The establishment of the atmospheric emission inventories of the ESCOMPTE program

Within the frame of the ESCOMPTE program, a spatial emission inventory and an emission database aimed at tropospheric photochemistry intercomparison modeling has been developed under the scientific supervision of the LPCA with the help of the regional coordination of Air Quality network AIRMARAIX. This inventory has been established for all categories of sources (stationary, mobile and biogenic sources) over a domain of 19,600 km² centered on the cities of Marseilles–Aix-en-Provence in the southeastern part of France with a spatial resolution of 1 km². A yearly inventory for 1999 has been established, and hourly emission inventories for 23 days of June and July 2000 and 2001, corresponding to the intensive measurement periods, have been produced. The 104 chemical species in the inventory have been selected to be relevant with respect to photochemistry modeling according to available data. The entire list of species in the inventory numbers 216 which will allow other future applications of this database. This database is presently the most detailed and complete regional emission database in France. In addition, the database structure and the emission calculation modules have been designed to ensure a better sustainability and upgradeability, being provided with appropriate maintenance software. The general organization and method is summarized and the results obtained for both yearly and hourly emissions are detailed and discussed. Some comparisons have been performed with the existing results in this region to ensure the congruency of the results. This leads to confirm the relevance and the consistency of the ESCOMPTE emission inventory.     

ArcTOP:TOPKAPI与GIS紧密连接的分布式水文模型系统

基于遥感（RS）和地理信息系统（GIS）的分布式水文模型已成为当前水文科学发展的前沿，地理信息建模系统是目前地理信息系统研究的热点问题之一。目前实现通用GIS空间分析功能与水文模型的集成主要有四种途径：水文模型嵌入CIS平台．GIS功能嵌入水文模型．水文模型和GIS的松散连接以及水文模型和CIS的紧密连接。本文提出的ArcTOP分布式水文模型系统，运用ArcViewGIS提供的宏语言，将以物理概念为基础的分布式水文模型TOPKAPI和CIS紧密连接，实现分布式水文模型和GIS的完全集成。     

Landslide triggering scenarios in homogeneous geological contexts: The area surrounding Acri (Calabria, Italy)

The paper describes a methodology to detect landslide triggering scenarios in geological homogeneous areas and for some specific landslide categories. In these scenarios, the rainfall–landslide relationship as well as the pluviometric load conditions influencing slope instability have to be investigated.The methodology is applied to an area located in northern Calabria (Italy) and affected by widespread and different slope instability phenomena. Outcropped, fractured, and deeply weathered crystalline rock masses, determining geologic homogeneous conditions, are present. In the same area, suitable and homogeneous climatic features have also been found.According to the methodology adopted, the hydrologic analysis of rainfall time-series is initially carried out notwithstanding historical data concerning landslide mobilization, but using simple models to determine critical pluviometric scenarios for the three landslide categories: shallow, medium-deep, and deep. Landslide-triggering scenarios individualized according to this procedure are less significant as compared to the landslide mobilization detected in the study area by means of historical research and ascribed to the three landslide categories according to geomorphologic analysis.Subsequently, the possible landslide triggering scenarios are outlined by carefully investigating the hydrologic analysis limited to the periods identified according to the historical data.In the study area and approximately for all the areas characterized by the outcrop of fractured and deeply weathered crystalline rocks, significant triggering scenarios can be outlined. In particular, shallow landslide triggers could be activated by rainfall events with intensities exceeding 90 mm/day and/or with amounts exceeding 160 mm. As for medium-deep and deep landslides, triggering mechanisms are more complicated; and effective rainfall contribution must be taken into account compared to groundwater storage. Moreover, a more complex link between deep landslides and precipitation is confirmed.The results obtained to date highlight the potential of this methodology, which enables us to define and progressively improve the knowledge framework by means of a work sequence integrating different disciplinary tools and results.     

Landslides in the Central Coalfield (Cantabrian Mountains, NW Spain): Geomorphological features, conditioning factors and methodological implications in susceptibility assessment

A geomorphological study focussing on slope instability and landslide susceptibility modelling was performed on a 278 km² area in the Nalón River Basin (Central Coalfield, NW Spain). The methodology of the study includes: 1) geomorphological mapping at both 1:5000 and 1:25,000 scales based on air-photo interpretation and field work; 2) Digital Terrain Model (DTM) creation and overlay of geomorphological and DTM layers in a Geographical Information System (GIS); and 3) statistical treatment of variables using SPSS and development of a logistic regression model. A total of 603 mass movements including earth flow and debris flow were inventoried and were classified into two groups according to their size. This study focuses on the first group with small mass movements (10⁰ to 10¹ m in size), which often cause damage to infrastructures and even victims. The detected conditioning factors of these landslides are lithology (soils and colluviums), vegetation (pasture) and topography. DTM analyses show that high instabilities are linked to slopes with NE and SW orientations, curvature values between − 6 and − 0.7, and slope values from 16° to 30°. Bedrock lithology (Carboniferous sandstone and siltstone), presence of Quaternary soils and sediments, vegetation, and the topographical factors were used to develop a landslide susceptibility model using the logistic regression method. Application of “zoom method” allows us to accurately detect small mass movements using a 5-m grid cell data even if geomorphological mapping is done at a 1:25,000 scale.     

Mapping landslide susceptibility from small datasets: A case study in the Pays de Herve (E Belgium)

A landslide susceptibility map is proposed for the Pays de Herve (E Belgium), where large landslides affect Cretaceous clay outcrop areas. Based on a Bayesian approach, this GIS-supported probabilistic map identifies the areas most susceptible to deep landslides. The database is comprised of the source areas of ten pre-existing landslides (i.e. a sample of 154 grid cells) and of six environmental data layers, namely lithology, proximity to active faults, slope angle and aspect, elevation and distance to the nearest valley-floor. A 30-m-resolution DEM from the Belgian National Geographical Institute is used for the analysis. Owing to the small size of the sample, a special cross-validation procedure of the susceptibility map is performed, which uses in an iterative way each of the landslides to test the predictive power of the map derived from the other landslides. Four different sets of variables are used to produce four susceptibility maps, whose prediction curves are compared. While the prediction rates associated with the models not involving the “proximity to active fault” criterion are comparable to those of the models considering this variable, strong weaknesses inherent in the fault data on which the latter rely suggest that the final susceptibility map should be based on a model that excludes any reference to fault. This highlights the difference between a triggering factor and determining factors, and in the same time broadens the scope of the produced map. A single reactivated slide is also used to test the possibility of predicting future reactivation of existing landslides in the area. Finally, the need for geomorphological control over the mathematical treatment is underlined in order to obtain realistic prediction maps.     

Reducing model complexity for explanation and prediction

Numerical models can be useful for explaining poorly understood phenomena or for reliable quantitative predictions. When modeling a multi-scale system, a ‘top-down’ approach—basing models on emergent variables and interactions, rather than explicitly on the much faster and smaller scale processes that give rise to them—facilitates both goals. Parameterizations representing emergent interactions range from highly simplified and abstracted to more quantitatively accurate. Empirically based large-scale parameterizations lead more reliably to accurate large-scale behavior than do parameterizations of much smaller scale processes. Conversely, purposefully simplified representations of model interactions can enhance a model's utility for explanation, clarifying the key feedbacks leading to an enigmatic behavior. For such potential insights to be relevant, the interactions in the model need to correspond to those in the ‘real’ system in some straightforward way. Such a correspondence usually holds for models constructed for predictive purposes, although this is not a requirement. The goals motivating a modeling endeavor help determine the most appropriate modeling strategies, as well as the most appropriate criteria for judging model usefulness.     

Application and verification of fuzzy algebraic operators to landslide susceptibility mapping

The aim of this study was to apply and to verify the use of fuzzy logic to landslide susceptibility mapping in the Gangneung area, Korea, using a geographic information system (GIS). For this aim, in the study, a data-derived model (frequency ratio) and a knowledge-derived model (fuzzy operator) were combined. Landslide locations were identified by changing the detection technique of KOMPSAT-1 images and checked by field studies. For landslide susceptibility mapping, maps of the topography, lineaments, soil, forest, and land cover were extracted from the spatial data sets, and the eight factors influencing landslide occurrence were obtained from the database. Using the factors and the identified landslide, the fuzzy membership values were calculated. Then fuzzy algebraic operators were applied to the fuzzy membership values for landslide susceptibility mapping. Finally, the produced map was verified by comparing with existing landslide locations for calculating prediction accuracy. Among the fuzzy operators, in the case in which the gamma operator (λ = 0.975) showed the best accuracy (84.68%) while the case in which the fuzzy or operator was applied showed the worst accuracy (66.50%).     

EFFECTS OF RIVER NETWORK WORKS AND SOIL CONSERVATION MEASURES ON RESERVOIR SILTATION

Knowledge of the morphological dynamics of a water course is essential for management of reservoir siltation. With an example of sedimentation in a reservoir in Basilicata, Italy, this paper demonstrates the effect on reservoir siltation of the hydraulic works, which are aimed to reduce sediment transport along the fluvial network and to prevent part of the sediment discharge from reaching the lake. The effect depends on the river type and on the the geological features of river basin slopes. The paper also shows how mass erosion can significantly contribute to development of reservoir siltation. Finally, preliminary results are provided about the time needed for river training works to be effective.     

Geochemical modeling of magma mixing and magma reservoir volumes during early episodes of Kīlauea Volcano’s Pu‘u ‘Ō‘ō eruption

Geochemical modeling of magma mixing allows for evaluation of volumes of magma storage reservoirs and magma plumbing configurations. A new analytical expression is derived for a simple two-component box-mixing model describing the proportions of mixing components in erupted lavas as a function of time. Four versions of this model are applied to a mixing trend spanning episodes 3–31 of Kilauea Volcano’s Puu Oo eruption, each testing different constraints on magma reservoir input and output fluxes. Unknown parameters (e.g., magma reservoir influx rate, initial reservoir volume) are optimized for each model using a non-linear least squares technique to fit model trends to geochemical time-series data. The modeled mixing trend closely reproduces the observed compositional trend. The two models that match measured lava effusion rates have constant magma input and output fluxes and suggest a large pre-mixing magma reservoir (46±2 and 49±1 million m³), with little or no volume change over time. This volume is much larger than a previous estimate for the shallow, dike-shaped magma reservoir under the Puu Oo vent, which grew from ∼3 to ∼10–12 million m³. These volumetric differences are interpreted as indicating that mixing occurred first in a larger, deeper reservoir before the magma was injected into the overlying smaller reservoir. Electronic Supplementary Material Supplementary material is available at and is accessible for authorized users.     

Prediction of surface horizontal displacements, and gravity and tilt changes caused by filling the Three Gorges Reservoir

 Horizontal displacements, and gravity and tilt changes induced by filling the Three Gorges Reservoir are modeled using elastic loading Green functions. When the water surface reaches its highest level, the effects become maximum on the reservoir banks. The longitudinal and latitudinal components of the horizontal displacements reach −8.2 and 7.7 mm respectively, gravity is increased by up to 3.4 mGal, and the prime vertical and meridian components of the tilt changes are −7.8 and −17.5 arcseconds respectively. Accordingly, the filling of the reservoir will influence values observed from global positioning system (GPS), gravimetry and tilt measurements in the area. The results given can be used to provide important corrections for extracting earthquake-related signals from observed data. Received: 19 January 2001 / Accepted: 3 September 2001