scholarly journals Picturing and modeling catchments by representative hillslopes

2017 ◽  
Vol 21 (2) ◽  
pp. 1225-1249 ◽  
Author(s):  
Ralf Loritz ◽  
Sibylle K. Hassler ◽  
Conrad Jackisch ◽  
Niklas Allroggen ◽  
Loes van Schaik ◽  
...  
Keyword(s):  
Expert Knowledge ◽  
Water Dynamics ◽  
Rainfall Runoff ◽  
Data Set ◽  
Physically Based Model ◽  
Optimal Representation ◽  
Physically Based ◽  
Physically Based Models ◽  
Streamflow Data ◽  
Runoff Events

Abstract. This study explores the suitability of a single hillslope as a parsimonious representation of a catchment in a physically based model. We test this hypothesis by picturing two distinctly different catchments in perceptual models and translating these pictures into parametric setups of 2-D physically based hillslope models. The model parametrizations are based on a comprehensive field data set, expert knowledge and process-based reasoning. Evaluation against streamflow data highlights that both models predicted the annual pattern of streamflow generation as well as the hydrographs acceptably. However, a look beyond performance measures revealed deficiencies in streamflow simulations during the summer season and during individual rainfall–runoff events as well as a mismatch between observed and simulated soil water dynamics. Some of these shortcomings can be related to our perception of the systems and to the chosen hydrological model, while others point to limitations of the representative hillslope concept itself. Nevertheless, our results confirm that representative hillslope models are a suitable tool to assess the importance of different data sources as well as to challenge our perception of the dominant hydrological processes we want to represent therein. Consequently, these models are a promising step forward in the search for the optimal representation of catchments in physically based models.

scholarly journals Rainfall-runoff modelling in a catchment with a complex groundwater flow system: application of the Representative Elementary Watershed (REW) approach

2005 ◽  
Vol 2 (3) ◽  
pp. 639-690 ◽  
Author(s):  
G. P. Zhang ◽  
H. H. G. Savenije
Keyword(s):  
River Basin ◽  
Overland Flow ◽  
Model Performance ◽  
Source Area ◽  
Subsurface Water ◽  
Watershed Hydrology ◽  
Rainfall Runoff ◽  
Data Set ◽  
Physically Based ◽  
Infiltration Excess

Abstract. Based on the Representative Elementary Watershed (REW) approach, the modelling tool REWASH (Representative Elementary WAterShed Hydrology) has been developed and applied to the Geer river basin. REWASH is deterministic, semi-distributed, physically based and can be directly applied to the watershed scale. In applying REWASH, the river basin is divided into a number of sub-watersheds, so called REWs, according to the Strahler order of the river network. REWASH describes the dominant hydrological processes, i.e. subsurface flow in the unsaturated and saturated domains, and overland flow by the saturation-excess and infiltration-excess mechanisms. Through flux exchanges among the different spatial domains of the REW, surface and subsurface water interactions are fully coupled. REWASH is a parsimonious tool for modelling watershed hydrological response. However, it can be modified to include more components to simulate specific processes when applied to a specific river basin where such processes are observed or considered to be dominant. In this study, we have added a new component to simulate interception using a simple parametric approach. Interception plays an important role in the water balance of a watershed although it is often disregarded. In addition, a refinement for the transpiration in the unsaturated zone has been made. Finally, an improved approach for simulating saturation overland flow by relating the variable source area to both the topography and the groundwater level is presented. The model has been calibrated and verified using a 4-year data set, which has been split into two for calibration and validation. The model performance has been assessed by multi-criteria evaluation. This work is the first full application of the REW approach to watershed rainfall-runoff modelling in a real watershed. The results demonstrate that the REW approach provides an alternative blueprint for physically based hydrological modelling.

scholarly journals Integrating remote sensing-derived evapotranspiration and ground-monitored discharge data for the improvement of hydrological modelling: A case study of the Sekong River Basin

2021 ◽  
Author(s):  
Raksmey Ang ◽  
S. Shrestha ◽  
Salvatore Virdis ◽  
Saurav KC
Keyword(s):  
Remote Sensing ◽  
Model Calibration ◽  
Hydrological Model ◽  
Hydrologic Model ◽  
Physically Based Model ◽  
Physically Based ◽  
Streamflow Data ◽  
The Impact ◽  
Streamflow Simulation ◽  
Calibration Approach

This study analyses the efficiency of integrating remotely sensed evapotranspiration into the process of hydrological model calibration. A joint calibration approach, employing both remote sensing-derived evapotranspiration and ground-monitored streamflow data was compared with a conventional ground-monitored streamflow calibration approach through physically-based hydrological, Soil and Water Assessment Tool (SWAT) model setups. The efficacy of the two calibration schemes was investigated in two modelling setups: 1) a physically-based model with only the outlet gauge available for calibration, and 2) a physically-based model with multiple gauges available for calibration. Joint calibration was found to enhance the skill of hydrological models in streamflow simulation compared to ground-monitored streamflow-only calibration at the unsaturated zone in the upstream area, where essential information on evapotranspiration is also required. Additionally, the use of remote sensing-derived evapotranspiration can significantly improve high flow compared to low flow simulation. A more consistent model performance improvement, obtained from using remote sensing-derived evapotranspiration data was found at gauged sites not used in the calibration, due to additional information on spatial evapotranspiration in internal locations being enhanced into a process-based model. Eventually, satellite-based evapotranspiration with fine resolution was found to be competent for calibrating and validating the hydrological model for streamflow simulation in the absence of measured streamflow data for model calibration. Furthermore, the impact of using evapotranspiration for hydrologic model calibration tended to be stronger at the upstream and tributary sub-basins than at downstream sub-basins.

scholarly journals Identifying rainfall-runoff events in discharge time series: A data-driven method based on Information Theory

2018 ◽  
Author(s):  
Stephanie Thiesen ◽  
Paul Darscheid ◽  
Uwe Ehret
Keyword(s):  
Predictive Power ◽  
Conditional Entropy ◽  
Data Driven ◽  
Rainfall Runoff ◽  
Discharge Time ◽  
Time Step ◽  
Data Set ◽  
Discharge Data ◽  
Previous Time Step ◽  
Runoff Events

Abstract. In this study, we propose a data-driven approach to automatically identify rainfall-runoff events in discharge time series. The core of the concept is to construct and apply discrete multivariate probability distributions to obtain probabilistic predictions of each time step being part of an event. The approach permits any data to serve as predictors, and it is non-parametric in the sense that it can handle any kind of relation between the predictor(s) and the target. Each choice of a particular predictor data set is equivalent to formulating a model hypothesis. Among competing models, the best is found by comparing their predictive power in a training data set with user-classified events. For evaluation, we use measures from Information Theory such as Shannon Entropy and Conditional Entropy to select the best predictors and models and, additionally, measure the risk of overfitting via Cross Entropy and Kullback–Leibler Divergence. As all these measures are expressed in bit, we can combine them to identify models with the best tradeoff between predictive power and robustness given the available data. We applied the method to data from the Dornbirnerach catchment in Austria distinguishing three different model types: Models relying on discharge data, models using both discharge and precipitation data, and recursive models, i.e., models using their own predictions of a previous time step as an additional predictor. In the case study, the additional use of precipitation reduced predictive uncertainty only by a small amount, likely because the information provided by precipitation is already contained in the discharge data. More generally, we found that the robustness of a model quickly dropped with the increase in the number of predictors used (an effect well known as the Curse of Dimensionality), such that in the end, the best model was a recursive one applying four predictors (three standard and one recursive): discharge from two distinct time steps, the relative magnitude of discharge in a 65-hour time window and event predictions from the previous time step. Applying the model reduced the uncertainty about event classification by 77.8 %, decreasing Conditional Entropy from 0.516 to 0.114 bits. Given enough data to build data-driven models, their potential lies in the way they learn and exploit relations between data unconstrained by functional or parametric assumptions and choices. And, beyond that, the use of these models to reproduce a hydrologist's way to identify rainfall-runoff events is just one of many potential applications.

scholarly journals Rainfall-runoff modelling in a catchment with a complex groundwater flow system: application of the Representative Elementary Watershed (REW) approach

2005 ◽  
Vol 9 (3) ◽  
pp. 243-261 ◽  
Author(s):  
G. P. Zhang ◽  
H. H. G. Savenije
Keyword(s):  
River Basin ◽  
Overland Flow ◽  
Subsurface Flow ◽  
Model Performance ◽  
Source Area ◽  
Watershed Hydrology ◽  
Rainfall Runoff ◽  
Data Set ◽  
Physically Based ◽  
Infiltration Excess

Abstract. Based on the Representative Elementary Watershed (REW) approach, the modelling tool REWASH (Representative Elementary WAterShed Hydrology) has been developed and applied to the Geer river basin. REWASH is deterministic, semi-distributed, physically based and can be directly applied to the watershed scale. In applying REWASH, the river basin is divided into a number of sub-watersheds, so called REWs, according to the Strahler order of the river network. REWASH describes the dominant hydrological processes, i.e. subsurface flow in the unsaturated and saturated domains, and overland flow by the saturation-excess and infiltration-excess mechanisms. The coupling of surface and subsurface flow processes in the numerical model is realised by simultaneous computation of flux exchanges between surface and subsurface domains for each REW. REWASH is a parsimonious tool for modelling watershed hydrological response. However, it can be modified to include more components to simulate specific processes when applied to a specific river basin where such processes are observed or considered to be dominant. In this study, we have added a new component to simulate interception using a simple parametric approach. Interception plays an important role in the water balance of a watershed although it is often disregarded. In addition, a refinement for the transpiration in the unsaturated zone has been made. Finally, an improved approach for simulating saturation overland flow by relating the variable source area to both the topography and the groundwater level is presented. The model has been calibrated and verified using a 4-year data set, which has been split into two for calibration and validation. The model performance has been assessed by multi-criteria evaluation. This work represents a complete application of the REW approach to watershed rainfall-runoff modelling in a real watershed. The results demonstrate that the REW approach provides an alternative blueprint for physically based hydrological modelling.

scholarly journals Identifying rainfall-runoff events in discharge time series: a data-driven method based on information theory

2019 ◽  
Vol 23 (2) ◽  
pp. 1015-1034 ◽  
Author(s):  
Stephanie Thiesen ◽  
Paul Darscheid ◽  
Uwe Ehret
Keyword(s):  
Predictive Power ◽  
Conditional Entropy ◽  
Data Driven ◽  
Rainfall Runoff ◽  
Time Step ◽  
Data Set ◽  
Discharge Data ◽  
Previous Time Step ◽  
Runoff Events

Abstract. In this study, we propose a data-driven approach for automatically identifying rainfall-runoff events in discharge time series. The core of the concept is to construct and apply discrete multivariate probability distributions to obtain probabilistic predictions of each time step that is part of an event. The approach permits any data to serve as predictors, and it is non-parametric in the sense that it can handle any kind of relation between the predictor(s) and the target. Each choice of a particular predictor data set is equivalent to formulating a model hypothesis. Among competing models, the best is found by comparing their predictive power in a training data set with user-classified events. For evaluation, we use measures from information theory such as Shannon entropy and conditional entropy to select the best predictors and models and, additionally, measure the risk of overfitting via cross entropy and Kullback–Leibler divergence. As all these measures are expressed in “bit”, we can combine them to identify models with the best tradeoff between predictive power and robustness given the available data. We applied the method to data from the Dornbirner Ach catchment in Austria, distinguishing three different model types: models relying on discharge data, models using both discharge and precipitation data, and recursive models, i.e., models using their own predictions of a previous time step as an additional predictor. In the case study, the additional use of precipitation reduced predictive uncertainty only by a small amount, likely because the information provided by precipitation is already contained in the discharge data. More generally, we found that the robustness of a model quickly dropped with the increase in the number of predictors used (an effect well known as the curse of dimensionality) such that, in the end, the best model was a recursive one applying four predictors (three standard and one recursive): discharge from two distinct time steps, the relative magnitude of discharge compared with all discharge values in a surrounding 65 h time window and event predictions from the previous time step. Applying the model reduced the uncertainty in event classification by 77.8 %, decreasing conditional entropy from 0.516 to 0.114 bits. To assess the quality of the proposed method, its results were binarized and validated through a holdout method and then compared to a physically based approach. The comparison showed similar behavior of both models (both with accuracy near 90 %), and the cross-validation reinforced the quality of the proposed model. Given enough data to build data-driven models, their potential lies in the way they learn and exploit relations between data unconstrained by functional or parametric assumptions and choices. And, beyond that, the use of these models to reproduce a hydrologist's way of identifying rainfall-runoff events is just one of many potential applications.

scholarly journals A Distributed Groundwater/Surface Water Model for the Suså-Catchment

1982 ◽  
Vol 13 (5) ◽  
pp. 311-322 ◽  
Author(s):  
Jens Chr Refsgaard ◽  
Eggert Hansen
Keyword(s):  
Surface Water ◽  
Hydrological Cycle ◽  
Water Model ◽  
Confined Aquifer ◽  
Groundwater Abstraction ◽  
Physically Based Model ◽  
Physical Mechanisms ◽  
Physically Based ◽  
Streamflow Data ◽  
Streamflow Depletion

A distributed physically based model of the entire land phase of the hydrological cycle has been developed for the Suså-area, covering about 1,000 km2 of Zealand, Denmark. The model is described in part I of the paper, also presented in this volume. The model's ability to simulate the streamflow depletion caused by a groundwater abstraction from a confined aquifer, overlaid by Quaternary drift deposits, has been tested on historical streamflow data from the catchment of Køge Å. This catchment has been heavily influenced by a major groundwater abstraction started in 1964. The physical mechanisms of streamflow depletion are discussed, and the effects of a groundwater abstraction on the recharge to the confined aquifer and on the streamflow are illustrated. General conclusions regarding streamflow depletion due to groundwater abstraction are made.

scholarly journals Impact of modellers' decisions on hydrological a priori predictions

2014 ◽  
Vol 18 (6) ◽  
pp. 2065-2085 ◽  
Author(s):  
H. M. Holländer ◽  
H. Bormann ◽  
T. Blume ◽  
W. Buytaert ◽  
G. B. Chirico ◽  
...  
Keyword(s):  
Initial Conditions ◽  
A Priori ◽  
Ungauged Catchments ◽  
Data Set ◽  
Additional Information ◽  
Hard Information ◽  
Process Understanding ◽  
Groundwater Response ◽  
Physically Based ◽  
Physically Based Models

Abstract. In practice, the catchment hydrologist is often confronted with the task of predicting discharge without having the needed records for calibration. Here, we report the discharge predictions of 10 modellers – using the model of their choice – for the man-made Chicken Creek catchment (6 ha, northeast Germany, Gerwin et al., 2009b) and we analyse how well they improved their prediction in three steps based on adding information prior to each following step. The modellers predicted the catchment's hydrological response in its initial phase without having access to the observed records. They used conceptually different physically based models and their modelling experience differed largely. Hence, they encountered two problems: (i) to simulate discharge for an ungauged catchment and (ii) using models that were developed for catchments, which are not in a state of landscape transformation. The prediction exercise was organized in three steps: (1) for the first prediction the modellers received a basic data set describing the catchment to a degree somewhat more complete than usually available for a priori predictions of ungauged catchments; they did not obtain information on stream flow, soil moisture, nor groundwater response and had therefore to guess the initial conditions; (2) before the second prediction they inspected the catchment on-site and discussed their first prediction attempt; (3) for their third prediction they were offered additional data by charging them pro forma with the costs for obtaining this additional information. Holländer et al. (2009) discussed the range of predictions obtained in step (1). Here, we detail the modeller's assumptions and decisions in accounting for the various processes. We document the prediction progress as well as the learning process resulting from the availability of added information. For the second and third steps, the progress in prediction quality is evaluated in relation to individual modelling experience and costs of added information. In this qualitative analysis of a statistically small number of predictions we learned (i) that soft information such as the modeller's system understanding is as important as the model itself (hard information), (ii) that the sequence of modelling steps matters (field visit, interactions between differently experienced experts, choice of model, selection of available data, and methods for parameter guessing), and (iii) that added process understanding can be as efficient as adding data for improving parameters needed to satisfy model requirements.

scholarly journals Integration of Demand Response and Photovoltaic Resources in Residential Segments

2018 ◽  
Vol 10 (9) ◽  
pp. 3030 ◽  
Author(s):  
Ana García-Garre ◽  
Antonio Gabaldón ◽  
Carlos Álvarez-Bel ◽  
María Ruiz-Abellón ◽  
Antonio Guillamón
Keyword(s):  
Demand Response ◽  
Electricity Markets ◽  
Renewable Sources ◽  
Physically Based Model ◽  
Energy Scenario ◽  
Physically Based ◽  
Physically Based Models ◽  
Final Energy Consumption ◽  
Demand Patterns ◽  
Consumption Demand

The development of renewable sources in residential segments is basic to achieve a sustainable energy scenario in the horizon 2030–2050 because these segments explain around 25% of the final energy consumption. Demand Response and its effective coordination with renewable are additional concerns for residential segments. This paper deals with two problems: the demonstration of cost-effectiveness of renewables in three different scenarios, and the application of the flexibility of demand, performing as energy storage systems, to efficiently manage the generation of renewable sources while improving benefits and avoiding penalties for the customer. A residential customer in Spain has been used as example. The work combines the use of a commercial simulator to obtain photovoltaic generation, the monitoring of customer to obtain demand patterns, and the development of a Physically-Based Model to evaluate the capability of demand to follow self-generation. As a main result, the integration of models (load/generation), neglected in practice in other approaches in the literature, allows customers to improve revenue up to 20% and reach a basic but important knowledge on how they can modify the demand, development of new skills and, in this way, learn how to deal with the characteristics and limitations of both Demand and Generation when a customer becomes a prosumer. This synergy amongst demand and generation physically-based models boosts the possibilities of customers in electricity markets.

scholarly journals KOHONEN NEURAL NETWORKS FOR RAINFALL-RUNOFF MODELING: CASE STUDY OF PIANCÓ RIVER BASIN

2013 ◽  
pp. 176-182 ◽  
Author(s):  
Camilo Allyson Simoes de Farias ◽  
Celso A. G. Santos ◽  
Artur M. G. Lourenço ◽  
Tatiane C. Carneiro
Keyword(s):  
Neural Networks ◽  
River Basin ◽  
Input Data ◽  
Missing Values ◽  
Rainfall Runoff ◽  
Data Set ◽  
Runoff Modeling ◽  
Proposed Model ◽  
Kohonen Neural Networks ◽  
Streamflow Data

The existence of long and reliable streamflow data records is essential to establishing strategies for the operation of water resources systems. In areas where streamflow data records are limited or present missing values, rainfall-runoff models are typically used for reconstruction and/or extension of river flow series. The main objective of this paper is to verify the application of Kohonen Neural Networks (KNN) for estimating streamflows in Piancó River. The Piancó River basin is located in the Brazilian semiarid region, an area devoid of hydrometeorological data and characterized by recurrent periods of water scarcity. The KNN are unsupervised neural networks that cluster data into groups according to their similarities. Such models are able to classify data vectors even when there are missing values in some of its components, a very common situation in rainfall-runoff modeling. Twenty two years of rainfall and streamflow monthly data were used in order to calibrate and test the proposed model. Statistical indexes were chose as criteria for evaluating the performance of the KNN model under four different scenarios of input data. The results show that the proposed model was able to provide reliable estimations even when there were missing values in the input data set.

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