Linking rivers to the rock record: Channel patterns and paleocurrent circular variance

Geology ◽  
2021 ◽  
Author(s):  
C.P. Galeazzi ◽  
R.P. Almeida ◽  
A.H. do Prado
Keyword(s):  
Water Discharge ◽  
Global Scale ◽  
Sedimentary Record ◽  
Discharge Data ◽  
Alluvial Rivers ◽  
Channel Pattern ◽  
Fluvial Deposits ◽  
Circular Variance ◽  
Rock Record ◽  
Channel Patterns

Alluvial rivers are the most important agents of sediment transport in continental basins, whose fluvial deposits enclose information related to the time when rivers were active. In order to extract the most information from fluvial deposits in the sedimentary record, it is imperative to quantify the natural variability of channel patterns at the global scale, explore what controls may influence their development, and investigate whether channel pattern information is preserved in the alluvial plains in order to develop tools for recognizing them in the sedimentary record. By surveying 361 reaches of modern alluvial rivers with available water discharge data at a global scale, we present a quantitative channel pattern classification based on sinuosity and channel count index applicable to the recognition in the rock record. A continuum of channel patterns ranging from high-sinuosity single channel to lowsinuosity multichannels is documented, along with the proportion of depositional elements in their alluvial plains and their conditions of occurrence. Preserved barforms in the alluvial plains of these rivers are used to infer and quantify paleoflow directions at the channel-belt scale and result in ranges of paleocurrent circular variance that may lead to channel pattern identification in the rock record. Data from this work indicate that the recognition of channel patterns may be used to predict paleogeographic features such as the scale of drainage basin area and discharge, slope, and annual discharge regimes.

scholarly journals Estimating water discharge from large radar altimetry datasets

2013 ◽  
Vol 17 (3) ◽  
pp. 923-933 ◽  
Author(s):  
A. C. V. Getirana ◽  
C. Peters-Lidard
Keyword(s):  
Land Surface ◽  
Amazon Basin ◽  
Water Discharge ◽  
Land Surface Model ◽  
Global Scale ◽  
Surface Model ◽  
Rating Curve ◽  
Radar Altimetry ◽  
Discharge Data ◽  
The Impact

Abstract. The objective of this study is to evaluate the potential of large altimetry datasets as a complementary gauging network capable of providing water discharge in ungauged regions. A rating curve-based methodology is adopted to derive water discharge from altimetric data provided by the Envisat satellite at 475 virtual stations (VS) within the Amazon basin. From a global-scale perspective, the stage–discharge relations at VS are built based on radar altimetry and outputs from a modeling system composed of a land surface model and a global river routing scheme. In order to quantify the impact of model uncertainties on rating-curve based discharges, a second experiment is performed using outputs from a simulation where daily observed discharges at 135 gauging stations are introduced in the modeling system. Discharge estimates at 90 VS are evaluated against observations during the curve fitting calibration (2002–2005) and evaluation (2006–2008) periods, resulting in mean normalized RMS errors as high as 39 and 15% for experiments without and with direct insertion of data, respectively. Without direct insertion, uncertainty of discharge estimates can be mostly attributed to forcing errors at smaller scales, generating a positive correlation between performance and drainage area. Mean relative streamflow volume errors (RE) of altimetry-based discharges varied from 15 to 84% for large and small drainage areas, respectively. Rating curves produced a mean RE of 51% versus 68% from model outputs. Inserting discharge data into the modeling system decreases the mean RE from 51 to 18%, and mean NRMSE from 24 to 9%. These results demonstrate the feasibility of applying the proposed methodology to the continental or global scales.

Remote sensed water discharge to analyze flood frequency

2021 ◽  
Author(s):  
Albert Kettner ◽  
Robert Brakenridge ◽  
Sagy Cohen
Keyword(s):  
Water Discharge ◽  
Global Scale ◽  
Flood Frequency ◽  
Balance Model ◽  
Joint Research ◽  
Discharge Data ◽  
Satellite Sensors ◽  
Multiple Sensor ◽  
Daily Discharge ◽  
Discharge Record

<p>Historical and current information regarding river discharge is essential, not only from a water management, energy, or global change perspective but also to better analyze, control and forecast flooding. However, globally the number of ground-based gauging stations declines, and data that is measured by ground-based gauging stations is often not, or shared with a considerable delay.</p><p>It has been demonstrated that existing satellite sensors can be utilized for useful discharge measurements without requiring ground-based information. The DFO – Flood Observatory uses the Advanced Microwave Scanning Radiometer band at 36.5 GHz (e.g. TRMM, AMSR‐E, AMSR2, GMP), pre-processed by the Joint Research Center (JRC) to estimate discharges. With a nearly-daily repeat interval, this microwave signal has been successfully applied to measure water discharge at a global scale, where the calibration of the microwave discharge signal to discharge units is accomplished by comparison to results from a global hydrological numerical model, the Water Balance Model (WBM), for a calibration period. Once calibrated, daily discharge can be back-calculated to January 1998, providing a daily discharge record for more than 20 years.</p><p>Here we present the methods used to utilize remote sensing to measure discharge. We indicate the challenges and how to overcome these when using a multiple sensor approach to capture daily discharges for over a 20-year period. And we show an example for the Amazon river, comparing the remote sensed discharge data with ground observations for multiple locations. Additionally, applications are shown on how this discharge can be combined with flood extent maps to analyze flood frequency.</p>

scholarly journals Water discharge estimates from large radar altimetry datasets in the Amazon basin

2012 ◽  
Vol 9 (6) ◽  
pp. 7591-7611 ◽  
Author(s):  
A. C. V. Getirana ◽  
C. Peters-Lidard
Keyword(s):  
Data Assimilation ◽  
Amazon Basin ◽  
Water Discharge ◽  
Rating Curve ◽  
Radar Altimetry ◽  
Discharge Data ◽  
Modeling Uncertainties ◽  
The Mean ◽  
Relative Errors ◽  
The Impact

Abstract. In this study, we evaluate the use of a large radar altimetry dataset as a complementary gauging network capable of providing water discharge in ungauged regions within the Amazon basin. A rating-curve-based methodology is adopted to derive water discharge from altimetric data provided by Envisat at 444 virtual stations (VS). The stage-discharge relations at VS are built based on radar altimetry and outputs from a global flow routing scheme. In order to quantify the impact of modeling uncertainties on rating-curve based discharges, another experiment is performed using simulated discharges derived from a simplified data assimilation procedure. Discharge estimates at 90 VS are evaluated against observations during the curve fitting calibration (2002–2005) and evaluation (2006–2008) periods, resulting in mean relative RMS errors as high as 52% and 12% for experiments without and with assimilation, respectively. Without data assimilation, uncertainty of discharge estimates can be mostly attributed to forcing errors at smaller scales, generating a positive correlation between performance and drainage area. Mean relative errors (RE) of altimetry-based discharges varied from 15% to 92% for large and small drainage areas, respectively. Rating curves produced a mean RE of 54% versus 68% from model outputs. Assimilating discharge data decreases the mean RE from 68% to 12%. These results demonstrate the feasibility of applying the proposed methodology to the regional or global scales. Also, it is shown the potential of satellite altimetry for predicting water discharge in poorly-gauged and ungauged river basins.

scholarly journals Global-scale human pressure evolution imprints on sustainability of river systems

2019 ◽  
Vol 23 (9) ◽  
pp. 3933-3944 ◽  
Author(s):  
Serena Ceola ◽  
Francesco Laio ◽  
Alberto Montanari
Keyword(s):  
Hot Spot ◽  
Global Scale ◽  
Mitigation Strategies ◽  
First Century ◽  
Human Pressure ◽  
River Systems ◽  
Anthropogenic Pressures ◽  
Discharge Data ◽  
The Sustainable Development ◽  
Major River

Abstract. Human pressures on river systems pose a major threat to the sustainable development of human societies in the twenty-first century. Previous studies showed that a large part of global river systems was already exposed to relevant anthropogenic pressures at the beginning of this century. A relevant question that has never been explained in the literature so far is whether these pressures are increasing in time, therefore representing a potential future challenge to the sustainability of river systems. This paper proposes an index we call “Differential Human Pressure on Rivers” (DHPR) to quantify the annual evolution of human pressure on river systems. DHPR identifies a per-year percentage increment (or decrement) of normalized human pressures on river systems (i.e., ratio of annual values to long-term average). This index, based on annual nightlights and stationary discharge data, is estimated for 2195 major river basins over a period of 22 years, from 1992 to 2013. The results show that normalized annual human pressure on river systems increased globally, as indicated by an average DHPR value of 1.9 % per year, whereby the greatest increase occurred in the northern tropical and equatorial areas. The evaluation of DHPR over this 22-year period allows the identification of hot-spot areas, therefore offering guidance on where the development and implementation of mitigation strategies and plans are most needed (i.e., where human pressure is strongly increasing).

Recognizing tide- and wave-dominated compound deltaic clinothems in the rock record

Geology ◽  
2020 ◽  
Vol 48 (12) ◽  
pp. 1149-1153
Author(s):  
Yang Peng ◽  
Cornel Olariu ◽  
Ronald J. Steel
Keyword(s):  
Seismic Data ◽  
Three Dimensional ◽  
Gravity Flow ◽  
Small Scale ◽  
Sedimentary Record ◽  
Sediment Gravity Flow ◽  
Rock Record ◽  
Storm Wave ◽  
Tidal Deposits

Abstract Many modern deltas exhibit a compound geometry that consists of a shoreline clinoform and a larger subaqueous clinoform connected through a subaqueous platform. Despite the ubiquity of compound clinoforms in modern deltas, very few examples have been documented from the ancient sedimentary record. We present recognition criteria for shelf compound-clinoform systems in both tide- and wave-dominated deltas by integration of ancient and modern examples from multiple types of data. The compound clinothem can be identified by using a combination of: (1) the three-dimensional (3-D) configuration identified in bathymetric or seismic data, (2) bipartite stacked regressive units, consisting of a lower muddy coarsening-to-fining-upward (CUFU) or coarsening-upward (CU) unit (30–100 m thick) and an overlying sandier CU unit (5–30 m thick) (together they represent the subaqueous and shoreline clinoform pair), and (3) distinct facies described herein, though both types of delta have highly bioturbated mudstone and siltstone bottomsets. Tide-dominated deltas have muddy foresets with tidal scours containing tidal rhythmites or inclined heterolithic strata in the subaqueous clinothem overlain by river and tidal deposits of the shoreline clinothem. Wave-dominated deltas show mainly wave-enhanced sediment-gravity-flow (WSGF) beds and some thin hummocky/swaley cross-stratified (HCS/SCS) sandstones toward the top in the subaqueous muddy foreset, and upward-thickening HCS/SCS and trough/planar cross-bedded sandstones interbedded with siltstones in the shoreline clinothem. The subaqueous platform, which links the clinoform couplet, shows evidence of frequent tidal or wave reworking and redeposition. The platform in tide-dominated deltas is characterized by tide-generated heterolithic strata (e.g., bidirectional current-rippled and cross-stratified sandstones, spring and neap tidal bundles, tidal rhythmites) with occasional storm-wave–influenced strata. In contrast, the wave-dominated platform comprises small-scale swales with scours and mud clasts and some WSGF deposits. The proposed criteria can aid in the recognition of compound deltaic clinothems in other basins, particularly those with limited amounts and/or types of data.

Estimation of paleo-discharge of the lost Saraswati River, north west India

2020 ◽  
Author(s):  
Zafar Beg ◽  
Kumar Gaurav ◽  
Sampat Kumar Tandon
Keyword(s):  
Catchment Area ◽  
Water Discharge ◽  
Satellite Image ◽  
Large River ◽  
Channel Width ◽  
Order Estimate ◽  
Alluvial Rivers ◽  
North West ◽  
First Order ◽  
Functional Relationships

<p>The lost Saraswati has been described as a large perennial river which was 'lost' in the desert towards the end of the 'Indus-Saraswati civilisation'. It has been suggested that this paleo river flowed in the Sutlej-Yamuna interfluve, parallel to the present-day Indus River. Today, in this interfluve an ephemeral river- the Ghaggar flows along the abandoned course of the ‘lost’ Saraswati River. We examine the hypothesis given by Yashpal et al. (1980) that two Himalayan-fed rivers Sutlej and Yamuna were the tributaries of the lost Saraswati River, and constituted the bulk of its paleo-discharge. Subsequently, the recognition of the occurrence of thick fluvial sand bodies in the subsurface and the presence of a large number of Harappan sites in the interfluve region have been used to suggest that the Saraswati River was a large perennial river. Further, the wider course of about 4-7 km recognised from satellite imagery of Ghaggar-Hakra belt in between Suratgarh and Anupgarh in the Thar strengthens this hypothesis.</p><p>            In this study, we have developed a methodology to estimate the paleo-discharge and paleo-width of the lost Saraswati River. In doing so, we rely on the hypothesis which suggests that the ancient Saraswati River used to carry the combined flow or some part thereof of the Yamuna, Sutlej and Ghaggar River catchments. The paleo-discharge of the river would compare with that of some of the large river of the Himalayan Foreland. These alluvial rivers are often called self-formed rivers, as they flow on the loose sediment and are subjected to erosion and deposition of channel bed and banks. The geometry of rivers such as width (W), depth (D) and slope (S) are primarily controlled by water discharge (Q) and catchment area (A). Various functional relationships have been developed to scale the alluvial rivers, which we have used to obtain the first-order estimate of the river discharge of the ‘lost’ Saraswati. A scaling relationship was established between the catchment area-channel width for 31 rivers and catchment area-discharge at 26 different locations on the rivers presently flowing on the Himalayan Foreland from Indus in the west to the Brahmaputra in the East. We found the width and discharge of all the Himalayan rivers scale in a similar way when they are plotted against their corresponding catchment area. Using these regime curves, we calculate the width and discharge of paleochannels of the Sutlej, Yamuna and Ghaggar rivers by measuring their corresponding catchment area from satellite images. Finally, we add the discharge and width obtained from each of the contributions of individual catchments (Yamuna, Sutlej and Ghaggar River) to estimate the paleo width and paleo discharge respectively of the Saraswati River. Our regime curves provide a first-order estimate of the paleo-discharge and paleo-width of the lost Saraswati ~2500 cumec and ~1000 m respectively. We also suggest that the 4-7 km channel width observed earlier on the satellite image corresponds to the channel belt width of the lost Saraswati River.</p>

scholarly journals Using artificial neural network (ANN) for prediction of sediment loads, application to the Mellah catchment, northeast Algeria

2017 ◽  
Vol 33 (1) ◽  
pp. 47-55 ◽  
Author(s):  
Housseyn Bouzeria ◽  
Abderrahmane N. Ghenim ◽  
Kamel Khanchoul
Keyword(s):  
Suspended Sediment ◽  
Time Series Data ◽  
Water Discharge ◽  
Absolute Error ◽  
Coefficient Of Determination ◽  
Series Data ◽  
Sediment Discharge ◽  
Statistical Parameters ◽  
Discharge Data ◽  
Sediment Loads

AbstractIn this study, we present the performances of the best training algorithm in Multilayer Perceptron (MLP) neural networks for prediction of suspended sediment discharges in Mellah catchment. Time series data of daily suspended sediment discharge and water discharge from the gauging station of Bouchegouf were used for training and testing the networks. A number of statistical parameters, i.e. root mean square error (RMSE), mean absolute error (MAE), coefficient of efficiency (CE) and coefficient of determination (R2) were used for performance evaluation of the model. The model produced satisfactory results and showed a very good agreement between the predicted and observed data. The results also showed that the performance of the MLP model was capable to capture the exact pattern of the sediment discharge data in the Mellah catchment.

scholarly journals Value of river discharge data for global-scale hydrological modeling

2007 ◽  
Vol 4 (6) ◽  
pp. 4125-4173 ◽  
Author(s):  
M. Hunger ◽  
P. Döll
Keyword(s):  
Correction Factor ◽  
River Discharge ◽  
Hydrological Modeling ◽  
Model Performance ◽  
Flow Characteristics ◽  
Global Scale ◽  
The Other ◽  
Station Correction ◽  
Discharge Data

Abstract. This paper investigates the value of observed river discharge data for global-scale hydrological modeling of a number of flow characteristics that are required for assessing water resources, flood risk and habitat alteration of aqueous ecosystems. An improved version of WGHM (WaterGAP Global Hydrology Model) was tuned in a way that simulated and observed long-term average river discharges at each station become equal, using either the 724-station dataset (V1) against which former model versions were tuned or a new dataset (V2) of 1235 stations and often longer time series. WGHM is tuned by adjusting one model parameter (γ) that affects runoff generation from land areas, and, where necessary, by applying one or two correction factors, which correct the total runoff in a sub-basin (areal correction factor) or the discharge at the station (station correction factor). The study results are as follows. (1) Comparing V2 to V1, the global land area covered by tuning basins increases by 5%, while the area where the model can be tuned by only adjusting γ increases by 8% (546 vs. 384 stations). However, the area where a station correction factor (and not only an areal correction factor) has to be applied more than doubles (389 vs. 93 basins), which is a strong drawback as use of a station correction factor makes discharge discontinuous at the gauge and inconsistent with runoff in the basin. (2) The value of additional discharge information for representing the spatial distribution of long-term average discharge (and thus renewable water resources) with WGHM is high, particularly for river basins outside of the V1 tuning area and for basins where the average sub-basin area has decreased by at least 50% in V2 as compared to V1. For these basins, simulated long-term average discharge would differ from the observed one by a factor of, on average, 1.8 and 1.3, respectively, if the additional discharge information were not used for tuning. The value tends to be higher in semi-arid and snow-dominated regions where hydrological models are less reliable than in humid areas. The deviation of the other simulated flow characteristics (e.g. low flow, inter-annual variability and seasonality) from the observed values also decreases significantly, but this is mainly due to the better representation of average discharge but not of variability. (3) The optimal sub-basin size for tuning depends on the modeling purpose. On the one hand, small basins between 9000 and 20 000 km2 show a much stronger improvement in model performance due to tuning than the larger basins, which is related to the lower model performance (with and without tuning), with basins over 60 000 km2 performing best. On the other hand, tuning of small basins decreases model consistency, as almost half of them require a station correction factor.

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