1,144 research outputs found
Kaposi’s Sarcoma-Associated Herpesvirus microRNAs
Kaposi’s sarcoma-associated herpesvirus (KSHV) is a human pathogenic γ-herpesvirus strongly associated with the development of Kaposi’s Sarcoma and B cell proliferative disorders, including primary effusion lymphoma (PEL). The identification and functional investigation of non-coding RNAs expressed by KSHV is a topic with rapidly emerging importance. KSHV miRNAs derived from 12 stem-loops located in the major latency locus have been the focus of particular attention. Recent studies describing the transcriptome-wide identification of mRNA targets of the KSHV miRNAs suggest that these miRNAs have evolved a highly complex network of interactions with the cellular and viral transcriptomes. Relatively few KSHV miRNA targets, however, have been characterized at a functional level. Here, our current understanding of KSHV miRNA expression, targets, and function will be reviewed
Monitoring infiltration with time-lapse relative gravity:An option for non-invasive determination of soil hydraulic parameters?
A review of the potential to establish a global, operational river monitoring based on Sentinel-3 water surface elevation observations
River monitoring from satellite radar altimetry in the Zambezi River basin
Satellite radar altimetry can be used to monitor surface water levels from space. While current and past altimetry missions were designed to study oceans, retracking the waveforms returned over land allows data to be retrieved for smaller water bodies or narrow rivers. The objective of this study is the assessment of the potential for river monitoring from radar altimetry in terms of water level and discharge in the Zambezi River basin. Retracked Envisat altimetry data were extracted over the Zambezi River basin using a detailed river mask based on Landsat imagery. This allowed for stage measurements to be obtained for rivers down to 80m wide with an RMSE relative to in situ levels of 0.32 to 0.72m at different locations. The altimetric levels were then converted to discharge using three different methods adapted to different data-availability scenarios: first with an in situ rating curve available, secondly with one simultaneous field measurement of cross-section and discharge, and finally with only historical discharge data available. For the two locations at which all three methods could be applied, the accuracies of the different methods were found to be comparable, with RMSE values ranging from 4.1 to 6.5% of the mean annual in situ gauged amplitude for the first method and from 6.9 to 13.8% for the second and third methods. The precision obtained with the different methods was analyzed by running Monte Carlo simulations and also showed comparable values for the three approaches with standard deviations found between 5.7 and 7.2% of the mean annual in situ gauged amplitude for the first method and from 8.7 to 13.0% for the second and third methods
Operational reservoir inflow forecasting with radar altimetry: The Zambezi case study
River basin management can greatly benefit from short-term river discharge
predictions. In order to improve model produced discharge forecasts, data
assimilation allows for the integration of current observations of the
hydrological system to produce improved forecasts and reduce prediction
uncertainty. Data assimilation is widely used in operational applications to
update hydrological models with in situ discharge or level measurements. In
areas where timely access to in situ data is not possible, remote sensing
data products can be used in assimilation schemes.
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While river discharge itself cannot be measured from space, radar altimetry
can track surface water level variations at crossing locations between the
satellite ground track and the river system called virtual stations (VS).
Use of radar altimetry versus traditional monitoring in operational settings
is complicated by the low temporal resolution of the data (between 10 and 35
days revisit time at a VS depending on the satellite) as well as the fact
that the location of the measurements is not necessarily at the point of
interest. However, combining radar altimetry from multiple VS with
hydrological models can help overcome these limitations.
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In this study, a rainfall runoff model of the Zambezi River basin is built
using remote sensing data sets and used to drive a routing scheme coupled to
a simple floodplain model. The extended Kalman filter is used to update the
states in the routing model with data from 9 Envisat VS. Model fit was
improved through assimilation with the Nash–Sutcliffe model efficiencies
increasing from 0.19 to 0.62 and from 0.82 to 0.88 at the outlets of two
distinct watersheds, the initial NSE (Nash–Sutcliffe efficiency) being low at one outlet due to large
errors in the precipitation data set. However, model reliability was poor in
one watershed with only 58 and 44% of observations falling in the
90% confidence bounds, for the open loop and assimilation runs
respectively, pointing to problems with the simple approach used to
represent model error
Satellite altimetry-derived water level for calibration of river morphological parameters in hydrodynamic models
Multi-Objective Optimization for Analysis of Changing Trade-Offs in the Nepalese Water-Energy-Food Nexus with Hydropower Development
While the water–energy–food nexus approach is becoming increasingly important for more efficient resource utilization and economic development, limited quantitative tools are available to incorporate the approach in decision-making. We propose a spatially explicit framework that couples two well-established water and power system models to develop a decision support tool combining multiple nexus objectives in a linear objective function. To demonstrate our framework, we compare eight Nepalese power development scenarios based on five nexus objectives: minimization of power deficit, maintenance of water availability for irrigation to support food self-sufficiency, reduction in flood risk, maintenance of environmental flows, and maximization of power export. The deterministic multi-objective optimization model is spatially resolved to enable realistic representation of the nexus linkages and accounts for power transmission constraints using an optimal power flow approach. Basin inflows, hydropower plant specifications, reservoir characteristics, reservoir rules, irrigation water demand, environmental flow requirements, power demand, and transmission line properties are provided as model inputs. The trade-offs and synergies among these objectives were visualized for each scenario under multiple environmental flow and power demand requirements. Spatially disaggregated model outputs allowed for the comparison of scenarios not only based on fulfillment of nexus objectives but also scenario compatibility with existing infrastructure, supporting the identification of projects that enhance overall system efficiency. Though the model is applied to the Nepalese nexus from a power development perspective here, it can be extended and adapted for other problems
Combining satellite radar altimetry, SAR surface soil moisture and GRACE total storage changes for hydrological model calibration in a large poorly gauged catchment
The availability of data is a major challenge for hydrological modelling in large parts of the world. Remote sensing data can be exploited to improve models of ungauged or poorly gauged catchments. In this study we combine three datasets for calibration of a rainfall-runoff model of the poorly gauged Okavango catchment in Southern Africa: (i) surface soil moisture (SSM) estimates derived from radar measurements onboard the Envisat satellite; (ii) radar altimetry measurements by Envisat providing river stages in the tributaries of the Okavango catchment, down to a minimum river width of about one hundred meters; and (iii) temporal changes of the Earth's gravity field recorded by the Gravity Recovery and Climate Experiment (GRACE) caused by total water storage changes in the catchment. The SSM data are shown to be helpful in identifying periods with over-respectively underestimation of the precipitation input. The accuracy of the radar altimetry data is validated on gauged subbasins of the catchment and altimetry data of an ungauged subbasin is used for model calibration. The radar altimetry data are important to condition model parameters related to channel morphology such as Manning's roughness. GRACE data are used to validate the model and to condition model parameters related to various storage compartments in the hydrological model (e.g. soil, groundwater, bank storage etc.). As precipitation input the FEWS-Net RFE, TRMM 3B42 and ECMWF ERA-Interim datasets are considered and compared
Unmanned aerial systems (UASS) for monitoring water surface elevation, bathymetry, surface velocity and discharge in streams
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