Fully Stochastic Distributed Methodology for Multivariate Flood Frequency Analysis

An adequate estimation of the extreme behavior of basin response is essential both for designing river structures and for evaluating their risk. The aim of this paper is to develop a new methodology to generate extreme hydrograph series of thousands of years using an event-based model. To this end, a spatial-temporal synthetic rainfall generator (RainSimV3) is combined with a distributed physically-based rainfall?runoff event-based model (RIBS). The use of an event-based model allows simulating longer hydrograph series with less computational and data requirements but need to characterize the initial basis state, which depends on the initial basin moisture distribution. To overcome this problem, this paper proposed a probabilistic calibration simulation approach, which considers the initial state and the model parameters as random variables characterized by a probability distribution though a Monte Carlo simulation. This approach is compared with two other approaches, the deterministic and the semi-deterministic approaches. Both approaches use a unique initial state. The deterministic approach also uses a unique value of the model parameters while the semi-deterministic approach obtains these values from its probability distribution through a Monte Carlo simulation, considering the basin variability. This methodology has been applied to the Corbès and Générargues basins, in the Southeast of France. The results show that the probabilistic approach offers the best fit. That means that the proposed methodology can be successfully used to characterize the extreme behavior of the basin considering the basin variability and overcoming the basin initial state problem

Rainfall-runoff modelling by using SM2RAIN-derived and state-of-the-art satellite rainfall products over Italy

Satellite rainfall products (SRPs) are becoming more accurate with ever increasing spatial and temporal resolution. This evolution can be beneficial for hydrological applications, providing new sources of information and allowing to drive models in ungauged areas. Despite the large availability of rainfall satellite data, their use in rainfall-runoff modelling is still very scarce, most likely due to measurement issues (bias, accuracy) and the hydrological community acceptability of satellite products. In this study, the real-time version (3B42-RT) of Tropical Rainfall Measurement Mission Multi-satellite Precipitation Analysis, TMPA, and a new SRP based on the application of SM2RAIN algorithm (Brocca et al., 2014) to the ASCAT (Advanced SCATterometer) soil moisture product, SM2RASC, are used to drive a lumped hydrologic model over four basins in Italy during the 4-year period 2010–2013. The need of the recalibration of model parameter values for each SRP is highlighted, being an important precondition for their suitable use in flood modelling. Results shows that SRPs provided, in most of the cases, performance scores only slightly lower than those obtained by using observed data with a reduction of Nash–Sutcliffe efficiency (NS) less than 30% when using SM2RASC product while TMPA is characterized by a significant deterioration during the validation period 2012–2013. Moreover, the integration between observed and satellite rainfall data is investigated as well. Interestingly, the simple integration procedure here applied allows obtaining more accurate rainfall input datasets with respect to the use of ground observations only, for 3 out 4 basins. Indeed, discharge simulations improve when ground rainfall observations and SM2RASC product are integrated, with an increase of NS between 2 and 42% for the 3 basins in Central and Northern Italy. Overall, the study highlights the feasibility of using SRPs in hydrological applications over the Mediterranean region with benefits in discharge simulations also in well gauged areas.1631731

A Review of the Applications of ASCAT Soil Moisture Products

Remote sensing of soil moisture has reached a level of good maturity and accuracy for which the retrieved products are ready to use in real-world applications. Due to the importance of soil moisture in the partitioning of the water and energy fluxes between the land surface and the atmosphere, a wide range of applications can benefit from the availability of satellite soil moisture products. Specifically, the Advanced SCATterometer (ASCAT) on board the series of Meteorological Operational (Metop) satellites is providing a near real time (and long-term, 9+ years starting from January 2007) soil moisture product, with a nearly daily (sub-daily after the launch of Metop-B) revisit time and a spatial sampling of 12.5 and 25 km. This study first performs a review of the climatic, meteorological, and hydrological studies that use satellite soil moisture products for a better understanding of the water and energy cycle. Specifically, applications that consider satellite soil moisture product for improving their predictions are analyzed and discussed. Moreover, four real examples are shown in which ASCAT soil moisture observations have been successfully applied toward: 1) numerical weather prediction, 2) rainfall estimation, 3) flood forecasting, and 4) drought monitoring and prediction. Finally, the strengths and limitations of ASCAT soil moisture products and the way forward for fully exploiting these data in real-world applications are discussed.228523062

Assessment of satellite derived rainfall and its use in the ACRU hydrological model.

Master of Science in Hydrology. University of KwaZulu-Natal, Pietermaritzburg, 2017.Many parts of southern Africa are considered water scarce regions. Therefore, sound management and decision making is important to achieve maximum usage with sustainability of the precious resource. Hydrological models are often used to inform management decisions; however model performance is directly linked to the quality of data that is input. Rainfall is a key aspect of hydrological systems. Understanding the spatial and temporal variations of rainfall is of paramount importance to make key management decisions within a management area. Rainfall is traditionally measured through the use of in-situ rain gauge measurements. However, rain gauge measurements poorly represent the spatial variations of rainfall and rain gauge networks are diminishing, especially in southern Africa. Due to the sparse distribution of rain gauges and the spatial problems associated with rain gauge measurements, the use of satellite derived rainfall is being increasingly advocated. The overall aim of this research study was to investigate the use of satellite derived rainfall into the ACRU hydrological model to simulate streamflow. Key objectives of the study included (i) the validation of satellite derived rainfall with rain gauge measurements, (ii) generation of time series of satellite derived rainfall to drive the ACRU hydrological model, and (iii) validation of simulated streamflow with measured streamflow. The products were evaluated in the upper uMngeni, upper uThukela (summer rainfall) as well as the upper and central Breede catchments (winter rainfall). The satellite rainfall products chosen for investigation in this study included TRMM 3B42, FEWS ARC2, FEWS RFE2, TAMSAT-3 and GPM. The satellite rainfall products were validated using rain gauges in and around the study sites from 1 January 2010 to 30 April 2017. The rainfall products performed differently at each location with high variation in daily magnitudes of rainfall. Total rainfall volumes over the period of analysis were generally in better agreement with rain gauge volumes with TRMM 3B42 tending to overestimate rainfall volumes whereas the other products underestimated rainfall volumes. The ACRU model was applied using satellite rainfall and rain gauge measurements in the aforementioned study catchments from 1 October 2007 to 30 September 2016. Streamflow results were generally poor and variable amongst products. Daily correlations of streamflow were poor. Total streamflow volumes were in better agreement with total volumes of observed streamflow. TRMM 3B42 and rain gauge driven simulations produced the best results in the summer rainfall region, whereas the FEWS driven simulations produced the best results in the winter rainfall region