Estimation of the Water Balance Using Observed Soil Water in the Nebraska Sandhills

Analyzing the dynamic hydrologic conditions of the Sandhills is critical for water and range management, sustainability of the Sandhills ecosystem as well as for dune stability. There are complex models available to quantify both surface and subsurface hydrological processes. However, we present in this study an application of a relatively simple model to arrive at best estimates of the water balance components. Using the Thornthwaite-Mather (TM) model, water balance components were estimated for 4 Automated Weather Data Network (AWDN) weather monitoring stations. Estimated averages of the water balance components suggested that mean annual precipitation of these four sites was only about 420 mm but water loss through plant evapotranspiration (ET) was 861 mm, with PET of about 1214 mm. Our investigation shows that there was surplus of water between December and March and a deficit occurs at the start of the growing season in May and extends through senescence in September-October. This study also suggests that the High Plains aquifer possibly met the plant water requirement during this deficit period as well as during the soil water extraction period, from May through September

Closure to Estimation of the Water Balance Using Observed Soil Water in the Nebraska Sandhills

We are thankful to Szilagyi [2010] for providing us an opportunity to discuss the important points of our paper [Sridhar and Hubbard, 2010]. We demonstrated a seasonal water balance assessment using the Modified Thornthwaite-Mather (TM) model in the Nebraska Sandhills. We computed the water budget for a few representative weather monitoring stations located in the Sandhills using the high resolution soil moisture data to assess the storage. In our water balance analysis, soil moisture storage is determined based on observed soil moisture and actual evapotranspiration, ETact was computed for each month using the change in storage in soil water and precipitation. If the change in storage is positive based on our observed soil moisture, we considered two scenarios and the least of the two is considered for computing actual ET

Breast Cancer Detection using Two Dimensional Principal Component Analysis and Back Propagation Neural Network

Breast cancer is the most common cancer which affects women around the world. It has been increasing over the years. Detection and diagnosis is the main factor for breast cancer control which increases the success rate of treatment, saves lives and reduce the cost. This paper proposes an efficient approach for breast cancer detection in mammogram breast images using two dimensional principal component analysis and back propagation neural network. The proposed approach consists of four step by step procedures namely preprocessing of breast images, image enhancement, feature extraction and classification. Two dimensional principal component analysis is used to obtain the features of the preprocessed and enhanced image. The reason for selecting two dimensional principal component analysis is it is easier to evaluate the covariance matrix accurately and less time is required to determine the corresponding features. Finally, Back propagation neural network is used to classify whether the given mammogram image is normal or abnormal. Simulation results are carried out using the proposed approach by considering MIAS data base. From the results, it is observed that proposed approach provide better accuracy

Development of the Soil Moisture Index to Quantify Agricultural Drought and Its “User Friendliness” in Severity-Area-Duration Assessment

This paper examines the role of soil moisture in quantifying drought through the development of a drought index using observed and modeled soil moisture. In Nebraska, rainfall is received primarily during the crop-growing season and the supply of moisture from the Gulf of Mexico determines if the impending crop year is either normal or anomalous and any deficit of rain leads to a lack of soil moisture storage. Using observed soil moisture from the Automated Weather Data Network (AWDN), the actual available water content for plants is calculated as the difference between observed or modeled soil moisture and wilting point, which is subsequently normalized with the site-specific, soil property–based, idealistic available water for plants that is calculated as the difference between field capacity and wilting point to derive the soil moisture index (SMI). This index is categorized into five classes from no drought to extreme drought to quantitatively assess drought in both space and time. Additionally, with the aid of an in-house hydrology model, soil moisture was simulated in order to compute model-based SMI and to compare the drought duration and severity for various sites. The results suggest that the soil moisture influence, a positive feedback process reported in many earlier studies, is unquestionably a quantitative indicator of drought. Also, the severity and duration of drought across Nebraska has a clear gradient from west to east, with the Panhandle region experiencing severe to extreme drought in the deeper soil layers for longer periods (\u3e200 days), than the central and southwestern regions (125–150 days) or the eastern regions about 100 days or less. The anomalous rainfall years can eliminate the distinction among these regions with regard to their drought extent, severity, and persistence, thus making drought a more ubiquitous phenomenon, but the recovery from drought can be subject to similar gradations. The spatial SMI maps presented in this paper can be used with the Drought Monitor maps to assess the local drought conditions more effectively

An Automated Framework for Detecting Change in the Source Code and Test Case Change Recommendation

Improvements and acceleration in software development have contributed towards high-quality services in all domains and all fields of industry, causing increasing demands for high-quality software developments. The industry is adopting human resources with high skills, advanced methodologies, and technologies to match the high-quality software development demands to accelerate the development life cycle. In the software development life cycle, one of the biggest challenges is the change management between the version of the source codes. Various reasons, such as changing the requirements or adapting available updates or technological upgrades, can cause the source code's version. The change management affects the correctness of the software service's release and the number of test cases. It is often observed that the development life cycle is delayed due to a lack of proper version control and due to repetitive testing iterations. Hence the demand for better version control-driven test case reduction methods cannot be ignored. The parallel research attempts propose several version control mechanisms. Nevertheless, most version controls are criticized for not contributing toward the test case generation of reduction. Henceforth, this work proposes a novel probabilistic rule-based test case reduction method to simplify the software development's testing and version control mechanism. Software developers highly adopt the refactoring process for making efficient changes such as code structure and functionality or applying changes in the requirements. This work demonstrates very high accuracy for change detection and management. This results in higher accuracy for test case reductions. The outcome of this work is to reduce the development time for the software to make the software development industry a better and more efficient world

Quality Control of Soil Water Data in ACIS – A Case Study in Nebraska

Soil moisture is the key state variable from both climate and hydrologic cycle assessment perspectives. Automated measurements of soil moisture were not possible in the past decades. Sensors deployed in the field with real-time monitoring networks such as the Automated Weather Data Network (AWDN) in Nebraska have not only become affordable but enhanced the monitoring capability of the network with valuable soil moisture data added to the existing stream of hourly and daily weather data for precipitation, air temperature, humidity, solar radiation, wind speed, and soil temperature. However, to assure the quality of the data, quality control (QC) tools are needed. Earlier studies lacked the QC of soil water data in general as they were not part of a network that routinely collected soil water measurements. This paper presents a systematic QC analysis and methodology to evaluate the performance of candidate QC techniques using spatiallyextenstive soil water dataset available from the AWDN network. The six tests included are based on the general behavior of soil moisture, the statistical characteristics of the measurements, the soil properties, and the precipitation measurements. The threshold, step change, and spatial regression test proved most effective in identifying data problems. The results demonstrate that these methods will lead to early identification of potential instrument failures and other disturbances to the soil water measurements

Jellyfish as an export commodity

Recently, jellyfish blooms have been reported with increased frequency from several parts of the world and it has been suggested that this phenomenon might be related to over-fishing and other human activities that are driving marine ecosystems off balance. It has contributed to the formulation of the “fishing down the food chain” hypothesis, which is based on the assumption that the reduction in large species marine predator populations is promoting the growth of organisms from lower levels of the food chain. Rising sea temperature is also considered as a reason for the occurrence of jellyfish fishery

Customer-engineer relationship management for converged ICT service companies

Thanks to the advent of converged communications services (often referred to as ‘triple play’), the next generation Service Engineer will need radically different skills, processes and tools from today’s counterpart. Why? in order to meet the challenges of installing and maintaining services based on multi-vendor software and hardware components in an IP-based network environment. The converged services environment is likely to be ‘smart’ and support flexible and dynamic interoperability between appliances and computing devices. These radical changes in the working environment will inevitably force managers to rethink the role of Service Engineers in relation to customer relationship management. This paper aims to identify requirements for an information system to support converged communications service engineers with regard to customer-engineer relationship management. Furthermore, an architecture for such a system is proposed and how it meets these requirements is discussed