A Regional Hydrological Model for Arid and Semi-Arid River Basins with Consideration of Irrigation (original) (raw)
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2022
The objective of the study was to configure the Hydrological Modeling System (HEC-HMS) in such a way that it could simulate all-important hydrological components (e.g., streamflow, soil moisture, snowmelt water, terrestrial water storage, baseflow, surface flow, and evapotranspiration) in the Three-River Headwater Region. However, the problem we faced was unsatisfactory simulations of these hydrological components, except streamflow. The main reason we found was the auto-calibration method of HEC-HMS because it generated irrational parameters, especially with the inclusion of Temperature Index Method and Soil Moisture Accounting (an advanced and complex loss method). Similar problems have been reported by different previous studies. To overcome these problems, we designed a comprehensive approach to estimate initial parameters and to calibrate the model manually in such a way that the model could simulate all the important hydrological components satisfactorily.
Hydrological simulation of the East River basin (Dongjiang) in China
Proceedings of the 4th International Symposium on Environmental Hydraulics & 14th Congress of Asia and Pacific Division, International Association of Hydraulic Engineering and Research, 15-18 December 2004, Hong Kong, 2004
Study region: The Rufiji basin, East Africa. Study focus: Rapid advances in global hydrological model (GHM) resolution, model features, and in situ and remotely sensed datasets are driving progress towards local relevance and application. Despite their increasing use, however, evaluation of local hydrological performance of GHMs is rare. In this paper, we examine the performance of a well-known GHM (LPJmL, recently modified to ∼9 km resolution) with and without modest steps to regionalise the model. We consider the Rufiji river basin, an economically important medium-size basin in eastern Africa. New hydrological insights for the region: Our results indicate that the unmodified GHM does provide a reasonable first approximation of spatial variability in mean flow conditions, but scores rather poorly on seasonal and inter-annual variability. For the model to achieve levels of performance indicators comparable with bespoke modelling, modifications to model inputs, additional runoff delay and wetland parameterization were required. The largest improvements are associated with adjustments in precipitation and enhanced runoff delay. With the modified version, as a proof of concept, we show that a well-known drying trend in a major tributary of the Rufiji can be explained by implementing irrigation abstractions in the model. Overall, the results suggest that with limited and fairly simple modification GHMs can be regionalised to allow their use for scenario testing and further exploration of key local processes in basins with limited observational data. 1. Introduction Global hydrological and land surface models (further GHMs) are undergoing rapid developmentever greater computational capacity and data storage, further supported by developments in remote sensing, the availability of gridded observation datasets and meteorological forcing data, have allowed model development at higher spatial resolution covering larger areas (e.g. Flörke et al., 2013; Sutanudjaja et al., 2017). These high-resolution models, with resolutions of 5 arc-minutes (∼9 km at the equator) or higher are able to model the hydrological cycle in catchments across the whole world uniformly and systematically, including in regions for which in situ observations are scarce or where the capacity to conduct continued and detailed hydrological modelling is limited. GHMs are now almost similar in scaleat least in spatial resolution, if not yet in process detail-to the domain of applied river
Modelling the streamflow of a river basin using enhanced hydro-meteorological data in Malaysia
Acta Horticulturae, 2017
Paddy rice is an important food crop in Malaysia providing food for the nation and yet its water demand is relatively high compared with the other crops. Increasing the problem are concerns about uncertainties in crop water demands, rainfall patterns and stream flows owing to climate change effects. Thus, predicting flows while accounting for climate change is critical in future. However, due to poor meteorological records in many catchments, a previous study presented a new meteorological data set gridded from daily observation data countrywide. In this study, the stream flow simulation for the Bernam River Basin was assessed using this data set as inputs to the soil and water assessment tool (SWAT) model. Flow calibration was done for 17 years and validation was performed for 8 years. The results showed that simulated values matched well with enhanced data values with (), NSE and PBIAS being 0.67, 0.62 and-9.4 for the calibration period and 0.62, 0.61 and-4.2 for the validation period. The study shows that the new data is applicable in the Bernam watershed, and that the SWAT model was able to predict flows reasonably well with these data, thus can be used to study future impacts on stream flow of this river.
A hydrograph simulation in the Huaihe River Basin (HRB) was investigated using two different models: a coupled land surface hydrological model (CLHMS), and a large-scale hydrological model (LSX-HMS). The NCEP-NCAR reanalysis dataset and observed precipitation data were used as meteorological inputs. The simulation results from both models were compared in terms of flood processes forecasting during high flow periods in the summers of 2003 and 2007, and partial high flow periods in 2000. The comparison results showed that the simulated streamflow by CLHMS model agreed well with the observations with Nash-Sutcliffe coefficients larger than 0.76, in both periods of 2000 at Lutaizi and Bengbu stations in the HRB, while the skill of the LSX-HMS model was relatively poor. The simulation results for the high flow periods in 2003 and 2007 suggested that the CLHMS model can simulate both the peak time and intensity of the hydrological processes, while the LSX-HMS model provides a delayed flo...
Simulation of rainfall – runoff process using HEC-HMS model for Chindwin River Basin
National Conference Science and Engineering, 2019
Hydrological modeling is a crucial and decisive tool to estimate hydrological process and the water resources availability. In this study, HEC-HMS 4.2 hydrological model is used to simulate stream flow in Chindwin River Basin situated in Northern west of Myanmar. Estimation of rainfall-runoff in a watershed based on the rate of precipitation and discharge at outlet is important in hydrologic studies. The main objective of this study is to simulate daily rainfall-runoff of Chindwin River. To compute runoff volume, peak runoff rate, base flow and flow routing methods-The initial and constant loss method, SCS unit hydrograph and Recession method, Lag routing methods are chosen, respectively. Model calibration with optimization method, validation and sensitivity analysis has been done. The Nash-Sutcliffe model efficiency criterion, the percentage error in volume, the percentage error in peak and net difference of observed and simulated time to peak, which were used for calibration model. The results have been found to range from (0.67 to 0.95), (-0.03 to 0.06), (-0.1 to 0.001) and (0 to 2 day) respectively. Finally, it can be concluded that model can be used with reasonable approximation in hydrologic simulation in Chindwin watershed.
2015
To estimate the impacts of future climate change on streamflow in the Tangwang River basin (TRB) in northeastern China, 2 hydrological models, the Soil and Water Assessment Tool and the Hydro-Informatic Modeling System, were used. These models are driven by future (2021−2050) local rainfall and temperature scenarios downscaled from global climate model (GCM) simulations from the fifth phase of the Coupled Model Intercomparison Project under 2 emission scenarios (Representative Concentration Pathway [RCP] 4.5 and RCP8.5). The downscaling of rainfall is done with the help of a multisite stochastic rainfall generator (MSRG), which extends the ‘Richardson type’ rainfall generator to a multisite approach using a modified series-independent and spatial-correlated random numbers method by linking its 4 parameters to large-scale circulations using least-squares regressions. An independent validation of the MSRG shows that it successfully preserves the major daily rainfall characteristics for wet and dry seasons. Relative to the reference period (1971−2000), the annual and wet season (April to October) streamflow during the future period (2021−2050) would decrease overall, which indicates that water resources and the potential flood risk would decline in the TRB. The slightly increased dry season (November to March) streamflow would, to some extent, contribute to the ‘spring drought’ over this region. Although rainfall is projected to remain un - changed in the wet season and the whole year, the increased total evapotranspiration due to the increase in temperature would lead to a decline in total streamflow for this basin. The projected streamflow changes from multiple GCMs in this paper could provide a glimpse into a very plausible future for the water resource management community, and would hence provide valuable ref - erences for the sustainable management of water and forest ecosystems under a changing climate.
Quaternary International, 2010
Available online xxx a b s t r a c t Gridded climate data sets are widely used in the analysis, modeling and forecasting of the consequences of climate change. The objective of this study is to compare the impact of different climate datasets (station vs. gridded) on the parameterization of a hydrological model (developed using SWAT2005) of the River Xiangxi, the largest tributary of Yangtze River in the Hubei part of the Three Gorges Reservoir. Climate data used in this study derive from two sources: point daily observations from the Xingshan meteorological station (STN) and gridded (0.5 Â 0.5 ) monthly observations of the CRU TS3.0 global dataset (CRU) downscaled to daily data using a weather generator. Data from 1970 to 1974 were applied for sensitivity analyses and autocalibration and subsequently validate hindcasts over the period [1976][1977][1978][1979][1980][1981][1982][1983][1984][1985][1986]. Despite there being only slight differences in mean annual precipitation (1003 mm vs. 1052 mm) between STN and CRU, the data differ more in their estimates of the number of rain days (136 vs. 112) and wet days standard deviation (11.75 mm vs. 18.49 mm). The mean, maximum and minimum temperatures from CRU are all lower than those from STN. SWAT parameter sensitivity analysis results show slight differences in the relative rank of the most sensitive parameters, with the differences mainly caused by the lower temperature and more intensive rainfall in CRU relative to STN. Autocalibrated parameters showed very similar values, except for the surface runoff lag coefficient which is higher for the CRU dataset compared to that derived from the STN dataset. Statistic results for discharge simulated based on CRU compared rather well with that based on STN CRU as evaluated using the standard statistics of the Nash-Sutcliffe efficiency, coefficient of determination, and percent error. The sensitivity analysis and autocalibration tool embedded in SWAT2005 is a powerful utility in hydrological modeling of the River Xiangxi, and the CRU dataset appears to be appropriate for application to hydrological modeling in this case, thus providing a good basis for climate change studies.
Water, 2015
A large-scale hydrological model (MIKE HYDRO) was established for the purpose of sustainable agricultural water management in the main stem Tarim River, located in northwest China. In this arid region, agricultural water consumption and allocation management are crucial to address the conflicts among irrigation water users from upstream to downstream. The results of model calibration indicated a close correlation between simulated and observed values. Scenarios with the change on irrigation strategies and land use distributions were investigated. Irrigation scenarios revealed that the available irrigation water has significant and varying effects on the yields of different crops. Irrigation water saving could reach up to 40% in the water-saving irrigation scenario. Land use scenarios illustrated that an increase of farmland area in the lower reach gravely aggravated the water deficit, while a decrease of farmland in the upper reaches resulted in considerable benefits for all sub-catchments. A substitution of crops was also investigated, which demonstrated
Review of studies on hydrological modelling in Malaysia
Modeling Earth Systems and Environment
Hydrological models are vital component and essential tools for water resources and environmental planning and management. In recent times, several studies have been conducted with a view of examining the compatibility of model results with streamflow measurements. Some modelers are of the view that even the use of complex modeling techniques does not give better assessment due to soil heterogeneity and climatic changes that plays vital roles in the behavior of streamflow. In Malaysia, several public domain hydrologic models that range from physically-based models, empirical models and conceptual models are in use. These include hydrologic modeling system (HEC-HMS), soil water assessment tool (SWAT), MIKE-SHE, artificial neural network (ANN). In view of this, a study was conducted to evaluate the hydrological models used in Malaysia, determine the coverage of the hydrological models in major river basins and to identify the methodologies used (specifically model performance and evaluation). The results of the review showed that 65% of the studies conducted used physical-based models, 37% used empirical models while 6% used conceptual models. Of the 65% of physical-based modelling studies, 60% utilized HEC-HMS an open source models, 20% used SWAT (public domain model), 9% used MIKE-SHE, MIKE 11 and MIKE 22, Infoworks RS occupied 7% while TREX and IFAS occupy 2% each. Thus, indicating preference for open access models in Malaysia. In the case of empirical models, 46% from the total of empirical researches in Malaysia used ANN, 13% used Logistic Regression (LR), while Fuzzy logic, Unit Hydrograph, Auto-regressive integrated moving average (ARIMA) model and support vector machine (SVM) contributed 8% each. Whereas the remaining proportion is occupied by Numerical weather prediction (NWP), land surface model (LSM), frequency ratio (FR), decision tree (DT) and weight of evidence (WoE). Majority of the hydrological modelling studies utilized one or more statistical measure of evaluating hydrological model performance (R, R 2 , NSE, RMSE, MAE, etc.) except in some few cases where no specific method was stated. Of the 70 papers reviewed in this study, 16 did not specify the type of model evaluation criteria they used in evaluating their studies, 17 utilized only one method while 37 used two or more methods. NSE with 27% was found to be the most widely used method of evaluating model performance; R and RMSE came second with a percentage use 24% each. R 2 (20%) was recorded as the third most widely used model evaluation criteria in Malaysia, MAE came fourth with 16% while PBIAS is the least with 11%.The findings of this work will serve as a guide to modelers in identifying the type of hydrological model they need to apply to a particular catchment for a particular problem. It will equally help water resources managers and policy makers in providing them with executive summary of hydrological studies and where more input is needed to achieve sustainable development.
Journal of Hydrology, 2006
Our study focuses on the simulation of heavy precipitation and floods over the Huaihe River Basin (270,000 km 2 ), one of the seven major river basins in China. The simulation covers two periods in 1998 (June 28-July 3, July 28-August 17) and a third period in 2003 (June 26-July 22). The former two periods, with eight meteorological cases each of duration 72-h, correspond to the Intensive Observation Period of HUBEX/MAGE (Huaihe River Basin Experiment/Monsoon Asian GEWEX Experiment). The period in 2003 with 10 cases is the second most severe flooding event on record. The Canadian atmospheric Mesoscale Compressible Community Model (MC2) is used for precipitation simulation in the hindcast mode for all cases. The Chinese Xinanjiang hydrological model driven by either rain gauge or MC2 precipitation is used to simulate hydrographs at the outlet of the Shiguanhe sub-basin (5930 km 2 ), part of the Huaihe River Basin. The MC2 precipitation is also evaluated using observations from rain gauges. Over the Huaihe River Basin, MC2 generally overestimates the basin-averaged precipitation. Three of the eight 1998 cases have a percentage error less than 50% with the fourth having an error of 54%, while six of the ten 2003 cases have errors less than 50%. The precipitation over five different sub-regions and the Shiguanhe sub-basin of the Huaihe River Basin from MC2 are also compared with values from the Chinese operational weather prediction model; the latter data are only available for the ten a v a i l a b l e a t w w w . s c i e n c e d i r e c t . c o m j o u r n a l h o m e p a g e : w w w . e l s e v i e r . c o m / l o c a t e / j h y d r o l simulation using rain gauge precipitation as revealed by the Nash-Sutcliffe coefficients of 0.91 for both summers of 1998 and 2003. The simulation using MC2 precipitation shows a reasonable agreement of flood timing and peak discharges with Nash-Sutcliffe coefficients of 0.63 and 0.87 for the two 1998 periods, and 0.60 for 2003. The encouraging results demonstrate the potential of using mesoscale model precipitation for flood forecast, which provides a longer lead time compared to traditional methods such as those based on rain gauges, statistical forecast or radar nowcasts.