scholarly journals Including spatial distribution in a data-driven rainfall-runoff model to improve reservoir inflow forecasting in Taiwan

2012 ◽  
Vol 28 (3) ◽  
pp. 1055-1070 ◽  
Author(s):  
Meng-Jung Tsai ◽  
Robert J. Abrahart ◽  
Nick J. Mount ◽  
Fi-John Chang
Keyword(s):  
Spatial Distribution ◽  
Data Driven ◽  
Rainfall Runoff ◽  
Inflow Forecasting ◽  
Reservoir Inflow ◽  
Rainfall Runoff Model ◽  
Runoff Model

scholarly journals Using NDII pattern for a semi-distributed rainfall-runoff model in tropical nested catchments

2020 ◽  
Author(s):  
Nutchanart Sriwongsitanon ◽  
Wasana Jandang ◽  
Thienchart Suwawong ◽  
Hubert H.~G. Savenije
Keyword(s):  
Spatial Distribution ◽  
Root Zone ◽  
Lag Time ◽  
Model Parameters ◽  
Model Structure ◽  
Rainfall Runoff ◽  
Moisture Storage ◽  
Rainfall Runoff Model ◽  
Runoff Model ◽  
Better Than

Abstract. A parsimonious semi-distributed rainfall-runoff model has been developed for flow prediction. In distribution, attention is paid to both timing of runoff and heterogeneity of moisture storage capacities within sub-catchments. This model is based on the lumped FLEXL model structure, which has proven its value in a wide range of catchments. To test the value of distribution, the gauged Upper Ping catchment in Thailand has been divided into 10 sub-catchments, which can be grouped into 5 gauged sub-catchments where internal performance is evaluated. To test the effect of timing, firstly excess rainfall was calculated for each sub-catchment, using the model structure of FLEXL. The excess rainfall was then routed to its outlet using the lag time from storm to peak flow (TlagF) and the lag time of recharge from the root zone to the groundwater (TlagS), as a function of catchment size. Subsequently, the Muskingum equation was used to route sub-catchment runoff to the downstream sub-catchment, before adding to runoff of the downstream sub-catchment, with the delay time parameter of the Muskingum equation being a function of channel length. Other model parameters of this semi-distributed FLEX-SD model were kept the same as in the calibrated FLEXL model of the entire Upper Ping basin, controlled by station P.1 located at the centre of Chiang Mai Province. The outcome of FLEX-SD was compared to: 1) observations at P.1; 2) the results of the calibrated FLEXL model; and 3) the semi-distributed URBS model - another established semi-distributed rainfall-runoff model. FLEX-SD showed better performance than URBS, but a bit lower than the calibrated FLEXL model with NSE of 0.74, 0.71, and 0.76, respectively. Subsequently, at the level of the gauged internal sub-catchments, runoff estimates of FLEX-SD were compared to observations and calibrated FLEXL model results. The results demonstrate that FLEX-SD provides more accurate runoff estimates at P.1, P.67 and P.75 stations which are located along the main Ping River, compared to those provided by the lumped calibrated FLEXL model. The results were less good at 2 tributary stations (P.20 and P.21), where calibrated FLEXL output performed better, while performance was similar at one tributary station (P.4A). Overall, FLEX-SD performed better than URBS at 5 out of 6 stations except at P.21. Subsequently, the effect of distributing moisture storage capacity was tested. Since the FLEX-SD uses the same Sumax value - the maximum moisture holding capacity of the root zone - for all sub-catchments, FLEX-SD-NDII was set-up making use of the spatial distribution of the NDII (the normalized difference infrared index). The readily available NDII appears to be a good proxy for moisture stress in the root zone, particularly during dry periods. The maximum moisture holding capacity in the root zone assumed to be a function of the maximum seasonal range of NDII values. The spatial distribution of this range among sub-catchments was used to calibrate the semi-distributed FLEX-SD-NDII model. The additional constraint by the NDII improved the performance of the model and the realism of the distribution. To test how well the model represents root zone soil moisture, the performance of the FLEX-SD-NDII model was compared to time series of the soil wetness index (SWI). The correlation between the root zone storage and the daily SWI appeared to be very good, even better than the correlation with the NDII, because NDII does not provide good estimates during wet periods. The SWI, which is partly model-based, was not used for calibration, but appeared to be an appropriate index for verification.

scholarly journals Temporal variability in snow distribution

2004 ◽  
Vol 38 ◽  
pp. 101-105 ◽  
Author(s):  
Eli Alfnes ◽  
Liss M. Andreassen ◽  
Rune V. Engeset ◽  
Thomas Skaugen ◽  
Hans-Christian Udnæs
Keyword(s):  
Spatial Distribution ◽  
Temporal Variability ◽  
Tree Line ◽  
Rainfall Runoff ◽  
Long Tail ◽  
Positive Direction ◽  
Snow Distribution ◽  
Rainfall Runoff Model ◽  
Southern Norway ◽  
Runoff Model

AbstractSnow-courses data have been collected in order to investigate the temporal variability of snow distribution in two catchments in southern Norway during the 2002 melt season. The profiles represent different elevations, aspects and terrain types. At snow maximum the spatial distribution of snow above the tree line was positively skewed (long tail in the positive direction), whereas the spatial distribution below the tree line followed a more normal distribution. During the snowmelt season the spatial distribution of snow became increasingly skewed. By separating the datasets into two terrain classes, alpine and forest, the snow distribution could be described by a time-variant gamma distribution function, one for each terrain class. The results of the study will be used to formulate a new snow routine in the Swedish rainfall–runoff model HBV, which is used for flood forecasting in Norway.

scholarly journals Physiographic Analysis of Tehri Dam Catchment and Development of GIUH Based Nash Model for Ungauged Rivers

2019 ◽  
Vol 14 (2) ◽  
pp. 215-230
Author(s):  
Niraj Kumar Agrawal ◽  
Anil Kumar Lohani ◽  
N. K. Goel
Keyword(s):  
Network Model ◽  
Monsoon Season ◽  
Small Rivers ◽  
Forecasting Model ◽  
Rainfall Runoff ◽  
Inflow Forecasting ◽  
Forecasting System ◽  
Rainfall Runoff Model ◽  
Tehri Dam ◽  
Runoff Model

Advanced information about incoming flows is required for operation of a variety of hydraulic structures including multipurpose storage hydropower projects. Inflow forecasts are used for optimum power generation during non -monsoon season and operation of gates and spillways during the flood season. In order to develop an inflow forecasting system for a reservoir, it has been observed that many a times number of ungauged rivers directly falling into the reservoirs are not accounted for. Such is the case for the Tehri Reservoir, where 16 small rivers/tributaries which are directly contributing to Tehri reservoir are ungauged. In the present study an attempt has been made to carry out physiographic objective Tehri catchment and to develop Geomorphological Instantaneous Unit Hydrograph (GIUH) for ungauged rivers/tributaries directly falling into the reservoir. GIUH developed for the ungauged rivers can be used to simulate the runoff from all the 16 ungauged rivers. Combining these GIUH models with a hydrological model of the other gauged rivers of the Tehri Catchment in the form of a network model provides a complete rainfall-runoff model. Thus, this study provides a useful input for the development of inflow forecasting model for the Tehri Dam as the network model can be used as flood forecasting model.

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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