Estimation of Initial Abstraction for Hydrological Modeling Based on Global Land Data Assimilation System–Simulated Datasets

AbstractInitial abstraction (Ia) is a sensitive parameter in hydrological models, and its value directly determines the amount of runoff. Ia, which is influenced by many factors related to antecedent watershed condition (AWC), is difficult to estimate due to lack of observed data. In the Soil Conservation Service curve number (SCS-CN) method, it is often assumed that Ia is 0.2 times the potential maximum retention S. Yet this assumption has frequently been questioned. In this paper, Ia/S and factors potentially influencing Ia were collected from rainfall–runoff events. Soil moisture and evaporation data were extracted from GLDAS-Noah datasets to represent AWC. Based on the driving factors of Ia, identified using the Pearson correlation coefficient and maximal information coefficient, artificial neural network (ANN)-estimated Ia was applied to simulate the selected flood events in the Hydrologic Engineering Center Hydrologic Modeling System (HEC-HMS) model. The results indicated that Ia/S varies over different events and different watersheds. Over 75% of the Ia/S values are less than 0.2 in the two study areas. The driving factors affecting Ia vary over different watersheds, and the antecedent precipitation index appears to be the most influential factor. Flood simulation by the HEC-HMS model using statistical Ia gives the best fitness, whereas applying ANN-estimated Ia outperforms the simulation with median Ia/S. For over 60% of the flood events, ANN-estimated Ia provided better fitness in flood peak and depth, with an average Nash–Sutcliffe efficiency coefficient of 0.76 compared to 0.71 for median Ia/S. The proposed ANN-estimated Ia is physically based and can be applied without calibration, saving time in constructing hydrological models.

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Assessing Groundwater Level with a Unified Seasonal Outlook and Hydrological Modeling Projection

Applied Sciences ◽

10.3390/app10248882 ◽

2020 ◽

Vol 10 (24) ◽

pp. 8882

Author(s):

Jing-Ying Huang ◽

Dong-Sin Shih

Keyword(s):

Groundwater Level ◽

Hydrological Modeling ◽

Seasonal Forecast ◽

Groundwater Table ◽

Seasonal Rainfall ◽

Water Model ◽

Annual Rainfall ◽

Efficiency Coefficient ◽

Hydrological Models ◽

Temporal And Spatial Distribution

Although the annual rainfall in Taiwan is high, water shortages still occasionally occur owing to its nonuniform temporal and spatial distribution. At these times, the groundwater is considered an acceptable alternative water source. Groundwater is of particular value because it is considered a clean and reliable source of fresh water. To prevent water scarcity, this study utilized seasonal forecasting by incorporating hydrological models to evaluate the seasonal groundwater level. The seasonal prospective issued by the Central Weather Bureau of Taiwan (CWB) was combined with weather generator data to construct seasonal weather forecasts as the input for hydrological models. A rainfall-runoff model, HEC-HMS, and a coupled groundwater and surface water model, WASH123D, were applied to simulate the seasonal groundwater levels. The Fengshan Creek basin in northern Taiwan was selected as a study site to test the proposed approach. The simulations demonstrated stability and feasibility, and the results agreed with the observed groundwater table. The calibrations indicated that the average errors of river stage were 0.850 for R2, 0.279 for root-mean-square error (RMSE), and 0.824 for efficiency coefficient (CE). The simulation also revealed that the simulated groundwater table corresponded with observed hydrographs very well (R2 of 0.607, RMSE of 0.282 m, and CE of 0.621). The parameters were verified in this study, and they were deemed practical and adequate for subsequent seasonal assessment. The seasonal forecast of 2018 at Guanxi station indicated that the 25th and 75th percentiles of simulated annual rainfall were within 1921–3285 mm and the actual annual rainfall was 2031 mm. Its seasonal rainfall outlook was around 30% accurate for forecasts of three consecutive months in 2018. Similarly, at Xinpu station, its seasonal rainfall outlook was about 40% accurate, and the amount of annual rainfall (1295 mm) was within the range of the 25th and 75th percentiles (1193–1852 mm). This revealed that the actual annual precipitations at both Guanxi and Xinpu station corresponded with the range of 25th and 75th percentiles of simulated rainfall, even if the accurate rate for the 3 month seasonal forecast had some error. The subsequent groundwater simulations were overestimated because the amount of actual rainfall was far lower than the average of the historical record in some dry season months. However, the amount of rainfall returned to normal values during the wet seasons, where the seasonal forecast and observation results were similar.

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A framework for visualizing the convergence performance of global optimization algorithms for hydrological models

10.5194/egusphere-egu2020-1792 ◽

2020 ◽

Author(s):

Tian Lan ◽

Kairong Lin ◽

Chong-Yu Xu ◽

Xiaohong Chen

Keyword(s):

Global Optimization ◽

Pearson Correlation ◽

Optimization Algorithms ◽

Hydrological Models ◽

Parameter Spaces ◽

Individual Parameter ◽

Convergence Behavior ◽

Convergence Performance ◽

Response Modes ◽

Maximal Information Coefficient

<p>The convergence performance of global optimization algorithms determines the reliability of the optimized parameter set of hydrological models, thereby affecting the prediction accuracy. This study applies advanced data analysis and visualization techniques to design a novel framework for characterizing and visualizing the convergence behavior of the optimization algorithms when used for the parameter calibration of hydrological models. First, we utilize violin plots to assess the convergence levels and speeds in individual parameter spaces (ECP-VP). The density distributions of violin plots match the possible properties of fitness landscapes. Then, the parallel coordinates techniques are used to simulate the dynamic convergence behavior and assess the convergence performance in multi-parameter space (ECP-PC). Furthermore, the possible mechanism for the effect of linear or nonlinear relationships between the parameters on the convergence performance is investigated using the maximal information coefficient (MIC) and the Pearson correlation coefficient (Pearson r). Finally, the effect of the parameter sensitivity on the convergence performance is analyzed. The proposed framework is applied in multi-period and multi-basin dynamic conditions as case studies. The results showed that the ECP-VP and ECP-PC techniques were well suited for the evaluation of the convergence performance of global optimization algorithms for hydrological models. The evaluation results provided valuable information on determining the reliability of the final optima, as well as the dominant response modes of hydrological models. It is also demonstrated that the convergence levels and speeds in pairwise parameter spaces depend on the linear correlations but not on the nonlinear correlation between the parameters. Additionally, there is no significant relationship between the sensitivity of the parameters and their convergence performance.</p>

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Analysis of an Extreme Weather Event in a Hyper Arid Region Using WRF-Hydro Coupling, Station, and Satellite data

10.5194/nhess-2018-226 ◽

2018 ◽

Author(s):

Youssef Wehbe ◽

Marouane Temimi ◽

Michael Weston ◽

Naira Chaouch ◽

Oliver Branch ◽

...

Keyword(s):

Soil Moisture ◽

United Arab Emirates ◽

Hydrological Modeling ◽

Flash Flood ◽

Pearson Correlation ◽

Global Radiation ◽

Wrf Model ◽

Extreme Weather ◽

Extreme Weather Event ◽

Weather Event

Abstract. This study investigates an extreme weather event that impacted the United Arab Emirates (UAE) in March 2016 using the Weather Research and Forecasting (WRF) model version 3.7.1 coupled with its hydrological modeling extension package (Hydro). Six-hourly forecasted forcing records at 0.5o spatial resolution, obtained from the NCEP Global Forecast System (GFS), are used to drive the three nested downscaling domains of both standalone WRF and coupled WRF/WRF-Hydro configurations for the recent flood-triggering storm. Ground and satellite observations over the UAE are employed to validate the model results. Precipitation, soil moisture, and cloud fraction retrievals from GPM (30-minute, 0.1o product), AMSR2 (daily, 0.1o product), and MODIS (daily, 5 km product), respectively, are used to assess the model output. The Pearson correlation coefficient (PCC), relative bias (rBIAS) and root-mean-square error (RMSE) are used as performance measures. Results show reductions of 24 % and 13 % in RMSE and rBIAS measures, respectively, in precipitation forecasts from the coupled WRF/WRF-Hydro model configuration, when compared to standalone WRF. The coupled system also shows improvements in global radiation forecasts, with reductions of 45 % and 12 % for RMSE and rBIAS, respectively. Moreover, WRF-Hydro was able to simulate the spatial distribution of soil moisture reasonably well across the study domain when compared to AMSR2 satellite soil moisture estimates, despite a noticeable dry/wet bias in areas where soil moisture is high/low. The demonstrated improvement, at the local scale, implies that WRF-Hydro coupling may enhance hydrologic forecasts and flash flood guidance systems in the region.

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Multi-source data assimilation for physically-based hydrological modeling of an experimental hillslope

10.5194/hess-2018-18 ◽

2018 ◽

Author(s):

Anna Botto ◽

Enrica Belluco ◽

Matteo Camporese

Keyword(s):

Soil Moisture ◽

Data Assimilation ◽

Hydrological Modeling ◽

Nonlinear Models ◽

Pressure Head ◽

Richards Equation ◽

Hydrological Models ◽

Filter Performance ◽

Trade Offs ◽

Source Data

Abstract. Data assimilation has been recently the focus of much attention for integrated surface-subsurface hydrological models, whereby joint assimilation of water table, soil moisture, and river discharge measurements with the ensemble Kalman filter (EnKF) have been extensively applied. Although the EnKF has been specifically developed to deal with nonlinear models, integrated hydrological models based on the Richards equation still represent a challenge, due to strong nonlinearities that may significantly affect the filter performance. Thus, more studies are needed to investigate the capabilities of the EnKF to correct the system state and identify parameters in cases where the unsaturated zone dynamics are dominant, as well as to quantify possible tradeoffs associated with assimilation of multi-source data. Here, the model CATHY (CATchment HYdrology) is applied to reproduce the hydrological dynamics observed in an experimental two-layered hillslope, equipped with tensiometers, water content reflectometer probes, and tipping bucket flow gages to monitor the hillslope response to a series of artificial rainfall events. Pressure head, soil moisture, and subsurface outflow are assimilated with the EnKF in a number of scenarios and the challenges and issues arising from the assimilation of multi-source data in this real-world test case are discussed. Our results demonstrate that the EnKF is able to effectively correct states and parameters even in a real application characterized by strong nonlinearities. However, multi-source data assimilation may lead to significant trade-offs: the assimilation of additional variables can lead to degradation of model predictions for other variables that were otherwise well reproduced. Furthermore, we show that integrated observations such as outflow discharge cannot compensate for the lack of well-distributed data in heterogeneous hillslopes.

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Comparison between dynamic and static sensitivity analysis approaches for impact assessment of different potential evapotranspiration methods on hydrological models performance

Journal of Hydrometeorology ◽

10.1175/jhm-d-20-0192.1 ◽

2021 ◽

Author(s):

Rodric Mérimé Nonki ◽

André Lenouo ◽

Christopher J. Lennard ◽

Raphael M. Tshimanga ◽

Clément Tchawoua

Keyword(s):

Sensitivity Analysis ◽

Impact Assessment ◽

Hydrological Modeling ◽

Potential Evapotranspiration ◽

Model Performance ◽

Estimation Methods ◽

Model Parameters ◽

Hydrological Models ◽

Sensitivity Approach ◽

Static Sensitivity

AbstractPotential Evapotranspiration (PET) plays a crucial role in water management, including irrigation systems design and management. It is an essential input to hydrological models. Direct measurement of PET is difficult, time-consuming and costly, therefore a number of different methods are used to compute this variable. This study compares the two sensitivity analysis approaches generally used for PET impact assessment on hydrological model performance. We conducted the study in the Upper Benue River Basin (UBRB) located in northern Cameroon using two lumped-conceptual rainfall-runoff models and nineteen PET estimation methods. A Monte-Carlo procedure was implemented to calibrate the hydrological models for each PET input while considering similar objective functions. Although there were notable differences between PET estimation methods, the hydrological models performance was satisfactory for each PET input in the calibration and validation periods. The optimized model parameters were significantly affected by the PET-inputs, especially the parameter responsible to transform PET into actual ET. The hydrological models performance was insensitive to the PET input using a dynamic sensitivity approach, while he was significantly affected using a static sensitivity approach. This means that the over-or under-estimation of PET is compensated by the model parameters during the model recalibration. The model performance was insensitive to the rescaling PET input for both dynamic and static sensitivities approaches. These results demonstrate that the effect of PET input to model performance is necessarily dependent on the sensitivity analysis approach used and suggest that the dynamic approach is more effective for hydrological modeling perspectives.

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Improving Meteorological Seasonal Forecasts for Hydrological Modeling in European Winter

Journal of Applied Meteorology and Climatology ◽

10.1175/jamc-d-19-0094.1 ◽

2020 ◽

Vol 59 (2) ◽

pp. 317-332

Author(s):

Nicky Stringer ◽

Jeff Knight ◽

Hazel Thornton

Keyword(s):

North Atlantic ◽

Large Scale ◽

Hydrological Modeling ◽

Strongly Correlated ◽

Hydrological Models ◽

Seasonal Forecasts ◽

The North ◽

Ensemble Mean ◽

The North Atlantic Oscillation ◽

Correlation Skill

AbstractRecent advances in the skill of seasonal forecasts in the extratropics during winter mean they could offer improvements to seasonal hydrological forecasts. However, the signal-to-noise paradox, whereby the variability in the ensemble mean signal is lower than would be expected given its correlation skill, prevents their use to force hydrological models directly. We describe a postprocessing method to adjust for this problem, increasing the size of the predicted signal in the large-scale circulation. This reduces the ratio of predictable components in the North Atlantic Oscillation (NAO) from 3 to 1. We then derive a large ensemble of daily sequences of spatially gridded rainfall that are consistent with the seasonal mean NAO prediction by selecting historical observations conditioned on the adjusted NAO forecasts. Over northern and southwestern Europe, where the NAO is strongly correlated with winter mean rainfall, the variability of the predicted signal in the adjusted rainfall forecasts is consistent with the correlation skill (they have a ratio of predictable components of ~1) and are as skillful as the unadjusted forecasts. The adjusted forecasts show larger predicted deviations from climatology and can be used to better assess the risk of extreme seasonal mean precipitation as well as to force hydrological models.

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Predictability of flood events in view of current meteorology and hydrology in the conditions of the Czech Republic

Soil and Water Research ◽

10.17221/2109-swr ◽

2008 ◽

Vol 2 (No. 4) ◽

pp. 156-168 ◽

Cited By ~ 4

Author(s):

L. Březková ◽

M. Šálek ◽

E. Soukalová ◽

M. Starý

Keyword(s):

Czech Republic ◽

Prediction Models ◽

Weather Prediction ◽

Warning System ◽

Economic Losses ◽

Hydrological Models ◽

Flood Events ◽

Common Error ◽

The Czech Republic ◽

Hydrological Forecast

In central Europe, floods are natural disasters causing the greatest economic losses. One way to reduce partly the flood-related damage, especially the loss of lives, is a functional objective forecasting and warning system that incorporates both meteorological and hydrological models. Numerical weather prediction models operate with horizontal spatial resolution of several dozens of kilometres up to several kilometres, nevertheless, the common error in the localisation of the heavy rainfall characteristic maxima is mostly several times as large as the grid size. The distributive hydrological models for the middle sized basins (hundreds to thousands of km<sup>2</sup>) operate with the resolution of hundreds of meters. Therefore, the (in) accuracy of the meteorological forecast can heavily influence the following hydrological forecast. In general, we can say that the shorter is the duration of the given phenomenon and the smaller area it hits, the more difficult is its prediction. The time and spatial distribution of the predicted precipitation is still one of the most difficult tasks of meteorology. Hydrological forecasts are created under the conditions of great uncertainty. This paper deals with the possibilities of the current hydrology and meteorology with regard to the predictability of the flood events. The Czech Hydrometeorological Institute is responsible by law for the forecasting flood service in the Czech Republic. For the precipitation and temperature forecasts, the outputs of the numerical model of atmosphere ALADIN are used. Moreover, the meteorological community has available operational outputs of many weather prediction models, being run in several meteorological centres around the world. For the hydrological forecast, the HYDROG and AQUALOG models are utilised. The paper shows examples of the hydrological flood forecasts from the years 2002–2006 in the Dyje catchment, attention being paid to floods caused by heavy rainfalls in the summer season. The results show that it is necessary to take into account the predictability of the particular phenomenon, which can be used in the decision making process during an emergency.

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Historical changes in frequency of extreme floods in Prague

Hydrology and Earth System Sciences ◽

10.5194/hess-19-4307-2015 ◽

2015 ◽

Vol 19 (10) ◽

pp. 4307-4315 ◽

Cited By ~ 19

Author(s):

L. Elleder

Keyword(s):

Flood Frequency ◽

Flood Frequency Analysis ◽

Flood Events ◽

Central European ◽

Extreme Floods ◽

Flood Peak ◽

The Past ◽

Documentary Sources ◽

Instrumental Period ◽

Peak Discharges

Abstract. This study presents a flood frequency analysis for the Vltava River catchment using a major profile in Prague. The estimates of peak discharges for the pre-instrumental period of 1118–1824 based on documentary sources were carried out using different approaches. 187 flood peak discharges derived for the pre-instrumental period augmented 150 records for the instrumental period of 1825–2013. Flood selection was based on Q10 criteria. Six flood-rich periods in total were identified for 1118–2013. Results of this study correspond with similar studies published earlier for some central European catchments, except for the period around 1750. Presented results indicate that the territory of the present Czech Republic might have experienced extreme floods in the past, comparable – with regard to peak discharge (higher than or equal to Q10) and frequency – to the flood events recorded recently.

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Improving calibration of two key parameters in Hydrologic Engineering Center hydrologic modelling system, and analysing the influence of initial loss on flood peak flows

Water Science & Technology ◽

10.2166/wst.2013.562 ◽

2013 ◽

Vol 68 (12) ◽

pp. 2718-2724 ◽

Cited By ~ 5

Author(s):

Musheng Lin ◽

Xingwei Chen ◽

Ying Chen ◽

Huaxia Yao

Keyword(s):

Return Period ◽

Flood Events ◽

Hydrologic Modelling ◽

K Value ◽

Flood Peak ◽

Initial Discharge ◽

Function Relationship ◽

Two Parameters ◽

Sensitive Parameters ◽

Modelling System

Parameter calibration is a key and difficult issue for a hydrological model. Taking the Jinjiang Xixi watershed of south-east China as the study area, we proposed methods to improve the calibration of two very sensitive parameters, Muskingum K and initial loss, in the Hydrologic Engineering Center hydrologic modelling system (HEC-HMS) model. Twenty-three rainstorm flood events occurring from 1972 to 1977 were used to calibrate the model using a trial-and-error approach, and a relationship between initial loss and initial discharge for these flood events was established; seven rainstorm events occurring from 1978 to 1979 were used to validate the two parameters. The influence of initial loss change on different return-period floods was evaluated. A fixed Muskingum K value, which was calibrated by assuming a flow wave velocity at 3 m/s, could be used to simulate a flood hydrograph, and the empirical power-function relationship between initial loss and initial discharge made the model more applicable for flood forecasting. The influence of initial loss on peak floods was significant but not identical for different flood levels, and the change rate of peak floods caused by the same initial loss change was more remarkable when the return period increased.

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Flow routing with Semi-distributed hydrological model HEC- HMS in case of Narayani River Basin

Journal of the Institute of Engineering ◽

10.3126/jie.v10i1.10877 ◽

2014 ◽

Vol 10 (1) ◽

pp. 45-58

Author(s):

Narayan Prasad Gautam

Keyword(s):

Water Resources ◽

River Basin ◽

Hydrological Modeling ◽

Numerical Solutions ◽

Peak Flow ◽

Channel Cross Section ◽

Hydrological Models ◽

Distributed Hydrological Model ◽

Element Discretization ◽

Verification Period

Routing is the modeling process to determine the outflow at an outlet from given inflow at upstream of the channel. A hydrological simulation model use mathematical equations that establish relationships between inputs and outputs of water system and simulates the catchment response to the rainfall input. Several hydrological models have been developed to assist in understanding of hydrologic system and water resources management. A model, once calibrated and verified on catchments, provides a multi-purpose tool for further analysis. Semi-Distributed models in hydrology are usually physically based in that they are defined in terms of theoretically acceptable continuum equations. They do, however, involve some degree of lumping since analytical solutions to the equations cannot be found, and so approximate numerical solutions, based on a finite difference or finite element discretization of the space and time dimensions, are implemented. Many rivers in Nepal are either ungauged or poorly gauged due to extreme complex terrains, monsoon climate and lack of technical and financial supports. In this context the role of hydrological models are extremely useful. In practical applications, hydrological routing methods are relatively simple to implement reasonably accurate. In this study, Gandaki river basin was taken for the study area. Kinematic wave method was used for overland routing and Muskingum cunge method was applied for channel routing to describe the discharge on Narayani river and peak flow attenuation and dispersion observed in the direct runoff hydrograph. Channel cross section parameters are extracted using HEC- GeoRAS extension tool of GIS. From this study result, Annual runoff, Peak flow and time of peak at the outlet are similar to the observed flow in calibration and verification period using trapezoidal channel. Hence Hydrological modeling is a powerful technique in the planning and development of integrated approach for management of water resources. DOI: http://dx.doi.org/10.3126/jie.v10i1.10877Journal of the Institute of Engineering, Vol. 10, No. 1, 2014 pp. 45-58

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