scholarly journals Rainfall Prediction with AMSR-E Soil Moisture Products Using SM2RAIN and Nonlinear Autoregressive Networks with Exogenous Input (NARX) for Poorly Gauged Basins: Application to the Karkheh River Basin, Iran

2018 ◽  
Author(s):  
Majid Fereidoon ◽  
Manfred Koch
Keyword(s):  
Soil Moisture ◽  
River Basin ◽  
Land Surface ◽  
Daily Rainfall ◽  
Rainfall Data ◽  
Rainfall Prediction ◽  
Nonlinear Autoregressive ◽  
Karkheh River Basin ◽  
The Karkheh River ◽  
Narx Model

Accurate estimates of daily rainfall are essential for understanding and modeling the physical processes involved in the interaction between the land surface and the atmosphere. In this study, daily satellite soil moisture observations from the Advanced Microwave Scanning Radiometer - Earth Observing System (AMSR-E) generated by implementing the standard NASA- algorithm are employed for estimating rainfall, firstly, through the use of recently developed approach, SM2RAIN (Brocca et al., 2013) and, secondly, the nonlinear autoregressive network with exogenous inputs (NARX) neural modelling at five climate stations in the Karkheh river basin (KRB), located in southwest Iran. In the SM2RAIN method, the period 1 January 2003 to 31 December 2005 is used for the calibration of algorithm and the remaining 9 months from 1 January 2006 to 30 September 2006 is used for the validation of the rainfall estimates. In the NARX model, the full study period is split into a training (1 January 2003 to 31 September 2005) and a testing (1 September 2005 to 30 September 2006) stage. For the prediction of the rainfall as the desired target (output), relative soil moisture changes from AMSR-E and measured air temperature time series are chosen as exogenous (external) inputs in NARX. The quality of the estimated rainfall data is evaluated by comparing it with observed rainfall data at the five rain gauges in terms of the correlation coefficient R, the RMSE and the statistical bias. For the SM2RAIN method, R ranges between 0.44 and 0.9 for all stations, whereas for the NARX- model the values are generally slightly lower. Moreover, the values of the bias for each station indicate that although SM2RAIN is likely to underestimate large rainfall intensities, due to the known effect of soil moisture saturation, its biases are somewhat lower than those of NARX. In conclusion, the results of the present study show that with the use of AMSR-E soil moisture products in the physically based SM2RAIN- algorithm as well as in the NARX neural network, rainfall for poorly gauged regions can be fairly predicted.

scholarly journals Rainfall Prediction with AMSR–E Soil Moisture Products Using SM2RAIN and Nonlinear Autoregressive Networks with Exogenous Input (NARX) for Poorly Gauged Basins: Application to the Karkheh River Basin, Iran

Water ◽  
2018 ◽  
Vol 10 (7) ◽  
pp. 964 ◽  
Author(s):  
Majid Fereidoon ◽  
Manfred Koch
Keyword(s):  
Soil Moisture ◽  
River Basin ◽  
Land Surface ◽  
Rainfall Data ◽  
Coefficient Of Determination ◽  
Climate Data ◽  
Nonlinear Autoregressive ◽  
Karkheh River Basin ◽  
The Karkheh River ◽  
Narx Model

Accurate estimates of daily rainfall are essential for understanding and modeling the physical processes involved in the interaction between the land surface and the atmosphere. In this study, daily satellite soil moisture observations from the Advanced Microwave Scanning Radiometer–Earth Observing System (AMSR–E) generated by implementing the standard National Aeronautics and Space Administration (NASA) algorithm are employed for estimating rainfall, firstly, through the use of recently developed approach, SM2RAIN and, secondly, the nonlinear autoregressive network with exogenous inputs (NARX) neural modelling at five climate stations in the Karkheh river basin (KRB), located in south-west Iran. In the SM2RAIN method, the period 1 January 2003 to 31 December 2005 is used for the calibration of algorithm and the remaining 9 months from 1 January 2006 to 30 September 2006 is used for the validation of the rainfall estimates. In the NARX model, the full study period is split into training (1 January 2003 to 31 September 2005) and testing (1 September 2005 to 30 September 2006) stages. For the prediction of the rainfall as the desired target (output), relative soil moisture changes from AMSR–E and measured air temperature time series are chosen as exogenous (external) inputs in NARX. The quality of the estimated rainfall data is evaluated by comparing it with observed rainfall data at the five rain gauges in terms of the coefficient of determination R2, the RMSE and the statistical bias. For the SM2RAIN method, R2 ranges between 0.32 and 0.79 for all stations, whereas for the NARX- model the values are generally slightly lower. Moreover, the values of the bias for each station indicate that although SM2RAIN is likely to underestimate large rainfall intensities, due to the known effect of soil moisture saturation, its biases are somewhat lower than those of NARX. Moreover, Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks–Climate Data Record (PERSIANN–CDR) is employed to evaluate its potential for predicting the ground-based observed station rainfall, but it is found to work poorly. In conclusion, the results of the present study show that with the use of AMSR–E soil moisture products in the physically based SM2RAIN algorithm as well as in the NARX neural network, rainfall for poorly gauged regions can be predicted satisfactorily.

scholarly journals Bias correction capabilities of quantile mapping methods for rainfall and temperature variables

2020 ◽  
Author(s):  
Maedeh Enayati ◽  
Omid Bozorg-Haddad ◽  
Javad Bazrafshan ◽  
Somayeh Hejabi ◽  
Xuefeng Chu
Keyword(s):  
River Basin ◽  
Bias Correction ◽  
Correction Method ◽  
Quantile Mapping ◽  
Topographic Features ◽  
Temperature Variable ◽  
Transformation Functions ◽  
Karkheh River Basin ◽  
The Karkheh River

Abstract This study aims to conduct a thorough investigation to compare the abilities of QM techniques as a bias correction method for the raw outputs from GCM/RCM combinations. The Karkheh River basin in Iran was selected as a case study, due to its diverse topographic features, to test the performances of the bias correction methods under different conditions. The outputs of two GCM/RCM combinations (ICHEC and NOAA-ESM) were acquired from the CORDEX dataset for this study. The results indicated that the performances of the QMs varied, depending on the transformation functions, parameter sets, and topographic conditions. In some cases, the QMs' adjustments even made the GCM/RCM combinations' raw outputs worse. The result of this study suggested that apart from DIST, PTF:scale, and SSPLIN, the rest of the considered QM methods can provide relatively improved results for both rainfall and temperature variables. It should be noted that, according to the results obtained from the diverse topographic conditions of the sub-basins, the empirical quantiles (QUANT) and robust empirical quantiles (RQUANT) methods proved to be excellent options to correct the bias of rainfall data, while all bias correction methods, with the notable exceptions of performed PTF:scale and SSPLIN, performed relatively well for the temperature variable.

Streamflow regionalization using a similarity approach in ungauged basins: Application of the geo-environmental signatures in the Karkheh River Basin, Iran

CATENA ◽  
2019 ◽  
Vol 182 ◽  
pp. 104128 ◽  
Author(s):  
Bahram Choubin ◽  
Karim Solaimani ◽  
Fereidoun Rezanezhad ◽  
Mahmoud Habibnejad Roshan ◽  
Arash Malekian ◽  
...  
Keyword(s):  
River Basin ◽  
Ungauged Basins ◽  
Karkheh River Basin ◽  
The Karkheh River

Food and water scenarios for the Karkheh River Basin, Iran

2010 ◽  
Vol 35 (4) ◽  
pp. 409-424 ◽  
Author(s):  
Sara Marjanizadeh ◽  
Charlotte de Fraiture ◽  
Willibald Loiskandl
Keyword(s):  
River Basin ◽  
Karkheh River Basin ◽  
The Karkheh River

scholarly journals Water productivity of irrigated wheat and maize in the Karkheh River basin of Iran

2009 ◽  
Vol 59 (3) ◽  
pp. 264-276 ◽  
Author(s):  
A. S. Qureshi ◽  
T. Oweis ◽  
P. Karimi ◽  
J. Porehemmat
Keyword(s):  
River Basin ◽  
Water Productivity ◽  
Irrigated Wheat ◽  
Karkheh River Basin ◽  
The Karkheh River

Simulation of hydrological processes and effects of engineering projects on the Karkheh River Basin and its wetland using SWAT2009

2015 ◽  
Vol 374 ◽  
pp. 144-153 ◽  
Author(s):  
Yasser Ghobadi ◽  
Biswajeet Pradhan ◽  
Gholam Abbas Sayyad ◽  
Keivan Kabiri ◽  
Yashar Falamarzi
Keyword(s):  
River Basin ◽  
Hydrological Processes ◽  
Engineering Projects ◽  
Karkheh River Basin ◽  
The Karkheh River

Extreme streamflow drought in the Karkheh river basin (Iran): probabilistic and regional analyses

2014 ◽  
Vol 76 (1) ◽  
pp. 327-346 ◽  
Author(s):  
Reza Zamani ◽  
Hossein Tabari ◽  
Patrick Willems
Keyword(s):  
River Basin ◽  
Regional Analyses ◽  
Karkheh River Basin ◽  
The Karkheh River

scholarly journals Simulation of Daily Rainfall Data using Articulated Weather Generator Model for Seasonal Prediction of ENSO-Affected Zones in Indonesia

2020 ◽  
Vol 52 (2) ◽  
pp. 143
Author(s):  
Andung Bayu Sekaranom ◽  
Emilya Nurjani ◽  
Rika Harini ◽  
Andi Syahid Muttaqin
Keyword(s):  
Large Scale ◽  
Southern Oscillation ◽  
Daily Rainfall ◽  
Regression Function ◽  
Seasonal Prediction ◽  
Weather Generator ◽  
Rainfall Data ◽  
Rainfall Simulation ◽  
Rainfall Prediction ◽  
Student’S T

Synthetic rainfall simulation using weather generator models is commonly used as a substitute at locations with incomplete or short rainfall data. It incorporates a method that can be developed into forecasts of future rainfall. This study was designed to modify a rainfall prediction system based on the principles of weather generator models and to test the validity of the modelling results. It processed the data collected from eight rain stations in zones affected by El-Nino Southern Oscillation (ENSO). A large-scale predictor, that is, SST prediction data in the Nino 3.4 region over the Pacific Ocean was used as the influencing variable in projecting rainfall for the following six months after the predefined dates. Rainfall data from weather stations and SST in 1960-2000 were analyzed to identify the effects of ENSO and build a statistical model based on the regression function. Meanwhile, the model was validated using the data from 2001 to 2007 by backtesting six months in a row. The analysis results showed that the model could simulate both low rainfall in the dry season and high one in the rainy season. Validation by the student's t-test confirmed that the six-month synthetic rain data at nearly all observed stations was homogenous. For this reason, the developed model can be potentially used as one of the season prediction systems.  

scholarly journals Spatial Heterogeneity in the Occurrence Probability of Rainstorms over China

Entropy ◽  
2018 ◽  
Vol 20 (12) ◽  
pp. 958
Author(s):  
Yan-Fang Sang
Keyword(s):  
Spatial Heterogeneity ◽  
River Basin ◽  
Yangtze River Basin ◽  
Daily Rainfall ◽  
Aridity Index ◽  
Rainfall Data ◽  
Occurrence Probability ◽  
Pearl River Basin ◽  
Entropy Index ◽  
Huai River

Detecting the spatial heterogeneity in the potential occurrence probability of water disasters is a foremost and critical issue for the prevention and mitigation of water disasters. However, it is also a challenging task due to the lack of effective approaches. In the article, the entropy index was employed and those daily rainfall data at 520 stations were used to investigate the occurrences of rainstorms in China. Results indicated that the entropy results were mainly determined by statistical characters (mean value and standard deviation) of rainfall data, and can categorically describe the spatial heterogeneity in the occurrence of rainstorms by considering both their occurrence frequencies and magnitudes. Smaller entropy values mean that rainstorm events with bigger magnitudes were more likely to occur. Moreover, the spatial distribution of entropy values kept a good relationship with the hydroclimate conditions, described by the aridity index. In China, rainstorms are more to likely occur in the Pearl River basin, Southeast River basin, lower-reach of the Yangtze River basin, Huai River basin, and southwest corner of China. In summary, the entropy index can be an effective alternative for quantifying the potential occurrence probability of rainstorms. Four thresholds of entropy value were given to distinguish the occurrence frequency of rainstorms as five levels: very high, high, mid, low and very low, which can be a helpful reference for the study of daily rainstorms in other basins and regions.

Sign in / Sign up

Export Citation Format

Share Document