|
Baum, Z. (2014). The Economic Impact of Water Scarcity Under Diverse Water Qualities and Desalination Policies: The Case of Israel..
|
|
Baum, Z. (2013). Assessing the impact of climate change on agriculture and a water economy with a diverse mix of water types – the Israeli case study..
|
|
Baum, Z., & Palatnik, R. R. (2013). Assessing the Impact of Climate Change on the Israeli Water Economy via a Linked CGE and Farm-Level Model..
|
Baum, Z., Palatnik, R. R., Kan, I., & Rapaport-Rom, M. (2016). Economic Impacts of Water Scarcity Under Diverse Water Salinities. Water Econs. Policy, 02(01), 1550013.
Abstract: Exploitation of alternative water sources is expected to grow in the decades to come in water-stressed countries with fast population growth, especially in regions where a further decline of natural freshwater availability is expected due to climate change. Increasing utilization of non-freshwater usually leads to salinity build-up in fields and water sources as well as accumulation of various pollutants — both having a considerable impact on the suitability of non-freshwater for irrigation due to constraints associated with crop salinity tolerance and food safety regulations. We developed a linked Computable General Equilibrium (CGE) — farm-level model of a water economy with representation for multiple water types characterized by different qualities. We employ the model to assess the impact of water shortage on the Israeli economy, where steadily growing water scarcity leads to an increasing utilization of alternative water sources. We simulate water shortage scenarios based on the Long Term National Master Plan for The Water Economy developed by the Israeli Water Authority (IWA). The linked CGE — farm-level model provides a mechanism for estimating the Constant Elasticity of Substitution (CES) rates between different irrigation water types used in agriculture. This mechanism accounts for the effects of salinity on yields and takes into consideration food safety regulations for irrigating crops with treated wastewater. We demonstrate that, in contrast to previous studies, CES rates between different water types are not identical. The CES rates obtained in our study have relatively low values, which can be attributed to the constraints associated with crop salinity tolerance and food safety regulations. Our results reveal that water shortage can lead to a significant decline of Israel’s GDP, where a considerable part of the decline is attributed to the decrease in agricultural outputs. The magnitude of the impact depends on the underlying assumptions regarding future desalination capacity. To further study the effect of desalination, we run simulations under various desalination levels and examine its impact on the GDP. We also examine the extent to which the impact of water shortage is sensitive to CES rates between different irrigation water types.
|
Bellocchi, G. (2015). Fuzzy-logic based multi-site crop model evaluation (Vol. 5).
Abstract: The most common way to evaluate simulation models is to quantify the agreement between observations and simulations via statistical metrics such as the root mean squared error and the linear regression coefficient of determination. It is agreed that the aggregation of metrics of different nature intro integrated indicators offers a valuable way to assess models. Expanded notions of model evaluation that have recently emerged, based on the trade-off between properties of the model and agreement between predictions and actual data under contrasting conditions, integrate sensitivity analysis measures and information criteria for model selection, as well as concepts of model robustness, and point to expert judgments to explore the importance of different metrics. As a FACCE MACSUR CropM-LiveM action, a composite indicator (MQIm: Model Quality Indicator for multi-site assessment) was elaborated, by a group of specialists, on metrics commonly used to evaluate crop models (with extension to grassland models) while also integrating aspects of model complexity and stability of performances. The indicator, based on fuzzy bounds applied to a set of weighed metrics, was first revised by a broader group of modellers and then assessed via questionnaire survey of scientists and end-users. We document a crop model evaluation in Europe and assess to what extent the MQIm reflects the main components of model quality and supports inferences about model performances. No Label
|
