scholarly journals Urban Runoff Simulation: How Do Land Use/Cover Change Patterning and Geospatial Data Quality Impact Model Outcome?

Water ◽  
2020 ◽  
Vol 12 (10) ◽  
pp. 2715
Author(s):  
Amnah Elaji ◽  
Wei Ji
Keyword(s):  
Land Use ◽  
Data Quality ◽  
Metropolitan Area ◽  
Hydrological Modeling ◽  
Urban Landscape ◽  
Urban Runoff ◽  
Geospatial Data ◽  
Rainfall Data ◽  
Runoff Simulation ◽  
Radar Rainfall

With the increase in global urbanization, satellite imagery and other types of geospatial data have been extensively used in urban landscape change research, which includes environmental modeling in order to assess the change impact on urban watersheds. For urban hydrological modeling, as a focus of this study, several related research questions are raised: (1) How sensitive are runoff simulation to land use and land cover change patterning? (2) How will input data quality impact the simulation outcome? (3) How effective is integrating and synthesizing various forms of geospatial data for runoff modeling? These issues were not fully or adequately addressed in previous related studies. With the aim of answering these questions as research objectives, we conducted a spatial land use and land cover (LULC) change analysis and an urban runoff simulation in the Blue River watershed in the Kansas City metropolitan area between 2003 and 2017. In this study, approaches were developed to incorporate the Hydrologic Engineering Center Hydrologic Modeling System (HEC-HMS) model with remote sensing, geographic information systems (GIS), and radar rainfall data. The impact of data quality on the model simulation outcome was also analyzed. The results indicate that there are no significant differences between simulated runoff responses in the two study years (2003 and 2017) due to spatial and temporal heterogeneity of urbanization processes in the region. While the metropolitan area has been experiencing remarkable urban development in the past few decades, the gain in built-up land in the study watershed during the study period is insignificant. On the other hand, the gain in vegetated land caused by forestation activities is offset by a decrease in farmland and grassland. The results show that increasing spatial data resolution does not necessarily or noticeably improve the HEC-HMS model performance or outcomes. Under these conditions, using Next Generation Weather Radar (NEXRAD) rainfall data in the simulation provides a satisfactory fit in hydrographs’ shapes, peak discharge amounts and time after calibration efforts, while they may overestimate the amount of rainfall as compared with gauge data. This study shows that the developed approach of synthesizing satellite, GIS, and radar rainfall data in hydrological modeling is effective and useful for incorporating urban landscape and precipitation change data in dynamic flood risk assessment at a watershed level.

scholarly journals Hydrological Modeling Approach Using Radar-Rainfall Ensemble and Multi-Runoff-Model Blending Technique

Water ◽  
2019 ◽  
Vol 11 (4) ◽  
pp. 850 ◽  
Author(s):  
Lee ◽  
Kang ◽  
Joo ◽  
Kim ◽  
Kim ◽  
...  
Keyword(s):  
Hydrological Modeling ◽  
Radar Data ◽  
Rainfall Data ◽  
Rainfall Runoff ◽  
Runoff Simulation ◽  
Radar Rainfall ◽  
Topographical Effects ◽  
Reservoir Regulation ◽  
Regulation Model ◽  
Runoff Model

The purpose of this study is to reduce the uncertainty in the generation of rainfall data and runoff simulations. We propose a blending technique using a rainfall ensemble and runoff simulation. To create rainfall ensembles, the probabilistic perturbation method was added to the deterministic raw radar rainfall data. Then, we used three rainfall-runoff models that use rainfall ensembles as input data to perform a runoff analysis: The tank model, storage function model, and streamflow synthesis and reservoir regulation model. The generated rainfall ensembles have increased uncertainty when the radar is underestimated, due to rainfall intensity and topographical effects. To confirm the uncertainty, 100 ensembles were created. The mean error between radar rainfall and ground rainfall was approximately 1.808–3.354 dBR. We derived a runoff hydrograph with greatly reduced uncertainty by applying the blending technique to the runoff simulation results and found that uncertainty is improved by more than 10%. The applicability of the method was confirmed by solving the problem of uncertainty in the use of rainfall radar data and runoff models.

scholarly journals The Role of the Spatial Distribution of Radar Rainfall on Hydrological Modeling for an Urbanized River Basin in Japan

Water ◽  
2019 ◽  
Vol 11 (8) ◽  
pp. 1703 ◽  
Author(s):  
Shakti P. C. ◽  
Tsuyoshi Nakatani ◽  
Ryohei Misumi
Keyword(s):  
River Basin ◽  
Spatial Resolution ◽  
Hydrological Modeling ◽  
Distribution Patterns ◽  
Rainfall Data ◽  
Model Parameters ◽  
Radar Rainfall ◽  
Rain Events ◽  
The Impact ◽  
Hydrological Applications

Recently, the use of gridded rainfall data with high spatial resolutions in hydrological applications has greatly increased. Various types of radar rainfall data with varying spatial resolutions are available in different countries worldwide. As a result of the variety in spatial resolutions of available radar rainfall data, the hydrological community faces the challenge of selecting radar rainfall data with an appropriate spatial resolution for hydrological applications. In this study, we consider the impact of the spatial resolution of radar rainfall on simulated river runoff to better understand the impact of radar resolution on hydrological applications. Very high-resolution polarimetric radar rainfall (XRAIN) data are used as input for the Hydrologic Engineering Center–Hydrologic Modeling System (HEC-HMS) to simulate runoff from the Tsurumi River Basin, Japan. A total of 20 independent rainfall events from 2012–2015 were selected and categorized into isolated/convective and widespread/stratiform events based on their distribution patterns. First, the hydrological model was established with basin and model parameters that were optimized for each individual rainfall event; then, the XRAIN data were rescaled at various spatial resolutions to be used as input for the model. Finally, we conducted a statistical analysis of the simulated results to determine the optimum spatial resolution for radar rainfall data used in hydrological modeling. Our results suggest that the hydrological response was more sensitive to isolated or convective rainfall data than it was to widespread rain events, which are best simulated at ≤1 km and ≤5 km, respectively; these results are applicable in all sub-basins of the Tsurumi River Basin, except at the river outlet.

scholarly journals Impact of Rain Gauges Distribution on the Runoff Simulation of a Small Mountain Catchment in Southern Ecuador

Water ◽  
2018 ◽  
Vol 10 (9) ◽  
pp. 1169 ◽  
Author(s):  
Adrián Sucozhañay ◽  
Rolando Célleri
Keyword(s):  
Hydrological Modeling ◽  
Rainfall Variability ◽  
Model Performance ◽  
Rainfall Data ◽  
Model Structure ◽  
Mountain Regions ◽  
Rainfall Estimation ◽  
Runoff Simulation ◽  
Rain Gauges ◽  
The Impact

In places with high spatiotemporal rainfall variability, such as mountain regions, input data could be a large source of uncertainty in hydrological modeling. Here we evaluate the impact of rainfall estimation on runoff modeling in a small páramo catchment located in the Zhurucay Ecohydrological Observatory (7.53 km2) in the Ecuadorian Andes, using a network of 12 rain gauges. First, the HBV-light semidistributed model was analyzed in order to select the best model structure to represent the observed runoff and its subflow components. Then, we developed six rainfall monitoring scenarios to evaluate the impact of spatial rainfall estimation in model performance and parameters. Finally, we explored how a model calibrated with far-from-perfect rainfall estimation would perform using new improved rainfall data. Results show that while all model structures were able to represent the overall runoff, the standard model structure outperformed the others for simulating subflow components. Model performance (NSeff) was improved by increasing the quality of spatial rainfall estimation from 0.31 to 0.80 and from 0.14 to 0.73 for calibration and validation period, respectively. Finally, improved rainfall data enhanced the runoff simulation from a model calibrated with scarce rainfall data (NSeff 0.14) from 0.49 to 0.60. These results confirm that in mountain regions model uncertainty is highly related to spatial rainfall and, therefore, to the number and location of rain gauges.

Assessing the impact of the urban landscape on heat wave episodes: a case study of the Metropolitan Area of Barcelona.

2020 ◽  
Author(s):  
Gara Villalba ◽  
Sergi Ventura ◽  
Joan Gilabert ◽  
Alberto Martilli ◽  
Alba Badia
Keyword(s):  
Land Use ◽  
Heat Wave ◽  
Metropolitan Area ◽  
Land Surface ◽  
Urban Areas ◽  
Urban Landscape ◽  
Urban Land Use ◽  
Anthropogenic Heat ◽  
Surface Model

<p>Currently, around 54% of the world's population is living in urban areas and this number is projected to increase by 66% by 2050. In the past years, cities have been experiencing heat wave episodes that affect the population. As the modern urban landscape is continually evolving, with green spaces and parks becoming a more integral component and with suburbs expanding outward from city centres into previously rural, agricultural, and natural areas, we need tools to learn how to best implement planning strategies that minimize heat waves.  In this study we use the Weather and Research Forecasting model (WRF) with a multi-layer layer scheme, the Building Effect Parameterization (BEP) coupled with the Building Energy Model (BEP+BEM, Salamanca and Martilli, 2010) to take into account the energy consumption of buildings and anthropogenic heat generated by air conditioning systems. The urban canopy scheme takes into account city morphology (e.g. building and street canyon geometry) and surface characteristics (e.g. albedo, heat capacity, emissivity, urban/vegetation fraction). The Community Land Surface Model (CLM) is used in WRF that uses 16 different plant functional types (PFTs) as the basis for land-use differentiation.  Furthermore, we use the Local Climate Zones (LCZ) classification which has 11 urban land use categories with specific thermal, radiative and geometric parameters of the buildings and ground to compute the heat and momentum fluxes in the urban areas.  The objective is to validate the model and establish relationships between urban morphology and land use with temperature, so that the model can be used to simulate land use scenarios to investigate the effectiveness of different mitigation strategies to lower urban temperatures during the summer months.</p><p> </p><p>We test the methods with the Metropolitan Area of Barcelona (AMB) as a case study. The AMB is representative of the Mediterranean climate, with mild winters and hot summers. With a heterogeneous urban landscape, the AMB covers 636 km<sup>2 </sup>(34% built, 23% agricultural, and 31% vegetation) and has more than five million habitants. We simulate the heat wave that occurred in August 2018, during which temperatures stayed between 30 and 40ºC for five consecutive days and compare results with observed data from five different weather stations. We then simulate a potential scenario changing land surface from built to vegetation, in accordance with Barcelona´s strategic climate plan, and the potential impact the land use change has on reducing heat wave episodes.</p>

scholarly journals Runoff Simulation of An Urban Drainage System Using Radar Rainfall Data

2013 ◽  
Vol 15 (3) ◽  
pp. 413-422 ◽  
Author(s):  
Na Rae Kang ◽  
Hui Seung Noh ◽  
Jong So Lee ◽  
Sang Hun Lim ◽  
Hung Soo Kim
Keyword(s):  
Drainage System ◽  
Rainfall Data ◽  
Urban Drainage ◽  
Runoff Simulation ◽  
Radar Rainfall ◽  
Urban Drainage System

scholarly journals Uncertainty of Flood Forecasting Based on Radar Rainfall Data Assimilation

2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
Author(s):  
Xinchi Chen ◽  
Liping Zhang ◽  
Christopher James Gippel ◽  
Lijie Shan ◽  
Shaodan Chen ◽  
...  
Keyword(s):  
Quantitative Estimation ◽  
Poor Performance ◽  
Statistical Weight ◽  
Rainfall Data ◽  
Precipitation Forecast ◽  
Data Input ◽  
Runoff Simulation ◽  
Radar Rainfall ◽  
Precipitation Measurement ◽  
Using Data

Precipitation is the core data input to hydrological forecasting. The uncertainty in precipitation forecast data can lead to poor performance of predictive hydrological models. Radar-based precipitation measurement offers advantages over ground-based measurement in the quantitative estimation of temporal and spatial aspects of precipitation, but errors inherent in this method will still act to reduce the performance. Using data from White Lotus River of Hubei Province, China, five methods were used to assimilate radar rainfall data transformed from the classifiedZ-Rrelationship, and the postassimilation data were compared with precipitation measured by rain gauges. The five sets of assimilated rainfall data were then used as input to the Xinanjiang model. The effect of precipitation data input error on runoff simulation was analyzed quantitatively by disturbing the input data using the Breeding of Growing Modes method. The results of practical application demonstrated that the statistical weight integration and variational assimilation methods were superior. The corresponding performance in flood hydrograph prediction was also better using the statistical weight integration and variational methods compared to the others. It was found that the errors of radar rainfall data disturbed by the Breeding of Growing Modes had a tendency to accumulate through the hydrological model.

scholarly journals Long-Term Simulation of Daily Streamflow Using Radar Rainfall and the SWAT Model: A Case Study of the Gamcheon Basin of the Nakdong River, Korea

2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
Author(s):  
Huiseong Noh ◽  
Jongso Lee ◽  
Narae Kang ◽  
Dongryul Lee ◽  
Hung Soo Kim ◽  
...  
Keyword(s):  
Swat Model ◽  
Rainfall Data ◽  
Nakdong River ◽  
Runoff Simulation ◽  
Spatiotemporal Resolution ◽  
Radar Rainfall ◽  
Daily Streamflow ◽  
The Nakdong River ◽  
Rain Radar ◽  
Better Than

In recent years, with the increasing need for improving the accuracy of hydrometeorological data, interests in rain-radar are also increasing. Accordingly, with high spatiotemporal resolution of rain-radar rainfall data and increasing accumulated data, the application scope of rain-radar rainfall data into hydrological fields is expanding. To evaluate the hydrological applicability of rain-radar rainfall data depending on the characteristics of hydrological model, this study appliedRgaugeandRradarto a SWAT model in the Gamcheon stream basin of the Nakdong River and analyzed the effect of rainfall data on daily streamflow simulation. The daily rainfall data forRgauge,RZ, andRKDPwere utilized as input data for the SWAT model. As a result of the daily runoff simulation for analysis periods usingRZ(P)andRKDP(P), the simulation which utilizedRgaugereflected the rainfall-runoff characteristics better than the simulations which appliedRZ(P)orRKDP(P). However, in the rainy or wet season, the simulations which utilizedRZ(P)orRKDP(P)were similar to or better than the simulation that appliedRgauge. This study reveals that analysis results and degree of accuracy depend significantly on rainfall characteristics (rainy season and dry season) and QPE algorithms when conducting a runoff simulation with radar.

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