Consistency of satellite-based precipitation products in space and over time compared with gauge observations and snow- hydrological modelling in the Lake Titicaca region

This paper proposes a protocol to assess the space–time consistency of 12 satellite-based precipitation products (SPPs) according to various indicators, including (i) direct comparison of SPPs with 72 precipitation gauges; (ii) sensitivity of streamflow modelling to SPPs at the outlet of four basins; and (iii) the sensitivity of distributed snow models to SPPs using a MODIS snow product as reference in an unmonitored mountainous area. The protocol was applied successively to four different time windows (2000–2004, 2004–2008, 2008–2012 and 2000–2012) to account for the space–time variability of the SPPs and to a large dataset composed of 12 SPPs (CMORPH–RAW v.1, CMORPH–CRT v.1, CMORPH–BLD v.1, CHIRP v.2, CHIRPS v.2, GSMaP v.6, MSWEP v.2.1, PERSIANN, PERSIANN–CDR, TMPA–RT v.7, TMPA–Adj v.7 and SM2Rain–CCI v.2), an unprecedented comparison. The aim of using different space scales and timescales and indicators was to evaluate whether the efficiency of SPPs varies with the method of assessment, time window and location. Results revealed very high discrepancies between SPPs. Compared to precipitation gauge observations, some SPPs (CMORPH–RAW v.1, CMORPH–CRT v.1, GSMaP v.6, PERSIANN, and TMPA–RT v.7) are unable to estimate regional precipitation, whereas the others (CHIRP v.2, CHIRPS v.2, CMORPH–BLD v.1, MSWEP v.2.1, PERSIANN–CDR, and TMPA–Adj v.7) produce a realistic representation despite recurrent spatial limitation over regions with contrasted emissivity, temperature and orography. In 9 out of 10 of the cases studied, streamflow was more realistically simulated when SPPs were used as forcing precipitation data rather than precipitation derived from the available precipitation gauge networks, whereas the SPP's ability to reproduce the duration of MODIS-based snow cover resulted in poorer simulations than simulation using available precipitation gauges. Interestingly, the potential of the SPPs varied significantly when they were used to reproduce gauge precipitation estimates, streamflow observations or snow cover duration and depending on the time window considered. SPPs thus produce space–time errors that cannot be assessed when a single indicator and/or time window is used, underlining the importance of carefully considering their space–time consistency before using them for hydro-climatic studies. Among all the SPPs assessed, MSWEP v.2.1 showed the highest space–time accuracy and consistency in reproducing gauge precipitation estimates, streamflow and snow cover duration.</p

The SCOOP 12 peptide regulates defense response and root development in Arabidopsis thaliana

Small secreted peptides are important players in plant development and stress response. Using a targeted in silico approach, we identified a family of 14 Arabidopsis genes encoding precursors of serine-rich endogenous peptides (PROSCOOP). Transcriptomic analyses revealed that one member of this family, PROSCOOP12, is involved in processes linked to biotic and oxidative stress as well as root growth. Plants defective in this gene were less susceptible to Erwinia amylovora infection and showed an enhanced root growth phenotype. In PROSCOOP12 we identified a conserved motif potentially coding for a small secreted peptide. Exogenous application of synthetic SCOOP12 peptide induces various defense responses in Arabidopsis. Our findings show that SCOOP12 has numerous properties of phytocytokines, activates the phospholipid signaling pathway, regulates reactive oxygen species response, and is perceived in a BAK1 co-receptor-dependent manner

Modeling denitrification in aquatic sediments

Author Posting. © The Author(s), 2008. This is the author's version of the work. It is posted here by permission of Springer for personal use, not for redistribution. The definitive version was published in Biogeochemistry 93 (2009): 159-178, doi:10.1007/s10533-008-9270-z.Sediment denitrification is a major pathway of fixed nitrogen loss from aquatic systems. Due to technical difficulties in measuring this process and its spatial and temporal variability, estimates of local, regional and global denitrification have to rely on a combination of measurements and models. Here we review approaches to describing denitrification in aquatic sediments, ranging from mechanistic diagenetic models to empirical parameterizations of nitrogen fluxes across the sediment-water interface. We also present a compilation of denitrification measurements and ancillary data for different aquatic systems, ranging from freshwater to marine. Based on this data compilation we reevaluate published parameterizations of denitrification. We recommend that future models of denitrification use (1) a combination of mechanistic diagenetic models and measurements where bottom waters are temporally hypoxic or anoxic, and (2) the much simpler correlations between denitrification and sediment oxygen consumption for oxic bottom waters. For our data set, inclusion of bottom water oxygen and nitrate concentrations in a multivariate regression did not improve the statistical fit.Financial support for AEG to work on the manuscript came from NSF NSF-DEB-0423565. KF, DB and DDT acknowledge support from NOAA CHRP grant NA07NOS4780191

Assessing uncertainties in climate change impacts on runoff in Western Mediterranean basins

International audienceThis paper investigates the uncertainties linked to climate change impacts on runoff in four mesoscale basins (900 to 1800 km 2) in the Mediterranean region. Runoff simulations were based on a daily conceptual model including a snow module. The model was calibrated and validated according to a differential split-sample test over a 20-year period and four competing criterions aiming to represent model structural uncertainty based on the concept of Pareto optimality. Five regional climate models (RCMs) from the Med-CORDEX initiative were used to provide temperature and precipitation projections under RCP8.5 by 2050. The RCMs' inability to realistically simulate reference climate (notably precipitation) led us to apply a monthly perturbation method in order to produce a range of climate scenarios. The structural uncertainty bounds obtained from the hydrological simulations over the reference period showed that the model was able to correctly reproduce observed runoff despite contrasted hydrological conditions in (and in between) the basins. Climate projections were shown to be convergent regarding temperatures, which could increase by about +1 to 3 • C on each basin. In contrast, no clear trends in precipitation could be put in evidence, some RCMs leading to a mean annual precipitation decrease (up to 64 %), and others to an increase (up to 33 %). The hydrological projections resulted from the combination of the hydrological simulation bounds with the range of climate projections. Despite the propagation of those uncertainties, the 2050 hydrological scenarios agreed on a significant runoff decrease (2-77 %) during spring on all basins. On the opposite, no clear trend in runoff could be observed for the other seasons

Reducing structural uncertainty in conceptual hydrological modelling in the semi-arid Andes

The use of lumped, conceptual models in hydrological impact studies requires placing more emphasis on the uncertainty arising from deficiencies and/or ambiguities in the model structure. This study provides an opportunity to combine a multiple-hypothesis framework with a multi-criteria assessment scheme to reduce structural uncertainty in the conceptual modelling of a mesoscale Andean catchment (1515 km(2)) over a 30-year period (1982-2011). The modelling process was decomposed into six model-building decisions related to the following aspects of the system behaviour: snow accumulation and melt, runoff generation, redistribution and delay of water fluxes, and natural storage effects. Each of these decisions was provided with a set of alternative modelling options, resulting in a total of 72 competing model structures. These structures were calibrated using the concept of Pareto optimality with three criteria pertaining to streamflow simulations and one to the seasonal dynamics of snow processes. The results were analyzed in the four-dimensional (4-D) space of performance measures using a fuzzy c-means clustering technique and a differential split sample test, leading to identify 14 equally acceptable model hypotheses. A filtering approach was then applied to these best-performing structures in order to minimize the overall uncertainty envelope while maximizing the number of enclosed observations. This led to retain eight model hypotheses as a representation of the minimum structural uncertainty that could be obtained with this modelling framework. Future work to better consider model predictive uncertainty should include a proper assessment of parameter equifinality and data errors, as well as the testing of new or refined hypotheses to allow for the use of additional auxiliary observations