A composite index for drought hazard assessment using CPC rainfall time series data

Hazard analysis is the first step in any disaster management activity. Drought is a serious environmental hazard strongly limiting the agricultural production in the tropical countries like India. A comprehensive drought hazard map is useful for multiple perspectives such as agriculture, environment and hydrology. In this study, daily rainfall data of the Climate Prediction Centre during the south-west monsoon season (June–September) of 12 years, over India was analysed. Based on rainfall and rainy days, six indicators of drought were generated which were then synthesized into a composite index of drought hazard for every 10 × 10 km pixel. The weights for the composite index were generated through variance approach. The index has effectively captured the spatial variations in meteorological drought across India by showing a typical pattern with increasing hazardous area from east to west. The drought hazard map also shows considerable agreement with the climate classification map and the drought proneness map reported by other studies. Thus, the current study presents a simple and novel approach for drought hazard analysis, using the routinely available geospatial rainfall data products. The methodology can be extended to other geographies and disasters too. Use of time series data of longer period would improve the reliability of the composite drought hazard index.     

Assessment of drought frequency, duration, and severity and its impact on pasture production in Mongolia

Drought frequency, duration, and severity and its impact on pasture productivity in the four main vegetation zones of Mongolia were analyzed using meteorological, soil moisture, and vegetation data during the growing season (April–August) of 1965–2010. Meteorological and pasture drought characteristics were explored using the Standardized Precipitation Index (SPI), the soil moisture anomalies percentile index (W _p), and Palmer Drought Severity Index (PDSI) on 1-month timescale. Generally, 35–37 (15–16 %) by SPI for meteorological drought while 27–29 (12–13 %) by W _p, and 16–21 (7–9 %) by PDSI for pasture drought with different durations were identified over the four vegetation zones during the study period. Most of these droughts (80 % by SPI and 50–60 % by both W _p and PDSI) observed during the entire events occurred on a 1-month duration with moderate intensity. Drought frequencies were not significantly (p > 0.05) different within the four zones. The frequency of the short-term meteorological droughts was observed relatively greater than pasture droughts; however, pasture droughts were more persistent and severe than meteorological droughts. The three indices show that the frequency and severity of droughts have slightly increased over the 46 years with significant (p < 0.05) dry conditions during the last decade of 2001–2010 in the four zones (except in the high mountain). The results showed the W _p was more highly significantly correlated with the precipitation anomalies (r = 0.68) and pasture production (r = 0.55) than PDSI (r = 0.51, p < 0.05 and r = 0.38, p < 0.10, respectively). A statistical model, based on pasture production and the W _p, suggested that the consecutive drought months contribution during the growing season was 30 % (p < 0.05) and that pasture production was more sensitive to the occurrence of droughts during June–August (R ² = 0.32, p < 0.05) as seen in 2000–2002 and 2007. We concluded that a greater severity and frequency of growing-season droughts, during the last decade of 2001–2010, have driven a reduction in pasture production in Mongolia.     

Tsunami hazard assessment of Canada

We present a preliminary probabilistic tsunami hazard assessment of Canadian coastlines from local and far-field, earthquake, and large submarine landslide sources. Analyses involve published historical, palaeotsunami and palaeoseismic data, modelling, and empirical relations between fault area, earthquake magnitude, and tsunami run-up. The cumulative estimated tsunami hazard for potentially damaging run-up (≥1.5 m) of the outer Pacific coastline is ~40–80 % in 50 years, respectively one and two orders of magnitude greater than the outer Atlantic (~1–15 %) and the Arctic (<1 %). For larger run-up with significant damage potential (≥3 m), Pacific hazard is ~10–30 % in 50 years, again much larger than both the Atlantic (~1–5 %) and Arctic (<1 %). For outer Pacific coastlines, the ≥1.5 m run-up hazard is dominated by far-field subduction zones, but the probability of run-up ≥3 m is highest for local megathrust sources, particularly the Cascadia subduction zone; thrust sources further north are also significant, as illustrated by the 2012 Haida Gwaii event. For Juan de Fuca and Georgia Straits, the Cascadia megathrust dominates the hazard at both levels. Tsunami hazard on the Atlantic coastline is dominated by poorly constrained far-field subduction sources; a lesser hazard is posed by near-field continental slope failures similar to the 1929 Grand Banks event. Tsunami hazard on the Arctic coastline is poorly constrained, but is likely dominated by continental slope failures; a hypothetical earthquake source beneath the Mackenzie delta requires further study. We highlight areas susceptible to locally damaging landslide-generated tsunamis, but do not quantify the hazard.     

The use of the weights-of-evidence modeling technique to predictive porphyry Cu potential mapping in Ahar–Arasbaran zone,Iran

The weights-of-evidence is a data-driven method that provides a simple approach to integration of diverse geo-data set information. In this study, we will use weights-of-evidence to build a model for predicting tracts in the Ahar–Arasbaran zone of Urumieh-Dokhtar orogenic belt (northwestern Iran) that are favorable for porphyry copper deposits. Weights of evidence are a data-driven method requiring known deposits and occurrences that are used as training points in the evaluated area. This zone hosts two major porphyry Cu deposits (The Sarcheshmeh deposit contains 450 million tonnes of sulfide ore with an average grade of 1.13 % Cu and 0.03 % Mo and Sungun deposit, which has 500 million tonnes of sulfide reserves grading 0.76 % Cu and 0.01 % Mo), and a number of subeconomic porphyry copper deposits are all associated with Mid- to Late Miocene diorite/granodiorite to quartz-monzonite stocks. Five evidential layers including geology, alteration, geochemistry, geophysics, and faulting are chosen for potential mapping. Weight factors were determined based on the applied method to generate last mineral prospectivity map. The studied area reduces to less than 11.78 %, while large zones are excluded for further studies. This result represents a significant area reduction and may help to better focus on mineral exploration targeting porphyry copper deposits in the Ahar–Arasbaran zone.     

Spatiotemporal variation of meteorological droughts based on the daily comprehensive drought index in the Haihe River basin,China

Meteorological droughts can affect large areas and may have serious environmental, social and economic impacts. These impacts depend on the severity, duration, and spatial extent of the precipitation deficit and the socioeconomic vulnerability of the affected regions. This paper examines the spatiotemporal variation of meteorological droughts in the Haihe River basin. Meteorological droughts events were diagnosed using daily meteorological data from 44 stations by calculating a comprehensive drought index (CI) for the period 1961–2011. Based on the daily CI values of each station over the past 50 years, the drought processes at each station were confirmed, and the severity, duration and frequency of each meteorological drought event were computed and analyzed. The results suggest the following conclusions: (1) the use of the CI index can effectively trace the development of drought and can also identify the duration and severity of each drought event; (2) the average drought duration was 57–85 days in each region of the Haihe River basin, and the region with the highest average values of drought duration and drought severity was Bohai Bay; (3) drought occurred more than 48 times over the study period, which is more than 0.95 times per year over the 50 years studied. The average frequencies of non-drought days, severe drought days and extreme drought days over the study period were 51.2, 3.2 and 0.4 %, respectively. Severe drought events mainly occurred in the south branch of the Hai River, and extreme drought events mainly occurred in the Shandong Peninsula and Bohai Bay; (4) the annual precipitation and potential evapotranspiration of the Haihe River basin show decreasing trends over the past 50 years. The frequency of severe drought and extreme drought events has increased in the past 20 years than during the period 1961–1990. The results of this study may serve as a reference point for decision regarding basin water resources management, ecological recovery and drought hazard vulnerability analysis.

     

Spatiotemporal variation of meteorological droughts based on the daily comprehensive drought index in the Haihe River basin,China

Meteorological droughts can affect large areas and may have serious environmental, social and economic impacts. These impacts depend on the severity, duration, and spatial extent of the precipitation deficit and the socioeconomic vulnerability of the affected regions. This paper examines the spatiotemporal variation of meteorological droughts in the Haihe River basin. Meteorological droughts events were diagnosed using daily meteorological data from 44 stations by calculating a comprehensive drought index (CI) for the period 1961–2011. Based on the daily CI values of each station over the past 50 years, the drought processes at each station were confirmed, and the severity, duration and frequency of each meteorological drought event were computed and analyzed. The results suggest the following conclusions: (1) the use of the CI index can effectively trace the development of drought and can also identify the duration and severity of each drought event; (2) the average drought duration was 57–85 days in each region of the Haihe River basin, and the region with the highest average values of drought duration and drought severity was Bohai Bay; (3) drought occurred more than 48 times over the study period, which is more than 0.95 times per year over the 50 years studied. The average frequencies of non-drought days, severe drought days and extreme drought days over the study period were 51.2, 3.2 and 0.4 %, respectively. Severe drought events mainly occurred in the south branch of the Hai River, and extreme drought events mainly occurred in the Shandong Peninsula and Bohai Bay; (4) the annual precipitation and potential evapotranspiration of the Haihe River basin show decreasing trends over the past 50 years. The frequency of severe drought and extreme drought events has increased in the past 20 years than during the period 1961–1990. The results of this study may serve as a reference point for decision regarding basin water resources management, ecological recovery and drought hazard vulnerability analysis.     

Probabilistic seismic hazard analysis of interconnected infrastructure: a case of Iranian high-pressure gas supply system

Seismic hazard maps are widely used for engineering design, land-use planning, and disaster mitigation. The development of the new seismic hazard map of Iran with regard to the specification of Iranian high-pressure gas network is based on probabilistic seismic hazard analysis using the historical and new earthquakes data, geology, tectonics, fault activity, and seismic zone models in Iran. The map displays the probabilistic estimates of peak ground acceleration for the return period of 2,475 year (2 % probability in 50 years). The results presented in this study will provide the basis for the preparation of risk map, the estimation of insurance premiums, finding best paths for future pipelines, planning, and relocating lifeline facilities especially for interconnected infrastructures.     

Landslide vulnerability and risk assessment for multi-hazard scenarios using airborne laser scanning data (LiDAR)

Landslide hazard, vulnerability, and risk-zoning maps are considered in the decision-making process that involves land use/land cover (LULC) planning in disaster-prone areas. The accuracy of these analyses is directly related to the quality of spatial data needed and methods employed to obtain such data. In this study, we produced a landslide inventory map that depicts 164 landslide locations using high-resolution airborne laser scanning data. The landslide inventory data were randomly divided into a training dataset: 70 % for training the models and 30 % for validation. In the initial step, a susceptibility map was developed using logistic regression approach in which weights were assigned to every conditioning factor. A high-resolution airborne laser scanning data (LiDAR) was used to derive the landslide conditioning factors for the spatial prediction of landslide hazard areas. The resultant susceptibility was validated using the area under the curve method. The validation result showed 86.22 and 84.87 % success and prediction rates, respectively. In the second stage, a landslide hazard map was produced using precipitation data for 15 years. The precipitation maps were subsequently prepared and show two main categories (two temporal probabilities) for the study area (the average for any day in a year and abnormal intensity recorded in any day for 15 years) and three return periods (15-, 10-, and 5-year periods). Hazard assessment was performed for the entire study area. In the third step, an element at risk map was prepared using LULC, which was considered in the vulnerability assessment. A vulnerability map was derived according to the following criteria: cost, time required for reconstruction, relative risk of landslide, risk to population, and general effect to certain damage. These criteria were applied only on the LULC of the study area because of lack of data on the population and building footprint and types. Finally, risk maps were produced using the derived vulnerability and hazard information. Thereafter, a risk analysis was conducted. The LULC map was cross-matched with the results of the hazard maps for the return period, and the losses were aggregated for the LULC. Then, the losses were calculated for the three return periods. The map of the risk areas may assist planners in overall landslide hazard management.     

Spatial and temporal analysis of drought risk during the crop-growing season over northeast China

Northeast China as an important agricultural zone for commercial and economic crop in China suffered from increased drought risk that seriously threatened agricultural production and food security in recent decades. Based on precipitation datasets from 71 stations from 1960 to 2009 and on the reliable statistical methods of the Mann–Kendall test, Sen’s slope and the Standardized Precipitation Index, we analyzed the temporal and spatial variation of drought occurrence during the crop-growing season (from May to September) and summer (from June to August). The results showed that regional mean precipitation during the crop-growing season and summer over the last 40 years has decreased at the rate of ?1.72 and ?1.12 mm/year, respectively. According to timescale analysis of abrupt changes, there were two distinct time series (1965–1983 and 1996–2009) with decreasing precipitation trends at a 95 % confidence level. A comparison between the two time series of these two periods demonstrated that more frequent and more severe drought occurred during 1996–2009. Furthermore, drought risk in recent decades has become even more serious both in severity and in extent. Especially in the crop-growing season of 2001 and summer of 2007, over 25 % (2.0 × 10⁵ km²) of study area experienced severe drought (serious and extreme droughts). Our results highlight the urgent need for the development of effective drought adaptations for cropland over northeast China.     

Determining the groundwater potential recharge zone and karst springs catchment area: Saldoran region,western Iran

Assessing the groundwater recharge potential zone and differentiation of the spring catchment area are extremely important to effective management of groundwater systems and protection of water quality. The study area is located in the Saldoran karstic region, western Iran. It is characterized by a high rate of precipitation and recharge via highly permeable fractured karstic formations. Pire-Ghar, Sarabe-Babaheydar and Baghe-rostam are three major karstic springs which drain the Saldoran anticline. The mean discharge rate and electrical conductivity values for these springs were 3, 1.9 and 0.98 m³/s, and 475, 438 and 347 μS/cm, respectively. Geology, hydrogeology and geographical information system (GIS) methods were used to define the catchment areas of the major karstic springs and to map recharge zones in the Saldoran anticline. Seven major influencing factors on groundwater recharge rates (lithology, slope value and aspect, drainage, precipitation, fracture density and karstic domains) were integrated using GIS. Geology maps and field verification were used to determine the weights of factors. The final map was produced to reveal major zones of recharge potential. More than 80 % of the study area is terrain that has a recharge rate of 55–70 % (average 63 %). Evaluating the water budget of Saldoran Mountain showed that the total volume of karst water emerging from the Saldoran karst springs is equal to the total annual recharge on the anticline. Therefore, based on the geological and hydrogeological investigations, the catchment area of the mentioned karst springs includes the whole Saldoran anticline.     

Delineation of groundwater potential zone in Chhatna Block, Bankura District, West Bengal, India using remote sensing and GIS techniques

Groundwater is one of the most valuable natural resources, which is an immensely important and dependable source of water supply in all climatic regions over the world. Groundwater is in demand in areas where surface water supply is inadequate and nonsexist in the Chhatna Block, Bankura district and is located on the eastern slope of Chotonagpur Plateau, which is mapped on 73 I/15, 73 I/16 and 73 M/3, and falls between latitude 23°10′–23°30′N and longitude 86°47′–87°02′E. It represents plain land and gentle slope, which is responsible for infiltration and groundwater recharge. The groundwater in this region is confined within the fracture zones and weathered residuum. The present investigation is, therefore, undertaken to delineate potential zones for groundwater development with the help of a remote-sensing study. IRS–LISS-III data along with other data sets, e.g., existing toposheets and field observation data, have been utilized to extract information on the hydrogeomorphic features which include valley fills, buried pediment moderate, buried pediment shallow and structural hills, lineament density contour and slope map of this hard rock terrain. The target of this study is to delineate the groundwater potential zones in Chhatna block, Bankura District, West Bengal. Satellite imagery, along with other data sets, has been utilized to extract information on the groundwater controlling features of this study area. Three features (hydrogeomorphology, slope, and lineaments) that influence groundwater occurrences were analyzed and integrated. All the information layers have been integrated through GIS analysis and the groundwater potential zones have been delineated. The weighted index overlay method has been followed to delineate groundwater potential zones. The results indicate that good to excellent groundwater potential zones are available in almost the entire block. The results show that there is good agreement between the predicted groundwater potential map and the existing groundwater borehole databases. The area is characterized by hard rock terrain—still due to the presence of planation surface along valley fills; it became the prospective zone. The area has been categorized into four distinct zones: excellent, good, fair and poor. Excellent groundwater potential zones constitute 30–35 % of the total block area; good groundwater potential zones occupy a majority of the block, covering approximately 55–60 % and the fair potential zones occupy about 10–15 % of the total block. Poor potential zones occupy a very insignificant portion (less than 1 %).     

Modeling forest fire risk in the northeast of Iran using remote sensing and GIS techniques

Fire in forested areas can be regarded as an environmental disaster which is triggered by either natural forces or anthropogenic activities. Fires are one of the major hazards in forested and grassland areas in the north of Iran. Control of fire is difficult, but it is feasible to map fire risk by geospatial technologies and thereby minimize the frequency of fire occurrences and damages caused by fire. The fire risk models provide a suitable concept to understand characterization of fire risk. Some models are map based, and they combine effectively different forest fire–causing variables with remote sensing data in a GIS environment for identifying and mapping forest fire risk. In this study, Structural Fire Index, Fire Risk Index, and a new index called Hybrid Fire Index were used to delineate fire risk in northeastern Iran that is subjected to frequent forest fire. Vegetation moisture, slope, aspect, elevation, distance from roads, and vicinity to settlements were used as the factors influencing accidental fire starts. These indices were set up by assigning subjective weight values to the classes of the layers based on their sensitivity ratio to fire. Hot spots data derived from MODIS satellite sensor were used to validate the indices. Assessment of the indices with receiver operating characteristic (ROC) curves shows that 76.7 % accuracy of the HFI outperformed the other two indices. According to the Hybrid Fire Index, 57.5 % of the study area is located under high-risk zone, 33 % in medium-risk zone, and the remaining 9.5 % area is located in low-risk zone.     

Assessing the effects of the climate change on land cover changes in different time periods

The present research evaluated the relation between the normalized difference vegetation index (NDVI) changes and the climate change during 2000–2014 in Qazvin Plain, Iran. Daily precipitation and mean temperature values during 2015–2040 and 2040–2065 were predicted using the statistical downscaling model (SDSM), and these values were compared with the values of the base period (2000–2014). The MODIS images (MOD13A2) were used for NDVI monitoring. In order to investigate the effects of climate changes on vegetation, the relationship between the NDVI and climatic parameters was assessed in monthly, seasonal, and annual time periods. According to the obtained results under the B2 scenario, the mean annual precipitation at Qazvin Station during 2015–2040 and 2040–2065 was 6.7 mm (9.3%) and 8.2 mm (11.36%) lower than the values in the base period, respectively. Moreover, the mean annual temperature in the mentioned periods was 0.7 and 0.92 °C higher than that in the base period, respectively. Analysis of the correlations between the NDVI and climatic parameters in different periods showed that there is a significant correlation between the seasonal temperature and NDVI (P < 0.01). Moreover, the NDVI will increase 0.009 and 0.011 during 2015–2040 and 2040–2065, respectively.     

Drought risk assessment in the western part of Bangladesh

Though drought is a recurrent phenomenon in Bangladesh, very little attention has been so far paid to the mitigation and preparedness of droughts. This article presents a method for spatial assessment of drought risk in Bangladesh. A conceptual framework, which emphasizes the combined role of hazard and vulnerability in defining risk, is used for the study. Standardized precipitation index method in a GIS environment is used to map the spatial extents of drought hazards in different time steps. The key social and physical factors that define drought vulnerability in the context of Bangladesh are identified and corresponding thematic maps in district level are prepared. Composite drought vulnerability map is developed through the integration of those thematic maps. The risk is computed as the product of the hazard and vulnerability. The result shows that droughts pose highest risk to the northern and northwestern districts of Bangladesh.     

Mapping flood susceptibility in an arid region of southern Iraq using ensemble machine learning classifiers: a comparative study

This study examined the efficacy of three machine ensemble classifiers, namely, random forest, rotation forest and AdaBoost, in assessing flood susceptibility in an arid region of southern Iraq. A dataset was created from flooded and non-flooded areas to train and validate the ensemble classifiers using a binary classification scheme (1—flood, 0—non-flood). The prepared dataset was then partitioned into two sets with a 70/30 ratio: 70% (2478 pixels) for training and 30% (1062 pixels) for testing. A total of 10 influential flood factors were selected and prepared based on data availability and a literature review. The selected factors were surface elevation, slope, plain curvature, topographic wetness index, stream power index, distance to rivers, drainage density, lithology, soil and land use/land cover. The information gain ratio was first utilised to explore the predictive abilities of the factors. The predictive performances of the three ensemble models were compared using six statistical measures: sensitivity, specificity, accuracy, kappa, root mean square error and area under the operating characteristics curve. The results revealed that the AdaBoost classifier was the best in terms of the statistical measures, followed by the random forest and rotation forest models. A flood susceptibility map was prepared based on the result of each classifier and classified into five zones: very low, low, moderate, high and very high. For the model with the best performance, i.e., the AdaBoost model, these zones were distributed over an area of 6002 km² (44%) for the very low–low zone, 2477 km² (18%) for the moderate zone and 5048 km² (40%) for the high–very high zones. This study proved the high capabilities of ensemble machine learning classifiers to decipher flood susceptibility zones in an arid region.     

Landslide susceptibility mapping at Vaz Watershed (Iran) using an artificial neural network model: a comparison between multilayer perceptron (MLP) and radial basic function (RBF) algorithms

Landslide susceptibility and hazard assessments are the most important steps in landslide risk mapping. The main objective of this study was to investigate and compare the results of two artificial neural network (ANN) algorithms, i.e., multilayer perceptron (MLP) and radial basic function (RBF) for spatial prediction of landslide susceptibility in Vaz Watershed, Iran. At first, landslide locations were identified by aerial photographs and field surveys, and a total of 136 landside locations were constructed from various sources. Then the landslide inventory map was randomly split into a training dataset 70 % (95 landslide locations) for training the ANN model and the remaining 30 % (41 landslides locations) was used for validation purpose. Nine landslide conditioning factors such as slope, slope aspect, altitude, land use, lithology, distance from rivers, distance from roads, distance from faults, and rainfall were constructed in geographical information system. In this study, both MLP and RBF algorithms were used in artificial neural network model. The results showed that MLP with Broyden–Fletcher–Goldfarb–Shanno learning algorithm is more efficient than RBF in landslide susceptibility mapping for the study area. Finally the landslide susceptibility maps were validated using the validation data (i.e., 30 % landslide location data that was not used during the model construction) using area under the curve (AUC) method. The success rate curve showed that the area under the curve for RBF and MLP was 0.9085 (90.85 %) and 0.9193 (91.93 %) accuracy, respectively. Similarly, the validation result showed that the area under the curve for MLP and RBF models were 0.881 (88.1 %) and 0.8724 (87.24 %), respectively. The results of this study showed that landslide susceptibility mapping in the Vaz Watershed of Iran using the ANN approach is viable and can be used for land use planning.     

Recognition of the regional lineaments of Iran: Using geospatial data and their implications for exploration of metallic ore deposits

The relationship between major structural lineaments and locations of ore deposits in Iran has been investigated using geospatial data. In the course of lineament extraction, satellite images, aeromagnetic data, digital elevation model (DEM) and structural maps were processed and the lineaments and large-scale faults were identified. The extracted lineaments, based on subjective assessment, from each dataset were imported into GIS software and the “lineament map of Iran” was prepared by data integration. The analysis for selecting significant lineament was mainly based on fault correlated lineament and lineament with field map fractures, which was sets as benchmarks for compiling a final output map. Four major regional lineament trends of N–S, E–W, NW–SE and NE–SW were identified in the data of all images, which are corresponded to the structural zones and the major fault systems of Iran. The mineral deposits (active and abandoned) and mineral indications database compiled are based on the published maps, papers, reports and the ore deposits data files of Geological Survey of Iran. Integrating the output of these two datasets by GIS software resulted in the “Combined Map of Lineaments and Gold, Copper, Lead, Zinc and Iron Deposits of Iran”. The number and distance of ore deposits toward the lineaments were processed by the counting and cumulative methods in the GIS software's. Approximately, over 90% of the ore deposits of Iran are located in the central part of the lineaments (15 km on each side) which are concordant with a definition of large lineament. About 50% of these mineral deposits are closer than 5 km to the lineaments. There are significant correlations between lineament density and intersections with ore deposits occurrences. The observed associations at this scale are informative in establishing exploration strategy and decreasing exploration risks for detailed work on ore deposit scale.     

Assessing cadmium risk in wheat grain using soil threshold values

At present, the prior-established threshold values are widely used to classify contaminated agricultural soils with heavy metals under the cultivation of a variety of crops, without considering the different sensitivity of plants to heavy metals. Evaluation of the characteristics of cadmium transfer from a polluted calcareous soil to cultivated wheat crop and assessment of the efficiency of using the threshold values to reflect the soil pollution risk by cadmium in Zanjan Zinc Town area at the northwest of Iran were the goals of this study. Totally, 65 topsoil (0–20 cm) and corresponding wheat samples of an agricultural region in the proximity of a metallurgical factory were collected and analyzed for cadmium concentration. The results revealed that industrial activities strongly control cadmium distribution in the studied soils. Relatively high bioavailable cadmium contents (mean 0.77 mg kg^?1) were found in the soils, notwithstanding their alkalinity. It was observed that just 22.5% of the studied area around the Zinc Town is covered by polluted soils with the cadmium concentration exceeding the maximum permissible concentration of 5 mg kg^?1, whereas cadmium concentration in wheat grains of 19 sampled plants is higher than the threshold value of 0.2 mg kg^?1. Among these polluted plants, a total of eight samples were grown in areas classified as unpolluted soils with cadmium, based on the soil threshold value. It seems that this misclassification of polluted soils is mainly related to the crop sensitivity to heavy metals uptake from the soil which should be considered.     

Dynamic risk prediction based on discriminant analysis for maize drought disaster

This study presents a discriminant analysis-based method for prediction of agriculture drought disaster risk. We selected the Chaoyang city in the Northeast China as the study area. We employed multi-scale standard precipitation index (SPI) to reflect drought hazard. We used the yield losses to indicate the drought disaster risk, which was divided into no, low, or high drought risk. We used the multi-scale SPI and drought disaster risk as the input factors for the discriminant analysis-based risk prediction model. The results showed that the model’s prediction accuracy varied between 40 and 82.4 %. The accuracy of high drought disaster risk category was higher than low and no drought disaster risk category. The prediction accuracy of the milky maturity stage was highest. We use leave-one-out cross-validation method to validate the model’s accuracy. And the results showed that the model validation accuracy of high drought group could reach 70.6 % in milky maturity stage. This study showed discriminant analysis is an effective and operable method for disaster risk prediction. This model can provide timely information for decision makers to make effective measures for drought disaster management and to reduce the drought effects to yields at the minimum level.     

A comparative frequency analysis of three standardized drought indices in the Poyang Lake basin,China

Regional drought frequency analysis was carried out in the Poyang Lake basin (PLB) from 1960–2014 based on three standardized drought indices: the standardized precipitation index (SPI), the standardized precipitation evapotranspiration index (SPEI) and the standardized Palmer drought index (SPDI). Drought events and characteristics were extracted. A Gumbel–Hougaard (GH) copula was selected to construct the bivariate probability distribution of drought duration and severity, and the joint return periods (T _a ) were calculated. Results showed that there were 50 (50 and 40) drought events in the past 55 years based on the SPI (SPEI and SPDI), and 9 (8 and 10) of them were severe with T _a more than 10 years, occurred in the 1960s, the 1970s and the 2000s. Overall, the three drought indices could detect the onset of droughts and performed similarly with regard to drought identification. However, for the SPDI, moisture scarcity was less frequent, but it showed more severe droughts with substantially higher severity and longer duration droughts. The conditional return period (Ts|d) was calculated for the spring drought in 2011, and it was 66a and 54a, respectively, based on the SPI and SPDI, which was consistent with the record. Overall, the SPI, only considering the precipitation, can as effectively as the SPEI and SPDI identify the drought process over the PLB under the present changing climate. However, drought is affected by climate and land-cover changes; thus, it is necessary to integrate the results of drought frequency analysis based on different drought indices to improve the drought risk management.