A nine-step approach for developing and implementing an “agricultural drought risk management plan”; case study: Alamut River basin in Qazvin,Iran

Occurrence of drought, as an inevitable natural climate feature, cannot be ceased while happening. However, costs of the consequences could be alleviated using mature scientific integrated approaches. To reduce the amount of damage, it is required to provide “Contingency” and “Mitigation” action plans. For this reason, development of efficient operating instructions for various regions based on weather conditions and field studies is needed as well as having a sophisticated understanding of socioeconomic situations. This paper describes an approach to provide the first national agricultural drought risk management plan for a river basin in Iran country as a pilot. The study lasted for 3 years as a national technical research project for the “soil conservation and watershed management research institute.” To reach the objectives, besides holding workshops and specialized think-tank meetings, field researches were done. Based on the socioeconomic data sources in the basin and the results of meetings by participation of local managers and residents, the final plan was developed. Moreover, in order to carry out this research, different climatic, agricultural and local information were collected in the watershed. In the next steps, potential risks and vulnerabilities of various agricultural sectors due to the hazard were evaluated. In this study, a nine-step approach to develop an agricultural drought risk management plan proposing different scientific–managerial phases based on the latest experts’ opinions, released international scientific best practices, and existing conditions governing the region was followed. With respect to the average income of US$ one million from agriculture and animal husbandry in the river basin, total drought loss varies from US$ 86,000 to US$ 258,000 for a range of light to very intense drought conditions, respectively. The setup of these nine executive phases defined monitoring, forecasting, and warning steps in working teams and managed the subprograms in partnership with stakeholders and decision-makers to mitigate the rate of drought damage from 30 to 47% (depending on the severity of the drought condition).

     

ANALYSIS OF FOLDS SOUTH OF MALAKAND AND ADJOINING AREAS, NORTH PAKISTAN

ANALYSIS OF FOLDS SOUTH OF MALAKAND AND ADJOINING AREAS, NORTH PAKISTAN1　Can啨ｒｏｔＪ,QuG .CuadernosdeGeologiaiberica ,1998,2 4:311～ 331. 2　DebelmasJ,MascleG .EnsSciencesdelaTerre[M ].Masson ,ed .1991.2 99.3　GaetaniM ,GarzantiE .AAPGBull ,1991,75 (9) :142 7～ 144 6 . 4　HendrixMS ,GrahamSA ,CarrollAR ,etal.GeolSocAmBull,1992 ,10 4:5 3～ 79.. 5　Jia ,Coll.PetroleumIndustryPress,Beijing .1997,2 95 . 6　JiaD ,LuHCaiD ,etal.AAPGBull ,8…     

Cd(II), Pb(II) and Zn(II) Removal from Contaminated Water by Biosorption Using Activated Sludge Biomass

Biosorption using activated sludge biomass (ASB) as a potentially sustainable technology for the treatment of wastewater containing different metal ions (Cd(II), Pb(II) and Zn(II)) was investigated. ASB metal uptake clearly competed with protons consumed by microbial biomass compared with control tests with non‐activated sludge biomass. Biosorption tests confirmed maximum exchange between metal ions and protons at pH 2.0–4.5. It was revealed by the study that the amount of metal ions released from the biomass increased with biomass sludge concentration. The result showed that maximum absorption of metal ions was observed for Cd(II) at pH 3.5, Pb(II) at pH 4.0, and pH 4.5 for Zn(II) ions. The maximum absorption capacities of ASB for Cd(II), Pb(II) and Zn(II) were determined to be 59.3, 68.5 and 86.5%, respectively. The biosorption of heavy metals was directly proportional to ASB stabilization corresponding to a reduction in heavy metals in the order of Cd < Pb < Zn. The order of increase of biosorption of metal ions in ASB was Zn(II) < Pb(II) < Cd(II), and this was opposite to that of non active sludge. The results indicate that ASB is a sustainable tools for the bioremediation of Cd(II), Pb(II) and Zn(II) ions from industrial sludge and wastewater treatment plants.     

2.5-D Gravity/Magnetic Model Studies in Sahl El Qaa Area, Southwestern Sinai, Egypt

The 2.5-D gravity-magnetic models of the upper crustal structures of Sahl El Qaa Area, Southwestern Sinai were constructed along seven profiles, focusing on the uppermost crustal layers to a depth of 4–5 km. In addition separation filtering process; spectral analysis and trend analysis were used to investigate the Bouguer and total intensity aeromagnetic field maps qualitatively and quantitatively. The study showed that the regional structures consist of tilted blocks in the form of a major NW-synclinal feature with an axis dipping northward. This feature is dissected by the NE trending cross faults forming horsts, grabens and step-fault structures. The tilted blocks are controlled by a major normal fault system and are greatly modified in the dip regime from north to south. They show a regional NW dip regime in northern and southern parts, where the depth to the basement reaches about 2–3 km in the down dip. In the central portion, the basin is dipping steeply to the east, with maximum depths attaining about 4–5 km.     

Drought analysis in Jordan under current and future climates

Droughts have adverse socioeconomic, agricultural, and environmental impacts that can be reduced by assessing and forecasting drought behavior. The paper presents detailed analyses of both meteorological and vegetative droughts over the period from 1970 to 2005. Standardized Precipitation Index (SPI) and Normalized Difference Vegetation Index (NDVI) have been used to quantify drought according to severity, magnitude and spatial distribution at the Hashemite Kingdom of Jordan. Results suggest that the country faced during the past 35 years frequent non-uniform drought periods in an irregular repetitive manner. Drought severity, magnitudes and life span increased with time from normal to extreme levels especially at last decade reaching magnitudes of more than 4. Generated NDVI maps spatial analyses estimate crop-area percentage damage due to severe and extremely severe drought events occurred during October, December, and February of 2000 to be about 10%, 45%, and 30%, respectively. In response to drought spatial extent, the paper suggest the presence of two drought types, local drought acting on one or more geographical climatic parts and national drought, of less common but more severe, that extend over the whole country. Droughts in Jordan act intensively during January, February and March and tend to shift position with time by alternative migrations from southern desert parts to northern desert parts and from the eastern desert parts to highlands and Jordan Rift Valley (JRV) at the west. The paper also investigates the potential use of Global Climate Model’s (GCM) to forecast future drought events from 2010 till 2040. Tukey HSD test indicates that ECHAM5OM GCM is capable to predicted rainfall variation at the country and suggests future droughts to become more intensive at the northern and southern desserts with 15% rainfall reduction factor, followed by 10% reduction at the JRV, and 5% at the highlands.     

Rancher and farmer perceptions of climate change in Nevada, USA

Farming and ranching communities in arid lands are vulnerable to the adverse impacts of climate change. We surveyed Nevada ranchers and farmers (n?=?481) during 2009–2010 to assess climate change related knowledge, assumptions, and perceptions. The large majority of this group agreed that we are in a period of climate change; however, only 29 % of them believed that human activity is playing a significant role. Female ranchers and farmers hold more scientifically accurate knowledge about climate change than do their male counterparts, regardless of Democratic or Republican affiliation. Partisan affiliation, political ideology, and gender have strong impacts on climate change knowledge and perceptions. Republican, conservative and male rural residents view climate change as a low national priority, less important to themselves, and less harmful to their communities. Female ranchers and farmers are more concerned about the negative impacts of climate change. We found that only 4 % of our subjects (n?=?299) attribute local environment changes to climate change or global warming. The knowledge gained from this study will help researchers and natural resource managers understand how to best communicate about climate change with rural communities, and support policy makers in identifying potentially effective adaptation and mitigation policies and outreach programs.     

Yield Forecasting of Spring Maize Using Remote Sensing and Crop Modeling in Faisalabad-Punjab Pakistan

Real time, accurate and reliable estimation of maize yield is valuable to policy makers in decision making. The current study was planned for yield estimation of spring maize using remote sensing and crop modeling. In crop modeling, the CERES-Maize model was calibrated and evaluated with the field experiment data and after calibration and evaluation, this model was used to forecast maize yield. A Field survey of 64 farm was also conducted in Faisalabad to collect data on initial field conditions and crop management data. These data were used to forecast maize yield using crop model at farmers’ field. While in remote sensing, peak season Landsat 8 images were classified for landcover classification using machine learning algorithm. After classification, time series normalized difference vegetation index (NDVI) and land surface temperature (LST) of the surveyed 64 farms were calculated. Principle component analysis were run to correlate the indicators with maize yield. The selected LSTs and NDVIs were used to develop yield forecasting equations using least absolute shrinkage and selection operator (LASSO) regression. Calibrated and evaluated results of CERES-Maize showed the mean absolute % error (MAPE) of 0.35–6.71% for all recorded variables. In remote sensing all machine learning algorithms showed the accuracy greater the 90%, however support vector machine (SVM-radial basis) showed the higher accuracy of 97%, that was used for classification of maize area. The accuracy of area estimated through SVM-radial basis was 91%, when validated with crop reporting service. Yield forecasting results of crop model were precise with RMSE of 255 kg ha^?1, while remote sensing showed the RMSE of 397 kg ha^?1. Overall strength of relationship between estimated and actual grain yields were good with R² of 0.94 in both techniques. For regional yield forecasting remote sensing could be used due greater advantages of less input dataset and if focus is to assess specific stress, and interaction of plant genetics to soil and environmental conditions than crop model is very useful tool.     

Markov CA,Multi Regression,and Multiple Decision Making for Modeling Historical Changes in Kirkuk City,Iraq

The cellular automata (CA) model is an important tool in land use change studies. Swift increases in population and long-term expectations of rapid urbanization have led to extensive land use change, and normal living conditions have affected the natural resources of the land. This paper highlights and analyzes the historical urban changes in Kirkuk City, Iraq, considering repeated changes undergone by the state such change as government infrastructures, wars, and economic blockade. In this paper, an integrated model, built-in multi regression model, and multi-criteria evaluation were considered to improve the representation of CA transition rules. Environmental and socioeconomic factors were used to produce Suitable Maps (SMs). These SMs were practicalities to create factor layers and weight usage, rating method process for variance expert decision-making groups, and geographic information systems for the periods 1984, 1990, 2000, and 2010. The roots of the equation (R²) values are compared and these values are chosen to produce a good model of suitable maps. The approach used in this study provides a mechanism for monitoring suitability maps in Kirkuk. Furthermore, the model Markov CA is implemented and evaluated. The results indicate that the model, its related concepts performs sufficiency     

Prediction of Land Cover Change Using Markov and Cellular Automata Models: Case of Al-Ain,UAE, 1992-2030

The UAE has witnessed rapid urban development and economic growth in recent years. With its ambitious vision to become one of the advanced nations by 2021, planners and policy-makers need to know the most likely direction of future urban development. In this study, remotely sensed imagery coupled with cellular automata models were used to predict land cover in Al Ain, the second largest city in the Emirate of Abu Dhabi. Markov and cellular automata models were used for 1992 and 2006 to predict land cover in 2012. Land Use and Land Cover maps for the study area were derived from 1992, 2006, and 2012 Landsat satellite images (TM, ETM⁺). The models achieved an overall accuracy of approximately 80 %. A Markov model was applied for 2006 and 2012 to predict land cover in 2030. The results conformed to the general trend of the Al Ain Master Plan 2030. This study demonstrates that remote sensing, with the availability of free Landsat data, is a viable technology that could be used to help in the prediction process especially in developing countries, where data availability is a problem.     

Influence of surface and vegetation characteristics on C-band radar measurements for soil moisture content

Soil moisture estimation using microwave remote sensing faces challenges of the segregation of influences mainly from roughness and vegetation. Under static surface conditions, it was found that Radarsat C-band SAR shows reasonably good correlation and sensitivity with changing soil moisture. Dynamic surface and vegetation conditions are supposed to result in a substantial reduction in radar sensitivity to soil moisture. A C-band scatterometer system (5.2 GHz) with a multi-polarization and multi-angular configuration was used 12 times to sense the soil moisture over a tall vegetated grass field. A score of vegetation and soil parameters were recorded on every occasion of the experiment. Three radar backscattering models Viz., Integral Equation Model (IEM), an empirical model and a volume scattering model, have been used to predict the backscattering phenomena. The volume scattering model, using the Distorted Born Approximation, is found to predict the backscattering phenomena reasonably well. But the surface scattering models are expectedly found to be inadequate for the purpose. The temporal variation of soil moisture does show good empirical relationship with the observed radar backscattering. But as the vegetation biomass increases, the radar shows higher sensitivity to the vegetation parameters compared to surface characteristics. A sensitivity analysis of the volume scattering model for all the parameters also reveals that the radar is more sensitive to plant parameters under high biomass conditions, particularly vegetation water content, but the sensitivity to surface characteristics, particularly to soil moisture, is also appreciable.