کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
4435069 1620137 2016 18 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Assessing the role of uncertain precipitation estimates on the robustness of hydrological model parameters under highly variable climate conditions
ترجمه فارسی عنوان
بررسی نقش برآوردهای بارش نامشخص در استحکام پارامترهای مدل هیدرولوژیکی در شرایط آب و هوایی بسیار متغیر
کلمات کلیدی
برآوردهای بارش مبتنی بر ماهواره؛ شرایط آب و هوایی بسیار متغیر؛ دیفرانسیل تقسیم نمونه؛ کالیبراسیون؛ استحکام پارامتر مدل؛ مدل سازی هیدرولوژیکی؛ آفریقای جنوبی
موضوعات مرتبط
مهندسی و علوم پایه علوم زمین و سیارات فرآیندهای سطح زمین
چکیده انگلیسی


• Seven satellite-based rainfall estimates are used to test robustness of model parameters.
• Best model performance are obtained with precipitation products that use gauge data for bias correction.
• Highly variable climate conditions are difficult to capture in a single and robust parameter set.
• Model parameters depend on the precipitation characteristics during calibration.
• Model parameters should only be transferred to similar target periods.

Study regionFour headwaters in Southern Africa.Study focusThe streamflow regimes in Southern Africa are amongst the most variable in the world. The corresponding differences in streamflow bias and variability allowed us to analyze the behavior and robustness of the LISFLOOD hydrological model parameters. A differential split-sample test is used for calibration using seven satellite-based rainfall estimates, in order to assess the robustness of model parameters. Robust model parameters are of high importance when they have to be transferred both in time and space. For calibration, the modified Kling-Gupta statistic was used, which allowed us to differentiate the contribution of the correlation, bias and variability between the simulated and observed streamflow.New hydrological insightsResults indicate large discrepancies in terms of the linear correlation (r), bias (β) and variability (γ) between the observed and simulated streamflows when using different precipitation estimates as model input. The best model performance was obtained with products which ingest gauge data for bias correction. However, catchment behavior was difficult to be captured using a single parameter set and to obtain a single robust parameter set for each catchment, which indicate that transposing model parameters should be carried out with caution. Model parameters depend on the precipitation characteristics of the calibration period and should therefore only be used in target periods with similar precipitation characteristics (wet/dry).

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Journal of Hydrology: Regional Studies - Volume 8, December 2016, Pages 112–129
نویسندگان
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