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Geological education plays a major role in the social, economic, and cultural growth and development of any country. India is a vast diversified country with wide-ranging geological features, consisting of rocks of all geological ages with well-developed physiographic divisions. The Indian lithospheric plate is a storehouse of vast georesources and also serves as a natural field laboratory for developing and testing numerous geological principles. The exploration of Indian georesources began in the past; with the organized exploration began way back in the 1830s. The number of institutions offering geological education is, however, comparatively low. Thus, Geology as a science discipline is less popular in comparison with other science subjects such as Physics, Chemistry, Zoology, Botany, and Environmental science, based on enrolment data of the learners enrolled in academic sessions from 2011-2012 to 2018-2019 of the Master's degree programs. Analysis indicates that teaching-learning resources of foreign origin are dominantly being adopted as learning resources in the teaching-learning pedagogy of degree programs in Geology, and are over-riding those of Indian origin. The COVID-19 pandemic is affecting geological education in two ways namely, the disruption of conventional (face-to-face) teaching together with not allowing learners of Geology programs to complete their compulsory out-door geological fieldwork assignments. The analysis of data gathered by an online questionnaire survey shows that the use of indigenous open educational resources in Geology appears as a plausible solution to overcome the learning barriers created by the COVID-19 pandemic, and also to enrich teaching-learning resources of Indian origin. This article provides an up-to-date detailed account of the necessity and evolution of geological education, the current popularity of Geology as a science discipline, the nature of available geological teaching-learning resources, the impact of the COVID-19 on geological education, and the role of open educational resources in providing quality and equitable geological education, whilst removing educational barriers created by COVID-19 in India.

Entre 2012 e 2015 houve uma seca intensa no semiárido baiano. Nesse período, a mídia impressa da Bahia divulgou notícias sobre esse fenômeno climático. Este artigo objetivou analisar qualitativa e quantitativamente as notícias sobre a seca 2012-2015 difundidas pela mídia, bem como a relação entre a difusão das matérias e o volume de precipitação local. O método consistiu em: análise de conteúdo das matérias publicadas pela mídia baiana e a análise conjunta dos dados de precipitação e a quantidade de notícias. Os resultados demonstraram relação entre o número de notícias e o comportamento da precipitação, contudo, foram verificados erros conceituais e adjetivos pejorativos à seca. Concluiu-se que as notícias ainda sustentam o discurso do combate à seca e ignoram as causas do risco da seca na região. Assim, a informação sobre a seca, difundida pela mídia, é mais um perigo que o sertanejo enfrenta.

The assessment of the response of slopes to precipitations is important for various applications, from water resources management to hazard assessment due to extreme rainfall events. It is well known that the underground conditions prior to the initiation of rainfall events control the hydrological processes that occur in slopes, affecting the water exchange through their boundaries. The present study aims at identifying hydrological variables to be monitored and modelled, suitable to improve the prediction of slope response to precipitations, for the case of a slope covered with loose pyroclastic coarse-grained soil overlaying a karstic bedrock, typical of southern Apennines (Italy). Field monitoring has been carried out for three years at the slope, including stream level recordings, meteorological recordings, and soil water content and suction measurements, which allowed setting up a physically based hydrological model of the slope, coupling the unsaturated flow in the soil cover with a perched aquifer developing in the fractured bedrock. To enlarge the field dataset, a synthetic dataset has been generated, linking a previously calibrated stochastic rainfall generator to the hydrological model. In this way, a synthetic dataset of 1000 years has been obtained, containing information on rainfall, aquifer water level and soil volumetric water content at different depths. Machine Learning techniques have been used to unwrap the relationships linking the studied variables, typically non-linear. The Random Forest technique has been used to assess the importance of each variable on the slope response, and the k-means clustering technique has been used to explore the geometrical disposition of data, so to identify seasonally recurrent different conditions controlling the slope response. The results indicate that the slope response, in terms of the fraction of rainwater remaining stored in the soil cover at the end of each rainfall event, can be predicted from the underground conditions prior to the rainfall initiation, weighting the role, on one hand, of the soil moisture excess above field capacity, controlling the ease of the water to flow in and out of the soil cover and, on the other hand, of the perched aquifer water level, that gives evidence of the activation of effective slope drainage.

Rainfall-triggered landslides are widespread geohazards, often characterized by shallow and fast movements. Their occurrence is reported in many mountainous areas, and its cumulative effects are sometimes comparable to great catastrophes (Banco Mundial, 2012). Particularly, southern Apennines of Campania (Italy), commonly covered by pyroclastic deposits laying upon karstic bedrock, are subjected to recurrent shallow landslides (Marino et al., 2021). Different triggering mechanisms have been hypothesized, and investigation on the hydrological processes predisposing slopes to failure is still needed. This study focuses on a slope where hydrometeorological monitoring has been carried out for several years, and landslides recently occurred. To assess the conditions leading to landslides, a 1000-year hourly synthetic dataset, mimicking the hydrological response of the slope to meteorological forcing, was generated. Specifically, a stochastic NSRP rainfall model was coupled with a hydrological model of the unsaturated flow through the soil cover, connected to a perched aquifer forming in the uppermost bedrock during the rainy season. Both the models had been previously calibrated based on field data (Greco et al, 2013, 2018; Marino et al, 2020).The synthetic dataset was analyzed with k-means clustering and Random Forest techniques, to identify the hydrologic conditions, before the onset of rainfall events, controlling the amount of rainwater remaining stored in the soil cover at the end of rainfall, thus affecting slope equilibrium. Stability was analyzed under the infinite slope hypothesis, considering the contribution of suction to unsaturated soil shear strength.The results show how the different hydrologic behaviors, related to slope underground water conditions before the onset of rainfall, as well as the total event rainfall, control slope stability. In fact, two different landslide triggering mechanisms are clearly identified. On one hand, when antecedent slope conditions hamper the fast drainage of infiltrating water out of the soil cover through the underlying fractured bedrock, typical of late autumn, slope failure is triggered by infiltration during the largest rainfall events, as almost all rainwater remains stored in the soil cover. On the other hand, when the bedrock is already filled with water previously drained from the soil cover, as at the end of very rainy autumns and winters, landslides can be triggered also by relatively small rainfall, as the bedrock cannot receive more water, and even exfiltration from the bedrock can occur.ReferencesBanco Mundial (2012). Análisis de la gestión del riesgo de desastres en Colombia: un aporte para la construcción de políticas públicas. https://doi.org/333.3109861/A56Greco R et al (2013). Hydrological modelling of a slope covered with shallow pyroclastic deposits from field monitoring data. https://doi.org/10.5194/hess-17-4001-2013Greco R et al (2021). Recurrent rainfall-induced landslides on the slopes with pyroclastic cover of Partenio Mountains (Campania, Italy): Comparison of 1999 and 2019 events. https://doi.org/10.1016/j.enggeo.2021.106160Greco et al (2018). Interaction between perched epikarst aquifer and unsaturated soil cover in the initiation of shallow landslides in pyroclastic soils. https://doi.org/10.3390/w10070948Marino et al (2021). Soil moisture information can improve shallow landslide forecasting using the hydrometeorological threshold approach. https://doi.org/ 10.1007/s10346-020-01420-8Marino et al (2021). Prediction of shallow landslides in pyroclastic-covered slopes by coupled modeling of unsaturated and saturated groundwater flow. https://doi.org/10.1007/s10346-020-01484

Water distribution networks (WDNs) are an important critical infrastructure, but they are increasingly at risk from contamination (WHO, 2014). The causes can be several: chlorination equipment malfunctioning, low pressure, contaminant intrusion in water tank, accidental cross-connection between drinking-water and non-drinking-water, etc... To limit potential threat to public health, it is advisable to install a network of sensors that can monitor water quality in real time and provide information about potential contamination risks. With the proliferation of IoT technologies and low-cost sensors capable of monitoring water quality parameters, it is now possible to implement a real-time monitoring network by overcoming the difficulties associated with biochemical analyses of water samples in a laboratory. Despite the modern technologies, the placement of sensors in the water network is still an open task for researchers. The main sensor placement methodologies use optimization techniques to minimize or maximize either single- or multi-objective functions (Ostfeld et al., 2008), but they require a calibrated model of the network, which is not always available because the calibration process is expensive and time-consuming.Recently, a novel approach (Santonastaso et al., 2021) based on the use of the topological centrality metric, which does not require hydraulic information and simulations, has been proposed, showing good effectiveness and easy applicability by water utilities to define locations for quality sensors, owing to its simplicity compared to optimization-based approaches.In this work, different weights such as the length of pipes, the diameter and the water demand were used to improve the performance of the adopted topological approach, as well as to evaluate the impact of the weight, used to compute centrality metrics, in relation to the most used objective functions: number of people exposed to the contaminant; number of detected contamination events; length of contaminated pipes; amount of contaminant consumed by users; detection time of contamination. ReferencesWorld Health Organization. (‎2014)‎. Water safety in distribution systems. World Health Organization. https://apps.who.int/iris/handle/10665/204422Ostfeld A, Uber JG, Salomons E, Berry JW, Hart WE, Phillips CA, Watson JP, Dorini G, Jonkergouw P, Kapelan Z, di Pierro F, Khu ST, Savic D, Eliades D, Polycarpou M, Ghimire SR, Barkdoll BD, Gueli R, Huang JJ, McBean EA, James W, Krause A, Leskovec J, Isovitsch S, Xu J, Guestrin C, VanBriesen J, Small M, Fischbeck P, Preis A, Propato M, Piller O, Trachtman GB, Wu ZY, Walski T (2008) The battle of the water sensor networks (BWSN): a design challenge for engineers and algorithms. J Water Resour Plan Manag 134:556–568. https://doi.org/10.1061/(ASCE)0733-9496(2008)134:6(556)Santonastaso, G., Di Nardo, A., Creaco, E. et al. Comparison of topological, empirical and optimization-based approaches for locating quality detection points in water distribution networks. Environ Sci Pollut Res 28, 33844–33853 (2021). https://doi.org/10.1007/s11356-020-10519-3

Many mountainous areas of Campania, Southern Italy, are characterized by steep slopes covered by layered granular unsaturated pyroclastic deposits, mainly consisting of ashes and pumices, laying upon fractured limestone bedrock. The total thickness of the soil is quite variable with a few meters (1.0 m–1.5 m) in the steepest part of the slopes, and larger at the foot. Shallow landslides are often triggered after large and intense precipitations, turning into destructive debris flows that cause heavy damage and victims. The slope of Cervinara, located around 40 km Northeast of Naples (Campania, Italy), was involved in a catastrophic debris flow between 15-16 December 1999, triggered by a rainfall event of 325 mm in about 48 h. Since 2001, hydrological monitoring activities have been carried out at the slope, by measuring precipitation depth, soil volumetric water content and capillary tension. Moreover, in December 2017 an automatic hydro-meteorological station has been installed at the elevation of 575 m a.s.l., near the scarp of the 1999 landslide. It allows the assessment of slope hydrological balance, by identifying the major hydrological processes involving the cover and the perched aquifer, which develops in the upper part of the fractured bedrock during the rainy season. Lately, since 1 December 2022, new monitoring activities started. A remotely accessible low-cost network has been installed moving away from the landslide scarp of 1999, for expanding the area interested by soil moisture monitoring. The tested prototype network is based on the use of capacitive sensors placed at nodes located 20 m apart from each other with a communication system within the domain of Internet of Things (IoT) technology. Specifically, the low-cost sensors network allowed measurements of soil water content, communicating through short-range wireless IoT system (i.e., Wi-Fi) thanks to ESP32 boards. The field data can be visualized remotely on ThingSpeakTM IoT platform on laptops and smartphones.The tested IoT-based low-cost network shows the potential to enhance the amount of monitored hydrological data at affordable cost, so to improve risk management in landslide-prone areas. The same IoT network architecture with diffuse measurements can be replicated with long-distance radio communication between nodes, which allows extending the mutual distance up to few kilometers.

The assessment of the response of slopes to precipitations is important for several applications: from drought associated problems to the evaluation of the occurrence of threatening events such as floods and landslides (Bogaard & Greco, 2016). This study aims at identifying the most important variables, that can be monitored in the field, suitable to describe the initial conditions that control the capability of a slope to store infiltrating water at the end of precipitation events. The case study of the slopes near the town of Cervinara, southern Italy, is presented, where field observations and laboratory experiments allowed the understanding of the water processes at different scales (Marino et al., 2020). A synthetic dataset, simulating the major hydraulic processes observed in the field, was generated to enrich the available data. It was built by simulating the response of the slope to a 1000-year long synthetic rainfall series, generated with the NSRP model, with a physically based model coupling the unsaturated flow in the coarse granular soil cover with the shallow aquifer hosted by the uppermost part of the underlying fractured limestone bedrock (Marino et al., 2021). The hydraulic behavior of the soil cover is modelled with the 1D Richards’ equation, while the aquifer, connected to the soil cover through its lower boundary condition, is modelled as a simple linear reservoir. Two variables expressing underground antecedent conditions, one hour before any rainfall event, were analyzed: mean water content in the uppermost meter of the soil cover and aquifer water level. The slope response was quantified as the fraction of rainwater remaining stored in the soil cover at the end of any rainfall event. The non-linear relationships linking the three variables were studied with clustering and random forest techniques, allowing the identification of three major hydrological conditions. The first one is linked to dry seasons, when the lowest aquifer water level coincides with soil water content below field capacity: in this condition, rainwater tends to remain completely stored in the soil at the end of rain events. Once the soil cover overcomes the field capacity, two different conditions are found. When the aquifer water level is high, active drainage through the soil-bedrock interface limits the increase of water stored in the soil cover. Conversely, when the aquifer water level is low, it corresponds to impeded drainage, i.e., there is little leakage from the soil cover to the bedrock. In this condition, most rainwater tends to remain stored in the soil cover even when it is already wet at the beginning of the rain event. References Bogaard, T., & Greco, R. (2016). Landslide hydrology: from hydrology to pore pressure. Wiley Interdisciplinary Reviews: Water, 3(3), 439–459. https://doi.org/10.1002/wat2.1126 Marino, P., Comegna, L., Damiano, E., Olivares, L., & Greco, R. (2020). Monitoring the hydrological balance of a landslide-prone slope covered by pyroclastic deposits over limestone fractured bedrock. Water (Switzerland), 12(12). https://doi.org/10.3390/w12123309 Marino, P., Santonastaso, G. F., Fan, X., & Greco, R. (2021). Prediction of shallow landslides in pyroclastic-covered slopes by coupled modeling of unsaturated and saturated groundwater flow. Landslides, 18(1), 31–41. https://doi.org/10.1007/s10346-020-01484-6

In the area of Casamicciola, on the island of Ischia, in the Gulf of Naples, on November 26, 2022, heavy rain triggered landslides that killed people and caused great damage to buildings and roads. Rain gauges on the island recorded heavy rainfall starting at midnight on November 25. The 6-hour cumulative rainfall (between 00:00 on 25/11 and 06:00 on 26/11) resulted 126 mm. The peak hourly rainfall at the two nearest rain gauges was 51.6 mm in Forio and 50.4 mm in Monte Epomeo, attained just before the triggering of the major landslide. The attainment of critical rainfall depth was so sudden, that rain gauges recordings did not allow deploying timely risk mitigation measures. In this context, an effective Landslide Early Warning System (LEWS), based not only on rain gauges, would be an important tool to mitigate the impact of landslides. The goal of a LEWS is to provide timely information to individuals and organizations, so that they can take appropriate actions to reduce the risk. These systems typically use a combination of monitoring networks and modeling techniques, to issue real-time warnings when the probability of a landslide becomes high. A well-designed LEWS can save lives, reduce property damage, and minimize the economic impact of the events. In this paper, a novel approach to LEWS, based on machine learning and radar data, is proposed. Specifically, a random forest model is trained to define pre-alarm thresholds based on radar measurements available on the portal MISTRAL (Mistral portal Meteo Italian SupercompuTing poRtAL), and on rainfall measurements from four rain gauges on the island of Ischia. Two concentric monitoring areas around the island of Ischia are divided into 16 sectors, and the model evaluates every five minutes the percentage of nodes in each sector where the rainfall height detected by the radar exceeds assigned thresholds, corresponding to pre-alarm stages. Preliminary results show the prospects of using machine learning in LEWS.

Citizen science has established itself as an important approach to the co-production of knowledge and public participation in scientific research. Combined with digital technologies and online tools, the approach has been celebrated as a path toward the democratization of science. However, only a few studies have investigated the role digital technologies play in shaping interactions between people and nature. Additionally, the role of context in shaping online and face-to-face participation in citizen science projects has yet to receive much attention. This article takes a citizen science initiative in the Brazilian Atlantic Forest in the state of São Paulo as an illustrative case of the emergence of unanticipated consequences of digital technologies. The emergence of a socio-material practice of animal identification through a popular instant-messaging app is described, allowing a better understanding of the role of digital technologies and the context framing citizen participation in challenging environments.

The assessment of the response of slopes to precipitation is important for various applications, from water supply management to hazard assessment due to extreme rainfall events. It is well known that the underground conditions prior to the initiation of rainfall events control the hydrological processes that occur in slopes, affecting the water exchange through their boundaries. The present study aims at identifying suitable variables to be monitored and modelled to predict the response of the slope to precipitations. A case study consisting of a loose pyroclastic coarse grained soil cover overlaying a karstic bedrock located in the southern Apennines (Italy) is described, where field monitoring has been carried out, comprising stream level recordings, meteorological recordings, and soil water content among others. Nevertheless, to enhance the field dataset, the slope hydraulic behaviour of the case study has been simulated with a physically based model linked to a synthetic rainfall time series, getting a consistent hourly timeseries dataset of 1000 years, containing information on rainfall, aquifer water level and soil volumetric water content at different depths. Machine Learning techniques have been used to unwrap the relationships amongst the studied variables, which relations are commonly non-linear. The Random Forest technique has been used to assess the way the slope response could be addressed and the importance of each variable on the slope response and the k-means clustering technique has been used to explore the geometrical disposition of data, and so the identification of seasonally recurrent different scenarios linked to the slope response. It has been shown that the slope response in terms of the rainwater being stored in the soil cover is naturally highly dependent on the rainfall amount, but water drainage and storage processes can be identified by normalizing the change in water storage with the rainfall depth. Indeed, with the methodology presented here, different hydrometeorological scenarios controlling major hydrological processes have been identified not only from the meteorological and seasonal behaviour but also from the underground conditions prior to the rainfall initiation, weighting the role, on one hand, of the field capacity value on the ease of the water to flow in and out of the soil cover and, on the other hand, of the ground water level, the increase of which gives evidence of the activation of slope drainage even during relatively intense rainfall events.

Preferential flow induced by desiccation cracks (PF-DC) has been proven to be an important hydrological effect that could cause various geotechnical engineering and ecological environment problems. Investigation on the PF-DC remains a great challenge due to the soil shrinking–swelling behavior. This work presents an experimental and numerical study of the PF-DC considering the dynamic changes of desiccation cracks. A soil column test was conducted under wetting–drying cycles to investigate the dynamic changes of desiccation cracks and their hydrological response. The ratios between the crack area and soil matrix area (crack ratio), crack aperture and depth were measured. The soil water content, matrix suction and water drainage were monitored. A new dynamic dual-permeability preferential flow model (DPMDy) was developed, which includes physically consistent functions in describing the variation of both porosity and hydraulic conductivity in crack and matrix domains. Its performance was compared to the single-domain model (SDM) and rigid dual-permeability model (DPM) with fixed crack ratio and hydraulic conductivity. The experimental results showed that the maximum crack ratio and aperture decreased when the evaporation intensity was excessively raised. The self-closure phenomenon of cracks and increased surficial water content was observed during low-evaporation periods. The simulation results showed that the matrix evaporation modeled by the DPMDy is lower than that of the SDM and DPM, but its crack evaporation is the highest. Compared to the DPM, the DPMDy simulated a faster pressure head building-up process in the crack domain and higher water exchange rates from the crack to the matrix domain during rainfall. Using a fixed crack ratio in the DPM, whether it is the maximum or the average value from the experiment data, will overestimate the infiltration fluxes of PF-DC but underestimate its contribution to the matrix domain. In conclusion, the DPMDy better described the underlying physics involving crack evolution and hydrological response with respect to the SDM and DPM. Further improvement of the DPMDy should focus on the hysteresis effect of the soil water retention curve and soil deformation during wetting–drying cycles.

O artigo discute as possibilidades do uso teórico do discurso na Geografia Escolar a partir da perspectiva dialógico-discursiva do Círculo de Mikhail Bakhtin. O desenvolvimento da discussão foi feito a partir do diálogo entre os conceitos desenvolvidos pelo referido autor de enunciado, signo ideológico e gêneros discursivos. Em seguida, foi apresentada e analisada uma faixa de anúncio como objeto de conhecimento em caráter de ilustração. Seguimos esse percurso com vistas a facilitar a apreensão da perspectiva adotada pensando em nosso leitor presumido, o professor de Geografia. Por fim foram discutidos alguns pontos que julgamos relevantes para o desenvolvimento do raciocínio geográfico na perspectiva adotada.

After the 2008 Wenchuan earthquake (Mw 7.9), increased occurrence of rainfall-induced debris flows was initially observed in the earthquake-hit region (Sichuan, China). In the following years, the frequency of debris flows gradually reduced, indicating a progressive recovery of stability of debris deposits accumulated along slopes and in gullies after the earthquake. To assess these dynamically changing conditions, empirical thresholds have been identified to predict post-seismic debris flow occurrence with two approaches: a meteorological approach based only on precipitation characteristics, and a hydrometeorological approach that also considers the hydrologic conditions before the onset of rainfall. Both used the available record of precipitations and debris flows that occurred between 2008 and 2015 in several gullies, tributary of the upper Minjiang river course, in Wenchuan county. Hydrometeorological thresholds for debris flows were identified at the gully catchment scale, by assessing the water balance with a simplified lumped hydrological model, based on the Budyko framework. The parameters of the model were estimated based on the scarce available information about the water balance of the entire watershed of the upper Minjiang. Simulated catchment water storage was used as a proxy of the moisture state of the slopes. The results indicate that both meteorological and hydrometeorological thresholds allow catching the progressive recovery of stability of the debris deposits. Specifically, the assessment of water balance at the catchment scale highlights the role played by the hydrological processes affecting the slopes, leading to the definition of reliable thresholds, that resulted robust despite the uncertainty of the estimated parameters of the hydrological model. Therefore, the hydrometeorological approach appears suitable to define thresholds for early warning of debris flows at the catchment scale.

Large areas of Campania (southern Italy) are characterized by steep slopes covered with shallow deposits of loose pyroclastic materials, laying upon bedrocks with different characteristics (i.e., limestones, dolomites, volcanic tuff). The pyroclastic covers, usually in unsaturated conditions, are frequently affected by rainfall-induced shallow landslides, which cause heavy damage to property and infrastructures and sometimes casualties. Owing to the brittle behavior of the involved soils, hardly exhibiting any deformation before failure, the occurrence of such landslides is not easily predictable, so that operational early warning systems for rainfall-induced landslides (LEWS) usually rely only on empirical thresholds based on precipitation information (i.e., intensity and duration of triggering rainfall event). Anyway, the reliability of landslide prediction would benefit from the inclusion of hydrological information about the condition of the slope cover before the onset of the triggering rainfall (e.g., Marino et al., 2020a).Three years of continuous field monitoring carried out at the slope of Cervinara, located around 40 km north-east of the city of Naples, where a destructive flowslide occurred in December 1999, have provided insight of the hydrological processes controlling the water balance of the pyroclastic deposits, laying upon a densely fractured limestone bedrock, where a temporary perched aquifer develops during the rainy season (Marino et al., 2020b). This knowledge allowed setting up a physically based model capable of identifying the seasonality of the predisposing conditions leading to slope failure (Greco et al., 2018; Marino et al., 2021). Aiming at identifying the hydrological processes mostly affecting landslide triggering, the model is coupled with a stochastic rainfall generator (i.e., the Neyman-Scott rectangular pulse model), previously calibrated based on 20 years hourly rainfall data, obtaining a 1000 years long synthetic series of the slope cover response to precipitations (in terms of soil suction, water content, perched aquifer water level, and leakage through the soil-bedrock interface). The obtained synthetic dataset of rainfall and hydrological variables have been analyzed with machine-learning techniques, so to identify the most effective combination of variables for landslide predictions.The analysis of the synthetic data allows identifying the most suitable variables to be monitored, for assessing the hydrologic conditions predisposing the slopes to failure. In fact, the obtained results are confirmed by the analysis of the available field monitoring data, indicating that coupling rainfall measurements with field and remote hydrological monitoring significantly improves landslide prediction.ReferencesGreco R, Marino P, Santonastaso GF, Damiano E (2018). Interaction between perched epikarst aquifer and unsaturated soil cover in the initiation of shallow landslides in pyroclastic soils. Water 10:948.Marino P, Peres DJ, Cancelliere A, Greco R, Bogaard TA (2020a). Soil moisture information can improve shallow landslide forecasting using the hydrometeorological threshold approach. Landslides 17(9): 2041-2054.Marino P, Comegna L, Damiano E, Olivares L, Greco R (2020b). Monitoring the Hydrological Balance of a Landslide-Prone Slope Covered by Pyroclastic Deposits over Limestone Fractured Bedrock. Water 12(12): 3309.Marino P, Santonastaso GF, Fan X, Greco R (2021). Prediction of shallow landslides in pyroclastic-covered slopes by coupled modeling of unsaturated and saturated groundwater flow. Landslides 18(1): 31-41.

The reliability of water distribution networks (WDN) can be defined as the ability of the system to meet water demands under both normal and abnormal conditions (Bao and Mays, 1990). The measure of reliability is influenced by many factors: possible failure of one or more components, unusual high demand, connectivity of the network, poor water quality, etc. Although reliability of WDN was originally defined by Goulter (1987) and Walters (1988), measuring reliability is an open challenge for researchers. Consequently, there is no established measure of WDN reliability.In recent years, many studies have attempted to mathematically define the reliability of water distribution networks and several synthetic indices are provided as proxy of reliability measures. The objective of this paper is to investigate the suitability of two of these metrics: one based on surplus of power head and other on Shannon’s informational entropy for the assessment of reliability of partitioned water distribution networks (WDNs). The creation of permanent DMAs involves permanently an alteration of the WDN by closing multiple lines at the same time, and, therefore, it is a more severe test than those commonly used in the scientific literature where a little or no disruption occurs to the operation of the WDN (e.g., segment isolation or demand amplification). In addition, the two metrics were compared with other known indicators of hydraulic performance to determine which of them is better suited to evaluate the reliability of water network partitioning. For this purpose, a medium-sized water distribution network in South of Italy was used as case study and the hydraulic simulations were performed with a pressure-driven approach using EPANET2.2 software. ReferencesBao, Y., Mays, L.W. (1990). "Model for water distribution system reliability", J. Hydrual. Eng., 116, 1119-1137Greco, R., Di Nardo, A., Santonastaso, G.F (2012). "Resilience and entropy as indices of robustness of water distribution networks", J. Hydroinformatics, 14 (3), 761–771.Gouher, I.C. (1987). "Current and future use of system analysis in water distribution network design", Civ. Engrg. Sys., 4(4), 175-184.Walters, G. (1988). "Optimal design of pipe networks: a review." Proc.,1st Int. Conf. on Compo and Water Resour., Vol. 2, Computational Mechanics Publications, Southampton, U.K., 21-31.

A seca é o principal fenômeno climático que ocorre no semiárido brasileiro. Nesta região, a constante ocorrência de eventos de seca atrelada aos inúmeros problemas sociais, acumulados ao longo da história desta região, resulta em riscos climáticos, sobretudo, para a produção agrícola. Os graves impactos econômicos e sociais enfrentados pela população do Semiárido, relacionados aos eventos de estiagens e secas, colocaram em destaque a importância do avanço de estudos sobre risco da seca à atividade agrícola na região. Nesse contexto, este estudo objetivou avaliar a distribuição espacial do risco climático referente à seca para a atividade agrícola nas mesorregiões Vale São Franciscano e Centro-Norte. Para a representação do risco climático, analisou-se o perigo climático (representado por categorias de seca) e a vulnerabilidade. As variáveis que compuseram a vulnerabilidade local foram: índice de manejo agrícola, índice de Firjan de Desenvolvimento Municipal (IFDM) e índice ambiental. Observaram-se tendências de diminuição das chuvas locais e altas condições de vulnerabilidades dos agricultores da região, de modo geral. Assim sendo, constatou-se que não há condição de risco baixo para os municípios analisados, independente da intensidade da seca. Conclui-se, portanto, que a seca, enquanto um evento de múltiplas proporções, tem seu risco agravado a depender das condições de vulnerabilidades locais.Palavras-chave: estiagem, perigo, vulnerabilidade. Hazard (drought) in the semi-arid region of Bahia: Climate Vulnerabilities and Risks ABSTRACTDrought is the main climatic phenomenon that occurs in the Brazilian semiarid region. In this region, the constant occurrence of drought events linked to numerous social problems, accumulated throughout the history of this region, results in climatic risks, especially for agricultural production. The serious economic and social impacts faced by the population of the semiarid region, related to the events of droughts and droughts, highlighted the importance of advancing studies on the risk of drought to agricultural activity in the region. In this context, this study aimed to evaluate the spatial distribution of climate risk related to drought for agricultural activity in the Vale São Franciscano and Centro-Norte regions. For the representation of climate risk, climate hazard (represented by drought categories) and vulnerability were analyzed. The variables that made up the local vulnerability were: agricultural management index, Firjan Municipal Development Index (IFDM) and environmental index. Tendencies of decreasing local rainfall and high vulnerability conditions for farmers in the region were observed, in general. Therefore, it was found that there is no low risk condition for the analyzed municipalities, regardless of the intensity of the drought. It is concluded, therefore, that drought, as an event of multiple proportions, has its risk aggravated depending on the conditions of local vulnerabilities.Keywords: drought, hazard, vulnerability

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