Abstract
River flow or runoff is an important water flux that can pose great threats to water security because of changes in its timing, magnitude, and seasonality. In this study, runoff variations in two different climate conditions, including the semi-arid and humid climates, were assessed using an integrated method based on the LARS-WG and Long Short-term Memory (LSTM) models. Runoff variations were simulated for the base period of 2001–2020 and it was forecasted for the future period of 2021–2040. Climatic parameters were evaluated using the LARS-WG statistical model for the selected stations under the RCPs scenarios. Then, outputs obtained under the RCPs scenarios, including minimum temperature, maximum temperature, precipitation, and sunshine hours, were considered as the input of the LSTM model. Results showed that the amount of mean runoff in spring for semi-arid areas and in spring and winter for humid areas will decrease in the next twenty years compared to the base period. For other seasons in both climates, the amount of runoff will increase. The maximum rainfall increase under the RCP8.5 scenario was 66.66 mm for the humid region and 30.06 mm for the semi-arid region compared to the base period. Also, the maximum temperature increase for the semi-arid region was 1.18 °C under the RCP8.5 scenario, and for the humid region was 1.07 °C under the RCP2.6 scenario. Results showed that the LSTM method can successfully model the future river flow values and river flow related to the previous day has a significant impact on the modeling process.
Similar content being viewed by others
Availability of Data and Material
The used datasets are obtained from Iranian Meteorological Organization and Regional Water.
Code Availability
Not applicable.
References
Berghuijs WR, Larsen JR, Van Emmerik TH, Woods RA (2017) A global assessment of runoff sensitivity to changes in precipitation, potential evaporation, and other factors. Water Resour Res 53(10):8475–8486
Bilhan O, Emiroglu ME, Kisi O (2011) Use of artificial neural networks for prediction of discharge coefficient of triangular labyrinth side weir in curved channels. Adv Eng Softw 42(4):208–214
Cai J, Varis O, Yin H (2017) China’s water resources vulnerability: a spatio-temporal analysis during 2003–2013. J Clean Prod 142:2901–2910
Cheng CT, Chau KW (2004) Flood control management system for reservoirs. Environ Modell Softw 19(12):1141–1150
Chung J, Gulcehre C, Cho K, Bengio Y (2015) Gated feedback recurrent neural networks. Int Conf Mach Learn 37:2067–2075
Dumka BB, Kumar P (2021) Modeling rainfall-runoff using Artificial Neural Network (ANNs) and Wavelet based ANNs (WANNs) for Haripura Dam, Uttarakhand. Indian J Ecol 48(1):271–274
Eray O, Mert C, Kisi O (2018) Comparison of multi-gene genetic programming and dynamic evolving neural-fuzzy inference system in modeling pan evaporation. Hydrol Res 49(4):1221–1233
Fotovatikhah F, Herrera M, Shamshirband S, Chau KW, Faizollahzadeh Ardabili S, Piran MJ (2018) Survey of computational intelligence as basis to big flood management: Challenges, research directions and future work. Eng Appl Comput Fluid Mech 12(1):411–437
Ghasempour R, Azamathulla HM, Roushangar K (2021) EEMD-and VMD-based hybrid GPR models for river streamflow point and interval predictions. Water Supply 21(7):3960–3975
Ghasempour R, Roushangar K (2022) The potential of integrated hybrid data processing techniques for successive-station streamflow prediction. Soft Comput 4:1–14
Ghorbani MA, Salmasi F, Saggi MK, Bhatia AS, Kahya E, Norouzi R (2020) Deep learning under H2O framework: A novel approach for quantitative analysis of discharge coefficient in sluice gates. J Hydroinform 22(6):1603–1619
Graves A, Schmidhuber J (2005) Framewise phoneme classification with bidirectional LSTM and other neural network architectures. Neural Net 18(5–6):602–610
Hagan MT, Demuth HB, Beale M (1997) Neural network design. PWS Publishing Co
Haghiabi AH, Parsaie A, Ememgholizadeh S (2018) Prediction of discharge coefficient of triangular labyrinth weirs using Adaptive Neuro Fuzzy Inference System. Alex Eng J 57(3):1773–1782
Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9(8):1735–1780
Houshyar M, Sobhani B, Hosseini SA (2018) Future Projection of maximum temperature in Urmia through downscaling output of Canesm2 Model. Geogr Plan 63(22):325–305
Hu C, Wu Q, Li H, Jian S, Li N, Lou Z (2018) Deep learning with a long short-term memory networks approach for rainfall-runoff simulation. Water 10(11):1543
Karami H, Karimi S, Bonakdari H, Shamshirband S (2018) Predicting discharge coefficient of triangular labyrinth weir using extreme learning machine, artificial neural network and genetic programming. Neural Comput Appl 29(11):983–989
Karimi M, Nabizadeh A (2017) Evaluating the effects of climate change on the climatic parameters of the Urmia Lake catchment during the years 2011–2040 using the WG-Lars model. J Geogr Reg Plan 22(65):285–265
Khosravi K, Golkarian A, Tiefenbacher JP (2022) Using optimized deep learning to predict daily streamflow: A comparison to common machine learning algorithms. Water Resour Manag 36(2):699–716
Maurer EP, Duffy PB (2005) Uncertainty in projections of streamflow changes due to climate change in California. Geophys Res Lett 32(3):L03704
Mekanik F, Imteaz MA, Talei A (2016) Seasonal rainfall forecasting by adaptive network-based fuzzy inference system (ANFIS) using large scale climate signals. Clim Dyn 46(9):3097–3111
Mohammadlou M, Haqizadeh A, Zainiwand H, Tahmasebipour N (2015) Evaluating the effects of climate change on the trend of temperature and rainfall changes in the Barandozchai watershed in West Azarbaijan province using atmospheric general circulation models. Geographical Space 16(56):151–168
Molajou A, Nourani V, Afshar A, Khosravi M, Brysiewicz A (2021) Optimal design and feature selection by genetic algorithm for emotional artificial neural network (EANN) in rainfall-runoff modeling. Water Resour Manag 35(8):2369–2384
Moradian S, Torabi Haghighi A, Asadi M, Mirbagheri SA (2022) Future changes in precipitation over northern europe based on a multi-model ensemble from CMIP6: Focus on Tana River Basin. Water Resour Manag 1–17
Perez-Alarcon A, Garcia-Cortes D, Fernandez-Alvarez JC, Martinez-Gonzalez Y (2022) Improving monthly rainfall forecast in a watershed by combining neural networks and autoregressive models. Environ Process 9(3):53
Racsko P, Szeidl L, Semenov M (1991) A serial approach to local stochastic weather models. Ecol Modell 57(1–2):27–41
Roy DK (2021) Long short-term memory networks to predict one-step ahead reference evapotranspiration in a subtropical climatic zone. Environ Process 8:911–941
Semenov MA, Brooks RJ (1999) Spatial interpolation of the LARS-WG stochastic weather generator in Great Britain. Clim Res 11(2):137–148
Semenov MA, Brooks RJ, Barrow EM, Richardson CW (1998) Comparison of the WGEN and LARS-WG stochastic weather generators for diverse climates. Clim Res 10(2):95–107
Silakhori E, Dahmardeh Ghaleno MR, Meshram SG, Alvandi E (2022) To assess the impacts of climate change on runoff in Golestan Province. Iran Nat Hazards 112(1):281–300
Singh P, Bengtsson L (2005) Impact of warmer climate on melt and evaporation for the rainfed, snowfed and glacierfed basins in the Himalayan region. J Hydrol 300(1–4):140–154
Stocker T (Ed.) (2014) Climate change 2013: the physical science basis: Working Group I contribution to the Fifth assessment report of the Intergovernmental Panel on Climate Change. Cambridge University press
Stocker TF, Qin D, Plattner GK (2013) Intergocernmental Panel on Climate Change. Summary for Policymakers. Climate change (2013) the physical science basis contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. UK and New York, NY, Cambridge
Sun G, Peng F, Mu M (2017) Uncertainty assessment and sensitivity analysis of soil moisture based on model parameter errors–Results from four regions in China. J Hydrol 555:347–360
Winter JM, Yeh PJF, Fu X, Eltahir EA (2015) Uncertainty in modeled and observed climate change impacts on A merican M idwest hydrology. Water Resour Res 51(5):3635–3646
Xiao C, Chen N, Hu C, Wang K, Gong J, Chen Z (2019) Short and mid-term sea surface temperature prediction using time-series satellite data and LSTM-AdaBoost combination approach. Remote Sens Environ 233:111358
Yaseen ZM, Jaafar O, Deo RC, Kisi O, Adamowski J, Quilty J, El-Shafie A (2016) Stream-flow forecasting using extreme learning machines: a case study in a semi-arid region in Iraq. J Hydrol 542:603–614
Zhang X, Tang Q, Liu X, Leng G, Di C (2018) Nonlinearity of runoff response to global mean temperature change over major global river basins. Geophys Res Lett 45(12):6109–6116
Author information
Authors and Affiliations
Contributions
Kiyoumars Roushangar: Project administration, Methodology, Conceptualization, Supervision, Review & Editing. Sadegh Abdelzad: Writing, Investigation, Formal analysis, Data Curation.
Corresponding author
Ethics declarations
Ethics Approval
Not applicable.
Consent to Participate
Not applicable.
Consent for Publication
Not applicable.
Conflicts of Interest/Competing Interests
The authors declare no conflicts of interest / competing interests.
Organizations.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Roushangar, K., Abdelzad, S. River Flow Modeling in Semi-Arid and Humid Regions Using an Integrated Method Based on LARS-WG and LSTM Models. Water Resour Manage 37, 3813–3831 (2023). https://doi.org/10.1007/s11269-023-03527-8
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11269-023-03527-8