Combining a data-driven approach with seasonal forecasts data to predicting reservoir water volume in the Mediterranean area.

2021 ◽  
Author(s):  
Antonio Francipane ◽  
Elisa Arnone ◽  
Leonardo Valerio Noto
Keyword(s):  
Water Balance ◽  
Water Level ◽  
Forecast Model ◽  
Water Balance Model ◽  
Water Levels ◽  
Data Driven ◽  
Balance Model ◽  
Water Volume ◽  
Reservoir Water ◽  
Preliminary Results

<p>Artificial reservoirs are one of the main water supply resources in the Mediterranean areas; their management can be strongly affected by the problems of drought and water scarcity. The reservoir water level is the result of the hydrological processes occurring in the upstream catchment, which, in turn, depend on meteorological variables, such as rainfall and temperature. It follows that a reliable forecast model of the meteorological forcing, along with a reliable water balance model, could enhance the correct management of a reservoir. With regard to the rainfall/temperature forecast model, the use of forecast climate data in the mid-term may provide further support for the future water level estimation of reservoirs.</p><p>From the perspective of the water balance model, instead, among the approaches used to predict the water levels for the next future, those based on data-driven methods have been demonstrated to be particularly capable of correctly reproducing the correlation between a dependent variable (e.g., water level, volume) and some covariates (e.g., temperature, precipitation).</p><p>This study describes the preliminary results of a novel application that exploits the Seasonal Forecast (SF) data, produced at the European Centre for Medium-Range Weather Forecasting (ECMWF), within a data-driven model aimed to predict the reservoir water volume at mid-term scale, up to 6 months ahead in four reservoirs of the Sicily (Italy) here considered as a case study. For each case, a NARX (Nonlinear AutoRegressive network with eXogenous inputs) neural network is calibrated to reproduce the monthly stored water volume starting from the monthly precipitation and mean monthly air temperature variables.</p><p>Preliminary results showed that the NARXs have the capability to reproduce the water levels in the investigated period (January 2017 - April 2020), including the variations during more or less dry periods. All this despite the SF data have not been previously treated with downscaling and/or bias correction techniques.</p>

scholarly journals Long-term forecast model of spring runoff on the Belaya river

2019 ◽  
Vol 486 (6) ◽  
pp. 723-726
Author(s):  
D. Yu. Vasil’ev ◽  
V. V. Vodopyanov ◽  
G. S. Zayzeva ◽  
Sh. I. Zakirzyanov ◽  
V. V. Semenov ◽  
...  
Keyword(s):  
Water Balance ◽  
Forecast Model ◽  
Water Balance Model ◽  
Balance Model ◽  
Spring Runoff ◽  
Marquardt Algorithm ◽  
Long Term Forecasting ◽  
Model Equations ◽  
The Stability

This article presents the results of long-term forecasting of spring runoff in the Belaya River basin, based on the water balance model. To optimize the structure and parameters of the water balance model equations, the Levenberg-Marquardt algorithm was used to impose restrictions on the input data values. The obtained values of the equations’ coefficients were checked according to the criterion D/s adopted in the hydrometeorological service. The reliability of the predictive method used was assessed by statistical calculations of the stability of their parameters and test calculations on an independent sample. All equations obtained during the numerical experiment may be suitable to make forecasts.

scholarly journals Sensitivity of a data-driven soil water balance model to estimate summer evapotranspiration along a forest chronosequence

2011 ◽  
Vol 15 (11) ◽  
pp. 3461-3473 ◽  
Author(s):  
J. A. Breña Naranjo ◽  
M. Weiler ◽  
K. Stahl
Keyword(s):  
Water Balance ◽  
Soil Water ◽  
Eddy Covariance ◽  
Water Balance Model ◽  
Soil Water Balance ◽  
Data Driven ◽  
Balance Model ◽  
Rooting Depth ◽  
Computational Time

Abstract. The hydrology of ecosystem succession gives rise to new challenges for the analysis and modelling of water balance components. Recent large-scale alterations of forest cover across the globe suggest that a significant portion of new biophysical environments will influence the long-term dynamics and limits of water fluxes compared to pre-succession conditions. This study assesses the estimation of summer evapotranspiration along three FLUXNET sites at Campbell River, British Columbia, Canada using a data-driven soil water balance model validated by Eddy Covariance measurements. It explores the sensitivity of the model to different forest succession states, a wide range of computational time steps, rooting depths, and canopy interception capacity values. Uncertainty in the measured EC fluxes resulting in an energy imbalance was consistent with previous studies and does not affect the validation of the model. The agreement between observations and model estimates proves that the usefulness of the method to predict summer AET over mid- and long-term periods is independent of stand age. However, an optimal combination of the parameters rooting depth, time step and interception capacity threshold is needed to avoid an underestimation of AET as seen in past studies. The study suggests that summer AET could be estimated and monitored in many more places than those equipped with Eddy Covariance or sap-flow measurements to advance the understanding of water balance changes in different successional ecosystems.

scholarly journals A multi-source satellite data approach for modelling Lake Turkana water level: calibration and validation using satellite altimetry data

2012 ◽  
Vol 16 (1) ◽  
pp. 1-18 ◽  
Author(s):  
N. M. Velpuri ◽  
G. B. Senay ◽  
K. O. Asante
Keyword(s):  
Water Balance ◽  
Satellite Altimetry ◽  
Lake Level ◽  
Water Balance Model ◽  
Water Levels ◽  
Balance Model ◽  
Altimetry Data ◽  
Lake Turkana ◽  
In Situ Data ◽  
Satellite Altimetry Data

Abstract. Lake Turkana is one of the largest desert lakes in the world and is characterized by high degrees of inter- and intra-annual fluctuations. The hydrology and water balance of this lake have not been well understood due to its remote location and unavailability of reliable ground truth datasets. Managing surface water resources is a great challenge in areas where in-situ data are either limited or unavailable. In this study, multi-source satellite-driven data such as satellite-based rainfall estimates, modelled runoff, evapotranspiration, and a digital elevation dataset were used to model Lake Turkana water levels from 1998 to 2009. Due to the unavailability of reliable lake level data, an approach is presented to calibrate and validate the water balance model of Lake Turkana using a composite lake level product of TOPEX/Poseidon, Jason-1, and ENVISAT satellite altimetry data. Model validation results showed that the satellite-driven water balance model can satisfactorily capture the patterns and seasonal variations of the Lake Turkana water level fluctuations with a Pearson's correlation coefficient of 0.90 and a Nash-Sutcliffe Coefficient of Efficiency (NSCE) of 0.80 during the validation period (2004–2009). Model error estimates were within 10% of the natural variability of the lake. Our analysis indicated that fluctuations in Lake Turkana water levels are mainly driven by lake inflows and over-the-lake evaporation. Over-the-lake rainfall contributes only up to 30% of lake evaporative demand. During the modelling time period, Lake Turkana showed seasonal variations of 1–2 m. The lake level fluctuated in the range up to 4 m between the years 1998–2009. This study demonstrated the usefulness of satellite altimetry data to calibrate and validate the satellite-driven hydrological model for Lake Turkana without using any in-situ data. Furthermore, for Lake Turkana, we identified and outlined opportunities and challenges of using a calibrated satellite-driven water balance model for (i) quantitative assessment of the impact of basin developmental activities on lake levels and for (ii) forecasting lake level changes and their impact on fisheries. From this study, we suggest that globally available satellite altimetry data provide a unique opportunity for calibration and validation of hydrologic models in ungauged basins.

scholarly journals Development and Utility Evaluation of a Multi-Composite Water Balance Model

2020 ◽  
Vol 20 (2) ◽  
pp. 239-250
Author(s):  
Yonghyeon Gwon ◽  
Kyunghwan Son ◽  
Kyoungdo Lee ◽  
Gyewoon Choi
Keyword(s):  
Water Balance ◽  
Water Supply ◽  
River Basin ◽  
Model Simulation ◽  
Water Balance Model ◽  
Weather Data ◽  
Balance Model ◽  
Reservoir Water ◽  
Balance Analysis ◽  
Daily Water

This study aimed to develop a water balance model capable of daily analysis of the water supply situation in a multi-composite area, evaluate the utility of the model, and conduct a water balance analysis. The multi-composite water balance model, which was developed to determine the daily water balance in an area, includes five modules: "Weather data build and area mean data," "Rainfall-runoff analysis," "Benefit area and demand estimation," "Reservoir water balance analysis," and "River basin water balance analysis." The study selected eight cities in northwestern Chungcheongnam-do in Korea as target areas and evaluated the utility of the water balance model. Further, the study used observation and model simulation data for its analysis, which found a high degree of accuracy as well as correlation. In addition, daily water balance analysis was conducted to estimate the potential supply, demand, supply, shortage, surplus supply, and shortage days in the river basin, while the ratio of shortage to demand was also determined to identify areas vulnerable to drought. In the future, it will be possible to establish drought countermeasures and facility operation plans by identifying areas with water supply vulnerability using the developed model.

scholarly journals Application of Neural Network Models and ANFIS for Water Level Forecasting of the Salve Faccha Dam in the Andean Zone in Northern Ecuador

Water ◽  
2021 ◽  
Vol 13 (15) ◽  
pp. 2011
Author(s):  
Pablo Páliz Larrea ◽  
Xavier Zapata Ríos ◽  
Lenin Campozano Parra
Keyword(s):  
Neural Network ◽  
Water Level ◽  
Clustering Algorithm ◽  
Network Models ◽  
Water Levels ◽  
Subtractive Clustering ◽  
Neural Network Models ◽  
Reservoir Water ◽  
Reservoir Water Level ◽  
Maximum Delay

Despite the importance of dams for water distribution of various uses, adequate forecasting on a day-to-day scale is still in great need of intensive study worldwide. Machine learning models have had a wide application in water resource studies and have shown satisfactory results, including the time series forecasting of water levels and dam flows. In this study, neural network models (NN) and adaptive neuro-fuzzy inference systems (ANFIS) models were generated to forecast the water level of the Salve Faccha reservoir, which supplies water to Quito, the Capital of Ecuador. For NN, a non-linear input–output net with a maximum delay of 13 days was used with variation in the number of nodes and hidden layers. For ANFIS, after up to four days of delay, the subtractive clustering algorithm was used with a hyperparameter variation from 0.5 to 0.8. The results indicate that precipitation was not influencing input in the prediction of the reservoir water level. The best neural network and ANFIS models showed high performance, with a r > 0.95, a Nash index > 0.95, and a RMSE < 0.1. The best the neural network model was t + 4, and the best ANFIS model was model t + 6.

Assessment of interbasin groundwater flows between catchments using a semi-distributed water balance model

2014 ◽  
Vol 519 ◽  
pp. 1848-1858 ◽  
Author(s):  
Francisco Pellicer-Martínez ◽  
José Miguel Martínez-Paz
Keyword(s):  
Water Balance ◽  
Water Balance Model ◽  
Balance Model
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