Automatic procedure for selecting flood events and identifying flood characteristics from daily streamflow data

Abstract. The understanding of low flows in rivers is paramount more than ever as demand for water increases on a global scale. At the same time, limited streamflow data to investigate this phenomenon, particularly in the tropics, makes the provision of accurate estimations in ungauged areas an ongoing research need. This paper analysed the potential of climatic and terrain attributes of 167 tropical and sub-tropical unregulated catchments to predict baseflow recession rates. Daily streamflow data (m3 s–1) from the Global River Discharge Center (GRDC) and a linear reservoir model were used to obtain baseflow recession coefficients (kbf) for these catchments. Climatic attributes included annual and seasonal indicators of rainfall and potential evapotranspiration. Terrain attributes included indicators of catchment shape, morphology, land cover, soils and geology. Stepwise regression was used to identify the best predictors for baseflow recession coefficients. Mean annual rainfall (MAR) and aridity index (AI) were found to explain 49% of the spatial variation of kbf. The rest of climatic indices and the terrain indices average catchment slope (SLO) and tree cover were also good predictors, but co-correlated with MAR. Catchment elongation (CE), a measure of catchment shape, was also found to be statistically significant, although weakly correlated. An analysis of clusters of catchments of smaller size, showed that in these areas, presumably with some similarity of soils and geology due to proximity, residuals of the regression could be explained by SLO and CE. The approach used provides a potential alternative for kbf parameterisation in ungauged catchments.

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Attribution of high resolution streamflow trends in Western Austria – an approach based on climate and discharge station data

Hydrology and Earth System Sciences ◽

10.5194/hess-19-1225-2015 ◽

2015 ◽

Vol 19 (3) ◽

pp. 1225-1245 ◽

Cited By ~ 15

Author(s):

C. Kormann ◽

T. Francke ◽

M. Renner ◽

A. Bronstert

Keyword(s):

Freezing Point ◽

Snow Accumulation ◽

Lower Altitude ◽

Study Region ◽

Daily Streamflow ◽

Streamflow Data ◽

The One ◽

Streamflow Trend ◽

Streamflow Trends

Abstract. The results of streamflow trend studies are often characterized by mostly insignificant trends and inexplicable spatial patterns. In our study region, Western Austria, this applies especially for trends of annually averaged runoff. However, analysing the altitudinal aspect, we found that there is a trend gradient from higher-altitude to lower-altitude stations, i.e. a pattern of mostly positive annual trends at higher stations and negative ones at lower stations. At mid-altitudes, the trends are mostly insignificant. Here we hypothesize that the streamflow trends are caused by the following two main processes: on the one hand, melting glaciers produce excess runoff at higher-altitude watersheds. On the other hand, rising temperatures potentially alter hydrological conditions in terms of less snowfall, higher infiltration, enhanced evapotranspiration, etc., which in turn results in decreasing streamflow trends at lower-altitude watersheds. However, these patterns are masked at mid-altitudes because the resulting positive and negative trends balance each other. To support these hypotheses, we attempted to attribute the detected trends to specific causes. For this purpose, we analysed trends of filtered daily streamflow data, as the causes for these changes might be restricted to a smaller temporal scale than the annual one. This allowed for the explicit determination of the exact days of year (DOYs) when certain streamflow trends emerge, which were then linked with the corresponding DOYs of the trends and characteristic dates of other observed variables, e.g. the average DOY when temperature crosses the freezing point in spring. Based on these analyses, an empirical statistical model was derived that was able to simulate daily streamflow trends sufficiently well. Analyses of subdaily streamflow changes provided additional insights. Finally, the present study supports many modelling approaches in the literature which found out that the main drivers of alpine streamflow changes are increased glacial melt, earlier snowmelt and lower snow accumulation in wintertime.

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Combining SAR and Optical Earth Observation with Hydraulic Simulation for Flood Mapping and Impact Assessment

Remote Sensing ◽

10.3390/rs12233980 ◽

2020 ◽

Vol 12 (23) ◽

pp. 3980

Author(s):

Emmanouil Psomiadis ◽

Michalis Diakakis ◽

Konstantinos X. Soulis

Keyword(s):

Impact Assessment ◽

Temporal Dynamics ◽

Flood Hazard ◽

Recurrence Interval ◽

Optical Data ◽

Flood Event ◽

Flood Events ◽

Hydraulic Modelling ◽

Flood Impact ◽

Flood Characteristics

Timely mapping, measuring and impact assessment of flood events are crucial for the coordination of flood relief efforts and the elaboration of flood management and risk mitigation plans. However, this task is often challenging and time consuming with traditional land-based techniques. In this study, Sentinel-1 radar and Landsat images were utilized in collaboration with hydraulic modelling to obtain flood characteristics and land use/cover (LULC), and to assess flood impact in agricultural areas. Furthermore, indirect estimation of the recurrence interval of a flood event in a poorly gauged catchment was attempted by combining remote sensing (RS) and hydraulic modelling. To this end, a major flood event that occurred in Sperchios river catchment, in Central Greece, which is characterized by extensive farming activity was used as a case study. The synergistic usage of multitemporal RS products and hydraulic modelling has allowed the estimation of flood characteristics, such as extent, inundation depth, peak discharge, recurrence interval and inundation duration, providing valuable information for flood impact estimation and the future examination of flood hazard in poorly gauged basins. The capabilities of the ESA Sentinel-1 mission, which provides improved spatial and temporal analysis, allowing thus the mapping of the extent and temporal dynamics of flood events more accurately and independently from the weather conditions, were also highlighted. Both radar and optical data processing methods, i.e., thresholding, image differencing and water index calculation, provided similar and satisfactory results. Conclusively, multitemporal RS data and hydraulic modelling, with the selected techniques, can provide timely and useful flood observations during and right after flood disasters, applicable in a large part of the world where instrumental hydrological data are scarce and when an apace survey of the condition and information about temporal dynamics in the influenced region is crucial. However, future missions that will reduce further revisiting times will be valuable in this endeavor.

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Changes in the mountain river discharge in the northern Tien Shan since the mid-20th Century: Results from the analysis of a homogeneous daily streamflow data set from seven catchments

Journal of Hydrology ◽

10.1016/j.jhydrol.2018.08.001 ◽

2018 ◽

Vol 564 ◽

pp. 1133-1152 ◽

Cited By ~ 9

Author(s):

M. Shahgedanova ◽

M. Afzal ◽

I. Severskiy ◽

Z. Usmanova ◽

Z. Saidaliyeva ◽

...

Keyword(s):

20Th Century ◽

River Discharge ◽

Tien Shan ◽

Mountain River ◽

Data Set ◽

Daily Streamflow ◽

Streamflow Data ◽

Northern Tien Shan

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Investigating the interaction of waves and river discharge during compound flooding at Breede Estuary, South Africa

10.5194/nhess-2021-220 ◽

2021 ◽

Author(s):

Sunna Kupfer ◽

Sara Santamaria-Aguilar ◽

Lara van Niekerk ◽

Melanie Lück-Vogel ◽

Athanasios Vafeidis

Keyword(s):

South Africa ◽

South African ◽

Hydrodynamic Model ◽

River Discharge ◽

Water Levels ◽

Flood Events ◽

Flood Magnitude ◽

Compound Events ◽

Flood Characteristics ◽

Water Depths

Abstract. Recent studies have drawn special attention to the significant dependencies between flood drivers and the occurrence of compound flood events in coastal areas. This study investigates compound flooding from tides, river discharge (Q) and specifically waves using a hydrodynamic model at Breede Estuary, South Africa. We quantify vertical and horizontal differences in flood characteristics caused by driver interaction, and assess the contribution of waves. Therefore, we compare flood characteristics resulting from compound flood scenarios to those in which single drivers are omitted. We find that flood characteristics are more sensitive to Q than to waves, particularly when the latter only coincide with high spring tides. When interacting with Q, however, the contribution of waves is high, causing 10–12 % larger flood extents and 45–85 cm higher water depths, as waves caused backwater effects and raised water levels inside the lower reaches of the estuary. With higher wave intensity, the first flooding began up to 12 hours earlier. Our findings provide insights on compound flooding in terms of flood magnitude and timing at a South African estuary and demonstrate the need to account for the effects of compound events, including waves, in future flood impact assessments of open South African estuaries.

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Physically-based distributed hydrological model calibration based on a short period of streamflow data: case studies in two Chinese basins

10.5194/hess-2016-192 ◽

2016 ◽

Cited By ~ 1

Author(s):

Wenchao Sun ◽

Yuanyuan Wang ◽

Xingqi Cui ◽

Jingshan Yu ◽

Depeng Zuo ◽

...

Keyword(s):

Uncertainty Analysis ◽

Model Calibration ◽

Calibration Data ◽

Hydrological Models ◽

Daily Streamflow ◽

Short Period ◽

Analysis Scheme ◽

Physically Based ◽

Streamflow Data ◽

Distributed Hydrological Models

Abstract. Physically-based distributed hydrological models are widely used for hydrological simulations in various environments. However, as with conceptual models, they are limited in data-sparse basin by the lack of streamflow data for calibration. Short periods of observational data (less than 1 year) may be obtained from the fragmentary historical records of past-existed gauging stations or from temporary gauging during field surveys, which might be of values for model calibration. This study explored how the use of limited continuous daily streamflow data might support the application of a physically-based distributed model in data-sparse basins. The influence of the length of observation period on the calibration of the widely applied Soil and Water Assessment Tool model was evaluated in two Chinese basins with differing climatic and geophysical characteristics. The evaluations were conducted by comparing calibrations based on short periods of data with calibrations based on data from a 3-year period, which were treated as benchmark calibrations for the two basins. To ensure the differences in the model simulations solely come from differences in the calibration data, the Generalized Likelihood Uncertainty Analysis scheme was employed for the automatic calibration and uncertainty analysis. In both basins, contrary to the common understanding of the need for observations over a period of several years, data records with lengths of less than 1 year were shown to calibrate the model effectively, i.e. performances similar to the benchmark calibrations were achieved. The model of wet Jinjiang Basin could be effectively calibrated using a shorter data record (1 month), compared with the arid Heihe Basin (6 months). Even though the two basins are very different, the results demonstrated that data from the wet season and wetter years performed better that data from the dry season and drier year. The results of this study demonstrated that short periods of observations could be a promising solution to the problem of calibration of physically-based distributed hydrological models in data-sparse basins and further researches similar to this study are required to gain more general understandings about the optimum number of observations needed for calibration when such model are applied to real data-sparse basins.

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A Comparison of Multiple Imputation Methods for Recovering Missing Data in Hydrological Studies

Civil Engineering Journal ◽

10.28991/cej-2021-03091747 ◽

2021 ◽

Vol 7 (9) ◽

pp. 1608-1619

Author(s):

Fatimah Bibi Hamzah ◽

Firdaus Mohd Hamzah ◽

Siti Fatin Mohd Razali ◽

Hafiza Samad

Keyword(s):

Missing Data ◽

Regression Tree ◽

Classification And Regression Tree ◽

Random Regression ◽

Training Dataset ◽

Percentage Error ◽

Efficiency Coefficient ◽

Imputation Methods ◽

Daily Streamflow ◽

Streamflow Data

Missing data is a common problem in hydrological studies; therefore, data reconstruction is critical, especially when it is crucial to employ all available resources, even incomplete records. Furthermore, missing data could have an impact on statistical analysis results, and the amount of variability in the data would not be fittingly anticipated. As a result, this study compared the performance of three imputation methods in predicting recurrence in streamflow datasets: robust random regression imputation (RRRI), k-nearest neighbours (k-NN), and classification and regression tree (CART). Furthermore, entire historical daily streamflow data from 2012 to 2014 (as training dataset) were utilised to assess and validate the effectiveness of the imputation methods in addressing missing streamflow data. Following that, all three methods coupled with multiple linear regression (MLR), were used to restore streamflow rates in Malaysia's Langat River Basin from 1978 to 2016. The estimation techniques effectiveness was evaluated using metrics inclusive of the Nash-Sutcliffe efficiency coefficient (CE), root-mean-square error (RMSE), and mean absolute percentage error (MAPE). The results confirmed that RRRI coupled with MLR (RRRI-MLR) had the lowest RMSE and MAPE values, outperforming all other techniques tested for filling missing data in daily streamflow datasets. This indicates that the RRRI-MLR is the best method for dealing with missing data in streamflow datasets. Doi: 10.28991/cej-2021-03091747 Full Text: PDF

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Streamflow drought: implication of drought definitions and its application for drought forecasting

10.5194/hess-2020-458 ◽

2020 ◽

Author(s):

Samuel J. Sutanto ◽

Henny A. J. Van Lanen

Keyword(s):

Moving Average ◽

Time Windows ◽

Early Warning Systems ◽

Streamflow Forecasting ◽

Drought Forecasting ◽

Comprehensive Overview ◽

Variable Threshold ◽

Daily Streamflow ◽

Streamflow Data ◽

Drought Early Warning

Abstract. Streamflow drought forecasting is a key element of contemporary Drought Early Warning Systems (DEWS). The term streamflow drought forecasting, rather than streamflow forecasting, however, has created confusion within the scientific hydro-meteorological community, as well as in operational weather and water management services. The way, how streamflow drought is defined, is the main reason for this misperception. The purpose of this study, therefore, is to provide a comprehensive overview of the differences within streamflow droughts using different identification approaches for European rivers, including an analysis of both historical drought and implications of forecasting of these extreme events. Streamflow data were obtained from a LISFLOOD hydrological model forced with gridded meteorological observed (known as LISFLOOD-Simulation Forced with Observed, SFO). The same model fed with seasonal meteorological forecasts of the European Centre for Medium-range Weather Forecasts system 5 (ECMWF SEAS 5) was used to obtain the forecasted streamflow. Streamflow droughts were analyzed using the Variable Threshold (VT), Fixed Threshold (FT), and the Standardized Streamflow Index (SSI). Our results clearly show that streamflow droughts derived from different approaches deviate from each other both in occurrence and timing, associated with different climate regions across Europe. The occurrence of FT drought is higher than droughts based upon VT and SSI, which highlights the importance of seasonality. FT drought happens earlier in the year than droughts obtained from VT and SSI. The use of aggregating daily streamflow data into monthly time windows for forecasting drought, such as the application of 30-day Moving Average (30DMA), is recommended to identify the VT and FT droughts. This approach will eliminate the undesired minor drought events, which are identified when using non-aggregated daily flow data. There is no unique hydrological drought definition that fits all purposes, hence developers of DEWS and end-users should clearly agree among themselves upon a sharp definition on which type of streamflow drought is required to be forecasted for a specific application.

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Past hydrological trends on the Pyrenees: towards a higher spatial heterogeneity

10.5194/egusphere-egu2020-10370 ◽

2020 ◽

Author(s):

Ane Zabaleta ◽

David Haro-Monteagudo ◽

Iñaki Antiguedad ◽

Santiago Beguería

Keyword(s):

Water Resources ◽

Land Use Changes ◽

Mountain Range ◽

Average Temperature ◽

Daily Streamflow ◽

Monthly Streamflow ◽

Streamflow Data ◽

Large Irrigation ◽

Annual Scale ◽

First Time

The Pyrenees are a fundamental source of water resources for the territories surrounding this mountain range and beyond, and like other mountainous areas they are very vulnerable under global change. The CLIMPY project (Interreg-POCTEFA) calculated an increase of 1.5 &#8451; on average temperature for this region in the last 60 years.One of the aims of the PIRAGUA project (Interreg-POCTEFA) is to make a regional and temporal characterisation of the water resources of the Pyrenees. To achieve that objective, a common standardized and homogenized database was created for the first time in this transboundary region with streamflow data measured by the different water agencies operating in the area (1956-2015).To avoid human impacted gauging stations (e.g. upstream reservoirs and large irrigation withdrawals), and to analyse only those with a reasonable quality, only a number of the initially obtained streamflow series were considered. A set of indicators was calculated from the selected daily streamflow series concerning mean, high and low flows at annual and monthly scales for different time periods ending in 2015.&#160;Results show that median discharge decreased an average of 30% in all gauging stations between 1956 through to 2015. High and low streamflow also decreased during the same period. On average, the number of days below the first quartile increased 10 days per decade, and the number of days above the third quartile decreased 6 days per decade. The interquartile range decreased 4% per decade on average showing that streamflow suffered a generalised reduction between 1956 and 2015. Regarding monthly streamflow, trends for median streamflow and the first quartile are similar to the annual scale. The most significant decrease is observed during spring (12-15% on average), and the lowest decrease occurs in the autumn (6-9% on average). Since 1986, trends change and streamflow increases are observed at some gauging stations with results that are spatially very heterogeneous. This inflection may be an effect of a more spatially heterogeneous climate in the recent past or of land use changes that are not regionally homogeneous, or a combination of both.

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