scholarly journals Simulation of river discharge in ungauged catchments by forcing GLDAS products to a hydrological model (a case study: Polroud basin, Iran)

2019 ◽  
Vol 20 (1) ◽  
pp. 277-286
Author(s):  
Hadis Pakdel Khasmakhi ◽  
Majid Vazifedoust ◽  
Safar Marofi ◽  
Abdollah Taheri Tizro
Keyword(s):  
River Discharge ◽  
Good Alternative ◽  
Observation Data ◽  
Ungauged Catchments ◽  
Discharge Data ◽  
The North ◽  
Temperature And Precipitation ◽  
North Of Iran ◽  
Land Data Assimilation ◽  
Land Data Assimilation System

Abstract Due to unavailability of sufficient discharge data for many rivers, an appropriate approach is required to provide accurate data for estimating discharge in ungauged watersheds. In this study, Global Land Data Assimilation System (GLDAS) datasets were integrated with Hydrologic Engineering Center Hydrologic Modeling System (HEC-HMS) to simulate the outlet river discharge in Polroud watershed, located in the North of Iran. Temperature and precipitation products generated by GLDAS were calibrated using regression analysis based on observation data for the period of 2004–2006. Then, river discharge was simulated by using HEC-HMS based on two different datasets (GLDAS meteorological product and gauged data) on the scale of the basin for the same period. The results clearly indicated that the forcing of GLDAS data into HEC-HMS model leads to promising results with acceptable correlation with observed data. Although, in comparison with direct GLDAS runoff products, the proposed approach improved the accuracy of river discharge, the problem of underestimation still reduces the expected accuracy. Because of global accessibility, GLDAS datasets would be a good alternative in ungauged or poorly gauged watersheds.

scholarly journals Blending SMAP, Noah and In Situ Soil Moisture Using Multiple Methods

2019 ◽  
Author(s):  
Ning Zhang ◽  
Steven M. Quiring ◽  
Trent W. Ford
Keyword(s):  
Soil Moisture ◽  
Error Variance ◽  
In Situ Measurements ◽  
The North ◽  
Sample Density ◽  
Land Data Assimilation ◽  
Land Data Assimilation System ◽  
Data Assimilation System ◽  
Simple Average

Abstract. Soil moisture can be obtained from in-situ measurements, satellite observations, and model simulations. This study evaluates different methods of combining model, satellite, and in-situ soil moisture data to provide an accurate and spatially-continuous soil moisture product. Three independent soil moisture datasets are used, including an in situ-based product that uses regression kriging (RK) with precipitation, SMAP L4 soil moisture, and model-simulated soil moisture from the Noah model as part of the North American Land Data Assimilation System. Triple collocation (TC), relative error variance (REV), and RK were used to estimate the error variance of each parent dataset, based on which the least squares weighting (LSW) was applied to blend the parent datasets. These results were also compared with that using simple average (AVE). The results indicated no significant differences between blended soil moisture datasets using errors estimated from TC, REV or RK. Moreover, the LSW did not outperform AVE. The SMAP L4 data have a significant negative bias (−18 %) comparing with in-situ measurements, and in-situ measurements are valuable for improving the accuracy of hybrid results. In addition, datasets using anomalies and percentiles have smaller errors than using volumetric water content, mainly due to the reduced bias. Finally, the in situ-based soil moisture and the simple-averaged product from in situ-based and Noah soil moisture are the two optimal datasets for soil moisture mapping. The in situ-based product performs better when the sample density is high, while the simple-averaged product performs better when the station density is low, or measurement sites are less representative.

scholarly journals Modelling and Forecasting of Ikpoba River Discharge in the Niger Delta Region using Adaptive Neuro-Fuzzy Inference System (ANFIS)

2020 ◽  
Vol 4 (2) ◽  
pp. 432-449
Author(s):  
S. Egbiki ◽  
J. O. Ehiorobo ◽  
O. C. Izinyon
Keyword(s):  
Climate Change ◽  
Fuzzy Inference System ◽  
River Discharge ◽  
Fuzzy Inference ◽  
Inference System ◽  
Discharge Data ◽  
Acceptable Error ◽  
Temperature And Precipitation ◽  
Discharge Temperature ◽  
Neuro Fuzzy

In this study, the discharge of Ikpoba River was modelled and forecasted using adaptive neuro-fuzzy inference system (ANFIS). The river daily discharge, temperature and precipitation data sets from year 1991 to 1995 were used. In applying the ANFIS, five models stages; model-1, model-2, model-3, model-4 and model-5 were created using MATLAB. Model-1 to 4 were created using only the river discharge data, while model-5 was created by incorporating temperature and precipitation to cater for the effect of climate change into model-4. Five performance evaluation criteria, coefficient of correlation (R), coefficient of determination (R2), mean square error (MSE), modelling efficiency (E) and index of agreement (IOA) were used for comparative analysis. The results showed that though Model 1 to 4 were able to predict the river discharge accurately, model-5 (when the effect of climate change was incorporated) performed better than the other four models with only discharge data. The training phase in model-5 showed an over-estimation of 0.043% of the observed target output sets while an over-estimation of 0.044% was observed in the testing phase. These are within acceptable error tolerance of +/-10% for data validation. This information is useful for integrated water resources planning and management.

STREAMFLOW PREDICTION IN UNGAUGED CATCHMENTS IN THE EAST COAST OF PENINSULAR MALAYSIA USING MULTIVARIATE STATISTICAL TECHNIQUES

2016 ◽  
Vol 78 (6-12) ◽  
Author(s):  
Salaudeen Abdul Razaq ◽  
Tarmizi Ismail ◽  
Arien Heryansyah ◽  
Umar Faruk L Awan ◽  
Mahiuddin Alamgir ◽  
...  
Keyword(s):  
River Discharge ◽  
East Coast ◽  
Risk Mitigation ◽  
Peninsular Malaysia ◽  
Regression Equations ◽  
Streamflow Prediction ◽  
Ungauged Catchments ◽  
Land Use Pattern ◽  
Multivariate Statistical ◽  
Discharge Data

The east coast of Peninsular Malaysia is one of the most vulnerable regions of Malaysia to hydrological disasters, which is believed to become more vulnerable due to climate change. Studies to have better understandings of the hydrological processes in the region are therefore, of paramount importance for disaster risk mitigation. However, unavailability of long-term river discharge data is one of the major constraints of hydrologic studies in the area. The major objective of this study is to predict river discharge in ungauged river basins in the study area. For this purpose, a set of multiple linear regression equations and exponential functions have been developed, which are expressed in the forms of multivariate equations. Available streamflow data along with other catchment characteristics from gauged catchments were used to develop the equations and were subsequently applied to the poorly gauged or ungauged catchments within the study area for prediction of streamflow. In this present study, 4 to 7 explanatory variables were selected as the input variables, which comprise of climatic, geomorphologic, geographic characteristics, soil properties, land use pattern and land cover of the area. Ten flow metrics as maximum, 0.05, 0.10, 0.25, 0.50, 0.75, 0.90, and 0.95, mean and minimum were therefore predicted. Thus, the results of the developed multivariate equations revealed the model to be capable of   predicting the desired flow metrics at ungauged catchments in the area under consideration with reasonable accuracy.

scholarly journals Similarity Assessment of Land Surface Model Outputs in the North American Land Data Assimilation System

2017 ◽  
Vol 53 (11) ◽  
pp. 8941-8965 ◽  
Author(s):  
Sujay V. Kumar ◽  
Shugong Wang ◽  
David M. Mocko ◽  
Christa D. Peters-Lidard ◽  
Youlong Xia
Keyword(s):  
Data Assimilation ◽  
North American ◽  
Land Surface ◽  
Land Surface Model ◽  
Surface Model ◽  
The North ◽  
Land Data Assimilation ◽  
Land Data Assimilation System ◽  
Data Assimilation System ◽  
Assimilation System
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