however, geomorphological environments, such as karst terrains, in which depressions are singular elements, on scales ranging from centimetres to kilometres, which are of intrinsic interest. The detection of these depressions is of significant interest in geomorphologic mapping because the development of large depressions is normal in karst terrains: potholes, blind valleys, dolines, uvalas and poljes. The smallest depressions that can be detected depend on the spatial resolution (pixel size) of the DEM. For example, depressions from centimetres to a few metres, such as some types of karren, cannot be detected if the raster digital elevation model has a spatial resolution greater than, say, 5 m (i.e., square 5m pixel). In this work we describe a method for the automatic detection and delineation of terrain depressions. First, we apply a very efficient algorithm to remove pits from the DEM. The terrain depressions are then obtained by subtracting the depression-free DEM from the original DEM. The final product is a digital map of depressions that facilitates the calculation of morphometric features such as the geometry of the depressions, the mean depth of the depressions, the density of depressions across the study area and the relationship between depressions and other variables such as altitude. The method is illustrated by applying it to data from the Sierra de las Nieves karst massif in the province of Málaga in Southern Spain. This is a carbonate aquifer that is drained by three main springs and in which the depressions play an important role in the recharge of the aquifer. A doline density map, produced from a map of 324 detected dolines/uvalas, identifies three main recharge areas of the three springs. Other morphometric results related to the size and direction of the dolines are also presented. Finally the dolines can be incorporated into a geomorphology map

Automatic detection and delineation of karst terrain depressions and its application in geomorphological mapping and morphometric analysis

Digital elevation models (DEM) are digital representations of topography that are especially suitable for numerical terrain analysis in earth sciences and engineering. One of the main quantitative uses of DEM is the automatic delineation of flow networks and watersheds in hydrology and geomorphology. In these applications (using both low-resolution and precision DEM) depressions hinder the inference of pathways and a lot of work has been done in designing algorithms that remove them so as to generate depression-free digital elevation models with no interruptions to flow. There are, however, geomorphological environments, such as karst terrains, in which depressions are singular elements, on scales ranging from centimetres to kilometres, which are of intrinsic interest. The detection of these depressions is of significant interest in geomorphologic mapping because the development of large depressions is normal in karst terrains: potholes, blind valleys, dolines, uvalas and poljes. The smallest depressions that can be detected depend on the spatial resolution (pixel size) of the DEM. For example, depressions from centimetres to a few metres, such as some types of karren, cannot be detected if the raster digital elevation model has a spatial resolution greater than, say, 5 m (i.e., square 5m pixel). In this work we describe a method for the automatic detection and delineation of terrain depressions. First, we apply a very efficient algorithm to remove pits from the DEM. The terrain depressions are then obtained by subtracting the depression-free DEM from the original DEM. The final product is a digital map of depressions that facilitates the calculation of morphometric features such as the geometry of the depressions, the mean depth of the depressions, the density of depressions across the study area and the relationship between depressions and other variables such as altitude. The method is illustrated by applying it to data from the Sierra de las Nieves karst massif in the province of Málaga in Southern Spain. This is a carbonate aquifer that is drained by three main springs and in which the depressions play an important role in the recharge of the aquifer. A doline density map, produced from a map of 324 detected dolines/uvalas, identifies three main recharge areas of the three springs. Other morphometric results related to the size and direction of the dolines are also presented. Finally the dolines can be incorporated into a geomorphology map

SPECTRAL ANALYSIS OF TOARCIAN SEDIMENTS FROM THE VALDORBIA SECTION (UMBRIA-MARCHE APENNINES): THE ASTRONOMICAL INPUT IN THE FORAMINIFERAL RECORD

Toarcian sections studied mainly in Europe have revealed the incidence of Milankovitch forcing with a well-developed, highly stable, 405 ky component of eccentricity, a short-term eccentricity of ~100 kyr, the cycle of obliquity ~36 kyr, and the precession signal at ~21 kyr. Cyclostratigraphic analysis of the Toarcian succession at the Valdorbia section (Umbria-Marche Apennines) was conducted based on time-series of foraminiferal assemblages. Well-developed cyclic patterns were obtained, with several significant cycles corresponding to thicknesses of 3.8-4.1 m / 5.8-6.3 m / 8.2 m / 10.4 m. Comparison with previous studies at the Valdorbia section led us to interpret the cycle of ~4 m as directly related with the short-term eccentricity (95-105 kyr). The rest of the cycles could be assigned to a periodicity of ~140-160 kyr, ~200 kyr and ~250 kyr, and interpreted as indirect signals of the long-term eccentricity, obliquity and precession, whose record would be impeded by the incompleteness of the studied succession and the sampling interval. Studied components in the foraminiferal assemblage show variable cyclostratigraphic patterns, allowing for a differentiation of groups based on similar registered cycles. These groups reveal different responses by the foraminiferal assemblage, associated with particular requirements, to the palaeoenvironmental changes of Milankovitch origin

Assessing Terrestrial Water Storage Variations in Southern Spain Using Rainfall Estimates and GRACE Data

This paper investigates the relationship between rainfall, groundwater and Gravity Recovery and Climate Experiment (GRACE) data to generate regional-scale estimates of terrestrial water storage variations in the Andalucía region of southern Spain. These estimates can provide information on groundwater depletion (caused by periods of low rainfall or droughts) and groundwater recovery. The spatial distribution of groundwater bodies in southern Spain is complex and current in situ groundwater monitoring methods are deficient, particularly in terms of obtaining representative samples and in implementing and maintaining groundwater monitoring networks. The alternative approach proposed here is to investigate the relationship between precipitation time series and changes in the terrestrial water storage estimated from GRACE observations. The results were validated against the estimated fluctuation in regional groundwater. The maximum correlation between the mean groundwater level and the GRACE observations is 0.69 and this occurs at a lag of one month because the variation in gravity is immediate, but rainfall water requires around one month to travel across the vadose zone before it reaches the groundwater table. Using graphical methods of accumulated deviations from the mean, we show that, in general, groundwater storage follows the smooth, multi-year trends of terrestrial water storage but with less short-term trends; the same is true of rainfall, for which the local trends are more pronounced. There is hysteresis-like behaviour in the variations in terrestrial water storage and in the variations of groundwater. In practical terms, this study shows that, despite the abnormal dryness of the Iberian Peninsula during the 2004–2010 drought, the depleted groundwater storage in Andalucía recovered almost to its pre-drought level by 2016. In addition, groundwater storage and terrestrial water storage show very similar trends but with a delay in the groundwater trendProjects PID2019-106435GB-I00 (Ministerio de Ciencia e Innovación)CGL2015-66835-P (Ministerio de Ciencia, Innovación y Universidades

Data-driven mapping of hourly wind speed and its potential energy resources: A sensitivity analysis

Renewable energies play a significant role to mitigate the impacts of climate change. In countries like Spain, there is a significant potential of wind energy production which might be a key resource. In this research, we obtain wind power at 80 meters height and wind turbine energy (assuming a specific turbine). To achieve this objective we produce an optimal mapping of the hourly “instantaneous surface wind speed” (height 10 m), based on the available data. An extensive region (Granada Province, south Spain) is studied with a spatial resolution of 300 m, during a long period (1996-2016). It allows us to assess the intra- and inter-daily variability of wind energy resources. Several interpolation approaches are tested and a cross validation experiment is applied to identify the optimal approach. The obtained maps were compared with the results obtained in the stations with two common frequency distributions (Rayleigh and Weibull). This is the first time that this sensitivity integrated analysis is performed over an extensive region (12600 km2) for a long time period (20 years) at fine spatiotemporal resolution (300 m, hourly scale). The results can be very valuable for a preliminary analysis of potential optimal location of wind energies facilities

Using numerical methods for map the spatiotemporal geogenic and anthropogenic influences on the groundwater in a detrital aquifer in south Spain

The presence of trace elements in water for domestic supply or irrigation could pose a significant toxic risk for health, due to direct consumption or bioaccumulation through the ingestion of vegetables irrigated with this water. This paper studies the presence of 41 trace elements plus nitrate and bromate in groundwater, using a multivariate statistical tool based on Principal Component Analysis and a geostatistical Kriging method to map the results. Principal Component Analysis revealed 11 significant principal components, which account for 82% and 81% of the total variance (information) respectively for the two dates analysed. Ordinary Kriging was applied to draw maps of the trace elements and PC scores. This research breaks new ground in terms of the large number of parameters used and in terms of the analysis of spatiotemporal variations in these parameters. The results obtained indicate that PC1 represents the natural quality of the aquifer (geogenic) and that there is little change in the average PC1 value between the two dates studied (June near the peak recharge point and November at the end of summer). Agriculture is the human activity that causes the greatest variations in the quality of the groundwater due to the use of fertilizers and due to watering crops with wastewater (PC7_J and PC5_N, June and November, respectively). Other elements of industrial origin, which are dangerous for human health, such as Pb, Cu and Cd, are grouped together in other principal components. The results show that the decline, or even complete absence, of natural recharge during the summer months leads to an increase in the TEs produced by human activity. This indicates that a temporary reduction in the natural recharge could worsen the quality of water resources. Based on the interpretation of the estimated maps, a synthetic map was created to show the spatial distribution of the areas affected by geogenic and anthropogenic factors. Studies with a global approach like this one are necessary in that the possible sources of pollution that could alter the quality of the groundwater and the amount of trace elements and other potentially harmful substances could increase as time goes by. The main advantage of the methodology proposed here is that it reduces the number of parameters, so simplifying the results. This makes it easier to interpret the results and manage the quality of the water.Research Group RNM-122 of the Junta de Andalucía (Spain