ADHI: The African Database of Hydrometric Indices (1950-2018)

2021 ◽  
Author(s):  
Nathalie Rouché ◽  
Yves Tramblay ◽  
Jean-Emmanuel Paturel ◽  
Gil Mahé ◽  
Jean-François Boyer ◽  
...  
Keyword(s):  
Time Series ◽  
Missing Data ◽  
River Discharge ◽  
Data Availability ◽  
Eastern Africa ◽  
African Continent ◽  
Discharge Data ◽  
Daily Discharge ◽  
Time Periods ◽  
Different Sources

<p>The African continent is probably the one with the lowest density of hydrometric stations currently measuring river discharge, despite the fact that the number of stations was quite important until the 70s. In addition, there is a major issue of data availability, since the different existing datasets are scattered across vast regions, heterogeneous and often with a large amount of missing data in the time series. The aim of this African Dataset of Hydrometric Indices (ADHI) is to provide a set of hydrometric indices computed from an unprecedented large set of daily discharge data in Africa. The ADHI database is based on a new streamflow dataset of 1466 gauging stations with an average record length of 33 years and for over 100 stations complete records are available over 50 years. ADHI is compiling data from different sources carefully checked, based on the historical databases of ORSTOM / IRD and the GRDC, including also other contributions from different countries and basin agencies. The criterion for a station to be included in ADHI is to have a minimum of 10 full years of daily discharge data between 1950 and 2018 with less than 5% missing data. Some time series originating from different sources were concatenated, after making sure the rating curves applied on the different time periods to compute river discharge were similar. Data records were scrutinized to identify suspicious discharge records and time periods where gap-filling methods have been applied to the original records. The selected stations are spread across the whole African continent, with the highest density in Western and Southern Africa and the lowest density in Eastern Africa. They are representative of most of the climate zones of Africa according the Köppen-Geiger climate classification. From this dataset, a large range of hydrological indices and flow signatures have been computed and made available to the scientific community (https://doi.org/10.23708/LXGXQ9). They are representing mean flow characteristics and extremes (low flows and floods) but also catchment characteristics, allowing to study the long-term evolution of hydrology in Africa and support the modelling efforts that aim at reducing the vulnerability of African countries to hydro-climatic variability.</p>

scholarly journals The African Database of Hydrometric Indices (ADHI)

2020 ◽  
Author(s):  
Yves Tramblay ◽  
Nathalie Rouché ◽  
Jean-Emmanuel Paturel ◽  
Gil Mahé ◽  
Jean-François Boyer ◽  
...  
Keyword(s):  
Quality Control ◽  
River Discharge ◽  
Scientific Community ◽  
Flow Characteristics ◽  
African Continent ◽  
Mean Flow ◽  
Discharge Data ◽  
Daily Discharge ◽  
The One ◽  
Different Sources

Abstract. The African continent is probably the one with the lowest density of hydrometric stations currently measuring river discharge, despite the fact that the number of operating stations was quite important until the 70s. This new African Database of Hydrometric Indices (ADHI) is compiling data from different sources carefully checked for quality control. It includes about 1500 stations with at least 10 years of daily discharge data over the period 1950–2018. The average record length is 19 years and for over 100 stations complete records are available over 50 years. With this dataset spanning most regions of the African continent, several hydrometric indices have been computed, representing mean flow characteristics and extremes (low flows and floods), and are made accessible to the scientific community. The database will be updated on a regular basis to include more hydrometric stations and longer time series of river discharge. The ADHI database is available for download at: https://doi.org/10.23708/LXGXQ9 (Tramblay and Rouché, 2020).

scholarly journals ADHI: the African Database of Hydrometric Indices (1950–2018)

2021 ◽  
Vol 13 (4) ◽  
pp. 1547-1560
Author(s):  
Yves Tramblay ◽  
Nathalie Rouché ◽  
Jean-Emmanuel Paturel ◽  
Gil Mahé ◽  
Jean-François Boyer ◽  
...  
Keyword(s):  
River Discharge ◽  
Flow Characteristics ◽  
African Continent ◽  
Mean Flow ◽  
Discharge Data ◽  
Climatic Regions ◽  
Wide Range ◽  
Daily Discharge ◽  
The One ◽  
Different Sources

Abstract. The African continent is probably the one with the lowest density of hydrometric stations currently measuring river discharge despite the fact that the number of operating stations was quite important until the 1970s. This new African Database of Hydrometric Indices (ADHI) provides a wide range of hydrometric indices and hydrological signatures computed from different sources of data after a quality control. It includes 1466 stations with at least 10 years of daily discharge data over the period 1950–2018. The average record length is 33 years, and 131 stations have complete records over 50 years. With this new dataset spanning most climatic regions of the African continent, several hydrometric indices have been computed, representing mean flow characteristics and extremes (low flows and floods), and are accessible to the scientific community. The database will be updated on a regular basis to include more hydrometric stations and longer time series of river discharge. The ADHI is available for download at: https://doi.org/10.23708/LXGXQ9 (Tramblay and Rouché, 2020).

scholarly journals Multi-variate infilling of missing daily discharge data on the Niger basin

2021 ◽  
Author(s):  
Ganiyu Titilope Oyerinde ◽  
Agnide E. Lawin ◽  
Oluwafemi E. Adeyeri
Keyword(s):  
Missing Data ◽  
River Discharge ◽  
Missing Values ◽  
Spatial Scales ◽  
Gap Filling ◽  
Discharge Data ◽  
Flow Duration ◽  
Daily Discharge ◽  
Flow Duration Curves ◽  
Discharge Patterns

Abstract The Niger basin have experienced historical drought episodes and floods in recent times. Reliable hydrological modelling has been hampered by missing values in daily river discharge data. We assessed the potential of using the Multivariate Imputation by Chained Equations (MICE) to estimate both continuous and discontinuous daily missing data across different spatial scales in the Niger basin. The study was conducted on 22 discharge stations that have missing data ranging from 2% to 70%. Four efficiency metrics were used to determine the effectiveness of MICE. The Flow Duration Curves (FDC) of observed and filled data were compared to determine how MICE captured the discharge patterns. Mann-Kendall, Modified Mann-Kendall, Pettit and Sen's Slope were used to assess the complete discharge trends using the gap-filled data. Results shows that MICE near perfectly filled the missing discharge data with Nash-Sutcliffe Efficiency (NSE) range of 0.94–0.99 for the calibration (1992–1994) period. Good fits were obtained between FDC of observed and gap-filled data in all considered stations. All the catchments showed significantly increasing discharge trend since 1990s after gap filling. Consequently, the use of MICE in handling missing data challenges across spatial scales in the Niger basin was proposed.

Discharge estimation and monitoring extreme events by satellite altimetry

2020 ◽  
Author(s):  
Rossella Belloni ◽  
Stefania Camici ◽  
Angelica Tarpanelli
Keyword(s):  
Time Series ◽  
Water Level ◽  
River Discharge ◽  
Satellite Altimetry ◽  
Global Scale ◽  
Discharge Time ◽  
Discharge Data ◽  
The World ◽  
Daily Discharge ◽  
Rating Curves

<p>In view of recent dramatic floods and drought events, the detection of trends in the frequency and magnitude of long time series of flood data is of scientific interest and practical importance. It is essential in many fields, from climate change impact assessment to water resources management, from flood forecasting to drought monitoring, for the planning of future water resources and flood protection systems. <br>To detect long-term changes in river discharge a dense, in space and time, network of monitoring stations is required. However, ground hydro-meteorological monitoring networks are often missing or inadequate in many parts of the world and the global supply of the available river discharge data is often restricted, preventing to identify trends over large areas.  <br>The most direct method of deriving such information on a global scale involves satellite earth observation. Over the last two decades, the growing availability of satellite sensors, and the results so far obtained in the estimation of river discharge from the monitoring of the water level through satellite radar altimetry has fostered the interest on this subject.  <br>Therefore, in the attempt to overcome the lack of long continuous observed time series, in this study satellite altimetry water level data are used to set-up a consistent, continuous and up-to-date daily discharge dataset for different sites across the world. Satellite-derived water levels provided by publicly available datasets (Podaac, Dahiti, River& Lake, Hydroweb and Theia) are used along with available ground observed river discharges to estimate rating curves. Once validated, the rating curves are used to fill and extrapolate discharge data over the whole period of altimetry water level observations. The advantage of using water level observations provided by the various datasets allowed to obtain discharge time series with improved spatio-temporal coverages and resolutions, enabling to extend the study on a global scale and to efficiently perform the analysis even for small to medium-sized basins.  <br>Long continuous discharge time series so obtained are used to perform a global trend analysis on extreme flood and drought events. Specifically, annual maximum discharge and peak-over threshold values are extracted from the simulated daily discharge time series, as proxy variables of independent flood events. For flood and drought events, a trend analysis is carried out to identify changes in the frequency and magnitude of extreme events through the Mann-Kendall (M-K) test and a linear regression model between time and the flood magnitude.  <br>The analysis has permitted to identify areas of the world prone to floods and drought, so that appropriate actions for disaster risk mitigation and continuous improvement in disaster preparedness, response, and recovery practices can be adopted. </p>

Learning recurrent transfer functions from data: From climate variability to high river discharge

2020 ◽  
Author(s):  
Mateusz Norel ◽  
Krzysztof Krawiec ◽  
Zbigniew Kundzewicz
Keyword(s):  
Time Series ◽  
River Discharge ◽  
Short Term Memory ◽  
Transfer Functions ◽  
Southern Oscillation ◽  
Flood Hazard ◽  
Atlantic Multidecadal Oscillation ◽  
Statistical Characteristics ◽  
Learning Tools ◽  
Discharge Data

<p>Interpretation of flood hazard and its variability remains a major challenge for climatologists, hydrologists and water management experts. This study investigates the existence of links between variability in high river discharge, worldwide, and inter-annual and inter-decadal climate oscillation indices: El Niño-Southern Oscillation, North Atlantic Oscillation, Pacific Interdecadal Oscillation, and Atlantic Multidecadal Oscillation. Global river discharge data used here stem from the ERA-20CM-R reconstruction at 0.5 degrees resolution and form a multidimensional time series, with each observation being a spatial matrix of estimated discharge volume. Elements of matrices aligned spatially form time series which were used to induce dedicated predictive models using machine learning tools, including multivariate regression (e.g. ARMA) and recurrent neural networks (RNNs), in particular the Long Short Term Memory model (LSTM) that proved to be effective in many other application areas. The models are thoroughly tested and juxtaposed in hindcasting mode on a separate test set and scrutinized with respect to their statistical characteristics. We hope to be able to contribute to improvement of interpretation of variability of flood hazard and reduction of uncertainty.</p>

scholarly journals Application of Vector Auto Regression Model for Rainfall-River Discharge Analysis

2015 ◽  
Vol 29 (1) ◽  
Author(s):  
Sri Hartini ◽  
Muhammad Pramono Hadi ◽  
Sudibyakto Sudibyakto ◽  
Aris Poniman
Keyword(s):  
Time Series ◽  
River Discharge ◽  
Time Series Data ◽  
Series Data ◽  
Var Model ◽  
Time Interval ◽  
Discharge Data ◽  
Vector Auto Regression ◽  
The Relationship ◽  
Auto Regression

River discharge quantity is highly depended on rainfall and initial condition of river discharge; hence, the river discharge has auto-correlation relationships. This study used Vector Auto Regression (VAR) model for analysing the relationship between rainfall and river discharge variables. VAR model was selected by considering the nature of the relationship between rainfall and river discharge as well as the types of rainfall and discharge data, which are in form of time series data. This research was conducted by using daily rainfall and river discharge data obtained from three weirs, namely Sojomerto and Juwero, in Kendal Regency and Glapan in Demak Regency, Central Java Province. Result of the causality tests shows significant relationship of both variables, those on the influence of rainfall to river discharge as well as the influence of river discharge to rainfall variables. The significance relationships of river discharge to rainfall indicate that the rainfall in this area has moved downstream. In addition, the form of VAR model could explain the variety of the relationships ranging between 6.4% - 70.1%. These analyses could be improved by using rainfall and river discharge time series data measured in shorter time interval but in longer period.

scholarly journals Calibration of hydrological models using flow-duration curves

2010 ◽  
Vol 7 (6) ◽  
pp. 9467-9522 ◽  
Author(s):  
I. K. Westerberg ◽  
J.-L. Guerrero ◽  
P. M. Younger ◽  
K. J. Beven ◽  
J. Seibert ◽  
...  
Keyword(s):  
Time Series ◽  
Input Data ◽  
Calibration Method ◽  
Observation Time ◽  
Input Output ◽  
Discharge Data ◽  
Flow Duration ◽  
Model Efficiency ◽  
Time Periods ◽  
Flow Duration Curves

Abstract. The degree of belief we have in predictions from hydrologic models depends on how well they can reproduce observations. Calibrations with traditional performance measures such as the Nash-Sutcliffe model efficiency are challenged by problems including: (1) uncertain discharge data, (2) variable importance of the performance with flow magnitudes, (3) influence of unknown input/output errors and (4) inability to evaluate model performance when observation time periods for discharge and model input data do not overlap. A new calibration method using flow-duration curves (FDCs) was developed which addresses these problems. The method focuses on reproducing the observed discharge frequency distribution rather than the exact hydrograph. It consists of applying limits of acceptability for selected evaluation points (EPs) of the observed uncertain FDC in the extended GLUE approach. Two ways of selecting the EPs were tested – based on equal intervals of discharge and of volume of water. The method was tested and compared to a calibration using the traditional model efficiency for the daily four-parameter WASMOD model in the Paso La Ceiba catchment in Honduras and for Dynamic TOPMODEL evaluated at an hourly time scale for the Brue catchment in Great Britain. The volume method of selecting EPs gave the best results in both catchments with better calibrated slow flow, recession and evaporation than the other criteria. Observed and simulated time series of uncertain discharges agreed better for this method both in calibration and prediction in both catchments without resulting in overpredicted simulated uncertainty. An advantage with the method is that the rejection criterion is based on an estimation of the uncertainty in discharge data and that the EPs of the FDC can be chosen to reflect the aims of the modelling application e.g. using more/less EPs at high/low flows. While the new method is less sensitive to epistemic input/output errors than the normal use of limits of acceptability applied directly to the time series of discharge, it still requires a reasonable representation of the distribution of inputs. Additional constraints might therefore be required in catchments subject to snow. The results suggest that the new calibration method can be useful when observation time periods for discharge and model input data do not overlap. The new method could also be suitable for calibration to regional FDCs while taking uncertainties in the hydrological model and data into account.

HETEROSKEDASTICITY AUTOCORRELATION ROBUST INFERENCE IN TIME SERIES REGRESSIONS WITH MISSING DATA

2018 ◽  
Vol 35 (03) ◽  
pp. 601-629
Author(s):  
Seung-Hwa Rho ◽  
Timothy J. Vogelsang
Keyword(s):  
Time Series ◽  
Missing Data ◽  
Regression Model ◽  
Critical Values ◽  
Missing Observations ◽  
Test Statistics ◽  
Time Series Regression ◽  
Bootstrap Methods ◽  
Time Periods ◽  
Equal Space

In this article, we investigate the properties of heteroskedasticity and autocorrelation robust (HAR) test statistics in time series regression settings when observations are missing. We primarily focus on the nonrandom missing process case where we treat the missing locations to be fixed asT→ ∞ by mapping the missing and observed cutoff dates into points on [0,1] based on the proportion of time periods in the sample that occur up to those cutoff dates. We consider two models, the amplitude modulated series (Parzen, 1963) regression model, which amounts to plugging in zeros for missing observations, and the equal space regression model, which simply ignores the missing observations. When the amplitude modulated series regression model is used, the fixed-blimits of the HAR test statistics depend on the locations of missing observations but are otherwise pivotal. When the equal space regression model is used, the fixed-blimits of the HAR test statistics have the standard fixed-blimits as in Kiefer and Vogelsang (2005). We discuss methods for obtaining fixed-bcritical values with a focus on bootstrap methods and find the naivei.i.d.bootstrap with missing dates fixed to be an effective and practical way to obtain the fixed-bcritical values.

scholarly journals Uncertainty in river discharge observations: a quantitative analysis

2009 ◽  
Vol 13 (6) ◽  
pp. 913-921 ◽  
Author(s):  
G. Di Baldassarre ◽  
A. Montanari
Keyword(s):  
River Discharge ◽  
River Flow ◽  
Flow Data ◽  
Rating Curve ◽  
Po River ◽  
Discharge Data ◽  
Model Code ◽  
Curve Method ◽  
Different Sources ◽  
Extrapolation Error

Abstract. This study proposes a framework for analysing and quantifying the uncertainty of river flow data. Such uncertainty is often considered to be negligible with respect to other approximations affecting hydrological studies. Actually, given that river discharge data are usually obtained by means of the so-called rating curve method, a number of different sources of error affect the derived observations. These include: errors in measurements of river stage and discharge utilised to parameterise the rating curve, interpolation and extrapolation error of the rating curve, presence of unsteady flow conditions, and seasonal variations of the state of the vegetation (i.e. roughness). This study aims at analysing these sources of uncertainty using an original methodology. The novelty of the proposed framework lies in the estimation of rating curve uncertainty, which is based on hydraulic simulations. These latter are carried out on a reach of the Po River (Italy) by means of a one-dimensional (1-D) hydraulic model code (HEC-RAS). The results of the study show that errors in river flow data are indeed far from negligible.

scholarly journals A New and Simplified Approach for Estimating the Daily River Discharge of the Tibetan Plateau Using Satellite Precipitation: An Initial Study on the Upper Brahmaputra River

2020 ◽  
Vol 12 (13) ◽  
pp. 2103
Author(s):  
Tian Zeng ◽  
Lei Wang ◽  
Xiuping Li ◽  
Lei Song ◽  
Xiaotao Zhang ◽  
...  
Keyword(s):  
Time Series ◽  
Tibetan Plateau ◽  
Real Time ◽  
River Discharge ◽  
The Tibetan Plateau ◽  
Time Lags ◽  
Precipitation Data ◽  
Precipitation Time Series ◽  
Discharge Data ◽  
Satellite Precipitation

Collecting in situ observations from remote, high mountain rivers presents major challenges, yet real-time, high temporal resolution (e.g., daily) discharge data are critical for flood hazard mitigation and river management. In this study, we propose a method for estimating daily river discharge (RD) based on free, operational remote sensing precipitation data (Tropical Rainfall Measuring Mission (TRMM), since 2001). In this method, an exponential filter was implemented to produce a new precipitation time series from daily basin-averaged precipitation data to model the time lag of precipitation in supplying RD, and a linear-regression relationship was constructed between the filtered precipitation time series and observed discharge records. Because of different time lags in the wet season (rainfall-dominant) and dry season (snowfall-dominant), the precipitation data were processed in a segmented way (from June to October and from November to May). The method was evaluated at two hydrological gauging stations in the Upper Brahmaputra (UB) river basin, where Nash–Sutcliffe Efficiency (NSE) coefficients for Nuxia (>0.85) and Yangcun (>0.80) indicate good performance. By using the degree-day method to estimate the snowmelt and acquire the time series of new active precipitation (rainfall plus snowmelt) in the target basins, the discharge estimations were improved (NSE > 0.9 for Nuxia) compared to the original data. This makes the method applicable for most rivers on the Tibetan Plateau, which are fed mainly by precipitation (including snowfall) and are subject to limited human interference. The method also performs well for reanalysis precipitation data (Chinese Meteorological Forcing Dataset (CMFD), 1980–2000). The real-time or historical discharges can be derived from satellite precipitation data (or reanalysis data for earlier historical years) by using our method.

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