A Seasonal to Decadal Calibration of 1990–2100 Eastern Canadian Freshwater Discharge Simulations by Observations, Data Models, and Neural Networks

داشتن درک درستی از چگونگی تغییرات زمانی و مکانی متغیر های هواشناسی در مطالعات محیطی از اهمیت بسزایی برخوردار است. اخیرا از مدل‌های منطقه ای پیش بینی عددی هواشناسی در تحقیقات بسیاری استفاده می شود. با این وجود برای بررسی چگونگی تغییرات زمانی و مکانی نتایج حاصل از مدل ها تحقیقات زیادی صورت نگرفته است. بر این اساس و با توجه به اهمیت این موضوع یک رویکرد زمانی - مکانی در این مقاله بررسی می شود. در این رویکرد نتایج پیش‌بینی مدل میان مقیاس 4MM5 با مجموعه داده های مشاهده ای، برای میانگین ماهانه و سالانه دما و بارش در دوره سال‌های 1990-2010 در منطقه شمال شرق ایران مورد تحلیل و بررسی قرار گرفت. در این تحقیق ارزیابی کمی شباهت مکانی و زمانی بین مجموعه داده‌ها و مدل آب و هوایی، و نیز شناسایی مناطق جغرافیایی و فصولی که در مدل سازی‌ها مشکل‌ساز می‌باشند انجام شده است. این کار با استفاده از نقشه‌های تهیه شده بر مبنای الگوریتم تشابه5 جبری، که یک روش موثر برای تحلیل مجموعه داده‌های مکانی مختلف، نقشه‌ها و مدل‌ها می‌باشد، انجام شد. مشخص شد که شباهت داده‌های واقعی با نتایج مدل برای متغیر دما بیش‌تر از بارندگی است. نتایج حاصل از مدل برای بارش و دما در فصل‌های گرم سال در مقایسه با داده‌های واقعی از لحاظ پراکندگی مقادیر تشابه، تناقض داشت. هم‌چنین در فصل‌های سرد سال پیش‌بینی دقیق‌تری به وسیله مدل ارائه شده است. علاوه بر این چند منطقه که در آنجا خطای پیش‌بینی بیش‌تر بود به وسیله الگوریتم نقشه‌پردازی تشابه شناسایی شدند. این تحلیل نشان داد که در محدوده‌های کوچکی از جنوب و ناحیه مرکزی منطقه مورد مطالعه بین مدل و داده‌های واقعی تشابه کم‌تری وجود دارد.

The Niangziguan Springs (NS) discharge is used as a proxy indicator of the variability of the karst groundwater system in relation to major climate indices such as El Niño–Southern Oscillation (ENSO), Pacific decadal oscillation (PDO), Indian summer monsoon (ISM), and west North Pacific monsoon (WNPM). The relationships between spring discharge and these climate indices are determined using the multitaper method (MTM), continuous wavelet transform (CWT), and wavelet transform coherence (WTC). Significant periodic components of spring discharge in the 1-, 3.4-, and 26.8-yr periodicities are identified and reconstructed for further investigation of the correlation between spring discharge and large-scale climate patterns on these time scales. Correlation coefficients and WTC between spring discharge and the climate indices indicate that variability in spring discharge is significantly and positively correlated with monsoon indices in the 1-yr periodicity and negatively correlated with ENSO in the 3.4-yr periodicity and PDO in the 26.8-yr periodicity. This suggests that the oscillations of the spring discharge on annual, interannual, and interdecadal time scales are dominated by monsoon, ENSO, and PDO in the NS basin, respectively. Results show that monsoons modulate the spring discharge by affecting local meteorological parameters. ENSO and PDO impact the variability of the NS discharge by affecting the climate conditions in northern China.

Hydraulic correction method (HCM) to enhance the efficiency of SRTM DEM in flood modeling

In this study, we used a geographic information system (GIS), remote sensing products and a digital elevation model (DEM) to prepare and to test the existing data sources and algorithms for a distributed, physically based, hydrological model, incorporating the gauging stations of the national networks of the Amazon basin. Watershed delineation for the Amazon sub‐basin system is a necessary first step in distributed hydrological modelling. The DEMs currently available for the Amazon basin are GTOPO30, which has a grid spacing of about 1 km, and SRTM, which can be freely obtained from the internet at a resolution of about 90 m. Each of these DEMs has different sources and consequently different kinds of uncertainties. We have tested the two DEMs, comparing the results obtained using both of them on different sub‐basins within the Amazonian basin. The delineation of the sub‐basins for the entire Amazonian basin, which is currently available on our project site (http://www.mpl.ird.fr/hybam/), has been obtained from the GTOPO30. With GTOPO30 alone, the D8 algorithm (which determines the direction of flow in eight neighbouring cells) does not always give the correct delineation of sub‐basins corresponding to the gauging stations. This problem occurs mainly in the very flat areas of the region. One way to overcome this problem is to burn‐in the DEM with a river network. The spatial precision of this river network must be compatible with that of the DEM, and homogeneous throughout the basin. We tested three river networks on the Negro River sub‐basins. We found that a suitable river network could be extracted by digitising a JERS‐1 mosaic image. The DEM GTOPO30 burned‐in with the JERS‐1 extracted river network made the correct distributed modelling of the Amazon gauged sub‐basins possible by determining water transfer times within the basin. Copyright © 2009 John Wiley & Sons, Ltd.

The principles and methods of automatic extraction of river network based on raster digital elevation model (DEM) were briefly introduced. The process to extract river network using the Arc Hydro Tools includes: depression filling, building flow direction matrix, building flow accumulation matrix, building river network. In order to study the effects of different resolution DEM on extraction of river network, Qing jiang River, for instance, the experiment results on the extraction of drainage network from the 10m, 90m, 160m resolution DEM data were compared. It can be concluded that on the Arc GIS platform, the best observable resolution is 90m, which's extraction results can both meet the requirements on the accuracy and efficiency. but in flat area deviation is existed in the extraction result. Therefore, from the overall automatic extraction of basin river network based on DEM is feasible.

Digital elevation model (DEM) has been frequently used for the reduction and management of flood risk. Various classification methods have been developed to extract DEM from point clouds. However, the accuracy and computational efficiency need to be improved. The objectives of this study were as follows: (1) to determine the suitability of a new method to produce DEM from unmanned aerial vehicle (UAV) and light detection and ranging (LiDAR) data, using a raw point cloud classification and ground point filtering based on deep learning and neural networks (NN); (2) to test the convenience of rebalancing datasets for point cloud classification; (3) to evaluate the effect of the land cover class on the algorithm performance and the elevation accuracy; and (4) to assess the usability of the LiDAR and UAV structure from motion (SfM) DEM in flood risk mapping. In this paper, a new method of raw point cloud classification and ground point filtering based on deep learning using NN is proposed and tested on LiDAR and UAV data. The NN was trained on approximately 6 million points from which local and global geometric features and intensity data were extracted. Pixel-by-pixel accuracy assessment and visual inspection confirmed that filtering point clouds based on deep learning using NN is an appropriate technique for ground classification and producing DEM, as for the test and validation areas, both ground and non-ground classes achieved high recall (>0.70) and high precision values (>0.85), which showed that the two classes were well handled by the model. The type of method used for balancing the original dataset did not have a significant influence in the algorithm accuracy, and it was suggested not to use any of them unless the distribution of the generated and real data set will remain the same. Furthermore, the comparisons between true data and LiDAR and a UAV structure from motion (UAV SfM) point clouds were analyzed, as well as the derived DEM. The root mean square error (RMSE) and the mean average error (MAE) of the DEM were 0.25 m and 0.05 m, respectively, for LiDAR data, and 0.59 m and –0.28 m, respectively, for UAV data. For all land cover classes, the UAV DEM overestimated the elevation, whereas the LIDAR DEM underestimated it. The accuracy was not significantly different in the LiDAR DEM for the different vegetation classes, while for the UAV DEM, the RMSE increased with the height of the vegetation class. The comparison of the inundation areas derived from true LiDAR and UAV data for different water levels showed that in all cases, the largest differences were obtained for the lowest water level tested, while they performed best for very high water levels. Overall, the approach presented in this work produced DEM from LiDAR and UAV data with the required accuracy for flood mapping according to European Flood Directive standards. Although LiDAR is the recommended technology for point cloud acquisition, a suitable alternative is also UAV SfM in hilly areas.

Watershed delineation is a required step when conducting any spatially distributed hydrological modelling. Automated approaches are often proposed to delineate a watershed based on a river network extracted from the digital elevation model (DEM) using the deterministic eight-neighbour (D8) method. However, a realistic river network cannot be derived from conventional DEM processing methods for a large flat area with a complex network of rivers, lakes, reservoirs, and polders, referred to as a plain river network region (PRNR). In this study, a new approach, which uses both hydrographic features and DEM, has been developed to address the problems of watershed delineation in PRNR. It extracts the river nodes and determines the flow directions of the river network based on a vector-based hydrographic feature data model. The river network, lakes, reservoirs, and polders are then used to modify the flow directions of grid cells determined by D8 approach. The watershed is eventually delineated into four types of catchments including lakes, reservoirs, polders, and overland catchments based on the flow direction matrix and the location of river nodes. Multiple flow directions of grid cells are represented using a multi-direction encoding method, and multiple outflows of catchments are also reflected in the topology of catchments. The proposed approach is applied to the western Taihu watershed in China. Comparisons between the results obtained from the D8 approach, the ‘stream burning’ approach, and those from the proposed approach clearly demonstrate an improvement of the new approach over the conventional approaches. This approach will benefit the development of distributed hydrological models in PRNR for the consideration of different types and multiple inlets and outlets of catchments. Copyright © 2015 John Wiley & Sons, Ltd.

Topography is a critical element in the hydrological response of a drainage basin and its availability in the form of Digital Elevation Models (DEM) has advanced the modelling of hydrological and hydraulic processes. However, progress experienced in these fields may stall, as intrinsic characteristics of free DEMs may limit new findings, while at the same time new releases of free, high-accuracy, global digital terrain models are still uncertain. In this paper, the limiting nature of free DEMs is dissected in the context of hydrogeomorphology. Nine sets of terrain data are analysed: the SRTM GL1 and GL3, HydroSHEDS, TINITALY, ASTER GDEM, EU DEM, VFP, ALOS AW3D30, MERIT and the TDX. In specific, the influence of three parameters are investigated, i.e., spatial resolution, hydrological reconditioning and vertical accuracy, on four relevant geomorphic terrain descriptors, namely the upslope contributing area, the local slope, the elevation difference and the flow path distance to the nearest stream, H and D, respectively. The Tanaro river basin in Italy is chosen as the study region and the newly released LiDAR for the Italian territory is used as benchmark to reassess vertical accuracies. In addition, the EU-Hydro photo-interpreted river network is used to compare DEM-based river networks. Most DEMs approximate well the frequency curve of elevations of the LiDAR, but this is not necessarily reflected in the representation of geomorphic features. For example, DEMs with finer spatial resolution present larger contributing areas; differences in the slope can reach 10%; between 5 m and 12 m H, none of the considered DEMs can faithfully represent the LiDAR; D presents significant variability between DEMs; and river network extraction can be problematic in flatter terrain. It is also found that the lowest mean absolute error (MAE) is given by the MERIT, 2.85 m, while the lowest root mean square error (RMSE) is given by the SRTM GL3, 4.83 m. Practical implications of choosing a DEM over another may be expected, as the limitations of any particular DEM in faithfully reproducing critical geomorphic terrain features may hinder our ability to find satisfactory answers to some pressing problems.

Extreme rainfall events drive the amount and spatial distribution of rainfall in the Amazon and are a key driver of forest dynamics across the basin. This study investigates how the 3-hourly predictions in the High Resolution Model Intercomparison Project (HighResMIP, a component of the recent Coupled Model Intercomparison Project, CMIP6) represent extreme rainfall events at annual, seasonal, and sub-daily time scales. TRMM 3B42 (Tropical Rainfall Measuring Mission) 3 h data were used as observations. Our results showed that eleven out of seventeen HighResMIP models showed the observed association between rainfall and number of extreme events at the annual and seasonal scales. Two models captured the spatial pattern of number of extreme events at the seasonal and annual scales better (higher correlation) than the other models. None of the models captured the sub-daily timing of extreme rainfall, though some reproduced daily totals. Our results suggest that higher model resolution is a crucial factor for capturing extreme rainfall events in the Amazon, but it might not be the sole factor. Improving the representation of Amazon extreme rainfall events in HighResMIP models can help reduce model rainfall biases and uncertainties and enable more reliable assessments of the water cycle and forest dynamics in the Amazon.

The Digital Elevation Model (DEM) derived from NASA's Shuttle Radar Topography Mission is the most accurate nearglobal elevation model that is publicly available. The characteristics, advantages, and disadvantages of Shuttle Radar Topography Mission (SRTM) data sets were reviewed and discussed briefly. In order to verify the effect of applying SRTM data sets in surface water hydrologic simulation, a tool set named Arc Hydro Tools that is utilized to extract watershed characteristics was introduced, developed as an ArcGIS interface. The Qingshuijiang watershed in Guizhou Province, Southwest China, was taken as a case study. Using the tool set, the river network and subwatersheds of main tributaries were delineated from CGIAR- CSI SRTM 90 m DEM. By comparing the river network delineated from CGIAR-CSI SRTM 90 m DEM with the actual river network and comparing areas of the subwatersheds delineated from CGIAR-CSI SRTM 90 m DEM with the actual areas of the subwatersheds, it can be concluded that the delineated river network is generally in accord with the actual river network, as well as the areas of the delineated subwatersheds. The CGIAR-CSI SRTM 90 m DEM will promote the use of geospatial science and applications for digital topography analysis, especially for surface water hydrologic simulation.

Accurate extraction of river network from the Digital Elevation Model (DEM) is a significant content in the application of a distributed hydrological model. However, the study of river network extraction based on DEM has some limitations, such as location offset, inaccurate parallel channel and short circuit of meandering channels. In this study, we proposed a new enhancement method for NASADEM V001 in the Danjiangkou Reservoir area. We used Surface Water Occurrence (SWO) and Sentinel-2 data to describe vertical limit differences between morphological units to complement actual flow path information from NASADEM data by a stream burning method. The differences between the extracted river network and the actual river network were evaluated in three different geographical regions. Compared with the actual river centerline, the location error of the river network extraction was significantly reduced. The average offset distances between river network extraction and the actual river network were 68.38, 36.99, and 21.59 m in the three test areas. Compared with NASADEM V001, the average offset distances in the three test areas were reduced by 7.26, 40.29, and 42.35%, respectively. To better estimate accuracy, we also calculated and compared the accuracy of the river network based on MERIT Hrdro and HydroSHEDS. The experimental results demonstrated that the method can effectively improve the accuracy of river network extraction and meet the needs of hydrological simulation.

InSAR and optical techniques represent two principal approaches for the generation of large-scale Digital Elevation Models (DEMs). Due to the inherent limitations of each technology, a single data source is insufficient to produce high-quality DEM products. The increasing deployment of satellites has generated vast amounts of InSAR and optical DEM data, thereby providing opportunities to enhance the quality of final DEM products through the more effective utilization of the existing data. Previous research has established that complete DEMs generated by InSAR technology can be combined with optical DEMs to produce a fused DEM with enhanced accuracy and reduced noise. Traditional DEM fusion methods typically employ weighted averaging to compute the fusion results. Theoretically, if the weights are appropriately selected, the fusion outcome can be optimized. However, in practical scenarios, DEMs frequently lack prior information on weights, particularly precise weight data. To address this issue, this study adopts a fully connected artificial neural network for elevation fusion prediction. This approach represents an advancement over existing neural network models by integrating local elevation and terrain as input features and incorporating curvature as an additional terrain characteristic to enhance the representation of terrain features. We also investigate the impact of terrain factors and local terrain feature as training features on the fused elevation outputs. Finally, three representative study areas located in Oregon, USA, and Macao, China, were selected for empirical validation. The terrain data comprise InSAR DEM, AW3D30 DEM, and Lidar DEM. The results indicate that compared to traditional neural network methods, the proposed approach improves the Root-Mean-Squared Error (RMSE) ranges, from 5.0% to 12.3%, and the Normalized Median Absolute Deviation (NMAD) ranges, from 10.3% to 26.6%, in the test areas, thereby validating the effectiveness of the proposed method.

Digital Elevation Models (DEMs) of Greenland provide the basic data for studying the Greenland ice sheet (GrIS), but little research quantitatively evaluates and compares the accuracy of various Greenland DEMs. This study uses IceBridge elevation data to evaluate the accuracies of the the Greenland Ice Map Project (GIMP)1 DEM, GIMP2 DEM, TanDEM-X, and ArcticDEM in their corresponding time ranges. This study also analyzes the impact of DEM accuracy and resolution on the accuracy of river network extraction. The results show that (1) within the time range covered by each DEM, TanDEM-X with an RMSE of 5.60 m has higher accuracy than the other DEMs in terms of absolute height accuracy, while GIMP1 has the lowest accuracy among the four Greenland DEMs, with an RMSE of 14.34 m. (2) Greenland DEMs are affected by regional errors and interannual changes. The accuracy in areas with elevations above 2000 m is higher than that in areas with elevations below 2000 m, and better accuracy is observed in the north than in the south. The stability of the ArcticDEM product is higher than those of the other three DEM products, and its RMSE standard deviation over multiple years is only 0.14 m. Therefore, the errors caused by the applications of DEMs with longer time spans are smaller. GIMP1 performs in an opposite manner, with a standard deviation of 2.39 m. (3) The river network extracted from TanDEM-X is close to the real river network digitized from remote sensing images, with an accuracy of 50.78%. The river network extracted from GIMP1 exhibits the largest errors, with an accuracy of only 8.83%. This study calculates and compares the accuracy of four Greenland DEMs and indicates that TanDEM-X has the highest accuracy, adding quantitative studies on the accuracy evaluation of various Greenland DEMs. This study also compares the results of different DEM river network extractions, verifies the impact of DEM accuracy on the accuracy of the river network extraction results, and provides an explorable direction for the hydrological analysis of Greenland as a whole.

Terrain slope and drainage networks are useful components to the basins morphometric characterization as well as to hydrologic modelling. One way to obtain the slope, drainage networks, and basins delineation is by their extraction from Digital Elevation Models (DEMs) and, therefore, their accuracy depends on the accuracy of the used DEM. Regional DEMs with high detail and accuracy are produced in many countries by National Mapping Agencies (NMA). However, the use of these products usually has associated costs. An alternative to those DEMs are the Global Digital Elevation Models (GDEMs) that can be accessed freely and cover almost the entire surface of the world. However, they are not as accurate as the regional DEMs obtained with other techniques. This study intends to assess if generating new, modified DEMs using altimetric data from the original GDEMs and the watercourses available for download in the collaborative project OpenStreetMap (OSM) improves the accuracy of the rebuilt DEMs, the slope derived from them, as well as the delineation of basins and the horizontal and vertical accuracy of the extracted drainage networks. The methodology is presented and applied to a study area located in the United Kingdom. The GDEMs used are of 30 m spatial resolution from the Shuttle Radar Topography Mission (SRTM 30). The accuracy of the original data and the data obtained with the proposed methodology is compared with a reference DEM, with a spatial resolution of 50 m, and the rivers network available at the Ordnance Survey website. The results mainly show an improvement of the horizontal accuracy of the drainage networks, but also a decrease of the systematic errors of the new DEMs, the derived slope, and the vertical position of the drainage networks, as well as the basin’s identification for a set of pour points.

Despite recent developments of continental and global vector‐based river networks, the impact of digital elevation model selection, stream initiation area and environmental parameters including land cover, and elevation, remain unexplored at large scales. To fill this gap, vector river networks based on multiple data sets are compared to the National Hydrography Dataset Plus High Resolution flowpaths. Using TauDEM, river networks from three conditioned Digital Elevation Models (DEMs) were produced at multiple thresholds for stream initiation. OpenCLC, a software package for the comparison of hydrographic networks, was used to compare digital hydrographic networks with the NHDPlus HR flowlines data set over more than 35,00 basins. Networks derived from the 12 m Tandem‐X data set showed similar results as the MERIT Hydro with 90 m resolution until the application of a sophisticated stream burning methodology improved performance significantly. The optimal CLC is obtained at 1‐km threshold for Hydrological Data and Maps Based on SHuttle Elevation Derivatives at multiple Scales and MERIT Hydro‐gridded data sets, quality declined with smaller thresholds. Spatial patterns in river‐network quality were observed and were associated with dominant land classification, with greater forest coverage associated with significantly better quality and greater wetland presence with lower quality networks. This study demonstrates user selection of DEM, and threshold combined with environmental factors (vegetation, water coverage, and precipitation) play a significant role in river‐network quality compared to the DEM selection, and that without sophisticated conditioning, a higher resolution base DEM does not necessarily produce a better river network.