Ingredients of German flood events

2020 ◽  
Author(s):  
Ralf Merz ◽  
Larisa Tarasova ◽  
Stefano Basso
Keyword(s):  
Soil Moisture ◽  
Snow Cover ◽  
Rainfall Intensity ◽  
Machine Learning Techniques ◽  
Rainfall Runoff ◽  
Flood Events ◽  
Runoff Generation ◽  
Regional Variability ◽  
Large Variability ◽  
Runoff Events

<p>Floods can be caused by a large variety of different processes, such as short, but intense rainfall bursts, long rainfall events, which are wetting up substantial parts of the catchment, or rain on snow cover or frozen soils. Although there is a plethora on studies analysing or modelling rainfall-runoff processes, it is still not well understood, what rainfall and runoff generation conditions are needed to generate flood runoff and how these characteristics vary between catchments. In this databased approach we decipher the ingredients of flood events occurred in 161 catchments across Germany. For each catchment rainfall-runoff events are separated from observed time series for the period 1950-2013, resulting in about 170,000 single events. A peak-over-threshold approach is used to select flood events out of these runoff events. For each event, spatially and temporally distributed rainfall and runoff generation characteristics, such as snow cover and soil moisture, as well as their interaction are derived. Then we decipher those event characteristics controlling flood event occurrence by using machine learning techniques.</p><p>On average, the most important event characteristic controlling flood occurrence in Germany is, as expected, event rainfall volume, followed by the overlap of rainfall and soil moisture and the extent of wet areas in the catchment (area with high soil moisture content). Rainfall intensity is another important characteristic. However, a large variability in its importance is noticeable between dryer catchments where short rainfall floods occur regularly and wetter catchments, where rainfall intensity might be less important for flood generation. To analyse the regional variability of flood ingredients, we cluster the catchments according to similarity in their flood controlling event characteristics and test how good the flood occurrence can be predicted from regionalised event characteristics. Finally, we analyse the regional variability of the flood ingredients in the light of climate and landscape catchment characteristics.</p>

scholarly journals Rapid changes in snow cover at low elevations in the Sierra Nevada, California, U.S.A.

1997 ◽  
Vol 25 ◽  
pp. 367-370 ◽  
Author(s):  
Richard Kattelmann
Keyword(s):  
Snow Cover ◽  
Sierra Nevada ◽  
Mountain Range ◽  
Rainfall Runoff ◽  
Runoff Generation ◽  
Distributed Models ◽  
Rapid Changes ◽  
Spatially Distributed ◽  
Precipitation Type ◽  
River Forecasting

Snow cover in the intermittent snow zone of the Sierra Nevada can occupy more than 10 000 km2 of the mountain range, but it has received relatively little attention in river forecasting. Snow is deposited at lower elevations only during the cold storms of winter, and remains there only for a few days or weeks. When cold storms have created a thin snow cover at low elevations, a subsequent warm storm can melt this snow in just a few hours and increase the runoff response dramatically. Operational hydrological models and river-forecasting procedures have tended to overlook contributions from the intermittent-snow zone, focusing instead on rainfall-runoff or melt from the snowpack zone at higher elevations. Data-collection efforts are minimal in this zone, too. Ideally, spatially distributed models of snowmelt and runoff generation are needed to account for the typically large differences in snow cover on different aspects in the intermittent snow zone. Although aircraft and satellite imagery would be most desirable to monitor the distribution of snow cover in the intermittent-snow zone, even a few climate stations that report precipitation type and snow presence would be a major improvement over the present situation in the Sierra Nevada.

scholarly journals Improved Curve Number Estimation in SWAT by Reflecting the Effect of Rainfall Intensity on Runoff Generation

Water ◽  
2019 ◽  
Vol 11 (1) ◽  
pp. 163 ◽  
Author(s):  
Dejian Zhang ◽  
Qiaoyin Lin ◽  
Xingwei Chen ◽  
Tian Chai
Keyword(s):  
Rainfall Intensity ◽  
Assessment Tool ◽  
Flow Simulation ◽  
Daily Rainfall ◽  
Curve Number ◽  
Base Flow ◽  
Data Sets ◽  
Rainfall Runoff ◽  
Runoff Generation ◽  
The Relationship

Determining the amount of rainfall that will eventually become runoff and its pathway is a crucial process in hydrological modelling. We proposed a method to better estimate curve number by adding an additional component (AC) to better account for the effects of daily rainfall intensity on rainfall-runoff generation. This AC is determined by a regression equation developed from the relationship between the AC series derived from fine-tuned calibration processes and observed rainfall series. When incorporated into the Soil and Water Assessment Tool and tested in the Anxi Watershed, it is found, overall, the modified SWAT (SWAT-ICN) outperformed the original SWAT (SWAT-CN) in terms of stream flow, base flow, and annual extreme flow simulation. These models were further evaluated with the data sets of two adjacent watersheds. Similar results were achieved, indicating the ability of the proposed method to better estimate curve number.

An Experimental Study on the Rainfall-Runoff Progress of Wheat under Different Slope Angle

2014 ◽  
Vol 912-914 ◽  
pp. 1986-1994
Author(s):  
Na Na Zhao ◽  
Fu Liang Yu ◽  
Chuan Zhe Li ◽  
Jia Liu ◽  
Hao Wang
Keyword(s):  
Regression Analysis ◽  
Soil Moisture ◽  
Power Function ◽  
Surface Runoff ◽  
Rainfall Intensity ◽  
Slope Angle ◽  
Subsurface Flow ◽  
Rainfall Runoff ◽  
Determination Coefficient ◽  
Initial Soil

Rainfall-runoff process plays an important role in hydrological cycle, and the study on the rainfall-runoff will provide foundation and basis for research on basin hydrology and flood forecasting. In this paper, the surface runoff and subsurface flow of wheat were observed in the laboratory by artificial rainfall, and analyzed the cumulated surface runoff and recession process of subsurface flow by regression analysis. In addition, the factors affected the runoff and response of soil moisture on the runoff coefficients was also discussed. Results showed that the rainfall intensity, soil coverage and slope had important influence on the surface runoff generation, and the surface runoff was observed when the total rainfall amount exceeded 32mm and 13mm for 5°and 15° slope respectively. The cumulative surface runoff could be expressed as a power function, which had higher determination coefficient R2 (0.92~0.999). The subsurface flow was only observed at the ripening period and wheat stubble treatment, and mainly affected by slope angle and initial soil moisture, whereas rainfall intensity showed little impact. The recession curve of subsurface flow can be described as a simple exponential expression or power function, which the determination coefficient was 0.88 and 0.94 by regression analysis, respectively. Moreover, there was an obvious threshold (approximately 30%) between the average initial soil moisture and runoff coefficients, which the runoff increased significantly as above the threshold.

scholarly journals Impact of rainfall spatial distribution on rainfall-runoff modelling efficiency and initial soil moisture conditions estimation

2011 ◽  
Vol 11 (1) ◽  
pp. 157-170 ◽  
Author(s):  
Y. Tramblay ◽  
C. Bouvier ◽  
P.-A. Ayral ◽  
A. Marchandise
Keyword(s):  
Soil Moisture ◽  
Initial Conditions ◽  
Radar Data ◽  
Rain Gauge ◽  
Production Model ◽  
Initial Condition ◽  
Rainfall Runoff ◽  
Flood Events ◽  
Moisture Index ◽  
Event Based

Abstract. A good knowledge of rainfall is essential for hydrological operational purposes such as flood forecasting. The objective of this paper was to analyze, on a relatively large sample of flood events, how rainfall-runoff modeling using an event-based model can be sensitive to the use of spatial rainfall compared to mean areal rainfall over the watershed. This comparison was based not only on the model's efficiency in reproducing the flood events but also through the estimation of the initial conditions by the model, using different rainfall inputs. The initial conditions of soil moisture are indeed a key factor for flood modeling in the Mediterranean region. In order to provide a soil moisture index that could be related to the initial condition of the model, the soil moisture output of the Safran-Isba-Modcou (SIM) model developed by Météo-France was used. This study was done in the Gardon catchment (545 km2) in South France, using uniform or spatial rainfall data derived from rain gauge and radar for 16 flood events. The event-based model considered combines the SCS runoff production model and the Lag and Route routing model. Results show that spatial rainfall increases the efficiency of the model. The advantage of using spatial rainfall is marked for some of the largest flood events. In addition, the relationship between the model's initial condition and the external predictor of soil moisture provided by the SIM model is better when using spatial rainfall, in particular when using spatial radar data with R2 values increasing from 0.61 to 0.72.

scholarly journals Rapid changes in snow cover at low elevations in the Sierra Nevada, California, U.S.A.

1997 ◽  
Vol 25 ◽  
pp. 367-370 ◽  
Author(s):  
Richard Kattelmann
Keyword(s):  
Snow Cover ◽  
Sierra Nevada ◽  
Mountain Range ◽  
Rainfall Runoff ◽  
Runoff Generation ◽  
Distributed Models ◽  
Rapid Changes ◽  
Spatially Distributed ◽  
Precipitation Type ◽  
River Forecasting

Snow cover in the intermittent snow zone of the Sierra Nevada can occupy more than 10 000 km2of the mountain range, but it has received relatively little attention in river forecasting. Snow is deposited at lower elevations only during the cold storms of winter, and remains there only for a few days or weeks. When cold storms have created a thin snow cover at low elevations, a subsequent warm storm can melt this snow in just a few hours and increase the runoff response dramatically. Operational hydrological models and river-forecasting procedures have tended to overlook contributions from the intermittent-snow zone, focusing instead on rainfall-runoff or melt from the snowpack zone at higher elevations. Data-collection efforts are minimal in this zone, too. Ideally, spatially distributed models of snowmelt and runoff generation are needed to account for the typically large differences in snow cover on different aspects in the intermittent snow zone. Although aircraft and satellite imagery would be most desirable to monitor the distribution of snow cover in the intermittent-snow zone, even a few climate stations that report precipitation type and snow presence would be a major improvement over the present situation in the Sierra Nevada.

scholarly journals Deep Learning with a Long Short-Term Memory Networks Approach for Rainfall-Runoff Simulation

Water ◽  
2018 ◽  
Vol 10 (11) ◽  
pp. 1543 ◽  
Author(s):  
Caihong Hu ◽  
Qiang Wu ◽  
Hui Li ◽  
Shengqi Jian ◽  
Nan Li ◽  
...  
Keyword(s):  
Deep Learning ◽  
Short Term Memory ◽  
Network Models ◽  
Machine Learning Techniques ◽  
Rainfall Runoff ◽  
Ann Model ◽  
Runoff Simulation ◽  
Simulation Performance ◽  
Artificial Neural Network Ann ◽  
Runoff Events

Considering the high random and non-static property of the rainfall-runoff process, lots of models are being developed in order to learn about such a complex phenomenon. Recently, Machine learning techniques such as the Artificial Neural Network (ANN) and other networks have been extensively used by hydrologists for rainfall-runoff modelling as well as for other fields of hydrology. However, deep learning methods such as the state-of-the-art for LSTM networks are little studied in hydrological sequence time-series predictions. We deployed ANN and LSTM network models for simulating the rainfall-runoff process based on flood events from 1971 to 2013 in Fen River basin monitored through 14 rainfall stations and one hydrologic station in the catchment. The experimental data were from 98 rainfall-runoff events in this period. In between 86 rainfall-runoff events were used as training set, and the rest were used as test set. The results show that the two networks are all suitable for rainfall-runoff models and better than conceptual and physical based models. LSTM models outperform the ANN models with the values of R 2 and N S E beyond 0.9, respectively. Considering different lead time modelling the LSTM model is also more stable than ANN model holding better simulation performance. The special units of forget gate makes LSTM model better simulation and more intelligent than ANN model. In this study, we want to propose new data-driven methods for flood forecasting.

scholarly journals Use of the KINFIL rainfall-runoff model on the Hukava catchment

2009 ◽  
Vol 4 (No. 1) ◽  
pp. 1-9
Author(s):  
P. Kovář ◽  
V. Kadlec
Keyword(s):  
Land Use ◽  
Land Use Changes ◽  
Practical Implementation ◽  
Rainfall Runoff ◽  
Flood Events ◽  
Event Duration ◽  
Rainfall Runoff Model ◽  
Runoff Model ◽  
Runoff Events ◽  
Small Catchments

The paper reports on the flood events on the forested Hukava catchment. It describes practical implementation of the KINFIL rainfall-runoff model. This model has been used for the reconstruction of the rainfall-runoff events and thus for the calibration of its parameters. The model was subsequently used to simulate the design discharges with an event duration of t<sub>d</sub> = 30, 60, and 300 min in the period of recurrence of 100 years, and during the scenario simulations of the land use change when 40% and 80% of the forest in the catchment had been cleared out and then replaced by permanent grasslands. The implementation of the KINFIL model supported by GIS proved to be a proper method for the flood runoff assessment on small catchments, during which different scenarios of the land use changes were tested.

scholarly journals The hierarchy of controls on snowmelt-runoff generation over seasonally-frozen hillslopes

2016 ◽  
Author(s):  
Anna E. Coles ◽  
Willemijn M. Appels ◽  
Brian G. McConkey ◽  
Jeffrey J. McDonnell
Keyword(s):  
Soil Moisture ◽  
Snow Cover ◽  
Water Content ◽  
Soil Surface ◽  
Snowmelt Runoff ◽  
Runoff Generation ◽  
Frozen Ground ◽  
Spring Runoff ◽  
Moisture Conditions ◽  
Seasonally Frozen Regions

Abstract. Understanding and modeling snowmelt-runoff generation in seasonally-frozen regions is a major challenge in hydrology. Partly, this is because the controls on hillslope-scale snowmelt-runoff generation are potentially extensive and their hierarchy is poorly understood. Understanding the relative importance of controls (e.g. topography, vegetation, land use, soil characteristics, and precipitation dynamics) on runoff response is necessary for model development, spatial extrapolation, and runoff classification schemes. Multiple interacting process controls, the nonlinearities between them, and the resultant threshold-like activation of runoff, typically are not observable in short-term experiments or single-season field studies. Therefore, long-term datasets and analyses are needed. Here, we use a 52-year dataset of runoff, precipitation, soil water content, snow cover, and meteorological data from three monitored c.5 ha hillslopes on the Canadian Prairies to determine the controls on snowmelt-runoff, their time-varying hierarchy, and the interactions between the controls. We use decision tree learning to extract information from the dataset on the controls on runoff ratio. Our analysis shows that there was a variable relationship between total spring runoff amount and either winter snowfall amount or snow cover water equivalent. Other factors came into play to control the fraction of precipitated water that infiltrated into the frozen ground. In descending order of importance, these were: total snowfall, snow cover, fall soil surface water content, melt rate, melt season length, and fall soil profile water content. While mid-winter warm periods in some years likely increased soil water content and/or led to development of impermeable ice lenses that affected the runoff response, hillslope memory of fall soil moisture conditions played a strong role in the spring runoff response. The hierarchy of these controls was condition-dependent, with the biggest differences between high and low snow cover seasons, and wet and dry fall soil moisture conditions. For example, when snow cover was high, the top three controls on runoff ratio matched the overall hierarchy of controls, with fall soil surface water content being the most important of these. By comparison, when snow cover was low, fall soil surface content was relatively unimportant and superseded by four other controls. Existing empirical methods for predicting infiltration into frozen ground failed to adequately predict runoff response at our site. Our analysis of the hierarchy of controls on meltwater runoff will aid in focusing new model approaches and understanding what to focus future measurement campaigns on in snowmelt-dominated, seasonally-frozen regions.

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