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Runoff prediction in ungauged catchments has been one of the major challenges in the past decades. However, due to the tremendous heterogeneity of the catchments, obstacles exist in deducing model parameters for ungauged catchments from gauged ones. We propose a novel approach to predict ungauged runoff with Catchment Morphing (CM) using a fully distributed model. CM is defined as by changing the catchment characteristics (area and slope here) from the baseline model built with a gauged catchment to model the ungauged ones. As a proof of concept, a case study on seven catchments in the UK has been used to demonstrate the proposed scheme. Comparing the predicted with measured runoff, the Nash-Sutcliffe efficiency (NSE) varies from 0.03 to 0.69 in six catchments. Moreover, NSEs are significantly improved (up to 0.81) when considering the discrepancy of percentage runoff between the target and baseline catchments. A distinct advantage has been experienced by comparing the CM with a traditional method for ungauged catchments. The advantages are: (a) less demand of the similarity between the baseline catchment and the ungauged catchment, (b) less demand of available data, and (c) potentially widely applicable in varied catchments. This study demonstrates the feasibility of the proposed scheme as a potentially powerful alternative to the conventional methods in runoff predictions of ungauged catchments. Clearly, more work beyond this pilot study is needed to explore and develop this new approach further to maturity by the hydrological community.

A crucial part in designing a robust water quality monitoring network is the selection of appropriate water quality sampling locations. Due to cost and time constraints, it is essential to identify and select these locations in an accurate and efficient manner. The main contribution of the present article is the development of a practical methodology for allocating critical sampling points in present and future conditions of the non-point sources under a case study of the Khoy watershed in northwest Iran, where financial resources and water quality data are limited. To achieve this purpose, the river mixing length method (RML) was applied to propose potential sampling points. A new non-point source potential pollution score (NPPS) was then proposed by the analytic network process (ANP) to classify the importance of each sampling point prior to selecting the most appropriate locations for a river system. In addition, an integrated cellular automata–Markov chain model (CA–Markov) was applied to simulate future change in non-point sources during the period 2026–2036. Finally, by considering anthropogenic activities through land-use mapping, the hierarchy value, the non-point source potential pollution score values and budget deficiency in the study area, the seven sampling points were identified for the present and the future. It is not expected, however, that the present location of the proposed sampling points will change in the future due to the forthcoming changes in non-point sources. The current study provides important insights into the design of a reliable water quality monitoring network with a high level of assurance under certain changes in non-point sources. Furthermore, the results of this study should be valuable for water quality monitoring agencies looking for a cost-effective approach for selecting sampling locations.

CLOUDET, a cloud detection and estimation algorithm for passive microwave imagers and sounders is presented. CLOUDET is based on a naive Bayes classifier and multilayer perceptron. It is applied to the special sensor microwave imager/sounder (SSMIS), and the ECMWF integrated forecast system (IFS) cloud liquid water information has been used to train the algorithm. CLOUDET is applicable to both ocean and land-surface types. CLOUDET has been developed and evaluated by employing two different groups of radiometric information, namely, the humidity channels near 183 GHz (humidity algorithm) and the window channels between 19 and 91 GHz (window algorithm). It has been revealed that both humidity and window algorithms can detect cloudy scenes over ocean at a confidence level of more than 90% (80% over land). The analysis indicates that the humidity algorithm has a better ability in detecting cloudy scenes over ocean than the window algorithm ( ${\bf CSI=0.98}$ vs. ${\bf CSI=0.93}$ ). The opposite is true over land-surface type, revealing a CSI of 0.85 by humidity algorithm as opposed to CSI of 0.88 by window algorithm. The estimation of cloud by the CLOUDET algorithm has also been very promising during the validation effort. In particular, the correlation coefficient obtained over ocean through the use of the window algorithm is 0.70 (MAE 0.04 mm and RMSE 0.09 mm). The presented algorithm CLOUDET can be served as a stand-alone tool to reject and identify the cloudy scenes as well as to estimate the cloud liquid water path amount prior to assimilating the radiances into numerical weather prediction model.

Traditional hydrological modelling assumes that the catchment does not change with time. However, due to changes of climate and catchment conditions, this stationarity assumption may not be valid in the future. It is a challenge to make the hydrological model adaptive to the future climate and catchment conditions. In this study IHACRES, a conceptual rainfall–runoff model, is applied to a catchment in southwest England. Long observation data (1961–2008) are used and seasonal calibration (only the summer) has been done since there are significant seasonal rainfall patterns. Initially, the calibration is based on changing the model parameters with time by adapting the parameters using the step forward and backward selection schemes. However, in the validation, both models do not work well. The problem is that the regression with time is not reliable since the trend may not be in a monotonic linear relationship with time. Therefore, a new scheme is explored. Only one parameter is selected for adjustment while the other parameters are set as the fixed and the regression of one optimised parameter is made not only against time but climate condition. The result shows that this nonstationary model works well both in the calibration and validation periods.

A large number of radar rainfall uncertainty (RRU) models have been proposed due to many error sources in weather radar measurements. It is recognized that these models should be integrated into overall uncertainty analysis schemes with other kinds of model uncertainties such as model parameter uncertainty when the radar rainfall is applied in hydrological modeling. We expect that the RRU model can be expressed in a mathematically extensible and simple format. However, the complexity of the RRU has been growing as more and more factors are considered such as spatio-temporal dependence and non-Gaussian distribution. This study analyzes how the RRU propagates through a hydrological model (the Xinanjiang model) and investigates which features of the RRU model have significant impacts on flow simulation. A RRU model named Multivariate Distributed Ensemble Generator (MDEG) is implemented in the Brue catchment in England under different model complexities. The generated ensemble rainfall values by MDEG are then input into the Xinanjiang model to produce uncertainty bands of ensemble flows. Comparison of five important indicators that describe the characteristics of uncertainty bands shows that the ensemble flows generated by MDEG with non-Gaussian marginal and joint distributions are close to the ones with Gaussian distributions. In addition, the dispersion of the uncertainty bands increases dramatically with the growth of the MDEG model complexity. It is concluded that the Gaussian marginal distribution and spatio-temporal dependence using Gaussian copula is considered to be the preferred configuration of the MDEG model for hydrological model uncertainty analysis. Further studies should be carried out in a variety of catchments under different climate conditions and geographical locations to check if the conclusion is valid beyond the Brue catchment under the British climate.

Global solar radiation is the driving force in hydrological cycle especially for evapotranspiration (ET) and is quite infrequently measured. This has led to the reliance on indirect techniques of estimation for data scarce regions. This study presents an improved technique that uses information from a numerical weather prediction (NWP) model (National Centre for Atmospheric Research NCAR's Mesoscale Meteorological model version 5 MM5), for the determination of a cloud cover index (CI), a major factor in the attenuation of the incident solar radiation. The cloud cover index (CI) together with the atmospheric transmission factor (KT) and output from a global clear sky solar radiation were then used for the estimation of global solar radiation for the Brue catchment located in the southwest of England. The results clearly show an improvement in the estimated global solar radiation in comparison to the prevailing approaches.

Reference evapotranspiration (ETo) is an important variable in hydrological modeling, which is not always available, especially for ungauged catchments. Satellite data, such as those available from the MODerate Resolution Imaging Spectroradiometer (MODIS), and global datasets via the European Centre for Medium Range Weather Forecasts (ECMWF) reanalysis (ERA) interim and National Centers for Environmental Prediction (NCEP) reanalysis are important sources of information for ETo. This study explored the seasonal performances of MODIS (MOD16) and Weather Research and Forecasting (WRF) model downscaled global reanalysis datasets, such as ERA interim and NCEP-derived ETo, against ground-based datasets. Overall, on the basis of the statistical metrics computed, ETo derived from ERA interim and MODIS were more accurate in comparison to the estimates from NCEP for all the seasons. The pooled datasets also revealed a similar performance to the seasonal assessment with higher agreement for the ERA interim (r = 0.96, RMSE = 2.76 mm/8 days; bias = 0.24 mm/8 days), followed by MODIS (r = 0.95, RMSE = 7.66 mm/8 days; bias = −7.17 mm/8 days) and NCEP (r = 0.76, RMSE = 11.81 mm/8 days; bias = −10.20 mm/8 days). The only limitation with downscaling ERA interim reanalysis datasets using WRF is that it is time-consuming in contrast to the readily available MODIS operational product for use in mesoscale studies and practical applications.

Numerical weather prediction (NWP) models are gaining more and more attention in providing high-resolution rainfall forecasts for real-time flood forecasting. In this study, the weather research and forecasting (WRF) model is integrated with the probability distribution model (PDM) to make real-time flow forecasts in a small catchment located in Southwest England. In order to improve the accuracy of the NWP rainfall and flow forecasts, dual real-time updating is carried out in the forecasting system through data assimilation. The three-dimensional variational data assimilation technique is coupled with the WRF model to assimilate radar reflectivity and traditional meteorological data; meanwhile, the autoregressive moving average model works with the rainfall–runoff model PDM to assimilate real-time flow observations. Four 24-h storm events with different characteristics of rainfall–runoff responses are selected from the study catchment to test the performance of the constructed forecasting system. The flood forecasting accuracy is found to be largely improved by incorporating the NWP forecasted rainfall when the lead time is beyond the catchment concentration time. The assimilation of radar and meteorological data also shows great advantage in improving the NWP rainfall forecasts.

The improvement of precipitation estimation with the use of radar-raingauge rainfall merging techniques is influenced by several factors, such as topography, storm types, raingauge network density for adjustment, data quality and the rainfall accumulation time. However, the influence of the raingauge network configuration on the performance of radar-raingauge merging methods is often ignored. The aim of this study is to compare and evaluate the performance of different radar-raingauge merging methods on various densities of raingauge network and raingauge network configurations. The analysis of the effect of the raingauge network density shows that the performance of Kriging merging methods increases with the increase of raingauge network density. The results also showed that the influence of raingauge network configuration on the spatial distribution of precipitation of the merged products is relatively smaller for the Kriging with radar-based error correction (KRE) and Kriging with external drift (KED) methods than for the ordinary Kriging method. This indicates that the inclusion of radar data in the KRE and KED methods helps to maintain the spatial distribution of precipitation on an hourly timescale. According to the statistical performance indicators and visual inspection of the merged rainfall fields, the KED outperforms the other radar-raingauge merging techniques, regardless of raingauge network density and configuration.

Uncertainty analysis of radar rainfall enables stakeholders and users have a clear knowledge of the possible uncertainty associated with the rainfall products. Long-term empirical modeling of the relationship between radar and gauge measurements is an efficient and practical method to describe the radar rainfall uncertainty. However, complicated variation of synoptic conditions makes the radar-rainfall uncertainty model based on historical data hard to extend in the future state. A promising solution is to integrate synoptic regimes with the empirical model and explore the impact of individual synoptic regimes on radar rainfall uncertainty. This study is an attempt to introduce season, one of the most important synoptic factor, into the radar rainfall uncertainty model and proposes a seasonal ensemble generator for radar rainfall using copula and autoregressive model. We firstly analyze the histograms of rainfall-weighted temperature, the radar-gauge relationships, and Box and Whisker plots in different seasons and conclude that the radar rainfall uncertainty has strong seasonal dependence. Then a seasonal ensemble generator is designed and implemented in a UK catchment under a temperate maritime climate, which can fully model marginal distribution, spatial dependence, temporal dependence and seasonal dependence of radar rainfall uncertainty. To test its performance, 12 typical rainfall events (4 for each season) are chosen to generate ensemble rainfall values. In each time step, 500 ensemble members are produced and the values of 5th to 95th percentiles are used to derive the uncertainty bands. Except several outliers, the uncertainty bands encompass the observed gauge rainfall quite well. The parameters of the ensemble generator vary considerably for each season, indicating the seasonal ensemble generator reflects the impact of seasons on radar rainfall uncertainty. This study is an attempt to simultaneously consider four key features of radar rainfall uncertainty and future study will investigate their impacts on the outputs of hydrological models with radar rainfall as input or initial conditions.

Hydrological models play a significant role in modelling river flow for decision making support in water resource management. In the past decades, many researchers have made a great deal of efforts in calibrating and validating various models, with each study being focused on one or two models. As a result, there is a lack of comparative analysis on the performance of those models to guide hydrologists to choose appropriate models for the individual climate and physical conditions. This paper describes a two-level meta-analysis to develop a matching system between catchment complexity (based on catchment significant features (CSFs)) and model types. The intention is to use the available CSF information for choosing the most suitable model type for a given catchment. In this study, the CSFs include the elements of climate, soil type, land cover and catchment scale. Specific choices of model types in small and medium catchments are further explored with all CSF information obtained. In particular, it is interesting to find that semi-distributed models are the most suitable model type for catchments with the area over 3000 km2, regardless of other CSFs. The potential methodology for expanding the matching system between catchment complexity and model complexity is discussed. Copyright © 2014 John Wiley & Sons, Ltd.

This paper describes a study on the combined impacts of antecedent earthquakes and droughts on disastrous debris flows. This is a novel attempt in quantifying such impacts using the effective peak acceleration (EPA) (to represent earthquakes) and standardized precipitation index (SPI) (to represent droughts). The study is based on the analysis of 116 disastrous debris flow events occurred in Mainland China in the last 100 years covering a wide spectrum of climate types and landforms. It has been found that the combined impacts from earthquakes and droughts on disastrous debris flows do exist and vary from low to very high according to different climate conditions and terrains. The impacts from earthquakes increase with the increased terrain relief, and the impacts from droughts are strongest in semi-humid climate condition (with reduced impacts in humid and semi-arid /arid climate conditions). Hypothetical explanations on the study discoveries have been proposed. This study reveals the possible reasons for the disastrous debris flow distributions around the world and has significant implications in paleo-climate-seismic analysis and disastrous debris flow risk management.

Due to the fact that weather radar is prone to several sources of errors, it is acknowledged that adjustment against ground observations such as rain gauges is crucial for radar measurement. Spatial matching of precipitation patterns between radar and rain gauge is a significant premise in radar bias corrections. It is a conventional way to construct radar-gauge pairs based on their vertical locations. However, due to the wind effects, the raindrops observed by the radar do not always fall vertically to the ground, and the raindrops arriving at the ground may not all be caught by the rain gauge. This study proposes a fully formulated scheme to numerically simulate the movement of raindrops in a three-dimensional wind field in order to adjust the wind-induced errors. The Brue catchment (135 km2) in Southwest England covering 28 radar pixels and 49 rain gauges is an experimental catchment, where the radar central beam height varies between 500 and 700 m. The 20 typical events (with durations of 6–36 h) are chosen to assess the correlation between hourly radar and gauge rainfall surfaces. It is found that for most events, the improved rates of correlation coefficients are greater than 10%, and nearly half of the events increase by 20%. With the proposed method, except four events, all the event-averaged correlation values are greater than 0.5. This work is the first study to tackle both wind effects on radar and rain gauges, which could be considered as one of the essential components in processing radar observational data in its hydrometeorological applications.

Soil Moisture and Ocean Salinity (SMOS) is the latest mission which provides flow of coarse resolution soil moisture data for land applications. However, the efficient retrieval of soil moisture for hydrological applications depends on optimally choosing the soil and vegetation parameters. The first stage of this work involves the evaluation of SMOS Level 2 products and then several approaches for soil moisture retrieval from SMOS brightness temperature are performed to estimate Soil Moisture Deficit (SMD). The most widely applied algorithm i.e. Single channel algorithm (SCA), based on tau-omega is used in this study for the soil moisture retrieval. In tau-omega, the soil moisture is retrieved using the Horizontal (H) polarisation following Hallikainen dielectric model, roughness parameters, Fresnel's equation and estimated Vegetation Optical Depth (tau). The roughness parameters are empirically calibrated using the numerical optimization techniques. Further to explore the improvement in retrieval models, modifications have been incorporated in the algorithms with respect to the sources of the parameters, which include effective temperatures derived from the European Center for Medium-Range Weather Forecasts (ECMWF) downscaled using the Weather Research and Forecasting (WRF)-NOAH Land Surface Model and Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST) while the s is derived from MODIS Leaf Area Index (LAI). All the evaluations are performed against SMD, which is estimated using the Probability Distributed Model following a careful calibration and validation integrated with sensitivity and uncertainty analysis. The performance obtained after all those changes indicate that SCA-H using WRF-NOAH LSM downscaled ECMWF LST produces an improved performance for SMD estimation at a catchment scale.

Crop yield estimation is of great importance to food security. Normalized Difference Vegetation Index (NDVI), as an effective crop monitoring tool, is extensively used in crop yield estimation. However, there are few studies focusing on the aspect of mixed crops grown together. In this study, a correlation-based approach for crop yield estimation is applied to three small counties (Jianshui, Luliang, and Qiubei) in the Nanpan River basin, Yunnan Province of China, and three main crops (paddy rice, winter wheat, and corn) in these areas are selected. Based on the correlation analysis between MODIS–NDVI data and crop yield, the crop planting areas as well as the best periods for a reliable estimation are identified. The best time is found approximately coinciding with the periods of heading, flowering, and filling of the crops. By Akaike’s information criterion, the most fit regression models with extracted NDVI in the corresponding crop planting areas are determined. They work reasonably well in small regions, especially in the areas where crop types are unknown exactly. Further improvements to the regression models are possible by incorporating other physical factors such as soil types and geographical information.

A hybrid technique for solar radiation estimation, a core part of hydrological cycle, is presented in this study which parameterises the cloud cover effect (cloud cover index) not just from the geostationary satellites but also the PSU/NCAR’s Mesoscale Modelling system (MM5) model. This, together with output from a global clear sky radiation model and observed datasets of temperature and precipitation are used as inputs within the Gamma test (GT) environment for the development of nonlinear models for global solar radiation estimation. The study also explores the ability of Gamma test to determine the optimum input combination and data length selection. Artificial neural network- and local linear regression-based nonlinear techniques are used to test the proposed methodology, and the results have shown a high degree of correlation between the observed and estimated values. It is believed that this study will initiate further exploration of GT for improving informed data and model selection.

Soil moisture plays a very important role in hydrological processes. It has been found in many studies that the surface soil moisture (SSM) is highly related to the diurnal change of the surface soil temperature (∆SST) at the same soil depth. However, some studies contradict this common belief with findings of a much stronger correlation between the SSM and the SST. In order to investigate this further, we have carried out for the first time a comparative assessment of the in-situ measured SST and ΔSST for SSM estimations, over two catchments with contrasting climate types and land uses (i.e. one in the UK and the other in Australia). In both catchments, the time point for the highest relationship between the SST and the SSM is explored. As a result, it is found the SST is more suitable to monitor the variability of the SSM than the ΔSST in both catchments. Moreover the proposed seasonal-based classification method further improves the SSM simulation results in both catchments, with a superior performance observed in the UK catchment (NSE = 0.900 and RMSE = 0.030). In the Australian catchment, a relatively weaker correlation is observed and some potential reasons are explained. The potential applications of the findings for remote sensing soil moisture retrievals are also discussed.

Changing climate leads to change of temporal dynamics of hydrological systems by affecting the catchment conditions. Considering climatic variations when calibrating a hydrological model can improve model performance, which allows parameter sets to vary according to sub-periods with different climate conditions. This study has explored climatic intra-annual variations by using two classification approaches to recognize the sub-periods with similar climatic patterns, Calendar-Based Grouping (CBG) method and Fuzzy C-Means (FCM) algorithm. The model performances of the sub-annual calibration schemes based on these two approaches are compared using the conceptual model IHACRES. The effect of time scales on sub-annual calibration schemes was also studied. Results indicate that the sub-annual calibration scheme based on CBG method performs better than that based on Rainfall-dominated FCM algorithm, since the CBG method has a better performance in recognizing temperature pattern, and the main source of catchment change is from the change of vegetation, which is mainly affected by temperature in the study site. The optimal time scale is dependent on the sub-annual calibration scheme, with bimonthly for CBG method and Temperature-dominated FCM algorithm and seasonal for Rainfall-dominated FCM algorithm. Overall, when using sub-annual calibration schemes, the selection of the partitioning method and time scale is very important to model performances

Rainfall rates derived from tipping bucket rain gauges generally ignore the detailed variation at a finer temporal scale that particularly occurs in light rainfall events. This study extends the exploration of using artificial neural networks (ANNs), in comparison with the conventional linear interpolation method (LIM) and the cubic spline algorithm (CSA) for rainfall rate estimation at fine temporal resolution using rain gauge data based on a case study at Chilbolton and Sparsholt observatories, UK. A supervised feed-forward neural network integrated with the backpropagation algorithm is used to identify the complex nonlinear relationships between input and target variables. The results indicate that the ANN considerably outperforms the CSA and LIM with higher Nash–Sutcliffe efficiency, lower root mean square error and lower rainfall amount differences when compared to the disdrometer observations when the model is trained within a broad span of input values. Consistent stability in accurately estimating rainfall rate in different sites shows the intrinsic advantage of ANNs in learning and self-adaptive abilities in modelling complex nonlinear relationships between the inputs and target variables.

The use of rainfall outputs from the latest convection-scale Weather Research and Forecasting (WRF) model is proven to be an effective way to extend the prediction lead time for flood forecasting. In this study, the effects of WRF domain configurations and spin-up time on rainfall simulations were evaluated at high temporal (sub-daily) and spatial (convective-permitting) scales for simulating a regional sub-daily extreme rainfall event occurred in Beijing, China. Seven objective verification metrics calculated against the ground precipitation observations and the ERA-Interim reanalysis, were analyzed jointly by the subjective verification to explore the likely best set of domain configurations and spin-up time. It was found that the rainfall simulations were quite sensitive to the change of the WRF domain size and spin-up time when evaluated at the convective scale. A model run with 1 : 5 : 5 horizontal downscaling ratio (1.6 km), 57 vertical layers (0.5 km), and 60-hour spin-up time covering Northern China exhibited the best skill in terms of the accuracy of rainfall intensity and the spatial correlation coefficient (R). Comparison made between the optimal run with the above set of the configurations and the initial run of the comparative test setup based on the most common settings revealed an evidential increase in each verification metric after the evaluation process, with R increased from 0.49 to 0.678, the relative error of point maximum precipitation rose from 0.41 to 0.881, and the spatial accumulated error fell by 43.22 %. In summary, the reevaluation of the domain configurations and spin-up time is of great importance and worthwhile in improving the accuracy and reliability of the rainfall simulations in the regional sub-daily heavy rainfall (SDHR) applications.

This study has compared hydrological model performances under different sub-annual period calibration schemes using two conceptual models, IHACRES and HYMOD. In several publications regarding sub-annual period calibration, the authors showed that such an approach generally performed better than the conventional whole period method. Hence, there are advantages in dividing (or clustering) the data into sub-annual periods for calibration. However, little attention has been paid to the issue of how to calibrate the non-continuous sub-annual period. It is therefore important to explore reliable calibration schemes for such a situation. Unlike the conventional whole period calibration which assumes time-invariant parameters for the entire calibration period, the model parameters vary in sub-annual calibration. We have explored two sub-annual calibration schemes, serial calibration scheme (SCS) and parallel calibration scheme (PCS). We assume that the relationships between the rainfall and runoff could be different for each sub-annual period and consider intra-annual variations of the system. The models are then evaluated for a different validation period to avoid over-fitting (or, over parameterisation) and the optimal sub-annual calibration period is explored. Overall, we have found that PCS performed slightly better than SCS and the optimal calibration periods are seasonal and bimonthly for IHACRES and biannual for HYMOD at the study catchment. Since there are pros and cons in both SCS and PCS, we recommend choosing the method depending on the purpose of the model usage. Although the catchment is specific in the study, the methodology proposed is general and applicable to other catchments.

This study explores rainfall spatial variability and its influence on runoff modelling. A novel assessment scheme integrated with coefficients of variance (CV) and Moran’s I is introduced to describe effective rainfall spatial variability. CV is widely accepted to identify rainfall variability through rainfall intensity, whereas Moran’s I reflects rainfall spatial autocorrelation. This new assessment framework combines these two indicators to assess the spatial variability derived from both rainfall intensity and distribution, which are crucial in determining the time and magnitude of runoff generation. Four model structures embedded in the Variable Infiltration Capacity (VIC) model are adopted for hydrological modelling in the Brue catchment of England. The models are assigned with 1, 3, 8 and 27 hydrological response units (HRUs) respectively and diverse rainfall spatial information for 236 events are extracted from 1995. This study investigates the model performance of different partitioning based on rainfall spatial variability through peak volume (Qp) and time to peak (Tp), along with the rainfall event process. The results show that models associated with dense spatial partitioning are broadly capable of capturing more spatial information with better performance. It is unnecessary to utilize models with high spatial density for simple rainfall events, though they show distinct advantages on complex events. With additional spatial information, Qp experiences a notable improvement over Tp. Moreover, seasonal patterns signified by the assessment scheme implies the feasibility of seasonal models.

We explore unit hydrograph (UH) properties influenced by catchment geomorphology that could be used in ungauged catchments. Unlike using gauged catchments, a robust approach with virtual catchments was adopted in deriving UH equations. Over 2000 virtual catchments were created from the baseline model of the Brue catchment, UK. A distributed model, SHETRAN, was used to generate runoff in these catchments. Using virtual catchments is feasible to control catchment geomorphologies, which could not be done with the real catchments due to their vast heterogeneity. Catchment characteristics of average slope, drainage length and a new index of catchment shape were examined of their influence on UH properties. The agreement of the results with the hydrological principles is a useful validation of the approach (e.g., the increasing slope led to quick response to peak (Tp) and high peak volume (Qp) of UH, whereas the drainage length presented an opposite trend). Catchment shape was shown to have a significant effect on UH properties. Compared with the widely used empirical equation from the U.K. Institute of Hydrology, the drawn conclusion recommends more indicators to be included to derive more comprehensive equations: apart from catchment geomorphologic properties, storm patterns including storm intensity and temporal distribution are also influential on the UH shape. The indicators in this study were limited to generate a sophisticated equation for use. However, these results can be considered as a testing case to gain more understanding in hydrologic processes for ungauged catchments with the help of the virtual catchment approach.

Storm water management systems depend on Intensity–Duration–Frequency (IDF) curves as a standard design tool. However, due to climate change, the extreme precipitation quantiles represented by IDF curves will be subject to alteration over time. Currently, a common approach is to adopt a single benchmark period for bias correction, which is inadequate in deriving reliable future IDF curves. This study assesses the expected changes between the IDF curves of the current climate and those of a projected future climate and the uncertainties associated with such curves. To provide future IDF curves, daily precipitation data simulated by a 1-km regional climate model were temporally bias corrected by using eight reference periods with a fixed length of 30 years and a moving window of 5 years between the cases for the period 1950–2014. Then the bias-corrected data were further disaggregated into ensemble of 5-min series by using an algorithm which combines the Nonparametric Prediction (NPRED) model and the method of fragments (MoF) framework. The algorithm uses the radar data to resample the disaggregated future rainfall fragments conditioned to the daily rainfall and temperature data. The disaggregated data were then aggregated into different durations based on concentration time. The results suggest that uncertainty in the percentage of change in the projected rainfall compared to the rainfall in the current climate varies significantly depending on which of the eight reference periods are used for the bias correction. Both the maximum projection of rainfall intensity and the maximum change in future projections are affected by using different reference periods for different frequencies and durations. Such an important issue has been largely ignored by the engineering community and this study has shown the importance of including the uncertainty of benchmarking periods in bias-correcting future climate projections.

A remarkable decline in the number of rain gauges is being faced in many areas of the world, as a compromise to the expensive cost of operating and maintaining rain gauges. The question of how to effectively deploy new or remove current rain gauges in order to create optimal rainfall information is becoming more and more important. On the other hand, larger-scaled, remotely sensed rainfall measurements, although poorer quality compared with traditional rain gauge rainfall measurements, provide an insight into the local storm characteristics, which are sought by traditional methods for designing a rain gauge network. Based on these facts, this study proposes a new methodology for rain gauge network design using remotely sensed rainfall datasets that aims to explore how many gauges are essential and where they should be placed. Principal component analysis (PCA) is used to analyze the redundancy of the radar grid network and to determine the number of rain gauges while the potential locations are determined by cluster analysis (CA) selection. The proposed methodology has been performed on 373 different storm events measured by a weather radar grid network and compared against an existing dense rain gauge network in southwestern England. Because of the simple structure, the proposed scheme could be easily implemented in other study areas. This study provides a new insight into rain gauge network design that is also a preliminary attempt to use remotely sensed data to solve the traditional rain gauge problems.

Dam failure constitutes a grave threat to human life. However, there is still a lack of systematic and comprehensive research on the loss of life (L) caused by dam break in China. From the perspective of protecting human life, a new calculation method for L occurred in dam break floods is put forward. Fourteen dam failure cases in China are selected as the basic data by three-dimensional stratified sampling, balancing spatial, vertical elevation and temporal representations, as well as considering various conditions of the dam collapse. The method includes three progressive steps: Firstly, some impact factors of loss of life (IFL) are selected by literature survey, i.e., severity of dam break flood (SF), population at risk (PR), understanding of dam break (UB), warning time (TW) and evacuation condition (EC). And the other IFL of weather during dam break (WB), dam break mode (MB), water storage (SW), building vulnerability (VB), dam break time (TB) and average distance from affected area to dam (DD) are also taken into account to get a more comprehensive consideration. According to disaster system and disaster risk, these eleven IFL are divided into four categories. Through the improved entropy method, eight key IFL are further selected out of the eleven. Secondly, four L modules are built based on four categories, which are L-causing factor module (M1), L-prone environment module (M2), affected body module (M3) and rescue condition module (M4). Eventually, by using two methods of multivariate nonlinear regression and leave-one-out cross-validation in combination with coupled four modules, the calculation method for L is established. Compared with the results of Graham method and D&M method, the result of the proposed one is much closer to the actual value and performs better in fitting effect and regional applicability. In the application, L calculation and consequence assessment are carried out in the example of Hengjiang reservoir that has already broken down. At the same time, L calculation and risk prediction are used in the analysis of Yunshan reservoir, which is under planning. The proposed method can not only be applied to estimate L and its rate (fL) under various types of dam break conditions in China, but also provide a reliable consequence assessment and prediction approach to reduce the risk of L.

Accurate soil moisture information can be assimilated into an operational hydrological model to greatly enhance its flood and drought forecasting performances. Although satellite soil moisture observations are useful information, their validations are generally hindered by the large spatial difference with the point-based measurements, hence they cannot be directly applied in hydrological modelling. This study adopts a widely applied operational hydrological model Xinanjiang (XAJ) as a hydrological validation tool because it is spatially more scale-matched. Two widely used microwave sensors (Soil Moisture and Ocean Salinity (SMOS) and Advanced Microwave Scanning Radiometer - Earth Observing System (AMSR-E)) are evaluated, over two basins (French Broad and Pontiac, respectively) with different climate types and vegetation covers. All soil moisture datasets evaluated in this study are procured for the period of January 2010 to October 2011, a period during which both SMOS and AMSR-E products are available for the study areas. The results demonstrate SMOS outperforms AMSR-E in the Pontiac basin (cropland); while both products perform poorly in the French Broad basin (forest). The Moderate-Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) thresholds of 0.81 and 0.64 (for cropland and forest basins, respectively) are very effective in dividing soil moisture datasets into ‘denser’ and ‘thinner’ vegetation periods. As a result, in the cropland, the statistical performance is further improved for both SMOS and AMSR-E (i.e., improved to Nash-Sutcliffe Efficiency (NSE) = 0.74, Root Mean Square Error (RMSE) = 0.0059 m and NSE = 0.58, RMSE = 0.0066 m for SMOS and AMER-E, respectively). The overall assessment suggests that SMOS is of reasonable quality in estimating basin-scale soil moisture at moderate-vegetated areas, and NDVI is a useful indicator for further improving the performance.

Given that radar-based rainfall has been broadly applied in hydrological studies, quantitative modelling of its uncertainty is critically important, as the error of input rainfall is the main source of error in hydrological modelling. Using an ensemble of rainfall estimates is an elegant solution to characterize the uncertainty of radar-based rainfall and its spatial and temporal variability. This paper has fully formulated an ensemble generator for radar precipitation estimation based on the copula method. Each ensemble member is a probable realization that represents the unknown true rainfall field based on the distribution of radar rainfall (RR) error and its spatial error structure. An uncertainty model consisting of a deterministic component and a random error factor is presented based on the distribution of gauge rainfall conditioned on the radar rainfall (GR|RR). Two kinds of copulas (elliptical and Archimedean copulas) are introduced to generate random errors, which are imposed by the dete...

Radar-based estimates of rainfall are affected by many sources of uncertainties, which would propagate through the hydrological model when radar rainfall estimates are used as input or initial conditions. An elegant solution to quantify these uncertainties is to model the empirical relationship between radar measurements and rain gauge observations (as the ‘ground reference’). However, most current studies only use a fixed and uniform model to represent the uncertainty of radar rainfall, without consideration of its variation under different synoptic regimes. Wind is such a typical weather factor, as it not only induces error in rain gauge measurements but also causes the raindrops observed by weather radar to drift when they reach the ground. For this reason, as a first attempt, this study introduces the wind field into the uncertainty model and designs the radar rainfall uncertainty model under different wind conditions. We separate the original dataset into three subsamples according to wind speed, which are named as WDI (0–2 m/s), WDII (2–4 m/s) and WDIII (>4 m/s). The multivariate distributed ensemble generator is introduced and established for each subsample. Thirty typical events (10 at each wind range) are selected to explore the behaviours of uncertainty under different wind ranges. In each time step, 500 ensemble members are generated, and the values of 5th to 95th percentile values are used to produce the uncertainty bands. Two basic features of uncertainty bands, namely dispersion and ensemble bias, increase significantly with the growth of wind speed, demonstrating that wind speed plays a considerable role in influencing the behaviour of the uncertainty band. On the basis of these pieces of evidence, we conclude that the radar rainfall uncertainty model established under different wind conditions should be more realistic in representing the radar rainfall uncertainty. This study is only a start in incorporating synoptic regimes into rainfall uncertainty analysis, and a great deal of more effort is still needed to build a realistic and comprehensive uncertainty model for radar rainfall data. Copyright © 2014 John Wiley & Sons, Ltd.

Water is undoubtedly the vital commodity for all living creatures and required for well-being of the human society. The present work is based on the surveys and chemical analyses performed on the collected groundwater samples in a part of the Ganga basin in order to understand the sources and evolution of the water quality in the region. The two standard indices such as water quality index and synthetic pollution index for the classification of water in the region are computed. The soil and sediment analysis are carried out with the help of X-ray diffractometer (XRD) for the identification of possible source of ions in water from rock and soil weathering. The dominant minerals which include quartz, muscovite, plagioclase, and orthoclase are reported in the area. The study further utilizes the multivariate statistical techniques for handling large and complex datasets in order to get better information about the groundwater quality. The following statistical methods such as cluster analysis (CA), factor analysis (FA), and principal component analysis (PCA) are applied to handle the large datasets and to understand the latent structure of the data. Through FA/PCAs, we have identified a total of 3 factors in pre-monsoon and 4 factors in post-monsoon season, which are responsible for the whole data structure. These factors explain 77.62 and 82.39 % of the total variance of the pre- and post-monsoon datasets. On the other hand, CA depicted the regions that have similar pollutants origin. The average value of synthetic pollution index of groundwater during pre-monsoon is 9.27, while during post-monsoon, it has been recorded as 8.74. On the other hand, the average values of water quality index of groundwater during pre-monsoon and post-monsoon seasons are found as 217.59 and 233.02, respectively. The study indicates that there occurs an extensive urbanization with gradual vast development of various small- and large-scale industries, which is responsible for degradation in water quality. The overall analysis reveals that the agricultural runoff, waste disposal, leaching, and irrigation with wastewater are the main causes of groundwater pollution followed by some degree of pollution from geogenic sources such as rock and soil weathering, confirmed through XRD analysis.

The authors have investigated an adaptive neuro fuzzy inference system (ANFIS) for the estimation of hydrometeors from the TRMM microwave imager (TMI). The proposed algorithm, named as Hydro-Rain algorithm, is developed in synergy with the TRMM precipitation radar (PR) observed hydrometeor information. The method retrieves rain rates by exploiting the synergistic relations between the TMI and PR observations in twofold steps. First, the fundamental hydrometeor parameters, liquid water path (LWP) and ice water path (IWP), are estimated from the TMI brightness temperatures. Next, the rain rates are estimated from the retrieved hydrometeor parameters (LWP and IWP). A comparison of the hydrometeor retrievals by the Hydro-Rain algorithm is done with the TRMM PR 2A25 and GPROF 2A12 algorithms. The results reveal that the Hydro-Rain algorithm has good skills in estimating hydrometeor paths LWP and IWP, as well as surface rain rate. An examination of the Hydro-Rain algorithm is also conducted on a super typhoon case, in which the Hydro-Rain has shown very good performance in reproducing the typhoon field. Nevertheless, the passive microwave based estimate of hydrometeors appears to suffer in high rain rate regimes, and as the rain rate increases, the discrepancies with hydrometeor estimates tend to increase as well.

Water vapour is the most variable constituent in the atmosphere which is responsible for serious noise in the optical satellite images. This research is focused on the vertical distribution of water vapour and deducing its possible effects on the atmospheric correction process. The vertical distribution of precipitable water vapour, water vapour mixing ratio with geopotential height and pressure were estimated through the weather research and forecasting (WRF) model by downscaling the National Center for Environmental Prediction (NCEP) global reanalysis product. In addition, the most widely used LANDSAT TM satellite image has been used for this assessment. The WRF model was applied with three domains centred on a LANDSAT captured image over the area. The 6S atmospheric correction code was utilised for viewing the effect of precipitable water vapour on satellite image correction. The analysis was conducted on two pressure levels (1,000 and 100 hPa) representing the troposphere and stratosphere, respectively. The validation of the atmospheric correction has been performed by estimating the land surface temperature (LST) over the Walnut Creek region and its comparison with the Soil Moisture Experiments in 2002 (SMEX02) LST field validation datasets. The overall analyses indicate a higher accuracy of LST repossession with 100 hPa corrected image.

Population increase and climate change are stretching not only the world’s but also Pakistan’s water resources. This has directly been responsible for the recurring patterns of floods and droughts in the country which emphasizes the importance of the fact that efficient practices need to be adopted for water resource sustainability. This study investigates the use of upland catchment information, comprising of hydrometeorological datasets for inflow prediction to the Tarbela reservoir (a multipurpose reservoir located on River Indus) using Artificial Neural Networks (ANN) and Regression Techniques (Standard and Step Wise). Input Combination and data length selection for all the selected techniques were performed with the aid of Gamma test (GT). This study has made a significant contribution for future water resource management within the Indus Basin as Tarbela is the main source of irrigation, water supply and hydropower generation in Pakistan along with flood control.

From complex server-dependent geographic information systems to simple sheets of paper, there is a myriad of flood risk visualisation approaches available, each with specific strengths and weaknesses. This paper discusses existing methods and tools to identify both common and novel features, and investigates how these traits affect their suitability for satisfying the requirements of different users and their technical requirements. The paper introduces a flexible web-browser-based tool focusing on delivering flood extents and key additional information (including critical infrastructure) in a user-friendly and resilient manner, without the need for network access or the installation of specialist software. Its development and implementation in the Midlands region of the UK's Environment Agency is discussed and its impact in use on flood incident management is investigated with the aid of user feedback.

IHACRES, a popular catchment-scale rainfall-runoff model for simulating streamflow hydrographs, has been widely used by the hydrological community. The standard IHACRES modelling system consists of a calibration scheme based on the grid search method for estimating the required parameters. This research found that such a calibration approach is time consuming and not always reliable in finding the best parameter values. Alternative model calibration approaches – genetic algorithms (GAs), constraint non-linear optimisation (CNLO) and un-constraint non-linear optimisation (UCNLO) – are explored in this study. A comparison using visual inspection and statistical R2 criterion was carried out to evaluate these parameter calibration approaches. It was found that significant improvements with the developed models could be achieved using CNLO, GA and UCNLO over the standard IHACRES method (with CNLO as the best in terms of calibration and validation accuracy). The paper also discusses the computational time issues of those schemes.

Summary It is broadly recognized that large uncertainties are associated with radar rainfall (RR) estimates, which could propagate in the hydrologic forecast system and contaminate its final outcomes. Ensemble generation of probable true rainfall is an elegant and practical solution to characterize the uncertainty of RR estimates and behavior in the hydrologic forecast system. In this study, we have proposed a fully formulated uncertainty model that can statistically quantify the characteristics of the RR errors and their spatial and temporal structure, which is a novel method of its kind in the radar data uncertainty field. The error model is established based on the distribution of gauge rainfall conditioned on radar rainfall (GR|RR). It’s spatial and temporal dependencies are simulated based on the t-copula function. With this proposed error model, a Multivariate Distributed Ensemble Generator (MDEG) driven by the copula and autoregressive filter is designed and applied in the Brue catchment (135 km 2 ), an extensively gauged site in the United Kingdom. The products from MDEG include a time series of ensemble rainfall fields with each of them representing a probable true rainfall. A series of tests show that the ensemble fields generated by MDEG have realistically maintained the spatial and temporal structure of the random error in RR as they have relatively low mean absolute errors (MAEs) of spatio-temporal correlation towards the observed ones. In addition, the results show that the simulated uncertainty bands derived by the 500 realizations of ensemble rainfall encompass most of the reference rain gauge measurements, indicating that the proposed scheme is statistically reliable.

Crop yield estimation by remote sensing is an important task in food security. Despite numerous studies in this field, there is a lack of a systematic analysis on the past studies to enable researchers to link individual case studies together. In this article, meta-analysis is adopted to pool various studies around the world and reveal the influences of the factors (such as crop types, soil orders, and climate systems) on the correlations between crop yield and remote-sensing data. The meta-analysis synthesizes the data on the effectiveness of NDVI (normalized difference vegetation index) on crop-yield estimation and it has been found that the correlation coefficient (r) varies significantly. Different correlation values are found in different crop types, with the highest 0.88 in cotton and the lowest 0.79 in sugar cane. They are found to be related to the single-leaf blades of the crops except for sugar cane. In addition, the meta-analysis results show that the correlations are also affected by the soil ...

One of the several methods in estimating flood quantiles in ungauged or data-scarce watersheds is regional frequency analysis. Amongst the approaches to regional frequency analysis, different clustering techniques have been proposed to determine hydrologically homogeneous regions in the literature. Recently, Self-Organization feature Map (SOM), a modern hydroinformatic tool, has been applied in several studies for clustering watersheds. However, further studies are still needed with SOM on the interpretation of SOM output map for identifying hydrologically homogeneous regions. In this study, two-level SOM and three clustering methods (fuzzy c-mean, K-mean, and Ward’s Agglomerative hierarchical clustering) are applied in an effort to identify hydrologically homogeneous regions in Mazandaran province watersheds in the north of Iran, and their results are compared with each other. Firstly the SOM is used to form a two-dimensional feature map. Next, the output nodes of the SOM are clustered by using unified distance matrix algorithm and three clustering methods to form regions for flood frequency analysis. The heterogeneity test indicates the four regions achieved by the two-level SOM and Ward approach after adjustments are sufficiently homogeneous. The results suggest that the combination of SOM and Ward is much better than the combination of either SOM and FCM or SOM and K-mean.

Weather radar has a potential to provide accurate short-term (0–3 h) forecasts of rainfall (i.e. radar nowcasts), which are of great importance in warnings and risk management for hydro-meteorological events. However, radar nowcasts are affected by large uncertainties, which are not only linked to limitations in the forecast method but also because of errors in the radar rainfall measurement. The probabilistic quantitative precipitation nowcasting approach attempts to quantify these uncertainties by delivering the forecasts in a probabilistic form. This study implements two forms of probabilistic quantitative precipitation nowcasting for a hilly area in the south of Manchester, namely, the theoretically based scheme [ensemble rainfall forecasts (ERF)-TN] and the empirically based scheme (ERF-EM), and explores which one exhibits higher predictive skill. The ERF-TN scheme generates ensemble forecasts of rainfall in which each ensemble member is determined by the stochastic realisation of a theoretical noise component. The so-called ERF-EM scheme proposed and applied for the first time in this study, aims to use an empirically based error model to measure and quantify the combined effect of all the error sources in the radar rainfall forecasts. The essence of the error model is formulated into an empirical relation between the radar rainfall forecasts and the corresponding ‘ground truth’ represented by the rainfall field from rain gauges measurements. The ensemble members generated by the two schemes have been compared with the rain gauge rainfall. The hit rate and the false alarm rate statistics have been computed and combined into relative operating characteristic curves. The comparison of the performance scores for the two schemes shows that the ERF-EM achieves better performance than the ERF-TN at 1-h lead time. The predictive skills of both schemes are almost identical when the lead time increases to 2 h. In addition, the relation between uncertainty in the radar rainfall forecasts and lead time is also investigated by computing the dispersion of the generated ensemble members. Copyright © 2013 John Wiley & Sons, Ltd.

The modern rain profiling algorithms in reflectivity (ZH) correction for two way path integrated attenuation (AH) using dual polarization radar at attenuating frequencies rely on differential propagation phase (ΦDP) measurements. To retrieve corrected reflectivity profiles from the measured profiles, these algorithms impose an external constraint in the inversion processes which is provided by total differential phase shifts (∆ΦDP) valid for rain cells only. However, there is a possibility of radar beams propagating through and beyond the bright band/melting layer region hence may observe non-rainy cells. In light of this, the present study explores the variation of reflectivity correction in different ∆ΦDP scenarios by varying the rain cells locality by +/− 500 m with respect to the “reference” rain cells location derived from a numerical weather prediction (NWP) model output. To implement this, the well-known ZPHI algorithm from the rain profiling algorithms group is applied to several rainfall events from a C-band dual polarization radar. Before applying the algorithms, a total of 162,415 raindrop spectra have been used to retrieve the algorithm coefficients through T-matrix scattering simulated dual polarized signatures. It is revealed that, in some cases, when the ΦDP profile in an attenuated beam is in increasing gradient near the rain layer top region, there could be a variation of about 1–2 dB in comparison with the “reference” attenuation corrected reflectivity. Such phenomenon is in accord with the reflectivity statistics when comparing with one neighboring C-band single polarization radar derived reflectivity data. Additionally, the remarkable agreement between the reflectivity profiles lends the applicability of the ZPHI algorithm in the attenuation correction with known rain/melting layer information.

Metam-sodium (MS) is widely used as a soil pre-plant fumigant as methyl bromide is phased out of agriculture. However, the information about how fumigation with MS affects the soil microbial community is still limited. In this study, a 66-day-long experiment was conducted to ascertain the effects of MS on soil substrate-induced respiration (SIR), microbial biomass nitrogen (MBN), NH4+-N and NO3--N concentrations, as well as the abundance of the total bacteria and fungi and the expression of genes involved in nitrogen cycling. In addition, 16S rRNA amplicon sequencing was used to investigate the effect of MS on the soil bacterial community. The half-lives of high and low doses of methyl isothiocyanate (MITC) are 10.51h and 9.93h, respectively. MS caused a short-term inhibition of SIR, MBN; had an accumulation effect on NH4+-N concentration in the short term; reduced the abundance of the total bacteria and fungi; and suppressed the expression of the nifH, AOA-amoA, anammox bacteria, nosZ, nirS, and narG. In addition, under the influence of MS, soil bacterial diversity decreased significantly in the long term, bacterial community structure was affected, and there was a shift in the predominant population; for example, some genera, such as Paenibacillus and Luteimonas, significantly increased in number. These changes in bacterial flora may be closely related to the growth of crops. Our study provides useful information for environmental safety assessments of MS in China.

Multiple criteria decision analysis (MCDA) methods have shown advantages in supporting decision-making with problems that confront conflicting objectives. However, current applications to complex environmental problems featuring the dynamic social sphere, particularly problems involving cultural heritage and nature, have yet to substantially reflect this. The dynamic social sphere reflects the demand for scenario forecasting in decision-making support. This knowledge gap has not been addressed sufficiently in MCDA research. A participatory MCDA method is hence proposed as a merger with Contingent Valuation Method (CVM) as the scenario forecasting. The MCDA is then carried out to tackle a complex environmental problem caused by traditional food production in a historic town, Daxi in Taiwan. The result reveals a remarkable willingness to support this issue of a historically significant industry causing detriment to environment (with WTP estimate of 128,700,000 USD from the public) and suggests a plan that applies multiple policy instruments rather than following a potentially adverse polluter-pays principle. This manifests the authors&rsquo, argument that recognition of heritage significance has dramatically affected selection of policy instruments and provides a critical recommendation to the local government which has struggled to find solutions. The proposed MCDA also highlights its participatory aspect for addressing issues involving cultural heritage, supported by several key steps, in particular the intervention-impact value tree building, the scenario forecasting and the sensitivity analysis.

Understanding and improving public flood risk perception is conducive to the implementation of effective flood risk management and disaster reduction policies. In the flood-prone city of Jingdezhen, flood disaster is one of the most destructive natural hazards to impact the society and economy. However, few studies have been attempted to focus on public flood risk perception in the small and medium-size city in China, like Jingdezhen. Therefore, the purpose of this study was to investigate the public flood risk perception in four districts of Jingdezhen and examine the related influencing factors. A questionnaire survey of 719 randomly sampled respondents was conducted in 16 subdistricts of Jingdezhen. Analysis of variance was conducted to identify the correlations between the impact factors and public flood risk perception. Then, the flood risk perception differences between different groups under the same impact factor were compared. The results indicated that the socio-demographic characteristics of the respondents (except occupation), flood experience, flood knowledge education, flood protection responsibility, and trust in government were strongly correlated with flood risk perception. The findings will help decision makers to develop effective flood risk communication strategies and flood risk reduction policies. (PDF) Analysis of the Public Flood Risk Perception in a Flood-Prone City: The Case of Jingdezhen City in China. Available from: https://www.researchgate.net/publication/328750736_Analysis_of_the_Public_Flood_Risk_Perception_in_a_Flood-Prone_City_The_Case_of_Jingdezhen_City_in_China [accessed Nov 12 2018].

There are many causes for the discrepancies between weather radar and rain gauges, and among these, displacement of raindrops due to wind drift – which is especially a problem with high-spatial resolution weather radar – is largely ignored in the published literature. This is mainly due to the lack of high-resolution three-dimensional wind fields and feasible treatment of the raindrop size distribution DSD. In this study, a new systematic approach is proposed to explore the radar–gauge relationship under the wind influence. The mass-weighted mean diameter of raindrops is derived for each radar grid from the DSD data. The reanalysis project ERA-40 data of the European Centre for Medium-range Weather Forecasts ECMWF are used to drive the numerical weather research and forecasting WRF model to generate high-resolution hourly three-dimensional wind fields. Trajectories and displacements of raindrops are then computed using a three-dimensional motion equation from the given radar beam height to the ground surface. Based on the radar rainfall surface interpolated by the bicubic spline method, the correlation of the radar–gauge pairs is used to validate the results. A case study with 20 storm events in the Brue catchment in South West England is chosen to evaluate the proposed scheme. It has been found that when wind drift is taken into account, the correlation coefficient in hourly gauge–radar comparisons can be enhanced by up to 30% and the average correlation coefficient for an event can be improved by 10%. However, there are still some situations in which the scheme fails to work, indicating the complexity and uncertainties in tackling this challenging problem. Further studies are needed to explore why those cases cause problems to the scheme and how it could be improved to cope with them.