Effects of climate change on spring wheat phenophase and water requirement in Heihe River basin,China

Climate change has significantly altered the temperature rhythm which is a key factor for the growth and phenophase of the crop. And temperature change further affects crop water requirement and irrigation system. In the north-west of China, one of the most important crop production bases is Heihe River basin where the observed phenological data is scarce. This study thus first adopted accumulated temperature threshold (ATT) method to define the phenological stages of the crop, and analysed the effect of climate change on phenological stages and water requirement of the crop during growing season. The results indicated the ATT was available for the determination of spring wheat phenological stages. The start dates of all phenological stages became earlier and the growing season length (days) was reduced by 7 days under climate change. During the growing season, water requirement without consideration of phenophase change has been increased by 26.1 mm, while that with consideration of phenophase change was featured in the decrease of water requirement by 50 mm. When temperature increased by 1°C on average, the changes were featured in the 2 days early start date of growing season, 2 days decrease of growing season length, and the 1.4 mm increase of water requirement, respectively.     

Decadal Changes in Water Quality and Net Productivity of a Shallow Danish Estuary Following Significant Nutrient Reductions

We utilized an extensive data set (1977–2013) from a water quality monitoring program to investigate the recovery of a Danish estuary following large reductions in total phosphorus (TP) and total nitrogen (TN) loading. Monthly rates of net transport and biogeochemical transformation of dissolved inorganic nitrogen (DIN) and phosphorus (DIP) were computed in two basins of the estuary using a box model approach, and oxygen-based rates of net ecosystem production (NEP) were determined. Since 1990, nutrient loading was reduced by 58 % for nitrogen and 80 % for phosphorus, causing significant decreases in DIN (60 %) and DIP (85 %) concentrations. Reductions in nutrient loadings and concentrations reduced annual chlorophyll levels by 50 % in the inner estuary and improved Secchi depth by approximately 1 m during the same period, particularly in the summer period. In the outer, deeper region of the estuary trends in water quality was less evident. Improvements in the inner estuary were strongly coupled to declines in DIN. Thresholds of DIN and DIP concentrations limiting phytoplankton growth indicated that both regions of the estuary were nitrogen limited. NEP rates indicated the development of more net autotrophic conditions over time that were likely associated with higher benthic primary production stimulated by improved light conditions. Box model computations revealed a modest reduction in summer net production of DIP over time, despite the persistence of elevated fluxes for several years after external loads were reduced. Since the mid-1990s, nutrient loading and transformation were stable while nutrient concentrations continued to decline and water quality improved in the inner estuary. The oligotrophication trajectory involved an initial fast transformation and modest retention of nutrients followed by a gradual decline in the rate of improvement towards a new stable condition.     

Review of water and soil contamination in and around Salihli geothermal field (Manisa,Turkey)

The town of Salihli is situated in Gediz Graben in the western Anatolia. This region is important in terms of industry, mining, geothermal energy, water sources, and agricultural production. Geothermal flow and anthropogenic activities in Salihli threaten the surrounding environment due to the contamination of cold groundwater, surface water, and soil. The goal of the present study is to determine the environmental effects of the geothermal and anthropogenic activities in Salihli on soil, stream sediments, and water. Stream sediments and farm soil have been contaminated by substances derived from geothermal and industrial effluents. To this end, the quality review of the water was completed and the heavy metal levels in stream sediment samples were measured to determine the extent of contamination. The elements As, B, Br, Fe, and Ni are the major contaminants present in surface water and groundwater in the study area. The concentrations of these elements excess tolerance limits of international water standards. Gibbsite, K-mica, kaolinite, sepiolite, halite, sulfur, willemite, and Pb(OH)₂ might be precipitated as scales at low temperatures on the soil; this could be interpreted as a resultant from soil contamination. The concentrations of 17 elements (As, Ba, B, Cd, Co, Cr, Cu, Fe, Hg, Li, Mo, Mn, Ni, Pb, Sb, Sr, and Zn) were measured in samples from stream sediments and surface soils. In the study area, especially geothermal and anthropogenic activities give rise to environmental pollution.     

Wavefield analysis of crosswell seismic data

Despite the numerous advantages of crosswell seismic data over surface seismic data, crosswell seismic geophysics is still underutilized and underdeveloped. The factors limiting the full utilization of crosswell data include the lack of standardized methods for processing and imaging the data. This is because crosswell data is not completely understood. To improve the understanding of crosswell data, we performed acoustic and elastic modeling of a west Texas carbonate oilfield data using finite difference methods and crosswell geometry. To account for the different wave modes in the field data, we decomposed the full data into its constituent wave modes. Results of the forward modeling show that elastic synthetic data is a better representation of crosswell field data than the popular acoustic synthetic data. Wavefield decomposition gave insight into the time-space kinematics behavior of the different wave modes that constitute the full data. Overall, the study improved our understanding of crosswell field data. The learning from this study has been utilized to perform data-driven reflection enhancement processing where the discerned characteristic of different seismic arrival is utilize to suppress unwanted and enhanced the desired wave modes. The processing reduced the complex data to only up-going P-P reflections that can be imaged to reveal the subtle geological structures of the oilfield.     

Temporal change of glaciers area and geomorphometric parameters in Parbati valley,Himachal Pradesh,India

Temporal change in the glacier coverage is analyzed for the period between 1962 and 2003 in Parbati valley, Himachal Pradesh. It is observed that the total glacier cover has been decreased by 17% ranging between 8 and 100% for individual glacier. The pattern of de-glaciation shows a high degree of shrinkage in outer zone of Parbati valley, while least shrinkage is observed in the inner valley. The present study is conducted to establish relationship between glacio-geomorphic parameters and glaciers shrinkage pattern to predict the future glacier cover in warming scenario. A systematic change is observed for glacio-geomorphic parameters associated with temporal change in glacier cover. It is observed that mean and minimum elevation, slope, relief and duration of insolation have changed substantially. Maximum elevation, plan/profile curvatures and aspect have shown less change from 1962 to 2003. A correlation matrix between glacio-geomorphic parameters for glaciers between 1962 and 2003 shows that the recent glaciers are much more controlled by terrain characteristics than that in the recent past.     

Combining AHP and genetic algorithms approaches to modify DRASTIC model to assess groundwater vulnerability: a case study from Jianghan Plain,China

Accurate identification of vulnerability areas is critical for groundwater resources protection and management. The present study employed the modified DRASTIC model to assess the groundwater vulnerability of Jianghan Plain, a major farming area in central China. DRASTICL model was developed by incorporating the land use factor to the original model. The ratings and weightings of the selected parameters were optimized by analytic hierarchy process (AHP) method and genetic algorithms (GAs) method, respectively. A combined AHP–GAs method was proposed to further develop this methodology. The unity-based normalization process was employed to categorize the vulnerability maps into four types, such as very high (>0.75), high (0.5–0.75), low (0.25–0.5), and very low (<0.25). The accuracy of vulnerability mapping was validated by Pearson’s correlation coefficient between vulnerability index and the nitrate concentration in groundwater and analysis of variance F statistic. The results revealed that the modified DRASTIC model had a large improvement over the conventional model. The correlation coefficient increased significantly from 41.07 to 75.31% after modification. Sensitivity analysis indicated that the depth to groundwater with 39.28% of mean effective weight was the most critical factor affecting the groundwater vulnerability. The developed vulnerability model proposed in this study could provide important objective information for groundwater and environmental management at local level and innovation for international researchers.     

Basin hydrogeological characterization using remote sensing,hydrogeochemical and isotope methods (the case of Baro-Akobo,Eastern Nile,Ethiopia)

Hydrogeochemical and isotopic signatures of the waters of the Baro-Akobo River Basin show deviation from signatures in other Ethiopian river basins. In this study, hydrogeochemical and isotope methods were employed to determine regional and local hydrogeology and characteristics of the basin. Optical, thermal and radar remote sensing products were used to update geological and structural maps of the basin and determine sampling points using the judgment sampling method. A total of 363 samples from wells, springs, rivers, lakes, swamps and rain were collected for this study, and an additional 270 water quality data sets were added from previous studies. These data were analyzed for their hydrogeochemical characteristics and isotope signatures. Analysis of the oxygen, deuterium and tritium isotopes shows the groundwater of the basin is modern water. Among all basins in Ethiopia, the Baro-Akobo Basin shows the highest enrichment. This indicates the proximity of the rainfall sources, which presumably are the Sudd and other wetlands in South Sudan. The hydrochemical properties of the waters show evapotranspiration is the dominant hydrologic process in the basin and explains the large amount of water that is lost in the lowland plain. Analysis of radon-222 shows no significant groundwater flux over the wetlands, which are part of Machar Marshes. This shows evaporation to be dominant hydrologic process in this zone. Results from all analyses help explain the limited holding capacity of the aquifers in the recharge zone and their vulnerability to anthropogenic impacts and climate variability. There is a trend of decreasing surface flow and rainfall and increasing water soil erosion.     

Mapping landslide susceptibility with frequency ratio,statistical index,and weights of evidence models: a case study in northern Iran

The main objective of this study is to investigate potential application of frequency ratio (FR), weights of evidence (WoE), and statistical index (SI) models for landslide susceptibility mapping in a part of Mazandaran Province, Iran. First, a landslide inventory map was constructed from various sources. The landslide inventory map was then randomly divided in a ratio of 70/30 for training and validation of the models, respectively. Second, 13 landslide conditioning factors including slope degree, slope aspect, altitude, plan curvature, stream power index, topographic wetness index, sediment transport index, topographic roughness index, lithology, distance from streams, faults, roads, and land use type were prepared, and the relationships between these factors and the landslide inventory map were extracted by using the mentioned models. Subsequently, the multi-class weighted factors were used to generate landslide susceptibility maps. Finally, the susceptibility maps were verified and compared using several methods including receiver operating characteristic curve with the areas under the curve (AUC), landslide density, and spatially agreed area analyses. The success rate curve showed that the AUC for FR, WoE, and SI models was 81.51, 79.43, and 81.27, respectively. The prediction rate curve demonstrated that the AUC achieved by the three models was 80.44, 77.94, and 79.55, respectively. Although the sensitivity analysis using the FR model revealed that the modeling process was sensitive to input factors, the accuracy results suggest that the three models used in this study can be effective approaches for landslide susceptibility mapping in Mazandaran Province, and the resultant susceptibility maps are trustworthy for hazard mitigation strategies.     

Support vector machines and feed-forward neural networks for spatial modeling of groundwater qualitative parameters

The present study attempts to model the spatial variability of three groundwater qualitative parameters in Guilan Province, northern Iran, using artificial neural networks (ANNs) and support vector machines (SVMs). Data collected from 140 observation wells for the years 2002–2014 were used. Five variables, X and Y coordinates of the observation well, distance of the observation well from the shoreline, areal average 6-month rainfall depth, and groundwater level at the day of water quality sampling, were considered as primary input variables. In addition, nine qualitative variables were also considered as auxiliary input variables. Electrical conductivity (EC), sodium concentration (Na⁺), and sulfate concentration (SO₄ ^2?) of the groundwater in the region were estimated using ANNs and SVMs with different input combinations. The results showed that both ANNs and SVMs work well when the only primary input variable is the well location. The ANN yielded an RMSE of 1.03 mEq/l for SO₄ ^2?, 1.05 mEq/l for Na⁺, and 203.17 μS/cm for EC, using the X and Y coordinates of the observation wells in the study area. In the case of SVM, these values were, respectively, 0.87, 0.87, and 176.68. Considering the auxiliary input variables (pH, EC, and the concentrations of Na⁺, K⁺, Ca²⁺, Mg²⁺, Cl^?, SO₄ ^2?, and HCO₃ ^?) resulted in a significant decrease in the RMSE of both ANNs (0.22, 0.30, and 33.04) and SVMs (0.26, 0.34, and 36.23). Comparing these RMSE values with those of cokriging interpolation technique (0.59, 0.98, and 177.59) indicated that ANNs and SVMs produced more accurate estimates of the three qualitative parameters. The relative importance of auxiliary input variables was also determined using Gamma test. The output uncertainty of ANNs and SVMs were determined using p-factor and d-factor. The results showed that SVMs have less uncertainty than ANNs.