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The estimation of water resources at basin scale requires modelling of all components of the hydrological system. Because of the great uncertainties associated with the estimation of each water cycle component and the large error in budget closure that results, water budget is rarely carried out explicitly. This paper fills the gap in providing a methodology for obtaining it routinely at daily and subdaily time scales. In this study, we use various strategies to improve water budget closure in a small basin of Italian Prealps. The specific objectives are: assessing the predictive performances of different Kriging methods to determine the most accurate precipitation estimates; using MODIS imagery data to assist in the separation of snowfall and rainfall; combining the Priestley-Taylor evapotranspiration model with the Budyko hypothesis to estimate at high resolution (in time and space) actual evapotranspiration (ET); using an appropriate calibration-validation strategy to forecast discharge spatially. For this, 18 years of spatial time series of precipitation, snow water equivalent, rainfall-runoff and ET at hourly time steps are simulated for the Posina River basin (Northeast Italy) using the JGrass-NewAGE system. Among the interpolation methods considered, local detrended kriging is seen to give the best performances in forecasting precipitation distribution. However, detrended Kriging gives better results in simulating discharges. The parameters optimized at the basin outlet over a five-year period show acceptable performances during the validation period at the outlet and at interior points of the basin. The use of the Budyko hypothesis to guide the ET estimation shows encouraging results, with less uncertainty than the values reported in literature. Aggregating at a long temporal scale, the mean annual water budget for the Posina River basin is about 1269 ± 372 mm (76.4%) runoff, 503.5 ± 35.5 mm (30%) evapotranspiration, and − 50 ± 129 mm ( − 4.2%) basin storage from basin precipitation of 1730 ± 344 mm. The highest interannual variability is shown for precipitation, followed by discharge. Evapotranspiration shows less interannual variability and is less dependent on precipitation. [ABSTRACT FROM AUTHOR]

This paper presents novel formulations of Mean field bias (MFB) and local bias (LB) correction schemes that incorporate conditional bias (CB) penalty. These schemes are based on the operational MFB and LB algorithms in the National Weather Service (NWS) Multisensor Precipitation Estimator (MPE). By incorporating CB penalty in the cost function of exponential smoothers, we are able to derive augmented versions of recursive estimators of MFB and LB. Two extended versions of MFB algorithms are presented, one incorporating spatial variation of gauge locations only (MFB-L), and the second integrating both gauge locations and CB penalty (MFB-X). These two MFB schemes and the extended LB scheme (LB-X) are assessed relative to the original MFB and LB algorithms (referred to as MFB-O and LB-O, respectively) through a retrospective experiment over a radar domain in north-central Texas, and through a synthetic experiment over the Mid-Atlantic region. The outcome of the former experiment indicates that introducing the CB penalty to the MFB formulation leads to small, but consistent improvements in bias and CB, while its impacts on hourly correlation and Root Mean Square Error (RMSE) are mixed. Incorporating CB penalty in LB formulation tends to improve the RMSE at high rainfall thresholds, but its impacts on bias are also mixed. The synthetic experiment suggests that beneficial impacts are more conspicuous at low gauge density (9 per 58,000 km 2 ), and tend to diminish at higher gauge density. The improvement at high rainfall intensity is partly an outcome of the conservativeness of the extended LB scheme. This conservativeness arises in part from the more frequent presence of negative eigenvalues in the extended covariance matrix which leads to no, or smaller incremental changes to the smoothed rainfall amounts. [ABSTRACT FROM AUTHOR]

Abstract: This paper considers a class of stochastic point process models, based on doubly stochastic Poisson processes, in the modelling of rainfall. We examine the application of this class of models, a neglected alternative to the widely-known Poisson cluster models, in the analysis of fine time-scale rainfall intensity. These models are mainly used to analyse tipping-bucket raingauge data from a single site but an extension to multiple sites is illustrated which reveals the potential of this class of models to study the temporal and spatial variability of precipitation at fine time-scales. [Copyright &y& Elsevier]

Abstract: Cascades have been used as models of precipitation for nearly 25years yet many basic questions remain unanswered and most applications have been to small or to regional scales. In this paper we revisit some of these issues and present an inter comparison of four global scale data sets each with exceptional characteristics: the hourly (and ≈200km) resolution Climate Prediction Center (CPC) gridded precipitation over the continental US, the three hourly global ECMWF reanalysis stratiform precipitation product at 1.5° resolution, the six hourly Twentieth Century reanalysis at 2° (1871–2008) and an analysis of 5300 orbits (1year) of the Tropical Rainfall Measuring Mission (TRMM) satellite rainfall over ±40° latitude. The data were analysed zonally, meridionally and in time. Each showed cascade structures; in space up to planetary scales and in time up to 5–10days. For each we estimated the moment scaling exponent (K(q)) as well as its characterisation near the mean (C 1) and the effective outer cascade scales. The comparison of the cascade structures in different directions indicate that although anomalies remain, they are relatively isotropic in (horizontal) space–time. For any given direction, the comparison of the different products indicates very similar but not identical scaling properties. In order to be properly inter calibrated at more than a unique resolution, the different products must have the same exponents and outer scales so that - while the similarities are encouraging – the remaining anomalies point to needed improvements in techniques for estimating areal rainfall. Our main conclusion is that the rain rate biases introduced by the measurement techniques are larger than the deviations from perfect log–log linearity (scaling) so that multifractal models will be needed for improving space–time precipitation measurements. Our analyses clarify various fundamental issues. For example, the CPC data show that at “weather” scales smaller than ≈2days in time; H =0.17±0.11 so that rain is apparently not the direct product of a cascade process (which would have H =0). Similarly, for the low frequency weather regime (scales >≈2weeks) we find H ≈−0.42 so that fluctuations tend to decrease rather than increase with scale and display long range statistical dependencies. Finally, we find power law probability tails with exponent q D ≈3 so that the orders of singularity are apparently not bounded, ruling out several model types including microcanonical and log-Poisson models. [Copyright &y& Elsevier]

The uncertainty in forecasted precipitation remains a major source of uncertainty in real time flood forecasting. Precipitation uncertainty consists of uncertainty in (i) the magnitude, (ii) temporal distribution, and (iii) spatial distribution of the precipitation. This paper presents a methodology for propagating the precipitation uncertainty through a deterministic rainfall-runoff-routing model for flood forecasting. It uses fuzzy set theory combined with genetic algorithms. The uncertainty due to the unknown temporal distribution of the precipitation is achieved by disaggregation of the precipitation into subperiods. The methodology based on fuzzy set theory is particularly useful where a probabilistic forecast of precipitation is not available. A catchment model of the Klodzko valley (Poland) built with HEC-1 and HEC-HMS was used for the application. The results showed that the output uncertainty due to the uncertain temporal distribution of precipitation can be significantly dominant over the uncertainty due to the uncertain quantity of precipitation. [Copyright &y& Elsevier]