Editorial: toward 50 years of 'Water Resources Research'

The first issue of 'Water Resources Research' (WRR) was published in March 1965 and, therefore, the year 2015 will present the exciting opportunity to celebrate the 50th anniversary of the journal. Naturally, this milestone will be seen as an occasion to look back on 50 years of research activity. The history of WRR provides a very interesting perspective on the development of hydrology and the legacy of the worldwide water resources community

Identification of coherent flood regions across Europe by using the longest streamflow records

This study compiles a new dataset, consisting of the longest available flow series from across Europe, and uses it to study the spatial and temporal clustering of flood events across the continent. Hydrological series at 102 gauging stations were collected from 25 European countries. Five geographically distinct large-scale homogeneous regions are identified: (i) an Atlantic region, (ii) a Continental region, (iii) a Scandinavian region, (iv) an Alpine region, and (v) a Mediterranean region. The months with a higher likelihood of flooding were identified in each region. The analysis of the clustering of annual counts of floods revealed an over-dispersion in the Atlantic and Continental regions, forming flood-rich and flood-poor periods, as well as an under-dispersion in the Scandinavian region that points to a regular pattern of flood occurrences at the inter-annual scale. The detection of trends in flood series is attempted by basing it on the identified regions, interpreting the results at a regional scale and for various time periods: 1900-1999; 1920-1999; 1939-1998 and 1956-1995. The results indicate that a decreasing trend in the magnitude of floods was observed mainly in the Continental region in the period 1920-1999 with 22% of the catchments revealing such a trend, as well as a decreasing trend in the timing of floods in the Alpine region in the period 1900-1999 with 75% of the catchments revealing this trend. A mixed pattern of changes in the frequency of floods over a threshold and few significant changes in the timing of floods were detected

Changing climate shifts timing of European floods

A warming climate is expected to have an impact on the magnitude and timing of river floods; however, no consistent large-scale climate change signal in observed flood magnitudes has been identified so far. We analyzed the timing of river floods in Europe over the past five decades, using a pan-European database from 4262 observational hydrometric stations, and found clear patterns of change in flood timing. Warmer temperatures have led to earlier spring snowmelt floods throughout northeastern Europe; delayed winter storms associated with polar warming have led to later winter floods around the North Sea and some sectors of the Mediterranean coast; and earlier soil moisture maxima have led to earlier winter floods in western Europe. Our results highlight the existence of a clear climate signal in flood observations at the continental scale

Applicability of kriging to regional flood estimation problem in Eastern Australia

Design flood estimation in ungauged catchments is a common problem in hydrology. Regional flood frequency analysis (RFFA) is widely used in design flood estimation at ungauged sites, which attempts to transfer flood characteristics from gauged catchments to ungauged ones. The most commonly adopted RFFA methods in Australia in the past included the Index Flood Method, Quantile Regression Technique and Probabilistic Rational Method; however, the new Australian Rainfall and Runoff (ARR) recommends a Parameter Regression Approach based on Log Pearson Type 3 distribution. This paper presents development of a new RFFA method in Australia based on ordinary kriging. It uses data from 558 gauged catchments from Victoria, New South Wales and Queensland States of Australia. These catchments are small to medium in size, with an upper limit of 1000 km2. Based on a leave-one-out validation technique, it has been found that the relative error values in design flood estimates by kriging are in the range of 28 to 36%, which are smaller than Australian Rainfall and Runoff (ARR) recommended RFFE Model. However, kriging shows a relatively higher degree of bias than the RFFE Model. The findings of this study will be useful to enhance the RFFE Model in Australia in near future by applying kriging

Evaluation of the predicted error of the soil moisture retrieval from C-band SAR by comparison against modelled soil moisture estimates over Australia

The Sentinel-1 will carry onboard a C-band radar instrument that will map the European continent once every four days and the global land surface at least once every twelve days with finest 5 × 20. m spatial resolution. The high temporal sampling rate a

Rtop: An R package for interpolation of data with a variable spatial support, with an example from river networks

Geostatistical methods have been applied only to a limited extent for spatial interpolation in applications where the observations have an irregular support, such as runoff characteristics along a river network and population health data. Several studies have shown the potential of such methods, but these developments have so far not led to easily accessible, versatile, easy to apply and open source software. Based on the top-kriging approach suggested by Skøien et al. (2006), we will here present the package rtop, which has been implemented in the statistical environment R (R Core Team, 2013). Taking advantage of the existing methods in R for analysis of spatial objects (Bivand et al., 2013), and the extensive possibilities for visualizing the results, rtop makes it easy to apply geostatistical interpolation methods when observations have a non-point spatial support. The package also offers integration with the intamap package for automatic interpolation and the possibility to run rtop through a Web Service.JRC.H.7-Climate Risk Managemen

How do Spatial Scale, Noise, and Reference Data affect Empirical Estimates of Error in ASAR Derived 1 km Resolution Soil Moisture?

The performance of the advanced synthetic aperture radar (ASAR) global mode (GM) surface soil moisture (SSM) data was studied over Australia by means of two widely used bivariate measures, the root-mean-square error (RMSE) and the Pearson correlation coefficient (R). By computing RMSE and R at multiple spatial scales and for different data combinations, we assessed how, and at which scales, the spatial sampling error, noise, and the choice of the reference data impact on RMSE and R. The results reveal large changes in RMSE and R with continental average values of 8% and 18% for the RMSE of relative soil moisture saturation and between 0.4 and 0.7 for R depending on the spatial scale of aggregation and the choice of reference data. The combined effect of noise and spatial sampling error accounted for a 79% RMSE increase at 1 km and predominated over the error due to the choise of the reference data also at 5 km scale. The effect of noise on RMSE strongly diminished at spatial scales ≥ km. By contrast, the impact of uncertainties in the reference data was larger on than on RMSE. This highlights the better potential of to estimate the benefit of observations prior to data assimilation. Based on our results, it is further suggested that a potential way for an improved ASAR GM SSM error assessment is to: 1) aggregate the data to ≥ km resolution to minimize the noise; 2) subtract the spatial sampling error within the coarse resolution footprint; and 3) remove the reference uncertainty using advanced techniques such as triple collocation

Operational readiness of microwave remote sensing of soil moisture for hydrologic applications

International audienceMicrowave remote sensing of soil moisture has been an active area of research since the 1970s but has yet found little use in operational applications. Given recent advances in retrieval algorithms and the approval of a dedicated soil moisture satellite, it is time to re-assess the potential of various satellite systems to provide soil moisture information for hydrologic applications in an operational fashion. This paper reviews recent progress made with retrieving surface soil moisture from three types of microwave sensors – radiometers, Synthetic Aperture Radars (SARs), and scatterometers. The discussion focuses on the operational readiness of the different techniques, considering requirements that are typical for hydrological applications. It is concluded that operational coarse-resolution (25–50 km) soil moisture products can be expected within the next few years from radiometer and scatterometer systems, while scientific and technological breakthroughs are still needed for operational soil moisture retrieval at finer scales (<1 km) from SAR. Also, further research on data assimilation methods is needed to make best use of the coarse-resolution surface soil moisture data provided by radiometer and scatterometer systems in a hydrologic context and to fully assess the value of these data for hydrological predictions