Search

The NASA Surface Water Ocean Topography (SWOT) mission and ESA Sentinel‐1, inter‐alia, propose to monitor inland waters from satellites. The radar altimeter and the microwave radiometer can be used to monitor water surface level and assess the cross‐sectional mean flow velocity, respectively. However, for most river sites bathymetric data are lacking, preventing the direct computation of discharge. In this context, a new methodology for simulating the bathymetry and estimating the discharge is proposed. The approach is based on entropy theory and can be applied using ground and satellite observations. Four parameters are needed and include channel roughness, water surface slope, channel bottom elevation, and an entropy parameter of flow depth. These parameters are estimated using a genetic algorithm by minimizing the error in the observed maximum surface velocity. Parameter uncertainty is considered through 1,000 random realizations based on different initial values. Eighteen streamflow measurements recorded from three different gauged river sites are used to benchmark the ground observations. The method included ENVISAT altimetry and Moderate Resolution Imaging Spectroradiometer (MODIS) data (2002‐2010) at one river site. Relative to ground measurements, the method provides good results with an error in channel area and discharge that, on average, does not exceed 10% on the 50th percentile. Relative to satellite measurements, channel area is well simulated with an error, on average, lower than 9%; discharge is less well simulated and represented by an error greater than 30% on the 50th percentile. The paper brings together important hydraulic and remote sensing information that combine to build a methodology to be applied to any riverThe paper can lay the groundwork for numerous remote sensing applications to estimating bathymetry, flow velocity, and discharge in riversA new perspective for noncontact streamflow measurements during high floods is addressed also at ungauged river sites

Rain gauges, weather radars, satellite sensors and modelled data from weather centres are used operationally for estimating the spatial-temporal variability of rainfall. However, the associated uncertainties can be very high, especially in poorly equipped regions of the world. Very recently, an innovative method, named SM2RAIN, that uses soil moisture observations to infer rainfall, has been proposed by Brocca et al. (2013) with very promising results when applied with in situ and satellite-derived data. However, a thorough analysis of the physical consistency of the SM2RAIN algorithm has not been carried out yet. In this study, synthetic soil moisture data generated from a physically-based soil water balance model are employed to check the reliability of the assumptions made in the SM2RAIN algorithm. Next, high quality and multiyear in situ soil moisture observations, at different depths (5-30 cm), and rainfall for ten sites across Europe are used for testing the performance of the algorithm, its limitations and applicability range.

The current research aims to predict the velocity distribution and discharge rates in rivers based on the entropy concept using only one surface velocity measurement. In this direction, first, the uncrewed aerial vehicle (UAV)‐based image acquisition technique was applied to collect the surface velocity distribution along two European rivers, the Sajó, and the Freiberger Mulde Rivers. Seven cross sections were chosen for the analysis. At each cross section, first, the entropic parameter Φ(M) was calibrated based on the maximum and mean velocity magnitudes, derived from Acoustic Doppler Current Profilers, respectively, showing a trend for all cross sections with a range of 0.6 < Φ(M) < 0.75. Next, the maximum surface velocity provided by the UAV was implemented as a single velocity input. Finally, the bathymetry data, herein collected by UAV, were considered as the input for the entropy approach. In this way, the entropy iterative method allowed estimating the mean flow velocity by identifying the location (dip) of maximum velocities across the river site and inferring the 2D velocity distribution. The results highlighted that the entropy approach can accurately predict the velocity distribution and discharge rates with a percentage error lower than 13%. Applying the uncrewed aerial vehicle‐based image acquisition technique to provide the maximum surface velocityEstimating the river discharge based on the Entropy concept relying on only maximum surface velocityPresenting cross‐sectional velocity distribution calculated by the Entropy approach through European rivers, Sajó, and Freiberger Mulde Applying the uncrewed aerial vehicle‐based image acquisition technique to provide the maximum surface velocity Estimating the river discharge based on the Entropy concept relying on only maximum surface velocity Presenting cross‐sectional velocity distribution calculated by the Entropy approach through European rivers, Sajó, and Freiberger Mulde

In many developing countries, due to economic constraints, a single station on a river reach is often equipped to record flow variables. On the other hand, hydrographs at the upstream sections may also be needed for especially assessing flooded areas. The upstream flow hydrograph prediction is called the reverse flood routing. There are some reverse flood routing pocedures requiring sophisticated methods together with substantial data requirements. This study proposes a new reverse flood routing procedure, based upon the simple kinematic wave (KW) equation, requiring only easily measurable downstream stage data. The KW equation is first averaged along a channel length at a fixed time, t, assuming that channel width is spatially constant, and then the spatially averaged equation is averaged in time, Δt. The temporally averaged terms are approximated as the arithmetical mean of the corresponding terms evaluated at time t and t + Δt. The Chezy roughness equation is employed for flow velocity, and the upstream flow stage hydrograph is assumed be described by a two parameter gamma distribution (Pearson Type III). The spatially averaged mean flow depth and lateral flow are related to the downstream flow stage. The resulting routing equation is thus obtained as a function of only downstream flow stage, meaning that the method mainly requires measurements of downstream flow stage data besides the mean values of channel length, channel width, roughness coefficient and bed slope. The optimal values of the parameters of reverse flood routing are obtained using the genetic algorithm. The calibration of the model is accomplished by using the measured downstream hydrographs. The validation is performed by comparing the model-generated upstream hydrographs against the measured upstream hydrographs. The proposed model is applied to generate upstream hydrographs at four different river reaches of Tiber River, located in central Italy. The length of river reaches varied from 20 to 65 km. Several upstream hydrographs at different stations on this river are generated using the developed method and compared with the observed hydrographs. The method predicts the time to peak with less than 5% error and peak rates with less than 10% error in the short river reaches of 20 km and 31 km. It also predicts the time to peak and peak rate in other two brances of 45 km and 65 km with less than 15% error. The method satisfactorily generates upstream hydrographs, with an overall mean absolute error (MAE) of 42 m3/s.

Soil moisture is a key state variable in hydrology, it controls the proportion of rainfall that infiltrates, runoff and evaporates from the land. For hydrological applications, soil moisture monitoring at catchment scale is required and, for that, microwave remote sensing sensors might be used. However, due to their coarse-spatial resolution, the skepticism on the suitability to retrieve the soil moisture at catchment scale takes still place. This work attempts to bring out if coarse resolution sensors for soil moisture monitoring can have some perspectives for hydrological applications. Two soil moisture products derived from the Advanced SCATterometer (ASCAT) and the Advanced Microwave Scanning Radiometer-EOS (AMSR-E) are used for this purpose. The analysis is addressed by investigating: (i) the reliability of product data in the estimation of the wetness conditions of a catchment antecedent to rainfall events, and (ii) the benefit on runoff prediction if data are assimilated into a rainfall-runoff model. Rainfall-runoff observations are taken from several catchments in Italy and Luxembourg for testing. Results reveal that ASCAT and AMSR-E soil moisture products can be conveniently used to improve runoff prediction thus opening new important challenges and opportunities for the use of this new sources of data in the operational hydrology.

An adaptive model for on-line stage forecasting is proposed for river reaches where significant lateral inflow contributions occur. The model is based on the Muskingum method and requires the estimation of four parameters if the downstream rating curve is unknown; otherwise only two parameters have to be determined. As the choice of the forecast lead time is linked to wave travel time along the reach, to increase the lead time, a schematization of two connected river reaches is also investigated. The variability of lateral inflow is accounted for through an on-line adaptive procedure. Calibration and validation of the model were carried out by applying it to different flood events observed in two equipped river reaches of the upper-middle Tiber basin in central Italy, characterized by a significant contributing drainage area. Even if the rating curve is unknown at the downstream section, the forecast stage hydrographs were found in good agreement with those observed. Errors in peak stage and time to peak along with the persistence coefficient values show that the model has potential as a practical tool for on-line flood risk management.

Entropy theory applied to hydrometric measurements enables establishing a relationship between the maximum and the mean velocities of flow passing a river section. In many developing countries, river discharge is still estimated by the velocity‐area method following the procedure established based on the point‐velocities measured at 0.6D depth from the water surface of many verticals of flow section. This study explores the establishment of a relationship between the maximum point‐velocity estimated at 0.6D depth of the vertical located at or nearer to the deepest flow depth, and the maximum point‐velocity occurring somewhere along the same vertical which in turn can be linked to the sectional mean flow velocity based on the established entropy theory. The appropriateness of the proposed two‐steps based approach of discharge estimation is first verified on the Tiber River and the Po River of Italy. The study finds that the values of the maximum point‐velocity estimated using the proposed approach closely matches with the recorded maximum flow velocity values along the same flow‐depths. After this verification, the proposed method was applied for discharge estimation at two Indian river gauging stations, where only the point‐velocity measurements at 0.6D depth locations of the verticals are recorded. The study finds that the discharges estimated by both the proposed approach and the velocity‐area method closely match with each other with the estimated Nash‐Sutcliffe Efficiency (NSE) values greater than 0.99. Thus the proposed two‐steps approach involving the entropy concept based relationship for discharge estimation enables to replace the tedious and time‐consuming velocity‐area approach. Proposes a new method to estimate discharge using u0.6D,maxvelocity measurement. Estimation of state equilibrium constant using the historical records of the velocity‐area methodIt is establishing a relationship between u0.6D,maxand umaxThe potential of the approach is demonstrated through uncertainty analysis for the Indian Rivers Proposes a new method to estimate discharge using u0.6D,maxvelocity measurement. Estimation of state equilibrium constant using the historical records of the velocity‐area method It is establishing a relationship between u0.6D,maxand umax The potential of the approach is demonstrated through uncertainty analysis for the Indian Rivers

Drought is the most frequent natural disaster in Algeria during the last century, with a severity ranging over the territory and causing enormous damages to agriculture and economy, especially in the north-west region of Algeria. The above issue motivated this study, which is aimed to analyse and predict droughts using the Standardized Precipitation Index (SPI). The analysis is based on monthly rainfall data collected during the period from 1960 to 2010 in seven plains located in the north-western Algeria. While a drought forecast with 2 months lead-time is addressed using an artificial neural network (ANN) model. Based on SPI values at different time scales (3-, 6-, 9-, and 12-months), the seven plains of north-western Algeria are severely affected by drought, conversely of the eastern part of the country, wherein drought phenomena are decreased in both duration and severity. The analysis also shows that the drought frequency changes according to the time scale. Moreover, the temporal analysis, without considering the autocorrelation effect on change point and monotonic trends of SPI series, depicts a negative trend with asynchronous in change-point timing. However, this becomes less significant at 3 and 6 months’ time scales if time series are modelled using the corrected and unbiased trend-free-pre-whitening (TFPWcu) approach. As regards the ANN-based drought forecast in the seven plains with 2 months of lead time, the multi-layer perceptron networks architecture with Levenberg–Marquardt calibration algorithm provides satisfactory results with the adjusted coefficient of determination (Radj2) higher than 0.81 and the root-mean-square-error (RMSE) and the mean absolute error (MAE) less than 0.41 and 0.23, respectively. Therefore, the proposed ANN-based drought forecast model can be conveniently adopted to establish with 2 months ahead adequate irrigation schedules in case of water stress and for optimizing agricultural production.

River discharge is an important quantity of the hydrologic cycle because it is essential for both scientific and operational applications related to water resources management and flood risk prevention. Streamflow measurements are sparse and for few sites along natural channels and, hence, they are not able to detect adequately the complexity of variation in surface water systems. Therefore, in recent years, the possibility to obtain river discharge estimates through remote sensing monitoring has received a great interest. In this context, the capability of the MODerate resolution Imaging Spectroradiometer (MODIS) for river discharge estimation is investigated here. Thanks to a very short revisiting time interval and a moderate spatial resolution (up to 250 m), MODIS has a significant potential for mapping flooded area extent and flow dynamics. Specifically, for the estimation of river discharge, the ratio of the MODIS channel 2 reflectance values between two pixels located within and outside the river is used. Time series of daily discharge between 2006 and 2010 measured at two gauging stations located along the Upper Tiber River basin (central Italy) are employed to test the procedure. The agreement between MODIS-derived and in situ discharge time series is found to be fairly good with correlation coefficient values close to 0.8.