کد مقاله کد نشریه سال انتشار مقاله انگلیسی ترجمه فارسی نسخه تمام متن
6410739 1332885 2015 21 صفحه PDF سفارش دهید دانلود رایگان
عنوان انگلیسی مقاله ISI
Significant uncertainty in global scale hydrological modeling from precipitation data errors
ترجمه فارسی عنوان
عدم اطمینان قابل توجه در مقیاس جهانی هیدرولوژیکی از خطاهای داده بارش
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کلمات کلیدی
مدلسازی هیدرولوژیکی جهانی، تحمیل عدم اطمینان، کالیبراسیون مدل، عدم قطعیت پارامتر
موضوعات مرتبط
مهندسی و علوم پایه علوم زمین و سیارات فرآیندهای سطح زمین
چکیده انگلیسی


- Forcing data quality and high CPU demand hamper global model calibration.
- Precipitation data errors introduce uncertainty in global hydrological modeling.
- Limited persistence in parameter performance over time, catchment and forcing.

SummaryIn the past decades significant progress has been made in the fitting of hydrologic models to data. Most of this work has focused on simple, CPU-efficient, lumped hydrologic models using discharge, water table depth, soil moisture, or tracer data from relatively small river basins. In this paper, we focus on large-scale hydrologic modeling and analyze the effect of parameter and rainfall data uncertainty on simulated discharge dynamics with the global hydrologic model PCR-GLOBWB. We use three rainfall data products; the CFSR reanalysis, the ERA-Interim reanalysis, and a combined ERA-40 reanalysis and CRU dataset. Parameter uncertainty is derived from Latin Hypercube Sampling (LHS) using monthly discharge data from five of the largest river systems in the world. Our results demonstrate that the default parameterization of PCR-GLOBWB, derived from global datasets, can be improved by calibrating the model against monthly discharge observations. Yet, it is difficult to find a single parameterization of PCR-GLOBWB that works well for all of the five river basins considered herein and shows consistent performance during both the calibration and evaluation period. Still there may be possibilities for regionalization based on catchment similarities. Our simulations illustrate that parameter uncertainty constitutes only a minor part of predictive uncertainty. Thus, the apparent dichotomy between simulations of global-scale hydrologic behavior and actual data cannot be resolved by simply increasing the model complexity of PCR-GLOBWB and resolving sub-grid processes. Instead, it would be more productive to improve the characterization of global rainfall amounts at spatial resolutions of 0.5° and smaller.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Journal of Hydrology - Volume 529, Part 3, October 2015, Pages 1095-1115
نویسندگان
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