带调节校正的调节流域融雪径流模型。

IF 5.7 3区 环境科学与生态学 Q1 WATER RESOURCES
Ninad Bhagwat, Xiaobing Zhou, Raja Nagisetty, Liping Jiang, Glenn Shaw, Martha Apple, Jeremy Clotfelter
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引用次数: 0

摘要

我们扩展了融雪径流模型(SRM)来模拟受调节流域的河流流量,从而产生了一个被称为扩展SRM (E-SRM)的改进框架,该框架集成了多年自动化批处理、嵌套迭代器和用于河流流量模拟的季节性分割算法。开发了一种简约的调节校正方法,从概念上将流域划分为一个原始的上游“子”流域和一个更大的、受调节的“母”流域。假设子流域和母流域之间的水文参数可传递。我们将E-SRM应用于美国蒙大拿州Morony流域(~ 59,400 km2;海拔范围:860-3418米)。该地区被细分为Morony和Canyon Ferry流域,后者被视为原始盆地进行校准。在Canyon Ferry进行校准后,使用Canyon Ferry、Hauser和Holter水坝的水流进行调节校正。验证在整个Morony流域进行。评估了三种方法学情景:(1)21年校准和21年验证;(2) 11年的校准和21年的验证;(3)使用奇数年进行校准,并在奇数年和偶数年进行验证。在所有情况下,将观察到的流量与调整后的流量进行比较,发现多个模型评估指标的性能都有所改善。其中包括绝对效率(例如Nash-Sutcliffe效率:- 0.16至0.74,- 0.43至0.59,- 0.16至0.67;克林-古普塔效率)和相对指标(如均方根误差、归一化均方根误差、平均绝对误差和体积差),突出了流量调节对SRM性能的显著影响。E-SRM框架为水资源管理的研究和实际应用提供了新的机会。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Snowmelt runoff model (SRM) for regulated watersheds with regulation-correction

We expanded the Snowmelt Runoff Model (SRM) to simulate streamflow in regulated watersheds, resulting in a modified framework termed the Expanded SRM (E-SRM) that integrates multi-year automated batch processing, nested iterators, and a seasonal divider algorithm for streamflow simulation. A parsimonious regulation-correction approach was developed that conceptually divides the watershed into a pristine upstream “daughter” subwatershed and a larger, regulated “mother” watershed. Hydrological parameter transferability was assumed between the daughter and mother watersheds. We applied the E-SRM to the Morony watershed in Montana, USA (~ 59,400 km2; elevation range: 860–3418 m). The area was subdivided into the Morony and Canyon Ferry watersheds, with the latter treated as a pristine basin for calibration. Following calibration at Canyon Ferry, regulation-correction was applied using streamflow from the Canyon Ferry, Hauser, and Holter dams. Validation was conducted over the entire Morony watershed. Three methodological scenarios were evaluated: (1) 21-year calibration and 21-year validation; (2) 11-year calibration and 21-year validation; and (3) calibration using odd years with validation on odd and even years. In all scenarios, comparison between observed and regulation-corrected streamflow showed improved performance across multiple model assessment metrics. These included both absolute (e.g. Nash–Sutcliffe Efficiency: from − 0.16 to 0.74, − 0.43 to 0.59, − 0.16 to 0.67; Kling–Gupta Efficiency) and relative (e.g. Root-Mean-Square Error, Normalized Root-Mean-Square Error, Mean Absolute Error, and Volume Difference) indicators, highlighting the significant impact of flow regulation on SRM performance. The E-SRM framework offers new opportunities for research and practical application in water resource management.

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来源期刊
Applied Water Science
Applied Water Science WATER RESOURCES-
CiteScore
9.90
自引率
3.60%
发文量
268
审稿时长
13 weeks
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