Showing papers by "Karim C. Abbaspour published in 2009"
TL;DR: In this paper, the authors used a hydrologic model of Iran to study the impact of future climate on the country's water resources using the Soil and Water Assessment Tool (SWAT) model and calibrated using daily river discharges and annual wheat yield data at a subbasin level.
Abstract: [1] As water resources become further stressed due to increasing levels of societal demand, understanding the effect of climate change on various components of the water cycle is of strategic importance in management of this essential resource. In this study, we used a hydrologic model of Iran to study the impact of future climate on the country's water resources. The hydrologic model was created using the Soil and Water Assessment Tool (SWAT) model and calibrated for the period from 1980 to 2002 using daily river discharges and annual wheat yield data at a subbasin level. Future climate scenarios for periods of 2010–2040 and 2070–2100 were generated from the Canadian Global Coupled Model (CGCM 3.1) for scenarios A1B, B1, and A2, which were downscaled for 37 climate stations across the country. The hydrologic model was then applied to these periods to analyze the effect of future climate on precipitation, blue water, green water, and yield of wheat across the country. For future scenarios we found that in general, wet regions of the country will receive more rainfall while dry regions will receive less. Analysis of daily rainfall intensities indicated more frequent and larger-intensity floods in the wet regions and more prolonged droughts in the dry regions. When aggregated to provincial levels, the differences in the predictions due to the three future scenarios were smaller than the uncertainty in the hydrologic model. However, at the subbasin level the three climate scenarios produced quite different results in the dry regions of the country, although the results in the wet regions were more or less similar.
428 citations
TL;DR: In this article, the authors used SWAT and SUFI-2 to calibrate and validate a hydrologic model of Iran based on river discharges and wheat yield, taking into consideration dam operations and irrigation practices.
Abstract: Knowledge of the internal renewable water resources of a country is strategic information which is needed for long-term planning of a nation's water and food security, among many other needs. New modelling tools allow this quantification with high spatial and temporal resolution. In this study we used the program Soil and Water Assessment Tool (SWAT) in combination with the Sequential Uncertainty Fitting program (SUFI-2) to calibrate and validate a hydrologic model of Iran based on river discharges and wheat yield, taking into consideration dam operations and irrigation practices. Uncertainty analyses were also performed to assess the model performance. The results were quite satisfactory for most of the rivers across the country. We quantified all components of the water balance including blue water flow (water yield plus deep aquifer recharge), green water flow (actual and potential evapotranspiration) and green water storage (soil moisture) at sub-basin level with monthly time-steps. The spatially aggregated water resources and simulated yield compared well with the existing data. The study period was 1990–2002 for calibration and 1980–1989 for validation. The results show that irrigation practices have a significant impact on the water balances of the provinces with irrigated agriculture. Concerning the staple food crop in the country, 55% of irrigated wheat and 57% of rain-fed wheat are produced every year in water-scarce regions. The vulnerable situation of water resources availability has serious implications for the country's food security, and the looming impact of climate change could only worsen the situation. This study provides a strong basis for further studies concerning the water and food security and the water resources management strategies in the country and a unified approach for the analysis of blue and green water in other arid and semi-arid countries. Copyright © 2008 John Wiley & Sons, Ltd.
284 citations
TL;DR: The results presented herein show that non-equilibrium sorption in macropores has a large impact on simulated solute transport for reactive compounds contained in LMB, which should be considered insolute transport models that are used for environmental risk assessments for such compounds.
Abstract: Pharmaceuticals and personal care products (PPCPs) carried in biosolids may reach surface waters or ground water when these materials are applied as fertilizer to agricultural land. During preferential flow conditions created by land application of liquid municipal biosolids (LMB), the residence time of solutes in the macropores may be too short for sorption equilibration. The physically based dual-permeability model MACRO is used in environmental risk assessments for pesticides and may have potential as an environmental risk assessment tool for PPCPs. The objective of this study was to evaluate MACRO and an updated version of MACRO that included non-equilibrium sorption in macropores using data from experiments conducted in eastern Ontario, Canada on the transport of three PPCPs (atenolol, carbamazepine, and triclosan), the nicotine metabolite cotinine, and the strongly sorbing dye rhodamine WT applied in LMB. Results showed that the MACRO model could not reproduce the measured rhodamine WT concentrations (Nash-Sutcliffe coefficient [NS] for the best simulation = -0.057) in drain discharge. The updated version resulted in better fits to measured data for PPCP (average NS = 0.97) and rhodamine WT (NS = 0.84) concentrations. However, it was not possible to simulate all compounds using the same set of hydraulic parameters, which indicates that the model does not fully account for all relevant processes. The results presented herein show that non-equilibrium sorption in macropores has a large impact on simulated solute transport for reactive compounds contained in LMB. This process should be considered in solute transport models that are used for environmental risk assessments for such compounds.
35 citations
TL;DR: In this article, the impacts of water reallocation on crop production and farmers' income were examined and issues relating to current compensation mechanisms were discussed. But the current compensation received by farmers is generally lower than the losses incurred due to reduced irrigation.
35 citations
TL;DR: In this article, the use of artificial neural networks (ANNs) in estimating soil shear strength from measured particle size distribution, topographic attributes, normalized difference vegetation index (NDVI), soil organic carbon (SOC), and CaCO3 were investigated.
32 citations
TL;DR: In this article, the uptake behavior of wheat and safflower was evaluated in a calcareous soil by using 12 undisturbed columns in which half were artificially contaminated and half were not contaminated.
Abstract: Accumulation of heavy metals (HMs) in cultivated soils is a continuing environmental problem in many parts of the world. An increase in HM concentration can enhance uptake of toxic metals by crops and enter the human food chain. In this study, the uptake behavior of wheat and safflower was evaluated in a calcareous soil by using 12 undisturbed columns in which half were artificially contaminated. Heavy metals in the form of CdCl2 (15 mg Cd kg− 1), CuSO4 (585 mg Cu kg− 1), Pb(NO3)2 (117 mg Pb kg− 1), and ZnCl2 (1094 mg Zn kg− 1) were sprayed on the soil surface and completely mixed in the top 10 cm. The background total concentrations of Cd, Cu, Pb and Zn were 1.6, 29.5, 17.5 and 61.2 mg kg− 1, respectively. After metal application, half of the columns (3 contaminated and 3 uncontaminated) were sown with wheat (Triticum aestivum) and the other half with safflower (Carthamus tinctorious) and grown for 74 days until maturity. After harvesting, soil columns were cut into 10-cm sections and analyzed for HNO3- ...
23 citations
TL;DR: In this paper, the results indicate that the ARIMA model is a more reliable model for monthly river flow forecasting applications in the basins under study, while the deseasonalized autoregressive moving average (DARMA) and Thomas-Fiering (TF) models are more reliable.
Abstract: River flow forecasting experiments are carried out for rivers located in the Karun Basin and its sub‐basins situated in the Southwestern Iran, because of the potential importance of these rivers for supplying relatively large amounts of water. More specifically, multiple linear regressions (MLR), autoregressive integrated moving average (ARIMA), deseasonalized autoregressive‐moving average (DARMA), and Thomas‐Fiering (TF) models were fitted to monthly, bimonthly, and seasonal river flow series. One‐step‐ahead forecasts for the test portion of the time series were generated using the selected set of candidate models. Forecasting performance of the models was compared based on the mean absolute error, root mean square error, normalized mean bias error and correlation coefficient between observed and forecasted values. The results indicate that the ARIMA model is a more reliable model for monthly river flow forecasting applications in the basins under study. For bimonthly and seasonal river flow for...
6 citations
01 Jun 2009
TL;DR: In this article, the authors used a global database of fluoride measurements, as well as global-scale information relevant to soil, geology, elevation, climate, and hydrology to evaluate several hybrid methods.
Abstract: There is an increasing interest in modeling groundwater contamination, particularly geogenic contaminant, on a large scale both from the researcher's as well as policy maker's point of view. However, modeling large scale groundwater contamination is very challenging due to the incomplete understanding of geochemical and hydrological processes in the aquifer. Despite the incomplete understanding, existing knowledge provides sufficient hints to develop predictive models of geogenic contamination. In this study we used a global database of fluoride measurements (>60,000 entities), as well as global-scale information relevant to soil, geology, elevation, climate, and hydrology to evaluate several hybrid methods. The hybrid methods were developed by combining two classification techniques including classification tree (CART) and knowledge based clustering (KBC) and three predictive techniques including multiple linear regression (MLR), adoptive neuro-fuzzy inference system (ANFIS) and logistic regression (LR). The results indicated that combination of classification techniques and nonlinear predictive method (ANFIS and LR) were more reliable than others and provided a better prediction capability. Among the different hybrid procedures, combination of KBC-ANFIS and also CART - ANFIS resulted in larger sensitivities and smaller false negative rates for both training and test data sets. However, as the CART classifier is very unstable and very sensitive to re-sampling, the combination of KBC and ANFIS or LR is preferred
3 citations