scholarly journals Fluxes from soil moisture measurements (FluSM v1.0): a data-driven water balance framework for permeable pavements

2021 ◽  
Vol 14 (4) ◽  
pp. 2127-2142
Author(s):  
Axel Schaffitel ◽  
Tobias Schuetz ◽  
Markus Weiler
Keyword(s):  
Soil Moisture ◽  
Water Balance ◽  
Input Parameter ◽  
Infiltration Rate ◽  
Data Driven ◽  
Minor Role ◽  
Water Fluxes ◽  
Parameter Uncertainties ◽  
Data Set ◽  
Permeable Pavements

Abstract. Water fluxes at the soil–atmosphere interface are a key piece of information for studying the terrestrial water cycle. However, measuring and modeling water fluxes in the vadose zone poses great challenges. While direct measurements require costly lysimeters, common soil hydrologic models rely on a correct parametrization, a correct representation of the involved processes, and the selection of correct initial and boundary conditions. In contrast to lysimeter measurements, soil moisture measurements are relatively cheap and easy to perform. Using such measurements, data-driven approaches offer the possibility to derive water fluxes directly. Here we present FluSM (fluxes from soil moisture measurements), which is a simple, parsimonious and robust data-driven water balancing framework. FluSM requires only a single input parameter (the infiltration rate) and is especially valuable for cases where the application of Richards-based models is critical. Since permeable pavements (PPs) present such a case, we apply FluSM on a recently published soil moisture data set to obtain the water balance of 15 different PPs over a period of 2 years. Consistent with findings from previous studies, our results show that vertical drainage dominates the water balance of PPs, while surface runoff plays only a minor role. An additional uncertainty analysis demonstrates the ability of the FluSM-approach for water balance studies, since input and parameter uncertainties only have a small effect on the characteristics of the derived water balances. Due to the lack of data on the hydrologic behavior of PPs under field conditions, our results are of special interest for urban hydrology.

scholarly journals Fluxes from Soil Moisture Measurements (FluSM v1.0). A Data-driven Water Balance Framework for Permeable Pavements

2020 ◽  
Author(s):  
Axel Schaffitel ◽  
Tobias Schuetz ◽  
Markus Weiler
Keyword(s):  
Soil Moisture ◽  
Water Balance ◽  
Water Cycle ◽  
Input Parameter ◽  
Data Driven ◽  
Minor Role ◽  
Water Fluxes ◽  
Parameter Uncertainties ◽  
Infiltration Capacity ◽  
Permeable Pavements

Abstract. Water fluxes at the soil-atmosphere interface are a key information for studying the terrestrial water cycle. However, measuring and modelling water fluxes in the vadose zone poses great challenges. While direct measurements require costly lysimeters, common soil hydrologic models rely on a correct parametrization, a correct representation of the involved processes and on the selection of correct initial and boundary conditions. In contrast to lysimeter measurements, soil moisture measurements are relatively cheap and easy to perform. Using such measurements, data-driven approaches offer the possibility to derive water fluxes directly. Here we present FluSM (Fluxes from Soil Moisture measurements), which is a simple, parsimonious and robust data-driven water balancing framework. FluSM requires only one single input parameter (the infiltration capacity) and is especially valuable for cases where the application of Richards based models is critical. Since Permeable Pavements (PPs) present such a case, we apply FluSM on a recently published soil moisture dataset to obtain the water balance of 15 different PPs over a period of two years. Consistent with findings from previous studies, our results show that vertical drainage dominates the water balance of PPs, while surface runoff plays only a minor role. An additional uncertainty analysis demonstrates the ability of the FluSM-approach for water balance studies, since input and parameter uncertainties have only small effects on the characteristics of the derived water balances. Due to the lack of data on the hydrologic behavior of PPs under field conditions, our results are of special interest for urban hydrology.

scholarly journals A new approach to estimating Mean Flow in the UK

2002 ◽  
Vol 6 (4) ◽  
pp. 709-720 ◽  
Author(s):  
M. G. R. Holmes ◽  
A. R. Young ◽  
A. Gustard ◽  
R. Grew
Keyword(s):  
Soil Moisture ◽  
Water Balance ◽  
Accurate Estimation ◽  
Mean Flow ◽  
Time Step ◽  
Runoff Water ◽  
Data Set ◽  
Water Balance Models ◽  
The Uk ◽  
Drying Curve

Abstract. Traditionally, the estimation of Mean Flow (MF) in ungauged catchments has been approached using conceptual water balance models or empirical formulae relating climatic inputs to stream flow. In the UK, these types of models have difficulty in predicting MF in low rainfall areas because the conceptualisation of soil moisture behaviour and its relationship with evaporation rates used is rather simplistic. However, it is in these dry regions where the accurate estimation of flows is most critical to effective management of a scarce resource. A novel approach to estimating MF, specifically designed to improve estimation of runoff in dry catchments, has been developed using a regionalisation of the Penman drying curve theory. The dynamic water balance style Daily Soil Moisture Accounting (DSMA) model operates at a daily time step, using inputs of precipitation and potential evaporation and simulates the development of soil moisture deficits explicitly. The model has been calibrated using measured MFs from a large data set of catchments in the United Kingdom. The performance of the DSMA model is superior to existing established steady state and dynamic water-balance models over the entire data set considered and the largest improvement is observed in very low rainfall catchments. It is concluded that the performance of all models in high rainfall areas is likely to be limited by the spatial representation of rainfall. Keywords: hydrological models, regionalisation, water resources, mean flow, runoff, water balance, Penman drying curve, soil moisture model

scholarly journals Unravelling abiotic and biotic controls on the seasonal water balance using data-driven dimensionless diagnostics

2017 ◽  
Vol 21 (6) ◽  
pp. 2817-2841 ◽  
Author(s):  
Simon Paul Seibert ◽  
Conrad Jackisch ◽  
Uwe Ehret ◽  
Laurent Pfister ◽  
Erwin Zehe
Keyword(s):  
Hydraulic Conductivity ◽  
Water Balance ◽  
Data Driven ◽  
Hydrological Process ◽  
Runoff Generation ◽  
Data Set ◽  
Runoff Formation ◽  
Topographic Gradient ◽  
Winter Runoff ◽  
Seasonal Runoff

Abstract. The baffling diversity of runoff generation processes, alongside our sketchy understanding of how physiographic characteristics control fundamental hydrological functions of water collection, storage, and release, continue to pose major research challenges in catchment hydrology. Here, we propose innovative data-driven diagnostic signatures for overcoming the prevailing status quo in catchment inter-comparison. More specifically, we present dimensionless double mass curves (dDMC) which allow inference of information on runoff generation and the water balance at the seasonal and annual timescales. By separating the vegetation and winter periods, dDMC furthermore provide information on the role of biotic and abiotic controls in seasonal runoff formation. A key aspect we address in this paper is the derivation of dimensionless expressions of fluxes which ensure the comparability of the signatures in space and time. We achieve this by using the limiting factors of a hydrological process as a scaling reference. We show that different references result in different diagnostics. As such we define two kinds of dDMC which allow us to derive seasonal runoff coefficients and to characterize dimensionless streamflow release as a function of the potential renewal rate of the soil storage. We expect these signatures for storage controlled seasonal runoff formation to remain invariant, as long as the ratios of release over supply and supply over storage capacity develop similarly in different catchments. We test the proposed methods by applying them to an operational data set comprising 22 catchments (12–166 km2) from different environments in southern Germany and hydrometeorological data from 4 hydrological years. The diagnostics are used to compare the sites and to reveal the dominant controls on runoff formation. The key findings are that dDMC are meaningful signatures for catchment runoff formation at the seasonal to annual scale and that the type of scaling strongly influences the diagnostic potential of the dDMC. Adding discrimination between growing season and winter period was of fundamental importance and easy to implement by means of a temperature-index model. More specifically, temperature aggregates explain over 70 % of the variability of the seasonal summer runoff coefficients. The results also show that the soil topographic index, i.e. the product of topographic gradient and saturated hydraulic conductivity, is significantly correlated with winter runoff coefficients, whereas the topographic gradient and the hydraulic conductivity alone are not. We conclude that proxies for gradients and resistances should be interpreted as a pair. Lastly, the dDMC concept reveals memory effects between summer and winter runoff regimes that are not relevant in spring between the transition from winter to summer.

scholarly journals Predicting the infiltration characteristics for semi-arid regions using regression trees

2021 ◽  
Author(s):  
Parveen Sihag ◽  
Munish Kumar ◽  
Saad Sh. Sammen
Keyword(s):  
Input Parameter ◽  
Regression Tree ◽  
Soil Surface ◽  
Infiltration Rate ◽  
Absolute Error ◽  
Coefficient Of Determination ◽  
Infiltration Process ◽  
Data Set ◽  
Cumulative Infiltration ◽  
Tree Methods

Abstract The study of infiltration process is considered as essential and necessary for all hydrology studies. Therefore, accurate predictions of infiltration characteristics are required to understand the behavior of subsurface flow of water through the soil surface. The aim of the current study is to simulate and improve the prediction accuracy of infiltration rate and cumulative infiltration of soil using regression tree methods. Experimental data recorded with a double ring infiltrometer for 17 different sites are used in this study. Three regression tree methods: Random tree, Random forest (RF) and M5 tree are employed to modelling the infiltration characteristics using the basic soil characteristics. The performance of the modelling approaches is compared in predicting the infiltration rate as well as cumulative infiltration, obtained results suggest that performance of RF model is better than other applied models with coefficient of determination (R2) = 0.97 & 0.97, root mean square error (RMSE) = 8.10 & 6.96 and mean absolute error (MAE) = 5.74 & 4.44 for infiltration rate and cumulative infiltration respectively. RF model is used to represent the infiltration characteristics of the study area. Moreover, parametric sensitivity is adopted to study the significance of each input parameter in estimating the infiltration process. Results suggest that time (t) is the most influencing parameter in predicting the infiltration process using this data set.

A Review and evaluation of Machine Translation methods for Lumasaaba

2020 ◽  
pp. 3-17
Author(s):  
Peter Nabende
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Machine Translation ◽  
Language Processing ◽  
Research Area ◽  
Data Driven ◽  
East African ◽  
Data Set ◽  
African Languages ◽  
Translation Methods

Natural Language Processing for under-resourced languages is now a mainstream research area. However, there are limited studies on Natural Language Processing applications for many indigenous East African languages. As a contribution to covering the current gap of knowledge, this paper focuses on evaluating the application of well-established machine translation methods for one heavily under-resourced indigenous East African language called Lumasaaba. Specifically, we review the most common machine translation methods in the context of Lumasaaba including both rule-based and data-driven methods. Then we apply a state of the art data-driven machine translation method to learn models for automating translation between Lumasaaba and English using a very limited data set of parallel sentences. Automatic evaluation results show that a transformer-based Neural Machine Translation model architecture leads to consistently better BLEU scores than the recurrent neural network-based models. Moreover, the automatically generated translations can be comprehended to a reasonable extent and are usually associated with the source language input.

scholarly journals A Data-Driven Surrogate Approach for the Temporal Stability Forecasting of Vegetation Covered Dikes

Water ◽  
2021 ◽  
Vol 13 (1) ◽  
pp. 107
Author(s):  
Elahe Jamalinia ◽  
Faraz S. Tehrani ◽  
Susan C. Steele-Dunne ◽  
Philip J. Vardon
Keyword(s):  
Numerical Simulation ◽  
Water Flux ◽  
Temporal Stability ◽  
Synthetic Data ◽  
Climatic Conditions ◽  
Training Data ◽  
Data Driven ◽  
Data Set ◽  
Surface Cracking ◽  
Real Time Analysis

Climatic conditions and vegetation cover influence water flux in a dike, and potentially the dike stability. A comprehensive numerical simulation is computationally too expensive to be used for the near real-time analysis of a dike network. Therefore, this study investigates a random forest (RF) regressor to build a data-driven surrogate for a numerical model to forecast the temporal macro-stability of dikes. To that end, daily inputs and outputs of a ten-year coupled numerical simulation of an idealised dike (2009–2019) are used to create a synthetic data set, comprising features that can be observed from a dike surface, with the calculated factor of safety (FoS) as the target variable. The data set before 2018 is split into training and testing sets to build and train the RF. The predicted FoS is strongly correlated with the numerical FoS for data that belong to the test set (before 2018). However, the trained model shows lower performance for data in the evaluation set (after 2018) if further surface cracking occurs. This proof-of-concept shows that a data-driven surrogate can be used to determine dike stability for conditions similar to the training data, which could be used to identify vulnerable locations in a dike network for further examination.

scholarly journals Estimating surface runoff and groundwater recharge in an urban catchment using a water balance approach

2021 ◽  
Vol 29 (7) ◽  
pp. 2411-2428
Author(s):  
Robin K. Weatherl ◽  
Maria J. Henao Salgado ◽  
Maximilian Ramgraber ◽  
Christian Moeck ◽  
Mario Schirmer
Keyword(s):  
Soil Moisture ◽  
Water Balance ◽  
Groundwater Recharge ◽  
Surface Runoff ◽  
Urban Areas ◽  
Water Cycle ◽  
Land Use Changes ◽  
Curve Number ◽  
Hydrograph Separation ◽  
Balance Approach

AbstractLand-use changes often have significant impact on the water cycle, including changing groundwater/surface-water interactions, modifying groundwater recharge zones, and increasing risk of contamination. Surface runoff in particular is significantly impacted by land cover. As surface runoff can act as a carrier for contaminants found at the surface, it is important to characterize runoff dynamics in anthropogenic environments. In this study, the relationship between surface runoff and groundwater recharge in urban areas is explored using a top-down water balance approach. Two empirical models were used to estimate runoff: (1) an updated, advanced method based on curve number, followed by (2) bivariate hydrograph separation. Modifications were added to each method in an attempt to better capture continuous soil-moisture processes and explicitly account for runoff from impervious surfaces. Differences between the resulting runoff estimates shed light on the complexity of the rainfall–runoff relationship, and highlight the importance of understanding soil-moisture dynamics and their control on hydro(geo)logical responses. These results were then used as input in a water balance to calculate groundwater recharge. Two approaches were used to assess the accuracy of these groundwater balance estimates: (1) comparison to calculations of groundwater recharge using the calibrated conceptual HBV Light model, and (2) comparison to groundwater recharge estimates from physically similar catchments in Switzerland that are found in the literature. In all cases, recharge is estimated at approximately 40–45% of annual precipitation. These conditions were found to closely echo those results from Swiss catchments of similar characteristics.

scholarly journals Diffuse approximation for identification of the mechanical properties of microcapsules

2020 ◽  
pp. 108128652097760
Author(s):  
Carlos Quesada ◽  
Claire Dupont ◽  
Pierre Villon ◽  
Anne-Virginie Salsac
Keyword(s):  
Mechanical Properties ◽  
Active Material ◽  
Liquid Core ◽  
Constitutive Law ◽  
Data Driven ◽  
Membrane Properties ◽  
Mechanical Resistance ◽  
Data Set ◽  
Diffuse Approximation ◽  
Capsule Size

A novel data-driven real-time procedure based on diffuse approximation is proposed to characterize the mechanical behavior of liquid-core microcapsules from their deformed shape and identify the mechanical properties of the submicron-thick membrane that protects the inner core through inverse analysis. The method first involves experimentally acquiring the deformed shape that a given microcapsule takes at steady state when it flows through a microfluidic microchannel of comparable cross-sectional size. From the mid-plane capsule profile, we deduce two characteristic geometric quantities that uniquely characterize the shape taken by the microcapsule under external hydrodynamic stresses. To identify the values of the unknown rigidity of the membrane and of the size of the capsule, we compare the geometric quantities with the values predicted numerically using a fluid-structure-interaction model by solving the three-dimensional capsule-flow interactions. The complete numerical data set is obtained off-line by systematically varying the governing parameters of the problem, i.e. the capsule-to-tube confinement ratio, and the capillary number, which is the ratio of the viscous to elastic forces. We show that diffuse approximation efficiently estimates the unknown mechanical resistance of the capsule membrane. We validate the data-driven procedure by applying it to the geometric and mechanical characterization of ovalbumin microcapsules (diameter of the order of a few tens of microns). As soon as the capsule is sufficiently deformed to exhibit a parachute shape at the rear, the capsule size and surface shear modulus are determined with an accuracy of 0.2% and 2.7%, respectively, as compared with 2–3% and 25% without it, in the best cases (Hu et al. Characterizing the membrane properties of capsules flowing in a square-section microfluidic channel: Effects of the membrane constitutive law. Phys Rev E 2013; 87(6): 063008). Diffuse approximation thus allows the capsule size and membrane elastic resistance to be provided quasi-instantly with very high precision. This opens interesting perspectives for industrial applications that require tight control of the capsule mechanical properties in order to secure their behavior when they transport active material.

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