<p>Agricultural areas are often artificially drained, especially in temperate and flat landscapes. This also applies to Denmark, where approximately half of the agricultural area is artificially drained, mostly with tile drains. The generated drain flow has significant impacts on various aspects of the hydrologic cycle such as groundwater recharge, flow paths and transport times. Consequently, drain flow is a major control on the transport of nutrients such as nitrogen. Yet, detailed knowledge of spatial and temporal variability of drain flow is inadequate due to insufficient observations of drain flow, lacking knowledge of drain infrastructure and issues of scale and hydrogeologic heterogeneity.</p> <p>The objective was to improve the simulation of both the spatial and temporal variability of drain flow in a large-scale hydrological model used to map nitrate transport. This model is a physically-based, distributed groundwater-surface water model of all of Denmark. It is a major challenge to simulate drain flow distribution in space and time with the national model due to its coarse horizontal resolution (500m or 100m), and the lack of drain flow observations at relevant scale. Hence, to achieve the objective, we gathered existing field-scale drain flow observations from all over Denmark. For these drain catchments, fine-scale (10m) physically based hydrological models were setup and calibrated against the drain flow observations. After successful calibration, the resulting simulated distributions of drain fraction (drain flow relative to precipitation) were regionalized to applicable areas across all of Denmark. The regionalization was performed using decision tree machine learning algorithms, and a set of topographic and geologic covariates available nationally at fine resolution. An analysis of spatial transferability of the machine learning algorithm allowed to limit predictions to applicable areas. Finally, these estimates of drain fraction are used in the calibration of the large-scale national hydrologic model, amongst other objective functions such as streamflow and groundwater heads.</p>

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Purpose - This research aims to uncover the ideologies and ethical perspectives behind electronic advertisements shown in Pakistan. The study examines the role of media adverts with an emphasis on stereotypic gender representation and spread of distorted information in shaping the minds of consumers through symbolic images and visual content. Design/methodology/approach - The current study is based on Peirce’s Semiotic Theory in 1860s, which analyzes the signs and visuals in mainstream television advertisements and unravel the social or cultural contexts underlying advertising discourse. Grounded in the theoretical framework of critical interpretation of electronic media, advertisements were interpreted using Semiotics and visuals features. The data for this study consists of three selected commercial broadcasts chosen randomly from all commercials aired on Pakistan’s most viewed TV channels (ARY, PTV, Geo, and Express). Lastly, Critical Discourse Analysis (CDA) has been used to critically examine TV advertisements to explore that how advertisers construct ideologies about gender stereotypes through signs, icons, and symbols. Findings – The findings revealed that electronic advertisements mostly represent gender roles stereotyping and promote unethical values against Islamic ideologies in Pakistan. Through the lens of Critical Discourse, it was found that advertisement’s content and symbols create meanings and convey the message with false narratives to consumers violating the ethical code of advertisements under the Pakistan Electronic Media Regulatory Authority (PEMRA) Pakistan. Originality/ value - The media revolution unfolds severe issues like women's objectification, ultra-modern culture, and misrepresentation of Islamic values. To date, many researchers have investigated the influence of advertisements on consumers in Western countries, but the Critical Semiotic Analysis of TV advertisements in complying with Islamic ideologies is never studied in Pakistan’s context. Practical Implications - The outcomes of this study will help advertising firms, Policymakers, and PEMRA by providing them with deep insights and knowledge about the perception of consumers and helping them to take strict measures to avoid anti-Islamic content that creates harm to Islamic values and civilization.

<p>Temporal drain flow dynamics and their underlying controlling factors are important for understanding the needs for water resource management in tile drained agricultural areas. The use of physics-based water flow models to understand tile drained systems is quite common. These physics-based models are complex and have high computational demand due to the high spatial and temporal dimensionality of the problem. We examine whether machine learning (ML) models can offer a simpler tool for water management.</p> <p> </p> <p>The main aim of our study is to assess the potential of ML tools for predicting drain flow with varying climate parameters and hydrogeological properties in different catchments in Denmark. We rely on unique data containing time series of daily drain flow in 26 field-scale tile drained catchments in Denmark: climate data (precipitation, potential evapotranspiration, temperature); geological properties (clay fraction, first sand layer thickness, first clay layer thickness); and topographical indexes (curvature, topographical wetness indexes, topographical position index, elevation etc.). The ML algorithm <em>XGBoost</em> is used to predict drain flow in the 26 drain catchments based on both static and dynamic variables. This algorithm also provides an independent measure of the value of information contained in variables related to climate, geology and topography for the prediction of tile drain flows.</p> <p> </p> <p>The ML approach examined could provide a more transferable, faster, and less computationally expensive tool to predict drain flow dynamics. Simultaneously, the results of the study offer insight into the underlying factors that control drain flow, allowing for improved data collection and physics-based model development.</p>

<p>Almost 50% of the agricultural land in Denmark is tile drained, and it includes a wide range of hydrogeological and topographical settings. These drains in the shallow groundwater system influence the hydrology and nutrient transport in subsurface and surface waters significantly. Therefore, it is critical to understand the share of drainage with respect to the recharge in shallow groundwater systems to get a holistic picture of drain flow dynamics in varied topographical and hydrogeological settings. To address these issues, multiple tile-drain catchments (28 sites, with measured drain flow timeseries) across Denmark are used to test the response of tile drains in varied topographical and hydrogeological settings on field scale. Using the national hydrological model of Denmark (DK-model) in MIKE-SHE as a basis, 10m resolution groundwater flow models for all the drain catchments are established. Combined calibration for all drain catchments is conducted by evaluating percent bias (PBIAS) and Kling-Gupta Efficiency (KGE) of simulated and observed discharge data using the Pareto Archived Dynamically Dimensioned Search (PADDS) of the OSTRICH optimization tool. Principal component analysis (PCA) on independent physical explanatory variables (and indexes) representing topography and hydrogeology is used to reduce all collected variables to significant variables only. Linear polynomial ridge regression is used to study whether independent explanatory variables are sufficient to represent drain flow distribution or whether additional information derived from the groundwater flow models is needed. In this presentation, we will show if the independent topographical and geological variables can predict drain flow fraction and among all explanatory variables, which variables play the most significant role. Moreover, the resulting groundwater flow model of Denmark will serve to produce a training dataset of drain flow fraction that can be used further with machine learning approaches to predict drain flow dynamics for all of Denmark. The results of the study will contribute to improved drain flow predictions across all of Denmark by improving the understanding of controls on drain flow behaviour.</p>