Projections of precipitation and temperature in Southern Iraq using a LARS-WG Stochastic weather generator

Khalaf, Rasul M.; Hussein, H.H.; Hassan, Waqed H.; Mohammed, Zaid M.; Nile, Basim K.

doi:10.1016/j.pce.2022.103224

Cited by 17 publications

(7 citation statements)

References 19 publications

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“…The standard deviation between the generated and observed monthly Pre signi cantly increased during months with higher Pre levels. This rise could potentially lead to a less accurate assessment of the model (Khalaf et al, 2022). The APSIM-Wheat successfully simulated three cultivars' main phenological development stages over calibration and validation (Figs.…”

Section: Discussionmentioning

confidence: 99%

A scrutiny of plasticity management in irrigated wheat systems under CMIP6 earth system models (case study: Golestan Province, Iran)

Hosseinpour,

Bagherikia,

Soughi

et al. 2024

Theor Appl Climatol

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Global wheat production has faced, and will persist in encountering many challenges. Therefore, developing a dynamic cultivation approach generated through modeling is crucial to coping with the challenges in speci c districts. The modeling can contribute to achieving global objectives of farmers' nancial independence and food security by enhancing the cropping systems. The current study aims to assess the effects of cultivars and sowing windows intricately on irrigated wheat production using the two models from Coupled Model Intercomparison Project Phase 6 (CMIP6), including ACCES-CM2 and HadGEM31-LL under two shared socioeconomic pathways (SSP245, and SSP585). A two-year on-farm experiment was conducted for parametrization and validation of the APSIM-Wheat model at two locations. The model reasonably simulated the days to anthesis, maturity, biomass production, and yield within all cultivars. The normalized root-meansquare error (RMSE) of the phenological stages was simulated and measured values were 5% and 2-4%, while the index of agreement (IOA) was in the range of 0.84-0.88 and 0.95-0.97. An acceptable agreement of the simulated biomass (RMSE = 5-7% and 0.91 − 0.78) and yield (RMSE = 6-11% and IOA = 0.70-0.94) was identi ed in the model. Afterward, the LARS-WG model generated the baseline (2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014) based on the weather data at the sites and projected the models for the near (2030-2049) and remote future (2050-2070). The models revealed that not only the average maximum and minimum temperatures will rise by 1.85°C and 1.62°C which will exacerbate the reference evapotranspiration (ET 0 ), but also the precipitation and solar radiation will reach + 58%, and + 0.25 Mj m − 2 . Our results clearly showed that precipitation volume over the growing seasons would elevate approximately two times as much as the baseline in the future, while there is a signi cant decrease in water productivity (WP) and yield from the intensive ET 0 . Based on the wheat simulation, the short-duration cultivar (Kalate) combined with the postponed planting (16-Dec) was determined as a practical alternative; nonetheless, both WP and yield signi cantly decreased by 40% and 7%, respectively (p < 0.05). In conclusion, identifying and analyzing future farming conditions (e.g., agro-climate, soil and crop management data) would provide a perception of the forthcoming scenarios. When applied, this knowledge can potentially mitigate the adverse impacts of climate change on global wheat production.* represents the statistical signi cance (p < 0.05) of the data's average compared to the baseline (1: ACCESS-CM2 and 2: HadGEM3).

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Section: Discussionmentioning

confidence: 99%

A scrutiny of plasticity management in irrigated wheat systems under CMIP6 earth system models (case study: Golestan Province, Iran)

Hosseinpour,

Bagherikia,

Soughi

et al. 2024

Theor Appl Climatol

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“…Because of a lack of available computing and human resources, it is not practical to employ all the CMIP5 GCMs to project and analyze the effects of climate change [4]. Accordingly, five models were chosen based on research done in Iraq that was best adapted to forecasting future climate change [1,11]. Future precipitation and maximum temperature estimates were created for the study using LRS-WG.…”

Section: 4long Ashton Research Station Modelmentioning

confidence: 99%

“…Furthermore, high-resolution climate forecasts are produced using the regional modeling system Producing Regional Climates for Impacts Studies (PRECIS) to determine the local-scale impacts of anticipated climatic changes [1]. Numerous scholars have examined the effects of climate change on arid and semiarid climates, [1,3,7,11,16,17] most did not consider the projection of weather variables to evaluate the meteorological drought. Mohammed and Hassan [1] and Khalaf et al [11] predicted future changes in the climate of the southern region of Iraq using five global climate models (GCMs): CSIRO-Mk3.6.0, HadGEM2-ES, CanESM2, MIROC5, and NorESM1-M, based on two emission scenarios: RCP8.5 and RCP4.5, during three selected periods: 2021-2040, 2051-2070, and 208-1100.…”

Section: Introductionmentioning

confidence: 99%

“…Numerous scholars have examined the effects of climate change on arid and semiarid climates, [1,3,7,11,16,17] most did not consider the projection of weather variables to evaluate the meteorological drought. Mohammed and Hassan [1] and Khalaf et al [11] predicted future changes in the climate of the southern region of Iraq using five global climate models (GCMs): CSIRO-Mk3.6.0, HadGEM2-ES, CanESM2, MIROC5, and NorESM1-M, based on two emission scenarios: RCP8.5 and RCP4.5, during three selected periods: 2021-2040, 2051-2070, and 208-1100. Mohammed and Scholz [7] used the UZRB as an example basin to illustrate how dry and semiarid climate conditions might affect long-term fluctuations and distributions of meteorological data on regional drought and aridity over the previous 35 years (1979-2014).…”

Section: Introductionmentioning

confidence: 99%

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Climate Change’s Impacts on Drought in Upper Zab Basin, Iraq: A Case Study

Mohammed,

Mohammed

2024

Tikrit j. eng. sci.

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Iraq has a semiarid and desert climate. Also, it is predicted to be more susceptible to global warming effects. In the present study, daily climatic data from the past and projected future of the Upper Zab Basin, located in northern Iraq and considered an example of arid and semiarid climate conditions, were simulated using LARS-WG 6.0, i.e., a stochastic weather generator. The model also estimated daily rainfall and temperature. Using the RCP4.5 and RCP8.5 main emission scenarios, the future climate throughout the current century was estimated utilizing the MIROC5, CanESM2, HadGEM2-ES, ESM1-M, and CSIRO-Mk3.6.0 general circulation models (GCMs). This estimation was performed considering the significant uncertainty of future climate estimates. The model, constructed using thirty years' worth of historical data, was validated using climate data from the Upper Zab Basin in northern Iraq (1990–2021). According to the data, the average monthly maximum temperature will decline by 2.15–6.20 °C under RCP4.5 and 1.81–6.10 °C under RCP8.5 by the end of the twenty-first era for the corresponding upstream and downstream sub-basins. Precipitation projections from all GCMs showed varying patterns. Given that some models, like CanESM2, expected a rise in precipitation, while others, like MIROC5, forecasted a future with no change in precipitation or a falling trend, which illustrates the significant level of uncertainty in precipitation forecast when only one model was utilized. Also, the downstream sub-basin suffered the most during the 1999–2000 and 2007–2008 droughts, with average RDIst values of -1.97 and -1.64, respectively. However, the upstream sub-basin had moderate to severe droughts in 1999–2000 and 2007–2008, with average RDIst values of -0.81 and -1.84, respectively. The water available in the research location will be significantly impacted by variations in rainfall and temperature.

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“…The SVE approximated models, widely used in the literature, benefit some practical assumptions and are as follows: (i) the Muskingum model, which uses observed field data; (ii) the grey‐box method, which uses the nonlinear hydraulic description and mass balance of check gates (Sharafati et al, 2021); (iii) the black‐box model, which develops linear and nonlinear models using measured data and system identification (Khan et al, 2018); (iv) the step response identification, integrator resonance model (Makar et al, 2022); (v) the neural networks, fuzzy and linear system‐based data‐driven models and pattern search methods (Khalaf et al, 2022; Moussa, 2018); and (vi) the ID model, which is the most widely used approximated model in which a linear model is introduced to the canal by dividing the canal into two parts: the uniform part that is a function of flow and backwater part with delayed inflow (Barkhordari & Shahdany, 2021). The double Q‐learning PID architecture of Carlucho et al (2019) was implemented to control ASCE's Canal 2 (Clemmens et al, 1998) by providing the canal's state‐space model as a linear model in the MATLAB environment.…”

Section: Introductionmentioning

confidence: 99%

AICSS: Automatic simulator–controller/optimizer model of open channels

Shahverdi

2023

Irrigation and Drainage

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The coupling between simulators and controllers in control systems of irrigation canals has always been a challenging issue. Consequently, simplified or approximated models of Saint‐Venant equations (SVEs) have unavoidably been used thus far. In this research, a coupled simulator–controller/optimizer model named Automatic Irrigation Canal Simulation System (AICSS) was developed to control irrigation conveyance system simulation (ICSS) using MATLAB, which allows setting input data, retrieving input or output data and opening, running and closing the ICSS model, enabling any real‐time modelling of canals with mathematical and statistical experiments as well as calibration of the discharge coefficient and Manning's roughness. To this end, several submodels were written in FORTRAN to automatically guide orders in ICSS received from MATLAB. The developed model is started by pressing the F5 key in MATLAB. To introduce the AICSS application and evaluate it, the proportional‐integral‐derivative (PID) controller was used and tested in the E1R1 Dez and Aghili East canals located in Khuzestan province (Iran), showing successful results when simulating and controlling the canals and leading to maximum and average water depth errors of 2.3% and 0.07% in the E1R1 canal and 5.3% and 0.36% in the Aghili East canal, respectively.

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Projections of precipitation and temperature in Southern Iraq using a LARS-WG Stochastic weather generator

Cited by 17 publications

References 19 publications

A scrutiny of plasticity management in irrigated wheat systems under CMIP6 earth system models (case study: Golestan Province, Iran)

A scrutiny of plasticity management in irrigated wheat systems under CMIP6 earth system models (case study: Golestan Province, Iran)

Climate Change’s Impacts on Drought in Upper Zab Basin, Iraq: A Case Study

AICSS: Automatic simulator–controller/optimizer model of open channels

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