Grain size analysis of surface fluvial sediments in rivers in Kelantan, Malaysia

Grain size spectrum and textural parameters for the fluvial sediment bed in seven tropical rivers of Kelantan, Malaysia are presented in this article. The samples were collected from six tributaries to the main Sungai Kelantan spanning approximately 248 km stretch of water streams. Sand or gravel dominated river was identified for each river using the sediment composition analysis. Textural pattern shows complicated profiles of mean size and no consistent decreasing grain size and gradation parameter were observed towards the downstream flow. Most of the samples fall under the category of either very poorly sorted or poorly sorted and has very platykurtic kurtosis distributions. CM diagram (C=one percentile in microns and M = median grain size in microns) suggested that the deposition of fine-grained sediment for samples with median grain size d50 <1 mm are either by rolling, rolling and saltation or saltation and suspension

Comparison and Assessment of Two Lumped AWBM and Semi-Distributed SWAT Models in Monthly Runoff Simulation of Gharah-Sou River in Kermanashah Province, Iran

In recent years determination of different components of water balance has been the focus of many hydrological modeling techniques. In most basins, it is not feasible to measure all the different quantities for a detailed and enhanced hydrological modeling. Therefore, it is necessary to select a model capable of simulating hydrological events with the least number of variables; while being simple to use. Hence, in this paper the monthly runoff of Ravansar Sanjabi basin, Kermanshah, Iran was simulated through AWBM and SWAT models. AWBM is a lumped model simulating the runoff in basins using rainfall and evaporation variables. On the other hand, SWAT model is a continuous and semi-distributive model, which can simulate the hydrological processes in basins through a wide range of information such as physical information of basins (soil, land use, slope) as well as weather data such as precipitation, temperature, wind, relative humidity, and solar radiation. Simulation results during the calibration and validation periods were evaluated through two statistical indices: Nash–Sutcliffe efficiency (NSE) and coefficient of determination, R2. Comparison of calculated statistical coefficients showed that SWAT model has better results in simulating monthly runoff in calibration and validation periods so that the calculated NSEcoefficient was equal to 0.7 and 0.81 respectively and 0.63 and 0.5 for AWBM model respectively

Investigation of the Hydrological Response to Meteorological Drought in Kashkan Sub-Catchments

The meteorological drought phenomenon and its relationship to the hydrological response of the catchments are of particular importance in the development of programs related to the comprehensive water management of the basin. In this study, a common period (1982-2017) in five corresponding hydrometric and rainfall stations in the Kashkan watershed was considered. The regression relationship between rainfall and discharge was investigated. Then, the standardized precipitation index (SPI) and the standardized discharge index (SDI) were calculated in time scales of 3, 6, 9, 12, 18, and 24 months. The interrelationships of SPI and SDI in sub- catchments were analyzed using the correlation method. The results showed that in more than 50% of the study period, the meteorological drought was close to normal. hydrological drought investigation of the sub-catchments showed that in the catchments with fewer karst formations, the frequency of years with severe drought was higher. The maximum drought with a severe situation in the Afrine catchment was 20% and the minimum was 6% in the Cham-Anjir catchment. The trend of changes in correlation coefficients between SPI and SDI in Sarab- Seyed Ali, Pol-e Dokhtar, and Cham-Anjir were similar and the maximum was at 18- and 24-month time scale with a coefficient determination of 0.67

Comparative Evaluation of GLDAS, ESA CCI SM and SMAP Soil Moisture with in situ Measurements (Case Study: Lorestan Province)

The aim of this study was to evaluate the effectiveness of estimated soil moisture data obtained from the GLDAS, ESA and SMAP sensor databases with the observed data of the Silakhor Agricultural Meteorological Station to investigate the spatial and temporal variation of soil moisture in Lorestan province. The data used in this research include the soil moisture data of the Silakhor station, GLDAS database, ESA center and SMAP sensor products during a six-year period (2016-2021). Estimated soil moisture data were evaluated against observed data using R2, RMSE and MAD statistics. The results showed that the SMAP satellite is associated with underestimation and the GLDAS model and the ESA satellite are associated with overestimation of soil moisture. However, in general, the estimated soil moisture values of the three mentioned sources have good accuracy. The value of the correlation coefficient between observed soil moisture data with soil moisture data obtained from SMAP and ESA satellites and GLDAS model was obtained as 0.62, 0.59 and 0.72 respectively, and in the combined case (SMAP, ESA and GLDAS) the value of correlation coefficient was increased to 0.77, therefore, it is suggested to use combine data to use soil moisture estimation

The development of automated spatial and temporal measurement system for lab-scale local scour

The spatial and temporal measurements of local scour around bridge piers provide the quantification of local scouring process. Most studies on a laboratory scale faced difficulties in obtaining a holistic local spatial variability around bridge pier, especially for continuous small time interval. Long experimental period, which could take up to few days, does not permit a consistent time interval spatial scour measurement due in particular to the physical constraints that exist under laboratory conditions. This study proposed an automated, cost-effective system which is capable of detecting changes in both spatial and temporal local scour. The system allows measurement to be made by using data recorder at an adjustable distance (± 0.1 mm) and angle (± 0.1˚) from the original position, which is programmed and controlled with an Arduino, which is an open source microcontroller with multiple capability of controlling electrical components such as motors and sensors. When the data recorder is in position, data is automatically captured and sequentially saved at a particular spatial interval. In this study, a web camera was used as a data recorder to capture images in the azimuthal plane for a one-hour interval. Images were captured for 30 seconds per measurement per position. The system was set up to monitor and measure the temporal and spatial local scour continuously in an 80-hour experiment. Results show that the location of maximum scour depth varied for different time intervals, and migrated from downstream to upstream of the pier. The rate of scour decreased as duration of experiment was increased. The system was able to provide a holistic view of both spatial and temporal variability in the development of local scour on a laboratory scale

Strengthening livelihood resilience in upper catchments of dry areas by integrated natural resources management

The Livelihood Resilience project evolved around the hypothesis that better integrated management can improve the livelihoods of poor farming communities and increase the environmental integrity and water productivity of upstream watersheds in dry areas. This hypothesis was tested by researchers from different Iranian research and executive organizations and farming communities in two benchmark research watersheds in upper Karkheh River Basin in Iran, under the guidance of the ICARDA scientists. Participatory technology development, water, soil, erosion, land degradation and vegetation assessments, livelihood, gender and policy analyses, and integrated workshops delivered a set of principles for watershed management in dry areas

Temporal monitoring of corn (Zea mays L.) yield using grami model, satellite imagery, and climate data in a semi-arid area

Corn yield estimation constitutes a critical issue in agricultural management and food supply, especially in demographic pressure and climate change contexts. In light of precision and smart agriculture, this study aims to develop a diagnostic approach to temporally monitor and estimate corn yields using GRAMI (a model for simulating the growth and yield of grain crops), satellite images, and climate data at regional scale. The GRAMI-corn model is controlled by vegetation indices (VIs) derived from Landsat 8 satellite images and calibrated by climate data. The model performed and validated using information collected from twenty-five cornfields in a semiarid region in Ravansar, Iran. The average of under- or over-estimate yields was 919 kg ha−1. In addition, the absolute error between the average observed and estimated yield values for the region was 19.21% for the 2016 corn season. The results using the GRAMI-corn model showed an acceptable agreement with field measurements

Hydrometeorology of large snowfall and snowmelt events in the Southern Alps of New Zealand.

Seasonal snowpack is an important element of mountain cryosphere. In the New Zealand’s Southern Alps/ Kā Tiritiri o te Moana, seasonal snow cover is of socio economic importance because of its key role in energy, agriculture and tourism sectors. Despite the extensive snow cover in such alpine regions, the knowledge of the snow processes such as large snowfall and snowmelt events is limited. Large snowfall and snowmelt events are a result of atmospheric circulation patterns that influence moisture transfers and surface climate in mountain regions. However, analysis of the relationship between large snowfall and snowmelt events and atmospheric forcing in alpine regions has remained a challenge mainly due to the scarcity of climate observations and snow measurements at higher altitudes. A better understanding of the synoptic-scale weather patterns can provide an insight into the distinct characteristics of atmospheric forcing impacting snow accumulation and snowmelt processes, especially in remote mountain regions. Therefore, the primary aim of this dissertation is to improve the understanding of synoptic-scale atmospheric forcing during large snowfall and snowmelt events in alpine regions. The New Zealand Southern Alps, surrounded by the Pacific Ocean and in the path of the westerly air flows represent a typical maritime environment, making them an ideal location for the study of alpine snow processes. To explore the synoptic climatology of large snowfall and snowmelt events, the 90th percentile value of daily snowfall (snowmelt) from three automatic weather stations (AWS) across the Southern Alps was used. A composite anomaly approach using reanalysis atmospheric data (i.e. sea level pressure, temperature and geopotential heights) was applied to characterize the main synoptic-scale hydrometerological conditions associated with these events. Additionally, an analysis of integrated vapour transport (IVT) was conducted in order to learn more about the moisture transport characteristics of precipitation during large snowfall and major snowmelt events associated with rain on snow (ROS). The application of IVT fields allowed to identify the distinct characteristics of moisture transports and the potential role of atmospheric rivers (ARs) in transferring moisture across the Tasman Sea towards the Southern Alps. Large snowfall events were found to account for 20-40% of total annual snow accumulation. Synoptic-scale atmospheric patterns influence the variability in timing and magnitude of large snowfall and snowmelt events. Weather patterns during large snowfall events in the Southern Alps are mainly characterised by strong negative anomalies of sea level pressure (SLP) and geopotential heights at 500 hPa (Z500) located over the southwest of New Zealand’s South Island. However, over the New Zealand region, the days leading to large snowfall events experienced positive anomalies of Z500 accompanied by positive anomalies of low-tropospheric temperatures (850 hPa and 1000 hPa). These positive anomalies were associated with the passage of relatively warm airflows over the Tasman Sea and across the Southern Alps. Troughing regimes were found to account for ~78% of large snowfall events. Large snowmelt events, however, were found to take place during both high pressure systems and troughing regimes, with the majority of rapid snowmelt events (~80%) occurring during high pressure systems with anomalously high temperatures. Observations of snowmelt at Mueller Hut revealed that even though snowmelt mostly occurs during spring, considerable melt (~300 mm day⁻¹) can also occur during winter month. These significant winter-melt events were found to be associated with rain-on-snow events. Anomalies of temperature revealed rising mid- and low-tropospheric temperatures (at 500, 700 and 850 hPa) during both high-pressure and troughing systems associated with large snowmelt events. Atmospheric rivers making landfall in the Southern Alps were found to impact the seasonal snowpacks in two ways. Firstly, they produce large snowfall events and secondly, they generate major spring- and winter-time rain-on-snow (ROS) events. While ARs accounted for majority of large snowfall events across the Southern Alps (~70%), they were also responsible for nine out of ten largest ROS events identified at Mueller Hut station near the Main Divide of the Southern Alps. Similar hydrometeorological characteristics (e.g. duration and shape) were identified for both rain-producing and snow-generating ARs; however, in terms of strength, the former were found to contain higher IVT values over the Southern Alps (up to ~822 kg m⁻¹ s⁻¹). AR-related ROS events were characterised by anomalously high temperatures, high advection of warm airflows and rising freezing level resulting in warm environments over the snowpacks, with air temperatures as high as ~10 °C, creating ideal conditions for rapid snowmelt at higher altitudes. The results of this study have improved the current knowledge of the hydrometeorological characteristics of snow processes in a mid-latitude maritime climate. Considering the high sensitivity of seasonal snowpacks in maritime environments to changes in atmospheric variables, the findings will contribute to the research into further quantifying the impacts of climate change on atmospheric circulation patterns as well as the timing and frequency of rain- and snow-producing ARs in such regions

Classification of Shoulder Implants in X-ray Images Using Deep Learning

Total Shoulder Arthroplasty (TSA) is a type of surgery in which the damaged ball of the shoulder is replaced with a prosthesis. Many years later, this prosthesis may be in need of servicing or replacement. In some situations, such as when the patient has changed his country of residence, the model and the manufacturer of the prosthesis may be unknown to the patient and primary doctor. Correct identification of the implant’s model prior to surgery is required for selecting the correct equipment and procedure. We present a novel way to automatically classify shoulder implants in X-ray images. We employ deep learning models and compare their performance to alternative classifiers, such as random forests and gradient boosting. We find that deep convolutional neural networks outperform other classifiers significantly if and only if out-of-domain data such as ImageNet is used to pre-train the models. In a data set containing X-ray images of shoulder implants from 4 manufacturers and 16 different models, deep learning is able to identify the correct manufacturer with an accuracy of approximately 80% in 10-fold cross validation, while other classifiers achieve an accuracy of 56% or less. We believe that this approach will be a useful tool in clinical practice, and it is likely applicable to other kinds of prostheses