Development and analysis of the Soil Water Infiltration Global database

In this paper, we present and analyze a novel global database of soil infiltration measurements, the Soil Water Infiltration Global (SWIG) database. In total, 5023 infiltration curves were collected across all continents in the SWIG database. These data were either provided and quality checked by the scientists who performed the experiments or they were digitized from published articles. Data from 54 different countries were included in the database with major contributions from Iran, China, and the USA. In addition to its extensive geographical coverage, the collected infiltration curves cover research from 1976 to late 2017. Basic information on measurement location and method, soil properties, and land use was gathered along with the infiltration data, making the database valuable for the development of pedotransfer functions (PTFs) for estimating soil hydraulic properties, for the evaluation of infiltration measurement methods, and for developing and validating infiltration models. Soil textural information (clay, silt, and sand content) is available for 3842 out of 5023 infiltration measurements ( ∼ 76%) covering nearly all soil USDA textural classes except for the sandy clay and silt classes. Information on land use is available for 76% of the experimental sites with agricultural land use as the dominant type ( ∼ 40%). We are convinced that the SWIG database will allow for a better parameterization of the infiltration process in land surface models and for testing infiltration models. All collected data and related soil characteristics are provided online in *.xlsx and *.csv formats for reference, and we add a disclaimer that the database is for public domain use only and can be copied freely by referencing it. Supplementary data are available at https://doi.org/10.1594/PANGAEA.885492 (Rahmati et al., 2018). Data quality assessment is strongly advised prior to any use of this database. Finally, we would like to encourage scientists to extend and update the SWIG database by uploading new data to it

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Not AvailableReliable and realistic streamflow forecasting is very important in hydrology, hydraulic, and water resources engineering as it can directly affect the dams operation and performance, groundwater recharge/exploitation, sediment conveyance capability of river, watershed management, etc. However, an accurate streamflow forecasting is not an easy task due to the high uncertainty associated with climate conditions and complexity of collecting and handling both spatial and non-spatial data. Therefore, hydrologists from all over the world have developed and adopted several types of data-driven techniques ranging from traditional stochastic time-series modeling to modern hybrid artificial intelligence models for future prediction of streamflow. In literature, studies dealing with streamflow forecasting used a variety of techniques having dissimilar concepts and characteristics, and streamflow datasets at different time scale such as daily, monthly, seasonal and yearly etc. This chapter first describes and classifies available data-driven techniques used in streamflow forecasting into suitable groups depending upon their characteristics. Then, growth of the salient data-driven models both single and hybrid such as time-series models, artificial neural network models, and other artificial intelligence models is discussed with their applications and comparisons as reported in studies on streamflow forecasting over time. Thereafter, current approaches used in the recent five-year streamflow-forecasting studies are briefly summarized. Also, challenges experienced by the researchers in applying data-driven techniques for streamflow forecasting are addressed. It is concluded that a vast scope exists for improving streamflow forecasts using emerging and modern tools and combining them with location-specific and in-depth knowledge of the physical processes occurring in the hydrologic system.Not Availabl

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Not AvailableThe performance of grasses and legumes in intercropping over its sole counterparts for the enhanced quality fodder production in the arid region of Kachchh, India was studied. The results showed that without adding any extra inputs cost on resource poor farmers of arid eco-system, intercropping of grass-legume yielded more quality fodder over its sole counterparts. Thus, it may be said that grass-legume intercropping system may be the best viable option for enhanced quality fodder production for the arid condition of the Kachchh for overall development of livestock sector. The findings of the study are applicable to all the arid regions of the world because enhanced quality fodder is main requirement for the arid system, wherein agriculture systems are livestock-centric and livestock remains main source of livelihood for the inhabitants.Not Availabl

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Not AvailableSpatial and temporal dynamics of groundwater levels provides vital information required for management of fast depleting groundwater resources in hard-rock aquifer systems. This study demonstrates application of multivariate statistical techniques to analyze spatial and temporal variations of 39-month period (May 2006 - July 2009) monthly groundwater levels at 50 monitoring sites and to understand principal factors most influencing the groundwater system in Ahar River catchment of Udaipur district, Rajasthan, India. Box-whisker plots drawn for mean monthly groundwater levels revealed that spatial variation of the groundwater levels was less during rainy season in comparison to that during dry season. The groundwater levels in the aquifer system were found to be largely influenced with rainfall occurrences in the area. Firstly, hierarchical cluster analysis technique was applied to classify 50 monitoring sites into different clusters according to behaviour of the groundwater levels. This resulted into four clusters of the groundwater levels at less than 22 linkage distance. The most (25.29 m) and the least (6.48 m) spatial variability of the groundwater levels were observed for clusters III and I, respectively. Furthermore, principal component analysis (PCA) technique was utilized to understand and identify the most significant variables influencing the groundwater levels in each of the four clusters of the groundwater levels. The first two principal components (PCs) explained 43-55% of the total variance. Based on the PCA, the significant PCs for clusters I and II were characterized as ‘topography factor’. On the other side, the significant PCs for clusters III and IV were termed as ‘geomorphologic’ and ‘land use’ factors, respectively.Not Availabl

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