Complex Orography Research Articles

Aerosol indirect effects in complex-orography areas: a numerical study over the Great Alpine Region

Abstract. Aerosols play a crucial role in climate through different feedback mechanisms, affecting radiation, clouds, and air column stability. This study focuses on the altitude dependence of the cloud-mediated indirect effects of aerosols in the Great Alpine Region (GAR), an area characterized by high pollution levels from anthropic activities in the Po Valley and a complex orography with some of the highest mountains in Europe. Using a regional atmospheric model, 5-year-long convective-permitting sensitivity experiments have been run with different surface aerosol fluxes. The results show that seasonal mean cloud cover, temperature, and precipitations are affected by the aerosol concentrations in the air column and that the response to pollution is both elevation- and season-dependent. The overall cloud cover increase with aerosol levels leads to either surface cooling or warming depending on the surface albedo (snow covered or not). Furthermore, different types of clouds have a different response: while the lifetime of low-pressure-system clouds and orographic clouds is generally increased at high levels of aerosols, convective clouds (typical of the summer season) can decrease at high levels of pollution due to the reduction in strong updrafts associated with an increased air column stability.

Low-Altitude Sensing of Urban Atmospheric Turbulence with UAV

The capabilities of a quadcopter in the hover mode for low-altitude sensing of atmospheric turbulence with high spatial resolution in urban areas characterized by complex orography are investigated. The studies were carried out in different seasons (winter, spring, summer, and fall), and the quadcopter hovered in the immediate vicinity of ultrasonic weather stations. The DJI Phantom 4 Pro quadcopter and AMK-03 ultrasonic weather stations installed in different places of the studied territory were used in the experiment. The smoothing procedure was used to study the behavior of the longitudinal and lateral spectra of turbulence in the inertial and energy production ranges. The longitudinal and lateral turbulence scales were estimated by the least-square fit method with the von Karman model as a regression curve. It is shown that the turbulence spectra obtained with DJI Phantom 4 Pro and AMK-03 generally coincide, with minor differences observed in the high-frequency region of the spectrum. In the inertial range, the behavior of the turbulence spectra shows that they obey the Kolmogorov–Obukhov “5/3” law. In the energy production range, the longitudinal and lateral turbulence scales and their ratio measured by DJI Phantom 4 Pro and AMK-03 agree to a good accuracy. Discrepancies in the data obtained with the quadcopter and the ultrasonic weather stations at the territory with complex orography are explained by the partial correlation of the wind velocity series at different measurement points and the influence of the inhomogeneous surface.

Links between precipitation, circulation weather types and orography in central Italy

AbstractRecent analyses of satellite and surface observations reported a negative annual rainfall trend in central Italy. The complex orography of the Apennines and the strong influence of climate change in the Mediterranean basin complicates the explanation of such trends and their spatial variability. This work aims at describing the link between circulation weather types, orography and precipitation patterns observed in central Italy, as a first step towards the understanding of such climate trends. Using ERA5 reanalysis data from 1951 to 2019, four weather types are identified as most responsible for the spatial variability of rainfall in central Italy. They are associated with cyclonic circulations characterized by high water vapour transport coming from west, south‐west, south‐east and north‐east. The analysis of wind speed and precipitation climatology for the period 1951–2019, as derived from both surface observations and reanalysis, confirms a strong influence of moist south‐westerly fluxes over all the domain, while the effect of north‐easterly fluxes remains confined to the Apennines and the Adriatic coast. ERA5 overestimates annual rainfall in most parts of the region, except on the north‐central Apennines where the underestimation reaches average values of 300 mm. The analysis of circulation weather types and their associated precipitation variability demonstrates that this deficit can be ascribed to a wrong representation of the orographic precipitation component as seen by ERA5 in correspondence of cyclonic north‐easterly and westerly flows impinging towards the north‐central Apennines. Indeed the precipitation associated with north‐easterly and westerly fluxes shows the strongest modulation by orography. This interaction and its possible modification by climate change effects should be considered in future studies investigating recent climate trends in central Italy.

An Evaluation of the Extreme Rainfall Event of 2010 over the Kabul River Basin using the WRF Model

Extreme precipitation events are among the most severe weather hazards. Knowledge about the spatial patterns underlying such events in the Upper Indus Basin is limited because estimating precipitation is very challenging due to the data scarcity and the complex orography. Numerical weather prediction models can be applied at a fine resolution to overcome this issue. The Advanced Research Weather Research and Forecasting (WRF) model version 3.8.1 was applied over the Kabul River Basin to simulate the temperature and precipitation of monsoon season 2010, i.e., 1st May to 16th September 2010. We considered the May month as a spin-up period. The initial and boundary conditions were derived from the National Oceanic and Atmospheric Administration, Climate Forecast System Reanalysis data. The model was set up by using two-nested domains with increasing horizontal resolution moving inward from 15km on domain d01 to 5km on domain d02. The simulations were compared with TRMM 3B42, and station data collected from the Pakistan Meteorological Department and Water and the Power Development Authority using bias, percentage bias, root mean square error, and Pearson correlation. The results revealed that the simulated precipitation was improved from d01 to d02. However, the model showed mixed results with overestimation of precipitation at some stations and underestimations at others. Simulated precipitation generally agreed better with TRMM than with station data. Overall, the results indicate that the WRF model can be used to simulate heavy precipitation in complex terrain.

Weather Radar in Complex Orography

Applications of weather radar data to complex orography are manifold, as are the problems. The difficulties start with the choice of suitable locations for the radar sites and their construction, which often involves long transport routes and harsh weather conditions. The next challenge is the 24/7 operation and maintenance of the remote, unmanned mountain stations, with high demands on the availability and stability of the hardware. The data processing and product generation also require solutions that have been specifically designed and optimised in a mountainous region. The reflection and shielding of the beam by the mountains, in particular, pose great challenges. This review article discusses the main problems and sources of error and presents solutions for the application of weather radar technology in complex orography. The review is focused on operational radars and practical applications, such as nowcasting and the automatic warning of thunderstorms, heavy rainfall, hail, flash floods and debris flows. The presented material is based, to a great extent, on experience collected by the authors in the Swiss Alps. The results show that, in spite of the major difficulties that emerge in mountainous regions, weather radar data have an important value for many practical quantitative applications.

Sensitivity of Pollutant Concentrations to the Turbulence Schemes of a Dispersion Modelling Chain over Complex Orography

Atmospheric circulation over mountainous regions is more complex than over flat terrain due to the interaction of flows on various scales: synoptic-scale flows, thermally-driven mesoscale winds and turbulent fluxes. In order to faithfully reconstruct the circulation affecting the dispersion and deposition of pollutants in mountainous areas, meteorological models should have a sub-kilometer grid spacing, where turbulent motions are partially resolved and the parametrizations of the sub-grid scale fluxes need to be evaluated. In this study, a modelling chain based on the Weather Research and Forecasting (WRF) model and the chemical transport model Flexible Air Quality Regional Model (FARM) is applied to estimate the pollutant concentrations at a 0.5 km horizontal resolution over the Aosta Valley, a mountainous region of the northwestern Alps. Two pollution episodes that occurred in this region are reconstructed: one summer episode dominated by thermally-driven winds, and one winter episode dominated by synoptic-scale flows. Three WRF configurations with specific planetary boundary layer and surface layer schemes are tested, and the numerical results are compared with the surface measurements of meteorological variables at twenty-four stations. For each WRF configuration, two different FARM runs are performed, with turbulence-related quantities provided by the SURface-atmosphere interFace PROcessor or directly by WRF. The chemical concentrations resulting from the different FARM runs are compared with the surface measurements of particulate matter of less than 10 µm in diameter and nitrogen dioxide taken at five air quality stations. Furthermore, these results are compared with the outputs of the modelling chain employed routinely by the Aosta Valley Environmental Protection Agency, based on FARM driven by COSMO-I2 (COnsortium for Small-scale MOdelling) at 2.8 km horizontal grid spacing. The pollution events are underestimated by the modelling chain, but the bias between simulated and measured surface concentrations is reduced using the configuration based on WRF turbulence parametrizations, which imply a reduced dispersion.

Mosaicking Weather Radar Retrievals from an Operational Heterogeneous Network at C and X Band for Precipitation Monitoring in Italian Central Apennines

Meteorological radar networks are suited to remotely provide atmospheric precipitation retrieval over a wide geographic area for severe weather monitoring and near-real-time nowcasting. However, blockage due to buildings, hills, and mountains can hamper the potential of an operational weather radar system. The Abruzzo region in central Italy’s Apennines, whose hydro-geological risks are further enhanced by its complex orography, is monitored by a heterogeneous system of three microwave radars at the C and X bands with different features. This work shows a systematic intercomparison of operational radar mosaicking methods, based on bi-dimensional rainfall products and dealing with both C and X bands as well as single- and dual-polarization systems. The considered mosaicking methods can take into account spatial radar-gauge adjustment as well as different spatial combination approaches. A data set of 16 precipitation events during the years 2018–2020 in the central Apennines is collected (with a total number of 32,750 samples) to show the potentials and limitations of the considered operational mosaicking approaches, using a geospatially-interpolated dense network of regional rain gauges as a benchmark. Results show that the radar-network pattern mosaicking, based on the anisotropic radar-gauge adjustment and spatial averaging of composite data, is better than the conventional maximum-value merging approach. The overall analysis confirms that heterogeneous weather radar mosaicking can overcome the issues of single-frequency fixed radars in mountainous areas, guaranteeing a better spatial coverage and a more uniform rainfall estimation accuracy over the area of interest.

Estimation of Potential Soil Erosion and Sediment Yield: A Case Study of the Transboundary Chenab River Catchment

Near real-time estimation of soil loss from river catchments is crucial for minimizing environmental degradation of complex river basins. The Chenab river is one of the most complex river basins of the world and is facing severe soil loss due to extreme hydrometeorological conditions, unpredictable hydrologic response, and complex orography. Resultantly, huge soil erosion and sediment yield (SY) not only cause irreversible environmental degradation in the Chenab river catchment but also deteriorate the downstream water resources. In this study, potential soil erosion (PSE) is estimated from the transboundary Chenab river catchment using the Revised Universal Soil Loss Equation (RUSLE), coupled with remote sensing (RS) and geographic information system (GIS). Land Use of the European Space Agency (ESA), Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) data, and world soil map of Food and Agriculture Organization (FAO)/The United Nations Educational, Scientific and Cultural Organization were incorporated into the study. The SY was estimated on monthly, quarterly, seasonal, and annual time-scales using sediment delivery ratio (SDR) estimated through the area, slope, and curve number (CN)-based approaches. The 30-year average PSE from the Chenab river catchment was estimated as 177.8, 61.5, 310.3, 39.5, 26.9, 47.1, and 99.1 tons/ha for annual, rabi, kharif, fall, winter, spring, and summer time scales, respectively. The 30-year average annual SY from the Chenab river catchment was estimated as 4.086, 6.163, and 7.502 million tons based on area, slope, and CN approaches. The time series trends analysis of SY indicated an increase of 0.0895, 0.1387, and 0.1698 million tons per year for area, slope, and CN-based approaches, respectively. It is recommended that the areas, except for slight erosion intensity, should be focused on framing strategies for control and mitigation of soil erosion in the Chenab river catchment.

On the need of bias adjustment for more plausible climate change projections of extreme heat

AbstractThe assessment of climate change impacts in regions with complex orography and land‐sea interfaces poses a challenge related to shortcomings of global climate models. Furthermore, climate indices based on absolute thresholds are especially sensitive to systematic model biases. Here we assess the effect of bias adjustment (BA) on the projected changes in temperature extremes focusing on the number of annual days with maximum temperature above 35°C. To this aim, we use three BA methods of increasing complexity (from simple scaling to empirical quantile mapping) and present a global analysis of raw and BA CMIP5 projections under different global warming levels. The main conclusions are (1) BA amplifies the magnitude of the climate change signal (in some regions by a factor 2 or more) achieving a more plausible representation of future heat threshold‐based indices; (2) simple BA methods provide similar results to more complex ones, thus supporting the use of simple and parsimonious BA methods in these studies.

Interannual and intra-annual cycles of satellite-derived chlorophyll-a concentrations in the Caspian Sea

Variability of phytoplankton in the Caspian Sea (CS) is related to the complex orography, the variety of physical and chemical processes, and climatic indices. Here, interannual and intra-annual variability of satellite-derived chlorophyll-a concentrations (Chl-a) were studied using wavelet analysis during 2002–2019 in different regions of the CS. Self-Organizing Maps (SOM) analysis performed to classify the CS into the areas of similar variability of satellite-derived Chl-a. Wavelet spectral analysis showed that the Chl-a variability regulated by four primary periodic cycles: 0.5-year, 1-year, 2 to 2.5-year, and 3 to 5-year. The 0.5 and 1-year wavelet cycles mostly depicted the intensity of seasonality patterns. The 2–2.5-year and 3–5-year cycles of Chl-a showed non-stationary coherence with corresponding low-frequency cycles of NAO and ENSO. The 3–5-year wavelet amplitudes of Chl-a strongly correlated with NAO and ENSO in the southern CS. Weak correlations of 2–2.5-year cycle wavelet amplitudes of Chl-a with NAO and ENSO suggested that variations do not always directly translate to a biological response. A negative anomaly in the Chl-a autumn peaks observed during 2011–2016 in the middle and southern CS, when NAO phases were persistently positive. The interannual variations of summer peaks in the northern CS, and autumn peaks in the middle and southern CS were broadly related to the precipitation. SST and wind stress. Moreover, it was shown that the Volga discharge has a significant influence on Chl-a variability in the northern CS.

Comparison of Top-Down and Bottom-Up Road Transport Emissions through High-Resolution Air Quality Modeling in a City of Complex Orography

Vehicular emissions are a predominant source of pollution in urban environments. However, inherent complexities of vehicular behavior are sources of uncertainties in emission inventories (EIs). We compare bottom-up and top-down approaches for estimating road transport EIs in Manizales, Colombia. The EIs were estimated using a COPERT model, and results from both approaches were also compared with the official top-down EI (estimated from IVE methodology). The transportation model PTV-VISUM was used for obtaining specific activity information (traffic volumes, vehicular speed) in bottom-up estimation. Results from COPERT showed lower emissions from the top-down approach than from the bottom-up approach, mainly for NMVOC (−28%), PM10 (−26%), and CO (−23%). Comparisons showed that COPERT estimated lower emissions than IVE, with higher differences than 40% for species such as PM10, NOX, and CH4. Furthermore, the WRF–Chem model was used to test the sensitivity of CO, O3, PM10, and PM2.5 predictions to the different EIs evaluated. All studied pollutants exhibited a strong sensitivity to the emission factors implemented in EIs. The COPERT/top-down was the EI that produced more significant errors. This work shows the importance of performing bottom-up EI to reduce the uncertainty regarding top-down activity data.

A didactic approach to the machine learning application to weather forecast

We propose a didactic approach to use the Machine Learning protocol in order to perform weather forecast. This study is motivated by the possibility to apply this method to predict weather conditions in proximity of the Etna and Stromboli volcanic areas, located in Sicily (south Italy). Here the complex orography may significantly influence the weather conditions due to Stau and Foehn effects, with possible impact on the air traffic of the nearby Catania and Reggio Calabria airports. We first introduce a simple thermodynamic approach, suited to provide information on temperature and pressure when the Stau and Foehn effects take place. Then, in order to gain information also on the rainfall accumulation, the Machine Learning approach is presented: according to this protocol, the model is able to “learn” from a set of input data constituted by the meteorological conditions (in our case dry, light rain, moderate rain and heavy rain) associated to the rainfall, measured in mm. We observe that, since in the input dataset provided by the Salina weather station the dry condition was the most common, the algorithm is very accurate in predicting it. Further improvements can be obtained by increasing the number of considered weather stations and the time interval.

Coordinated Multicast/Unicast Transmission on 5G: A Novel Approach for Linear Broadcasting

Linear broadcasting services, with a scheduled programming, constitute a paramount telecommunication service for today’s society. Although the existing technology is mature, current linear broadcast systems have serious limitations when providing service to moving users or users placed in areas with complex orography and poor signal quality. To overcome these limitations, 3GPP 5G standard has included a work item to support 5G multicast/broadcast services for future Release 17. This paper investigates the integration of point-to-point (unicast) communication with cellular multicast/broadcast on 5G technology to extend the current support of linear broadcasting services. This integration relies on the use mobile edge computing (MEC) at the 5G base station (gNB) to host a dynamic adaptive streaming over HTTP (DASH) server that is coordinated with the multicast transmission to complement the broadcast service. This approach join the reliability of point-to-point communications, with dedicated resources for each user, with the spectrum efficiency of multi-cast communications, where a set of users share common resources. The cooperation between those unicast and multicast schemes allows those users whose coverage is not good enough, to complete the linear broadcast flow through the point-to-point transmission via MEC. The benefits of such approach have been assessed with simulations in a realistic scenario that considers a vehicle moving across a sparsely populated region in southern Spain. Results reveals that throughput and bitrate playback (reproduction rate) are greatly improved when unicast/multicast integration is enabled since the number of stalling events is reduced significantly.

A data-driven approach to forecasting ground-level ozone concentration

The ability to forecast the concentration of air pollutants in an urban region is crucial for decision-makers wishing to reduce the impact of pollution on public health through active measures (e.g. temporary traffic closures). In this study, we present a machine learning approach applied to forecasts of the day-ahead maximum value of ozone concentration for several geographical locations in southern Switzerland. Due to the low density of measurement stations and to the complex orography of the use-case terrain, we adopted feature selection methods instead of explicitly restricting relevant features to a neighborhood of the prediction sites, as common in spatio-temporal forecasting methods. We then used Shapley values to assess the explainability of the learned models in terms of feature importance and feature interactions in relation to ozone predictions. Our analysis suggests that the trained models effectively learned explanatory cross-dependencies among atmospheric variables. Finally, we show how weighting observations helps to increase the accuracy of the forecasts for specific ranges of ozone’s daily peak values.

Regional and Seasonal Precipitation and Drought Trends in Ganga–Brahmaputra Basin

Satellite-based precipitation products can be a better alternative of rain gauges for hydro-meteorological studies in data-poor regions. This study aimed to evaluate how regional and seasonal precipitation and drought patterns had changed in the Ganga–Brahmaputra Basin between 1983 and 2020 with PERSIANN-CDR precipitation data. The spatial pattern of winter drought, monsoon drought, and Standardized Precipitation Index (SPI) calculated for different time scales were evaluated using principal component analysis. Ganga–Brahmaputra is one of the most populated river basins that flows through different geographical regions. Rain gauges are heterogeneously distributed in the basin due to its complex orography, highlighting the significance of gridded precipitation products over gauge observations for climate studies. Annual and monthly precipitation trends between 1983 and 2020 were evaluated using the original and modified Mann–Kendall trend test, and annual precipitation in the basin was found to be declining at a rate of 5.8 mm/year. An increasing trend was observed in pre-monsoon rainfall, whereas precipitation exhibited a decreasing trend for other months. Results of the Pettitt test showed precipitation time series was inhomogeneous and changepoint occurred around 2000. Decreasing trends of SPI indicated increasing frequency and intensity of drought events. Winter drought showed a clear spatial pattern in the basin; however, SPIs calculated for different time scales and monsoon drought had complex spatial patterns. This study demonstrates the applicability of satellite-based PERSIANN-CDR precipitation data in climate research in the Ganga–Brahmaputra Basin.

Mid-Holocene monsoons in South and Southeast Asia: dynamically downscaled simulations and the influence of the Green Sahara

Abstract. Proxy records suggest that the Northern Hemisphere during the mid-Holocene (MH), to be assumed herein to correspond to 6000 years ago, was generally warmer than today during summer and colder in the winter due to the enhanced seasonal contrast in the amount of solar radiation reaching the top of the atmosphere. The complex orography of both South and Southeast Asia (SA and SEA), which includes the Himalayas and the Tibetan Plateau (TP) in the north and the Western Ghats mountains along the west coast of India in the south, renders the regional climate complex and the simulation of the intensity and spatial variability of the MH summer monsoon technically challenging. In order to more accurately capture important regional features of the monsoon system in these regions, we have completed a series of regional climate simulations using a coupled modeling system to dynamically downscale MH global simulations. This regional coupled modeling system consists of the University of Toronto version of the Community Climate System Model version 4 (UofT-CCSM4), the Weather Research and Forecasting (WRF) regional climate model, and the 3D Coastal and Regional Ocean Community model (CROCO). In the global model, we have taken care to incorporate Green Sahara (GS) boundary conditions in order to compare with standard MH simulations and to capture interactions between the GS and the monsoon circulations in India and SEA. Comparison of simulated and reconstructed climates suggest that the dynamically downscaled simulations produce significantly more realistic anomalies in the Asian monsoon than the global climate model, although they both continue to underestimate the inferred changes in precipitation based upon reconstructions using climate proxy information. Monsoon precipitation over SA and SEA is also greatly influenced by the inclusion of a GS, with a large increase particularly being predicted over northern SA and SEA, and a lengthening of the monsoon season. Data–model comparisons with downscaled simulations outperform those with the coarser global model, highlighting the crucial role of downscaling in paleo data–model comparison.

Espacios agrarios, asentamientos prehispánicos y tecnología LiDAR en el área costera central del Ecuador

<p class="VARAbstract">The use of Light Detection and Ranging (LiDAR) technology has revolutionised pre-Hispanic archaeology in large tracts of Central America (Canuto et. al. 2018; Fisher et al. 2016; Garrison et al. 2019; Chase & Weishampel, 2016). This contribution is particularly significant in areas in which jungles and a complex orography had precluded the implementation of large-scale landscape archaeology studies.</p><p class="VARAbstract">Although this technology has not been used in South America as intensively as in Central America (Mexico, Guatemala and Honduras, among others), LiDAR is beginning to be used to answer questions concerning the interaction between the landscape, pre-Hispanic societies and their economic exploitation strategies (Amazonia, Peru) (Van Valkenburg et al., 2020). This work analyses one of the most important archaeological areas in central Ecuador, Cerros de Hojas-Jaboncillo (Figs. 1 & 2), which has traditionally been identified as a major centre of the Manteña culture. This culture spanned the current provinces of Manabí, Santa Elena and Guayas between the 8<sup>th</sup> and 15<sup>th</sup> centuries AD.</p><p class="VARAbstract">Although the site was discovered in the early 20th century (Saville, 1907 & 1910; Estrada, 1957) on the slopes and summits of the Chongón-Colonche and Balzar hills range (formerly called Sierra de Bálsamo), it has not been until recent moments that the characterization of its extension, its spatial conformation, as well as the main geoarchaeological characteristics of the site have begun to be possible (López, 2008; Delgado, 2009; Tobar, 2012; Lunniss, 2012; Veintimilla-Bustamante, 2012). The limitations of previous studies were largely due to the prevalence of thick sub-tropical forests, whose trees are sometimes 14 m tall.</p><p class="VARNormal">In 2016, a large-scale LiDAR flight covering 147.78 km² at a density of 9 ppm² has revealed the main features of the archaeological complex, including various sites that illustrate a multi-nuclear settlement pattern (Figs. 5, 8 & 10). The study suggests that settlements, between 0.5 and 3 ha in size (Fig. 8), followed a common pattern, standing on small long hilltop plateaus. The complexes were visually connected and were linked by a road network.</p><p class="VARNormal">There is little doubt that one of the most important discoveries concerns the relationship between archaeological sites and complex agrarian systems, which point to the exploitation of wide areas. These had irrigation systems that involved rainwater retention and water resources maximisation (Figs. 6 & 8). The system made intensive rainwater use during the humid season (December-May); it also took advantage of the coastal mists that ensure precipitations on the hilltops.</p><p class="VARNormal">At the same time, the LiDAR survey has established differences between the settlement pattern in the north-eastern sector, in which the interaction between settlements and the agro-system is not so obvious, and the south-western sector, whose settlements are clearly related to wide terraced systems.</p><p class="VARNormal">These results will allow for comparisons between Cerros de Hojas-Jaboncillo and other known Manteño sites, such as Japotó (Bouchard, 2010), Agua Blanca (McEwan & Silva, 1992) and Ligüiqui. All of them point to the continued occupation of the same areas, and the continuity of some sites at least between the Guangala period (500 BC.-600 AD) and the late Manteño period (12<sup>th</sup>-15<sup>th</sup> centuries), as seen in Salango and its territory (Béarez, Gay & Lunniss, 2012).</p><p class="VARNormal">Most research in the 20<sup>th</sup> century has argued for an increase in social complexity during the classic Manteño period (8<sup>th</sup>-11<sup>th</sup> centuries), following a diffusionist explanatory pattern (Estrada, 1957). However, the LiDAR analysis of the Cerros de Hojas-Jaboncillo site points to prolonged periods of stable management of the environment, a response to the need to make use of the available resources. Archaeology, through large-scale landscape studies and the in-depth characterisation of sites through wide-ranging sampling methodologies can contribute to answering the question as to whether environmental management led to the formation of a “Manteño state” or whether this society can be better defined as a chiefdom (Touchard-Houlbert, 2010).</p><p class="VARNormal"><strong>Highlights:</strong></p><ul><li><p>The study focused on the LiDAR research of the Cerro de Hojas-Jaboncillo, the most important pre-Hispanic site in the Manabi province (Ecuador).</p></li><li><p>LiDAR technology has allowed users to identify an intense occupation in a forest area, where until now, it was impossible to detect archaeological structures.</p></li><li><p>The study has established the close relationship between the pre-Hispanic occupation model and the use of complex irrigation systems.</p></li></ul>

The sequence of heavy precipitation and flash flooding of 12 and 13 September 2019 in eastern Spain. Part II: A hydro-meteorological predictability analysis based on convection-permitting ensemble strategies

AbstractOn 12 and 13 September 2019, widespread flash flooding caused devastating effects across eastern Spain. Within the framework of the HyMeX program, this study examines predictability of the long-lasting heavy precipitation episode (HPE) conducive to flash flooding. A set of short-range, convection-permitting ensemble prediction systems (EPSs) is built to cope with different sources of meteorological uncertainty. Specifically, the performance of an Ensemble Kalman Filter, tailored bred vectors and stochastic model parameterizations is compared to more standard ensemble generation techniques such as dynamical downscaling and multiple physics. Results indicate EPS focusing on sampling model uncertainties related to parameterization of subgrid process are skillful, especially when deep convection and its interaction with complex orography are important. Furthermore, representation of small-scale thermodynamical aspects is improved through data assimilation, leading to an enhanced forecasting skill as well. Nevertheless, predictability remains relatively low at the catchment scale in terms of exceedance probabilities in cumulative precipitation and peak discharge. The analysis presented herein could serve as a basis for the future implementation of real-time flash flood warning systems based on skillful meteorological EPSs over small-to-medium, semi-arid watersheds in eastern Spain and, by extension, over the flood-prone Western Mediterranean region.

Hail characteristics in Serbia based on data obtained from the network of hail suppression system stations

AbstractSerbia is located in the mid‐latitudes of the Northern Hemisphere and is exposed to frequent hailfall. Consequently, a hail suppression system was gradually developed during the last 54 years. In this study, data from densely distributed hail suppression stations were used to better understand the hail characteristics. Two data subsets are differentiated: the first from the territory of Serbia, excluding the northern province of Vojvodina, during 1981–2015 (P1) and the second from the entire territory of Serbia during 2002–2015 (P2). The distribution of hail size shifted to smaller diameters in P2 compared to P1. The hailfall lasted less than 5 min in about 75% of events while the duration was greater than or equal to 10 min in only 8%. The influence of Serbia's complex orography on hailfall is reflected in the greater average annual number of days with hailfall in mountainous areas, with a maximum mean value of 1.2 in the southwestern part of Serbia, while the maximum number was 2.7. Hail and small hail occurred mainly in the afternoon and evening, with 92.24% of cases being observed from 12:00 to 21:00 local time (UTC + 1). A positive monotonic trend in the annual number of days with hailfall was observed. In both subsets, there was no statistically significant trend in hail frequency. The average seasonal duration of hail and small hail falls showed a decreasing trend in P1. Results showed that the first occurrence of hail falls did not appear earlier in the season.

Analysis of Rainfall Erosivity Trends 1980–2018 in a Complex Terrain Region (Abruzzo, Central Italy) from Rain Gauges and Gridded Datasets

The erosive capacity of precipitation depends on its intensity, volume, and duration. The rainfall erosivity factor (R) of the Universal Soil Loss Equation (USLE) requires high frequency (subhourly) data. When these are not available, R can be estimated from simplified indices such as the Modified Fournier Index (MFI), the Precipitation Concentration Index (PCI), and the Seasonality Index (SI), which are computed from monthly precipitation. We calculated these indices for 34 stations in the complex terrain Abruzzo region (central Italy) during 1980–2018, based on both gauge (point) and grid datasets. Using 30-min rainfall data of 14 stations, we verified that MFI and PCI are reliable predictors of R (R2 = 0.91, RMSE = 163.6 MJ mm ha−1 h−1 year−1). For MFI, grid data do not capture the peaks in high-altitude stations and the low values in some inland areas, detected by the point dataset. Grid data show significant MFI positive trends in 74% of the stations, while the point data display significant positive trends in only 26% of stations and significant negative trends in four stations in the inland areas. The grid data complex orography requires preliminary validation work.