Using synthetic flow duration curves for rainfall-runoff model calibration at ungauged sites

2000 ◽  
Vol 14 (1) ◽  
pp. 117-133 ◽  
Author(s):  
P.-S. Yu ◽  
T.-C. Yang
Keyword(s):  
Model Calibration ◽  
Rainfall Runoff ◽  
Flow Duration ◽  
Ungauged Sites ◽  
Flow Duration Curves ◽  
Rainfall Runoff Model ◽  
Runoff Model

Transférabilité d’un modèle pluie–débit pour la simulation de courbes de débits classés sur des petits bassins non jaugés de l’Amazonie

2008 ◽  
Vol 35 (9) ◽  
pp. 999-1008 ◽  
Author(s):  
Claudio J.C. Blanco ◽  
Yves Secretan ◽  
Anne-Catherine Favre
Keyword(s):  
Power Production ◽  
Hydroelectric Power ◽  
Rainfall Runoff ◽  
Flow Duration ◽  
Evaluation Approach ◽  
Ungauged Sites ◽  
Flow Duration Curves ◽  
Rainfall Runoff Model ◽  
Runoff Model ◽  
Small Catchments

In Amazonia, because the small catchments are ungauged, it is not possible to analyse them, for example, for hydroelectric power production. Thus, the objective of this paper is to study the transferability of a rainfall–runoff model to simulate flow duration curves for the production of hydroelectric power. The approach is based on the transfer of the impulse response of a model calibrated on two gauged catchments, allowing the evaluation approach permutation between these two catchments. We have, respectively, 7 years and 2 years and 2 months of rainfall and runoff data for these catchments. A sensitivity analysis of the transferability calibration to the sample size is carried out to determine the shortest flow period gauged on the receptor catchment, which produces results comparable to those calibrated with the maximum samples size. This analysis evaluates fieldwork on the ungauged sites of the region.

scholarly journals Model analysis of forest thinning impacts on the water resources during hydrological drought periods

2021 ◽  
Author(s):  
Hiroki Momiyama ◽  
Tomo'omi Kumagai ◽  
Tomohiro Egusa
Keyword(s):  
Water Resources ◽  
Meteorological Data ◽  
Rainfall Runoff ◽  
Forest Thinning ◽  
Flow Duration ◽  
Mountain Catchment ◽  
Data Generator ◽  
Flow Duration Curves ◽  
Rainfall Runoff Model ◽  
Runoff Model

In Japan, there has recently been an increasing call for forest thinning to conserve water resources from forested mountain catchments in terms of runoff during prolonged drought periods of the year. How their water balance and the resultant runoff are altered by forest thinning is examined using a combination of 8-year hydrological observations, 100-year meteorological data generator output, and a semi-process-based rainfall-runoff model. The rainfall-runoff model is developed based on TOPMODEL assuming that forest thinning has an impact on runoff primarily through an alteration in canopy interception. The main novelty in this analysis is that the availability of the generated 100-year meteorological data allows the investigations of the forest thinning impacts on mountain catchment water resources under the most severer drought conditions. The model is validated against runoff observations conducted at a forested mountain catchment in the Kanto region of Japan for the period 2010–2017. It is demonstrated that the model reproduces temporal variations in runoff and evapotranspiration at inter- and intra-annual time scales, resulting in well reproducing the observed flow duration curves. On the basis of projected flow duration curves for the 100-year, despite the large increase in an annual total runoff with ordinary intensifying thinning, low flow rates, i.e., water resources from the catchment in the drought period in the year, in both normal and drought years were impacted by the forest thinning to a lesser extent. Higher catchment water retention capacity appreciably enhanced the forest thinning effect on increasing available water resources.

scholarly journals A comparative assessment of rainfall–runoff modelling against regional flow duration curves for ungauged catchments

2017 ◽  
Vol 21 (11) ◽  
pp. 5647-5661 ◽  
Author(s):  
Daeha Kim ◽  
Il Won Jung ◽  
Jong Ahn Chun
Keyword(s):  
Rainfall Runoff ◽  
Local Calibration ◽  
Ungauged Catchments ◽  
Humid Climate ◽  
Timing Information ◽  
Flow Duration ◽  
Regional Flow ◽  
Flow Duration Curves ◽  
Rainfall Runoff Model ◽  
Runoff Model

Abstract. Rainfall–runoff modelling has long been a special subject in hydrological sciences, but identifying behavioural parameters in ungauged catchments is still challenging. In this study, we comparatively evaluated the performance of the local calibration of a rainfall–runoff model against regional flow duration curves (FDCs), which is a seemingly alternative method of classical parameter regionalisation for ungauged catchments. We used a parsimonious rainfall–runoff model over 45 South Korean catchments under semi-humid climate. The calibration against regional FDCs was compared with the simple proximity-based parameter regionalisation. Results show that transferring behavioural parameters from gauged to ungauged catchments significantly outperformed the local calibration against regional FDCs due to the absence of flow timing information in the regional FDCs. The behavioural parameters gained from observed hydrographs were likely to contain intangible flow timing information affecting predictability in ungauged catchments. Additional constraining with the rising limb density appreciably improved the FDC calibrations, implying that flow signatures in temporal dimensions would supplement the FDCs. As an alternative approach in data-rich regions, we suggest calibrating a rainfall–runoff model against regionalised hydrographs to preserve flow timing information. We also suggest use of flow signatures that can supplement hydrographs for calibrating rainfall–runoff models in gauged and ungauged catchments.

Multicore Parallel Genetic Algorithm with Tabu Strategy for Rainfall-Runoff Model Calibration

2017 ◽  
Vol 22 (8) ◽  
pp. 04017024 ◽  
Author(s):  
Shengli Liao ◽  
Qianying Sun ◽  
Chuntian Cheng ◽  
Ruhong Zhong ◽  
Huaying Su
Keyword(s):  
Genetic Algorithm ◽  
Model Calibration ◽  
Rainfall Runoff ◽  
Parallel Genetic Algorithm ◽  
Rainfall Runoff Model ◽  
Runoff Model
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