Complexity‐reduced in the hydroclimatological modelling of aquifer's discharge

Climate variability induces considerable interannual fluctuations in spring discharge, especially in mountain areas, where groundwater is recharged mainly by rain and snow melt. This study presents the discharge climatological model (DISCLIM), which was developed to test a complexity‐reduced approach to perform historical reconstruction in the lack of physical assumptions. The Mount Cervialto aquifer (Southern Italy) is the test site, where a powerful karst spring is monitored since the 1920s and is very sensitive to climatic conditions. DISCLIM incorporates seasonal precipitation and climate indicators only. Despite its simplicity, DISCLIM has been able to well estimate the yearly fluctuations of discharge hydrological, explaining about 90% of the interannual variability at the calibration stage, and more than 80% at validation stage. This means that DISCLIM can be easily used for estimating the discharge in historical times, when no all the hydrological balance data are available for the purpose.     

A submicrometer NMOS multiplexer-demultiplexer chip set for 622.08-Mb/s SONET applications

The development of a low-power 12-channel multiplexer-demultiplexer pair that is clocked at the standard synchronous optical network (SONET) rate of 622.08 MHz is discussed. Each device has been integrated in silicon using a 0.75- mu m NMOS VLSI technology that provides high fabrication yield at relatively low cost. Highlighted are the analog interface circuits of the two chips. These include a phase splitter and amplifier for the maser clock input, a precision 50- Omega output driver for high-speed synchronous-transport-signal-12 (STS-12) data, as well as input amplifier and an output stage for low-speed differential STS-1 data.<>     

Embedded DRAM: more than just a memory

Dynamic random access memory has been a viable semiconductor storage medium for more than three decades. Surprisingly, it has only been in the past three years that attempts to combine DRAM with meaningful amounts of Boolean logic, on the same substrate, have occurred. Although much fanfare has accompanied this technological breakthrough, commonly referred to as embedded DRAM, few system designers appreciate the complexity of this new technology, let alone its applicability to other circuit forms. This article provides background information about embedded DRAM technology, provide suggestions on how structural and electrical elements of the embedded DRAM era might be reused in other circuits, and review circuit theory that is directly attributed to the DRAM technology progression     

Space–time prediction of rainfall-induced shallow landslides through a combined probabilistic/deterministic approach,optimized for initial water table conditions

In landslide-prone areas the magnitude of events is related to recurring rainfall intensity. In a large sector of the Sannio Apennines (Southern Italy), predictive mapping of recurrent shallow landslides was undertaken by combining deterministic and probabilistic predictive approaches. This, with the aim to minimize the negative influence of the uniform distribution of the initial water table depth in steady condition that usually influence the theoretical instability resulting from the application of methods for large-scale estimation. The deterministic approach was performed by means of the Transient Rainfall Infiltration and Grid-based Regional Slope-stability model to obtain triggering maps in multi-temporal transient pore-water pressures. The optimized physical modeling was validated by back-analysis on large-magnitude landslide events which occurred in 2003 by means of the introduction of two cross-mapping correlation indexes. Subsequently, different predictive scenarios were proposed for different probabilistic return periods of the rainstorm events. The output data permitted the definition of a linear log regression curve to estimate the theoretical instability of the study area. This curve is defined as a function of cumulative precipitation, duration and return periods of the possible rainfall events.     

Spatial Uncertainty Modeling of Climate Processes for Extreme Hydrogeomorphological Events Hazard Monitoring

The growing concern of the possible impact of natural disasters and extreme events on the environment has recently created new demands for information from, and assessment by, environmental engineers and climatologists. Mediterranean river basins encompass a diverse range of valley and floodplain environments and are affected by many different processes, such as hydroclimate, land-use, catchment geology, and relief. The objective of this paper is to develop a methodology for exploratory rainstorms data analysis and uncertainty estimation in regional hydrogeomorphological frequency analysis (HFA). A general framework is proposed for designing spatial variability of rainstorms with assigned return periods (T) inducing multiple damaging hydrogeomorphological events in the Campania region, south Italy. To this end, the analysis of precipitation, involving means techniques of regional HFA and geostatistical theory integrated approaches, is a subject of great interest. However, the hydrogeomorphological consequences depend on complex interactions between extreme precipitation and torrential characteristics of the landscape. In Mediterranean environments, climatic fluctuations in hydrological regime, especially those exceeding rain thresholds for an acceptable range of flexibility, can be the main causes of relevant hydrogeomorphological impacts. An algorithm for the characterization of this impact termed with the acronym DRHI (Design Rainstorms Hazard Index) in this paper is presented first. Next, the continuous DRHI data are converted at each location using a binary variable indicator transform based on critical thresholds. In the third step, the expansion of DRHI-soft information from point to landscapes were assessed geostatistically using the records of 70 rain stations of the Department of Civil Protection established by Regional Monitoring Networks. In this way, threshold values for extreme hydrogeomorphological impacts were selected based on geographical distribution patterns of precipitation maxima with a duration of 3?h for a return period of 10, 20, and 50 years. Moreover, the map-based method provides the identification where future infill sampling should be focused in support of more precise characterization.     

Geospatial Rainfall Modelling at Eastern Nepalese Highland from Ground Environmental Data

The study presents a geospatial knowledge transfer framework by accommodating precipitation maps for the Eastern Nepalese Highland (ENH) across an area of about 100,000 km². For this remote area, precipitation–elevation relationships are not homogeneously distributed, but present a chaotic gradient of correlations at altitude ranges. This is mainly due to impervious orography, extreme climate, and data scarcity (most of the rain gauges in Himalaya are located at valley bottoms). Applying geostatistical models (e.g. multivariate geospatial approaches) is difficult in these zones. This makes the ENH an interesting test area where we obtained monthly precipitation spatial patterns for a 30-year period (1961–1990). The aim was to both capture orographic meso-α spatial regimen (~30 km) and local pattern variability (~10 km). Data from 58 FAO raingauges were used plus data from an atmospheric weather station (AWS Pyramid) operating at 5,050 m a.s.l., used to compensate the gap of precipitation pattern presents in the area surrounding the Mount Everest. In these complex orographically remote areas of the Himalayas, monsoon precipitation systems exhibit important topographical interactions and spatial correlations, depending on the scale at which the primary variable (e.g., precipitation) and co-variables (e.g., elevation) are recorded and analysed. Precipitations were assessed for months—May, July and September—representative of the monsoon season. For the rainiest month (July), cokriging indicated a range of precipitation values from ~100 mm over the Tibetan Plateau to ~500 mm in the southern part of Nepal, up to ~900 mm towards the pre-Himalayan range. For July, cokriging precipitation map also showed correspondence with the map of vegetation pattern, and therein lies the clue to using multivariate geostatistical models as flexible approaches for estimating precipitation spatial patterns in remote areas.     

Predicting Monthly Spring Discharges Using a Simple Statistical Model

Current precipitation and past climate variability induce considerable intermonthly fluctuations in spring discharges. This study presents the DISHMET model (Discharge Hydro-Climatological Model) developed to perform historical spring reconstructions in the lack of physical assumptions. We analyzed discharge data of the Caraventa spring, located on the southern side of Mount La Montagna in Southern Italy, which has been monitored since the 1996s. The La Montagna aquifer is tectonically and litologically complex and deformed bedding controls the groundwater flow. Due to this aspect a parsimonious model should be more suitable than a complex model in spring discharge estimation. Thus, the DISHMET model incorporates monthly and annual precipitation only. The model is able to estimate sufficiently well the monthly fluctuations of groundwater discharge. DISHMET can be easily used to assess historical discharge, even when hydrological data is discontinuously available. The magnitude of this discharge is linked to the frequency and type of weather patterns transiting over the central Mediterranean area during the autumn and winter seasons. It is mainly related to the local precipitation that recharges the Mt. La Montagna aquifer. An analysis of antecedent rainfall and spring discharge reveal moderate to strong relationships.