Surface runoff prediction and comparison using IHACRES and GR4J lumped models in the Mono catchment, West Africa

Abstract. This study aims to assess simulated surface runoff before and after dam construction in the Mono catchment (West Africa) using two lumped models: GR4J (Rural Engineering with 4 Daily Parameters) and IHACRES (Identification of unit Hydrographs and Component flows from Rainfall, Evapotranspiration and Stream data) over two different periods (1964–1986 and 1988–2010). Daily rainfall, mean temperature, evapotranspiration and discharge in situ data were collected for the period 1964–2010. After the model's initialization, calibration and validation; performances analysis have been carried out using multi-objectives functions developed in R software (version 3.5.3). The results indicate that statistical metrics such as the coefficient of determination (R2), the Kling–Gupta Efficiency (KGE), the Nash–Sutcliffe coefficient (NSE) and the Percent of Bias (PBIAS) provide satisfactory insights over the first period of simulation (1964–1986) and low performances over the second period of simulation (1988–2010). In particular, IHACRES model underestimates extreme high runoff of Mono catchment between 1964 and 1986. Conversely, GR4J model overestimates extreme high runoff and has been found to be better for runoff prediction of the river only between 1964 and 1986. Moreover, the study deduced that the robustness of runoff simulation between 1964 and 1986 is better than between 1988 and 2010. Therefore, the weakness of simulated runoff between 1988 and 2010 was certainly due to dam management in the catchment. The study suggests that land cover changes impacts, soil proprieties and climate may also affect surface runoff in the catchment.

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An Assessment of a Proposed Hybrid Neural Network for Daily Flow Prediction in Arid Climate

Modelling and Simulation in Engineering ◽

10.1155/2014/635018 ◽

2014 ◽

Vol 2014 ◽

pp. 1-10 ◽

Cited By ~ 2

Author(s):

Milad Jajarmizadeh ◽

Sobri Harun ◽

Mohsen Salarpour

Keyword(s):

Neural Network ◽

Transfer Functions ◽

Heuristic Method ◽

Daily Rainfall ◽

Hybrid Network ◽

Coefficient Of Determination ◽

Training Algorithms ◽

Rainfall Runoff ◽

Runoff Simulation ◽

Hidden Layer

Rainfall-runoff simulation in hydrology using artificial intelligence presents the nonlinear relationships using neural networks. In this study, a hybrid network presented as a feedforward modular neural network (FF-MNN) has been developed to predict the daily rainfall-runoff of the Roodan watershed at the southern part of Iran. This FF-MNN has three layers—input, hidden, and output. The hidden layer has two types of neural expert or module. Hydrometeorological data of the catchment were collected for 21 years. Heuristic method was used to develop the MNN for exploring daily flow generalization. Two training algorithms, namely, backpropagation with momentum and Levenberg-Marquardt, were used. Sigmoid and linear transfer functions were employed to explore the network’s optimum behavior. Cross-validation and predictive uncertainty assessments were carried out to protect overtiring and overparameterization, respectively. Results showed that the FF-MNN could satisfactorily predict stream flow during testing period. The Nash-Sutcliff coefficient, coefficient of determination, and root mean square error obtained using MNN during training and test periods were 0.85, 0.85, and 39.4 and 0.57, 0.58, and 32.2, respectively. The predictive uncertainties for both periods were 0.39 and 0.44, respectively. Generally, the study showed that the FF-MNN can give promising prediction for rainfall-runoff relations.

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Rainfall-Runoff Modeling Using the HEC-HMS Model for the Al-Adhaim River Catchment, Northern Iraq

Hydrology ◽

10.3390/hydrology8020058 ◽

2021 ◽

Vol 8 (2) ◽

pp. 58

Author(s):

Ahmed Naseh Ahmed Hamdan ◽

Suhad Almuktar ◽

Miklas Scholz

Keyword(s):

Geographical Information Systems ◽

Hydrological Model ◽

Daily Rainfall ◽

Curve Number ◽

Geographical Information ◽

Coefficient Of Determination ◽

Rainfall Runoff ◽

Time Step ◽

River Catchment ◽

Northern Iraq

It has become necessary to estimate the quantities of runoff by knowing the amount of rainfall to calculate the required quantities of water storage in reservoirs and to determine the likelihood of flooding. The present study deals with the development of a hydrological model named Hydrologic Engineering Center (HEC-HMS), which uses Digital Elevation Models (DEM). This hydrological model was used by means of the Geospatial Hydrologic Modeling Extension (HEC-GeoHMS) and Geographical Information Systems (GIS) to identify the discharge of the Al-Adhaim River catchment and embankment dam in Iraq by simulated rainfall-runoff processes. The meteorological models were developed within the HEC-HMS from the recorded daily rainfall data for the hydrological years 2015 to 2018. The control specifications were defined for the specified period and one day time step. The Soil Conservation Service-Curve number (SCS-CN), SCS Unit Hydrograph and Muskingum methods were used for loss, transformation and routing calculations, respectively. The model was simulated for two years for calibration and one year for verification of the daily rainfall values. The results showed that both observed and simulated hydrographs were highly correlated. The model’s performance was evaluated by using a coefficient of determination of 90% for calibration and verification. The dam’s discharge for the considered period was successfully simulated but slightly overestimated. The results indicated that the model is suitable for hydrological simulations in the Al-Adhaim river catchment.

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Application of digital image processing in slope surface runoff velocity analysis under simulated rainfall conditions

Hydrology Research ◽

10.2166/nh.2017.040 ◽

2017 ◽

Vol 49 (4) ◽

pp. 1304-1312

Author(s):

Tiexiong Gong ◽

Yuanjun Zhu

Keyword(s):

Image Processing ◽

Surface Runoff ◽

Lateral Diffusion ◽

Ease Of Use ◽

Velocity Analysis ◽

Slope Gradient ◽

Tracer Method ◽

Dye Tracer ◽

The Mean ◽

High Runoff

Abstract To have accurate runoff velocity, there is need to improve dye tracer method for estimating surface runoff velocity. This can enhance the calculations of relevant hydrologic parameters that will lead to a better understanding of hydrological processes and soil erosion. In this study, an integrated dye tracer and image processing method (IPV) and dye tracer method (AOV), respectively, were used to estimate runoff velocity under three slope gradients (5°, 10°, and 15°) and three slope positions (up-slope, mid-slope, and down-slope). The results showed more variation in runoff velocity under IPV than AOV. Both IPV and AOV were positively correlated with slope gradient. IPV values were close to AOV ones for slope gradients ≤5°, but were significantly different for slope gradients ≥10°. The mean AOV value was 10.6% higher than that of IPV. Regression analysis showed that compared with AOV, IPV overestimated and underestimated runoff under low and high runoff velocity conditions, respectively. The use of image processing in IPV was advantageous because of its ease of use with fewer artificial errors and its suitability for lateral diffusion of runoff. Irrespectively, additional studies are needed to verify and/or improve further the use of this method in runoff velocity analysis.

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An assessment of a daily rainfall erosivity model for New South Wales

Soil Research ◽

10.1071/sr9960139 ◽

1996 ◽

Vol 34 (1) ◽

pp. 139 ◽

Cited By ~ 62

Author(s):

B Yu ◽

CJ Rosewell

Keyword(s):

New South ◽

New South Wales ◽

Daily Rainfall ◽

Climate Change Impacts ◽

Seasonal Distribution ◽

Coefficient Of Determination ◽

Rainfall Erosivity ◽

South Wales ◽

Parameter Values ◽

Variance Explained

A rainfall erosivity model using daily rainfall amounts to estimate rainfall erosivity was tested for 29 sites in New South Wales to see whether such a model could adequately describe the temporal variation and seasonal distribution of rainfall erosivity. The coefficient of determination varied from 0.57 to 0.97 and the average discrepancy between actual and estimated seasonal distribution was no more than 3%. A set of parameter values for sites without pluviograph data was recommended for New South Wales. With this set of recommended parameter values, the percentage of total variance explained was decreased to 44%–89% for the 29 sites. Large errors, however, can occur when estimating extreme storm erosivity with large return periods. The daily erosivity model could be used for determining the seasonal distribution of rainfall erosivity or for simulating changes to rainfall erosivity as part of climate change impacts assessment.

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Spatio-temporal modeling of surface runoff in ungauged sub-catchments of Subarnarekha river basin using SWAT

MAUSAM ◽

10.54302/mausam.v72i3.1309 ◽

2021 ◽

Vol 72 (3) ◽

pp. 597-606

Author(s):

CHINMAYA PANDA ◽

DWARIKA MOHAN DAS ◽

B. C. SAHOO ◽

B. PANIGRAHI ◽

K. K. SINGH

Keyword(s):

Surface Runoff ◽

Assessment Tool ◽

Nutrient Loss ◽

Coefficient Of Determination ◽

Annual Rainfall ◽

Model Parameters ◽

Runoff Generation ◽

Temporal Modeling ◽

Spatio Temporal ◽

R Factors

In this present study, Soil and Water Assessment Tool (SWAT) embedded with ArcGIS interface has been used to simulate the surface runoff from the un-gauged sub-catchments in the upper catchment of Subarnarekha basin. Model calibration and validation were performed with the help of Sequential Uncertainty Fitting (SUFI-2) in-built in the SWAT-CUP package (SWAT Calibration Uncertainty Programs). The model was calibrated for a period from 1996 to 2008 with 3 years warm up period (1996-1998) and validated for a period of 5 years from 2009 to 2013. The model evaluation was performed by Nash - Sutcliffe coefficient (NSE), Coefficient of determination (R2) and Percentage Bias (PBIAS). The degree of uncertainty was evaluated by P and R factors. Basing upon the R2, NSE and PBIAS values respectively, of the order of 0.90, 0.90 and -12%, during calibration and 0.85, 0.83 and -15% during validation, substantiate performance of the model. All uncertainties of model parameters have been well taken by the P and R factors respectively, of the order of 0.95 and 0.77 during calibration and 0.82 and 0.87 during validation. The runoff generation from 19 sub-catchments of Adityapur catchment varies from 29.2-44.1% of the annual rainfall and average surface runoff simulated for the entire catchment is 545 mm. As the surface runoff generated in most of the sub-catchments amounts to above 30% of rainfall, it is recommended for adequate number of structural interventions at appropriate locations in the catchment to store the rainfall excess for providing irrigation, recharging groundwater and restricting the sediment and nutrient loss.

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Influence of pit removal algorithms on surface runoff simulation

Journal of the Croatian Association of Civil Engineers ◽

10.14256/jce.1543.2015 ◽

2017 ◽

Vol 69 (03) ◽

pp. 183-198

Keyword(s):

Surface Runoff ◽

Runoff Simulation

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A regional similarity-based approach for sub-daily rainfall nonparametric generation

RBRH ◽

10.1590/2318-0331.252020190054 ◽

2020 ◽

Vol 25 ◽

Author(s):

Milena Guerra de Aguilar ◽

Veber Afonso Figueiredo Costa

Keyword(s):

Daily Rainfall ◽

High Temporal Resolution ◽

Rainfall Time Series ◽

Storm Events ◽

Runoff Simulation ◽

K Nearest Neighbors ◽

Method Of Fragments ◽

Block Maxima ◽

Regional Similarity ◽

Doce River

ABSTRACT Rainfall time series with high temporal resolution are required for estimating storm events for the design of urban drainage systems, for performing rainfall-runoff simulation in small catchments and for modeling flash-floods. Nonetheless, large and continuous sub-daily rainfall samples are often unavailable. For dealing with the limited availability of high-resolution rainfall records, in both time and space, this paper explored an alternative version of the k-nearest neighbors algorithm, coupled with the method of fragments (KNN-MOF model), which utilizes a state-based logic for simulating consecutive wet days and a regionalized similarity-based approach for sampling fragments from hydrologically similar nearby stations. The proposed disaggregation method was applied to 40 rainfall gauging stations located in the São Francisco and Doce river catchments. Disaggregation of daily rainfall was performed for the durations of 60, 180 and 360 minutes. Results indicated the model presented an appropriate performance to disaggregate daily rainfall, reasonably reproducing sub-daily summary statistics. In addition, the annual block-maxima behavior, even for low exceedance probabilities, was relatively well described, although not all expected variability in the quantiles was properly summarized by the model. Overall, the proposed approach proved a sound and easy to implement alternative for simulating continuous sub-daily rainfall amounts from coarse-resolution records.

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Evaluation of Zenith Tropospheric Delay Derived from Ray-Traced VMF3 Product over the West African Region Using GNSS Observations

Advances in Meteorology ◽

10.1155/2021/8836806 ◽

2021 ◽

Vol 2021 ◽

pp. 1-14

Author(s):

Samuel Osah ◽

Akwasi A. Acheampong ◽

Collins Fosu ◽

Isaac Dadzie

Keyword(s):

West Africa ◽

Mean Squared Error ◽

Pearson Correlation ◽

Coefficient Of Determination ◽

Tropospheric Delay ◽

International Gnss Service ◽

African Region ◽

Precipitable Water Vapour ◽

Alternative Source ◽

Zenith Tropospheric Delay

The growing demand for Global Navigation Satellite System (GNSS) technology has necessitated the establishment of a vast and ever-growing network of International GNSS Service (IGS) tracking stations worldwide. The IGS provides highly accurate and highly reliable daily time-series Zenith Tropospheric Delay (ZTD) products using data from the member sites towards the use of GNSS for precise geodetic, climatological, and meteorological applications. However, if for reasons like poor internet connectivity, equipment failure, and power outages, the IGS station is inaccessible or malfunctioning, and gaps are created in the data archive resulting in degrading the quality of the ZTD and precipitable water vapour (PWV) estimation. To address this challenge as a means of providing an alternative data source to improve the continuous availability of ZTD data and as a backup data in the event that the IGS site data are missing or unavailable in West Africa, this paper compares the sitewise operational Vienna Mapping Functions 3 (VMF3) ZTD product with the IGS final ZTD product over five IGS stations in West Africa. Eight different statistical evaluation metrics, such as the mean bias (MB), mean absolute error (MAE), root mean squared error (RMSE), Pearson correlation coefficient (r), coefficient of determination (r2), refined index of agreement (IAr), Nash–Sutcliffe coefficient of efficiency (NSE), and the fraction of prediction within a factor of two (FAC2), are employed to determine the degree of agreement between the VMF3 and IGS tropospheric products. The results show that the VMF3-ZTD product performed excellently and matches very well with the IGS final ZTD product with an average MB, MAE, RMSE, r, r2, NSE, IAr, and FAC2 of 0.38 cm, 0.87 cm, 1.11 cm, 0.988, 0.976, 0.967, 0.992, and 1.00 (100%), respectively. This result is an indication that the VMF3-ZTD product is accurate enough to be used as an alternative source of ZTD data to augment the IGS final ZTD product for positioning and meteorological applications in West Africa.

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RETRIEVAL OF SEA SURFACE TEMPERATURE OVER POTERAN ISLAND WATER OF INDONESIA WITH LANDSAT 8 TIRS IMAGE: A PRELIMINARY ALGORITHM

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprsarchives-xl-2-w4-87-2015 ◽

2015 ◽

Vol XL-2/W4 ◽

pp. 87-90 ◽

Cited By ~ 6

Author(s):

M. A. Syariz ◽

L. M. Jaelani ◽

L. Subehi ◽

A. Pamungkas ◽

E. S. Koenhardono ◽

...

Keyword(s):

Surface Temperature ◽

Sea Surface Temperature ◽

Brightness Temperature ◽

Sea Surface ◽

Coefficient Of Determination ◽

Landsat 8 ◽

In Situ Data ◽

The North ◽

Thermal Band

The Sea Surface Temperature (SST) retrieval from satellites data Thus, it could provide SST data for a long time. Since, the algorithms of SST estimation by using Landsat 8 Thermal Band are sitedependence, we need to develop an applicable algorithm in Indonesian water. The aim of this research was to develop SST algorithms in the North Java Island Water. The data used are in-situ data measured on April 22, 2015 and also estimated brightness temperature data from Landsat 8 Thermal Band Image (band 10 and band 11). The algorithm was established using 45 data by assessing the relation of measured in-situ data and estimated brightness temperature. Then, the algorithm was validated by using another 40 points. The results showed that the good performance of the sea surface temperature algorithm with coefficient of determination (R2) and Root Mean Square Error (RMSE) of 0.912 and 0.028, respectively.

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