Spatiotemporal Configuration of Hydrographic Variability in Terminos Lagoon: Implications for Fish Distribution

Paz-Ríos, Carlos E.; Sosa-López, Atahualpa; Torres‐Rojas, Yassir Edén

doi:10.1007/s12237-023-01250-6

Cited by 4 publications

(1 citation statement)

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“…Precipitation is an indispensable part of the water cycle and the material basis of runoff formation [1]. In recent years, under the joint influence of both global climate change and subtropical highs, the precipitation has gradually presented the characteristics of uneven spatial and temporal distribution and high intensity in flood season, further forming the turbulent surface runoff [2,3]. The resulting flood disasters are gradually becoming more common and complex.…”

Section: Introductionmentioning

confidence: 99%

An Improved Xin’anjiang Hydrological Model for Flood Simulation Coupling Snowmelt Runoff Module in Northwestern China

Tan,

Dong,

Hou

et al. 2023

Water

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The Xin’anjiang hydrological model (XHM) is the practical tool for runoff simulation and flood forecasting in most regions in China, but it still presents some challenges when applied to Northwest China, where the river runoff mostly comes from high-temperature snowmelt, as the model lacks such a functional module. In this study, the improved XHM coupling snowmelt module is presented to complete the existing XHM for better suitability for flood simulation in areas dominated by snowmelt. The improved model includes four sub-models: evapotranspiration, runoff yield, runoff separation, and runoff routing, where the snowmelt runoff module is introduced in both the runoff yield and separation sub-models. The watershed is divided into two types, non-snow areas with lower altitudes and snow-covered areas with higher altitudes, to study the mechanism of runoff production and separation. The evaluation index, determination coefficients (R2), mean square error (MSE), and Nash efficiency coefficients (NSE) are used to assess the improved XHM’s effect by comparing it with the traditional model. Results show that the R2 of the improved XHM coupled with snowmelt are around 0.7 and 0.8 at the Zamashk and Yingluoxia stations, respectively, while the MSE and NSE are also under 0.4 and above 0.6, respectively. The absolute value of error of both flood peaks in the Yingluoxia station simulated by improved XHM is only 10% and 6%, and that of traditional XHM is 32% and 40%, indicating that the peak flow and flood process can be well simulated and showing that the improved XHM coupled with snowmelt constructed in this paper can be applied to the flood forecasting of the Heihe River Basin. The critical temperature of snow melting and degree-day factor of snow are more sensitive compared with other parameters related to snow melting, and the increasing trend of peak flow caused by both decreased critical temperature and increased degree-day factor occurs only when the value of the model’s state (snow reserve) is higher. These results can expand the application scope in snow-dominated areas of the XHM, providing certain technical references for flood forecasting and early warning of other snowmelt-dominated river basins.

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Section: Introductionmentioning

confidence: 99%