Topographic and meteorological influences on spatial scaling of heavy convective rainfall in mountainous regions

Characterizing the spatial scaling and dynamics of convective precipitation in mountainous terrain and the development of downscaling methods to transfer precipitation fields from one scale to another is the overall motivation for this research. Substantial progress has been made on characterizing the space-time organization of Midwestern convective systems and tropical rainfall, which has led to the development of statistical/dynamical downscaling models. Space-time analysis and downscaling of orographic precipitation has received less attention due to the complexities of topographic influences. This study uses multiscale statistical analysis to investigate the spatial scaling of organized thunderstorms that produce heavy rainfall and flooding in mountainous regions. Focus is placed on the eastern and western slopes of the Appalachian region and the Front Range of the Rocky Mountains. Parameter estimates are analyzed over time and attention is given to linking changes in the multiscale parameters with meteorological forcings and orographic influences on the rainfall. Influences of geographic regions and predominant orographic controls on trends in multiscale properties of precipitation are investigated. Spatial resolutions from 1 km to 50 km are considered. This range of spatial scales is needed to bridge typical scale gaps between distributed hydrologic models and numerical weather prediction (NWP) forecasts and attempts to address the open research problem of scaling organized thunderstorms and convection in mountainous terrain down to 1-4 km scales.

PROBABILISTIC MODELING OF RAINFALL INDUCED LANDSLIDE HAZARD ASSESSMENT IN SAN JUAN LA LAGUNA, SOLOLÁ, GUATEMALA

The municipality of San Juan La Laguna, Guatemala is home to approximately 5,200 people and located on the western side of the Lake Atitlán caldera. Steep slopes surround all but the eastern side of San Juan. The Lake Atitlán watershed is susceptible to many natural hazards, but most predictable are the landslides that can occur annually with each rainy season, especially during high-intensity events. Hurricane Stan hit Guatemala in October 2005; the resulting flooding and landslides devastated the Atitlán region. Locations of landslide and non-landslide points were obtained from field observations and orthophotos taken following Hurricane Stan. This study used data from multiple attributes, at every landslide and non-landslide point, and applied different multivariate analyses to optimize a model for landslides prediction during high-intensity precipitation events like Hurricane Stan. The attributes considered in this study are: geology, geomorphology, distance to faults and streams, land use, slope, aspect, curvature, plan curvature, profile curvature and topographic wetness index. The attributes were pre-evaluated for their ability to predict landslides using four different attribute evaluators, all available in the open source data mining software Weka: filtered subset, information gain, gain ratio and chi-squared. Three multivariate algorithms (decision tree J48, logistic regression and BayesNet) were optimized for landslide prediction using different attributes. The following statistical parameters were used to evaluate model accuracy: precision, recall, F measure and area under the receiver operating characteristic (ROC) curve. The algorithm BayesNet yielded the most accurate model and was used to build a probability map of landslide initiation points. The probability map developed in this study was also compared to the results of a bivariate landslide susceptibility analysis conducted for the watershed, encompassing Lake Atitlán and San Juan. Landslides from Tropical Storm Agatha 2010 were used to independently validate this study’s multivariate model and the bivariate model. The ultimate aim of this study is to share the methodology and results with municipal contacts from the author's time as a U.S. Peace Corps volunteer, to facilitate more effective future landslide hazard planning and mitigation.

Quantitative high-resolution warm season rainfall recorded in varved sediments of Lake Oeschinen, northern Swiss Alps: calibration and validation AD 1901–2008

High-resolution, well-calibrated records of lake sediments are critically important for quantitative climate reconstructions, but they remain a methodological and analytical challenge. While several comprehensive paleotemperature reconstructions have been developed across Europe, only a few quantitative high-resolution studies exist for precipitation. Here we present a calibration and verification study of lithoclastic sediment proxies from proglacial Lake Oeschinen (46°30′N, 7°44′E, 1,580 m a.s.l., north–west Swiss Alps) that are sensitive to rainfall for the period AD 1901–2008. We collected two sediment cores, one in 2007 and another in 2011. The sediments are characterized by two facies: (A) mm-laminated clastic varves and (B) turbidites. The annual character of the laminae couplets was confirmed by radiometric dating (210Pb, 137Cs) and independent flood-layer chronomarkers. Individual varves consist of a dark sand-size spring-summer layer enriched in siliciclastic minerals and a lighter clay-size calcite-rich winter layer. Three subtypes of varves are distinguished: Type I with a 1–1.5 mm fining upward sequence; Type II with a distinct fine-sand base up to 3 mm thick; and Type III containing multiple internal microlaminae caused by individual summer rainstorm deposits. Delta-fan surface samples and sediment trap data fingerprint different sediment source areas and transport processes from the watershed and confirm the instant response of sediment flux to rainfall and erosion. Based on a highly accurate, precise and reproducible chronology, we demonstrate that sediment accumulation (varve thickness) is a quantitative predictor for cumulative boreal alpine spring (May–June) and spring/summer (May–August) rainfall (rMJ = 0.71, rMJJA = 0.60, p < 0.01). Bootstrap-based verification of the calibration model reveals a root mean squared error of prediction (RMSEPMJ = 32.7 mm, RMSEPMJJA = 57.8 mm) which is on the order of 10–13 % of mean MJ and MJJA cumulative precipitation, respectively. These results highlight the potential of the Lake Oeschinen sediments for high-resolution reconstructions of past rainfall conditions in the northern Swiss Alps, central and eastern France and south-west Germany.

Transport of volatile chlorinated hydrocarbons in unsaturated aggregated media

Transport of volatile hydrocarbons in soils is largely controlled by interactions of vapours with the liquid and solid phase. Sorption on solids of gaseous or dissolved comPounds may be important. Since the contact time between a chemical and a specific sorption site can be rather short, kinetic or mass-transfer resistance effects may be relevant. An existing mathematical model describing advection and diffusion in the gas phase and diffusional transport from the gaseous phase into an intra-aggregate water phase is modified to include linear kinetic sorption on ps-solid and water-solid interfaces. The model accounts for kinetic mass transfer between all three phases in a soil. The solution of the Laplace-transformed equations is inverted numerically. We performed transient column experiments with 1,1,2-Trichloroethane, Trichloroethylene, and Tetrachloroethylene using air-dry solid and water-saturated porous glass beads. The breakthrough curves were calculated based on independently estimated parameters. The model calculations agree well with experimental data. The different transport behaviour of the three compounds in our system primarily depends on Henry's constants.

MODELING REACTIVE GAS UPTAKE, TRANSPORT, AND TRANSFORMATION IN AGGREGATED SOILS

Gas diffusion research in soils covers, to a large extent, the transport behavior of practically insoluble gases. We extend the mathematical description of gas transport to include reactive gaseous components that hydrolyze in water such as SO2 and CO2. The path between the free atmosphere and the microporous niches is modeled by assuming penetration through gas-filled macropores, air-water phase transfer, and diffusion and speciation in the liquid phase. For hydrolyzable gases, the rate of mass transfer into and the total absorption capacity of the soil solution may be high. Both the capacity and the transfer rate are influenced by the soil-solution pH; for high pH, they become extremely high for SO2. The soil absorption of such gases is also influenced by soil structure. Well-aerated, near-neutral soils are a potentially important sink for SO2.

Aggregated Multicast — A Comparative Study

Though IP multicast is resource ef£cient in delivering data to a group of members simultaneously, it suffers from scalability problem with the number of concurrently active multicast groups because it requires a router to keep forwarding state for every multicast tree passing through it. To solve this state scalability problem, we proposed a scheme, called aggregated multicast. The key idea is that multiple groups are forced to share a single delivery tree. In our earlier work, we introduced the basic concept of aggregated multicast and presented some initial results to show that multicast state can be reduced. In this paper, we develop a more quantitative assessment of the cost/bene£t trade-offs. We propose an algorithm to assign multicast groups to delivery trees with controllable cost and introduce metrics to measure multicast state and tree management overhead for multicast schemes. We then compare aggregated multicast with conventional multicast schemes, such as source speci£c tree scheme and shared tree scheme. Our extensive simulations show that aggregated multicast can achieve signi£cant routing state and tree management overhead reduction while containing the expense of extra resources (bandwidth waste and tunnelling overhead). We conclude that aggregated multicast is a very cost-effective and promising direction for scalable transit domain multicast provisioning.

Rainfall input, throughfall and stemflow of nutrients in a central African rain forest dominated by ectomycorrhizal trees

Incident rainfall is a major source of nutrient input to a forest ecosystem and the consequent throughfall and stemflow contribute to nutrient cycling. These rain-based fluxes were measured over 12 mo in two forest types in Korup National Park, Cameroon, one with low (LEM) and one with high (HEM) ectomycorrhizal abundances of trees. Throughfall was 96.6 and 92.4% of the incident annual rainfall (5370 mm) in LEM and HEM forests respectively; stemflow was correspondingly 1.5 and 2.2%. Architectural analysis showed that ln(funneling ratio) declined linearly with increasing ln(basal area) of trees. Mean annual inputs of N, P, K, Mg and Ca in incident rainfall were 1.50, 1.07, 7.77, 5.25 and 9.27 kg ha(-1), and total rain-based inputs to the forest floor were 5.0, 3.2, 123.4, 14.4 and 37.7 kg ha-1 respectively. The value for K is high for tropical forests and that for N is low. Nitrogen showed a significantly lower loading of throughfall and stemflow in HEM than in LEM forest, this being associated in the HEM forest with a greater abundance of epiphytic bryophytes which may absorb more N. Incident rainfall provided c. 35% of the gross input of P to the forest floor (i. e., rain-based plus small litter inputs), a surprisingly high contribution given the sandy P-poor soils. At the start of the wet season leaching of K from the canopy was particularly high. Calcium in the rain was also highest at this time, most likely due to washing off of dry-deposited Harmattan dusts. It is proposed that throughfall has an important `priming' function in the rapid decomposition of litter and mineralization of P at the start of the wet season. The contribution of P inputted from the atmosphere appears to be significant when compared to the rates of P mineralization from leaf litter.

Ecosystem functions are resistant to extreme changes to rainfall regimes in a mesotrophic grassland

Major changes to rainfall regimes are predicted for the future but the effect of such changes on terrestrial ecosystem function is largely unknown. We created a rainfall manipulation experiment to investigate the effects of extreme changes in rainfall regimes on ecosystem functioning in a grassland system. We applied two rainfall regimes; a prolonged drought treatment (30 % reduction over spring and summer) and drought/downpour treatment (long periods of no rainfall interspersed with downpours), with an ambient control. Both rainfall manipulations included increased winter rainfall. We measured plant community composition, CO2 fluxes and soil nutrient availability. Plant species richness and cover were lower in the drought/downpour treatment, and showed little recovery after the treatment ceased. Ecosystem processes were less affected, possibly due to winter rainfall additions buffering reduced summer rainfall, which saw relatively small soil moisture changes. However, soil extractable P and ecosystem respiration were significantly higher in rainfall change treatments than in the control. This grassland appears fairly resistant, in the short term, to even the more extreme rainfall changes that are predicted for the region, although prolonged study is needed to measure longer-term impacts. Differences in ecosystem responses between the two treatments emphasise the variety of ecosystem responses to changes in both the size and frequency of rainfall events. Given that model predictions are inconsistent there is therefore a need to assess ecosystem function under a range of potential climate change scenarios.

Plant and arthropod community sensitivity to rainfall manipulation but not nitrogen enrichment in a successional grassland ecosystem

Grasslands provide many ecosystem services including carbon storage, biodiversity preservation and livestock forage production. These ecosystem services will change in the future in response to multiple global environmental changes, including climate change and increased nitrogen inputs. We conducted an experimental study over 3 years in a mesotrophic grassland ecosystem in southern England. We aimed to expose plots to rainfall manipulation that simulated IPCC 4th Assessment projections for 2100 (+15 % winter rainfall and −30 % summer rainfall) or ambient climate, achieving +15 % winter rainfall and −39 % summer rainfall in rainfall-manipulated plots. Nitrogen (40 kg ha−1 year−1) was also added to half of the experimental plots in factorial combination. Plant species composition and above ground biomass were not affected by rainfall in the first 2 years and the plant community did not respond to nitrogen enrichment throughout the experiment. In the third year, above-ground plant biomass declined in rainfall-manipulated plots, driven by a decline in the abundances of grass species characteristic of moist soils. Declining plant biomass was also associated with changes to arthropod communities, with lower abundances of plant-feeding Auchenorrhyncha and carnivorous Araneae indicating multi-trophic responses to rainfall manipulation. Plant and arthropod community composition and plant biomass responses to rainfall manipulation were not modified by nitrogen enrichment, which was not expected, but may have resulted from prior nitrogen saturation and/or phosphorus limitation. Overall, our study demonstrates that climate change may in future influence plant productivity and induce multi-trophic responses in grasslands.