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Bahareh Kamali
Leibniz Centre for Agricultural Landscape Research, Eberswalder Straße 84, 15374, Müncheberg, Germany

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Journal article
Published: 16 March 2021 in European Journal of Agronomy
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Crop models were originally developed for application at the field scale but are increasingly used to assess the impact of climate and/or agronomic practices on crop growth and yield and water dynamics at larger scales. This raises the question of how data aggregation approaches affect outputs when using crop models at large spatial scales. This study investigates how input and output data aggregation affected simulated rainfed and irrigated potato yield and irrigation water requirement (IWR) across potato production areas in Tasmania, Australia. First, the yield and IWR with aggregated model inputs at 15, 25 and 40 km resolutions (input aggregation) was simulated. Second, simulated model outputs generated with high-resolution input data were aggregated to 15, 25 and 40 km resolutions (output aggregation) and compared to the corresponding yield and IWR with simulations based on input data aggregation. Finally, the differences (D) (DY and DIWR for yield and IWR, respectively) between grids using input and output aggregation were evaluated. The results indicate that the effect of input and output data aggregation on yield depends on water-driven factors including plant available water capacity (PAWC), rainfall and irrigation. Maximum D values were found for rainfed yield (4.4 t ha−1) and IWR (137 mm). DY variations were correlated with the differences of PAWC caused by data aggregation in 82 % of potato production areas. Differences between aggregation methods were reduced when growing season rainfall increased. We conclude that PAWC and the source of water (rainfall or rainfall + irrigation) explained the larger errors associated with the input and output data aggregation on simulated potato yield and IWR. Future studies should consider the data aggregation method in their assessment to minimize errors and therefore produce higher quality advice or farming decisions.

ACS Style

Jonathan J. Ojeda; Ehsan Eyshi Rezaei; Tomas A. Remenyi; Heidi A. Webber; Stefan Siebert; Holger Meinke; Mathew A. Webb; Bahareh Kamali; Rebecca M.B. Harris; Darren B. Kidd; Caroline L. Mohammed; John McPhee; Jose Capuano; Frank Ewert. Implications of data aggregation method on crop model outputs – The case of irrigated potato systems in Tasmania, Australia. European Journal of Agronomy 2021, 126, 126276 .

AMA Style

Jonathan J. Ojeda, Ehsan Eyshi Rezaei, Tomas A. Remenyi, Heidi A. Webber, Stefan Siebert, Holger Meinke, Mathew A. Webb, Bahareh Kamali, Rebecca M.B. Harris, Darren B. Kidd, Caroline L. Mohammed, John McPhee, Jose Capuano, Frank Ewert. Implications of data aggregation method on crop model outputs – The case of irrigated potato systems in Tasmania, Australia. European Journal of Agronomy. 2021; 126 ():126276.

Chicago/Turabian Style

Jonathan J. Ojeda; Ehsan Eyshi Rezaei; Tomas A. Remenyi; Heidi A. Webber; Stefan Siebert; Holger Meinke; Mathew A. Webb; Bahareh Kamali; Rebecca M.B. Harris; Darren B. Kidd; Caroline L. Mohammed; John McPhee; Jose Capuano; Frank Ewert. 2021. "Implications of data aggregation method on crop model outputs – The case of irrigated potato systems in Tasmania, Australia." European Journal of Agronomy 126, no. : 126276.

Primary research article
Published: 06 July 2020 in Global Change Biology
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Smallholder farmers in sub‐Saharan Africa (SSA) currently grow rainfed maize with limited inputs including fertilizer. Climate change may exacerbate current production constraints. Crop models can help quantify the potential impact of climate change on maize yields, but a comprehensive multi‐model assessment of simulation accuracy and uncertainty in these low‐input systems is currently lacking. We evaluated the impact of varying [CO2], temperature and rainfall conditions on maize yield, for different nitrogen (N) inputs (0, 80, 160 kg N ha‐1) for five environments in SSA, including cool sub‐humid Ethiopia, cool semi‐arid Rwanda, hot sub‐humid Ghana and hot semi‐arid Mali and Benin using an ensemble of 25 maize models. Models were calibrated with measured grain yield, plant biomass, plant N, leaf area index, harvest index and in‐season soil water content from two‐year experiments in each country to assess their ability to simulate observed yield. Simulated responses to climate change factors were explored and compared between models. Calibrated models reproduced measured grain yield variations well with average rRMSE of 26%, although uncertainty in model prediction was substantial (CV = 28%). Model ensembles gave greater accuracy than any model taken at random. Nitrogen fertilization controlled the response to variations in [CO2], temperature and rainfall. Without N fertilizer input, maize (i) benefited less from an increase in atmospheric [CO2], (ii) was less affected by higher temperature or decreasing rainfall and (iii) was more affected by increased rainfall because N leaching was more critical. The model inter‐comparison revealed that simulation of daily soil N supply and N leaching plays a crucial role in simulating climate change impacts for low‐input systems. Climate change and N input interactions have strong implications for the design of robust adaptation practices across SSA, because the impact of climate change will be modified if farmers intensify maize production with more mineral fertilizer.

ACS Style

Gatien N. Falconnier; Marc Corbeels; Kenneth J. Boote; François Affholder; Myriam Adam; Dilys S. MacCarthy; Alex C. Ruane; Claas Nendel; Anthony M. Whitbread; Éric Justes; Lajpat R. Ahuja; Folorunso M. Akinseye; Isaac N. Alou; Kokou A. Amouzou; Saseendran S. Anapalli; Christian Baron; Bruno Basso; Frédéric Baudron; Patrick Bertuzzi; Andrew J. Challinor; Yi Chen; Delphine Deryng; Maha Elsayed; Babacar Faye; Thomas Gaiser; Marcelo Galdos; Sebastian Gayler; Edward Gerardeaux; Michel Giner; Brian Grant; Gerrit Hoogenboom; Esther S. Ibrahim; Bahareh Kamali; Kurt Christian Kersebaum; Soo‐Hyung Kim; M Van Der Laan; Louise Leroux; Jon I. Lizaso; Bernardo Maestrini; Elizabeth A. Meier; Fasil Mequanint; Alain Ndoli; Cheryl H. Porter; Eckart Priesack; Dominique Ripoche; Tesfaye S. Sida; Upendra Singh; Ward N. Smith; Amit Srivastava; Sumit Sinha; Fulu Tao; Peter J. Thorburn; Dennis Timlin; Bouba Traore; Tracy Twine; Heidi Webber. Modelling climate change impacts on maize yields under low nitrogen input conditions in sub‐Saharan Africa. Global Change Biology 2020, 26, 5942 -5964.

AMA Style

Gatien N. Falconnier, Marc Corbeels, Kenneth J. Boote, François Affholder, Myriam Adam, Dilys S. MacCarthy, Alex C. Ruane, Claas Nendel, Anthony M. Whitbread, Éric Justes, Lajpat R. Ahuja, Folorunso M. Akinseye, Isaac N. Alou, Kokou A. Amouzou, Saseendran S. Anapalli, Christian Baron, Bruno Basso, Frédéric Baudron, Patrick Bertuzzi, Andrew J. Challinor, Yi Chen, Delphine Deryng, Maha Elsayed, Babacar Faye, Thomas Gaiser, Marcelo Galdos, Sebastian Gayler, Edward Gerardeaux, Michel Giner, Brian Grant, Gerrit Hoogenboom, Esther S. Ibrahim, Bahareh Kamali, Kurt Christian Kersebaum, Soo‐Hyung Kim, M Van Der Laan, Louise Leroux, Jon I. Lizaso, Bernardo Maestrini, Elizabeth A. Meier, Fasil Mequanint, Alain Ndoli, Cheryl H. Porter, Eckart Priesack, Dominique Ripoche, Tesfaye S. Sida, Upendra Singh, Ward N. Smith, Amit Srivastava, Sumit Sinha, Fulu Tao, Peter J. Thorburn, Dennis Timlin, Bouba Traore, Tracy Twine, Heidi Webber. Modelling climate change impacts on maize yields under low nitrogen input conditions in sub‐Saharan Africa. Global Change Biology. 2020; 26 (10):5942-5964.

Chicago/Turabian Style

Gatien N. Falconnier; Marc Corbeels; Kenneth J. Boote; François Affholder; Myriam Adam; Dilys S. MacCarthy; Alex C. Ruane; Claas Nendel; Anthony M. Whitbread; Éric Justes; Lajpat R. Ahuja; Folorunso M. Akinseye; Isaac N. Alou; Kokou A. Amouzou; Saseendran S. Anapalli; Christian Baron; Bruno Basso; Frédéric Baudron; Patrick Bertuzzi; Andrew J. Challinor; Yi Chen; Delphine Deryng; Maha Elsayed; Babacar Faye; Thomas Gaiser; Marcelo Galdos; Sebastian Gayler; Edward Gerardeaux; Michel Giner; Brian Grant; Gerrit Hoogenboom; Esther S. Ibrahim; Bahareh Kamali; Kurt Christian Kersebaum; Soo‐Hyung Kim; M Van Der Laan; Louise Leroux; Jon I. Lizaso; Bernardo Maestrini; Elizabeth A. Meier; Fasil Mequanint; Alain Ndoli; Cheryl H. Porter; Eckart Priesack; Dominique Ripoche; Tesfaye S. Sida; Upendra Singh; Ward N. Smith; Amit Srivastava; Sumit Sinha; Fulu Tao; Peter J. Thorburn; Dennis Timlin; Bouba Traore; Tracy Twine; Heidi Webber. 2020. "Modelling climate change impacts on maize yields under low nitrogen input conditions in sub‐Saharan Africa." Global Change Biology 26, no. 10: 5942-5964.

Special section
Published: 01 January 2020 in Vadose Zone Journal
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Agroecosystem models need to reliably simulate all biophysical processes that control crop growth, particularly the soil water fluxes and nutrient dynamics. As a result of the erosion history, truncated and colluvial soil profiles coexist in arable fields. The erosion‐affected field‐scale soil spatial heterogeneity may limit agroecosystem model predictions. The objective was to identify the variation in the importance of soil properties and soil profile modifications in agroecosystem models for both agronomic and environmental performance. Four lysimeters with different soil types were used that cover the range of soil variability in an erosion‐affected hummocky agricultural landscape. Twelve models were calibrated on crop phenological stages, and model performance was tested against observed grain yield, aboveground biomass, leaf area index, actual evapotranspiration, drainage, and soil water content. Despite considering identical input data, the predictive capability among models was highly diverse. Neither a single crop model nor the multi‐model mean was able to capture the observed differences between the four soil profiles in agronomic and environmental variables. The model's sensitivity to soil‐related parameters was apparently limited and dependent on model structure and parameterization. Information on phenology alone seemed insufficient to calibrate crop models. The results demonstrated model‐specific differences in the impact of soil variability and suggested that soil matters in predictive agroecosystem models. Soil processes need to receive greater attention in field‐scale agroecosystem modeling; high‐precision weighable lysimeters can provide valuable data for improving the description of soil–vegetation–atmosphere process in the tested models.

ACS Style

Jannis Groh; Efstathios Diamantopoulos; Xiaohong Duan; Frank Ewert; Michael Herbst; Maja Holbak; Bahareh Kamali; Kurt‐Christian Kersebaum; Matthias Kuhnert; Gunnar Lischeid; Claas Nendel; Eckart Priesack; Jörg Steidl; Michael Sommer; Thomas Pütz; Harry Vereecken; Evelyn Wallor; Tobias K.D. Weber; Martin Wegehenkel; Lutz Weihermüller; Horst H. Gerke. Crop growth and soil water fluxes at erosion‐affected arable sites: Using weighing lysimeter data for model intercomparison. Vadose Zone Journal 2020, 19, 1 .

AMA Style

Jannis Groh, Efstathios Diamantopoulos, Xiaohong Duan, Frank Ewert, Michael Herbst, Maja Holbak, Bahareh Kamali, Kurt‐Christian Kersebaum, Matthias Kuhnert, Gunnar Lischeid, Claas Nendel, Eckart Priesack, Jörg Steidl, Michael Sommer, Thomas Pütz, Harry Vereecken, Evelyn Wallor, Tobias K.D. Weber, Martin Wegehenkel, Lutz Weihermüller, Horst H. Gerke. Crop growth and soil water fluxes at erosion‐affected arable sites: Using weighing lysimeter data for model intercomparison. Vadose Zone Journal. 2020; 19 (1):1.

Chicago/Turabian Style

Jannis Groh; Efstathios Diamantopoulos; Xiaohong Duan; Frank Ewert; Michael Herbst; Maja Holbak; Bahareh Kamali; Kurt‐Christian Kersebaum; Matthias Kuhnert; Gunnar Lischeid; Claas Nendel; Eckart Priesack; Jörg Steidl; Michael Sommer; Thomas Pütz; Harry Vereecken; Evelyn Wallor; Tobias K.D. Weber; Martin Wegehenkel; Lutz Weihermüller; Horst H. Gerke. 2020. "Crop growth and soil water fluxes at erosion‐affected arable sites: Using weighing lysimeter data for model intercomparison." Vadose Zone Journal 19, no. 1: 1.

Journal article
Published: 19 November 2019 in Science of The Total Environment
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Input data aggregation affects crop model estimates at the regional level. Previous studies have focused on the impact of aggregating climate data used to compute crop yields. However, little is known about the combined data aggregation effect of climate (DAEc) and soil (DAEs) on irrigation water requirement (IWR) in cool-temperate and spatially heterogeneous environments. The aims of this study were to quantify DAEc and DAEs of model input data and their combined impacts for simulated irrigated and rainfed yield and IWR. The Agricultural Production Systems sIMulator Next Generation model was applied for the period 1998–2017 across areas suitable for potato (Solanum tuberosum L.) in Tasmania, Australia, using data at 5, 15, 25 and 40 km resolution. Spatial variances of inputs and outputs were evaluated by the relative absolute difference (rAD¯) between the aggregated grids and the 5 km grids. Climate data aggregation resulted in a rAD¯ of 0.7–12.1%, with high values especially for areas with pronounced differences in elevation. The rAD¯ of soil data was higher (5.6–26.3%) than rAD¯ of climate data and was mainly affected by aggregation of organic carbon and maximum plant available water capacity (i.e. the difference between field capacity and wilting point in the effective root zone). For yield estimates, the difference among resolutions (5 km vs. 40 km) was more pronounced for rainfed (rAD¯ = 14.5%) than irrigated conditions (rAD¯ = 3.0%). The rAD¯ of IWR was 15.7% when using input data at 40 km resolution. Therefore, reliable simulations of rainfed yield require a higher spatial resolution than simulation of irrigated yields. This needs to be considered when conducting regional modelling studies across Tasmania. This study also highlights the need to separately quantify the impact of input data aggregation on model outputs to inform about data aggregation errors and identify those variables that explain these errors.

ACS Style

Jonathan J. Ojeda; Ehsan Eyshi Rezaei; Tomas A. Remenyi; Mathew A. Webb; Heidi A. Webber; Bahareh Kamali; Rebecca M.B. Harris; Jaclyn N. Brown; Darren B. Kidd; Caroline L. Mohammed; Stefan Siebert; Frank Ewert; Holger Meinke. Effects of soil- and climate data aggregation on simulated potato yield and irrigation water requirement. Science of The Total Environment 2019, 710, 135589 .

AMA Style

Jonathan J. Ojeda, Ehsan Eyshi Rezaei, Tomas A. Remenyi, Mathew A. Webb, Heidi A. Webber, Bahareh Kamali, Rebecca M.B. Harris, Jaclyn N. Brown, Darren B. Kidd, Caroline L. Mohammed, Stefan Siebert, Frank Ewert, Holger Meinke. Effects of soil- and climate data aggregation on simulated potato yield and irrigation water requirement. Science of The Total Environment. 2019; 710 ():135589.

Chicago/Turabian Style

Jonathan J. Ojeda; Ehsan Eyshi Rezaei; Tomas A. Remenyi; Mathew A. Webb; Heidi A. Webber; Bahareh Kamali; Rebecca M.B. Harris; Jaclyn N. Brown; Darren B. Kidd; Caroline L. Mohammed; Stefan Siebert; Frank Ewert; Holger Meinke. 2019. "Effects of soil- and climate data aggregation on simulated potato yield and irrigation water requirement." Science of The Total Environment 710, no. : 135589.

Journal article
Published: 03 November 2019 in Sustainability
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Drought events have significant impacts on agricultural production in Sub-Saharan Africa (SSA), as agricultural production in most of the countries relies on precipitation. Socio-economic factors have a tremendous influence on whether a farmer or a nation can adapt to these climate stressors. This study aims to examine the extent to which these factors affect maize vulnerability to drought in SSA. To differentiate sensitive regions from resilient ones, we defined a crop drought vulnerability index (CDVI) calculated by comparing recorded yield with expected yield simulated by the Environmental Policy Integrated Climate (EPIC) model during 1990–2012. We then assessed the relationship between CDVI and potential socio-economic variables using regression techniques and identified the influencing variables. The results show that the level of fertilizer use is a highly influential factor on vulnerability. Additionally, countries with higher food production index and better infrastructure are more resilient to drought. The role of the government effectiveness variable was less apparent across the SSA countries due to being generally stationary. Improving adaptations to drought through investing in infrastructure, improving fertilizer distribution, and fostering economic development would contribute to drought resilience.

ACS Style

Bahareh Kamali; Karim C. Abbaspour; Bernhard Wehrli; Hong Yang. A Quantitative Analysis of Socio-Economic Determinants Influencing Crop Drought Vulnerability in Sub-Saharan Africa. Sustainability 2019, 11, 6135 .

AMA Style

Bahareh Kamali, Karim C. Abbaspour, Bernhard Wehrli, Hong Yang. A Quantitative Analysis of Socio-Economic Determinants Influencing Crop Drought Vulnerability in Sub-Saharan Africa. Sustainability. 2019; 11 (21):6135.

Chicago/Turabian Style

Bahareh Kamali; Karim C. Abbaspour; Bernhard Wehrli; Hong Yang. 2019. "A Quantitative Analysis of Socio-Economic Determinants Influencing Crop Drought Vulnerability in Sub-Saharan Africa." Sustainability 11, no. 21: 6135.

Journal article
Published: 12 October 2018 in Water
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Robust calibration of hydrologic models is critical for simulating water resource components; however, the time-consuming process of calibration sometimes impedes the accurate parameters’ estimation. The present study compares the performance of two approaches applied to overcome the computational costs of automatic calibration of the HEC-HMS (Hydrologic Engineering Center-Hydrologic Modeling System) model constructed for the Tamar basin located in northern Iran. The model is calibrated using the Particle Swarm Optimization (PSO) algorithm. In the first approach, a machine learning algorithm, i.e., Artificial Neural Network (ANN) was trained to act as a surrogate for the original HMS (ANN-PSO), while in the latter, the computational tasks were distributed among different processors. Due to inefficacy of preliminary ANN-PSO, an efficient adaptive technique was employed to boost training and accelerate the convergence of optimization. We found that both approaches were helpful in improving computational efficiency. For jointly-events calibrations schemes, meta-models outperformed parallelization due to effective exploration of calibration space, where parallel processing was not practical owing to the time required for data sharing and collecting among many clients. Model approximation using meta-models becomes highly complex, particularly in the presence of combining more events, because larger numbers of samples and much longer training times are required.

ACS Style

Majid Taie Semiromi; Sorush Omidvar; Bahareh Kamali. Reducing Computational Costs of Automatic Calibration of Rainfall-Runoff Models: Meta-Models or High-Performance Computers? Water 2018, 10, 1440 .

AMA Style

Majid Taie Semiromi, Sorush Omidvar, Bahareh Kamali. Reducing Computational Costs of Automatic Calibration of Rainfall-Runoff Models: Meta-Models or High-Performance Computers? Water. 2018; 10 (10):1440.

Chicago/Turabian Style

Majid Taie Semiromi; Sorush Omidvar; Bahareh Kamali. 2018. "Reducing Computational Costs of Automatic Calibration of Rainfall-Runoff Models: Meta-Models or High-Performance Computers?" Water 10, no. 10: 1440.

Accepted manuscript
Published: 08 June 2018 in Environmental Research Letters
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Crop yields exhibit known responses to droughts. However, quantifying crop drought vulnerability is often not straightforward, because components of vulnerability are not defined in a standardized and spatially comparable quantity in most cases and it must be defined on a fine spatial resolution. This study aims to develop a physical crop drought vulnerability index through linking the Drought Exposure Index (DEI) with the Crop Sensitivity Index (CSI) in Sub-Saharan Africa. Two different DEIs were compared. One was derived from the cumulative distribution functions fitted to precipitation and the other from the difference between precipitation and potential evapotranspiration. DEIs were calculated for one, three, six, nine, and twelve-month time scales. Similarly, CSI was calculated by fitting a cumulative distribution function to maize yield simulated using the Environmental Policy Integrated Climate (EPIC) model. Using a power function, curves were fitted to CSI and DEI relations resulting in different shapes explaining the severity of vulnerability. The results indicated that the highest correlation was found between CSI and DEI obtained from the difference between precipitation and potential evapotranspiration in one, three, and six-month time scales. Our findings show that Southern African countries and some regions of Sahelian strip are highly vulnerable to drought due to experiencing more water stress, whereas vulnerability in Central African countries pertains to temperature stresses. The proposed methodology provides complementary information on quantifying different degrees of vulnerabilities and the underlying reasons. The methodology can be applied to different regions and spatial scales.

ACS Style

Bahareh Kamali; Karim C. Abbaspour; Anthony Lehmann; Bernhard Wehrli; Hong Yang. Spatial assessment of maize physical drought vulnerability in sub-Saharan Africa: Linking drought exposure with crop failure. Environmental Research Letters 2018, 13, 074010 .

AMA Style

Bahareh Kamali, Karim C. Abbaspour, Anthony Lehmann, Bernhard Wehrli, Hong Yang. Spatial assessment of maize physical drought vulnerability in sub-Saharan Africa: Linking drought exposure with crop failure. Environmental Research Letters. 2018; 13 (7):074010.

Chicago/Turabian Style

Bahareh Kamali; Karim C. Abbaspour; Anthony Lehmann; Bernhard Wehrli; Hong Yang. 2018. "Spatial assessment of maize physical drought vulnerability in sub-Saharan Africa: Linking drought exposure with crop failure." Environmental Research Letters 13, no. 7: 074010.

Journal article
Published: 01 February 2018 in European Journal of Agronomy
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ACS Style

Bahareh Kamali; Karim C. Abbaspour; Anthony Lehmann; Bernhard Wehrli; Hong Yang. Uncertainty-based auto-calibration for crop yield – the EPIC+ procedure for a case study in Sub-Saharan Africa. European Journal of Agronomy 2018, 93, 57 -72.

AMA Style

Bahareh Kamali, Karim C. Abbaspour, Anthony Lehmann, Bernhard Wehrli, Hong Yang. Uncertainty-based auto-calibration for crop yield – the EPIC+ procedure for a case study in Sub-Saharan Africa. European Journal of Agronomy. 2018; 93 ():57-72.

Chicago/Turabian Style

Bahareh Kamali; Karim C. Abbaspour; Anthony Lehmann; Bernhard Wehrli; Hong Yang. 2018. "Uncertainty-based auto-calibration for crop yield – the EPIC+ procedure for a case study in Sub-Saharan Africa." European Journal of Agronomy 93, no. : 57-72.

Journal article
Published: 11 January 2018 in Global and Planetary Change
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Drought as a slow-onset phenomenon inflicts important losses to agriculture where the degree of vulnerability depends not only on physical variables such as precipitation and temperature, but also on societal preparedness. While the scopes of physical and social vulnerability are very different in nature, studies distinguishing these two aspects have been lacking. In this study we address the physical and social aspects of drought vulnerability of maize (CDVIphy and CDVIsoc) in Sub-Saharan Africa (SSA). To quantify vulnerability, we applied a probabilistic framework combining a Drought Exposure Index (DEI) with a physical or social Crop Failure Index, CFIphy or CFIsoc, respectively. DEI was derived from the exceedance probability of precipitation. Maize yields, simulated using the Environmental Policy Integrated Climate (EPIC) model, were used to build CFIphy, whereas the residual of simulated and FAO recorded yields were used to construct CFIsoc. The results showed that southern and partially central Africa are more vulnerable to physical drought as compared to other regions. Central and western Africa, however, are socially highly vulnerable. Comparison of CDVIphy and CDVIsoc revealed that societal factors cause more vulnerability than physical variables in almost all SSA countries except Nigeria and South Africa. We conclude that quantification of both drought vulnerabilities help a better characterization of droughts and identify regions where more investments in drought preparedness are required.

ACS Style

Bahareh Kamali; Karim C. Abbaspour; Bernhard Wehrli; Hong Yang. Drought vulnerability assessment of maize in Sub-Saharan Africa: Insights from physical and social perspectives. Global and Planetary Change 2018, 162, 266 -274.

AMA Style

Bahareh Kamali, Karim C. Abbaspour, Bernhard Wehrli, Hong Yang. Drought vulnerability assessment of maize in Sub-Saharan Africa: Insights from physical and social perspectives. Global and Planetary Change. 2018; 162 ():266-274.

Chicago/Turabian Style

Bahareh Kamali; Karim C. Abbaspour; Bernhard Wehrli; Hong Yang. 2018. "Drought vulnerability assessment of maize in Sub-Saharan Africa: Insights from physical and social perspectives." Global and Planetary Change 162, no. : 266-274.

Journal article
Published: 10 December 2017 in Climate and Development
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This study contributes to a better understanding of climate change adaptation by investigating different farming systems and by including cognitive factors as explanatory variables. We compared a food crop and a horticultural farming system, regarding applied adaptation measures and factors influencing adaptation. The data were based on a field survey of 267 smallholder farmers in Laikipia County of Kenya. A binary logistic regression was conducted against individual adaptation measures to identify determinants of adaptation. Adaptation measures employed by food crop farmers were mainly risk-reducing, such as mixed- and inter-cropping, planting early-maturing crop varieties and early planting. In contrast, horticultural farmers tended to focus more on intensifying crop production and applied crop rotation, irrigation and application of agro-chemicals, artificial fertilizer and manure. Factors positively influencing adaptation included access to extension services and risk perception among horticultural farmers, and access to workforce and farmers groups among food crop farmers. Furthermore, food crop farmers with access to less risk-prone income sources than agriculture seemed to have less motivation to adapt. The study showed that as climate change progresses, social differences between horticultural and food crop farmers are likely to increase, hence leading to inequalities in adaptation at local levels. Adaptation planners need to address these differences if sustainable adaptation is to be achieved.

ACS Style

Julia Olivera Stefanovic; Hong Yang; Yuan Zhou; Bahareh Kamali; Sarah Ayeri Ogalleh. Adaption to climate change: a case study of two agricultural systems from Kenya. Climate and Development 2017, 11, 319 -337.

AMA Style

Julia Olivera Stefanovic, Hong Yang, Yuan Zhou, Bahareh Kamali, Sarah Ayeri Ogalleh. Adaption to climate change: a case study of two agricultural systems from Kenya. Climate and Development. 2017; 11 (4):319-337.

Chicago/Turabian Style

Julia Olivera Stefanovic; Hong Yang; Yuan Zhou; Bahareh Kamali; Sarah Ayeri Ogalleh. 2017. "Adaption to climate change: a case study of two agricultural systems from Kenya." Climate and Development 11, no. 4: 319-337.

Journal article
Published: 01 October 2017 in Environmental Modelling & Software
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ACS Style

Seyed Saeid Ashraf Vaghefi; Nazanin Abbaspour; Bahareh Kamali; Karim C. Abbaspour. A toolkit for climate change analysis and pattern recognition for extreme weather conditions – Case study: California-Baja California Peninsula. Environmental Modelling & Software 2017, 96, 181 -198.

AMA Style

Seyed Saeid Ashraf Vaghefi, Nazanin Abbaspour, Bahareh Kamali, Karim C. Abbaspour. A toolkit for climate change analysis and pattern recognition for extreme weather conditions – Case study: California-Baja California Peninsula. Environmental Modelling & Software. 2017; 96 ():181-198.

Chicago/Turabian Style

Seyed Saeid Ashraf Vaghefi; Nazanin Abbaspour; Bahareh Kamali; Karim C. Abbaspour. 2017. "A toolkit for climate change analysis and pattern recognition for extreme weather conditions – Case study: California-Baja California Peninsula." Environmental Modelling & Software 96, no. : 181-198.

Journal article
Published: 16 September 2017 in Water
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A large number of local and global databases for soil, land use, crops, and climate are now available from different sources, which often differ, even when addressing the same spatial and temporal resolutions. As the correct database is unknown, their impact on estimating water resource components (WRC) has mostly been ignored. Here, we study the uncertainty stemming from the use of multiple databases and their impacts on WRC estimates such as blue water and soil water for the Karkheh River Basin (KRB) in Iran. Four climate databases and two land use maps were used to build multiple configurations of the KRB model using the soil and water assessment tool (SWAT), which were similarly calibrated against monthly river discharges. We classified the configurations based on their calibration performances and estimated WRC for each one. The results showed significant differences in WRC estimates, even in models of the same class i.e., with similar performance after calibration. We concluded that a non-negligible level of uncertainty stems from the availability of different sources of input data. As the use of any one database among several produces questionable outputs, it is prudent for modelers to pay more attention to the selection of input data.

ACS Style

Bahareh Kamali; Karim C. Abbaspour; Hong Yang. Assessing the Uncertainty of Multiple Input Datasets in the Prediction of Water Resource Components. Water 2017, 9, 709 .

AMA Style

Bahareh Kamali, Karim C. Abbaspour, Hong Yang. Assessing the Uncertainty of Multiple Input Datasets in the Prediction of Water Resource Components. Water. 2017; 9 (9):709.

Chicago/Turabian Style

Bahareh Kamali; Karim C. Abbaspour; Hong Yang. 2017. "Assessing the Uncertainty of Multiple Input Datasets in the Prediction of Water Resource Components." Water 9, no. 9: 709.

Journal article
Published: 30 March 2017 in Water
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Studies using Drought Hazard Indices (DHIs) have been performed at various scales, but few studies associated DHIs of different drought types with climate change scenarios. To highlight the regional differences in droughts at meteorological, hydrological, and agricultural levels, we utilized historic and future DHIs derived from the Standardized Precipitation Index (SPI), Standardized Runoff Index (SRI), and Standardized Soil Water Index (SSWI), respectively. To calculate SPI, SRI, and SSWI, we used a calibrated Soil and Water Assessment Tool (SWAT) for the Karkheh River Basin (KRB) in Iran. Five bias-corrected Global Circulation Models (GCMs) under two Intergovernmental Panel on Climate Change (IPCC) scenarios projected future climate. For each drought type, we aggregated drought severity and occurrence probability rate of each index into a unique DHI. Five historic droughts were identified with different characteristics in each type. Future projections indicated a higher probability of severe and extreme drought intensities for all three types. The duration and frequency of droughts were predicted to decrease in precipitation-based SPI. However, due to the impact of rising temperature, the duration and frequency of SRI and SSWI were predicted to intensify. The DHI maps of KRB illustrated the highest agricultural drought exposures. Our analyses provide a comprehensive way to monitor multilevel droughts complementing the existing approaches.

ACS Style

Bahareh Kamali; Delaram Houshmand Kouchi; Hong Yang; Karim C. Abbaspour. Multilevel Drought Hazard Assessment under Climate Change Scenarios in Semi-Arid Regions—A Case Study of the Karkheh River Basin in Iran. Water 2017, 9, 241 .

AMA Style

Bahareh Kamali, Delaram Houshmand Kouchi, Hong Yang, Karim C. Abbaspour. Multilevel Drought Hazard Assessment under Climate Change Scenarios in Semi-Arid Regions—A Case Study of the Karkheh River Basin in Iran. Water. 2017; 9 (4):241.

Chicago/Turabian Style

Bahareh Kamali; Delaram Houshmand Kouchi; Hong Yang; Karim C. Abbaspour. 2017. "Multilevel Drought Hazard Assessment under Climate Change Scenarios in Semi-Arid Regions—A Case Study of the Karkheh River Basin in Iran." Water 9, no. 4: 241.

Journal article
Published: 17 February 2016 in Ecohydrology
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Soil erosion threatens both soil and water resources and has increased globally because of the removal of natural vegetation and the intensification of existing agriculture. Brazil is privileged by a large proportion of natural vegetation and abundant freshwater. Recently, modifications of the Brazilian Forest Act (BFA) have been approved, which offer landowners that had committed illegal riparian deforestation in the past amnesty from reforestation, and further reductions of riparian protected areas are currently discussed. Here, we used the Soil and Water Assessment Tool to simulate river discharge and sediment exports in a typical human-impacted Brazilian catchment, the Rio das Mortes catchment. By restoring the riparian vegetation according to the BFA and ignoring amnesties to land owners, the current annual sediment export of the catchment of 0·830 t ha−1 was reduced by 29·4% according to our model. Further, simulated reforestation twice the size demanded by the BFA resulted in a 31·4% reduction of the current sediment export. However, reforestation of a 5-m homogeneous riparian corridor only, as currently discussed in the Federal Brazilian State of São Paulo, reduced sediment exports by only 23·8%, not considering expected additional erosion due to deforestation outside the simulated reforested 5-m corridor. Our study is the first catchment-wide assessment of the role of riparian vegetation in preventing soil erosion in Brazil. Its results support intensive reforestation efforts of the riparian zone and point to substantial negative effects of further reductions of the protected riparian corridor width and amnesties from reforestation to land owners. Copyright © 2016 John Wiley & Sons, Ltd.

ACS Style

José A. F. Monteiro; Bahareh Kamali; Raghavan Srinivasan; Karim Abbaspour; Björn Gücker. Modelling the effect of riparian vegetation restoration on sediment transport in a human-impacted Brazilian catchment. Ecohydrology 2016, 9, 1289 -1303.

AMA Style

José A. F. Monteiro, Bahareh Kamali, Raghavan Srinivasan, Karim Abbaspour, Björn Gücker. Modelling the effect of riparian vegetation restoration on sediment transport in a human-impacted Brazilian catchment. Ecohydrology. 2016; 9 (7):1289-1303.

Chicago/Turabian Style

José A. F. Monteiro; Bahareh Kamali; Raghavan Srinivasan; Karim Abbaspour; Björn Gücker. 2016. "Modelling the effect of riparian vegetation restoration on sediment transport in a human-impacted Brazilian catchment." Ecohydrology 9, no. 7: 1289-1303.

Journal article
Published: 17 October 2012 in Hydrological Processes
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This study presents single‐objective and multi‐objective particle swarm optimization (PSO) algorithms for automatic calibration of Hydrologic Engineering Center‐ Hydrologic Modeling Systems rainfall‐runoff model of Tamar Sub‐basin of Gorganroud River Basin in north of Iran. Three flood events were used for calibration and one for verification. Four performance criteria (objective functions) were considered in multi‐objective calibration where different combinations of objective functions were examined. For comparison purposes, a fuzzy set‐based approach was used to determine the best compromise solutions from the Pareto fronts obtained by multi‐objective PSO. The candidate parameter sets determined from different single‐objective and multi‐objective calibration scenarios were tested against the fourth event in the verification stage, where the initial abstraction parameters were recalibrated. A step‐by‐step screening procedure was used in this stage while evaluating and comparing the candidate parameter sets, which resulted in a few promising sets that performed well with respect to at least three of four performance criteria. The promising sets were all from the multi‐objective calibration scenarios which revealed the outperformance of the multi‐objective calibration on the single‐objective one. However, the results indicated that an increase of the number of objective functions did not necessarily lead to a better performance as the results of bi‐objective function calibration with a proper combination of objective functions performed as satisfactorily as those of triple‐objective function calibration. This is important because handling multi‐objective optimization with an increased number of objective functions is challenging especially from a computational point of view. Copyright © 2012 John Wiley & Sons, Ltd.

ACS Style

Bahareh Kamali; S. Jamshid Mousavi; Karim C Abbaspour. Automatic calibration of HEC-HMS using single-objective and multi-objective PSO algorithms. Hydrological Processes 2012, 27, 4028 -4042.

AMA Style

Bahareh Kamali, S. Jamshid Mousavi, Karim C Abbaspour. Automatic calibration of HEC-HMS using single-objective and multi-objective PSO algorithms. Hydrological Processes. 2012; 27 (26):4028-4042.

Chicago/Turabian Style

Bahareh Kamali; S. Jamshid Mousavi; Karim C Abbaspour. 2012. "Automatic calibration of HEC-HMS using single-objective and multi-objective PSO algorithms." Hydrological Processes 27, no. 26: 4028-4042.

Journal article
Published: 10 May 2011 in Journal of Hydroinformatics
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This study presents the application of an uncertainty-based technique for automatic calibration of the well-known Hydrologic Engineering Center-Hydrologic Modelling System (HEC-HMS) model. Sequential uncertainty fitting (SUFI2) approach has been used in calibration of the HEC-HMS model built for Tamar basin located in north of Iran. The basin was divided into seven sub-basins and three routing reaches with 24 parameters to be estimated. From the four events, three were used for calibration and one for verification. Each event was initially calibrated separately. As there was no unique parameter set identified, all events were then calibrated jointly. Based on the scenarios of separately and jointly calibrated events, different candidate parameter sets were inputted to the model verification stage where recalibration of initial abstraction parameters commenced. Some of the candidate parameter sets with no physically meaningful parameter values were withdrawn after recalibration. Then new ranges of parameters were identified based on minimum and maximum values of the remaining parameter sets. The new parameter ranges were used in an uncertainty analysis using SUFI2 technique resulting in much narrower parameter intervals that can simulate both verification and calibration events satisfactorily in a probabilistic sense. Results show that the SUFI2 technique linked to HEC-HMS as a simulation–optimization model can provide a basis for performing uncertainty-based automatic calibration of event-based hydrologic models.

ACS Style

S. Jamshid Mousavi; K. C. Abbaspour; Bahareh Kamali; M. Amini; H. Yang. Uncertainty-based automatic calibration of HEC-HMS model using sequential uncertainty fitting approach. Journal of Hydroinformatics 2011, 14, 286 -309.

AMA Style

S. Jamshid Mousavi, K. C. Abbaspour, Bahareh Kamali, M. Amini, H. Yang. Uncertainty-based automatic calibration of HEC-HMS model using sequential uncertainty fitting approach. Journal of Hydroinformatics. 2011; 14 (2):286-309.

Chicago/Turabian Style

S. Jamshid Mousavi; K. C. Abbaspour; Bahareh Kamali; M. Amini; H. Yang. 2011. "Uncertainty-based automatic calibration of HEC-HMS model using sequential uncertainty fitting approach." Journal of Hydroinformatics 14, no. 2: 286-309.