Journal of Hydrologic Engineering http://yetl.yabesh.ir/yetl1/handle/yetl/19026 2026-08-26T18:47:11Z Experimental Investigation and Parameter Inversion of Radial Solute Transport Accounting for the Skin Effect http://yetl.yabesh.ir/yetl1/handle/yetl/4311741 Experimental Investigation and Parameter Inversion of Radial Solute Transport Accounting for the Skin Effect Yuan, Kehan; Zhu, Qi; Ma, Teng; Wang, Ning; Li, Xu; Li, Na AbstractRadial solute tracer tests are a key method for estimating aquifer parameters, yet their accuracy often is compromised by skin effects around the wellbore resulting from drilling or completion activities. A clear understanding of how skin ... 2026-01-01T00:00:00Z Response of Wildfires to Concurrent and Consecutive Drought and Hot Extremes across the Globe http://yetl.yabesh.ir/yetl1/handle/yetl/4311740 Response of Wildfires to Concurrent and Consecutive Drought and Hot Extremes across the Globe Ma, Qian; Hao, Zengchao; Xie, Lulu AbstractWildfires can cause disastrous impacts on natural and human systems. Hydroclimatic extremes, such as droughts and high temperature anomalies, can affect the occurrence, intensity, and spread of wildfires. Moreover, these extremes may occur ... 2026-01-01T00:00:00Z Climate-Driven Shifts in the Geographic Dependency of the NRCS Initial Abstraction Ratio http://yetl.yabesh.ir/yetl1/handle/yetl/4311739 Climate-Driven Shifts in the Geographic Dependency of the NRCS Initial Abstraction Ratio Moglen, Glenn E.; Miller, Julianne J.; Gifford, Justin; Chen, Tianyang AbstractThe appropriate selection of the initial abstraction ratio in the Natural Resources Conservation Service (NRCS) curve number method has been debated within the hydrologic engineering community. Building on recent work that introduced the concept ...Practical ApplicationsThis work uses the future climate-based projections made available by the National Oceanographic and Atmospheric Administration (NOAA) Atlas 15 pilot project that provides future precipitation estimates across the State of Montana as ... 2026-01-01T00:00:00Z Integrating Hydrological, Physical, and Chemical Factors for Soil Moisture Prediction Using Advanced Machine Learning Models http://yetl.yabesh.ir/yetl1/handle/yetl/4311738 Integrating Hydrological, Physical, and Chemical Factors for Soil Moisture Prediction Using Advanced Machine Learning Models Al-Juaidi, Ahmed E. M. AbstractAccurately predicting soil moisture content (SMC) is critical for effective water management and sustainable agriculture. This study demonstrates that combining hydrological factors, such as rainfall, with soil physical characteristics—including ... 2026-01-01T00:00:00Z