scholarly journals Estimation of long-term external nutrient loading from watersheds to Lake Biwa by a combined rainfall-runoff model and loading-discharge curve approach

2020 ◽  
Vol 14 (4) ◽  
pp. 143-149
Author(s):  
Huu Le Tien ◽  
Kenji Okubo ◽  
Phuong Ho Thi ◽  
Mitsuyo Saito
2001 ◽  
Vol 5 (4) ◽  
pp. 554-562 ◽  
Author(s):  
R. Ragab ◽  
D. Moidinis ◽  
J. Albergel ◽  
J. Khouri ◽  
A. Drubi ◽  
...  

Abstract. The objective of this work was to assess the performance of the newly developed HYDROMED model. Three catchments with hill reservoirs were selected. They are El-Gouazine and Kamech in Tunisia and Es Sindiany in Syria. The rainfall, the spillway flow and volume of water in the reservoirs were used as input to the model. Events that generated spillway flow were preferred for calibration. The results confirmed that the HYDROMED model is capable of reproducing the runoff volume at all the three sites. In calibrating single events, the model performance was high as measured by the Nash-Sutcliffe criterion for goodness of fit. In some events this value was as high as 98%. In simulation mode, the highest Nash-Sutcliffe criterion value was close to 70% in the El-Gouazine and Kamech catchments and close to 50% in the Es Sindiany catchment. Given the limited information available, especially on the unrecorded releases in the three catchments, the hydrological impact of site geology (e.g. Kamech), the unrecorded operator intervention during the spillway flow (e.g. Es Sindiany) and other unaccounted factors (e.g siltation, evaporation, etc.), these results are by and large very encouraging. However, they could be further improved as and when more information on the unrecorded parameters becomes available. Additionally, the results of this work highlighted the need for long term records with a large number of significant events that are able to generate spillway flow to obtain more consistent and reliable parameter values. It also highlights the need for more accurately recorded releases for irrigation and other uses. As these results are encouraging, more tests on those three and other sites are planned. Keywords: HYDROMED, rainfall-runoff model, Mediterranean, conceptual model


2020 ◽  
Author(s):  
Vasileios Kourakos ◽  
Theano Iliopoulou ◽  
Panayiotis Dimitriadis ◽  
Demetris Koutsoyiannis ◽  
Vassilios Kaleris ◽  
...  

<p>Runoff simulation using hydrological models has a key role in water resources<br>management. Thus, there is a need to investigate how rainfall-runoff models preserve<br>the stochastic characteristics of real-world streamflow data. It is also useful to compare<br>the stochastic properties of output with those of input processes (rainfall) and internal<br>state variables (soil moisture), with focus on marginal distribution tails and long-term<br>persistence. To this aim, we perform a case study using the ENNS rainfall-runoff model<br>with real and synthetic rainfall time series, and for all processes we study the marginal<br>distributions and the dependence structures. In the analyses we use recently developed<br>stochastic tools such as K-moments and climacograms.</p>


2013 ◽  
Vol 43 (4) ◽  
pp. 327-350 ◽  
Author(s):  
Zuzana Štefunková ◽  
Kamila Hlavčová ◽  
Milan Lapin

Abstract In the study the potential impact of climate change on river runoff in the upper Hron River, V´ah River, and Laborec River basin was evaluated using the Hron conceptual spatially-lumped rainfall-runoff model, which was driven by regional circulation models of atmosphere. The rainfall-runoff model was calibrated with data from the 1981-1995 period and validated with data from the 1996-2010 period. Changes in climate variables in the future were expressed by three different regional climate change projections: KNMI, MPI and ALADIN-Climate for the period 1961-2100. Changes in the seasonal runoff distribution were evaluated by a comparison of the simulated long-term mean monthly discharges in the river basin outlets in future decades with the present stage.


2021 ◽  
Author(s):  
Jamie Lee Stevenson ◽  
Christian Birkel ◽  
Aaron J. Neill ◽  
Doerthe Tetzlaff ◽  
Chris Soulsby

Water ◽  
2021 ◽  
Vol 13 (9) ◽  
pp. 1226
Author(s):  
Pakorn Ditthakit ◽  
Sirimon Pinthong ◽  
Nureehan Salaeh ◽  
Fadilah Binnui ◽  
Laksanara Khwanchum ◽  
...  

Accurate monthly runoff estimation is crucial in water resources management, planning, and development, preventing and reducing water-related problems, such as flooding and droughts. This article evaluates the monthly hydrological rainfall-runoff model’s performance, the GR2M model, in Thailand’s southern basins. The GR2M model requires only two parameters: production store (X1) and groundwater exchange rate (X2). Moreover, no prior research has been reported on its application in this region. The 37 runoff stations, which are located in three sub-watersheds of Thailand’s southern region, namely; Thale Sap Songkhla, Peninsular-East Coast, and Peninsular-West Coast, were selected as study cases. The available monthly hydrological data of runoff, rainfall, air temperature from the Royal Irrigation Department (RID) and the Thai Meteorological Department (TMD) were collected and analyzed. The Thornthwaite method was utilized for the determination of evapotranspiration. The model’s performance was conducted using three statistical indices: Nash–Sutcliffe Efficiency (NSE), Correlation Coefficient (r), and Overall Index (OI). The model’s calibration results for 37 runoff stations gave the average NSE, r, and OI of 0.657, 0.825, and 0.757, respectively. Moreover, the NSE, r, and OI values for the model’s verification were 0.472, 0.750, and 0.639, respectively. Hence, the GR2M model was qualified and reliable to apply for determining monthly runoff variation in this region. The spatial distribution of production store (X1) and groundwater exchange rate (X2) values was conducted using the IDW method. It was susceptible to the X1, and X2 values of approximately more than 0.90, gave the higher model’s performance.


2012 ◽  
Vol 26 (26) ◽  
pp. 3953-3961 ◽  
Author(s):  
Jiangmei Luo ◽  
Enli Wang ◽  
Shuanghe Shen ◽  
Hongxing Zheng ◽  
Yongqiang Zhang

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