A Novel Physically Based Distributed Model for Irrigation Districts’ Water Movement

The water movement research in irrigation districts is important for food production. Many hydrological models have been proposed to simulate the water movement on the regional scale, yet few of them have comprehensively considered processes in the irrigation districts. A novel physically based distributed model, the Irrigation Districts Model (IDM), was constructed in this study to address this problem. The model combined the 1D canal and ditch flow, the 1D soil water movement, the 2D groundwater movement, and the water interactions among these processes. It was calibrated and verified with two-year experimental data from Shahaoqu Sub-Irrigation Area in Hetao Irrigation District. The overall water balance error is 2.9% and 1.6% for the two years, respectively. The Nash–Sutcliffe efficiency coefficient (NSE) of water table depth and soil water content is 0.72 and 0.64 in the calibration year and 0.68 and 0.64 in the verification year. The results show good correspondence between the simulation and observation. It is practicable to apply the model in water movement research of irrigation districts.

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Using digital soil hydraulic maps to simulate water balance at the regional scale (southern Portugal) - impact of spatial resolution and modelling approach

10.5194/ismc2021-46 ◽

2021 ◽

Author(s):

Ana R. Oliveira ◽

Ana Horta ◽

Tiago Ramos

Keyword(s):

Water Balance ◽

Soil Water ◽

Soil Texture ◽

Regional Scale ◽

Water Dynamics ◽

Irrigation District ◽

Geostatistical Simulation ◽

Southern Portugal ◽

Soil Hydraulic ◽

Modelling Approaches

<p>Modelling of soil physical, chemical, and biological processes is critical to improve the understanding of soil functions, the effect of agricultural practices on soil degradation, and appropriate soil management strategies. However, the use of such tools at the regional scale is largely limited by the lack of accurate mapping of soil texture and soil hydraulic properties (SHP). To overcome this limitation, SHP digital maps were obtained using two modelling approaches. One used a national harmonized soil texture database and geostatistical simulation to create soil texture maps which were further used as input data to derive SHP maps using local pedotransfer functions (PTFs). The other approach used SHP maps produced by T&#243;th et al (2017) using soil texture from the product SoilsGrids (Hengl et al, 2017). The SHP maps from both approaches were produced at two spatial resolutions: 250 m and 1000 m. This study aims to evaluate the usefulness of such SHP maps to simulate soil water dynamics and biomass growth at the regional scale using the MOHID-Land model. This model describes the movement of water in the porous medium based on mass and momentum conservation equations that are computed in a 3D grid domain using a finite volume approach. Crop development is simulated using a modified version of the EPIC model. The SHP maps produced using the two modelling approaches and considering two spatial resolutions (250 and 1000 m) were used as inputs for the hydraulic characteristics of soils. Simulations were compared for an irrigation area (Roxo Irrigation District), located in southern Portugal. Results revealed the differences in the components of the soil water balance, with soil inputs from local data being able to better portray landscape heterogeneity.</p>

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Comparison of soil moisture fields estimated by catchment modelling and remote sensing: a case study in South Africa

Hydrology and Earth System Sciences ◽

10.5194/hess-12-751-2008 ◽

2008 ◽

Vol 12 (3) ◽

pp. 751-767 ◽

Cited By ~ 28

Author(s):

T. Vischel ◽

G. G. S. Pegram ◽

S. Sinclair ◽

W. Wagner ◽

A. Bartsch

Keyword(s):

South Africa ◽

Remote Sensing ◽

Soil Moisture ◽

Hydrological Model ◽

Regional Scale ◽

Remotely Sensed ◽

Hydrological Models ◽

Good Correspondence ◽

Physically Based

Abstract. The paper compares two independent approaches to estimate soil moisture at the regional scale over a 4625 km2 catchment (Liebenbergsvlei, South Africa). The first estimate is derived from a physically-based hydrological model (TOPKAPI). The second estimate is derived from the scatterometer on board the European Remote Sensing satellite (ERS). Results show a good correspondence between the modelled and remotely sensed soil moisture, particularly with respect to the soil moisture dynamic, illustrated over two selected seasons of 8 months, yielding regression R2 coefficients lying between 0.68 and 0.92. Such a close similarity between these two different, independent approaches is very promising for (i) remote sensing in general (ii) the use of hydrological models to back-calculate and disaggregate the satellite soil moisture estimate and (iii) for hydrological models to assimilate the remotely sensed soil moisture.

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Neuro-fuzzy systems to estimate reference evapotranspiration

Water SA ◽

10.4314/wsa.v45i2.10 ◽

2019 ◽

Vol 45 (2 April) ◽

Cited By ~ 1

Author(s):

Mousaab Zakhrouf ◽

Hamid Bouchelkia ◽

Madani Stamboul

Keyword(s):

Meteorological Data ◽

Regional Scale ◽

Subtractive Clustering ◽

Efficiency Coefficient ◽

Climate Data ◽

Neuro Fuzzy ◽

Physically Based ◽

Fuzzy C Means Clustering ◽

Daily Evapotranspiration ◽

Semi Empirical

Routine and rapid estimation of evapotranspiration (ET) at regional scale is of great significance for agricultural, hydrological and climatic studies. A large number of empirical or semi-empirical equations have been developed for assessing ET from meteorological data. The FAO-56 PM is one of the most important methods used to estimate evapotranspiration. The advantage of FAO-56 PM is a physically based method that requires a large number of climatic parameter data. In this paper, the potential of two types of neuro-fuzzy system, including ANFIS based on subtractive clustering (S_ANFIS), ANFIS based on the fuzzy C-means clustering method (F_ANFIS), and multiple linear regression (MLR), were used in modelling daily evapotranspiration (ET0). For this purpose various daily climate data – air temperature (T), relative humidity (RH), wind speed (U) and insolation duration (ID) – from Dar El Beidain Algiers, Algeria, were used as inputs for the ANFIS and MLR models to estimate the ET0 obtained by FAO-56 based on the Penman-Monteith equation. The obtained results show that the performances of S_ANFIS model yield superior to those of F_ANFIS and MLR models. It can be judged from results of the Nash-Sutcliffe efficiency coefficient (EC) where S_ANFIS (EC = 94.01%) model can improve the performances of F_ANFIS (EC = 93.00%) and MLR (EC = 92.12%) during the test period, respectively.

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Performance of HYDRUS-1D for simulating water movement in water-repellent soils

Canadian Journal of Soil Science ◽

10.1139/cjss-2017-0116 ◽

2018 ◽

Vol 98 (3) ◽

pp. 407-420 ◽

Cited By ~ 8

Author(s):

Xiaofang Wang ◽

Yi Li ◽

Yichen Wang ◽

Chuncheng Liu

Keyword(s):

Soil Water ◽

Water Movement ◽

Water Dynamics ◽

Dynamics Simulation ◽

Water Repellent ◽

Efficiency Coefficient ◽

Wetting Front ◽

Statistical Parameters ◽

Volumetric Soil Water Content ◽

Hydrus 1D

Soil water repellency affects soil water movement during infiltration significantly. The HYDRUS software has been popularly applied in soil water dynamics simulation for many years, but its performance in water-repellent (WR) soils has not been assessed thoroughly. Our objectives are to assess the performance of HYDRYUS-1D for cumulative infiltration (CI), wetting front (Zf), and volumetric soil water content (θv) during horizontal imbibition and vertical infiltration in wettable, slightly WR, and strongly WR soils. The key parameters of α and n in water retention curves were inversely estimated by RETension Curve software. The α and n were calibrated inversely until the observed data fitted the simulated values well enough. The α and n were then used for validation using three statistical parameters including relative root-mean-square error, R2, and Nash–Sutcliffe efficiency coefficient. The performances of calibration and validation for wettable, slightly, and strongly WR soils were good enough to be used for further simulations (RRMSE ≤20.2% for calibration and ≤21.1% for validation). Soil water movements for strongly WR soils of variable ponded depth during vertical infiltration were simulated. For Lou soil, as the ponded depth increased from 4 to 10 cm, the CI and Zf increased 2.08 and 5.5 cm, respectively. The simulations for the other three soils also showed gradually increased CI and Zf values. In conclusion, the performances of HYDRUS-1D in four different soil types with changing WR levels were good, which confirmed the application of HYDRUS-1D in WR soils.

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Comparison of soil moisture fields estimated by catchment modelling and remote sensing: a case study in South Africa

Hydrology and Earth System Sciences Discussions ◽

10.5194/hessd-4-2273-2007 ◽

2007 ◽

Vol 4 (4) ◽

pp. 2273-2306 ◽

Cited By ~ 3

Author(s):

T. Vischel ◽

G. Pegram ◽

S. Sinclair ◽

W. Wagner ◽

A. Bartsch

Keyword(s):

South Africa ◽

Remote Sensing ◽

Soil Moisture ◽

Hydrological Model ◽

Regional Scale ◽

Remotely Sensed ◽

Hydrological Models ◽

Good Correspondence ◽

Physically Based

Abstract. The paper compares two independent approaches to estimate soil moisture at the regional scale over a 4625 km2 catchment (Liebenbergsvlei, South Africa). The first estimate is derived from a physically-based hydrological model (TOPKAPI). The second estimate is derived from the scatterometer on board on the European Remote Sensing satellite (ERS). Results show a very good correspondence between the modelled and remotely sensed soil moisture, illustrated over two selected seasons of 8 months by regression R2 coefficients lying between 0.78 and 0.92. Such a close similarity between these two different, independent approaches is very promising for (i) remote sensing in general (ii) the use of hydrological models to back-calculate and disaggregate the satellite soil moisture estimate and (iii) for hydrological models to assimilate the remotely sensed soil moisture.

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Regional Analyses of Rainfall-Induced Landslide Initiation in Upper Gudbrandsdalen (South-Eastern Norway) Using TRIGRS Model

Geosciences ◽

10.3390/geosciences11010035 ◽

2021 ◽

Vol 11 (1) ◽

pp. 35

Author(s):

Luca Schilirò ◽

José Cepeda ◽

Graziella Devoli ◽

Luca Piciullo

Keyword(s):

Numerical Simulations ◽

Regional Scale ◽

Shallow Landslides ◽

South Eastern ◽

Physically Based ◽

Triggering Conditions ◽

Cover Thickness ◽

Regional Analyses ◽

Factor Maps ◽

Eastern Norway

In Norway, shallow landslides are generally triggered by intense rainfall and/or snowmelt events. However, the interaction of hydrometeorological processes (e.g., precipitation and snowmelt) acting at different time scales, and the local variations of the terrain conditions (e.g., thickness of the surficial cover) are complex and often unknown. With the aim of better defining the triggering conditions of shallow landslides at a regional scale we used the physically based model TRIGRS (Transient Rainfall Infiltration and Grid-based Regional Slope stability) in an area located in upper Gudbrandsdalen valley in South-Eastern Norway. We performed numerical simulations to reconstruct two scenarios that triggered many landslides in the study area on 10 June 2011 and 22 May 2013. A large part of the work was dedicated to the parameterization of the numerical model. The initial soil-hydraulic conditions and the spatial variation of the surficial cover thickness have been evaluated applying different methods. To fully evaluate the accuracy of the model, ROC (Receiver Operating Characteristic) curves have been obtained comparing the safety factor maps with the source areas in the two periods of analysis. The results of the numerical simulations show the high susceptibility of the study area to the occurrence of shallow landslides and emphasize the importance of a proper model calibration for improving the reliability.

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A Data-Driven Method for the Temporal Estimation of Soil Water Potential and Its Application for Shallow Landslides Prediction

Water ◽

10.3390/w13091208 ◽

2021 ◽

Vol 13 (9) ◽

pp. 1208

Author(s):

Massimiliano Bordoni ◽

Fabrizio Inzaghi ◽

Valerio Vivaldi ◽

Roberto Valentino ◽

Marco Bittelli ◽

...

Keyword(s):

Machine Learning ◽

Water Potential ◽

Soil Water ◽

Temporal Trends ◽

Soil Water Potential ◽

Shallow Landslides ◽

Water Dynamics ◽

Data Driven ◽

Machine Learning Techniques ◽

Physically Based

Soil water potential is a key factor to study water dynamics in soil and for estimating the occurrence of natural hazards, as landslides. This parameter can be measured in field or estimated through physically-based models, limited by the availability of effective input soil properties and preliminary calibrations. Data-driven models, based on machine learning techniques, could overcome these gaps. The aim of this paper is then to develop an innovative machine learning methodology to assess soil water potential trends and to implement them in models to predict shallow landslides. Monitoring data since 2012 from test-sites slopes in Oltrepò Pavese (northern Italy) were used to build the models. Within the tested techniques, Random Forest models allowed an outstanding reconstruction of measured soil water potential temporal trends. Each model is sensitive to meteorological and hydrological characteristics according to soil depths and features. Reliability of the proposed models was confirmed by correct estimation of days when shallow landslides were triggered in the study areas in December 2020, after implementing the modeled trends on a slope stability model, and by the correct choice of physically-based rainfall thresholds. These results confirm the potential application of the developed methodology to estimate hydrological scenarios that could be used for decision-making purposes.

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Modeling Soil Water Movement in Pedostructure

10.13031/2013.20766 ◽

2006 ◽

Author(s):

Rabi H. Mohtar ◽

Erik Braudeau

Keyword(s):

Soil Water ◽

Water Movement ◽

Soil Water Movement

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Analysis of Soil Water Response to Grass Transpiration

Soil and Water Research ◽

10.17221/6510-swr ◽

2013 ◽

Vol 1 (No. 3) ◽

pp. 85-98

Author(s):

Dohnal Michal ◽

Dušek Jaromír ◽

Vogel Tomáš ◽

Herza Jiří

Keyword(s):

Soil Water ◽

Water Uptake ◽

Preferential Flow ◽

Water Movement ◽

Control Volume ◽

Water Pressure ◽

Lower Boundary ◽

Root Water Uptake ◽

Water Dynamics ◽

Soil Water Dynamics

This paper focuses on numerical modelling of soil water movement in response to the root water uptake that is driven by transpiration. The flow of water in a lysimeter, installed at a grass covered hillslope site in a small headwater catchment, is analysed by means of numerical simulation. The lysimeter system provides a well defined control volume with boundary fluxes measured and soil water pressure continuously monitored. The evapotranspiration intensity is estimated by the Penman-Monteith method and compared with the measured lysimeter soil water loss and the simulated root water uptake. Variably saturated flow of water in the lysimeter is simulated using one-dimensional dual-permeability model based on the numerical solution of the Richards’ equation. The availability of water for the root water uptake is determined by the evaluation of the plant water stress function, integrated in the soil water flow model. Different lower boundary conditions are tested to compare the soil water dynamics inside and outside the lysimeter. Special attention is paid to the possible influence of the preferential flow effects on the lysimeter soil water balance. The adopted modelling approach provides a useful and flexible framework for numerical analysis of soil water dynamics in response to the plant transpiration.

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