scholarly journals A geostatistical data-assimilation technique for enhancing macro-scale rainfall-runoff simulations

2017 ◽  
Author(s):  
Alessio Pugliese ◽  
Simone Persiano ◽  
Stefano Bagli ◽  
Paolo Mazzoli ◽  
Juraj Parajka ◽  
...  
Keyword(s):  
Scale Model ◽  
Rainfall Runoff ◽  
Daily Streamflow ◽  
Macro Scale ◽  
Streamflow Data ◽  
Flow Duration Curves ◽  
Data Assimilation Technique ◽  
The Relationship ◽  
Simulated Time ◽  
Simulated Time Series

Abstract. Our study develops and tests a geostatistical technique for locally enhancing macro-scale rainfall-runoff simulations on the basis of observed streamflow data that were not used in calibration. We consider Tyrol (Austria and Italy) and two different types of daily streamflow data: macro-scale rainfall-runoff simulations at 11 prediction nodes and observations at 46 gauged catchments. The technique consists of three main steps: (1) period-of-record flow-duration curves (FDCs) are geostatistically predicted at target ungauged basins, for which macro-scale model runs are available; (2) residuals between geostatistically predicted FDCs and FDCs constructed from simulated streamflow series are computed; (3) the relationship between duration and residuals is used for enhancing simulated time series at target basins. We apply the technique in cross-validation to 11 gauged catchments, for which simulated and observed streamflow series are available over the period 1980–2010. Our results show that (1) the procedure can significantly enhance macro-scale simulations (regional NSE increases from nearly zero to ≈ 0.7) and (2) improvements are significant for low gauging network densities (i.e. 1 gauge per 2000 km2).

scholarly journals A geostatistical data-assimilation technique for enhancing macro-scale rainfall–runoff simulations

2018 ◽  
Vol 22 (9) ◽  
pp. 4633-4648 ◽  
Author(s):  
Alessio Pugliese ◽  
Simone Persiano ◽  
Stefano Bagli ◽  
Paolo Mazzoli ◽  
Juraj Parajka ◽  
...  
Keyword(s):  
Scale Model ◽  
Rainfall Runoff ◽  
Daily Streamflow ◽  
Macro Scale ◽  
Streamflow Data ◽  
Flow Duration Curves ◽  
Data Assimilation Technique ◽  
The Relationship ◽  
Simulated Time ◽  
Simulated Time Series

Abstract. Our study develops and tests a geostatistical technique for locally enhancing macro-scale rainfall–runoff simulations on the basis of observed streamflow data that were not used in calibration. We consider Tyrol (Austria and Italy) and two different types of daily streamflow data: macro-scale rainfall–runoff simulations at 11 prediction nodes and observations at 46 gauged catchments. The technique consists of three main steps: (1) period-of-record flow–duration curves (FDCs) are geostatistically predicted at target ungauged basins, for which macro-scale model runs are available; (2) residuals between geostatistically predicted FDCs and FDCs constructed from simulated streamflow series are computed; (3) the relationship between duration and residuals is used for enhancing simulated time series at target basins. We apply the technique in cross-validation to 11 gauged catchments, for which simulated and observed streamflow series are available over the period 1980–2010. Our results show that (1) the procedure can significantly enhance macro-scale simulations (regional LNSE increases from nearly zero to ≈0.7) and (2) improvements are significant for low gauging network densities (i.e. 1 gauge per 2000 km2).

scholarly journals The Impact of Wind on the Rainfall-Runoff Relationship in Urban High-Rise Building Areas

2021 ◽  
Author(s):  
Xichao Gao ◽  
Zhiyong Yang ◽  
Dawei Han ◽  
Kai Gao ◽  
Qian Zhu
Keyword(s):  
Wind Speed ◽  
Theoretical Framework ◽  
Scale Model ◽  
Runoff Coefficient ◽  
Rainfall Runoff ◽  
Wind Drift ◽  
High Rise ◽  
High Rise Building ◽  
The Impact ◽  
The Relationship

Abstract. Wind drift has a significant influence on the rainfall-runoff relationship in urban high-rise building areas since the oblique rainfall caused by the wind drift can interact with the building walls. However, the impact of the rainfall inclination angle on the rainfall-runoff process in urban high-rise building areas has not been studied. In this study, the relationship between wind and the rainfall-runoff process in such areas was explored. A theoretical framework was developed to describe their relationship, including a computational fluid dynamics (CFD) method to obtain the relationship between wind speed and rainfall inclination and a newly derived equation to describe the relationship between rainfall inclination and the runoff coefficient. Subsequently, a laboratory scale model experiment was conducted to verify the proposed framework. The main results are that (1) the runoff coefficient calculated by the proposed theoretical framework is highly consistent with that obtained from the laboratory experiment; (2) the runoff coefficient of urban high-rise building areas increases with wind speed; (3) the change of the runoff coefficient for the experiment with larger raindrop is 0.047 when the wind speed increases from 0 to 5.9 m/s while that for the experiment with smaller raindrop is 0.064, which means that the rainfall with larger droplets is less influenced by the wind.

scholarly journals Detection and predictive modeling of chaos in finite hydrological time series

2005 ◽  
Vol 12 (1) ◽  
pp. 41-53 ◽  
Author(s):  
S. Khan ◽  
A. R. Ganguly ◽  
S. Saigal
Keyword(s):  
Time Series ◽  
Predictive Modeling ◽  
Finite Time ◽  
Simulated Data ◽  
Chaotic Signal ◽  
Hydrologic Systems ◽  
Hydrologic Data ◽  
Streamflow Data ◽  
Simulated Time ◽  
Simulated Time Series

Abstract. The ability to detect the chaotic signal from a finite time series observation of hydrologic systems is addressed in this paper. The presence of random and seasonal components in hydrological time series, like rainfall or runoff, makes the detection process challenging. Tests with simulated data demonstrate the presence of thresholds, in terms of noise to chaotic-signal and seasonality to chaotic-signal ratios, beyond which the set of currently available tools is not able to detect the chaotic component. The investigations also indicate that the decomposition of a simulated time series into the corresponding random, seasonal and chaotic components is possible from finite data. Real streamflow data from the Arkansas and Colorado rivers are used to validate these results. Neither of the raw time series exhibits chaos. While a chaotic component can be extracted from the Arkansas data, such a component is either not present or can not be extracted from the Colorado data. This indicates that real hydrologic data may or may not have a detectable chaotic component. The strengths and limitations of the existing set of tools for the detection and modeling of chaos are also studied.

Daily streamflow prediction using an LSTM neural network in Alpine catchments

2020 ◽  
Author(s):  
Mohit Anand ◽  
Peter Molnar ◽  
Nadav Peleg
Keyword(s):  
Hydrological Model ◽  
Hydrological Processes ◽  
Subsurface Water ◽  
Rainfall Runoff ◽  
Streamflow Prediction ◽  
Temperature Dependent ◽  
Daily Streamflow ◽  
Highly Nonlinear ◽  
Streamflow Data ◽  
Alpine Catchments

<p>Prediction of rainfall-runoff response in Alpine catchments is complex because hydrological processes vary strongly in space and time, they are elevation and temperature dependent, subsurface water stores are heterogeneous, snow plays an important role, and runoff response is fast. As a result, the transformation of rainfall into runoff is highly nonlinear. Machine Learning (ML) methods are suitable for reproducing such nonlinearities between input and output data and have been used for streamflow prediction. Recurrent Neural Networks (RNNs) with memory states, such as Long and Short-Term Memory (LSTM) models, are particularly suitable for hydrological variables that are dependent in time. An example of a recent application of LSTM to the rainfall-runoff transformation in many catchments in the USA showed that the LSTM model can learn physically meaningful catchment embeddings from precipitation-temperature-streamflow data, and performs comparably to widely used conceptual hydrological models (Kratzert et al., 2019).</p><p>In this study, we tested the LSTM approach on high-quality daily data from 23 Alpine catchments in Switzerland with three goals in mind. First, the LSTM model was trained and validated using daily climate variables (precipitation, air temperature, sunshine duration) and streamflow data on all catchments individually and the performance was compared to a distributed hydrological model (PREVAH). The performance of the LSTM model was in many (but not in all) cases better than the hydrological model. Second, a single LSTM model was trained in all catchments simultaneously, embedding terrain attributes extracted from the Digital Elevation Model (DEM). In this way differences between catchments related to the elevation and temperature dependent hydrological processes, such as snow accumulation and melt, evapotranspiration, runoff generation, etc., can be captured. We show the performance of this model and evaluate the regionalization potential provided by it. Third, the LSTM model was applied in an ensemble forecasting context, and we discuss the benefits and limitations this application brings compared to forecasting with a process-based hydrological model.</p>

scholarly journals The role of climatic and terrain attributes in estimating baseflow recession in tropical catchments

2010 ◽  
Vol 14 (11) ◽  
pp. 2193-2205 ◽  
Author(s):  
J. L. Peña-Arancibia ◽  
A. I. J. M. van Dijk ◽  
M. Mulligan ◽  
L. A. Bruijnzeel
Keyword(s):  
Potential Evapotranspiration ◽  
Aridity Index ◽  
Global Scale ◽  
Tree Cover ◽  
Annual Rainfall ◽  
Ungauged Catchments ◽  
Ongoing Research ◽  
Daily Streamflow ◽  
Terrain Attributes ◽  
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.

scholarly journals Scale-dependent intrinsic entropies of complex time series

2016 ◽  
Vol 374 (2065) ◽  
pp. 20150204 ◽  
Author(s):  
Jia-Rong Yeh ◽  
Chung-Kang Peng ◽  
Norden E. Huang
Keyword(s):  
Time Series ◽  
Time Scales ◽  
Spatial Scales ◽  
Multiple Time Scales ◽  
Multiple Time ◽  
Complex Time ◽  
Multi Scale ◽  
Mode Decomposition ◽  
Simulated Time ◽  
Simulated Time Series

Multi-scale entropy (MSE) was developed as a measure of complexity for complex time series, and it has been applied widely in recent years. The MSE algorithm is based on the assumption that biological systems possess the ability to adapt and function in an ever-changing environment, and these systems need to operate across multiple temporal and spatial scales, such that their complexity is also multi-scale and hierarchical. Here, we present a systematic approach to apply the empirical mode decomposition algorithm, which can detrend time series on various time scales, prior to analysing a signal’s complexity by measuring the irregularity of its dynamics on multiple time scales. Simulated time series of fractal Gaussian noise and human heartbeat time series were used to study the performance of this new approach. We show that our method can successfully quantify the fractal properties of the simulated time series and can accurately distinguish modulations in human heartbeat time series in health and disease.

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