scholarly journals Basin Scale Performance of a Distributed Rainfall-Runoff Model Using Uncertainty Modelling Approach in Data Scarce Region

2018 ◽  
Author(s):  
Madaka Harold Tumbo
Keyword(s):  
Rainfall Runoff ◽  
Uncertainty Modelling ◽  
Basin Scale ◽  
Rainfall Runoff Model ◽  
Runoff Model ◽  
Modelling Approach

Overcoming the challenges of using a rainfall–runoff model to estimate the impacts of groundwater extraction on low flows in an ephemeral stream

2013 ◽  
Vol 45 (1) ◽  
pp. 58-72 ◽  
Author(s):  
K. M. Ivkovic ◽  
B. F. W. Croke ◽  
R. A. Kelly
Keyword(s):  
River System ◽  
Groundwater Extraction ◽  
Rainfall Runoff ◽  
Low Flows ◽  
Water Accounting ◽  
River Reach ◽  
Streamflow Data ◽  
Rainfall Runoff Model ◽  
Runoff Model ◽  
Modelling Approach

Simple modelling approaches such as a spatially lumped, rainfall–runoff model offer a number of advantages in the management of water resources including the relative ease with which groundwater and surface water accounts can be evaluated at the river-reach scale in data-poor areas. However, rainfall–runoff models are generally not well suited for use in ephemeral river systems because of their inability to simulate abrupt transitions from flow to no-flow periods and the highly non-linear rainfall–runoff relationships that exist in low yielding catchments. This paper discusses some of the challenges of using a rainfall–runoff model to assess the impacts of groundwater extraction on low flows within an ephemeral river system and demonstrates how these challenges were overcome during the development of the IHACRES_GW (Identification of Hydrographs And Component flows from Rainfall, Evaporation and Streamflow data – with Ground Water store) model. Details on the model algorithms, calibration, validation and objective function fits are provided. The performance of the IHACRES_GW model in Cox's Creek (Namoi Valley, Australia), and 13 additional areas investigated, suggests that this simple modelling approach may be of considerable utility for water accounting, especially when attempting to evaluate the impacts of groundwater extraction on low flows in similar systems.

scholarly journals Toward an operational tool to simulate green roof hydrological impact at the basin scale: a new version of the distributed rainfall–runoff model Multi-Hydro

2016 ◽  
Vol 74 (8) ◽  
pp. 1845-1854 ◽  
Author(s):  
Pierre-Antoine Versini ◽  
Auguste Gires ◽  
Ioulia Tchinguirinskaia ◽  
Daniel Schertzer
Keyword(s):  
Positive Impact ◽  
Green Roof ◽  
Green Roofs ◽  
Peak Discharge ◽  
Rainfall Runoff ◽  
Runoff Volume ◽  
Basin Scale ◽  
Wide Range ◽  
Rainfall Runoff Model ◽  
Runoff Model

Currently widespread in new urban projects, green roofs have shown a positive impact on urban runoff at the building scale: decrease and slow-down of the peak discharge, and decrease of runoff volume. The present work aims to study their possible impact at the catchment scale, more compatible with stormwater management issues. For this purpose, a specific module dedicated to simulating the hydrological behaviour of a green roof has been developed in the distributed rainfall–runoff model (Multi-Hydro). It has been applied on a French urban catchment where most of the building roofs are flat and assumed to accept the implementation of a green roof. Catchment responses to several rainfall events covering a wide range of meteorological situations have been simulated. The simulation results show green roofs can significantly reduce runoff volume and the magnitude of peak discharge (up to 80%) depending on the rainfall event and initial saturation of the substrate. Additional tests have been made to assess the susceptibility of this response regarding both spatial distributions of green roofs and precipitation. It appears that the total area of greened roofs is more important than their locations. On the other hand, peak discharge reduction seems to be clearly dependent on spatial distribution of precipitation.

scholarly journals Effects of streamflow isotope sampling strategies on the calibration of a tracer‐aided rainfall‐runoff model

2021 ◽  
Author(s):  
Jamie Lee Stevenson ◽  
Christian Birkel ◽  
Aaron J. Neill ◽  
Doerthe Tetzlaff ◽  
Chris Soulsby
Keyword(s):  
Sampling Strategies ◽  
Rainfall Runoff ◽  
Rainfall Runoff Model ◽  
Runoff Model

scholarly journals Performance Evaluation of a Two-Parameters Monthly Rainfall-Runoff Model in the Southern Basin of Thailand

Water ◽  
2021 ◽  
Vol 13 (9) ◽  
pp. 1226
Author(s):  
Pakorn Ditthakit ◽  
Sirimon Pinthong ◽  
Nureehan Salaeh ◽  
Fadilah Binnui ◽  
Laksanara Khwanchum ◽  
...  
Keyword(s):  
Exchange Rate ◽  
Rainfall Runoff ◽  
Management Planning ◽  
Runoff Variation ◽  
Groundwater Exchange ◽  
Monthly Runoff ◽  
Rainfall Runoff Model ◽  
Two Parameters ◽  
Runoff Model

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.

Effects of conditional parameterization on performance of rainfall-runoff model regarding hydrologic non-stationarity

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