scholarly journals Impact of Precipitation Pre-Processing Methods on Hydrological Model Performance using High-Resolution Gridded Dataset

Water ◽  
2020 ◽  
Vol 12 (3) ◽  
pp. 840 ◽  
Author(s):  
Salam A. Abbas ◽  
Yunqing Xuan
Keyword(s):  
High Resolution ◽  
Model Calibration ◽  
Hydrological Model ◽  
Estimation Method ◽  
Model Performance ◽  
Point Estimation ◽  
Processing Methods ◽  
Essential Components ◽  
The Impact ◽  
Point Estimation Method

Effective representation of precipitation inputs is one of the essential components in hydrological model structures, especially when gauge measurements for the modelled catchment are sparse. Assessment of the impact of precipitation pre-processing is often nontrivial as precipitation data are very limited in the first place. In this paper, we demonstrate a study using a semi-distributed hydrological model, the Soil and Water Assessment Tool (SWAT) to examine the impact of different precipitation pre-processing methods on model calibration and the overall model performance with regards to the operational use. A river catchment in the UK is modelled to test against the three pre-processing methods: the Centroid Point Estimation Method (CPEM), the Grid Area Method (GAM) and the Grid Point Method (GPM). Cross-calibration and validation are then carried out by using the high-resolution Centre for Ecology & Hydrology–Gridded Estimate Areal Rainfall (CEH-GEAR) dataset. The results show that the proposed methods GAM and GPM can improve the model calibration significantly against the one calibrated with the existing CPEM method used by the model; the performance differences in the validation among the calibrated models, however, remain small and become irrelevant. The findings indicate that it is preferable to always make use of high-quality rainfall data, when available, with a better pre-processing method, even with models that are previously calibrated with low-quality rainfall inputs. It is also shown that such improvements are affected by the size of catchment and become less significant for smaller catchments.

scholarly journals Hydrological model optimization using multi-gauge calibration (MGC) in a mountainous region

2020 ◽  
Author(s):  
Sead Ahmed Swalih ◽  
Ercan Kahya
Keyword(s):  
Climate Change ◽  
Model Calibration ◽  
Hydrological Model ◽  
Model Performance ◽  
Spatial Dimension ◽  
Model Parameters ◽  
Climate Projections ◽  
Predictive Capacity ◽  
Black Sea Region ◽  
The Impact

Abstract It is a challenge for hydrological models to capture complex processes in a basin with limited data when estimating model parameters. This study aims to contribute in this field by assessing the impact of incorporating spatial dimension on the improvement of model calibration. Hence, the main objective of this study was to evaluate the impact of multi-gauge calibration in hydrological model calibration for Ikizdere basin, Black Sea Region in Turkey. In addition, we have incorporated the climate change impact assessment for the study area. Four scenarios were tested for performance assessment of calibration: (1) using downstream flow data (DC), (2) using upstream data (UC), (3) using upstream and downstream data (Multi-Gauge Calibration – MGC), and (4) using upstream and then downstream data (UCDC). The results have shown that using individual gauges for calibration (1 and 2) improve the local predictive capacity of the model. MGC calibration significantly improved the model performance for the whole basin unlike 1 and 2. However, the local gauge calibrations statistical performance, compared to MGC outputs, was better for local areas. The UCDC yields the best model performance and much improved predictive capacity. Regarding the climate change, we did not observe an agreement amongst the future climate projections for the basin towards the end of the century.

scholarly journals Hydrological Model Calibration in Data-Limited Catchments Using Non-Continuous Data Series with Different Lengths

10.29007/5hv1 ◽  
2018 ◽  
Author(s):  
Chuanzhe Li ◽  
Jia Liu ◽  
Fuliang Yu ◽  
Jiyang Tian ◽  
Yang Wang ◽  
...  
Keyword(s):  
Model Calibration ◽  
Hydrological Model ◽  
Potential Evapotranspiration ◽  
Model Performance ◽  
Data Series ◽  
Observation Data ◽  
Calibration Data ◽  
Series Length ◽  
Streamflow Data ◽  
The Impact

This paper evaluates the effects of calibration data series length on the performance of a hydrological model in data-limited catchments where data are non-continuous and fragmental. Non-continuous calibration periods were used for more independent streamflow data for SIMHYD model calibration. Nash-Sutcliffe efficiency and percentage water balance error were used as performance measures. The particle swarm optimization method was used to calibrate the rainfall-runoff models. Different lengths of data series ranging from one year to ten years were used to study the impact of calibration data series length. Fifty-five relatively unimpaired catchments located all over Australia with daily precipitation, potential evapotranspiration, and streamflow data were tested to obtain more general conclusions. The results show that longer calibration data series do not necessarily result in better model performance. Our results may have useful and interesting implications for the efficiency of using limited observation data for hydrological model calibration in different climates.

scholarly journals Allocation of EV Public Charging Station in Renewable based Distribution Network using HHO Considering Uncertainties and Traffic Congestion

2021 ◽  
Author(s):  
Arnab Pal ◽  
Aniruddha Bhattacharya ◽  
Ajoy Kumar Chakraborty
Keyword(s):  
Distribution System ◽  
Traffic Congestion ◽  
Distribution Network ◽  
Estimation Method ◽  
Point Estimation ◽  
Rank Test ◽  
Voltage Profile ◽  
Charging Station ◽  
Soft Computing Techniques ◽  
Point Estimation Method

Abstract Electric vehicle (EV) is the growing vehicular technology for sustainable development to reduce carbon emission and to save fossil fuel. The charging station (CS) is necessary at appropriate locations to facilitate the EV owners to charge their vehicle as well as to keep the distribution system parameters within permissible limits. Besides that, the selection of a charging station is also a significant task for the EV user to reduce battery energy wastage while reaching the EV charging station. This paper presents a realistic solution for the allocation of public fast-charging stations (PFCS) along with solar distributed generation (SDG). A 33 node radial distribution network is superimposed with the corresponding traffic network to allocate PFCSs and SDGs. Two interconnected stages of optimization are used in this work. The first part deals with the optimization of PFCS’s locations and SDG’s locations with sizes, to minimize the energy loss and to improve voltage profile using harris hawk optimization (HHO) and few other soft computing techniques. The second part handles the proper assignment of EVs to the PFCSs with less consumption of the EV’s energy considering the road distances with traffic congestion using linear programming (LP), where the shortest paths are decided by Dijkstra's algorithm. The 2m point estimation method (2m PEM) is employed to handle the uncertainties associated with EVs and SDGs. The robustness of solutions are tested using wilcoxon signed rank test and quade test.

scholarly journals Evaluating the impact of grid cell properties in spatial discretization of groundwater model for a tropical karst catchment in Rote Island, Indonesia

2016 ◽  
Vol 48 (6) ◽  
pp. 1757-1772 ◽  
Author(s):  
Dua K. S. Y. Klaas ◽  
Monzur Alam Imteaz ◽  
Arul Arulrajah
Keyword(s):  
Estimation Method ◽  
Model Performance ◽  
Grid Cell ◽  
Specific Yield ◽  
Spatial Discretization ◽  
Observation Well ◽  
Improve Model ◽  
Karst Catchment ◽  
The Impact ◽  
Tropical Karst

Abstract To assess the effect of three grid cell properties (size, mean slope of the surface and distance between centre of grid and observation well) on groundwater models' performances, a tropical karst catchment characterized by monsoonal season in Rote Island, Indonesia was selected. Here, MODFLOW was used to develop models with five different spatial discretization schemes: 10 × 10 m, 20 × 20 m, 30 × 30 m, 40 × 40 m and 50 × 50 m. Using parameter estimation method, hydraulic conductivity and specific yield values over a selection of pilot points were estimated. The trends of the performances were calculated at each observation well in order to recommend the most appropriate location for observation well placement in terms of topographical characteristic. It is confirmed that the deterioration of model performance is mainly controlled by the increase of distance between well and centre of the cell, and the mean slope of the surface. Results reveal that model performance increases substantially for areas of low slope (<3%) and medium slope (3–10%) for a smaller grid cell size. Therefore, to improve model performance, it is recommended that the observations wells are placed in areas of low and medium slopes.

Hydrological modelling and anthropogenic water use

2021 ◽  
Author(s):  
Ponnambalam Rameshwaran ◽  
Ali Rudd ◽  
Vicky Bell ◽  
Matt Brown ◽  
Helen Davies ◽  
...  
Keyword(s):  
Climate Change ◽  
Water Use ◽  
Hydrological Model ◽  
Model Performance ◽  
River Flows ◽  
Discharge Data ◽  
New Approach ◽  
Water Demands ◽  
The Impact ◽  
Grid Based

<p>Despite Britain’s often-rainy maritime climate, anthropogenic water demands have a significant impact on river flows, particularly during dry summers. In future years, projected population growth and climate change are likely to increase the demand for water and lead to greater pressures on available freshwater resources.</p><p>Across England, abstraction (from groundwater, surface water or tidal sources) and discharge data along with ‘Hands off Flow’ conditions are available for thousands of individual locations; each with a licence for use, an amount, an indication of when abstraction can take place, and the actual amount of water abstracted (generally less than the licence amount). Here we demonstrate how these data can be used in combination to incorporate anthropogenic artificial influences into a grid-based hydrological model. Model simulations of both high and low river flows are generally improved when abstractions and discharges are included, though for some catchments model performance decreases. The new approach provides a methodological baseline for further work investigating the impact of anthropogenic water use and projected climate change on future river flows.</p>

scholarly journals Two-Stage Optimal Scheduling of Large-Scale Renewable Energy System Considering the Uncertainty of Generation and Load

2020 ◽  
Vol 10 (3) ◽  
pp. 971 ◽  
Author(s):  
Xiangyu Kong ◽  
Shuping Quan ◽  
Fangyuan Sun ◽  
Zhengguang Chen ◽  
Xingguo Wang ◽  
...  
Keyword(s):  
Renewable Energy ◽  
Demand Response ◽  
Large Scale ◽  
Thermal Power ◽  
Estimation Method ◽  
Point Estimation ◽  
Low Carbon ◽  
System Operation ◽  
Two Stage ◽  
Point Estimation Method

With the development of smart grid and low-carbon electricity, a high proportion of renewable energy is connected to the grid. In addition, the peak-valley difference of system load increases, which makes the traditional grid scheduling method no longer suitable. Therefore, this paper proposes a two-stage low-carbon economic scheduling model considering the characteristics of wind, light, thermal power units, and demand response at different time scales. This model not only concerns the deep peak state of thermal power units under the condition of large-scale renewable energy, but also sets the uncertain models of PDR (Price-based Demand Response) virtual units and IDR (Incentive Demand Response) virtual units. Taking the system operation cost and carbon treatment cost as the target, the improved bat algorithm and 2PM (Two-point Estimation Method) are used to solve the problem. The introduction of climbing costs and low load operating costs can more truly reflect the increased cost of thermal power units. Meanwhile, the source-load interaction can weigh renewable energy limited costs and the increased costs of balancing volatility. The proposed method can be applied to optimal dispatch and safe operation analysis of the power grid with a high proportion of renewable energy. Compared with traditional methods, the total scheduling cost of the system can be reduced, and the rights and obligations of contributors to system operation can be guaranteed to the greatest extent.

An IPEM for optimal control of uncertain beam-moving mass systems with saturation nonlinearity

2017 ◽  
Vol 24 (13) ◽  
pp. 2760-2781
Author(s):  
Xiao-Xiao Liu ◽  
Xing-Min Ren
Keyword(s):  
Nonlinear Control ◽  
Vibration Suppression ◽  
Estimation Method ◽  
Feedback System ◽  
Random Excitation ◽  
Point Estimation ◽  
Maximum Principles ◽  
Moving Mass ◽  
Nonlinear Feedback ◽  
Point Estimation Method

This paper addresses the vibration control of single-span beams subjected to a moving mass by coupling the saturated nonlinear control and an improved point estimation method (IPEM). An optimal nonlinear feedback control law, for a kind of uncertain linear system with actuator nonlinearities, is derived using the combination of Pontryagin's maximum principles and the improved point estimation method. The stability of the feedback system is guaranteed using a Lyapunov function. In order to obtain the instantaneously probabilistic information of output responses, a novel moment approach is presented by combining the improved point estimation method, the maximum entropy methodology and the probability density evolution theory. In addition to the consideration of stochastic system parameters, the external loadings are considered as a nonstationary random excitation and a moving sprung mass, respectively. The proposed strategy is then used to perform vibration suppression analysis and parametric sensitivity analysis of the given beam. From numerical simulation results, it is deduced that the improved point estimation method is a priority approach to the optimal saturated nonlinear control of stochastic beam systems. This observation has widespread applications and prospects in vehicle–bridge interaction and missile–gun systems.

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