scholarly journals The First Comparisons of IMERG and the Downscaled Results Based on IMERG in Hydrological Utility over the Ganjiang River Basin

Water ◽  
2018 ◽  
Vol 10 (10) ◽  
pp. 1392 ◽  
Author(s):  
Ziqiang Ma ◽  
Xiao Tan ◽  
Yuan Yang ◽  
Xi Chen ◽  
Guangyuan Kan ◽  
...  
Keyword(s):  
River Basin ◽  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Vital Role ◽  
Hydrological Models ◽  
Observation Methods ◽  
Satellite Retrievals ◽  
Precipitation Estimates ◽  
Streamflow Simulation ◽  
Ganjiang River Basin

Rainfall information is a prerequisite to and plays a vital role in driving hydrological models. However, limited by the observation methods, the obtained precipitation data, at present, are still too coarse. In this study, a new downscaling method was proposed to obtain high spatial resolution (~1 km/hourly) precipitation estimates based on Integrated Multi-satellitE Retrievals for GPM (IMERG) data at hourly scale. Compared with original IMERG data, the downscaled precipitation results showed the similar spatial patterns with those of original IMERG data, but with finer spatial resolution. In addition, the downscaled precipitation estimates were further analyzed to quantify their improvements using the Coupled Routing and Excess STorage (CREST) model across Ganjiang River basin. Compared with the observed streamflow, the downscaled precipitation results showed satisfying hydrological performance, with Nash-Sutcliffe Coefficient of Efficiency (NSCE), Root Mean Square Error (RMSE), Relative Bias (BIAS), and Correlation Coefficient (CC). The improvement in terms of four statistic metrics in terms of streamflow simulation also indicated great potential of hydrological utility for the downscaled precipitation results.

scholarly journals Remote Sensing Retrieval of Turbidity in Alpine Rivers based on high Spatial Resolution Satellites

2019 ◽  
Vol 11 (24) ◽  
pp. 3010 ◽  
Author(s):  
Weihua Liu ◽  
Siyuan Wang ◽  
Ruixia Yang ◽  
Yuanxu Ma ◽  
Ming Shen ◽  
...  
Keyword(s):  
Remote Sensing ◽  
River Basin ◽  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Light Attenuation ◽  
Optical Parameter ◽  
Retrieval Models ◽  
Kaidu River ◽  
Alpine Rivers ◽  
Variation Trends

Turbidity, relating to underwater light attenuation, is an important optical parameter for water quality evaluation. Satellite estimation of turbidity in alpine rivers is challenging for common ocean color retrieval models due to the differences in optical properties of the water bodies. In this study, we present a simple two-band semi-analytical turbidity (2BSAT) retrieval model for estimating turbidity in five alpine rivers with varying turbidity from 1.01 to 284 NTU. The model was calibrated and validated, respectively, while using one calibration dataset that was obtained from the Three Parallel Rivers basin and two independent validation datasets that were obtained from the Kaidu River basin and the Yarlung Zangbo River basin. The results show that the model has excellent performance in deriving turbidity in alpine rivers. We verified the consistency of the simulated reflectance and satellite-based reflectance and calibrated the 2BSAT model for the specified bands of high spatial resolution satellites in order to achieve the goal of remote sensing monitoring. It is concluded that the model can be used for the quantitative monitoring of turbidity in alpine rivers using satellite images. Based on the model, we used the Sentinel-2 images from one year to identify the seasonal patterns of turbidity of five alpine rivers and the Landsat series images from 1989 to 2018 to analyze the turbidity variation trends of these rivers. The results indicate that the turbidity of these alpine rivers usually presents the highest level in summer, followed by spring and autumn, and the lowest in winter. Meanwhile, the variation trends of turbidity over the past 30 years present distinctly different characteristics in the five rivers.

scholarly journals Spatial Downscaling of TRMM Precipitation Data Using an Optimal Subset Regression Model with NDVI and Terrain Factors in the Yarlung Zangbo River Basin, China

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Jinping Liu ◽  
Wanchang Zhang ◽  
Ning Nie
Keyword(s):  
River Basin ◽  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Rain Gauge ◽  
Precipitation Data ◽  
Rain Gauge Data ◽  
Optimal Subset ◽  
Yarlung Zangbo River ◽  
Gauge Data ◽  
Subset Regression

High accuracy, high spatial resolution precipitation data is important for understanding basin-scale hydrology and the spatiotemporal distributions of regional precipitation. The objective of this study was to develop a reliable statistical downscaling algorithm to produce high quality, high spatial resolution precipitation products from Tropical Rainfall Monitoring Mission (TRMM) 3B43 data over the Yarlung Zangbo River Basin using an optimal subset regression (OSR) model combined with multiple topographical factors, the Normalized Difference Vegetation Index (NDVI), and observational data from rain gauge stations. After downscaling, the bias between TRMM 3B43 and rain gauge data decreased considerably from 0.397 to 0.109, the root-mean-square error decreased from 235.16 to 124.60 mm, and the r2 increased from 0.54 to 0.61, indicating significant improvement in the spatial resolution and accuracy of the TRMM 3B43 data. Moreover, the spatial patterns of both precipitation rates of change and their corresponding p value statistics were consistent between the downscaled results and the original TRMM 3B43 during the 2001–2014 period, which verifies that the downscaling method performed well in the Yarlung Zangbo River Basin. Its high performance in downscaling precipitation was also proven by comparing with other models. All of these findings indicate that the proposed approach greatly improved the quality and spatial resolution of TRMM 3B43 rainfall products in the Yarlung Zangbo River Basin, for which rain gauge data is limited. The potential of the post-real-time Integrated Multi-satellite Retrievals for Global Precipitation Measurement (IMERG) downscaled precipitation product was also demonstrated in this study.

Use of Radar Quantitative Precipitation Estimates (QPEs) for Improved Hydrological Model Calibration and Flood Forecasting

2021 ◽  
Author(s):  
Dayal Wijayarathne ◽  
Paulin Coulibaly ◽  
Sudesh Boodoo ◽  
David Sills
Keyword(s):  
Real Time ◽  
Model Calibration ◽  
Hydrological Model ◽  
Flood Forecasting ◽  
Hydrological Models ◽  
Radar Rainfall ◽  
Precipitation Estimates ◽  
Quantitative Precipitation Estimates ◽  
Streamflow Simulation ◽  
Rainfall Estimates

AbstractFlood forecasting is essential to minimize the impacts and costs of floods, especially in urbanized watersheds. Radar rainfall estimates are becoming increasingly popular in flood forecasting because they provide the much-needed real-time spatially distributed precipitation information. The current study evaluates the use of radar Quantitative Precipitation Estimates (QPEs) in hydrological model calibration for streamflow simulation and flood mapping in an urban setting. Firstly, S-band and C-band radar QPEs were integrated into event-based hydrological models to improve the calibration of model parameters. Then, rain gauge and radar precipitation estimates’ performances were compared for hydrological modeling in an urban watershed to assess radar QPE's effects on streamflow simulation accuracy. Finally, flood extent maps were produced using coupled hydrological-hydraulic models integrated within the Hydrologic Engineering Center- Real-Time Simulation (HEC-RTS) framework. It is shown that the bias correction of radar QPEs can enhance the hydrological model calibration. The radar-gauge merging obtained a KGE, MPFC, NSE, and VE improvement of about + 0.42, + 0.12, + 0.78, and − 0.23, respectively for S-band and + 0.64, + 0.36, + 1.12, and − 0.34, respectively for C-band radar QPEs. Merged radar QPEs are also helpful in running hydrological models calibrated using gauge data. The HEC-RTS framework can be used to produce flood forecast maps using the bias-corrected radar QPEs. Therefore, radar rainfall estimates could be efficiently used to forecast floods in urbanized areas for effective flood management and mitigation. Canadian flood forecasting systems could be efficiently updated by integrating bias-corrected radar QPEs to simulate streamflow and produce flood inundation maps.

scholarly journals Evaluation of Manning’s n Roughness Coefficient in Arid Environments by Using SAR Backscatter

2018 ◽  
Vol 10 (10) ◽  
pp. 1505 ◽  
Author(s):  
Yuval Sadeh ◽  
Hai Cohen ◽  
Shimrit Maman ◽  
Dan Blumberg
Keyword(s):  
Surface Roughness ◽  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Roughness Coefficient ◽  
Arid Environments ◽  
Hydrological Models ◽  
Radar Backscatter ◽  
Manning’S Roughness Coefficient ◽  
Manning’S N ◽  
Manning's N

The prediction of arid region flash floods (magnitude and frequency) is essential to ensure the safety of human life and infrastructures and is commonly based on hydrological models. Traditionally, catchment characteristics are extracted using point-based measurements. A considerable improvement of point-based observations is offered by remote sensing technologies, which enables the determination of continuous spatial hydrological parameters and variables, such as surface roughness, which significantly influence runoff velocity and depth. Hydrological models commonly express the surface roughness using Manning’s roughness coefficient (n) as a key variable. The objectives were thus to determine surface roughness by exploiting a new high spatial resolution spaceborne synthetic aperture radar (SAR) technology and to examine the correlation between radar backscatter and Manning’s roughness coefficient in an arid environment. A very strong correlation (R2 = 0.97) was found between the constellation of small satellites for Mediterranean basin observation (COSMO)-SkyMed SAR backscatter and surface roughness. The results of this research demonstrate the feasibility of using an X-band spaceborne sensor with high spatial resolution for the evaluation of surface roughness in flat arid environments. The innovative method proposed to evaluate Manning’s n roughness coefficient in arid environments with sparse vegetation cover using radar backscatter may lead to improvements in the performance of hydrological models.

scholarly journals Deep Hybrid Network for Land Cover Semantic Segmentation in High-Spatial Resolution Satellite Images

Information ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 230
Author(s):  
Sultan Daud Khan ◽  
Louai Alarabi ◽  
Saleh Basalamah
Keyword(s):  
High Resolution ◽  
Land Cover ◽  
Spatial Resolution ◽  
Satellite Images ◽  
High Spatial Resolution ◽  
Multiple Scales ◽  
Semantic Segmentation ◽  
Vital Role ◽  
Hybrid Network ◽  
High Resolution Satellite Images

Land cover semantic segmentation in high-spatial resolution satellite images plays a vital role in efficient management of land resources, smart agriculture, yield estimation and urban planning. With the recent advancement in remote sensing technologies, such as satellites, drones, UAVs, and airborne vehicles, a large number of high-resolution satellite images are readily available. However, these high-resolution satellite images are complex due to increased spatial resolution and data disruption caused by different factors involved in the acquisition process. Due to these challenges, an efficient land-cover semantic segmentation model is difficult to design and develop. In this paper, we develop a hybrid deep learning model that combines the benefits of two deep models, i.e., DenseNet and U-Net. This is carried out to obtain a pixel-wise classification of land cover. The contraction path of U-Net is replaced with DenseNet to extract features of multiple scales, while long-range connections of U-Net concatenate encoder and decoder paths are used to preserve low-level features. We evaluate the proposed hybrid network on a challenging, publicly available benchmark dataset. From the experimental results, we demonstrate that the proposed hybrid network exhibits a state-of-the-art performance and beats other existing models by a considerable margin.

scholarly journals Study on the Applicability of the Hargreaves Potential Evapotranspiration Estimation Method in CREST Distributed Hydrological Model (Version 3.0) Applications

Water ◽  
2018 ◽  
Vol 10 (12) ◽  
pp. 1882 ◽  
Author(s):  
Zhansheng Li ◽  
Yuan Yang ◽  
Guangyuan Kan ◽  
Yang Hong
Keyword(s):  
River Basin ◽  
Hydrological Model ◽  
Potential Evapotranspiration ◽  
Estimation Method ◽  
Estimation Methods ◽  
Distributed Hydrological Model ◽  
Model Driven ◽  
Model Version ◽  
Streamflow Simulation ◽  
Ganjiang River Basin

The potential evapotranspiration (PET) is an important input to the hydrological model and its compatibility has an important influence on the model applications. The applicability of the Hargreaves-Samani (HS) PET estimation method in Coupled Routing and Excess STorage distributed hydrological model version 3.0 (CREST 3.0 model) was studied in a typical humid region, Ganjiang River Basin, in Southern China. The PET estimation methods were evaluated based on the streamflow simulation accuracies using the CREST 3.0 model driven by different PET products with various spatial resolutions. The Penman-Monteith (PM) equation-based PET estimation method was adopted as the reference PET estimation method in this study. The results demonstrated that PET obtained from the HS method was larger than that generated by the PM method, and the CREST 3.0 model driven by both HS and PM-based PET products can simulate the streamflow temporal variations equally well in annual time scale. Compared with the PM method, the HS method was more stable and robust in driving CREST 3.0 model under the scenarios of different spatial resolutions. In addition, during the validation period (2007–2009) with 2003–2006 as the calibration period, the HS outperformed PM considering the streamflow simulation accuracy. Therefore, the HS method was not only applicable to CREST 3.0 model with flexible spatial resolutions, but also can be an alternative method to PM method in CREST 3.0 model streamflow simulation applications in Ganjiang River Basin. The study results will not only increase the confidence on the applicability of the HS method in hydrological simulation in Ganjiang River Basin, but also prove the flexibility of CREST 3.0 model in terms of PET input, which will expand the application range of the CREST 3.0 model.

scholarly journals High-Spatial-Resolution Population Exposure to PM2.5 Pollution Based on Multi-Satellite Retrievals: A Case Study of Seasonal Variation in the Yangtze River Delta, China in 2013

2019 ◽  
Vol 11 (23) ◽  
pp. 2724 ◽  
Author(s):  
Wang ◽  
Li ◽  
Gao ◽  
Yim ◽  
Shen ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Population Density ◽  
Spatial Resolution ◽  
Yangtze River ◽  
High Spatial Resolution ◽  
Yangtze River Delta ◽  
Population Exposure ◽  
Satellite Retrievals ◽  
The Yangtze River Delta ◽  
Pm2.5 Concentration

To assess the health risk of PM2.5, it is necessary to accurately estimate the actual exposure level of the population to PM2.5. However, the spatial distribution of PM2.5 may be inconsistent with that of the population, making it necessary for a high-spatial-resolution and refined assessment of the population exposure to air pollution. This study takes the Yangtze River Delta (YRD) Region as an example since it has a high-density population and a high pollution level. The brightness reflectance of night-time light, and MODIS-based (Moderate Resolution Imaging Spectroradiometer) vegetation index, elevation, and slope information are used as independent variables to construct a random-forest (RF) model for the estimation of the population spatial distribution, before any combination with the PM2.5 data retrieved from MODIS. This enables assessment of the population exposure to PM2.5 (i.e., intensity of population exposure to PM2.5 and population-weighted PM2.5 concentration) at a 3-km resolution, using the year 2013 as an example. Results show that the variance explained for the RF-model-estimated population density reaches over 80%, while the estimated errors in half of counties are < 20%, indicating the high accuracy of the estimated population. The spatial distribution of population exposure to PM2.5 exhibits an obvious urban–suburban–rural difference consistent with the population distribution but inconsistent with the PM2.5 concentration. High and low PM2.5 concentrations are mainly distributed in the northern and southern YRD Region, respectively, with the mean proportions of the population exposed to PM2.5 concentrations > 35μg/m3 close to 100% in all four seasons. A high-level population exposure to PM2.5 is mainly found in Shanghai, most of the Jiangsu Province, the central Anhui Province, and some coastal cities of the Zhejiang Province. The highest risk of population exposure to PM2.5 occurs in winter, followed by spring and autumn, and the lowest in summer, consistent with the PM2.5 seasonal variation. Seasonal-averaged population-weighted PM2.5 concentrations are different from PM2.5 concentrations in the region, which are closely related to the urban-exposed population density and pollution levels. This work provides a novel assessment of the proposed population-density exposure to PM2.5 by using multi-satellite retrievals to determine the high-spatial-resolution risk of air pollution and detailed regional differences in the population exposure to PM2.5.

Land cover data of Upper Parana River Basin, South America, at high spatial resolution

2019 ◽  
Vol 83 ◽  
pp. 101926 ◽  
Author(s):  
Anderson Paulo Rudke ◽  
Thais Fujita ◽  
Daniela Sanches de Almeida ◽  
Marilia Moreira Eiras ◽  
Ana Carolina Freitas Xavier ◽  
...  
Keyword(s):  
South America ◽  
Land Cover ◽  
River Basin ◽  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Paraná River ◽  
Parana River ◽  
Land Cover Data ◽  
Upper Paraná River ◽  
Upper Paraná River Basin

scholarly journals High resolution (1 km) satellite rainfall estimation from SM2RAIN applied to Sentinel-1: Po River Basin as case study

2021 ◽  
Author(s):  
Paolo Filippucci ◽  
Luca Brocca ◽  
Raphael Quast ◽  
Luca Ciabatta ◽  
Carla Saltalippi ◽  
...  
Keyword(s):  
Soil Moisture ◽  
High Resolution ◽  
River Basin ◽  
Spatial Resolution ◽  
Temporal Resolution ◽  
High Spatial Resolution ◽  
Added Value ◽  
Rainfall Estimation ◽  
Po River ◽  
Po River Basin

Abstract. Satellite sensors to infer rainfall measurements have become widely available in the last years, but their spatial resolution usually exceed 10 kilometres, due to technological limitation. This poses an important constraint on their use for application such as water resource management, index insurance evaluation or hydrological models, which require more and more detailed information. In this work, the algorithm SM2RAIN (Soil Moisture to Rain) for rainfall estimation is applied to a high resolution soil moisture product derived from Sentinel-1, named S1-RT1, characterized by 1 km spatial resolution (500 m spacing), and to the 25 km ASCAT soil moisture (12.5 km spacing), resampled to the same grid of S1-RT1, to obtain rainfall products with the same spatial and temporal resolution over the Po River basin. In order to overcome the need of calibration and to allow its global application, a parameterized version of SM2RAIN algorithm was adopted along with the standard one. The capabilities in estimating rainfall of each obtained product were then compared, to assess both the parameterized SM2RAIN performances and the added value of Sentinel-1 high spatial resolution. The results show that good estimates of rainfall are obtainable from Sentinel-1 when considering aggregation time steps greater than 1 day, since to the low temporal resolution of this sensor (from 1.5 to 4 days over Europe) prevents its application to infer daily rainfall. On average, the ASCAT derived rainfall product performs better than S1-RT1 one, even if the performances are equally good when 30 days accumulated rainfall is considered, being the mean Pearson’s correlation of the rainfall obtained from ASCAT and S1-RT1 equal to 0.74 and 0.73, respectively, using the parameterized SM2RAIN. Notwithstanding this, the products obtained from Sentinel-1 outperform those from ASCAT in specific areas, like in valleys inside mountain regions and most of the plains, confirming the added value of the high spatial resolution information in obtaining spatially detailed rainfall. Finally, the parameterized products performances are similar to those obtained with SM2RAIN calibration, confirming the reliability of the parameterized algorithm for rainfall estimation in this area and fostering the possibility to apply SM2RAIN worldwide even without the availability of a rainfall benchmark product.

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