scholarly journals Continuous Modeling of the Mkurumudzi River Catchment in Kenya Using the HEC-HMS Conceptual Model: Calibration, Validation, Model Performance Evaluation and Sensitivity Analysis

Hydrology ◽  
2018 ◽  
Vol 5 (3) ◽  
pp. 44 ◽  
Author(s):  
Wendso Ouédraogo ◽  
James Raude ◽  
John Gathenya
Keyword(s):  
Sensitivity Analysis ◽  
Stream Flow ◽  
Model Performance ◽  
Base Flow ◽  
Low Flow ◽  
Coefficient Of Determination ◽  
Flow Index ◽  
Model Parameters ◽  
Time Step ◽  
Continuous Modeling

The Mkurumudzi River originates in the Shimba hills and runs through Kwale County on the Kenyan Coast. Study on this river has been informed by the many economic activities that the river supports, which include sugarcane plantations, mining, tourism and subsistence farming. The main objective of this study was to use the soil moisture accounting (SMA) model specified in the Hydrologic Engineering Center-Hydrologic Modeling System (HEC-HMS) settings for the continuous modeling of stream flow in the Mkurumudzi catchment. Data from past years were compared with observed stream flow data in order to evaluate whether the model can be used for further prediction. The calibration was performed using data from 1988 to 1991 and validation for the period from 1992 to 1995 at a daily time step. The model performance was evaluated based on computed statistical parameters and visual checking of plotted hydrographs. For the calibration period of the continuous modeling, the performance of the model was very good, with a coefficient of determination R2 = 0.80, Nash-Sutcliffe Efficiency NSE = 0.80, index of agreement d = 0.94, and a Root Mean Squared Error (RMSE)/observations’ standard deviation ratio—RSR = 0.46. Similarly, the continuous model performance for the validation period was good, with R2 = 0.67, NSE = 0.65, RSR = 0.62 and d = 0.88. Based on these performance results, the SMA model in the HEC-HMS was found to give a satisfactory prediction of stream flow in the Mkurumudzi Catchment. The sensitivity analysis of the model parameters was performed, and the different parameters were ranked according to their sensitivity in terms of percent change in simulated runoff volume, peaks, Nash-Efficiency, seven-day low flow and base flow index. Sensitivity analysis helped to understand the relationships between the key model parameters and the variables.

scholarly journals Conditioning rainfall-runoff model parameters for ungauged catchments and land management impacts analysis

2009 ◽  
Vol 13 (6) ◽  
pp. 893-904 ◽  
Author(s):  
N. Bulygina ◽  
N. McIntyre ◽  
H. Wheater
Keyword(s):  
Parameter Space ◽  
Land Management ◽  
Base Flow ◽  
Distributed Model ◽  
Flow Index ◽  
Model Parameters ◽  
Ungauged Catchments ◽  
Management Interventions ◽  
Simulated Event ◽  
Base Flow Index

Abstract. Data scarcity and model over-parameterisation, leading to model equifinality and large prediction uncertainty, are common barriers to effective hydrological modelling. The problem can be alleviated by constraining the prior parameter space using parameter regionalisation. A common basis for regionalisation in the UK is the HOST database which provides estimates of hydrological indices for different soil classifications. In our study, Base Flow Index is estimated from the HOST database and the power of this index for constraining the parameter space is explored. The method is applied to a highly discretised distributed model of a 12.5 km2 upland catchment in Wales. To assess probabilistic predictions against flow observations, a probabilistic version of the Nash-Sutcliffe efficiency is derived. For six flow gauges with reliable data, this efficiency ranged between 0.70 and 0.81, and inspection of the results shows that the model explains the data well. Knowledge of how Base Flow Index and interception losses may change under future land use management interventions was then used to further condition the model. Two interventions are considered: afforestation of grazed areas, and soil degradation associated with increased grazing intensity. Afforestation leads to median reduction in modelled runoff volume of 24% over the simulated 3 month period; and a median peak flow reduction ranging from 12 to 15% over the six gauges for the largest simulated event. Uncertainty in all results is low compared to prior uncertainty and it is concluded that using Base Flow Index estimated from HOST is a simple and potentially powerful method of conditioning the parameter space under current and future land management.

scholarly journals Multiple causes of nonstationarity in the Weihe annual low-flow series

2018 ◽  
Vol 22 (2) ◽  
pp. 1525-1542 ◽  
Author(s):  
Bin Xiong ◽  
Lihua Xiong ◽  
Jie Chen ◽  
Chong-Yu Xu ◽  
Lingqi Li
Keyword(s):  
Frequency Analysis ◽  
Driving Forces ◽  
Potential Evapotranspiration ◽  
Aridity Index ◽  
Base Flow ◽  
Low Flow ◽  
Distribution Model ◽  
Flow Index ◽  
Explanatory Variables ◽  
Distribution Parameters

Abstract. Under the background of global climate change and local anthropogenic activities, multiple driving forces have introduced various nonstationary components into low-flow series. This has led to a high demand on low-flow frequency analysis that considers nonstationary conditions for modeling. In this study, through a nonstationary frequency analysis framework with the generalized linear model (GLM) to consider time-varying distribution parameters, the multiple explanatory variables were incorporated to explain the variation in low-flow distribution parameters. These variables are comprised of the three indices of human activities (HAs; i.e., population, POP; irrigation area, IAR; and gross domestic product, GDP) and the eight measuring indices of the climate and catchment conditions (i.e., total precipitation P, mean frequency of precipitation events λ, temperature T, potential evapotranspiration (EP), climate aridity index AIEP, base-flow index (BFI), recession constant K and the recession-related aridity index AIK). This framework was applied to model the annual minimum flow series of both Huaxian and Xianyang gauging stations in the Weihe River, China (also known as the Wei He River). The results from stepwise regression for the optimal explanatory variables show that the variables related to irrigation, recession, temperature and precipitation play an important role in modeling. Specifically, analysis of annual minimum 30-day flow in Huaxian shows that the nonstationary distribution model with any one of all explanatory variables is better than the one without explanatory variables, the nonstationary gamma distribution model with four optimal variables is the best model and AIK is of the highest relative importance among these four variables, followed by IAR, BFI and AIEP. We conclude that the incorporation of multiple indices related to low-flow generation permits tracing various driving forces. The established link in nonstationary analysis will be beneficial to analyze future occurrences of low-flow extremes in similar areas.

Comparison between dynamic and static sensitivity analysis approaches for impact assessment of different potential evapotranspiration methods on hydrological models performance

2021 ◽  
Author(s):  
Rodric Mérimé Nonki ◽  
André Lenouo ◽  
Christopher J. Lennard ◽  
Raphael M. Tshimanga ◽  
Clément Tchawoua
Keyword(s):  
Sensitivity Analysis ◽  
Impact Assessment ◽  
Hydrological Modeling ◽  
Potential Evapotranspiration ◽  
Model Performance ◽  
Estimation Methods ◽  
Model Parameters ◽  
Hydrological Models ◽  
Sensitivity Approach ◽  
Static Sensitivity

AbstractPotential Evapotranspiration (PET) plays a crucial role in water management, including irrigation systems design and management. It is an essential input to hydrological models. Direct measurement of PET is difficult, time-consuming and costly, therefore a number of different methods are used to compute this variable. This study compares the two sensitivity analysis approaches generally used for PET impact assessment on hydrological model performance. We conducted the study in the Upper Benue River Basin (UBRB) located in northern Cameroon using two lumped-conceptual rainfall-runoff models and nineteen PET estimation methods. A Monte-Carlo procedure was implemented to calibrate the hydrological models for each PET input while considering similar objective functions. Although there were notable differences between PET estimation methods, the hydrological models performance was satisfactory for each PET input in the calibration and validation periods. The optimized model parameters were significantly affected by the PET-inputs, especially the parameter responsible to transform PET into actual ET. The hydrological models performance was insensitive to the PET input using a dynamic sensitivity approach, while he was significantly affected using a static sensitivity approach. This means that the over-or under-estimation of PET is compensated by the model parameters during the model recalibration. The model performance was insensitive to the rescaling PET input for both dynamic and static sensitivities approaches. These results demonstrate that the effect of PET input to model performance is necessarily dependent on the sensitivity analysis approach used and suggest that the dynamic approach is more effective for hydrological modeling perspectives.

scholarly journals Forecasting Low Stream Flow Rate Using Monte—Carlo Simulation of Perigiali Stream, Kavala City, NE Greece

Proceedings ◽  
2018 ◽  
Vol 2 (11) ◽  
pp. 580 ◽  
Author(s):  
Thomas Papalaskaris ◽  
Theologos Panagiotidis
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Flow Rate ◽  
Stream Flow ◽  
Simulation Method ◽  
Base Flow ◽  
Low Flow ◽  
Rate Data ◽  
Storm Flow ◽  
Management Plans

A small number of scientific research studies with reference to extremely low flow conditions, have been conducted in Greece, so far. Predicting future low stream flow rate values is an essential and of paramount importance task when compiling watershed and drought management plans, designing water reservoirs and general hydraulic works capacity, calculating hydrological and drought low flow values, separating groundwater base flow and storm flow of storm hydrographs etc. The Monte-Carlo simulation method generates multiple attempts to define the anticipated value of a random (hydrological in this specific case) variable. The present study compiles, correspondingly, artificial low stream flow time series of both the same part of the year (2016) as well as a part of the calendar year (2017), based on the stream flow data observed during the same two different interval periods of the years 2016 and 2017, using a 3-inches U.S.G.S. modified portable Parshall flume, a 3-inches conventional portable Parshall flume, a 3-inches portable Montana (short Parshall) flume and a 90° V-notched triangular shaped sharp crested portable weir plate. The recorded data were plotted against the fitted one and the results were demonstrated through interactive tables providing us the ability to effectively evaluate the simulation procedure performance. Finally, we plot the observed against the calculated low stream flow rate data, compiling a log-log scale chart which provides a better visualization of the discrepancy ratio statistical performance metric and calculate statistics featuring the comparison between the recorded and the forecasted low stream flow rate data.

scholarly journals Sensitivity-guided evaluation of the HBV hydrological model parameterization

2012 ◽  
Vol 15 (3) ◽  
pp. 967-990 ◽  
Author(s):  
M. B. Zelelew ◽  
K. Alfredsen
Keyword(s):  
Sensitivity Analysis ◽  
Hydrological Model ◽  
River Basin Management ◽  
Low Flow ◽  
Model Parameters ◽  
Sensitivity Index ◽  
Analysis Method ◽  
Flow Conditions ◽  
Model Parameterization ◽  
Sensitivity Analysis Method

Applying hydrological models for river basin management depends on the availability of the relevant data information to constrain the model residuals. The estimation of reliable parameter values for parameterized models is not guaranteed. Identification of influential model parameters controlling the model response variations either by main or interaction effects is therefore critical for minimizing model parametric dimensions and limiting prediction uncertainty. In this study, the Sobol variance-based sensitivity analysis method was applied to quantify the importance of the HBV conceptual hydrological model parameterization. The analysis was also supplemented by the generalized sensitivity analysis method to assess relative model parameter sensitivities in cases of negative Sobol sensitivity index computations. The study was applied to simulate runoff responses at twelve catchments varying in size. The result showed that varying up to a minimum of four to six influential model parameters for high flow conditions, and up to a minimum of six influential model parameters for low flow conditions can sufficiently capture the catchments' responses characteristics. To the contrary, varying more than nine out of 15 model parameters will not make substantial model performance changes on any of the case studies.

scholarly journals Conditioning rainfall-runoff model parameters for ungauged catchments and land management impacts analysis

2009 ◽  
Vol 6 (2) ◽  
pp. 1907-1938 ◽  
Author(s):  
N. Bulygina ◽  
N. McIntyre ◽  
H. Wheater
Keyword(s):  
Parameter Space ◽  
Land Management ◽  
Base Flow ◽  
Distributed Model ◽  
Flow Index ◽  
Model Parameters ◽  
Ungauged Catchments ◽  
Management Interventions ◽  
Simulated Event ◽  
Base Flow Index

Abstract. Data scarcity and model over-parameterisation, leading to model equifinality and large prediction uncertainty, are common barriers to effective hydrological modelling. The problem can be alleviated by constraining the prior parameter space using parameter regionalization. A common basis for regionalization in the UK is the HOST database which provides estimates of hydrological indices for different soil classifications. In our study, Base Flow Index is estimated from the HOST database and the power of this index for constraining the parameter space is explored. The method is applied to a highly discretized distributed model of a 12.5 km2 upland catchment in Wales. To assess probabilistic predictions against flow observations, a probabilistic version of the Nash-Sutcliffe efficiency is derived. For six flow gauges with reliable data, this efficiency ranged between 0.70 and 0.81, and inspection of the results shows that the model explains the data well. Knowledge of how Base Flow Index and interception losses may change under future land use management interventions was then used to further condition the model. Two interventions are considered: afforestation of grazed areas, and soil degradation associated with increased grazing intensity. Afforestation leads to median reduction in modelled runoff volume of 24% over the simulated 3 month period; and a median peak flow reduction ranging from 12–15% over the six gauges for the largest simulated event. Uncertainty in all results is suprisingly low and it is concluded that using Base Flow Index estimated from HOST is a simple and potentially powerful method of conditioning the parameter space under current and future land management.

scholarly journals Hydrologic analysis for river Nyando using SWAT

2011 ◽  
Vol 8 (1) ◽  
pp. 1765-1797 ◽  
Author(s):  
A. O. Opere ◽  
B. N. Okello
Keyword(s):  
Stream Flow ◽  
Input Data ◽  
Lake Victoria ◽  
Base Flow ◽  
Weather Data ◽  
Series Data ◽  
Organic Chemical ◽  
Primary Role ◽  
Time Step ◽  
Run Off

Abstract. The Nyando River is one of the major Rivers in the Lake Victoria Basin. It drains parts of Nandi, Kericho and Nyando districts. It has a catchment area of about 3600 km−2 of Western Kenya and an average discharge of approximately 15 m3 s−1, and has within it some of the most severe problems of environmental degradation and deepening poverty found anywhere in Kenya. The Nyando River drains into the Winam Gulf of Lake Victoria and is a major contributor of sediment. The primary role of GIS in hydrological modeling is to integrate the ever increasing volumes of diverse spatial and non spatial data. This can be the model input or output. Recent advance in GIS (hardware and software) technology offer unprecedented capabilities for storing and manipulating large quantities of detailed, spatially-distributed watershed data (ASCE, 1999). SWAT, which is an interface of Arc View GIS, uses Arc View to prepare input data and display the model output as spatial maps, charts or time series data. This makes it easy to study and display the information for assimilation by SWAT. SWAT is a continuous time model that operates on a daily/sub-daily time step. It is physically based and can operate on large basins for long periods of time (Arnold et al., 1998). The basic model inputs are rainfall, maximum and minimum temperature, radiation, wind speed, relative humidity, land cover, soil and elevation (DEM). The watershed is subdivided into sub-basins that are spatially related to one another. Routing in stream channel is divided in to Water, Sediment, nutrients and organic chemical routing (Neitsch et al., 2002a). Stream flow data was available for two Stations 1GD03 and 1GD07. The stations had data ranging from 1950 to 1997, though they had missing gaps. Rainfall data were available for twelve rainfall recording stations in and around the basin. The collected data ranges between 1960 and 2000 though there were quite a number of missing data. The other weather data used were temperature data (maximum and minimum) for Kericho and Kisumu Meteorological stations. During the study the available water capacity (SOL_AWC) was varied within the range of ±0.05 mm of water/mm of soil. The result showed that SOL_AWC affects the stream flow. SOL_AWC affects both the surface flow and base flow. An increase in SOL_AWC results in decrease on the stream flow because of increase in the ability of the soil to hold more water. An increase in the initial curve number (CN2) increases the stream flow, but the effect is more pronounced on the effects on surface run off. The slightly increase in total stream flow could be as a result of ration of surface run off to base flow. The amount of stream flow contributed by the base flow was more than 50% of the total stream flow as show by base flow separation. The goodness of fit between observed and simulated stream flow was assessed for the aforementioned (1GD03) station, the R2 was found to be 0.24 while the NSE was 0.46 respectively. The low value of R2 and NSE could be attributed to lots of data gaps in the station and also the effects of combined tributaries. The station is located about 10 km upstream of Ahero Bridge just before the flood plain. The model over estimated the low flows at this station while the high flows were well estimated. The performance of the model varied depending on the available input data. The coefficient of determination R2 varies for observed and simulated stream flow at River gauging Station. The relationship between land use/cover change and stream flow is very significant in Nyando basin. The observation made is that with decreased Forest Cover up to 0% there is increased stream flow mean and peak and increased forests cover i.e. 100% results in decreased mean and peak stream flow.

Impact of Climate Change on the Spatiotemporal Distribution of Stream Flow and Sediment Yield of Darbandikhan Watershed, Iraq

2020 ◽  
Vol 38 (2A) ◽  
pp. 265-276
Author(s):  
Mahmoud S. Al- Khafaji ◽  
Rana D. Al- Chalabi
Keyword(s):  
Climate Change ◽  
Sediment Yield ◽  
Stream Flow ◽  
Assessment Tool ◽  
Coefficient Of Determination ◽  
Base Period ◽  
Time Step ◽  
Impact Of Climate Change ◽  
Global Circulation Models ◽  
The Impact

The impact of climate change on stream flow and sediment yield in Darbandikhan Watershed is an important challenge facing the water resources in Diyala River, Iraq. This impact was investigated using five Global Circulation Models (GCM) based climate change projection models from the A1B scenario of medium emission. The Soil and Water Assessment Tool (SWAT) was used to compute the temporal and spatial distribution of streamflow and sediment yield of the study area for the period 1984 to 2050. The daily-observed flow recorded in Darbandikhan Dam for the period from 1984 to 2013 was used as a base period for future projection. The initial results of SWAT were calibrated and validated using SUFI-2 of the SWAT-CUP program in daily time step considering the values of the Nash-Sutcliffe Efficiency (NSE) coefficient of determination (R2) as a Dual objective function. Results of NSE and R2 during the calibration (validation) periods were equal to 0.61 and 0.62(0.53 and 0.68), respectively. In addition, the average future prediction for the five climate models indicated that the average yearly flow and sediment yield in the watershed would decrease by about 49% and 44%, respectively, until the year 2050 compared with these of the base period from 1984 to 2013. Moreover, spatial analysis shows that 89.6 % and 90 % of stream flow and sediment come from the Iranian part of Darbandikhan watershed while the remaining small percent comes from Iraq, respectively. However, the middle and southern parts of Darbandikhan Watershed contribute by most of the stream...

scholarly journals Does the Complexity of Evapotranspiration and Hydrological Models Enhance Robustness?

2018 ◽  
Vol 10 (8) ◽  
pp. 2837 ◽  
Author(s):  
Dereje Birhanu ◽  
Hyeonjun Kim ◽  
Cheolhee Jang ◽  
Sanghyun Park
Keyword(s):  
Potential Evapotranspiration ◽  
Estimation Method ◽  
Model Performance ◽  
Low Flow ◽  
Estimation Methods ◽  
Model Parameters ◽  
Hydrological Models ◽  
Shuffled Complex Evolution ◽  
University Of Arizona ◽  
Optimizing Model

In this study, five hydrological models of increasing complexity and 12 Potential Evapotranspiration (PET) estimation methods of different data requirements were applied in order to assess their effect on model performance, optimized parameters, and robustness. The models were applied over a set of 10 catchments that are located in South Korea. The Shuffled Complex Evolution-University of Arizona (SCE-UA) algorithm was implemented to calibrate the hydrological models for each PET input while considering similar objective functions. The hydrological models’ performance was satisfactory for each PET input in the calibration and validation periods for all of the tested catchments. The five hydrological models’ performance were found to be insensitive to the 12 PET inputs because of the SCE-UA algorithm’s efficiency in optimizing model parameters. However, the five hydrological models’ parameters in charge of transforming the PET to actual evapotranspiration were sensitive and significantly affected by the PET complexity. The values of the three statistical indicators also agreed with the computed model evaluation index values. Similarly, identical behavioral similarities and Dimensionless Bias were observed in all of the tested catchments. For the five hydrological models, lack of robustness and higher Dimensionless Bias were seen for high and low flow as well as for the Hamon PET input. The results indicated that the complexity of the hydrological models’ structure and the PET estimation methods did not necessarily enhance model performance and robustness. The model performance and robustness were found to be mainly dependent on extreme hydrological conditions, including high and low flow, rather than complexity; the simplest hydrological model and PET estimation method could perform better if reliable hydro-meteorological datasets are applied.

scholarly journals A trading-space-for-time approach to probabilistic continuous streamflow predictions in a changing climate – accounting for changing watershed behavior

2011 ◽  
Vol 15 (11) ◽  
pp. 3591-3603 ◽  
Author(s):  
R. Singh ◽  
T. Wagener ◽  
K. van Werkhoven ◽  
M. E. Mann ◽  
R. Crane
Keyword(s):  
Spatial Relationship ◽  
Model Performance ◽  
Climate Scenario ◽  
Hydrologic Model ◽  
Climatic Conditions ◽  
Low Flow ◽  
Parameter Estimates ◽  
Model Parameters ◽  
Watershed Model ◽  
The Impact

Abstract. Projecting how future climatic change might impact streamflow is an important challenge for hydrologic science. The common approach to solve this problem is by forcing a hydrologic model, calibrated on historical data or using a priori parameter estimates, with future scenarios of precipitation and temperature. However, several recent studies suggest that the climatic regime of the calibration period is reflected in the resulting parameter estimates and model performance can be negatively impacted if the climate for which projections are made is significantly different from that during calibration. So how can we calibrate a hydrologic model for historically unobserved climatic conditions? To address this issue, we propose a new trading-space-for-time framework that utilizes the similarity between the predictions under change (PUC) and predictions in ungauged basins (PUB) problems. In this new framework we first regionalize climate dependent streamflow characteristics using 394 US watersheds. We then assume that this spatial relationship between climate and streamflow characteristics is similar to the one we would observe between climate and streamflow over long time periods at a single location. This assumption is what we refer to as trading-space-for-time. Therefore, we change the limits for extrapolation to future climatic situations from the restricted locally observed historical variability to the variability observed across all watersheds used to derive the regression relationships. A typical watershed model is subsequently calibrated (conditioned) on the predicted signatures for any future climate scenario to account for the impact of climate on model parameters within a Bayesian framework. As a result, we can obtain ensemble predictions of continuous streamflow at both gauged and ungauged locations. The new method is tested in five US watersheds located in historically different climates using synthetic climate scenarios generated by increasing mean temperature by up to 8 °C and changing mean precipitation by −30% to +40% from their historical values. Depending on the aridity of the watershed, streamflow projections using adjusted parameters became significantly different from those using historically calibrated parameters if precipitation change exceeded −10% or +20%. In general, the trading-space-for-time approach resulted in a stronger watershed response to climate change for both high and low flow conditions.

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