AI-based runoff simulation based on remote sensing observations: A case study of two river basins in the United States and Canada

Data-driven techniques are used extensively for hydrologic time-series prediction. We created various data-driven models (DDMs) based on machine learning: long short-term memory (LSTM), support vector regression (SVR), extreme learning machines, and an artificial neural network with backpropagation, to define the optimal approach to predicting streamflow time series in the Carson River (California, USA) and Montmorency (Canada) catchments. The moderate resolution imaging spectroradiometer (MODIS) snow-coverage dataset was applied to improve the streamflow estimate. In addition to the DDMs, the conceptual snowmelt runoff model was applied to simulate and forecast daily streamflow. The four main predictor variables, namely snow-coverage (S-C), precipitation (P), maximum temperature (T_max), and minimum temperature (T_min), and their corresponding values for each river basin, were obtained from National Climatic Data Center and National Snow and Ice Data Center to develop the model. The most relevant predictor variable was chosen using the support vector machine-recursive feature elimination feature selection approach. The results show that incorporating the MODIS snow-coverage dataset improves the models' prediction accuracies in the snowmelt-dominated basin. SVR and LSTM exhibited the best performances (root mean square error = 8.63 and 9.80) using monthly and daily snowmelt time series, respectively. In summary, machine learning is a reliable method to forecast runoff as it can be employed in global climate forecasts that require high-volume data processing.     

Multi-objective conflict resolution optimization model for reservoir’s selective depth water withdrawal considering water quality

Environmental Science and Pollution Research - This paper develops a multi-objective conflict resolution simulation-optimization model based on a leader-follower game to resolve conflicts between...     

Cadmium contamination in agricultural soils of Bangladesh and management by application of organic amendments: evaluation of field assessment and pot experiments

Environmental Geochemistry and Health - In recent years, cadmium (Cd) contamination in agricultural soils and its subsequent transfer to crops is one of the high-priority environmental and public...     

Assessment of geochemical modeling applications and research hot spots—a year in review

Environmental Geochemistry and Health - Geochemical modeling has been employed in several fields of science and engineering in recent years. This review seeks to provide an overview of case studies...     

Photocatalytic degradation of the acetaminophen by nanocrystal-engineered TiO2 thin film in batch and continuous system

• Photocatalytic activity was improved in TiO₂ thin film by rapid thermal annealing. • Photoreactor was designed for TiO₂ thin film. • Considerable reusability and durability of prepared photocatalysts were studied. Un-biodegradable pharmaceuticals are one of the major growing threats in the wastewaters. In the current study, TiO₂ thin film photocatalysts were designed by nanocrystal engineering and fabricated for degradation of the acetaminophen (ACE) in a photocatalytic reaction under UV light irradiation in batch and continuous systems. The photocatalyst was prepared by sputtering and then engineered by thermal treatment (annealing at 300℃ (T300) and 650℃ (T650)). The annealing effects on the crystallinity and photocatalytic activity of the TiO₂ film were completely studied; it was found that annealing at higher temperatures increases the surface roughness and grain size which are favorable for photocatalytic activity due to the reduction in the recombination rate of photo-generated electron-hole pairs. For the continuous system, a flat plate reactor (FPR) was designed and manufactured. The photocatalytic performance was decreased with the increase of flow rate because the higher flow rate caused to form the thicker film of the liquid in the reactor and reduced the UV light received by photocatalyst. The reusability and durability of the samples after 6 h of photocatalytic reaction showed promising performance for the T650 sample (annealed samples in higher temperatures).     

Vertical oil dispersion profile under non-breaking regular waves

An experimental program was conducted to investigate vertical oil dispersion of surface oil spills under non-breaking regular waves. The variation in oil concentration caused by oil dispersion in a water column was studied to determine the vertical oil dispersion profile. The experiments were performed using different waves characteristics for different volumes of oil spill to evaluate the variation in oil concentration at three depths at two sampling stations. The correlations between oil concentration and the main parameters of wave characteristics, oil spill volume, sampling depth, and distance of sampling stations to spill location were assessed. The results revealed that the trend of variation in oil concentration versus wave steepness is linear. The results obtained from experimental measurements indicated that the oil concentrations at mid-depth were 44–77 % and the concentrations near the flume bed were 12–33 % of the concentration near the water surface.     

Analyses on phenological and morphological variations of mangrove forests along the southwest coast of Bangladesh

Drastic changes in river discharge and salinity levels are threatening the phenology and morphology of the coastal mangrove forests of the Sundarbans of Bangladesh. We have used AVHRR GIMMS (1985–2006) and MODIS (2005–2010) satellite Normalized Difference Vegetation Index (NDVI) data to identify the temporal variation of the phenology of the mangroves. Linear interpolation and Fourier-based adjustment were applied to remove noise from the NDVI time series. Then linear regression analysis on a single area (8 km ? 8 km) and a composite of 36 areas for three NDVI statistics the annual minimum, annual average, and annual maximum were performed--over the time periods 1985–1990, 1990–2000, 2000–2006 and 2005–2010 to identify possible functional changes in NDVI time series around the Sundarbans. Furthermore, we used fourteen LANDSAT images spanning the period 1989–2010 to estimate the spatiotemporal rate of shoreline changes over the three time periods 1989–2000, 2000–2006, and 2006–2010. A decreasing trend in the annual minimum NDVI was observed in most of the areas of the Sundarbans for the period 1990–2000. During the years 2000–2006, the trends of the three NDVI statistics became significantly positive, indicating an improvement of the mangrove phenology. In the period 2005–2010, a decreasing trend in all the NDVI variables was again dominant. The coast underwent rapid erosion from 1989–2000 and 2006–2010. However, the rate substantially declined between 2000 and 2006, when accretion was dominant. The advent of the upstream Farakka barrage caused a significant reduction in the Ganges-Gorai River discharge and increased the salinity in and around the Sundarbans. Our study concludes that this may be responsible for the degradation of mangrove phenology and accelerated erosion in the earlier and recent periods. In the interim, 2000–2006, improved river discharge and salinity levels due to the Ganges water sharing agreement (1996) and dredging of the Gorai River bed (1998–1999) enhanced the mangrove phenology and helped the coast to gain land.     

Interaction of selenium and cadmium in wheat at different salinities

Abstract

Interaction of Se up to 8?mg?kg^?1 soil and Cd at 5 and 15?mg?kg^?1 soil in wheat at different salinities was evaluated. Se concentrations in wheat shoots were progressively increased in parallel to Se in soil but decreased by addition of Cd. With increasing salinity, Cd concentrations increased and Se decreased. At low salinity, low addition of Se to the soil reduced Cd accumulation. At moderate and high salinities, only high Se amendment led to decline in Cd uptake, at the expense of reduction in biomass yield. Se at 0.5?mg?kg^?1 soil alleviated the negative effect of Cd on shoot yield.     

Oxidative stress in the freshwater cyprinid crucian carp (Carassius carassius L.) upon chronic exposure to endosulfan

Oxidative stress and antioxidant responses of crucian carp, upon chronic exposure to endosulfan, were evaluated in vivo. The lethal concentration (LC_50–96?h) was 70 μg L^?1; on its basis, the fish were exposed to endosulfan at 20, 35, and 50 μg L^?1 and autopsy was done on days 1, 2, 3, 4, 7, 14, 21, 28, and 35. Lipid peroxidation was induced in a concentration-dependent manner, being highest at 50 μg L^?1 (3/4 LC_{50–96 h}, sub-lethal concentration-I, SL-I) on day 4 (720% versus control), followed in its extent (490%) at 30 μg L^?1 (1/2 LC_{50–96 h}, sub-lethal concentration-II, SL-II) on day 7 and lowest (260%) at 10 μg L^?1 (1/4 LC_{50–96 h}, sub-lethal concentration-III, SL-III) on day 14. Glutathione showed a concentration- and time-dependent elevation in the initial phase, with highest level on day 4 (180%) at SL-I, but showed significant reduction in all test concentrations from day 21 of post-exposure. Superoxide dismutase was decreased significantly throughout the study, with highest reduction (63%) on day 4 at SL-I; catalase increased in all test concentrations up to day 14 but showed a significant decrease from the day 28 of post-exposure. The potential role of these parameters as indicators of pesticide pollution in aquatic systems is discussed.     

Space-time stick-breaking processes for small area disease cluster estimation

We propose a space-time stick-breaking process for the disease cluster estimation. The dependencies for spatial and temporal effects are introduced by using space-time covariate dependent kernel stick-breaking processes. We compared this model with the space-time standard random effect model by checking each model’s ability in terms of cluster detection of various shapes and sizes. This comparison was made for simulated data where the true risks were known. For the simulated data, we have observed that space-time stick-breaking process performs better in detecting medium- and high-risk clusters. For the real data, county specific low birth weight incidences for the state of South Carolina for the years 1997–2007, we have illustrated how the proposed model can be used to find grouping of counties of higher incidence rate.