Spatial Representativeness Research Articles

Integrating life cycle assessment (LCA) and quantitative risk assessment (QRA) to address model uncertainties: defining a landfill reference case under varying environmental and engineering conditions

PurposeModel uncertainties in life cycle assessment (LCA) can arise due to the lack or limited knowledge of the system (epistemic), or due to the inherent spatial and/or temporal variability of the processes that characterize it (stochastic). Scenario analysis is often used to address such uncertainties. However, the analysis does not provide information on which scenario is more representative of the system, or more likely to occur. To account for model uncertainties in LCA, the approach presented here estimates the likelihood of occurrence of the scenarios by assigning probabilities to the events characterizing them.MethodAn integrated life cycle assessment (LCA) and quantitative risk assessment (QRA) approach was developed to include the likelihood of occurrence of future events, and their consequences, in the definition of one representative scenario. The integration of the two tools was conceived at a framework level, with the inclusion of probability estimates as weighting factors for the scenario results. These probability estimates for each scenario allow improving the spatial and temporal representativeness of the model of the system.The approach was applied to a landfill case study to test its applicability in defining the impacts of a landfill reference scenario. Twelve scenarios were developed to represent the potential variation of landfill conditions over time. In particular, the degradation and failure of the containment systems, and the flooding of the site, were the main events considered in the scenario definition. An event tree was built to estimate the probability of occurrence of each scenario.ResultsExpected values and standard deviations were computed for all impact categories based on the impacts and their probabilities. Additional information on the probability of obtaining a certain impact was further provided by the impact curve built as function of the impacts and their cumulative probability.ConclusionsOverall, the results of the study highlight the potential of the approach and the relevance of the information that the results can provide on the impact of a landfill, and more generally a system, under varying conditions over time.

Elucidating local pollution and site representativeness at the Jungfraujoch, Switzerland through parallel aerosol measurements at an adjacent mountain ridge

Many long-term air pollution and climate monitoring stations face the issue of increasing anthropogenic activities in their vicinity. Furthermore, the spatial representativeness of the sites is often not entirely understood especially in mountainous terrain with complex topographic features. This study presents a 5-year comparison of parallel aerosol measurements (total particle number concentration and equivalent black carbon mass concentration) at the Jungfraujoch in the Swiss Alps (JFJ, 3580 m a.s.l.), and an adjacent mountain ridge, the Jungfrau East Ridge (JER, 3705 m a.s.l.), in 1000 m air-line distance to the main site. The parallel aerosol measurements reveal characteristic differences in the diurnal variations between the two sites under certain specific meteorological conditions. Our analysis estimates that on 20%–40% of the days local activities at the Jungfraujoch have a clear influence on the measured time series of the total aerosol number concentration and the equivalent black carbon mass concentration. This influence is mainly seen in form of strong isolated spikes rather than by an increase in the on-site background concentration. They can thus be flagged during the data quality assurance process and filtered from those measurement parameters available at high time resolution. Removing the spikes from the original time series results in daily mean values for the total aerosol number concentration and equivalent black carbon mass concentration that are 5%–10% lower compared to the original signals. During nighttime with hardly any local pollution sources that cause spikes this percentage decreases towards 0%. The signal baselines at the Jungfraujoch and Jungfrau East Ridge correlate well during more than 50% of the days.

Generation of a long-term daily gridded precipitation dataset for the Upper Indus Basin (UIB) through temporal Reconstruction, Correction & Informed Regionalization-“ReCIR”

This study attempted to generate a long-term (1961–2010) daily gridded precipitation dataset for the Upper Indus Basin (UIB) with orographic adjustments so as to generate realistic precipitation estimates, enabling hydrological and water resource investigations that can close the water balance, that is difficult, if not impossible to achieve with the currently available precipitation data products for the basin. The procedure includes temporal reconstruction of precipitation series at points where data were not recorded prior to the mid-nineties, followed by a regionalization of the precipitation series to a smaller scale across the basin (0.125 ° × 0.125 °), while introducing adjustments for the orographic effect and changes in glacier storage. The reconstruction process involves interpolation of the precipitation at virtual locations of the current (1995-) dense observational network, followed by corrections for frequency and intensity and adjustments for temporal trends at these virtual locations. The data generated in this way were further validated for temporal and spatial representativeness through evaluation of SWAT-modelled streamflow responses against observed flows across the UIB. The results show that the calibrated SWAT-simulated daily discharge at the basin outlet as well as at different sub-basin outlets, when forcing the model with the reconstructed precipitation of years 1973–1996, is almost identical to that when forcing it with the reference precipitation data (1997–2008). Finally, the spatial distribution pattern of the reconstructed (1961–1996) and reference (1997–2008) precipitation were also found consistent across the UIB, reflecting well the large-scale atmospheric-circulation pattern in the region.

Biomass burning combustion efficiency observed from space using measurements of CO and NO2 by the TROPOspheric Monitoring Instrument (TROPOMI)

Abstract. The global fire emission inventories depend on ground and airborne measurements of species-specific emission factors (EFs), which translate dry matter losses due to fires to actual trace gas and aerosol emissions. The EFs of nitrogen oxides (NOx) and carbon monoxide (CO) can function as a proxy for combustion efficiency to distinguish flaming from smoldering combustion. The uncertainties in these EFs remain large as they are limited by the spatial and temporal representativeness of the measurements. The global coverage of satellite observations has the advantage of filling this gap, making these measurements highly complementary to ground-based or airborne data. We present a new analysis of biomass burning pollutants using space-borne data to investigate the spatiotemporal efficiency of fire combustion. Column measurements of nitrogen dioxide and carbon monoxide (XNO2 and XCO) from the TROPOspheric Monitoring Instrument (TROPOMI) are used to quantify the relative atmospheric enhancements of these species over different fire-prone regions around the world. We find spatial and temporal patterns in the ΔXNO2 ∕ ΔXCO ratio that point to distinct differences in biomass burning behavior. Such differences are induced by the burning phase of the fire (e.g., high-temperature flaming vs. low-temperature smoldering combustion) and burning practice (e.g., the combustion of logs, coarse woody debris and soil organic matter vs. the combustion of fine fuels such as savanna grasses). The sampling techniques and the signal-to-noise ratio of the retrieved ΔXNO2 ∕ ΔXCO signals were quantified with WRF-Chem experiments and showed similar distinct differences in combustion types. The TROPOMI measurements show that the fraction of surface smoldering combustion is much larger for the boreal forest fires in the upper Northern Hemisphere and peatland fires in Indonesia. These types of fires cause a much larger increase (3 to 6 times) in ΔXCO relative to ΔXNO2 than elsewhere in the world. The high spatial and temporal resolution of TROPOMI also enables the detection of spatial gradients in combustion efficiency at smaller regional scales. For instance, in the Amazon, we found higher combustion efficiency (up to 3-fold) for savanna fires than for the nearby tropical deforestation fires. Out of two investigated fire emission products, the TROPOMI measurements support the broad spatial pattern of combustion efficiency rooted in GFED4s. Meanwhile, TROPOMI data also add new insights into regional variability in combustion characteristics that are not well represented in the different emission inventories, which can help the fire modeling community to improve their representation of the spatiotemporal variability in EFs.

An Evaluation of MODIS Global Evapotranspiration Product as Satellite-Based Evapotranspiration Data for Supporting Precision Agriculture in West Papua - Indonesia

Precision Agriculture has been a significant issue since the middle of the 1980s. Evapotranspiration is one of the main parameters in precision agriculture to analyze real water needs in the agriculture area and managing water resources. Traditionally evapotranspiration estimates by directly measured methods, i.e., lysimeter, pan-evaporation, eddy covariance, Bowen ratio, soil water, and climate data analysis. These methods are expensive techniques with low spatial representativeness. The utilization of remote sensing technology is expected to be an alternative solution for providing evapotranspiration data with a cost-effective and high spatial representative. This research aims to evaluate the MODIS global evapotranspiration as satellite-based evapotranspiration in estimating evapotranspiration in West Papua. Four (4) statistical parameters, i.e., mean error (ME), root means square error (RMSE), relative bias (RB), and mean bias factor (MBF), are using for evaluation. The research showed that MODIS global evapotranspiration was overestimated in estimating evapotranspiration in West Papua. However, MODIS global evapotranspiration has an acceptable accuracy in estimating evapotranspiration in West Papua indicated by ME = 0.66 mm/day, RMSE = 0.94 mm/day, RB = 0.27, and MBF = 0.81. Therefore, MODIS global evapotranspiration can be used as an alternative solution for providing evapotranspiration data in West Papua with a cost-effective.

A Utility Model for Photo Selection in Mobile Crowdsensing

Existing mobile photo crowdsensing approaches focus on the participant-to-server photo pre-selection, i.e., reducing the photo redundancy from participants to a server. The server may still receive plenty of photos for a target area. Yet, another important problem is to select a proper photo subset of an area from the server to a requester. This is a challenging problem because the selected subset with a small size should attain both coverage on the PoIs - Points of Interest (i.e., photo coverage of the area) and quality on the views (i.e., view quality). In this paper, we propose a novel and generic server-to-requester photo selection approach even when there are neither photo shooting direction information nor reference photos. A utility model is designed to measure photo merits of coverage and quality by exploiting photos' spatial distribution and visual representativeness. We present two photo selection schemes, basic and PoI number-aware, to maximize the photo selection utility with multiple levels of granularity. Experimental results on real-world datasets show that our basic scheme outperforms the baselines by an average of 33% and 18.7% on photo coverage and view quality, respectively. Our PoI number-aware scheme can yield an additionally 44.8 percent improvement on the photo coverage performance.

Validation of SMAP Soil Moisture at Terrestrial National Ecological Observatory Network (NEON) Sites Show Potential for Soil Moisture Retrieval in Forested Areas

Soil moisture influences forest health, fire occurrence and extent, and insect and pathogen impacts, creating a need for regular, globally extensive soil moisture measurements that can only be achieved by satellite-based sensors, such as NASA's soil moisture active passive (SMAP). However, SMAP data for forested regions, which account for ∼20% of land cover globally, are flagged as unreliable due to interference from vegetation water content, and forests were underrepresented in previous validation efforts, preventing an assessment of measurement accuracy in these biomes. Here we compare over twelve thousand SMAP soil moisture measurements, representing 88 site-years, to <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in situ</i> soil moisture measurements from forty National Ecological Observatory Network (NEON) sites throughout the US, half of which are forested. At unforested NEON sites, agreement with SMAP soil moisture (unbiased RMSD: 0.046 m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−3</sup> ) was similar to previous sparse network validations (which include inflation of the metric due to spatial representativeness errors). For the forested sites, SMAP achieved a reasonable level of accuracy (unbiased RMSD: 0.06 m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−3</sup> or 0.053 m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−3</sup> after accounting for random representativeness errors) indicating SMAP is sensitive to changes in soil moisture in forest ecosystems. Moreover, we identified that both an index of vegetation water content and canopy height were related to mean difference (MD), which incorporates measurement bias and representativeness bias, and suggests a potential approach to improve SMAP algorithm parameterization for forested regions. In addition, expanding the number and extent of soil moisture measurements at forested validation sites would likely further reduce MD by minimizing representativeness errors.

Ring widths of Rhododendron shrubs reveal a persistent winter warming in the central Himalaya

Himalayan Mountains provide unique opportunities for the extension of shrub-ring based dendroclimatology beyond the upper tree limit. However, little is known about limiting climate factors of shrub growth under harsh environmental conditions. We established a new ring-width chronology of a Himalayan shrub rhododendron (Rhododendron campanulatum D. Don) at the upper Krummholz treeline in the Mt. Gaurishankar massif, central Himalaya, Nepal. Bootstrapped correlation analysis showed positive relationships between radial growth and temperatures of all months from previous November to current October. Correlations were the highest with winter (December-February) minimum temperature (r = 0.781, p < 0.001), indicating that radial growth of R. campanulatum is strongly sensitive to winter minimum temperature. The linear regression model explained 61 % of the actual winter minimum temperature variance during the calibration period 1960–2013. Periods of low and high minimum winter temperatures in the central Himalaya were consistent with cool and warm episodes found by other regional winter temperature reconstructions from the Himalayas and the Tibetan Plateau. Spatial correlation analysis with land surface temperatures revealed the spatial representativeness of our reconstruction for a larger geographical territory over the Himalayas and the Tibetan Plateau. Furthermore, winter temperature in the central Himalaya is teleconnected with the December-February India-Burma trough. The persistent increasing winter temperature in recent decades in the central Himalaya coincides with continental-scale warming. Alpine vegetation in humid regions of the Himalayas may benefit from winter warming via an earlier start and extension of the growing season, as long as moisture availability is sufficient.

ECOSTRESS and CIMIS: A Comparison of Potential and Reference Evapotranspiration in Riverside County, California

The ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) provides remotely-sensed estimates of evapotranspiration at 70 m spatial resolution every 1–5 days, sampling across the diurnal cycle. This study, in partnership with an operational water management organization, the Eastern Municipal Water District (EMWD) in Southern California, was conducted to evaluate estimates of evapotranspiration under ideal conditions where water is not limited. EMWD regularly uses a ground-based network of reference evapotranspiration (ETo) from the California Irrigation Management Information System (CIMIS); yet, there are gaps in spatial coverage and questions of spatial representativeness and consistency. Space-based potential evapotranspiration (PET) estimates, such as those from ECOSTRESS, provide consistent spatial coverage. We compared ECOSTRESS ETo (estimated from PET) to CIMIS ETo at five CIMIS sites in Riverside County, California from July 2018–June 2020. We found strong correlations between CIMIS ETo and ECOSTRESS ETo across all five sites (R2 = 0.89, root mean square error (RMSE) = 0.11 mm hr−1). Both CIMIS and ECOSTRESS ETo captured similar seasonal patterns through the study period as well as diurnal variability. There were site-specific differences in the relationship between ECOSTRESS AND CIMIS, in part due to spatial heterogeneity around the station site. Consequently, careful examination of landscapes surrounding CIMIS sites must be considered in future comparisons. These results indicate that ECOSTRESS successfully retrieves PET that is comparable to ground-based reference ET, highlighting the potential for providing observation-driven guidance for irrigation management across spatial scales.

Geostatistical evaluation of the design of the precipitation stable isotope monitoring network for Slovenia and Hungary

A detailed knowledge of the stable isotope signature of precipitation is the basis of investigations in a variety of scientific fields and applications. To obtain robust and reliable results, the representativity of the currently operating (at least, as of 2018) precipitation stable isotope monitoring stations across Slovenia (n = 8) and Hungary (n = 9) was evaluated on the basis of amount-weighted annual averages with the aim of revealing any redundantly (i.e. over-) represented or un(der)represented areas. In the case of the latter, optimal locations for additional sites were suggested in Slovenia and Hungary. The networks of both countries are design-based systems that need to be fine-tuned for long-term optimized operation. The evaluation of the monitoring network was performed taking into consideration the stations operating in Slovenia and Hungary, as well as closely situated ones operating in neighboring countries. The evaluation was carried out in nine different combinations, using spatial simulated annealing, with regression kriging variance as a quality measure. The results showed that (i) there are over- and un(der)represented areas in the network, an issue requiring remedial action, (ii) the mutual information exchange of the precipitation stable isotope monitoring networks of Slovenia and Hungary increases the precision of precipitation δ18O estimation by ~0.3‰ in a 15–30 km wide zone near the borders, and (iii) by an even greater degree in the neighboring countries’ stations. The current research may be termed pioneering in the matter of the detailed geostatistical assessment of spatial representativity of a precipitation stable isotope monitoring network, and as such, can serve as an example for future studies aiming for the spatial optimization of other regional precipitation stable isotope monitoring networks.

Evaluating Sentinel-5P TROPOMI tropospheric NO2 column densities with airborne and Pandora spectrometers near New York City and Long Island Sound.

Airborne and ground-based Pandora spectrometer NO2 column measurements were collected during the 2018 Long Island Sound Tropospheric Ozone Study (LISTOS) in the New York City/Long Island Sound region, which coincided with early observations from the Sentinel-5P TROPOspheric Monitoring Instrument (TROPOMI) instrument. Both airborne- and ground-based measurements are used to evaluate the TROPOMI NO2 Tropospheric Vertical Column (TrVC) product v1.2 in this region, which has high spatial and temporal heterogeneity in NO2. First, airborne and Pandora TrVCs are compared to evaluate the uncertainty of the airborne TrVC and establish the spatial representativeness of the Pandora observations. The 171 coincidences between Pandora and airborne TrVCs are found to be highly correlated (r2 =0.92 and slope of 1.03), with the largest individual differences being associated with high temporal and/or spatial variability. These reference measurements (Pandora and airborne) are complementary with respect to temporal coverage and spatial representativity. Pandora spectrometers can provide continuous long-term measurements but may lack areal representativity when operated in direct-sun mode. Airborne spectrometers are typically only deployed for short periods of time, but their observations are more spatially representative of the satellite measurements with the added capability of retrieving at subpixel resolutions of 250m×250m over the entire TROPOMI pixels they overfly. Thus, airborne data are more correlated with TROPOMI measurements (r2 = 0.96) than Pandora measurements are with TROPOMI (r2 = 0.84). The largest outliers between TROPOMI and the reference measurements appear to stem from too spatially coarse a priori surface reflectivity (0.5°) over bright urban scenes. In this work, this results during cloud-free scenes that, at times, are affected by errors in the TROPOMI cloud pressure retrieval impacting the calculation of tropospheric air mass factors. This factor causes a high bias in TROPOMI TrVCs of 4%–11%. Excluding these cloud-impacted points, TROPOMI has an overall low bias of 19%–33% during the LISTOS timeframe of June–September 2018. Part of this low bias is caused by coarse a priori profile input from the TM5-MP model; replacing these profiles with those from a 12 km North American Model–Community Multiscale Air Quality (NAMCMAQ) analysis results in a 12%–14% increase in the TrVCs. Even with this improvement, the TROPOMI-NAMCMAQ TrVCs have a 7%–19% low bias, indicating needed improvement in a priori assumptions in the air mass factor calculation. Future work should explore additional impacts of a priori inputs to further assess the remaining low biases in TROPOMI using these datasets.

Spatial and temporal representativeness of point measurements for nitrogen dioxide pollution levels in cities

Abstract. In many cities around the world the overall air quality is improving, but at the same time nitrogen dioxide (NO2) trends show stagnating values and in many cases could not be reduced below air quality standards recommended by the World Health Organization (WHO). Many large cities have built monitoring stations to continuously measure different air pollutants. While most stations follow defined rules in terms of measurement height and distance to traffic emissions, the question remains of how representative are those point measurements for the city-wide air quality. The question of the spatial coverage of a point measurement is important because it defines the area of influence and coverage of monitoring networks, determines how to assimilate monitoring data into model simulations or compare to satellite data with a coarser resolution, and is essential to assess the impact of the acquired data on public health. In order to answer this question, we combined different measurement data sets consisting of path-averaging remote sensing data and in situ point measurements in stationary and mobile setups from a measurement campaign that took place in Munich, Germany, in June and July 2016. We developed an algorithm to strip temporal from spatial patterns in order to construct a consistent NO2 pollution map for Munich. Continuous long-path differential optical absorption spectroscopy (LP DOAS) measurements were complemented with mobile cavity-enhanced (CE) DOAS, chemiluminescence (CL) and cavity attenuated phase shift (CAPS) instruments and were compared to monitoring stations and satellite data. In order to generate a consistent composite map, the LP DOAS diurnal cycle has been used to normalize for the time of the day dependency of the source patterns, so that spatial and temporal patterns can be analyzed separately. The resulting concentration map visualizes pollution hot spots at traffic junctions and tunnel exits in Munich, providing insights into the strong spatial variations. On the other hand, this database is beneficial to the urban planning and the design of control measures of environment pollution. Directly comparing on-street mobile measurements in the vicinity of monitoring stations resulted in a difference of 48 %. For the extrapolation of the monitoring station data to street level, we determined the influence of the measuring height and distance to the street. We found that a measuring height of 4 m, at which the Munich monitoring stations measure, results in 16 % lower average concentrations than a measuring height of 1.5 m, which is the height of the inlet of our mobile measurements and a typical pedestrian breathing height. The horizontal distance of most stations to the center of the street of about 6 m also results in an average reduction of 13 % compared to street level concentration. A difference of 21 % in the NO2 concentrations remained, which could be an indication that city-wide measurements are needed for capturing the full range and variability of concentrations for assessing pollutant exposure and air quality in cities.

Estimates of pedestrian exposure to atmospheric pollution using high-resolution modelling in a real traffic hot-spot

Atmospheric pollution is a very relevant risk for the human health, in particular in urban environments, where most people lives and high levels of pollution are found. Population exposure is traditionally estimated through concentration recorded at air quality monitoring stations (AQMS) or modelled at a spatial resolution of the order of 1 km2. However, these methodologies have limitations in urban areas where strong gradients of concentration, even in the same street, exist. In addition, the movements of pedestrians make difficult to compute reliable estimates of pollutant concentration to which people are exposed to. In this context, the main objective of this study is to estimate the exposure of pedestrians to ambient nitrogen oxides (NOx) concentrations with high spatial resolution in a real urban traffic hot-spot under different methodologies. To achieve this objective, a novel methodology which combines high-resolution NOx concentrations from computational fluid dynamic (CFD) simulations with the pedestrian flows obtained by pedestrian mobility microsimulations is applied to an urban area of Madrid, Spain. High-resolution maps show pedestrian exposure peaks, at bus stops and crosswalks, that cannot be captured by the simpler methods based on spatial average concentration (SAC) or concentration measured in an AQMS. Total daily exposure obtained is 1.19 · 109 person s μg m−3, while SAC and AQMS concentration methods yielded 9–23% and 30–40% lower values. In conclusion, the proposed methodology allows to determine the areas with higher exposure in order to design local strategies to reduce the impact on human health. In addition, from a more general point of view, the total exposure in the studied area is better estimated by using spatial average concentration than through concentration recorded by AQMS. The assessment of the spatial representative of AQMS becomes necessary to use AQMS concentration to evaluate air quality and population exposure of an urban area.

An Asian Summer Monsoon-Related Relative Humidity Record from Tree-Ring δ18O in Gansu Province, North China

The monsoon fringe region in North China (NC) is also an ecologically fragile zone. Improving our comprehension of the paleoclimate variations and their driving mechanisms in this region has great significance for environmental protection and agricultural economic development. In order to provide more reliable data for future climate forecasting and reduce the effects of climatic disasters in NC, we established a 328-year stable oxygen isotope (δ18O) chronology based on four Pinus tabulaeformis Carr. from Mt. Hasi, Gansu Province, and found that the tree-ring δ18O inherited the signals of summer (July–August) monsoonal precipitation δ18O (δ18OP). Correlation function analysis indicated that the tree-ring δ18O series responded significantly to the observed local relative humidity from July to August (RHJA) with r = −0.65 (n = 55, p &lt; 0.001). Based on the clear physiological mechanism, we reconstructed the RHJA variations from 1685 to 2012 using a transfer function. Our reconstruction was very stable and had strong spatial representativeness, it was significantly positively correlated with Asian summer monsoon (ASM) indices, indicating that our reconstruction reflected the variations of ASM to a large extent. The RHJA series successfully captured the weakening of the ASM since the 1930s. There was a close connection between the reconstructed sequence and the East Pacific sea surface temperature (SST). Further analyses revealed that El Niño–Southern Oscillation (ENSO) and Pacific Decadal Oscillation (PDO) may play important roles in the summer monsoon precipitation in NC.

Isoscape of amount-weighted annual mean precipitation tritium (3H) activity from 1976 to 2017 for the Adriatic–Pannonian region – AP3H_v1 database

Abstract. Tritium (3H) as a constituent of the water molecule is an important natural tracer in hydrological sciences. The anthropogenic tritium introduced into the atmosphere unintentionally became an excellent tracer of processes on a time scale of up to 100 years. A prerequisite for tritium applications is to know the distribution of tritium activity in precipitation. Here we present a database of isoscapes derived from 41 stations for amount-weighted annual mean tritium activity in precipitation for the period 1976 to 2017 on spatially continuous interpolated 1 km×1 km grids for the Adriatic–Pannonian region (called the AP3H_v1 database), with a special focus on post-2010 years, which are not represented by existing global models. Five stations were used for out-of-sample evaluation of the model performance, independently confirming its capability of reproducing the spatiotemporal tritium variability in the region. The AP3H database is capable of providing reliable spatiotemporal input for hydrogeological application at any place within Slovenia, Hungary, and their surroundings. Results also show a decrease in the average spatial representativity of the stations regarding tritium activity in precipitation from ∼440 km in 1970s, when bomb tritium still prevailed in precipitation, to ∼235 km in the 2010s. The post-2010 isoscapes can serve as benchmarks for background tritium activity for the region, helping to determine potential future local increases in technogenic tritium from these backgrounds. The gridded tritium isoscape is available in NetCDF-4 at https://doi.org/10.1594/PANGAEA.896938 (Kern et al., 2019).

Temporal and spatial variabilities in surface mass balance at the EGRIP site, Greenland from 2009 to 2017

Temporal variability in surface mass balance (SMB) on the Greenland ice sheet is important for understanding the mass balance of the ice sheet. Additionally, knowledge of the spatial variability in SMB at ice core drilling sites helps to interpret the spatial representativeness of SMB data obtained from a single ice core. In this study, to investigate the spatiotemporal variability in recent SMB in the East Greenland Ice Core Project (EGRIP) area in the northeastern Greenland ice sheet, pit observations were made at six sites in the summers of 2016–2018. In all pits, depth profiles of water isotope ratios showed clear seasonal variations. The annual SMB differed from site to site, which is probably due to post-depositional redistribution of snow caused by wind erosion and snowdrift. However, the multiple-site averages of annual SMBs, which ranged from 134 to 157 mm w. e. yr−1 (average 146 mm w. e. yr−1) during 2009–2017, were very similar. This indicates that annual SMBs in the EGRIP area were nearly constant in this period. The seasonal SMBs in the EGRIP area tended to be larger in the summer–winter period than in the winter–summer period.

A Scheme to Estimate Diurnal Cycle of Evapotranspiration from Geostationary Meteorological Satellite Observations

The diurnal cycle of evapotranspiration (ET) is significant in studying the dynamics of land–atmosphere interactions. The diurnal ET cycle can be considered as an indicator of dry/wet surface conditions. However, the accuracy of current models in estimating the diurnal ET cycle is generally low. This study developed an improved scheme to estimate the diurnal cycle of ET by solving the surface energy balance equation combined with simplified parameterization, with daily ET as the constraint. Meteosat Second Generation (MSG) land surface temperature, and longwave and shortwave radiation products were the primary inputs. Daily ET was from the remote sensing-based ETMonitor model. The estimated instantaneous (30 min) ET from the improved scheme outperformed the official MSG instantaneous ET product when compared with in situ half-hourly measurements at 35 flux sites from the FLUXNET2015 dataset, and was also comparable with European Center for Medium-Range Weather Forecasts (ECMWF) ERA5 ET data, with an R2 of 0.617 and root mean square error (RMSE) of 65.8 W/m2 for the improved scheme. Results were largely improved compared with those without daily ET as the constraint. The improved method was stable for the estimation of ET’s diurnal cycle at the similar atmospheric conditions and the accuracy was comparative at different land cover surfaces. Errors in the input variables and the simplification of surface heat flux parameterization affected surface energy balance closure, which can lead to instability of the solution of constants in the simplified parameterization and further to the uncertainty of ET’s diurnal cycle estimation. Measurement errors, different source areas in measured variables, and inconsistent spatial representativeness between remote sensing and site measurements also impacted the evaluation.

Analyzing Spatio-Temporal Factors to Estimate the Response Time between SMOS and In-Situ Soil Moisture at Different Depths

A comprehensive understanding of temporal variability of subsurface soil moisture (SM) is paramount in hydrological and agricultural applications such as rainfed farming and irrigation. Since the SMOS (Soil Moisture and Ocean Salinity) mission was launched in 2009, globally available satellite SM retrievals have been used to investigate SM dynamics, based on the fact that useful information about subsurface SM is contained in their time series. SM along the depth profile is influenced by atmospheric forcing and local SM properties. Until now, subsurface SM was estimated by weighting preceding information of remotely sensed surface SM time series according to an optimized depth-specific characteristic time length. However, especially in regions with extreme SM conditions, the response time is supposed to be seasonally variable and depends on related processes occurring at different timescales. Aim of this study was to quantify the response time by means of the time lag between the trend series of satellite and in-situ SM observations using a Dynamic Time Warping (DTW) technique. DTW was applied to the SMOS satellite SM L4 product at 1 km resolution developed by the Barcelona Expert Center (BEC), and in-situ near-surface and root-zone SM of four representative stations at multiple depths, located in the Soil Moisture Measurements Station Network of the University of Salamanca (REMEDHUS) in Western Spain. DTW was customized to control the rate of accumulation and reduction of time lag during wetting and drying conditions and to consider the onset dates of pronounced precipitation events to increase sensitivity to prominent features of the input series. The temporal variability of climate factors in combination with crop growing seasons were used to indicate prevailing SM-related processes. Hereby, a comparison of long-term precipitation recordings and estimations of potential evapotranspiration (PET) allowed us to estimate SM seasons. The spatial heterogeneity of land use was analyzed by means of high-resolution images of Normalized Difference Vegetation Index (NDVI) from Sentinel-2 to provide information about the level of spatial representativeness of SMOS observations to each in-situ station. Results of the spatio-temporal analysis of the study were then evaluated to understand seasonally and spatially changing patterns in time lag. The time lag evolution describes a variable characteristic time length by considering the relevant processes which link SMOS and in-situ SM observation, which is an important step to accurately infer subsurface SM from satellite time series. At a further stage, the approach needs to be applied to different SM networks to understand the seasonal, climate- and site-specific characteristic behaviour of time lag and to decide, whether general conclusions can be drawn.

Comparison of observed borehole temperatures in Antarctica with simulations using a forward model driven by climate model outputs covering the past millennium

Abstract. The reconstructed surface-temperature time series from boreholes in Antarctica have significantly contributed to our understanding of multidecadal and centennial temperature changes and thus provide a good way to evaluate the ability of climate models to reproduce low-frequency climate variability. However, up to now, there has not been any systematic model–data comparison based on temperature from boreholes at a regional or local scale in Antarctica. Here, we discuss two different ways to perform such a comparison using borehole measurements and the corresponding reconstructions of surface temperature at the West Antarctic Ice Sheet (WAIS) Divide, Larissa, Mill Island, and Styx Glacier in Antarctica. The standard approach is to compare the surface temperature simulated by the climate model at the grid cell closest to each site with the reconstructions in the time domain derived from the borehole temperature observations. Although some characteristics of the reconstructions, for instance the nonuniform smoothing, limit to some extent the model–data comparison, several robust features can be evaluated. In addition, a more direct model–data comparison based on the temperature measured in the boreholes is conducted using a forward model that simulates explicitly the subsurface temperature profiles when driven with climate model outputs. This comparison in the depth domain is not only generally consistent with observations made in the time domain but also provides information that cannot easily be inferred from the comparison in the time domain. The major results from these comparisons are used to derive metrics that can be applied for future model–data comparison. We also describe the spatial representativity of the sites chosen for the metrics. The long-term cooling trend in West Antarctica from 1000 to 1600 CE (−1.0 ∘C) is generally reproduced by the models but often with a weaker amplitude. The 19th century cooling in the Antarctic Peninsula (−0.94 ∘C) is not reproduced by any of the models, which tend to show warming instead. The trend over the last 50 years is generally well reproduced in West Antarctica and at Larissa (Antarctic Peninsula) but overestimated at other sites. The wide range of simulated trends indicates the importance of internal variability in the observed trends and shows the value of model–data comparison to investigate the response to forcings.

ESTIMACIÓN DE LA FRACCIÓN INHALADA DE CONTAMINANTES PRIMARIOS DEL AIRE EN LA CIUDAD DE MEDELLÍN

Intake fraction was estimated for measured pollutants in Medellín city: suspended particulates (PM10 and PM2.5), carbon monoxide (CO) and nitrogen oxides (NOx). The applicable method for primary pollutants was used, according to the existing air quality data in terms of emissions and concentrations, as a factor that can be used in the evaluation of the impact on human health due to the sources of air pollution. The data of emissions, attributable exposure concentration, amount population and breathing rates were processed for calculation. Given the importance of the spatial and temporal relationship of the variables, all data were from 2015, the last period in which the inventory of atmospheric emissions was updated. Areas of spatial representativeness were considered, obtaining cells with a CSF greater than 0.9 in Politécnico Jaime Isaza Cadavid (MED-PJIC) (PM10 and CO) and Universidad Nacional Núcleo el Volador (MED-UNNV) (PM2.5 and NOx) stations. Yearly averages of the intake fraction were: 475 and 29 ppm for PM10 and CO, respectively, at MED-PJIC station, and 151 and 64 ppm for PM2.5 and NOx, respectively, at MED-UNNV station. The intake fraction values were analyzed too by weighted respiration rates by age groups, and by hourly, daily, monthly and daily weekday averages of concentration and pollutant emission values.