This paper is the outcome of a community initiative to identify major unsolved scientific problems in hydrology motivated by a need for stronger harmonisation of research efforts. The procedure involved a public consultation through online media, followed by two workshops through which a large number of potential science questions were collated, prioritised, and synthesised. In spite of the diversity of the participants (230 scientists in total), the process revealed much about community priorities and the state of our science: a preference for continuity in research questions rather than radical departures or redirections from past and current work. Questions remain focused on the process-based understanding of hydrological variability and causality at all space and time scales. Increased attention to environmental change drives a new emphasis on understanding how change propagates across interfaces within the hydrological system and across disciplinary boundaries. In particular, the expansion of the human footprint raises a new set of questions related to human interactions with nature and water cycle feedbacks in the context of complex water management problems. We hope that this reflection and synthesis of the 23 unsolved problems in hydrology will help guide research efforts for some years to come. ARTICLE HISTORY

Several techniques for the detection of discontinuities in temperature series are evaluated. Eight homogenization techniques were compared using simulated datasets reproducing a vast range of possible situations. The simulated data represent homogeneous series and series having one or more steps. Although the majority of the techniques considered in this study perform very well, two methods seem to work slightly better than the others: the standard normal homogeneity test without trend, and the multiple linear regression technique. Both methods are distinctive because of their sensitivity concerning homogeneous series and their ability to detect one or several steps properly within an inhomogeneous series.

[1] This paper presents an intercomparison of eight statistical tests to detect inhomogeneities in climatic data. The objective was to select those that are more suitable for precipitation data in the southern and central regions of the province of Quebec, Canada. The performances of these methods were evaluated by simulation on several thousands of homogeneous and inhomogeneous synthetic series. These series were generated to reproduce the statistical characteristics of typical precipitations observed in the southern and central parts of the province of Quebec and nearby areas, Canada. It was found that none of these methods was efficient for all types of inhomogeneities, but some of them performed substantially better than others: the bivariate test, the Jaruskova's method, and the standard normal homogeneity test. Techniques such as the Student sequential test and the two-phase regression led to the worst performances. The analysis of the performances of each method in several situations allowed the design of an optimal procedure that takes advantage of the strengths of the best performing techniques.

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Trends and variations in daily temperature and precipitation indices in southern Québec

International audienceA remote sensing-based surface energy balance model is developed to explicitly represent the energy fluxes of four surface components of agricultural fields including bare soil, unstressed green vegetation, non-transpiring green vegetation, and standing senescent vegetation. Such a four-source representation (SEB-4S) is achieved by a consistent physical interpretation of the edges and vertices of the polygon (named T − fvg polygon) obtained by plotting surface temperature (T) as a function of fractional green vegetation (fvg) and the polygon (named T − alpha polygon) obtained by plotting T as a function of surface albedo (alpha). To test the performance of SEB-4S, a T − alpha image-based model and a T − fvg image-based model are implemented as benchmarks. The three models are tested over a 16 km by 10 km irrigated area in northwestern Mexico during the 2007-2008 agricultural season. Input data are composed of ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) thermal infrared, Formosat-2 shortwave, and station-based meteorological data. The fluxes simulated by SEB-4S, the T − alpha image-based model, and the T − fvg image-based model are compared on seven ASTER overpass dates with the in situ measurements collected at six locations within the study domain. The evapotranspiration simulated by SEB-4S is significantly more accurate and robust than that predicted by the models based on a single (either T − fvg or T − alpha) polygon. The improvement provided with SEB-4S reaches about 100 W m−2 at low values and about 100 W m−2 at the seasonal peak of evapotranspiration as compared with boththe T − alpha and T − fvg image-based models. SEB-4S can be operationally applied to irrigated agricultural areas using high-resolution solar/thermal remote sensing data, and has potential to further integrate microwave-derived soil moisture as additional constraint on surface soil energy and water fluxes

Land surface temperature (LST) is an important variable involved in the Earth's surface energy and water budgets and a key component in many aspects of environmental research. The Landsat program, jointly carried out by NASA and the USGS, has been recording thermal infrared data for the past 40 years. Nevertheless, LST data products for Landsat remain unavailable. The atmospheric correction (AC) method commonly used for mono-window Landsat thermal data requires detailed information concerning the vertical structure (temperature, pressure) and the composition (water vapor, ozone) of the atmosphere. For a given coordinate, this information is generally obtained through either radio-sounding or atmospheric model simulations and is passed to the radiative transfer model (RTM) to estimate the local atmospheric correction parameters. Although this approach yields accurate LST data, results are relevant only near this given coordinate. To meet the scientific community's demand for high-resolution LST maps, we developed a new software tool dedicated to processing Landsat thermal data. The proposed tool improves on the commonly-used AC algorithm by incorporating spatial variations occurring in the Earth's atmosphere composition. The ERA-Interim dataset (ECMWFmeteorological organization) was used to retrieve vertical atmospheric conditions, which are available at a global scale with a resolution of 0.125 degrees and a temporal resolution of 6 h. A temporal and spatial linear interpolation of meteorological variables was performed to match the acquisition dates and coordinates of the Landsat images. The atmospheric correction parameters were then estimated on the basis of this reconstructed atmospheric grid using the commercial RTMsoftware MODTRAN. The needed surface emissivity was derived from the common vegetation index NDVI, obtained from the red and near-infrared (NIR) bands of the same Landsat image. This permitted an estimation of LST for the entire image without degradation in resolution. The software tool, named LANDARTs, which stands for Landsat automatic retrieval of surface temperatures, is fully automatic and coded in the programming language Python. In the present paper, LANDARTs was used for the local and spatial validation of surface temperature obtained from a Landsat dataset covering two climatically contrasting zones: southwestern France and central Tunisia. Long-term datasets of in situ surface temperature measurements for both locations were compared to corresponding Landsat LST data. This temporal comparison yielded RMSE values ranging from 1.84 • C-2.55 • C. Landsat surface temperature data obtained with LANDARTs were then spatially compared using the ASTER data products of kinetic surface temperatures (AST08) for both geographical zones. This comparison yielded a satisfactory RMSE of about 2.55 • C. Finally, a sensitivity analysis for the effect of spatial validation on the LST correction process showed a variability of up to 2 • C for an entire Landsat image, confirming that the proposed s...

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