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

Elevation gaps in fluvial sandbar deposition and their implications for paleodepth estimation

Geology ◽  
2020 ◽  
Vol 48 (7) ◽  
pp. 718-722
Author(s):  
Jason S. Alexander ◽  
Brandon J. McElroy ◽  
Snehalata Huzurbazar ◽  
Marissa L. Murr
Keyword(s):  
Large Data ◽  
Accurate Estimation ◽  
Systematic Bias ◽  
Flow Depth ◽  
Mean Flow ◽  
Data Set ◽  
Bed Elevation ◽  
The Difference ◽  
Base Elevation ◽  
Thickness Measurements

Abstract Accurate estimation of paleo–streamflow depth from outcrop is important for estimation of channel slopes, water discharges, sediment fluxes, and basin sizes of ancient river systems. Bar-scale inclined strata deposited from slipface avalanching on fluvial bar margins are assumed to be indicators of paleodepth insofar as their thickness approaches but does not exceed formative flow depths. We employed a unique, large data set from a prolonged bank-filling flood in the sandy, braided Missouri River (USA) to examine scaling between slipface height and measures of river depth during the flood. The analyses demonstrated that the most frequent slipface height observations underestimate study-reach mean flow depth at peak stage by a factor of 3, but maximum values are approximately equal to mean flow depth. At least 70% of the error is accounted for by the difference between slipface base elevation and mean bed elevation, while the difference between crest elevation and water surface accounts for ∼30%. Our analysis provides a scaling for bar-scale inclined strata formed by avalanching and suggests risk of systematic bias in paleodepth estimation if mean thickness measurements of these deposits are equated to mean bankfull depth.

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 Modelling field scale water partitioning using on-site observations in sub-Saharan rainfed agriculture

2010 ◽  
Vol 14 (4) ◽  
pp. 627-638 ◽  
Author(s):  
H. Makurira ◽  
H. H. G. Savenije ◽  
S. Uhlenbrook
Keyword(s):  
Soil Moisture ◽  
Water Balance ◽  
Crop Growth ◽  
Deep Percolation ◽  
Farming Practices ◽  
Field Scale ◽  
Runoff Water ◽  
Rainfed Farming ◽  
Dry Spell ◽  
Water Partitioning

Abstract. Smallholder rainfed farming systems generally realise sub-optimal crop yields which are largely attributed to dry spell occurrences during crop growth stages. However, through the introduction of appropriate farming practices, it is possible to substantially increase yield levels even with little and highly variable rainfall. The presented results follow research conducted in the Makanya catchment in northern Tanzania where gross rainfall amounts to less than 400 mm/season which is insufficient to support staple food crops (e.g. maize). The yields from farming system innovations (SIs), which are basically alternative cultivation techniques, are compared against traditional farming practices. The SIs tested in this research are runoff harvesting used in combination with in-field trenches and soil bunds (fanya juus). These SIs aim to reduce soil and nutrient loss from the field and, more importantly, promote in-field infiltration and water retention. Water balance components have been observed in order to study water partitioning processes for the "with" and "without" SI scenarios. Based on rainfall, soil evaporation, transpiration, runoff and soil moisture measurements, a water balance model has been developed to simulate soil moisture variations over the growing season. Simulation results show that, during the field trials, the average productive transpiration flow ranged between 1.1–1.4 mm d−1 in the trial plots compared to 0.7–1.0 mm d−1 under traditional tillage practice. Productive transpiration processes accounted for 23–29% while losses to deep percolation accounted for 33–48% of the available water. The field system has been successfully modelled using the spreadsheet-based water balance 1-D model. Conclusions from the research are that the SIs that were tested are effective in enhancing soil moisture retention at field scale and that diversions allow crop growth moisture conditions to be attained with early rains. From the partitioning analysis, it is also concluded that there is more scope for efficient utilisation of the diverted runoff water if storage structures could be installed to minimise runoff and deep percolation and, hence, regulate water flow to the root zone when required.

A Long-Term Land Surface Hydrologic Fluxes and States Dataset for China

2014 ◽  
Vol 15 (5) ◽  
pp. 2067-2084 ◽  
Author(s):  
Xue-Jun Zhang ◽  
Qiuhong Tang ◽  
Ming Pan ◽  
Yin Tang
Keyword(s):  
Soil Moisture ◽  
Water Balance ◽  
Snow Cover ◽  
Land Surface ◽  
Hydrologic Model ◽  
Reliable Estimate ◽  
Time Step ◽  
Snow Cover Extent ◽  
Monthly Streamflow

Abstract A long-term consistent and comprehensive dataset of land surface hydrologic fluxes and states will greatly benefit the analysis of land surface variables, their changes and interactions, and the assessment of land–atmosphere parameterizations for climate models. While some offline model studies can provide balanced water and energy budgets at land surface, few of them have presented an evaluation of the long-term interaction of water balance components over China. Here, a consistent and comprehensive land surface hydrologic fluxes and states dataset for China using the Variable Infiltration Capacity (VIC) hydrologic model driven by long-term gridded observation-based meteorological forcings is developed. The hydrologic dataset covers China with a 0.25° spatial resolution and a 3-hourly time step for 1952–2012. In the dataset, the simulated streamflow matches well with the observed monthly streamflow at the large river basins in China. Given the water balance scheme in the VIC model, the overall success at runoff simulations suggests that the long-term mean evapotranspiration is also realistically estimated. The simulated soil moisture generally reproduces the seasonal variation of the observed soil moisture at the ground stations where long-term observations are available. The modeled snow cover patterns and monthly dynamics bear an overall resemblance to the Northern Hemisphere snow cover extent data from the National Snow and Ice Data Center. Compared with global product of a similar nature, the dataset can provide a more reliable estimate of land surface variables over China. The dataset, which will be publicly available via the Internet, may be useful for hydroclimatological studies in China.

scholarly journals Historical gridded reconstruction of potential evapotranspiration for the UK

2018 ◽  
Author(s):  
Maliko Tanguy ◽  
Christel Prudhomme ◽  
Katie Smith ◽  
Jamie Hannaford
Keyword(s):  
Input Data ◽  
Potential Evapotranspiration ◽  
Temperature Data ◽  
Accurate Estimation ◽  
Polynomial Method ◽  
Minor Effect ◽  
Hydrological Models ◽  
Time Step ◽  
Naive Method ◽  
The Uk

Abstract. Potential Evapotranspiration (PET) is a necessary input data for most hydrological models and is usually needed at a daily or shorter time-step. An accurate estimation of PET requires many input climate variables which are in most cases not available prior to the 1960s for the UK, nor indeed most parts of the world. Therefore, when applying hydrological models for reconstructing earlier periods, modellers have to rely on PET estimation derived from simplified methods. Given that only monthly observed temperature data is readily available for the late 19th and early 20th century at a national scale for the UK, the objective of this work was to derive the best possible UK-wide gridded PET dataset with the limited data available. To that end, firstly, a combination of (i) seven temperature-based PET equations, (ii) four different calibration approaches and (iii) seven input temperature data were evaluated against a gridded daily PET product based on the physically-based Penman-Monteith equation (the CHESS PET dataset), the rationale being that this provides a reliable ground-truth PET dataset for evaluation purposes, given that no directly observed, distributed PET datasets exist. The performance of the models was also compared to the simplest possible estimation of PET (naïve method) in the absence of any available climate data, the CHESS PET daily long term average (the period from 1961 to 1990 was chosen for this study), or CHESS-PET daily climatology. The analysis revealed that the type of calibration and the input temperature dataset had only a minor effect in the accuracy of the PET estimations at catchment scale. From the seven equations tested, only the calibrated version of the McGuinness-Bordne equation was able to outperform the naïve method and was therefore used to derive the gridded, reconstructed dataset. The equation was calibrated using 43 catchments across Great Britain. The dataset produced is a 5-km gridded PET dataset for the period 1891 to 2015, using as input data for the PET equation the Met Office 5-km monthly gridded temperature data available for that time period, which was disaggregated to daily temperature using pchip (piecewise cubic hermite interpolating polynomial) method. The dataset includes daily and monthly PET grids and is complemented with a suite of mapped performance metrics to help users assess the quality of the data spatially. The data can be accessed here: https://doi.org/10.5285/17b9c4f7-1c30-4b6f-b2fe-f7780159939c.

scholarly journals Satellite soil moisture data assimilation for improved operational continental water balance prediction

2020 ◽  
Author(s):  
Siyuan Tian ◽  
Luigi J. Renzullo ◽  
Robert C. Pipunic ◽  
Julien Lerat ◽  
Wendy Sharples ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Data Assimilation ◽  
Water Balance ◽  
Surface Soil ◽  
Root Zone ◽  
In Situ Observation ◽  
Surface Soil Moisture ◽  
Time Step ◽  
Australian Water

Abstract. A simple and effective two-step data assimilation framework was developed to improve soil moisture representation in an operational large-scale water balance model. The first step is the sequential state updating process that exploits temporal covariance statistics between modelled and satellite-derived soil moisture to produce analysed estimates. The second step is to use analysed surface moisture estimates to impart mass conservation constraints (mass redistribution) on related states and fluxes of the model in a post-analysis adjustment after the state updating at each time step. In this study, we apply the data assimilation framework to the Australian Water Resources Assessment Landscape model (AWRA-L) and evaluate its impact on the model's accuracy against in-situ observations across water balance components. We show that the correlation between simulated surface soil moisture and in-situ observation increases from 0.54 (open-loop) to 0.77 (data assimilation). Furthermore, indirect verification of root-zone soil moisture using remotely sensed vegetation time series across cropland areas results in significant improvements of 0.11 correlation units. The improvements gained from data assimilation can persist for more than one week in surface soil moisture estimates and one month in root-zone soil moisture estimates, thus demonstrating the efficacy of this data assimilation framework.

scholarly journals Neural network estimation of the scour cone geometry around outlet in the pressure flushing

2013 ◽  
Vol 14 (4) ◽  
pp. 540-549
Keyword(s):  
Neural Network ◽  
Flow Velocity ◽  
Three Dimensional ◽  
Accurate Estimation ◽  
Mean Flow ◽  
Data Set ◽  
Network Estimation ◽  
Artificial Neural Network Ann ◽  
Bp Model ◽  
Mean Flow Velocity

When flushing carry out as pressure condition, a scour cone is performed around the outlet. As the flow around the outlet in the pressure flushing is three dimensional, therefore that it is difficult to establish a general empirical model to provide accurate estimation for scour cone volume and length. In this study artificial neural network (ANN) with multi-layer perception which using backpropagation algorithm (MLP/BP) was used. The scour cone volume (Vf) and length (Lf) were modeled as a function of three variables; water depth (HW), mean flow velocity through outlet (uf) and mean grain diameter (D50). For training and testing model, experimental data in two forms of original and non-dimensional are selected. The results of this research indicate that MLP/BP model can predict the scour cone volume and length. Finally, sensitivity analysis with original and nondimensional data set show that mean flow velocity through outlet (uf) and uf / √(g (Gs-1)D50) have a greater influence on scour cone volume and length rather than other parameters.

scholarly journals Satellite soil moisture data assimilation for improved operational continental water balance prediction

2021 ◽  
Vol 25 (8) ◽  
pp. 4567-4584
Author(s):  
Siyuan Tian ◽  
Luigi J. Renzullo ◽  
Robert C. Pipunic ◽  
Julien Lerat ◽  
Wendy Sharples ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Data Assimilation ◽  
Water Balance ◽  
Surface Soil ◽  
Root Zone ◽  
In Situ Observation ◽  
Surface Soil Moisture ◽  
Time Step ◽  
Australian Water

Abstract. A simple and effective two-step data assimilation framework was developed to improve soil moisture representation in an operational large-scale water balance model. The first step is a Kalman-filter-type sequential state updating process that exploits temporal covariance statistics between modelled and satellite-derived soil moisture to produce analysed estimates. The second step is to use analysed surface moisture estimates to impart mass conservation constraints (mass redistribution) on related states and fluxes of the model using tangent linear modelling theory in a post-analysis adjustment after the state updating at each time step. In this study, we assimilate satellite soil moisture retrievals from both Soil Moisture Active Passive (SMAP) and Soil Moisture and Ocean Salinity (SMOS) missions simultaneously into the Australian Water Resources Assessment Landscape model (AWRA-L) using the proposed framework and evaluate its impact on the model's accuracy against in situ observations across water balance components. We show that the correlation between simulated surface soil moisture and in situ observation increases from 0.54 (open loop) to 0.77 (data assimilation). Furthermore, indirect verification of root-zone soil moisture using remotely sensed Enhanced Vegetation Index (EVI) time series across cropland areas results in significant improvements from 0.52 to 0.64 in correlation. The improvements gained from data assimilation can persist for more than 1 week in surface soil moisture estimates and 1 month in root-zone soil moisture estimates, thus demonstrating the efficacy of this data assimilation framework.

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