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Accurate and timely monitoring of streamflow and its variation is crucial for water resources management in watersheds. This study aimed at evaluating the performance of two process-driven conceptual rainfall-runoff models (HBV: Hydrologiska Byråns Vattenbalansavdelning, and NRECA: Non Recorded Catchment Areas) and seven hybrid models based on three artificial intelligence (AI) methods (adaptive neuro-fuzzy inference system (ANFIS), support vector machine (SVM), and group method of data handling (GMDH)) in simulating streamflow in four river basins in Indonesia. HBV and NRECA were developed based on precipitation data. Various combinations of 1-month lagged precipitation data together with outputs of HBV and NRECA were used for developing ANFIS and SVM models, and the best results of ANFIS and SVM formed the inputs to GMDH. Results showed that AI-based hybrid models have generally led to more accurate streamflow estimates compared with HBV and NRECA, and the GMDH model had the best performance at Cipero, Kedungdowo, Notog, and Sukowati stations, with RMSEs of 12.21, 6.07, 20.35, and 24.2 m3 s−1, respectively. More accurate estimation of peak values in training set at Cipero and Sukowati stations, and in both training and testing sets at Kedungdowo station was another advantage of GMDH. Hybrid models based on AI methods can be suitable alternatives to hydrological models, particularly in watersheds where there is a lack of measured data (e.g. climatic parameters, land cover-plant growth data, soil data, stream conditions, and properties of groundwater aquifers), provided that appropriate inputs are used.
Babak Mohammadi; Roozbeh Moazenzadeh; Kevin Christian; Zheng Duan. Improving streamflow simulation by combining hydrological process-driven and artificial intelligence-based models. Environmental Science and Pollution Research 2021, 1 -17.
AMA StyleBabak Mohammadi, Roozbeh Moazenzadeh, Kevin Christian, Zheng Duan. Improving streamflow simulation by combining hydrological process-driven and artificial intelligence-based models. Environmental Science and Pollution Research. 2021; ():1-17.
Chicago/Turabian StyleBabak Mohammadi; Roozbeh Moazenzadeh; Kevin Christian; Zheng Duan. 2021. "Improving streamflow simulation by combining hydrological process-driven and artificial intelligence-based models." Environmental Science and Pollution Research , no. : 1-17.
Remote Sensing, as a driver for water management decisions, needs further integration with monitoring water quality programs, especially in developing countries. Moreover, usage of remote sensing approaches has not been broadly applied in monitoring routines. Therefore, it is necessary to assess the efficacy of available sensors to complement the often limited field measurements from such programs and build models that support monitoring tasks. Here, we integrate field measurements (2013–2019) from the Mexican national water quality monitoring system (RNMCA) with data from Landsat-8 OLI, Sentinel-3 OLCI, and Sentinel-2 MSI to train an extreme learning machine (ELM), a support vector regression (SVR) and a linear regression (LR) for estimating Chlorophyll-a (Chl-a), Turbidity, Total Suspended Matter (TSM) and Secchi Disk Depth (SDD). Additionally, OLCI Level-2 Products for Chl-a and TSM are compared against the RNMCA data. We observed that OLCI Level-2 Products are poorly correlated with the RNMCA data and it is not feasible to rely only on them to support monitoring operations. However, OLCI atmospherically corrected data is useful to develop accurate models using an ELM, particularly for Turbidity (R2 = 0.7). We conclude that remote sensing is useful to support monitoring systems tasks, and its progressive integration will improve the quality of water quality monitoring programs.
Leonardo Arias-Rodriguez; Zheng Duan; José Díaz-Torres; Mónica Basilio Hazas; Jingshui Huang; Bapitha Kumar; Ye Tuo; Markus Disse. Integration of Remote Sensing and Mexican Water Quality Monitoring System Using an Extreme Learning Machine. Sensors 2021, 21, 4118 .
AMA StyleLeonardo Arias-Rodriguez, Zheng Duan, José Díaz-Torres, Mónica Basilio Hazas, Jingshui Huang, Bapitha Kumar, Ye Tuo, Markus Disse. Integration of Remote Sensing and Mexican Water Quality Monitoring System Using an Extreme Learning Machine. Sensors. 2021; 21 (12):4118.
Chicago/Turabian StyleLeonardo Arias-Rodriguez; Zheng Duan; José Díaz-Torres; Mónica Basilio Hazas; Jingshui Huang; Bapitha Kumar; Ye Tuo; Markus Disse. 2021. "Integration of Remote Sensing and Mexican Water Quality Monitoring System Using an Extreme Learning Machine." Sensors 21, no. 12: 4118.
Solar radiation plays a pivotal role in the energy balance at the Earth's surface, evaporation, snow melting, water requirements of plants, and hydrological control of catchments. In this work, performance of ERA-Interim (a reanalysis dataset) was examined to estimate solar radiation at Ahvaz, BandarAbbas, and Kermanshah weather stations representing the even spatial distribution over Iran using eight empirical models and an artificial intelligence-based model (SVM: Support Vector Machine). In the calibration set, SVM exhibited the best performance with RMSEs of 249, 299 and 437 J.cm−2.day−1 at the aforementioned stations, respectively. In validation set, SVM reduced the errors in the estimates of solar radiation by 2.5 and 7.3 percent compared to the best empirical model at Ahvaz station (Abdallah model, RMSE = 242 J.cm−2.day−1) and Kermanshah station (Angstrom-Prescott model, RMSE = 315 J.cm−2.day−1), respectively. During the validation at BandarAbbas station, Bahel and Abdallah model (RMSE = 309 J.cm−2.day−1), Angstrom-Prescott model (RMSE = 310 J.cm−2.day−1) and SVM (RMSE = 312 J.cm−2.day−1) showed a relatively similar performance. The results also showed that the ERA-Interim dataset can be a comparatively suitable alternative to some of the empirical models, where radiation or the input parameters of empirical models are not directly measured, with RMSEs of 382.81, 320.82 and 414.1 J.cm−2.day−1 at Ahvaz, BandarAbbas, and Kermanshah stations, respectively (in validation phase); although its error rates are significant compared with the SVM model, and substituting it for artificial intelligence-based models is not recommended.
Babak Mohammadi; Roozbeh Moazenzadeh; Quoc Bao Pham; Nadhir Al-Ansari; Khalil Ur Rahman; Duong Tran Anh; Zheng Duan. Application of ERA-Interim, empirical models, and an artificial intelligence-based model for estimating daily solar radiation. Ain Shams Engineering Journal 2021, 1 .
AMA StyleBabak Mohammadi, Roozbeh Moazenzadeh, Quoc Bao Pham, Nadhir Al-Ansari, Khalil Ur Rahman, Duong Tran Anh, Zheng Duan. Application of ERA-Interim, empirical models, and an artificial intelligence-based model for estimating daily solar radiation. Ain Shams Engineering Journal. 2021; ():1.
Chicago/Turabian StyleBabak Mohammadi; Roozbeh Moazenzadeh; Quoc Bao Pham; Nadhir Al-Ansari; Khalil Ur Rahman; Duong Tran Anh; Zheng Duan. 2021. "Application of ERA-Interim, empirical models, and an artificial intelligence-based model for estimating daily solar radiation." Ain Shams Engineering Journal , no. : 1.
The multiple-year drought that started in 2011 and reached climax in 2015 was the most severe and prolonged one in the semiarid northeastern (NE) Brazil in recent decades. This study aimed to investigate the reservoir surface water volume (SWV) variation in NE Brazil from 2009 to 2017 in four representative regions covering a total area of approximately 10,000 km2 there and encompassing 2,140 reservoirs (areas range from 0.003 to 21 km2). High-resolution (10 m) digital elevation models (DEMs) were generated from the TanDEM-X data acquired during October–December 2015 to represent the reservoirs' bathymetric maps. The water extents in the reservoirs were delineated from high-resolution (6.5 m) RapidEye images acquired during 2009–2017. The combination of the aforementioned two variables yielded reservoir SWV with an accuracy of 0.64 × 106–1.06 × 106 m3, corresponding to 3.1%–5.6% of the maximum SWV in the reservoirs. The results showed that: 1) 81%–99% of the reservoirs in the four regions were from the groups with maximum water extent 50 ha and contributed 40%–98% to the regional SWV; 2) From 2009 to 2017, reservoir SWV in the four regions decreased at the rates of 2.3 × 106–17.8 × 106 m3/year; and 3) The SWV in the reservoirs responded differently to the regional terrestrial water budget, i.e. the differences between precipitation and evapotranspiration (P-ET). This study filled the data gap of bathymetric maps for the 2140 reservoirs, regardless of their sizes and macrophyte coverage. The SWV variations derived in those reservoirs over a period covering the recent drought can support better preparedness for drought in NE Brazil and better understanding of the regional hydrology in semi-arid regions.
Shuping Zhang; Saskia Foerster; Pedro Medeiros; José Carlos de Araújo; Zheng Duan; Axel Bronstert; Bjoern Waske. Mapping regional surface water volume variation in reservoirs in northeastern Brazil during 2009–2017 using high-resolution satellite images. Science of The Total Environment 2021, 789, 147711 .
AMA StyleShuping Zhang, Saskia Foerster, Pedro Medeiros, José Carlos de Araújo, Zheng Duan, Axel Bronstert, Bjoern Waske. Mapping regional surface water volume variation in reservoirs in northeastern Brazil during 2009–2017 using high-resolution satellite images. Science of The Total Environment. 2021; 789 ():147711.
Chicago/Turabian StyleShuping Zhang; Saskia Foerster; Pedro Medeiros; José Carlos de Araújo; Zheng Duan; Axel Bronstert; Bjoern Waske. 2021. "Mapping regional surface water volume variation in reservoirs in northeastern Brazil during 2009–2017 using high-resolution satellite images." Science of The Total Environment 789, no. : 147711.
Palmer Drought Severity Index (PDSI) is known as a robust agricultural drought index since it considers the water balance conditions in the soil. It has been widely used as a reference index for monitoring agricultural drought. In this study, the PDSI time series were calculated for nine synoptic stations to monitor agricultural drought in semi-arid region located at Zagros mountains of Iran. Autoregressive Moving Average (ARMA) was used as the stochastic model while Radial Basis Function Neural Network (RBFNN) and Support Vector Machine (SVM) were applied as Machine Learning (ML)-based techniques. According to the time series analysis of PDSI, for the driest months the most PDSI drought events are normal drought and mild drought conditions. As an innovation, Dragonfly Algorithm (DA) was used in this study to optimize the SVM’s parameters, called as the hybrid SVM-DA model. It is worthy to mention that the hybrid SVM-DA is developed as a meta-innovative model for the first time in hydrological studies. The novel hybrid SVM-DA paradigm could improve the SVM’s accuracy up to 29% in predicting PDSI and therefore was found as the superior model. The best statistics for this model were obtained as Root Mean Squared Error (RMSE) = 0.817, Normalized RMSE (NRMSE) = 0.097, Wilmott Index (WI) = 0.940, and R = 0.889. The Mean Absolute Error values of the PDSI predictions via the novel SVM-DA model were under 0.6 for incipient drought, under 0.7 for mild and moderate droughts. In general, the error values in severe and extreme droughts were more than the other classes; however, the hybrid SVM-DA was the best-performing model in most of the cases.
Pouya Aghelpour; Babak Mohammadi; Saeid Mehdizadeh; Hadigheh Bahrami-Pichaghchi; Zheng Duan. A novel hybrid dragonfly optimization algorithm for agricultural drought prediction. Stochastic Environmental Research and Risk Assessment 2021, 1 -19.
AMA StylePouya Aghelpour, Babak Mohammadi, Saeid Mehdizadeh, Hadigheh Bahrami-Pichaghchi, Zheng Duan. A novel hybrid dragonfly optimization algorithm for agricultural drought prediction. Stochastic Environmental Research and Risk Assessment. 2021; ():1-19.
Chicago/Turabian StylePouya Aghelpour; Babak Mohammadi; Saeid Mehdizadeh; Hadigheh Bahrami-Pichaghchi; Zheng Duan. 2021. "A novel hybrid dragonfly optimization algorithm for agricultural drought prediction." Stochastic Environmental Research and Risk Assessment , no. : 1-19.
Terrestrial water storage (TWS) is a crucial indicator of regional water balance and water resources changes. Due to limited hydrological observations, we combined the Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On (GRACE-FO) products using the Long Short-Term Memory (LSTM) neural network to monitor the TWS changes from April 2002 to March 2020 over the closed Qaidam Basin in northwest China and examined the impacts of climate and meteorological changes on TWS variations. The results indicated that the LSTM model, driven by the cumulative precipitation, temperature, and Global Land Data Assimilation System datasets, was reliable for use in reconstruction of the GRACE products in the closed basin. The TWS variations featured seasonal variation characteristics and a significant upward trend at internal-annual scales, which were tested via linear statistics and a modified Mann–Kendall method. The increasing trend is likely to remain strongly sustainable in the near future with a Hurst index over 0.75 in most regions. Moreover, the TWS oscillation has a periodicity and nonlinearity increase trend of 0.43 mm/month as observed using ensemble empirical mode decomposition analysis, and the TWS components (including snow water equivalent, soil moisture, and groundwater) demonstrate discordant increasing trends in the basin. Under climate change conditions, teleconnection factors have strong impacts on TWS variability, particularly for the Pacific Decadal Oscillation index with a significant negative correlation by cross wavelet transform technology. Nonetheless, the increase in TWS is primarily influenced by precipitation increases and is more sensitive to the accumulated precipitation in this region. In this study, the GRACE products in combination with GRACE-FO data may help us to better understand the spatiotemporal characterization of TWS in Qaidam Basin, which will provide an important support for the water resource management and ecological environment protection in such data-scarce regions.
Linyong Wei; Shanhu Jiang; Liliang Ren; Hongbing Tan; Wanquan Ta; Yi Liu; Xiaoli Yang; Linqi Zhang; Zheng Duan. Spatiotemporal changes of terrestrial water storage and possible causes in the closed Qaidam Basin, China using GRACE and GRACE Follow-On data. Journal of Hydrology 2021, 598, 126274 .
AMA StyleLinyong Wei, Shanhu Jiang, Liliang Ren, Hongbing Tan, Wanquan Ta, Yi Liu, Xiaoli Yang, Linqi Zhang, Zheng Duan. Spatiotemporal changes of terrestrial water storage and possible causes in the closed Qaidam Basin, China using GRACE and GRACE Follow-On data. Journal of Hydrology. 2021; 598 ():126274.
Chicago/Turabian StyleLinyong Wei; Shanhu Jiang; Liliang Ren; Hongbing Tan; Wanquan Ta; Yi Liu; Xiaoli Yang; Linqi Zhang; Zheng Duan. 2021. "Spatiotemporal changes of terrestrial water storage and possible causes in the closed Qaidam Basin, China using GRACE and GRACE Follow-On data." Journal of Hydrology 598, no. : 126274.
Soil moisture content is an important hydrological and climatic variable with applications in a wide range of domains. The high spatial variability of soil moisture cannot be well captured from conventional point-based in-situ measurements. Remote sensing offers a feasible way to observe spatial pattern of soil moisture from regional to global scales. Microwave remote sensing has long been used to estimate Surface Soil Moisture Content (SSMC) at lower spatial resolutions (>1km), but few accurate options exist in the higher spatial resolution (<1km) domain. This study explores the capabilities of deep learning in the high-resolution domain of remotely sensed SSMC by using a Convolutional Neural Network (CNN) to estimate SSMC from Sentinel-1 acquired Synthetic Aperture Radar (SAR) imagery. The developed model incorporates additional SSMC predictors such as Normalized Difference Vegetation Index (NDVI), temperature, precipitation, and soil type to yield a more accurate estimation than traditional empirical formulas that focus solely on the conversion of backscatter signals to relative soil moisture. This also makes the developed model less sensitive to site-specific conditions and increases the model applicability outside the training domain. The model is developed and tested with in-situ soil moisture measurements in Denmark from a dense network maintained by HOBE (Danish Hydrological Observatory). The unique advantage of the developed model is its transferability across climate zones, which has been historically absent in many prior models. This would open up opportunities for high-resolution soil moisture mapping through remote sensing in areas with relatively few soil moisture gauges. A reliable high-resolution soil moisture platform at good temporal resolution would allow for more precise erosion modelling, flood forecasting, drought monitoring, and precision agriculture.
Nicklas Simonsen; Zheng Duan. Development of a deep learning-based method for estimating surface soil moisture at high spatial resolution from Sentinel-1 satellite data. 2021, 1 .
AMA StyleNicklas Simonsen, Zheng Duan. Development of a deep learning-based method for estimating surface soil moisture at high spatial resolution from Sentinel-1 satellite data. . 2021; ():1.
Chicago/Turabian StyleNicklas Simonsen; Zheng Duan. 2021. "Development of a deep learning-based method for estimating surface soil moisture at high spatial resolution from Sentinel-1 satellite data." , no. : 1.
Rapid warming in northern high latitudes during the past two decades may have profound impacts on the structures and functioning of ecosystems. Understanding how ecosystems respond to climatic change is crucial for the prediction of climate-induced changes in plant phenology and productivity. Here we investigate spatial patterns of polynomial trends in ecosystem productivity for northern (> 30 °N) biomes and their relationships with climatic drivers during 2000–2018. Based on a moderate resolution (0.05°) of satellite data and climate observations, we quantify polynomial trend types and change rates of ecosystem productivities using plant phenology index (PPI), a proxy of gross primary productivity (GPP), and a polynomial trend identification scheme (Polytrend). We find the yearly-integrated PPI (PPIINT) shows a high degree of agreement with an OCO-2-based solar‐induced chlorophyll fluorescence GPP product (GOSIF-GPP) for distinct spatial patterns of trend types of ecosystem productivities. The averaged slope for linear trends of GPP is found positive across all the biomes, among which deciduous broadleaved and evergreen needle-leaved forests show the highest and lowest rates respectively. The evergreen needle-leaved forests, low shrub, and permanent wetland show linear trends in PPIINT over more than 50% of the covered area and permanent wetland also shows a large fraction of the area with the quadratic and cubic trends. Spatial patterns of linear trends for growing season sum of temperature, precipitation, and photosynthetic active radiation have been quantified. Based on the partial correlations between PPIINT and climate drivers, we found that there is a consistent shift of dominant drivers from temperature or radiation to precipitation across all the biomes except the permeant wetland when the trend type of ecosystem productivity changes from linear to non-linear. This may imply precipitation changes in recent years may determine the linear or non-linear responses of ecosystem productivity to climate change. Our results highlight the importance of understanding how changes in climatic drivers may affect the overall responses of ecosystems productivity. Our findings will facilitate the sustainable management of ecosystems accounting for the resilience of ecosystem productivity and phenology to future climate change.
Wenxin Zhang; Hongxiao Jin; Sadegh Jamali; Zheng Duan; Mousong Wu; Huaiwei Sun; Youhua Ran. Elucidating climatic controls of polynomial trends in interannual variations of northern ecosystem productivities. 2021, 1 .
AMA StyleWenxin Zhang, Hongxiao Jin, Sadegh Jamali, Zheng Duan, Mousong Wu, Huaiwei Sun, Youhua Ran. Elucidating climatic controls of polynomial trends in interannual variations of northern ecosystem productivities. . 2021; ():1.
Chicago/Turabian StyleWenxin Zhang; Hongxiao Jin; Sadegh Jamali; Zheng Duan; Mousong Wu; Huaiwei Sun; Youhua Ran. 2021. "Elucidating climatic controls of polynomial trends in interannual variations of northern ecosystem productivities." , no. : 1.
Soil moisture is an Essential Climate Variable (ECV) that plays an important role in land surface-atmosphere interactions. Accurate monitoring of soil moisture is essential for many studies in water, energy and carbon cycles. However, soil moisture is characterized with high spatial and temporal variability, making conventional point-based in-situ measurements difficult to sufficiently capture these variabilities given the often lack of dense in-situ network for most regions. Considerable efforts have been made to explore satellite remote sensing, hydrological and land surface models in estimating and mapping soil moisture, leading to increasing availability of different gridded soil moisture products at various spatial and temporal resolutions. The accuracy of an individual product varies between regions and needs to be evaluated in order to guide the selection of the most suitable products for certain applications. Such evaluation will also benefit product development and improvements. The most common (traditional) evaluation method is to calculate error metrics of the evaluated products with in-situ measurements as ground truth. The triple collocation (TC) analysis has been widely used and demonstrated powerful in evaluation of various products for different geophysical variables when ground truth is not available.
The Integrated Carbon Observation System (ICOS) is a research infrastructure with aim to quantify the greenhouse gas balance of Europe and adjacent regions. A standardized network of more than 140 research stations in 13 member states has been established and is operated by ICOS to provide direct measurements of climate relevant variables. The ICOS Carbon Portal offers a 'one-stop shop' freely for all ICOS data products at https://www.icos-cp.eu/observations/carbon-portal. This study evaluates for the first time a large number of different satellite-based and reanalysis surface soil moisture products at varying spatial and temporal resolutions using ICOS measurements from 2015 over Sweden. Evaluated products include ESA CCI, ASCAT, SMAP, SMOS, Sentinel-1 derived, ERA5 and GLDAS products. In order to quantify spatial patterns of errors of each individual product, TC analysis is applied to different combinations of gridded products for spatial evaluation across entire Sweden. The performance of products in different seasons and years is evaluated. The similarity and difference among different products for the drought period in the year 2018 is particularly assessed. This study is expected to improve our understanding of the applicability and limitations of various gridded soil moisture products in the Nordic region.
Zheng Duan; Nina del Rosario; Jianzhi Dong; Hongkai Gao; Jian Peng; Yang Lu; Junzhi Liu; Alex Vermeulen. Quantifying errors of multiple gridded soil moisture products in Sweden using triple collocation analysis and traditional evaluation method with ICOS data. 2021, 1 .
AMA StyleZheng Duan, Nina del Rosario, Jianzhi Dong, Hongkai Gao, Jian Peng, Yang Lu, Junzhi Liu, Alex Vermeulen. Quantifying errors of multiple gridded soil moisture products in Sweden using triple collocation analysis and traditional evaluation method with ICOS data. . 2021; ():1.
Chicago/Turabian StyleZheng Duan; Nina del Rosario; Jianzhi Dong; Hongkai Gao; Jian Peng; Yang Lu; Junzhi Liu; Alex Vermeulen. 2021. "Quantifying errors of multiple gridded soil moisture products in Sweden using triple collocation analysis and traditional evaluation method with ICOS data." , no. : 1.
Amongst the impacts of climate change, those arising from extreme hydrological events are expected to cause the greatest impacts. To assess the changes in temperature and precipitation and their impacts on the discharge in the upper Yangtze Basin from pre-industrial to the end of 21st century, four hydrological models were integrated with four global climate models. Results indicated that mean discharge was simulated to increase slightly for all hydrological models forced by all global climate models during 1771–1800 and 1871–1900 relative to the 1971–2000 reference period, whereas the change directions in mean discharge were not consistent among the four global climate models during 2070–2099, with increases from HadGEM2-ES and MIROC5, and decreases from GFDL-ESM2M and IPSL-CM5A-LR. Additionally, our results indicated that decreases in precipitation may always result in the decrease in mean discharge, but increases in precipitation did not always lead to increases in discharge due to high temperature rise. The changes in extreme flood events with different return intervals were also explored. These extreme events were projected to become more intense and frequent in the future, which could have potential devastating impacts on the society and ecosystem in this region.
Yanjuan Wu; Gang Luo; Cai Chen; Zheng Duan; Chao Gao. Using Integrated Hydrological Models to Assess the Impacts of Climate Change on Discharges and Extreme Flood Events in the Upper Yangtze River Basin. Water 2021, 13, 299 .
AMA StyleYanjuan Wu, Gang Luo, Cai Chen, Zheng Duan, Chao Gao. Using Integrated Hydrological Models to Assess the Impacts of Climate Change on Discharges and Extreme Flood Events in the Upper Yangtze River Basin. Water. 2021; 13 (3):299.
Chicago/Turabian StyleYanjuan Wu; Gang Luo; Cai Chen; Zheng Duan; Chao Gao. 2021. "Using Integrated Hydrological Models to Assess the Impacts of Climate Change on Discharges and Extreme Flood Events in the Upper Yangtze River Basin." Water 13, no. 3: 299.
The diurnal cycle of precipitation is a fundamental mode of climatic variability. Based on an hourly 0.1° × 0.1° gauge-satellite merged precipitation dataset, which is provided by the China Meteorological Data Service Center, this study used the K-means clustering algorithm to map the diurnal cycles of precipitation in summer over China. The obtained maps can objectively delineate irregular regions with similar patterns of diurnal cycles, and many of these regions resemble large-scale geomorphic units such as the North China Plain and the valley of the Yarlung Zangbo River, which verifies the importance of macroterrain for diurnal cycles of precipitation. In addition to total precipitation, precipitation events with different durations, which are often related to different types of precipitation (e.g., convective precipitation and stratiform precipitation), were analyzed separately. Compared with the previous studies using predefined region boundaries, the main advantage of this study is that the obtained maps can show more spatial details, through which we can get intuitive and in-depth understandings of the regional characteristics and spatial patterns of the diurnal cycles of precipitation over China.
Junzhi Liu; Lei Yang; Jingchao Jiang; Weihua Yuan; Zheng Duan. Mapping diurnal cycles of precipitation over China through clustering. Journal of Hydrology 2020, 592, 125804 .
AMA StyleJunzhi Liu, Lei Yang, Jingchao Jiang, Weihua Yuan, Zheng Duan. Mapping diurnal cycles of precipitation over China through clustering. Journal of Hydrology. 2020; 592 ():125804.
Chicago/Turabian StyleJunzhi Liu; Lei Yang; Jingchao Jiang; Weihua Yuan; Zheng Duan. 2020. "Mapping diurnal cycles of precipitation over China through clustering." Journal of Hydrology 592, no. : 125804.
Although the Tropical Rainfall Measurement Mission (TRMM) has come to an end, the evaluation of TRMM satellite precipitation is still of great significance for the improvement of the Global Precipitation Measurement (GPM). In this paper, the hydrological utility of TRMM Multi-satellite Precipitation Analysis (TMPA) 3B42 RTV7/V7 precipitation products was evaluated using the variable infiltration capacity (VIC) hydrological model in the upper Yangtze River basin. The main results show that (1) TMPA 3B42V7 had a reliable performance in precipitation estimation compared with the gauged precipitation on both spatial and temporal scales over the upper Yangtze River basin. Although TMPA 3B42V7 slightly underestimated precipitation, TMPA 3B42RTV7 significantly overestimated precipitation at daily and monthly time scales; (2) the simulated runoff by the VIC hydrological model showed a high correlation with the gauged runoff and lower bias at daily and monthly time scales. The Nash–Sutcliffe coefficient of efficiency (NSCE) value was as high as 0.85, the relative bias (RB) was −6.36% and the correlation coefficient (CC) was 0.93 at the daily scale; (3) the accuracy of the 3B42RTV7-driven runoff simulation had been greatly improved by using the hydrological calibration parameters obtained from 3B42RTV7 compared with that of gauged precipitation. A lower RB (14.38% vs. 66.58%) and a higher CC (0.87 vs. 0.85) and NSCE (0.71 vs. −0.92) can be found at daily time scales when we use satellite data instead of gauged precipitation data to calibrate the VIC model. However, the performance of the 3B42V7-driven runoff simulation did not improve in the same operation accordingly. The cause might be that the 3B42V7 satellite products have been adjusted by gauged precipitation. This study suggests that it might be better to calibrate the parameters using satellite data in hydrological simulations, especially for unadjusted satellite data. This study is not only helpful for understanding the assessment of multi-satellite precipitation products in large-scale and complex areas in the upper reaches of the Yangtze River, but also can provide a reference for the hydrological utility of the satellite precipitation products in other river basins of the world.
Bin Zhu; Yuhan Huang; Zengxin Zhang; Rui Kong; Jiaxi Tian; Yichen Zhou; Sheng Chen; Zheng Duan. Evaluation of TMPA Satellite Precipitation in Driving VIC Hydrological Model over the Upper Yangtze River Basin. Water 2020, 12, 3230 .
AMA StyleBin Zhu, Yuhan Huang, Zengxin Zhang, Rui Kong, Jiaxi Tian, Yichen Zhou, Sheng Chen, Zheng Duan. Evaluation of TMPA Satellite Precipitation in Driving VIC Hydrological Model over the Upper Yangtze River Basin. Water. 2020; 12 (11):3230.
Chicago/Turabian StyleBin Zhu; Yuhan Huang; Zengxin Zhang; Rui Kong; Jiaxi Tian; Yichen Zhou; Sheng Chen; Zheng Duan. 2020. "Evaluation of TMPA Satellite Precipitation in Driving VIC Hydrological Model over the Upper Yangtze River Basin." Water 12, no. 11: 3230.
Spatial downscaling is an effective way to obtain precipitation with sufficient spatial details. The performance of downscaling is typically determined by the empirical statistical relationships between precipitation and the used auxiliary variables. In this study, we conducted a comprehensive comparison of five empirical statistical methods for spatial downscaling of GPM IMERG V06B monthly and annual precipitation with a relatively long time series from 2001 to 2015 over a typical semi-arid to arid area (Gansu province, China). These methods included two parametric regression methods (univariate regression, or UR; multivariate regression, or MR) and three machine learning methods (artificial neural network, or ANN; support vector machine, or SVM; random forests, or RF), which were used to downscale the satellite precipitation from 0.1° (∼10 km) to 1 km spatial resolution. Five commonly used indices which were normalized differential vegetation index (NDVI), elevation, land surface temperature (LST), and latitude and longitude were selected as auxiliary variables. The downscaled results were validated using a total of 80 rain gauge station data during 2001–2015. Results showed that latitude had the overall largest correlation with IMERG annual precipitation, also evidenced by feature importance measurements in RF. The downscaled results at monthly scale were overall consistent with the results at annual scale. The machine learning-based methods had better predictive ability of the original IMERG precipitation than parametric regression methods, with larger coefficient of determination (R 2) and smaller root-mean-square error (RMSE) as well as relative root-mean-square error (RRMSE). The downscaled 1 km IMERG precipitation by parametric regression methods had obvious underestimations (positive residual errors) in the south and east of Gansu province and overestimations (negative residual errors) in the west. In addition, the validation results of parametric regression downscaling methods showed large improvements after residual correction, while the improvements were small in the machine learning-based methods. However, the interpolation algorithm included in residual correction can cause certain errors in the downscaled results due to the ignorance of precipitation spatial heterogeneity. The machine learning-based RF downscaling had the smallest residual errors and the overall best validation results, showing great potentials to provide accurate precipitation with high spatial resolution.
Cheng Chen; Qiuwen Chen; Binni Qin; Shuhe Zhao; Zheng Duan. Comparison of Different Methods for Spatial Downscaling of GPM IMERG V06B Satellite Precipitation Product Over a Typical Arid to Semi-Arid Area. Frontiers in Earth Science 2020, 8, 1 .
AMA StyleCheng Chen, Qiuwen Chen, Binni Qin, Shuhe Zhao, Zheng Duan. Comparison of Different Methods for Spatial Downscaling of GPM IMERG V06B Satellite Precipitation Product Over a Typical Arid to Semi-Arid Area. Frontiers in Earth Science. 2020; 8 ():1.
Chicago/Turabian StyleCheng Chen; Qiuwen Chen; Binni Qin; Shuhe Zhao; Zheng Duan. 2020. "Comparison of Different Methods for Spatial Downscaling of GPM IMERG V06B Satellite Precipitation Product Over a Typical Arid to Semi-Arid Area." Frontiers in Earth Science 8, no. : 1.
Glacier retreat caused by global warming alters the hydrological regime and poses far-reaching challenges to water resources and nature conservation of the headwater of Yangtze River, and its vast downstream regions with dense population. However, there is still lack of a robust modeling framework of the “climate-glacier-streamflow” in this water tower region, to project the future changes of glacier mass balance, glacier geometry, and the consequent impacts on runoff. Moreover, it is imperative to use the state-of-the-art sixth phase Coupled Model Intercomparison Project (CMIP6) to assess glacio-hydrology variations in future. In this study, we coupled a glacio-hydrological model (FLEXG) with a glacier retreat method (Δh-parameterization) to simulate glacio-hydrological processes in the Dongkemadi Glacier (over 5155 m.a.s.l), which has the longest continuous glacio-hydrology observation on the headwater of Yangtze River. The FLEXG-Δh model was forced with in-situ observed meteorological data, radar ice thickness, remote sensing topography and land cover data, and validated by measured runoff. The results showed that the model was capable to simulate hydrological processes in this glacierized basin, with Kling-Gupta efficiency (IKGE) of daily runoff simulation 0.88 in calibration and 0.70 in validation. Then, forcing by the bias-corrected meteorological forcing from the eight latest CMIP6 Earth system models under two climate scenarios (RCP2.6 and RCP8.5), we assessed the impact of future climate change on glacier response and its hydrological effects. The results showed that, to the end of simulation in 2100, the volume of the Dongkemadi Glacier would continuously retreat. For the RCP2.6 and RCP8.5 scenarios, the glacier volume will decrease by 8.7×108 m3 (74%) and 10.8×108 m3 (92%) respectively in 2100. The glacier runoff will increase and reach to peak water around 2060 to 2085, after this tipping point water resources will likely decrease.
Hongkai Gao; Zijing Feng; Tong Zhang; Yuzhe Wang; Xiaobo He; Hong Li; Xicai Pan; Ze Ren; Xi Chen; Wenxin Zhang; Zheng Duan. Assessing glacier retreat and its impact on water resources in a headwater of Yangtze River based on CMIP6 projections. Science of The Total Environment 2020, 765, 142774 .
AMA StyleHongkai Gao, Zijing Feng, Tong Zhang, Yuzhe Wang, Xiaobo He, Hong Li, Xicai Pan, Ze Ren, Xi Chen, Wenxin Zhang, Zheng Duan. Assessing glacier retreat and its impact on water resources in a headwater of Yangtze River based on CMIP6 projections. Science of The Total Environment. 2020; 765 ():142774.
Chicago/Turabian StyleHongkai Gao; Zijing Feng; Tong Zhang; Yuzhe Wang; Xiaobo He; Hong Li; Xicai Pan; Ze Ren; Xi Chen; Wenxin Zhang; Zheng Duan. 2020. "Assessing glacier retreat and its impact on water resources in a headwater of Yangtze River based on CMIP6 projections." Science of The Total Environment 765, no. : 142774.
This study evaluated the suitability of the latest retrospective Integrated Multi-satellitE Retrievals for Global Precipitation Measurement V06 (IMERG) Final Run product with a relatively long period (beginning from June 2000) for drought monitoring over mainland China. First, the accuracy of IMERG was evaluated by using observed precipitation data from 807 meteorological stations at multiple temporal (daily, monthly, and yearly) and spatial (pointed and regional) scales. Second, the IMERG-based standardized precipitation index (SPI) was validated and analyzed through statistical indicators. Third, a light–extreme–light drought-event process was adopted as the case study to dissect the latent performance of IMERG-based SPI in capturing the spatiotemporal variation of drought events. Our results demonstrated a sufficient consistency and small error of the IMERG precipitation data against the gauge observations with the regional mean correlation coefficient (CC) at the daily (0.7), monthly (0.93), and annual (0.86) scales for mainland China. The IMERG possessed a strong capacity for estimating intra-annual precipitation changes; especially, it performed well at the monthly scale. There was a strong agreement between the IMERG-based SPI values and gauge-based SPI values for drought monitoring in most regions in China (with CCs above 0.8). In contrast, there was a comparatively poorer capability and notably higher heterogeneity in the Xinjiang and Qinghai-Tibet Plateau regions with more widely varying statistical metrics. The IMERG featured the advantage of satisfactory spatiotemporal accuracy in terms of depicting the onset and extinction of representative drought disasters for specific consecutive months. Furthermore, the IMERG has obvious drought monitoring abilities, which was also complemented when compared with the Remotely Sensed Information using Artificial Neural Networks Climate Data Record (PERSIANN-CDR), Climate Hazards Group Infrared Precipitation with Stations (CHIRPS), and Tropical Rainfall Measuring Mission Multi-satellite Precipitation Analysis (TMPA) 3B42V7. The outcomes of this study demonstrate that the retrospective IMERG can provide a more competent data source and potential opportunity for better drought monitoring utility across mainland China, particularly for eastern China.
Linyong Wei; Shanhu Jiang; Liliang Ren; Linqi Zhang; Menghao Wang; Zheng Duan. Preliminary Utility of the Retrospective IMERG Precipitation Product for Large-Scale Drought Monitoring over Mainland China. Remote Sensing 2020, 12, 2993 .
AMA StyleLinyong Wei, Shanhu Jiang, Liliang Ren, Linqi Zhang, Menghao Wang, Zheng Duan. Preliminary Utility of the Retrospective IMERG Precipitation Product for Large-Scale Drought Monitoring over Mainland China. Remote Sensing. 2020; 12 (18):2993.
Chicago/Turabian StyleLinyong Wei; Shanhu Jiang; Liliang Ren; Linqi Zhang; Menghao Wang; Zheng Duan. 2020. "Preliminary Utility of the Retrospective IMERG Precipitation Product for Large-Scale Drought Monitoring over Mainland China." Remote Sensing 12, no. 18: 2993.
Model realism is of vital importance in science of hydrology, in terms of realistic representation of hydrological processes and reliability of future prediction. Here, we employed a stepwise modeling approach that leverages flexible model structures and multi-source observations for robust streamflow simulation and internal variables validation with improved model realism. This framework is demonstrated in Yigong Zangbu River (YZR) basin, a data scarce glacier basin in the upper Brahmaputra River. We designed six experiments (Exp1–6) to use modeling as a tool to understand hydrological processes in this remote cold basin with extremely high altitude. In Exp1, we started with a distributed rainfall-runoff model (FLEXD) - representing the case that snow and glacier processes were ignored. Then, we stepwisely added snow and glacier processes into FLEXD, denoted as FLEXD-S (Exp2) and FLEXD-SG (Exp3), respectively, and such improvement of model structure led to significantly improved streamflow estimates. To explore the impact of different precipitation forcing on model performance, FLEXD-SG was driven by Theissen average (Exp3) and three individual stations’ precipitation (Exp4–6). The model realism was tested by observed hydrograph, snow cover area (SCA) and glacier mass balance (GMB). Results showed that a robust and realistic hydrological modeling system was achieved in Exp6. In this modeling study, we learned that: 1) stepwise modeling is effective in investigating catchment behavior, and snow and glacier melting are the dominant hydrological processes in the YZR basin; 2) internal variables validation is beneficial to test model realism in data scarce basin; 3) the FLEXD-SG model calibrated by only one year hydrograph is sufficient to reproduce snow and glacier variations; 4) precipitation of a single station as forcing data could outperform Theissen average; 5) based on the well tested model configuration in Exp6, we analyzed simulated results, and reconstructed the long term hydrography (1961–2013), to support the potential competence for decision making on water resources management in practice. The proposed framework may significantly improve our skills in hydrological modeling over data-poor regions.
Hongkai Gao; Jianzhi Dong; Xi Chen; Huayang Cai; Zhiyong Liu; Zhihao Jin; Dehua Mao; Zongji Yang; Zheng Duan. Stepwise modeling and the importance of internal variables validation to test model realism in a data scarce glacier basin. Journal of Hydrology 2020, 591, 125457 .
AMA StyleHongkai Gao, Jianzhi Dong, Xi Chen, Huayang Cai, Zhiyong Liu, Zhihao Jin, Dehua Mao, Zongji Yang, Zheng Duan. Stepwise modeling and the importance of internal variables validation to test model realism in a data scarce glacier basin. Journal of Hydrology. 2020; 591 ():125457.
Chicago/Turabian StyleHongkai Gao; Jianzhi Dong; Xi Chen; Huayang Cai; Zhiyong Liu; Zhihao Jin; Dehua Mao; Zongji Yang; Zheng Duan. 2020. "Stepwise modeling and the importance of internal variables validation to test model realism in a data scarce glacier basin." Journal of Hydrology 591, no. : 125457.
Remote-sensing-based machine learning approaches for water quality parameters estimation, Secchi Disk Depth (SDD) and Turbidity, were developed for the Valle de Bravo reservoir in central Mexico. This waterbody is a multipurpose reservoir, which provides drinking water to the metropolitan area of Mexico City. To reveal the water quality status of inland waters in the last decade, evaluation of MERIS imagery is a substantial approach. This study incorporated in-situ collected measurements across the reservoir and remote sensing reflectance data from the Medium Resolution Imaging Spectrometer (MERIS). Machine learning approaches with varying complexities were tested, and the optimal model for SDD and Turbidity was determined. Cross-validation demonstrated that the satellite-based estimates are consistent with the in-situ measurements for both SDD and Turbidity, with R2 values of 0.81 to 0.86 and RMSE of 0.15 m and 0.95 nephelometric turbidity units (NTU). The best model was applied to time series of MERIS images to analyze the spatial and temporal variations of the reservoir’s water quality from 2002 to 2012. Derived analysis revealed yearly patterns caused by dry and rainy seasons and several disruptions were identified. The reservoir varied from trophic to intermittent hypertrophic status, while SDD ranged from 0–1.93 m and Turbidity up to 23.70 NTU. Results suggest the effects of drought events in the years 2006 and 2009 on water quality were correlated with water quality detriment. The water quality displayed slow recovery through 2011–2012. This study demonstrates the usefulness of satellite observations for supporting inland water quality monitoring and water management in this region.
Leonardo F. Arias-Rodriguez; Zheng Duan; Rodrigo Sepúlveda; Sergio I. Martinez-Martinez; Markus Disse. Monitoring Water Quality of Valle de Bravo Reservoir, Mexico, Using Entire Lifespan of MERIS Data and Machine Learning Approaches. Remote Sensing 2020, 12, 1586 .
AMA StyleLeonardo F. Arias-Rodriguez, Zheng Duan, Rodrigo Sepúlveda, Sergio I. Martinez-Martinez, Markus Disse. Monitoring Water Quality of Valle de Bravo Reservoir, Mexico, Using Entire Lifespan of MERIS Data and Machine Learning Approaches. Remote Sensing. 2020; 12 (10):1586.
Chicago/Turabian StyleLeonardo F. Arias-Rodriguez; Zheng Duan; Rodrigo Sepúlveda; Sergio I. Martinez-Martinez; Markus Disse. 2020. "Monitoring Water Quality of Valle de Bravo Reservoir, Mexico, Using Entire Lifespan of MERIS Data and Machine Learning Approaches." Remote Sensing 12, no. 10: 1586.
Model realism testing is of vital importance in science of hydrology, in terms of realistic representation of hydrological processes and reliability of future prediction. We conducted three modeling case studies in cold regions of China, i.e. the upper Heihe River basin, the Urumqi Glacier No.1 basin, and the Yigong Zangbu River basin, to test the importance of stepwise modeling and internal fluxes validation to improve model realism.
In the upper Heihe River basin, we used four progressively more complex hydrological models (FLEXL, FLEXD, FLEXT0 and FLEXT), to stepwisely account for distributed forcing inputs, tailor-made model structure for different landscapes, and the realism constraints of parameters and fluxes. We found that the stepwise modeling framework helped hydrological processes understanding, and the tailor-made model structure and realism constraints improved model transferability to two nested basins.
In the Urumqi Glacier No. 1 basin, with 52% of the area covered by glaciers, we developed a conceptual glacier-hydrological model (FLEXG) and tested its performance to reproduce the hydrograph, and separate the discharge into contributions from glacier and nonglacier areas, and establish estimates of the annual glacier mass balance (GMB), the annual equilibrium line altitude (ELA), and the daily snow water equivalent (SWE). We found that the FLEXG model, involving effects of topography aspect, was successfully transferred and upscaled to a larger catchment without recalibration.
In the Yigong Zangbu River basin, with 41.4% glacier area, we designed three models (FLEXD, FLEX-S, FLEX-SG) to stepwisely understand the impact of snow, glacier to reproduce historic streamflow. We found that by involving snow and glacier modules, the model performance was dramatically improved. Although the daily streamflow of FLEX-SG reached up to 0.93 Kling-Gupta Efficiency (KGE) in calibration, it significantly overestimated snow cover area (SCA) and glacier mass balance (GMB). With satellite measured precipitation lapse rate, we improved FLEX-SG model realism not only to reproduce hydrography but also SCA and GMB.
Hongkai Gao; Ze Ren; Zheng Duan. Stepwise modeling and the importance of internal fluxes validation to improve hydrological model realism: three case studies in cold regions of China. 2020, 1 .
AMA StyleHongkai Gao, Ze Ren, Zheng Duan. Stepwise modeling and the importance of internal fluxes validation to improve hydrological model realism: three case studies in cold regions of China. . 2020; ():1.
Chicago/Turabian StyleHongkai Gao; Ze Ren; Zheng Duan. 2020. "Stepwise modeling and the importance of internal fluxes validation to improve hydrological model realism: three case studies in cold regions of China." , no. : 1.
Precipitation is an important component of the water cycle. Precipitation is characterized with high temporal and spatial variability. Accurate measurements of precipitation at high spatiotemporal resolution are essential for many applications in the fields of hydrology, meteorology and ecology. The traditional rain gauge stations provide direct measurements of rainfall at the surface but at a limited scale; rain gauge measurements are often considered as point-based measurements that are insufficient to represent the spatial variability of rainfall over a certain region, especially in the case of sparse rain gauge network. Satellite remote sensing has been developing with great ability of being used for estimating various water cycle components at different temporal and spatial scales. Considerable efforts have been made to develop satellite precipitation products at different spatial and temporal resolutions over the global or quasi-global scale. The majority of global/quasi-global precipitation products are at the spatial resolution of 0.25° (~25 km) with very few products at 0.05°-0.10° resolution. The usefulness of satellite precipitation products has been increasingly recognized but the relative coarse spatial resolution is still a limitation for many applications such as hydrological modelling at basin scales that generally require precipitation data at a desirable higher spatial resolution (e.g. 1 km). Over recent years, numerous spatial downscaling procedures/methods have been proposed to obtain precipitation products at higher spatial resolution. The relationships between precipitation and various auxiliary land-surface variables were explored and incorporated into spatial downscaling procedures using a large range of regression algorithms. Advanced machine learning and geostatistical methods have also been innovatively used to develop spatial downscaling procedures.
The aim of this study is to present a comprehensive review of studies on spatial downscaling of satellite precipitation products over the recent years. We will summarize the proposed spatial downscaling methods, investigated auxiliary land-surface variables and the evaluation strategy. The performance of spatial downscaling methods in studied regions and their applications will be compared and discussed in terms of advantages and limitations. Finally, we will conclude this paper with outlook on future research needs and associated challenges about spatial downscaling of satellite precipitation products.
Zheng Duan; Cheng Chen; Hongkai Gao; Jian Peng. A review of spatial downscaling of satellite precipitation products. 2020, 1 .
AMA StyleZheng Duan, Cheng Chen, Hongkai Gao, Jian Peng. A review of spatial downscaling of satellite precipitation products. . 2020; ():1.
Chicago/Turabian StyleZheng Duan; Cheng Chen; Hongkai Gao; Jian Peng. 2020. "A review of spatial downscaling of satellite precipitation products." , no. : 1.
Hydrological modelling is an important tool to improve our understanding of hydrological processes of river basins and to predict impacts of climate change and environmental change on water resources. Precipitation is a key component of the hydrological cycle, and the most important driver/input data for hydrological models. Accurate precipitation measurements at desirable temporal and spatial resolution are essential for achieving reasonable performance of hydrological modelling. Compared to the conventional measurements from point-based rain gauge stations, remote sensing of precipitation with satellite sensors and ground-based radar can expand observational coverage and provide regional precipitation at varying temporal and spatial resolutions. Radars can provide sampling at very high resolution but also tend to contain significant errors in precipitation estimates. The Deutscher Wetterdienst (DWD; German Weather Service) developed the RADOLAN (RADar-OnLine-ANeichung) method (a real-time, gauge-adjustment and correction procedure) to generate precipitation estimates (termed as RADOLAN product) from the German Doppler radar network. More recently (2017), the DWD published a reanalysis of radar data to generate RADKLIM (RADarKLIMatologie) precipitation product using upgraded correction algorithms and additional offline gauge adjustment.
This study presents the first assessment of the performance of two high spatial resolution (1 km) radar-based precipitation products (RADOLAN and RADKLIM) in streamflow simulation using the hydrological model SWAT (Soil and Water Assessment Tool) in Germany. We also evaluate the performance of conventional point-based rain gauge data and a satellite precipitation product in driving SWAT for streamflow simulation. The selected satellite product is CHIRPS (Climate Hazards Group InfraRed Precipitation with Station data) because of its well reported good performance and the relative higher spatial resolution (0.05°). The Vils Basin located in Bavaria, Germany is chosen as the study area. Performance of investigated precipitation product is assessed by comparing simulated streamflow using calibrated SWAT model against measured streamflow at basin outlet at both daily and monthly time scales. The model calibration is performed using the SWAT-CUP program with measured streamflow. Different calibration procedures are also investigated to analyze the influence on model performance. This study presents and discusses the accuracy and uncertainty of using ground-based radar and satellite precipitation products in driving SWAT model for daily and monthly streamflow simulation. Our findings are expected to provide beneficial feedback to product developers for further improvements, and to inform local end-users about the quality of investigated precipitation products.
Zheng Duan; Edward Duggan; Ye Qing; Ye Tuo. Assessing the performance of radar-based and satellite precipitation products in hydrological modelling with SWAT in Vils Basin, Germany. 2020, 1 .
AMA StyleZheng Duan, Edward Duggan, Ye Qing, Ye Tuo. Assessing the performance of radar-based and satellite precipitation products in hydrological modelling with SWAT in Vils Basin, Germany. . 2020; ():1.
Chicago/Turabian StyleZheng Duan; Edward Duggan; Ye Qing; Ye Tuo. 2020. "Assessing the performance of radar-based and satellite precipitation products in hydrological modelling with SWAT in Vils Basin, Germany." , no. : 1.