scholarly journals Adequacy of Near Real-Time Satellite Precipitation Products in Driving Flood Discharge Simulation in the Fuji River Basin, Japan

2021 ◽  
Vol 11 (3) ◽  
pp. 1087
Author(s):  
Li Zhou ◽  
Mohamed Rasmy ◽  
Kuniyoshi Takeuchi ◽  
Toshio Koike ◽  
Hemakanth Selvarajah ◽  
...  
Keyword(s):  
Real Time ◽  
River Basin ◽  
Bias Correction ◽  
Large Scale ◽  
River Flow ◽  
Hydrologic Model ◽  
Satellite Precipitation ◽  
Flood Discharge ◽  
Discharge Simulation ◽  
The Impact

Flood management is an important topic worldwide. Precipitation is the most crucial factor in reducing flood-related risks and damages. However, its adequate quality and sufficient quantity are not met in many parts of the world. Currently, near real-time satellite precipitation products (NRT SPPs) have great potential to supplement the gauge rainfall. However, NRT SPPs have several biases that require corrections before application. As a result, this study investigated two statistical bias correction methods with different parameters for the NRT SPPs and evaluated the adequacy of its application in the Fuji River basin. We employed Global Satellite Mapping of Precipitation (GSMaP)-NRT and Integrated Multi-satellitE Retrievals for GPM (IMERG)-Early for NRT SPPs as well as BTOP model (Block-wise use of the TOPMODEL (Topographic-based hydrologic model)) for flood runoff simulation. The results showed that the corrected SPPs by the 10-day ratio based bias correction method are consistent with the gauge data at the watershed scale. Compared with the original SPPs, the corrected SPPs improved the flood discharge simulation considerably. GSMaP-NRT and IMERG-Early have the potential for hourly river-flow simulation on a basin or large scale after bias correction. These findings can provide references for the applications of NRT SPPs in other basins for flood monitoring and early warning applications. It is necessary to investigate the impact of number of ground observation and their distribution patterns on bias correction and hydrological simulation efficiency, which is the future direction of this study.

scholarly journals Assessing the impact of hydrodynamics on large-scale flood wave propagation – a case study for the Amazon Basin

2017 ◽  
Vol 21 (1) ◽  
pp. 117-132 ◽  
Author(s):  
Jannis M. Hoch ◽  
Arjen V. Haag ◽  
Arthur van Dam ◽  
Hessel C. Winsemius ◽  
Ludovicus P. H. van Beek ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
Hydrodynamic Model ◽  
Large Scale ◽  
Hydrologic Model ◽  
Flood Events ◽  
Coupled Models ◽  
Validation Parameters ◽  
Directional Coupling ◽  
Inundation Extent ◽  
The Impact

Abstract. Large-scale flood events often show spatial correlation in neighbouring basins, and thus can affect adjacent basins simultaneously, as well as result in superposition of different flood peaks. Such flood events therefore need to be addressed with large-scale modelling approaches to capture these processes. Many approaches currently in place are based on either a hydrologic or a hydrodynamic model. However, the resulting lack of interaction between hydrology and hydrodynamics, for instance, by implementing groundwater infiltration on inundated floodplains, can hamper modelled inundation and discharge results where such interactions are important. In this study, the global hydrologic model PCR-GLOBWB at 30 arcmin spatial resolution was one-directionally and spatially coupled with the hydrodynamic model Delft 3D Flexible Mesh (FM) for the Amazon River basin at a grid-by-grid basis and at a daily time step. The use of a flexible unstructured mesh allows for fine-scale representation of channels and floodplains, while preserving a coarser spatial resolution for less flood-prone areas, thus not unnecessarily increasing computational costs. In addition, we assessed the difference between a 1-D channel/2-D floodplain and a 2-D schematization in Delft 3D FM. Validating modelled discharge results shows that coupling PCR-GLOBWB to a hydrodynamic routing scheme generally increases model performance compared to using a hydrodynamic or hydrologic model only for all validation parameters applied. Closer examination shows that the 1-D/2-D schematization outperforms 2-D for r2 and root mean square error (RMSE) whilst having a lower Kling–Gupta efficiency (KGE). We also found that spatial coupling has the significant advantage of a better representation of inundation at smaller streams throughout the model domain. A validation of simulated inundation extent revealed that only those set-ups incorporating 1-D channels are capable of representing inundations for reaches below the spatial resolution of the 2-D mesh. Implementing 1-D channels is therefore particularly of advantage for large-scale inundation models, as they are often built upon remotely sensed surface elevation data which often enclose a strong vertical bias, hampering downstream connectivity. Since only a one-directional coupling approach was tested, and therefore important feedback processes are not incorporated, simulated discharge and inundation extent for both coupled set-ups is generally overpredicted. Hence, it will be the subsequent step to extend it to a two-directional coupling scheme to obtain a closed feedback loop between hydrologic and hydrodynamic processes. The current findings demonstrating the potential of one-directionally and spatially coupled models to obtain improved discharge estimates form an important step towards a large-scale inundation model with a full dynamic coupling between hydrology and hydrodynamics.

scholarly journals Comparing bias correction methods in downscaling meteorological variables for hydrologic impact study in an arid area in China

2014 ◽  
Vol 11 (11) ◽  
pp. 12659-12696 ◽  
Author(s):  
G. H. Fang ◽  
J. Yang ◽  
Y. N. Chen ◽  
C. Zammit
Keyword(s):  
Water Resources ◽  
River Basin ◽  
Bias Correction ◽  
Meteorological Data ◽  
Hydrologic Model ◽  
Temperature Correction ◽  
Arid Area ◽  
Tarim River ◽  
Tarim River Basin ◽  
Impact Study

Abstract. Water resources are essential to the ecosystem and social economy in the desert and oasis of the arid Tarim River Basin, Northwest China, and expected to be vulnerable to climate change. Regional Climate Models (RCM) have been proved to provide more reliable results for regional impact study of climate change (e.g. on water resources) than GCM models. However, it is still necessary to apply bias correction before they are used for water resources research due to often considerable biases. In this paper, after a sensitivity analysis on input meteorological variables based on Sobol' method, we compared five precipitation correction methods and three temperature correction methods to the output of a RCM model with its application to the Kaidu River Basin, one of the headwaters of the Tarim River Basin. Precipitation correction methods include Linear Scaling (LS), LOCal Intensity scaling (LOCI), Power Transformation (PT), Distribution Mapping (DM) and Quantile Mapping (QM); and temperature correction methods include LS, VARIance scaling (VARI) and DM. These corrected precipitation and temperature were compared to the observed meteorological data, and then their impacts on streamflow were also compared by driving a distributed hydrologic model. The results show: (1) precipitation, temperature, solar radiation are sensitivity to streamflow while relative humidity and wind speed are not, (2) raw RCM simulations are heavily biased from observed meteorological data, which results in biases in the simulated streamflows, and all bias correction methods effectively improved theses simulations, (3) for precipitation, PT and QM methods performed equally best in correcting the frequency-based indices (e.g. SD, percentile values) while LOCI method performed best in terms of the time series based indices (e.g. Nash–Sutcliffe coefficient, R2), (4) for temperature, all bias correction methods performed equally well in correcting raw temperature. (5) For simulated streamflow, precipitation correction methods have more significant influence than temperature correction methods and the performances of streamflow simulations are consistent with these of corrected precipitation, i.e. PT and QM methods performed equally best in correcting flow duration curve and peak flow while LOCI method performed best in terms of the time series based indices. The case study is for an arid area in China based on a specific RCM and hydrologic model, but the methodology and some results can be applied to other area and other models.

Machine learning approaches for improving near-real-time IMERG rainfall estimates by integrating Cloud Properties from NOAA CDR PATMOSX

2021 ◽  
Author(s):  
Zhi Zhang ◽  
Dagang Wang ◽  
Jianxiu Qiu ◽  
Jinxin Zhu ◽  
Tingli Wang
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
River Basin ◽  
Central China ◽  
Support Vector ◽  
Learning Approaches ◽  
Satellite Precipitation ◽  
Cloud Properties ◽  
Spatial Coverage ◽  
Extreme Gradient Boosting

AbstractThe Global Precipitation Measurement (GPM) mission provides satellite precipitation products with an unprecedented spatio-temporal resolution and spatial coverage. However, its near-real-time (NRT) product still suffers from low accuracy. This study aims to improve the early run of the Integrated Multi-satellitE Retrievals for GPM (IMERG) by using four machine learning approaches, i.e., support vector machine (SVM), random forest (RF), artificial neural network (ANN), and Extreme Gradient Boosting (XGB). The cloud properties are selected as the predictors in addition to the original IMERG in these approaches. All the four approaches show similar improvement, with 53%-60% reduction of root-mean-square error (RMSE) compared with the original IMERG in a humid area, i.e., the Dongjiang River Basin (DJR) in southeastern China. The improvements are even greater in a semi-arid area, i.e., the Fenhe River Basin (FHR) in central China, the RMSE reduction ranges from 63%-66%. The products generated by the machine learning methods performs similarly to or even outperform than the final run of IMERG. Feature importance analysis, a technique to evaluate input features based on how useful they are in predicting a target variable, indicates that the cloud height and the brightness temperature are the most useful information in improving satellite precipitation products, followed by the atmospheric reflectivity and the surface temperature. This study shows that a more accurate NRT precipitation product can be produced by combining machine learning approaches and cloud information, which is of importance for hydrological applications that requires NRT precipitation information including flood monitoring.

scholarly journals Spatial disaggregation of bias-corrected GCM precipitation for improved hydrologic simulation: Ping River Basin, Thailand

2007 ◽  
Vol 11 (4) ◽  
pp. 1373-1390 ◽  
Author(s):  
D. Sharma ◽  
A. Das Gupta ◽  
M. S. Babel
Keyword(s):  
River Basin ◽  
Bias Correction ◽  
Hydrological Modeling ◽  
Correction Method ◽  
Hydrologic Model ◽  
Global Climate Models ◽  
Model Parameters ◽  
Bias Correction Method ◽  
Spatial Disaggregation ◽  
Grid Nodes

Abstract. Global Climate Models (GCMs) precipitation scenarios are often characterized by biases and coarse resolution that limit their direct application for basin level hydrological modeling. Bias-correction and spatial disaggregation methods are employed to improve the quality of ECHAM4/OPYC SRES A2 and B2 precipitation for the Ping River Basin in Thailand. Bias-correction method, based on gamma-gamma transformation, is applied to improve the frequency and amount of raw GCM precipitation at the grid nodes. Spatial disaggregation model parameters (β,σ2), based on multiplicative random cascade theory, are estimated using Mandelbrot-Kahane-Peyriere (MKP) function at q=1 for each month. Bias-correction method exhibits ability of reducing biases from the frequency and amount when compared with the computed frequency and amount at grid nodes based on spatially interpolated observed rainfall data. Spatial disaggregation model satisfactorily reproduces the observed trend and variation of average rainfall amount except during heavy rainfall events with certain degree of spatial and temporal variations. Finally, the hydrologic model, HEC-HMS, is applied to simulate the observed runoff for upper Ping River Basin based on the modified GCM precipitation scenarios and the raw GCM precipitation. Precipitation scenario developed with bias-correction and disaggregation provides an improved reproduction of basin level runoff observations.

scholarly journals Effect of Land Use Land Cover and Climate Change on River Flow and Soil Loss in Didessa River Basin, South West Blue Nile, Ethiopia

Hydrology ◽  
2018 ◽  
Vol 6 (1) ◽  
pp. 2 ◽  
Author(s):  
Kinati Chimdessa ◽  
Shoeb Quraishi ◽  
Asfaw Kebede ◽  
Tena Alamirew
Keyword(s):  
Climate Change ◽  
Land Use ◽  
Land Cover ◽  
River Basin ◽  
Sediment Yield ◽  
Soil Loss ◽  
River Flow ◽  
Land Use Land Cover ◽  
The Impact ◽  
Soil Losses

In the Didessa river basin, which is found in Ethiopia, the human population number is increasing at an alarming rate. The conversion of forests, shrub and grasslands into cropland has increased in parallel with the population increase. The land use/land cover change (LULCC) that has been undertaken in the river basin combined with climate change may have affected the Didessa river flow and soil loss. Therefore, this study was designed to assess the impact of LULCC on the Didessa river flow and soil loss under historical and future climates. Land use/land cover (LULC) of the years 1986, 2001 and 2015 were independently combined with the historical climate to assess their individual impacts on river flow and soil loss. Further, the impact of future climates under Representative Concentration Pathways (RCP2.6, RCP4.5 and RCP8.5) scenarios on river flow and soil loss was assessed by combining the pathways with the 2015 LULC. A physically based Soil and Water Assessment Tool (SWAT2012) model in the ArcGIS 10.4.1 interface was used to realize the purpose. Results of the study revealed that LULCC that occurred between 1986 and 2015 resulted in increased average sediment yield by 20.9 t ha−1 yr−1. Climate change under RCP2.6, RCP4.5 and RCP8.5 combined with 2015 LULC increased annual average soil losses by 31.3, 50.9 and 83.5 t ha−1 yr−1 compared with the 2015 LULC under historical climate data. It was also found that 13.4%, 47.1% and 87.0% of the total area may experience high soil loss under RCP2.6, RCP4.5 and RCP8.5, respectively. Annual soil losses of five top-priority sub catchments range from 62.8 to 57.7 per hectare. Nash Stuncliffe Simulation efficiency (NSE) and R2 values during model calibration and validation indicated good agreement between observed and simulated values both for flow and sediment yield.

scholarly journals Statistical and Hydrological Evaluations of Multi-Satellite Precipitation Products over Fujiang River Basin in Humid Southeast China

2018 ◽  
Vol 10 (12) ◽  
pp. 1898 ◽  
Author(s):  
Weiwei Sun ◽  
Jun Ma ◽  
Gang Yang ◽  
Weiyue Li
Keyword(s):  
Real Time ◽  
River Basin ◽  
Local Community ◽  
Pearson Correlation ◽  
Warm Season ◽  
Correlation Coefficients ◽  
Cold Season ◽  
Optimal Interpolation ◽  
Distributed Model ◽  
Satellite Precipitation

The purpose of the paper is to evaluate the quality and hydrological utility of four popular satellite precipitation products, including the Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) product (3B42V7), near real-time product (3B42RT), and the Climate Prediction Center (CPC) MORPHing technique (CMORPH) satellite–gauge merged product (CMORPH BLD) and bias-corrected product (CMORPH CRT) over Fujiang River basin, China. First, we provided a statistical assessment of the four precipitation products at multiple spatiotemporal scales. The results show that: (1) all the products except 3B42RT capture the spatial pattern of annual precipitation fairly well; (2) in general, CMORPH BLD benefits from the application of the probability density function-optimal interpolation (PDF-OI) gauge adjustment algorithm and performs best among all the products with Pearson correlation coefficients (CC) of 0.84 and 0.94, equitable threat score (ETS) of 0.56 and 0.63 in grid and basin scales, respectively, followed by 3B42V7 and CMORPH CRT; whereas 3B42RT performs worst across all the metrics; (3) according to the occurrence frequencies of rainfall, satellite estimates mainly fall into the bin of 0–1 mm/day and tend to underestimate light precipitation. In addition, the performance of all the products in warm season is much better than in cold season in both grid and basin scales. Subsequently, a physically based distributed model is established to further evaluate the hydrological utility of different precipitation products. The results reveal that: (1) the errors in precipitation products mainly propagate into hydrological simulations, resulting in the best hydrological performance in CMORPH BLD in both daily and monthly scales after recalibrating the model, while 3B42RT shows limited skills in reproducing the daily observed hydrograph; (2) after recalibrating the model with the respective satellite data, significant improvements are observed for all the products; (3) CMORPH BLD no longer shows its superiority during near-real-time monitoring of floods. There is still a great challenge for the application of current satellite-based estimates into local flood monitoring. This study could be used as guidance for choosing alternative satellite precipitation products for hydrological applications in a local community, particularly in basins in which rainfall gauges are scarce.

scholarly journals Attribution Analysis of Hydrological Drought Risk Under Climate Change and Human Activities: A Case Study on Kuye River Basin in China

Water ◽  
2019 ◽  
Vol 11 (10) ◽  
pp. 1958 ◽  
Author(s):  
Zhang ◽  
Wang ◽  
Zhou
Keyword(s):  
Climate Change ◽  
River Basin ◽  
Human Activities ◽  
Large Scale ◽  
Research Area ◽  
Drought Severity ◽  
Drought Risk ◽  
Drought Duration ◽  
Recurrence Period ◽  
The Impact

This study conducted quantitative diagnosis on the impact of climate change and human activities on drought risk. Taking the Kuye river basin (KRB) in China as the research area, we used variation point diagnosis, simulation of precipitation and runoff, drought risk assessment, and attribution quantification. The results show that: (1) the annual runoff sequence of KRB changed significantly after 1979, which was consistent with the introduction of large-scale coal mining; (2) under the same drought recurrence period, the drought duration and severity in the human activity stage were significantly worse than in the natural and simulation stages, indicating that human activities changed the drought risk in this area; and (3) human activities had little impact on drought severity in the short duration and low recurrence period, but had a greater impact in the long duration and high recurrence period. These results provide scientific guidance for the management, prevention, and resistance of drought; and guarantee sustainable economic and social development in the KRB.

Lessons from a Large-Scale Experiment on the Use of Smartphone Apps to Collect Travel Diary Data: The “City Logger” for the Greater Golden Horseshoe Area

2020 ◽  
Vol 2674 (7) ◽  
pp. 299-311
Author(s):  
Ahmadreza Faghih Imani ◽  
Chris Harding ◽  
Siva Srikukenthiran ◽  
Eric J. Miller ◽  
Khandker Nurul Habib
Keyword(s):  
Real Time ◽  
Travel Behavior ◽  
Large Scale ◽  
Smartphone Apps ◽  
Systematic Assessment ◽  
Travel Diary ◽  
Scale Experiment ◽  
App Design ◽  
The Impact ◽  
The City

Smartphones offer a potential alternative to collect high-quality information on the travel patterns of individuals without burdening the respondents with reporting every detail of their travel. Smartphone apps have recently become a common tool for travel survey data collection around the world, especially for multiday surveys. However, there still exists a lack of systematic assessment of issues related to smartphone app-based surveys, such as the impact of app design or the recruitment method on the collected data. Through a large-scale experiment (named the City Logger), this paper assesses the data produced by the City Logger app, to better understand recruitment avenues (targeted invitation versus crowdsourcing), and examine differences in respondents’ travel behavior recruited through crowdsourcing methods. The paper also examines how app design, and particularly the user input method for trip validation, influences participants’ responses. The results indicate that, while crowdsourcing recruitment is promising, it might not yet be the best way to capture a true representation of the population. For app design, a combination of real-time and travel diary approaches is recommended. An ideal app would prompt users real-time and create a travel diary, so users can validate, edit, or delete the recorded information.

scholarly journals Understanding Future Water Challenges in a Highly Regulated Indian River Basin—Modelling the Impact of Climate Change on the Hydrology of the Upper Narmada

Water ◽  
2020 ◽  
Vol 12 (6) ◽  
pp. 1762 ◽  
Author(s):  
Nathan Rickards ◽  
Thomas Thomas ◽  
Alexandra Kaelin ◽  
Helen Houghton-Carr ◽  
Sharad K. Jain ◽  
...  
Keyword(s):  
Climate Change ◽  
Water Resources ◽  
River Basin ◽  
Large Scale ◽  
Monsoon Season ◽  
Future Climate Change ◽  
Impact Of Climate Change ◽  
Global Circulation Models ◽  
The Impact ◽  
Availability Assessment

The Narmada river basin is a highly regulated catchment in central India, supporting a population of over 16 million people. In such extensively modified hydrological systems, the influence of anthropogenic alterations is often underrepresented or excluded entirely by large-scale hydrological models. The Global Water Availability Assessment (GWAVA) model is applied to the Upper Narmada, with all major dams, water abstractions and irrigation command areas included, which allows for the development of a holistic methodology for the assessment of water resources in the basin. The model is driven with 17 Global Circulation Models (GCMs) from the Coupled Model Intercomparison Project Phase 5 (CMIP5) ensemble to assess the impact of climate change on water resources in the basin for the period 2031–2060. The study finds that the hydrological regime within the basin is likely to intensify over the next half-century as a result of future climate change, causing long-term increases in monsoon season flow across the Upper Narmada. Climate is expected to have little impact on dry season flows, in comparison to water demand intensification over the same period, which may lead to increased water stress in parts of the basin.

scholarly journals Evaluation of bias-adjusted satellite precipitation estimations for extreme flood events in Langat river basin, Malaysia

2019 ◽  
Vol 51 (1) ◽  
pp. 105-126 ◽  
Author(s):  
Eugene Zhen Xiang Soo ◽  
Wan Zurina Wan Jaafar ◽  
Sai Hin Lai ◽  
Faridah Othman ◽  
Ahmed Elshafie ◽  
...  
Keyword(s):  
River Basin ◽  
Bias Correction ◽  
Monsoon Season ◽  
Rainfall Threshold ◽  
Careful Consideration ◽  
Northeast Monsoon ◽  
Satellite Precipitation ◽  
Extreme Floods ◽  
Extreme Flood ◽  
Langat River

Abstract Even though satellite precipitation products have received an increasing amount of attention in hydrology and meteorology, their estimations are prone to bias. This study investigates the three approaches of bias correction, i.e., linear scaling (LS), local intensity scaling (LOCI) and power transformation (PT), on the three advanced satellite precipitation products (SPPs), i.e., CMORPH, TRMM and PERSIANN over the Langat river basin, Malaysia by focusing on five selected extreme floods due to northeast monsoon season. Results found the LS scheme was able to match the mean precipitation of every SPP but does not correct standard deviation (SD) or coefficient of variation (CV) of the estimations regardless of extreme floods selected. For LOCI scheme, only TRMM and CMORPH estimations in certain floods have showed some improvement in their results. This might be due to the rainfall threshold set in correcting process. PT scheme was found to be the best method as it improved most of the statistical performances as well as the rainfall distribution of the floods. Sensitivity of the parameters used in the bias correction is also investigated. PT scheme is found to be least sensitive in correcting the daily SPPs compared to the other two schemes. However, careful consideration should be given for correcting the CMORPH and PERSIANN estimations.

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