Stable virulence levels in the HIV epidemic of Switzerland over two decades

OBJECTIVE: To determine whether the virulence of HIV-1 has been changing since its introduction into Switzerland. DESIGN: A prospective cohort study of HIV-1 infected individuals with well-characterized pre-therapy disease history. METHODS: To minimize the effect of recently imported viruses and ethnicity-associated host factors, the analysis was restricted to the white, north-west-European majority population of the cohort. Virulence was characterized by the decline slope of the CD4 cell count (n = 817 patients), the decline slope of the CD4:CD8 ratio (n = 815 patients) and the viral setpoint (n = 549 patients) in untreated patients with sufficient data points. Linear regression models were used to detect correlations between the date of diagnosis (ranging between 1984 and 2003) and the virulence markers, controlling for gender, exposure category, age and CD4 cell count at entry. RESULTS: We found no correlation between any of the virulence markers and the date of diagnosis. Inspection of short-term trends confirmed that virulence has fluctuated around a stable level over time. CONCLUSIONS: The lack of long-term time trends in the virulence markers indicates that HIV-1 is not evolving towards increasing or decreasing virulence at a perceptible rate. Both highly virulent and attenuated strains have apparently been unable to spread at the population level. This result suggests that either the evolution of virulence may be slow or inhibited due to evolutionary constraints, or HIV-1 may have already evolved to optimal virulence in the human host.

An Instrument Design for Space-Based Optical Observation of Space Debris

Currently, observations of space debris are primarily performed with ground-based sensors. These sensors have a detection limit at some centimetres diameter for objects in Low Earth Orbit (LEO) and at about two decimetres diameter for objects in Geostationary Orbit (GEO). The few space-based debris observations stem mainly from in-situ measurements and from the analysis of returned spacecraft surfaces. Both provide information about mostly sub-millimetre-sized debris particles. As a consequence the population of centimetre- and millimetre-sized debris objects remains poorly understood. The development, validation and improvement of debris reference models drive the need for measurements covering the whole diameter range. In 2003 the European Space Agency (ESA) initiated a study entitled “Space-Based Optical Observation of Space Debris”. The first tasks of the study were to define user requirements and to develop an observation strategy for a space-based instrument capable of observing uncatalogued millimetre-sized debris objects. Only passive optical observations were considered, focussing on mission concepts for the LEO, and GEO regions respectively. Starting from the requirements and the observation strategy, an instrument system architecture and an associated operations concept have been elaborated. The instrument system architecture covers the telescope, camera and onboard processing electronics. The proposed telescope is a folded Schmidt design, characterised by a 20 cm aperture and a large field of view of 6°. The camera design is based on the use of either a frame-transfer charge coupled device (CCD), or on a cooled hybrid sensor with fast read-out. A four megapixel sensor is foreseen. For the onboard processing, a scalable architecture has been selected. Performance simulations have been executed for the system as designed, focussing on the orbit determination of observed debris particles, and on the analysis of the object detection algorithms. In this paper we present some of the main results of the study. A short overview of the user requirements and observation strategy is given. The architectural design of the instrument is discussed, and the main tradeoffs are outlined. An insight into the results of the performance simulations is provided.

Effectively Selecting a Target Population for a Future Comparative Study

When comparing a new treatment with a control in a randomized clinical study, the treatment effect is generally assessed by evaluating a summary measure over a specific study population. The success of the trial heavily depends on the choice of such a population. In this paper, we show a systematic, effective way to identify a promising population, for which the new treatment is expected to have a desired benefit, using the data from a current study involving similar comparator treatments. Specifically, with the existing data we first create a parametric scoring system using multiple covariates to estimate subject-specific treatment differences. Using this system, we specify a desired level of treatment difference and create a subgroup of patients, defined as those whose estimated scores exceed this threshold. An empirically calibrated group-specific treatment difference curve across a range of threshold values is constructed. The population of patients with any desired level of treatment benefit can then be identified accordingly. To avoid any ``self-serving'' bias, we utilize a cross-training-evaluation method for implementing the above two-step procedure. Lastly, we show how to select the best scoring system among all competing models. The proposals are illustrated with the data from two clinical trials in treating AIDS and cardiovascular diseases. Note that if we are not interested in designing a new study for comparing similar treatments, the new procedure can also be quite useful for the management of future patients who would receive nontrivial benefits to compensate for the risk or cost of the new treatment.

Semiparametric Normal Transformation Models for Spatially Correlated Survival Data

There is an emerging interest in modeling spatially correlated survival data in biomedical and epidemiological studies. In this paper, we propose a new class of semiparametric normal transformation models for right censored spatially correlated survival data. This class of models assumes that survival outcomes marginally follow a Cox proportional hazard model with unspecified baseline hazard, and their joint distribution is obtained by transforming survival outcomes to normal random variables, whose joint distribution is assumed to be multivariate normal with a spatial correlation structure. A key feature of the class of semiparametric normal transformation models is that it provides a rich class of spatial survival models where regression coefficients have population average interpretation and the spatial dependence of survival times is conveniently modeled using the transformed variables by flexible normal random fields. We study the relationship of the spatial correlation structure of the transformed normal variables and the dependence measures of the original survival times. Direct nonparametric maximum likelihood estimation in such models is practically prohibited due to the high dimensional intractable integration of the likelihood function and the infinite dimensional nuisance baseline hazard parameter. We hence develop a class of spatial semiparametric estimating equations, which conveniently estimate the population-level regression coefficients and the dependence parameters simultaneously. We study the asymptotic properties of the proposed estimators, and show that they are consistent and asymptotically normal. The proposed method is illustrated with an analysis of data from the East Boston Ashma Study and its performance is evaluated using simulations.

MORTALITY IN THE MEDICARE POPULATION AND CHRONIC EXPOSURE TO FINE PARTICULATE AIR POLLUTION

Prospective cohort studies have provided evidence on longer-term mortality risks of fine particulate matter (PM2.5), but due to their complexity and costs, only a few have been conducted. By linking monitoring data to the U.S. Medicare system by county of residence, we developed a retrospective cohort study, the Medicare Air Pollution Cohort Study (MCAPS), comprising over 20 million enrollees in the 250 largest counties during 2000-2002. We estimated log-linear regression models having as outcome the age-specific mortality rate for each county and as the main predictor, the average level for the study period 2000. Area-level covariates were used to adjust for socio-economic status and smoking. We reported results under several degrees of adjustment for spatial confounding and with stratification into by eastern, central and western counties. We estimated that a 10 µg/m3 increase in PM25 is associated with a 7.6% increase in mortality (95% CI: 4.4 to 10.8%). We found a stronger association in the eastern counties than nationally, with no evidence of an association in western counties. When adjusted for spatial confounding, the estimated log-relative risks drop by 50%. We demonstrated the feasibility of using Medicare data to establish cohorts for follow-up for effects of air pollution. Particulate matter (PM) air pollution is a global public health problem (1). In developing countries, levels of airborne particles still reach concentrations at which serious health consequences are well-documented; in developed countries, recent epidemiologic evidence shows continued adverse effects, even though particle levels have declined in the last two decades (2-6). Increased mortality associated with higher levels of PM air pollution has been of particular concern, giving an imperative for stronger protective regulations (7). Evidence on PM and health comes from studies of acute and chronic adverse effects (6). The London Fog of 1952 provides dramatic evidence of the unacceptable short-term risk of extremely high levels of PM air pollution (8-10); multi-site time-series studies of daily mortality show that far lower levels of particles are still associated with short-term risk (5)(11-13). Cohort studies provide complementary evidence on the longer-term risks of PM air pollution, indicating the extent to which exposure reduces life expectancy. The design of these studies involves follow-up of cohorts for mortality over periods of years to decades and an assessment of mortality risk in association with estimated long-term exposure to air pollution (2-4;14-17). Because of the complexity and costs of such studies, only a small number have been conducted. The most rigorously executed, including the Harvard Six Cities Study and the American Cancer Society’s (ACS) Cancer Prevention Study II, have provided generally consistent evidence for an association of long- term exposure to particulate matter air pollution with increased all-cause and cardio-respiratory mortality (2,4,14,15). Results from these studies have been used in risk assessments conducted for setting the U.S. National Ambient Air Quality Standard (NAAQS) for PM and for estimating the global burden of disease attributable to air pollution (18,19). Additional prospective cohort studies are necessary, however, to confirm associations between long-term exposure to PM and mortality, to broaden the populations studied, and to refine estimates by regions across which particle composition varies. Toward this end, we have used data from the U.S. Medicare system, which covers nearly all persons 65 years of age and older in the United States. We linked Medicare mortality data to (particulate matter less than 2.5 µm in aerodynamic diameter) air pollution monitoring data to create a new retrospective cohort study, the Medicare Air Pollution Cohort Study (MCAPS), consisting of 20 million persons from 250 counties and representing about 50% of the US population of elderly living in urban settings. In this paper, we report on the relationship between longer-term exposure to PM2.5 and mortality risk over the period 2000 to 2002 in the MCAPS.

Cost effectiveness of home based population screening for Chlamydia trachomatis in the UK: economic evaluation of chlamydia screening studies (ClaSS) project

OBJECTIVE: To investigate the cost effectiveness of screening for Chlamydia trachomatis compared with a policy of no organised screening in the United Kingdom. DESIGN: Economic evaluation using a transmission dynamic mathematical model. SETTING: Central and southwest England. PARTICIPANTS: Hypothetical population of 50,000 men and women, in which all those aged 16-24 years were invited to be screened each year. MAIN OUTCOME MEASURES: Cost effectiveness based on major outcomes averted, defined as pelvic inflammatory disease, ectopic pregnancy, infertility, or neonatal complications. RESULTS: The incremental cost per major outcome averted for a programme of screening women only (assuming eight years of screening) was 22,300 pounds (33,000 euros; $45,000) compared with no organised screening. For a programme screening both men and women, the incremental cost effectiveness ratio was approximately 28,900 pounds. Pelvic inflammatory disease leading to hospital admission was the most frequently averted major outcome. The model was highly sensitive to the incidence of major outcomes and to uptake of screening. When both were increased the cost effectiveness ratio fell to 6200 pound per major outcome averted for screening women only. CONCLUSIONS: Proactive register based screening for chlamydia is not cost effective if the uptake of screening and incidence of complications are based on contemporary empirical studies, which show lower rates than commonly assumed. These data are relevant to discussions about the cost effectiveness of the opportunistic model of chlamydia screening being introduced in England.

Primary care physician supply and other key determinants of health care utilisation: The case of Switzerland

Background The Swiss government decided to freeze new accreditations for physicians in private practice in Switzerland based on the assumption that demand-induced health care spending may be cut by limiting care offers. This legislation initiated an ongoing controversial public debate in Switzerland. The aim of this study is therefore the determination of socio-demographic and health system-related factors of per capita consultation rates with primary care physicians in the multicultural population of Switzerland. Methods The data were derived from the complete claims data of Swiss health insurers for 2004 and included 21.4 million consultations provided by 6564 Swiss primary care physicians on a fee-for-service basis. Socio-demographic data were obtained from the Swiss Federal Statistical Office. Utilisation-based health service areas were created and were used as observational units for statistical procedures. Multivariate and hierarchical models were applied to analyze the data. Results Models within the study allowed the definition of 1018 primary care service areas with a median population of 3754 and an average per capita consultation rate of 2.95 per year. Statistical models yielded significant effects for various geographical, socio-demographic and cultural factors. The regional density of physicians in independent practice was also significantly associated with annual consultation rates and indicated an associated increase 0.10 for each additional primary care physician in a population of 10,000 inhabitants. Considerable differences across Swiss language regions were observed with reference to the supply of ambulatory health resources provided either by primary care physicians, specialists, or hospital-based ambulatory care. Conclusion The study documents a large small-area variation in utilisation and provision of health care resources in Switzerland. Effects of physician density appeared to be strongly related to Swiss language regions and may be rooted in the different cultural backgrounds of the served populations.

Characterization of a stem cell population in lung cancer A549 cells

We isolated a stem cell subpopulation from human lung cancer A549 cells using FACS/Hoechst 33342. This side population (SP), which comprised 24% of the total cell population, totally disappeared after treatment with the selective ABCG 2 inhibitor fumitremorgin C. In a repopulation study, isolated SP and non-SP cells were each able to generate a heterogeneous population of SP and non-SP cells, but this repopulation occurred more rapidly in SP cells than non-SP. An MTT assay and cell cycle distribution analysis reveal a similar profile between SP and non-SP groups. However, in the presence of doxorubicin (DOX) and methotrexate (MTX), SP cells showed significantly lower Annexin V staining when compared to non-SP cells. Taken together, these results demonstrate that SP cells have an active regeneration capacity and high anti-apoptotic activity compared with non-SP cells. Furthermore, our GeneChip data revealed a heightened mRNA expression of ABCG2 and ABCC2 in SP cells. Overall these data explain why the SP of A549 has a unique ability to resist DOX and MTX treatments. Therefore, we suggest that the expression of the ABCG2 transporter plays an important role in the multidrug resistance phenotype of A549 SP cells.

Perceptions of water conditions and management in the Sonora River Basin, Sonora, Mexico

The effects of climate change are expected to be very severe in arid regions. The Sonora River Basin, in the northwestern state of Sonora, Mexico, is likely to be severely affected. Some of the anticipated effects include precipitation variability, intense storm events, higher overall temperatures, and less available water. In addition, population in Sonora, specifically the capital city of Hermosillo, is increasing at a 1.5% rate and current populations are near 700,000. With the reduction in water availability and an increase in population, Sonora, Mexico is expected to experience severe water resource issues in the near future. In anticipation of these changes, research is being conducted in an attempt to improve water management in the Sonora River Basin, located in the northwestern part of Sonora. This research involves participatory modeling techniques designed to increase water manager awareness of hydrological models and their use as integrative tools for water resource management. This study was conducted as preliminary research for the participatory modeling grant in order to gather useful information on the population being studied. This thesis presents research from thirty-four in-depth interviews with water managers, citizens, and agricultural producers in Sonora, Mexico. Data was collected on perceptions of water quantity and quality in the basin, thoughts on current water management practices, perceptions of climate change and its management, experience with, knowledge of, and trust in hydrological models as water management tools. Results showed that the majority of interviewees thought there was not enough water to satisfy their daily needs. Most respondents also agreed that the water available was of good quality, but that current management of water resources was ineffective. Nearly all interviewees were aware of climate change and thought it to be anthropogenic. May reported experiencing higher temperatures, precipitation changes, and higher water scarcity and attributed those fluctuations to climate change. 65% of interviewees were at least somewhat familiar with hydrological models, though only 28% had ever used them or their output. Even with model usage results being low, 100% of respondents believed hydrological models to be very useful water management tools. Understanding how water, climate change, and hydrological models are perceived by this population of people is essential to improving their water management practices in the face of climate change.

Genetic association studies under the population stratification, family pedigree and application to genome-wide association studies

This dissertation has three separate parts: the first part deals with the general pedigree association testing incorporating continuous covariates; the second part deals with the association tests under population stratification using the conditional likelihood tests; the third part deals with the genome-wide association studies based on the real rheumatoid arthritis (RA) disease data sets from Genetic Analysis Workshop 16 (GAW16) problem 1. Many statistical tests are developed to test the linkage and association using either case-control status or phenotype covariates for family data structure, separately. Those univariate analyses might not use all the information coming from the family members in practical studies. On the other hand, the human complex disease do not have a clear inheritance pattern, there might exist the gene interactions or act independently. In part I, the new proposed approach MPDT is focused on how to use both the case control information as well as the phenotype covariates. This approach can be applied to detect multiple marker effects. Based on the two existing popular statistics in family studies for case-control and quantitative traits respectively, the new approach could be used in the simple family structure data set as well as general pedigree structure. The combined statistics are calculated using the two statistics; A permutation procedure is applied for assessing the p-value with adjustment from the Bonferroni for the multiple markers. We use simulation studies to evaluate the type I error rates and the powers of the proposed approach. Our results show that the combined test using both case-control information and phenotype covariates not only has the correct type I error rates but also is more powerful than the other existing methods. For multiple marker interactions, our proposed method is also very powerful. Selective genotyping is an economical strategy in detecting and mapping quantitative trait loci in the genetic dissection of complex disease. When the samples arise from different ethnic groups or an admixture population, all the existing selective genotyping methods may result in spurious association due to different ancestry distributions. The problem can be more serious when the sample size is large, a general requirement to obtain sufficient power to detect modest genetic effects for most complex traits. In part II, I describe a useful strategy in selective genotyping while population stratification is present. Our procedure used a principal component based approach to eliminate any effect of population stratification. The paper evaluates the performance of our procedure using both simulated data from an early study data sets and also the HapMap data sets in a variety of population admixture models generated from empirical data. There are one binary trait and two continuous traits in the rheumatoid arthritis dataset of Problem 1 in the Genetic Analysis Workshop 16 (GAW16): RA status, AntiCCP and IgM. To allow multiple traits, we suggest a set of SNP-level F statistics by the concept of multiple-correlation to measure the genetic association between multiple trait values and SNP-specific genotypic scores and obtain their null distributions. Hereby, we perform 6 genome-wide association analyses using the novel one- and two-stage approaches which are based on single, double and triple traits. Incorporating all these 6 analyses, we successfully validate the SNPs which have been identified to be responsible for rheumatoid arthritis in the literature and detect more disease susceptibility SNPs for follow-up studies in the future. Except for chromosome 13 and 18, each of the others is found to harbour susceptible genetic regions for rheumatoid arthritis or related diseases, i.e., lupus erythematosus. This topic is discussed in part III.

Identification of pathogen and host-response markers correlated with periodontal disease

BACKGROUND: Periodontitis is the major cause of tooth loss in adults and is linked to systemic illnesses, such as cardiovascular disease and stroke. The development of rapid point-of-care (POC) chairside diagnostics has the potential for the early detection of periodontal infection and progression to identify incipient disease and reduce health care costs. However, validation of effective diagnostics requires the identification and verification of biomarkers correlated with disease progression. This clinical study sought to determine the ability of putative host- and microbially derived biomarkers to identify periodontal disease status from whole saliva and plaque biofilm. METHODS: One hundred human subjects were equally recruited into a healthy/gingivitis group or a periodontitis population. Whole saliva was collected from all subjects and analyzed using antibody arrays to measure the levels of multiple proinflammatory cytokines and bone resorptive/turnover markers. RESULTS: Salivary biomarker data were correlated to comprehensive clinical, radiographic, and microbial plaque biofilm levels measured by quantitative polymerase chain reaction (qPCR) for the generation of models for periodontal disease identification. Significantly elevated levels of matrix metalloproteinase (MMP)-8 and -9 were found in subjects with advanced periodontitis with Random Forest importance scores of 7.1 and 5.1, respectively. The generation of receiver operating characteristic curves demonstrated that permutations of salivary biomarkers and pathogen biofilm values augmented the prediction of disease category. Multiple combinations of salivary biomarkers (especially MMP-8 and -9 and osteoprotegerin) combined with red-complex anaerobic periodontal pathogens (such as Porphyromonas gingivalis or Treponema denticola) provided highly accurate predictions of periodontal disease category. Elevated salivary MMP-8 and T. denticola biofilm levels displayed robust combinatorial characteristics in predicting periodontal disease severity (area under the curve = 0.88; odds ratio = 24.6; 95% confidence interval: 5.2 to 116.5). CONCLUSIONS: Using qPCR and sensitive immunoassays, we identified host- and bacterially derived biomarkers correlated with periodontal disease. This approach offers significant potential for the discovery of biomarker signatures useful in the development of rapid POC chairside diagnostics for oral and systemic diseases. Studies are ongoing to apply this approach to the longitudinal predictions of disease activity.

Identification of mesenchymal stromal cells in human lung parenchyma capable of differentiating into aquaporin 5-expressing cells

The lack of effective therapies for end-stage lung disease validates the need for stem cell-based therapeutic approaches as alternative treatment options. In contrast with exogenous stem cell sources, the use of resident progenitor cells is advantageous considering the fact that the lung milieu is an ideal and familiar environment, thereby promoting the engraftment and differentiation of transplanted cells. Recent studies have shown the presence of multipotent 'mesenchymal stem cells' in the adult lung. The majority of these reports are, however, limited to animal models, and to date, there has been no report of a similar cell population in adult human lung parenchyma. Here, we show the identification of a population of primary human lung parenchyma (pHLP) mesenchymal stromal cells (MSCs) derived from intraoperative normal lung parenchyma biopsies. Surface and intracellular immunophenotyping by flow cytometry revealed that cultures do not contain alveolar type I epithelial cells or Clara cells, and are devoid of the following hematopoietic markers: CD34, CD45 and CXCR4. Cells show an expression pattern of surface antigens characteristic of MSCs, including CD73, CD166, CD105, CD90 and STRO-1. As per bone marrow MSCs, our pHLP cells have the ability to differentiate along the adipogenic, osteogenic and chondrogenic mesodermal lineages when cultured in the appropriate conditions. In addition, when placed in small airway growth media, pHLP cell cultures depict the expression of aquaporin 5 and Clara cell secretory protein, which is identified with that of alveolar type I epithelial cells and Clara cells, respectively, thereby exhibiting the capacity to potentially differentiate into airway epithelial cells. Further investigation of these resident cells may elucidate a therapeutic cell population capable of lung repair and/or regeneration.

Prediction of residential radon exposure of the whole Swiss population: comparison of model-based predictions with measurement-based predictions

Radon plays an important role for human exposure to natural sources of ionizing radiation. The aim of this article is to compare two approaches to estimate mean radon exposure in the Swiss population: model-based predictions at individual level and measurement-based predictions based on measurements aggregated at municipality level. A nationwide model was used to predict radon levels in each household and for each individual based on the corresponding tectonic unit, building age, building type, soil texture, degree of urbanization, and floor. Measurement-based predictions were carried out within a health impact assessment on residential radon and lung cancer. Mean measured radon levels were corrected for the average floor distribution and weighted with population size of each municipality. Model-based predictions yielded a mean radon exposure of the Swiss population of 84.1 Bq/m(3) . Measurement-based predictions yielded an average exposure of 78 Bq/m(3) . This study demonstrates that the model- and the measurement-based predictions provided similar results. The advantage of the measurement-based approach is its simplicity, which is sufficient for assessing exposure distribution in a population. The model-based approach allows predicting radon levels at specific sites, which is needed in an epidemiological study, and the results do not depend on how the measurement sites have been selected.

Comparison of Newer-Generation Drug-Eluting With Bare-Metal Stents in Patients With Acute ST-Segment Elevation Myocardial Infarction: A Pooled Analysis of the EXAMINATION (clinical Evaluation of the Xience-V stent in Acute Myocardial INfArcTION) and COMFORTABLE-AMI (Comparison of Biolimus Eluted From an Erodible Stent Coating With Bare Metal Stents in Acute ST-Elevation Myocardial Infarction) Trials

OBJECTIVES This study sought to study the efficacy and safety of newer-generation drug-eluting stents (DES) compared with bare-metal stents (BMS) in an appropriately powered population of patients with ST-segment elevation myocardial infarction (STEMI). BACKGROUND Among patients with STEMI, early generation DES improved efficacy but not safety compared with BMS. Newer-generation DES, everolimus-eluting stents, and biolimus A9-eluting stents, have been shown to improve clinical outcomes compared with early generation DES. METHODS Individual patient data for 2,665 STEMI patients enrolled in 2 large-scale randomized clinical trials comparing newer-generation DES with BMS were pooled: 1,326 patients received a newer-generation DES (everolimus-eluting stent or biolimus A9-eluting stent), whereas the remaining 1,329 patients received a BMS. Random-effects models were used to assess differences between the 2 groups for the device-oriented composite endpoint of cardiac death, target-vessel reinfarction, and target-lesion revascularization and the patient-oriented composite endpoint of all-cause death, any infarction, and any revascularization at 1 year. RESULTS Newer-generation DES substantially reduce the risk of the device-oriented composite endpoint compared with BMS at 1 year (relative risk [RR]: 0.58; 95% confidence interval [CI]: 0.43 to 0.79; p = 0.0004). Similarly, the risk of the patient-oriented composite endpoint was lower with newer-generation DES than BMS (RR: 0.78; 95% CI: 0.63 to 0.96; p = 0.02). Differences in favor of newer-generation DES were driven by both a lower risk of repeat revascularization of the target lesion (RR: 0.33; 95% CI: 0.20 to 0.52; p < 0.0001) and a lower risk of target-vessel infarction (RR: 0.36; 95% CI: 0.14 to 0.92; p = 0.03). Newer-generation DES also reduced the risk of definite stent thrombosis (RR: 0.35; 95% CI: 0.16 to 0.75; p = 0.006) compared with BMS. CONCLUSIONS Among patients with STEMI, newer-generation DES improve safety and efficacy compared with BMS throughout 1 year. It remains to be determined whether the differences in favor of newer-generation DES are sustained during long-term follow-up.

LakeMIP Kivu: evaluating the representation of a large, deep tropical lake by a set of one-dimensional lake models

The African great lakes are of utmost importance for the local economy (fishing), as well as being essential to the survival of the local people. During the past decades, these lakes experienced fast changes in ecosystem structure and functioning, and their future evolution is a major concern. In this study, for the first time a set of one-dimensional lake models are evaluated for Lake Kivu (2.28°S; 28.98°E), East Africa. The unique limnology of this meromictic lake, with the importance of salinity and subsurface springs in a tropical high-altitude climate, presents a worthy challenge to the seven models involved in the Lake Model Intercomparison Project (LakeMIP). Meteorological observations from two automatic weather stations are used to drive the models, whereas a unique dataset, containing over 150 temperature profiles recorded since 2002, is used to assess the model’s performance. Simulations are performed over the freshwater layer only (60 m) and over the average lake depth (240 m), since salinity increases with depth below 60 m in Lake Kivu and some lake models do not account for the influence of salinity upon lake stratification. All models are able to reproduce the mixing seasonality in Lake Kivu, as well as the magnitude and seasonal cycle of the lake enthalpy change. Differences between the models can be ascribed to variations in the treatment of the radiative forcing and the computation of the turbulent heat fluxes. Fluctuations in wind velocity and solar radiation explain inter-annual variability of observed water column temperatures. The good agreement between the deep simulations and the observed meromictic stratification also shows that a subset of models is able to account for the salinity- and geothermal-induced effects upon deep-water stratification. Finally, based on the strengths and weaknesses discerned in this study, an informed choice of a one-dimensional lake model for a given research purpose becomes possible.