Browsing by Subject "runoff"

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Access status: Open Access ,
Ocena zmian odpływu w zlewni rzeki Widawki w roku hydrologicznym 2010 pod wpływem oddziaływania inwestycji górniczo-energetycznej w rejonie Bełchatowa
(Wydawnictwa AGH, 2011) Wachowiak, Grzegorz; Galiniak, Grzegorz; Jończyk, Waldemar; Martyniak, Renata
Changes in water conditions (surface and groundwater) are one of the important elements of the impact of open pit lignite mining and associated energy production on the environment. In this article, the authors characterize (for the hydrological year 2010) the impact of the mine and power station »Belchatow« on the river runoff in the catchment of Widawka river. This influence is manifested primarily by providing the hydrographic network with drainage water coming from the mine, by flow-reducing influence of the depression cone, as well as by abstractions for the power station. The analysis of changes in the outflow was based on the determination of river flows (actual and reconstructed as in natural conditions) and on the size of anthropogenic factors.
Access status: Open Access ,
Surface water runoff estimation: a review of methods incorporating terrain shape
(Wydawnictwa AGH, 2025) Ramirez-Yara, Yessica; Malinowska, Agnieszka A.; Hejmanowski, Ryszard
Surface runoff is a key variable in the water balance, representing excess water that exceeds soil infiltration capacity and is not absorbed by drains. Accurate estimation of surface runoff is crucial for flood prevention, optimizing agricultural water use, and detecting irregular water capture. Various computational approaches, including machine learning, deep learning, statistical models, and hydraulic simulations, have been developed to estimate runoff. However, despite extensive research, many models overlook the influence of terrain characteristics – such as topography, slope, and surface roughness – leading to potential inaccuracies in runoff prediction. This study conducts a comprehensive bibliographic review of state-of-the-art research on surface runoff estimation, with a focus on methods that integrate digital terrain models (DTMs), remote sensing, and computational modeling techniques. Through this analysis, a specific research niche was identified and verified, highlighting the need for terrain-sensitive runoff models that better incorporate topographic variables into hydrological modeling. By evaluating and comparing existing methodologies, this review provides insights into the most effective approaches for runoff estimation and offers recommendations for selecting the appropriate models based on landscape and hydrological conditions. It also presents potential solutions that may pave the way for a better understanding of runoff processes.
Access status: Open Access ,
Surface water runoff estimation: a review of methods incorporating terrain shape
(2025) Ramirez Yara, Yessica Natalia; Malinowska, Agnieszka A.; Hejmanowski, Ryszard
Wydział Geodezji Górniczej i Inżynierii Środowiska
Surface runoff is a key variable in the water balance, representing excess water that exceeds soil infiltration capacity and is not absorbed by drains. Accurate estimation of surface runoff is crucial for flood prevention, optimizing agricultural water use, and detecting irregular water capture. Various computational approaches, including machine learning, deep learning, statistical models, and hydraulic simulations, have been developed to estimate runoff. However, despite extensive research, many models overlook the influence of terrain characteristics—such as topography, slope, and surface roughness—leading to potential inaccuracies in runoff prediction. This study conducts a comprehensive bibliographic review of state-of-the-art research on surface runoff estimation, with a focus on methods that integrate digital terrain models (DTMs), remote sensing, and computational modeling techniques. Through this analysis, a specific research niche was identified and verified, highlighting the need for terrain-sensitive runoff models that better incorporate topographic variables into hydrological modeling. By evaluating and comparing existing methodologies, this review provides insights into the most effective approaches for runoff estimation and offers recommendations for selecting the appropriate models based on landscape and hydrological conditions. It also presents potential solutions that may pave the way for a better understanding of runoff processes.