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Land surface models such as the Joint UK Land Environment Simulator (JULES) are increasingly used for hydrological assessments because of their state-of-the-art representation of physical processes and versatility. Unlike statistical models and AI models, the JULES model simulates the physical water flux under given meteorological conditions, allowing us to understand and investigate the cause and effect of environmental processes changes. Here we explore the possibility of this approach using a case study in the Atibaia river basin, which serves as a major water supply for metropolitan regions of Campinas and São Paulo, Brazil. The watershed is suffering increasing hydrological risks, which could be attributed to environmental changes, such as urbanization and agricultural activity. The increasing risks highlight the importance to evaluate the land surface processes of the watershed systematically. We explore the use of local precipitation collection complement with multiple sources of global reanalysis data to simulate the basin hydrology. Our results show that the coarse resolution of rainfall data is the main reason to reduce model performance. Despite this shortcoming, key hydrological fluxes in the basin can be represented by the JULES model simulations. [ABSTRACT FROM AUTHOR]

Bias correction is a necessary post-processing procedure in order to use regional climate model (RCM)-simulated local climate variables as the input data for hydrological models due to systematic errors of RCMs. Most of the present bias-correction methods adjust statistical properties between observed and simulated data based on a predefined duration (e.g. a month or a season). However, there is a lack of analysis of the optimal period for bias correction. This study attempted to address the question whether there is an optimal number for bias-correction groups (i.e. optimal bias-correction period). To explore this we used a catchment in southwest England with the regional climate model HadRM3 precipitation data. The proposed methodology used only one grid of RCM in the Exe catchment, one emissions scenario (A1B) and one member (Q0) among 11 members of HadRM3. We tried 13 different bias-correction periods from 3-day to 360-day (i.e. the whole of one year) correction using the quantile mapping method. After the bias correction a low pass filter was used to remove the high frequencies (i.e. noise) followed by estimating Akaike’s information criterion. For the case study catchment with the regional climate model HadRM3 precipitation, the results showed that a bias-correction period of about 8 days is the best. We hope this preliminary study on the optimum number bias-correction period for daily RCM precipitation will stimulate more research to improve the methodology with different climatic conditions. Future efforts on several unsolved problems have been suggested, such as how strong the filter should be and the impact of the number of bias correction groups on river flow simulations.Editor M.C. Acreman Associate editor S. Kanae [ABSTRACT FROM PUBLISHER]

Bias correction is a necessary post-processing procedure in order to use regional climate model (RCM) simulated local climate variables as the input data for hydrological models owing to systematic errors of RCMs. Most of present bias correction methods adjust statistical properties between observed and simulated data on the basis of calendar periods, e.g. month or season. However, this matching statistic is only a necessary condition, not a sufficient condition, as temporal distribution of the precipitation between observed and simulated data is ignored. This study suggests an improved bias correction scheme that considers not only statistical properties but also the temporal distribution between the time series of observed and modelled data. The ratio of the observed precipitation to simulated precipitation is used to compare the behaviour between the observed and modelled precipitation data, and three criteria are proposed when dividing bias correction periods: (1) underestimation of precipitation, (2) stability of /underestimation of precipitation, (2) stability of precipitation ratio and (3) oscillation of precipitation ratio. The results show that the output of the proposed bias correction method follows the trend of the observed precipitation better than that of the conventional bias correction method. This study indicates that temporal distribution should not be ignored when choosing a comparison period for bias correction. However, the study is only a preliminary attempt to address this important issue, and we hope it will stimulate more research activities to improve the methodology. Future efforts on several unsolved problems have been suggested such as how to find out the optimal group number to avoid the overfitting and underfitting conditions. Copyright © 2014 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Land surface models and satellite remote sensing are among the modern soil moisture retrieval techniques that can be used over large areas. However, the lack of 'ground truth' soil moisture measurements is still an obstacle in the evaluation and validation of soil moisture retrievals. In this study, a new scheme is used to assess soil moisture retrieval from both the NOAH Land surface Model (LSM) coupled with the fifth generation Mesoscale Model MM5 and the Advanced Microwave Scanning Radiometer AMSR-E. The proposed scheme is based on the strong correlation between changes in soil storage from rainfall runoff events and changes in the retrieved soil moisture either from the MM5-NOAH LSM or the AMSR-E. The aim of this study is to compare the application of the proposed scheme between these two different approaches for soil moisture estimation. The MM5-NOAH LSM provides soil moisture estimations at three different layers with depths 0-10 cm (surface layer 1), 10-40 cm (layer 2) and 40-100 cm (layer 3). In this study, the combined soil moisture over the top two layers (first and second) and the combined soil moisture over the first three layers (first, second and third) are used to account for the entire soil column for assessing the estimated soil moisture using changes in the storage from the water balance. The results have shown that the MM5 soil moisture from the combined top two layers has the better performance than either of the individual layers when compared to the catchment water storage. The results also show that the MM5-NOAH LSM soil moisture estimates have a slightly better performance than the AMSR-E surface soil moisture measurement. This preliminary assessment shows the benefits of using hydrological data in the validation of soil moisture retrieval methods. Copyright © 2013 Royal Meteorological Society [ABSTRACT FROM AUTHOR]

The mesoscale Numerical Weather Prediction (NWP) model is gaining popularity among the hydrometeorological community in providing high-resolution rainfall forecasts at the catchment scale. Although the performance of the model has been verified in capturing the physical processes of severe storm events, the modelling accuracy is negatively affected by significant errors in the initial conditions used to drive the model. Several meteorological investigations have shown that the assimilation of real-time observations, especially the radar data can help improve the accuracy of the rainfall predictions given by mesoscale NWP models. The aim of this study is to investigate the effect of data assimilation for hydrological applications at the catchment scale. Radar reflectivity together with surface and upper-air meteorological observations is assimilated into the Weather Research and Forecasting (WRF) model using the three-dimensional variational data-assimilation technique. Improvement of the rainfall accumulation and its temporal variation after data assimilation is examined for four storm events in the Brue catchment (135.2 km2) located in southwest England. The storm events are selected with different rainfall distributions in space and time. It is found that the rainfall improvement is most obvious for the events with one-dimensional evenness in either space or time. The effect of data assimilation is even more significant in the innermost domain which has the finest spatial resolution. However, for the events with two-dimensional unevenness of rainfall, i.e. the rainfall is concentrated in a small area and in a short time period, the effect of data assimilation is not ideal. WRF fails in capturing the whole process of the highly convective storm with densely concentrated rainfall in a small area and a short time period. A shortened assimilation time interval together with more efficient utilisation of the weather radar data might help improve the effectiveness of data assimilation in such cases. Copyright © 2012 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Sustainable water resources management require scientifically sound information on precipitation, as it plays a key role in hydrological responses in a catchment. In recent years, mesoscale weather models in conjunction with hydrological models have gained great attention as they can provide high-resolution downscaled weather variables. Many cumulus parameterization schemes (CPSs) have been developed and incorporated into three-dimensional Pennsylvania State University/National Center for Atmospheric Research (PSU/NCAR) mesoscale model 5 (MM5). This study has performed a comprehensive evaluation of four CPSs (the Anthes-Kuo, Grell, Betts-Miller and Kain-Fritsch93 schemes) to identify how their inclusion influences the mesoscale model's precipitation estimation capabilities. The study has also compared these four CPSs in terms of variability in rainfall estimation at various horizontal and vertical levels. For this purpose, the MM5 was nested down to resolution of 81 km for Domain 1 (domain span 21 × 81 km) and 3 km for Domain 4 (domain span 16 × 3 km), respectively, with vertical resolutions at 23, 40 and 53 vertical levels. The study was carried out at the Brue catchment in Southwest England using both the ERA-40 reanalysis data and the land-based observation data. The performances of four CPs were evaluated in terms of their ability to simulate the amount of cumulative rainfall in 4 months in 1995 representing the four seasonal months, namely, January (winter), March (spring), July (summer) and October (autumn). It is observed that the Anthes-Kuo scheme has produced inferior precipitation values during spring and autumn seasons while simulations during winter and summer were consistently good. The Betts-Miller scheme has produced some reasonable results, particularly at the small-scale domain (3 km grid size) during winter and summer. The KF2 scheme was the best scheme for the larger-scale (81 km grid size) domain during winter season at both 23 and 53 vertical levels. This scheme tended to underestimate rainfall for other seasons including the small-scale domain (3 km grid size) in the mesoscale. The Grell scheme was the best scheme in simulating rainfall rates, and was found to be superior to other three schemes with consistently better results in all four seasons and in different domain scales. Copyright © 2011 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Limited area numerical weather predication (NWP) models such as MM5 have become a popular method for generating rainfall estimates for hydrological analysis, particularly for catchments where rainfall data are sparse. Although several studies have been undertaken to investigate the appropriateness of MM5 parameterization schemes for hydrological applications, the size of the nested domains and the distance between them have been overlooked as a source of uncertainty in model precipitation estimates for hydrological purposes. This study examines the uncertainty of model rainfall estimates derived from MM5 by varying the domain size and the distance between the domains. The results from this study show that domain size and buffer zone have a significant impact on model rainfall estimates, which should not be overlooked by hydrologists. Copyright © 2010 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]