@article{KamaliStellaBergMohnickeetal.2022,
  author    = {Kamali, Bahareh and Stella, Tommaso and Berg-Mohnicke, Michael and Pickert, J{\"u}rgen and Groh, Jannis and Nendel, Claas},
  title     = {Improving the simulation of permanent grasslands across Germany by using multi-objective uncertainty-based calibration of plant-water dynamics},
  series = {European journal of agronomy},
  volume    = {134},
  journal   = {European journal of agronomy},
  publisher = {Elsevier},
  address   = {Amsterdam},
  issn      = {1161-0301},
  doi       = {10.1016/j.eja.2022.126464},
  pages     = {17},
  year      = {2022},
  abstract  = {The dynamics of grassland ecosystems are highly complex due to multifaceted interactions among their soil, water, and vegetation components. Precise simulations of grassland productivity therefore rely on accurately estimating a variety of parameters that characterize different processes of these systems. This study applied three calibration schemes - a Single-Objective (SO-SUFI2), a Multi-Objective Pareto (MO-Pareto), and, a novel Uncertainty-Based Multi-Objective (MO-SUFI2) - to estimate the parameters of MONICA (Model for Nitrogen and Carbon Simulation) agro-ecosystem model in grassland ecosystems across Germany. The MO-Pareto model is based on a traditional Pareto optimality concept, while the MO-SUFI2 optimizes multiple target variables considering their level of prediction uncertainty. We used measurements of leaf area index, aboveground biomass, and soil moisture from experimental data at five sites with different intensities of cutting regimes (from two to five cutting events per season) to evaluate model performance. Both MO-Pareto and MO-SUFI2 outperformed SO-SUFI2 during calibration and validation. The comparison of the two MO approaches shows that they do not necessarily conflict with each other, but MO-SUFI2 provides complementary information for better estimations of model parameter uncertainty. We used the obtained parameter ranges to simulate grassland productivity across Germany under different cutting regimes and quantified the uncertainty associated with estimated productivity across regions. The results showed higher uncertainty in intensively managed grasslands compared to extensively managed grasslands, partially due to a lack of high-resolution input information concerning cutting dates. Furthermore, the additional information on the quantified uncertainty provided by our proposed MO-SUFI2 method adds deeper insights on confidence levels of estimated productivity. Benefiting from additional management data collected at high resolution and ground measurements on the composition of grassland species mixtures appear to be promising solutions to reduce uncertainty and increase model reliability.},
  language  = {en}
}
@article{KamaliLoriteWebberetal.2022,
  author    = {Kamali, Bahareh and Lorite, Ignacio J. and Webber, Heidi A. and Rezaei, Ehsan Eyshi and Gabaldon-Leal, Clara and Nendel, Claas and Siebert, Stefan and Ramirez-Cuesta, Juan Miguel and Ewert, Frank and Ojeda, Jonathan J.},
  title     = {Uncertainty in climate change impact studies for irrigated maize cropping systems in southern Spain},
  series = {Scientific reports},
  volume    = {12},
  journal   = {Scientific reports},
  number    = {1},
  publisher = {Macmillan Publishers Limited,},
  address   = {London},
  issn      = {2045-2322},
  doi       = {10.1038/s41598-022-08056-9},
  pages     = {13},
  year      = {2022},
  abstract  = {This study investigates the main drivers of uncertainties in simulated irrigated maize yield under historical conditions as well as scenarios of increased temperatures and altered irrigation water availability. Using APSIM, MONICA, and SIMPLACE crop models, we quantified the relative contributions of three irrigation water allocation strategies, three sowing dates, and three maize cultivars to the uncertainty in simulated yields. The water allocation strategies were derived from historical records of farmer's allocation patterns in drip-irrigation scheme of the Genil-Cabra region, Spain (2014-2017). By considering combinations of allocation strategies, the adjusted R-2 values (showing the degree of agreement between simulated and observed yields) increased by 29\% compared to unrealistic assumptions of considering only near optimal or deficit irrigation scheduling. The factor decomposition analysis based on historic climate showed that irrigation strategies was the main driver of uncertainty in simulated yields (66\%). However, under temperature increase scenarios, the contribution of crop model and cultivar choice to uncertainty in simulated yields were as important as irrigation strategy. This was partially due to different model structure in processes related to the temperature responses. Our study calls for including information on irrigation strategies conducted by farmers to reduce the uncertainty in simulated yields at field scale.},
  language  = {en}
}
@article{KamaliJahanbakhshiDogaruetal.2022,
  author    = {Kamali, Bahareh and Jahanbakhshi, Farshid and Dogaru, Diana and Dietrich, J{\"o}rg and Nendel, Claas and AghaKouchak, Amir},
  title     = {Probabilistic modeling of crop-yield loss risk under drought: a spatial showcase for sub-Saharan Africa},
  series = {Environmental research letters},
  volume    = {17},
  journal   = {Environmental research letters},
  number    = {2},
  publisher = {IOP Publishing},
  address   = {Bristol},
  issn      = {1748-9326},
  doi       = {10.1088/1748-9326/ac4ec1},
  pages     = {15},
  year      = {2022},
  abstract  = {Assessing the risk of yield loss in African drought-affected regions is key to identify feasible solutions for stable crop production. Recent studies have demonstrated that Copula-based probabilistic methods are well suited for such assessment owing to reasonably inferring important properties in terms of exceedance probability and joint dependence of different characterization. However, insufficient attention has been given to quantifying the probability of yield loss and determining the contribution of climatic factors. This study applies the Copula theory to describe the dependence between drought and crop yield anomalies for rainfed maize, millet, and sorghum crops in sub-Saharan Africa (SSA). The environmental policy integrated climate model, calibrated with Food and Agriculture Organization country-level yield data, was used to simulate yields across SSA (1980-2012). The results showed that the severity of yield loss due to drought had a higher magnitude than the severity of drought itself. Sensitivity analysis to identify factors contributing to drought and high-temperature stresses for all crops showed that the amount of precipitation during vegetation and grain filling was the main driver of crop yield loss, and the effect of temperature was stronger for sorghum than for maize and millet. The results demonstrate the added value of probabilistic methods for drought-impact assessment. For future studies, we recommend looking into factors influencing drought and high-temperature stresses as individual/concurrent climatic extremes.},
  language  = {en}
}