scholarly journals Using NDII patterns to constrain semi-distributed rainfall-runoff models in tropical nested catchments

2021 ◽  
Author(s):  
Nutchanart Sriwongsitanon ◽  
Wasana Jandang ◽  
Thienchart Suwawong ◽  
Hubert H. G. Savenije
Keyword(s):  
Soil Moisture ◽  
Root Zone ◽  
Lag Time ◽  
Model Parameters ◽  
Model Structure ◽  
Rainfall Runoff ◽  
Annual Average ◽  
Moisture Storage ◽  
Rainfall Runoff Model ◽  
Runoff Model

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 32 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, 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 the internal stations; 2) the calibrated FLEXL model; and 3) the semi-distributed URBS model - another established semi-distributed rainfall-runoff model. FLEX-SD showed better or similar performance both during calibration and especially in validation. Subsequently, we tried to distribute the moisture storage capacity by constraining FLEX-SD on patterns of the NDII (normalized difference infrared index). The readily available NDII appears to be a good proxy for moisture stress in the root zone during dry periods. The maximum moisture holding capacity in the root zone is assumed to be a function of the maximum seasonal range of NDII values, and the annual average NDII values to construct 2 alternative models: FLEX-SD-NDIIMax-Min and FLEX-SD-NDIIAvg, respectively. The additional constraint on the moisture holding capacity by the NDII improved both model performance and the realism of the distribution. Distribution of Sumax using annual average NDII values was found to be well correlated with the percentage of evergreen forest in 31 sub-catchments. Spatial average NDII values were proved to be highly corresponded with the root zone soil moisture of the river basin, not only in the dry season but also in the water limited ecosystem. To check 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 validation.

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.

DEVELOPMENT OF A CONCEPTUAL DETERMINISTIC RAINFALL-RUNOFF MODEL

1973 ◽  
Vol 4 (3) ◽  
pp. 147-170 ◽  
Author(s):  
STEN BERGSTRÖM ◽  
ARNE FORSMAN
Keyword(s):  
Time Interval ◽  
Model Structure ◽  
Rainfall Runoff ◽  
Soil Moisture Storage ◽  
Lumped Parameter ◽  
Moisture Storage ◽  
Rainfall Runoff Model ◽  
Physical Laws ◽  
Parameter Values ◽  
Runoff Model

This progress report outlines the main principles for the development of a simple conceptual rainfall-runoff model at the Swedish Meteorological and Hydrological Institute. The HBV-2 Model is based on lumped-parameter approximations to the physical laws governing infiltration, percolation and runoff formation. The time interval is one day. The model structure includes a soil moisture storage, an upper zone storage and a lower zone storage. A procedure for evaluating the parameter values is described. Examples of applications to several test catchments in various hydrologic settings are included.

scholarly journals Subgrid Parameterization of the Soil Moisture Storage Capacity for a Distributed Rainfall-Runoff Model

Water ◽  
2015 ◽  
Vol 7 (12) ◽  
pp. 2691-2706 ◽  
Author(s):  
Weijian Guo ◽  
Chuanhai Wang ◽  
Xianmin Zeng ◽  
Tengfei Ma ◽  
Hai Yang
Keyword(s):  
Soil Moisture ◽  
Storage Capacity ◽  
Rainfall Runoff ◽  
Soil Moisture Storage ◽  
Moisture Storage ◽  
Rainfall Runoff Model ◽  
Runoff Model

scholarly journals Regionalizing rainfall–runoff model parameters to predict the daily streamflow of ungauged catchments in the dry tropics

2009 ◽  
Vol 40 (5) ◽  
pp. 433-444 ◽  
Author(s):  
David A. Post
Keyword(s):  
Input Data ◽  
Water Yield ◽  
Model Parameters ◽  
Rainfall Runoff ◽  
Wet Season ◽  
Daily Streamflow ◽  
Total Length ◽  
Dry Tropics ◽  
Rainfall Runoff Model ◽  
Runoff Model

A methodology has been derived which allows an estimate to be made of the daily streamflow at any point within the Burdekin catchment in the dry tropics of Australia. The input data requirements are daily rainfall (to drive the rainfall–runoff model) and mean average wet season rainfall, total length of streams, percent cropping and percent forest in the catchment (to regionalize the parameters of the rainfall–runoff model). The method is based on the use of a simple, lumped parameter rainfall–runoff model, IHACRES (Identification of unit Hydrographs And Component flows from Rainfall, Evaporation and Streamflow data). Of the five parameters in the model, three have been set to constants to reflect regional conditions while the other two have been related to physio-climatic attributes of the catchment under consideration. The parameter defining total catchment water yield (c) has been estimated based on the mean average wet season rainfall, while the streamflow recession time constant (τ) has been estimated based on the total length of streams, percent cropping and percent forest in the catchment. These relationships have been shown to be applicable over a range of scales from 68–130,146 km2. However, three separate relationships were required to define c in the three major physiographic regions of the Burdekin: the upper Burdekin, Bowen and Suttor/lower Burdekin. The invariance of the relationships with scale indicates that the dominant processes may be similar across a range of scales. The fact that different relationships were required for each of the three major regions indicates the geographic limitations of this regionalization approach. For most of the 24 gauged catchments within the Burdekin the regionalized rainfall–runoff models were nearly as good as or better than the rainfall–runoff models calibrated to the observed streamflow. In addition, models often performed better over the simulation period than the calibration period. This indicates that future improvements in regionalization should focus on improving the quality of input data and rainfall–runoff model conceptualization rather than on the regionalization procedure per se.

Analysis of parameter uncertainty of a flow and quality stormwater model

2009 ◽  
Vol 60 (3) ◽  
pp. 717-725 ◽  
Author(s):  
C. B. S. Dotto ◽  
A. Deletic ◽  
T. D. Fletcher
Keyword(s):  
Water Quality ◽  
Bayesian Methods ◽  
Flow Model ◽  
Model Parameters ◽  
Rainfall Runoff ◽  
Sources Of Uncertainty ◽  
Music Model ◽  
Rainfall Runoff Model ◽  
Water Quality Models ◽  
Runoff Model

Uncertainty is intrinsic to all monitoring programs and all models. It cannot realistically be eliminated, but it is necessary to understand the sources of uncertainty, and their consequences on models and decisions. The aim of this paper is to evaluate uncertainty in a flow and water quality stormwater model, due to the model parameters and the availability of data for calibration and validation of the flow model. The MUSIC model, widely used in Australian stormwater practice, has been investigated. Frequentist and Bayesian methods were used for calibration and sensitivity analysis, respectively. It was found that out of 13 calibration parameters of the rainfall/runoff model, only two matter (the model results were not sensitive to the other 11). This suggests that the model can be simplified without losing its accuracy. The evaluation of the water quality models proved to be much more difficult. For the specific catchment and model tested, we argue that for rainfall/runoff, 6 months of data for calibration and 6 months of data for validation are required to produce reliable predictions. Further work is needed to make similar recommendations for modelling water quality.

Effect of rainfall data errors on the optimized values of model parameters

Soil Research ◽  
1982 ◽  
Vol 20 (1) ◽  
pp. 15
Author(s):  
WC Boughton ◽  
FT Sefe
Keyword(s):  
Storage Capacity ◽  
Rainfall Data ◽  
Model Parameters ◽  
Similar Magnitude ◽  
Rainfall Runoff ◽  
Data Errors ◽  
Cast Doubt ◽  
Rainfall Runoff Model ◽  
Induced Changes ◽  
Runoff Model

The rainfall input to a rainfall-runoff model was arbitrarily increased and decreased in order to determine the magnitude of corresponding changes in optimized values of the model parameters. The optimized capacities of moisture stores representing surface storage capacity of a catchment changed by average amounts of +24% and -20% as rainfall input was changed by +10% and -10%, respectively. Values of other parameters showed changes of similar magnitude, but there was no uniformity in the magnitude of induced changes from catchment to catchment. The results cast doubt on the validity of relating optimized values of model parameters to physical characteristics of catchments.

scholarly journals Calibration of hydrological model parameters for ungauged catchments

2007 ◽  
Vol 11 (2) ◽  
pp. 703-710 ◽  
Author(s):  
A. Bárdossy
Keyword(s):  
Hydrological Model ◽  
Model Performance ◽  
Model Parameters ◽  
Hydrological Models ◽  
Rainfall Runoff ◽  
Ungauged Catchments ◽  
Rainfall Runoff Model ◽  
Climate Statistics ◽  
Runoff Model ◽  
German Part

Abstract. The parameters of hydrological models for catchments with few or no discharge records can be estimated using regional information. One can assume that catchments with similar characteristics show a similar hydrological behaviour and thus can be modeled using similar model parameters. Therefore a regionalisation of the hydrological model parameters on the basis of catchment characteristics is plausible. However, due to the non-uniqueness of the rainfall-runoff model parameters (equifinality), a workflow of regional parameter estimation by model calibration and a subsequent fit of a regional function is not appropriate. In this paper a different approach for the transfer of entire parameter sets from one catchment to another is discussed. Parameter sets are considered as tranferable if the corresponding model performance (defined as the Nash-Sutclife efficiency) on the donor catchment is good and the regional statistics: means and variances of annual discharges estimated from catchment properties and annual climate statistics for the recipient catchment are well reproduced by the model. The methodology is applied to a set of 16 catchments in the German part of the Rhine catchments. Results show that the parameters transfered according to the above criteria perform well on the target catchments.

scholarly journals A Rainfall - Runoff Model for the Highly Regulated Lake Taupo Catchment, Using a Constrained Ensemble Kalman Filter to Improve the Accuracy and Reliability of Model Output

2021 ◽  
Author(s):  
◽  
Deborah Maxwell
Keyword(s):  
Decision Making ◽  
Kalman Filter ◽  
Conceptual Model ◽  
Ensemble Kalman Filter ◽  
Lake Level ◽  
Model Structure ◽  
Model Output ◽  
Rainfall Runoff ◽  
Rainfall Runoff Model ◽  
Runoff Model

<p>Lake Taupo is the effective source of the Waikato River. The Waikato Power Scheme relies on the outflow from the lake for moderated flows throughout the year. As the lake is maintained between a 1.4m operating range, it is the inflows to the lake that determine the amount of water available to the scheme for electricity generation. These inflows have not been modelled in any detail prior to this dissertation. This dissertation aims to develop a predictive rainfall-runoff model that can provide accurate and reliable inflow and lake level forecasts for the Lake Taupo catchment. Model formulation is guided by a fundamental understanding of catchment hydrologic principles and an in-depth assessment of catchment hydrologic behaviour. The model is a semi-distributed physically-consistent conceptual model which aims to provide a parsimonious representation of different storages and flow pathways through a catchment. It has three linear sub-surface stores. Drainage to these stores is related to the size of the saturation zone, utilising the concept of a variable source area. This model is used to simulate inflows from gauged unregulated sub-catchments. It is also used to estimate the inflow from ungauged areas through regionalisation. For regulated sub-catchments, the model is modified to incorporate available data and information relating to the relevant scheme‟s operation, resource consent conditions and other physical and legislative constraints. The output from such models is subject to considerable uncertainty due to simplifications in the model structure, estimated parameter values and imperfect driving data. For robust decision making, it is important this uncertainty is reduced to within acceptable levels. In this study, a constrained Ensemble Kalman Filter (EnKF) is applied to the four unregulated gauged catchments to deal with model structure and data uncertainties. Used in conjunction with Monte Carlo simulations, all three sources of uncertainty are addressed. Simple mass and flux constraints are applied to the four (soil storage, baseflow, interflow and fastflow) model states. Without these constraints states can be adjusted beyond what is physically possible, compromising the integrity of model output. It is demonstrated that the application of a constrained EnKF improves the accuracy and reliability of model output.Due to the complexity of the Tongariro Power Scheme (TPS) and the limited data available to model it, the conceptual model is not suitable. Rather, a statistical probability analysis is used to estimate the discharge from this scheme given the month of the year, day of the week and hour of the day. Model output is combined and converted into a corresponding change in lake level. The model is evaluated over a wide range of hydrological and meteorological conditions. An in-depth critical evaluation is undertaken on eight events chosen a priori as representation of both extreme and „usual‟ conditions. The model provides reasonable predictions of lake level given the uncertainty with the TPS, complexity of the catchment and data/information constraints. The model performs particularly well in „normal‟ and dry conditions but also does a good job during rainfall events in light of errors associated with driving data. However, for real-time operational use the integration of the model with meteorological forecasts is required. Model recalibration would be required due to the issue of moving from point estimation to areal rainfall data. Once this is achieved, this operational model would allow robust decision-making and efficient management of the water resource for the Waikato Power Scheme. Although there is room for improvement, there is considerable scope for extending the application of the constrained EnKF and techniques for incorporating regulation to other catchments both in New Zealand and internationally.</p>

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