scholarly journals Fuzzy and process modelling of contour ridge water dynamics

2018 ◽  
Vol 378 ◽  
pp. 3-10
Author(s):  
Alexander Mhizha ◽  
John Ndiritu
Keyword(s):  
Fuzzy Logic ◽  
Soil Moisture ◽  
Rainwater Harvesting ◽  
Root Zone ◽  
Process Modelling ◽  
Water Dynamics ◽  
Field Scale ◽  
Model Soil ◽  
Distributed Process ◽  
Using Data

Abstract. Contour ridges are an in-situ rainwater harvesting technology developed initially for soil erosion control but are currently also widely promoted for rainwater harvesting. The effectiveness of contour ridges depends on geophysical, hydro-climatic and socio economic factors that are highly varied in time and space. Furthermore, field-scale data on these factors are often unavailable. This together with the complexity of hydrological processes at field scale limits the application of classical distributed process modelling to highly-instrumented experimental fields. This paper presents a framework that combines fuzzy logic and process-based approach for modelling contour ridges for rainwater harvesting where detailed field data are not available. Water balance for a representative contour-ridged field incorporating the water flow processes across the boundaries is integrated with fuzzy logic to incorporate the uncertainties in estimating runoff. The model is tested using data collected during the 2009/2010 and 2010/2011 rainfall seasons from two contour-ridged fields in Zhulube located in the semi-arid parts of Zimbabwe. The model is found to replicate soil moisture in the root zone reasonably well (NSE = 0.55 to 0.66 and PBIAS = −1.3 to 6.1 %). The results show that combining fuzzy logic and process based approaches can adequately model soil moisture in a contour ridged-field and could help to assess the water dynamics in contour ridged fields.

scholarly journals Field-scale soil moisture bridges the spatial-scale gap between drought monitoring and agricultural yields

2020 ◽  
Author(s):  
Noemi Vergopolan ◽  
Sitian Xiong ◽  
Lyndon Estes ◽  
Niko Wanders ◽  
Nathaniel W. Chaney ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Spatial Scale ◽  
Root Zone ◽  
Maize Yield ◽  
Remotely Sensed ◽  
Drought Monitoring ◽  
Surface Model ◽  
Field Scale ◽  
Yield Data ◽  
Scale Modeling

Abstract. Soil moisture is highly variable in space, and its deficits (i.e. droughts) plays an important role in modulating crop yields and its variability across landscapes. Limited hydroclimate and yield data, however, hampers drought impact monitoring and assessment at the farmer field-scale. This study demonstrates the potential of field-scale soil moisture simulations to advance high-resolution agricultural yield prediction and drought monitoring at the smallholder farm field-scale. We present a multi-scale modeling approach that combines HydroBlocks, a physically-based hyper-resolution Land Surface Model (LSM), and machine learning. We applied HydroBlocks to simulate root zone soil moisture and soil temperature in Zambia at 3-hourly 30-m resolution. These simulations along with remotely sensed vegetation indices, meteorological conditions, and data describing the physical properties of the landscape (topography, land cover, soil properties) were combined with district-level maize data to train a random forest model (RF) to predict maize yields at the district- and field-scale (250-m) levels. Our model predicted yields with a coefficient of variation (R2) of 0.61, Mean Absolute Error (MAE) of 349 kg ha−1, and mean normalized error of 22 %. We captured maize losses due to the 2015/2016 El Niño drought at similar levels to losses reported by the Food and Agriculture Organization (FAO). Our results revealed that soil moisture is the strongest and most reliable predictor of maize yield, driving its spatial and temporal variability. Consequently, soil moisture was also the most effective indicator of drought impacts in crops when compared with precipitation, soil and air temperatures, and remotely-sensed NDVI-based drought indices. By combining field-scale root zone soil moisture estimates with observed maize yield data, this research demonstrates how field-scale modeling can help bridge the spatial scale discontinuity gap between drought monitoring and agricultural impacts.

Cosmic Ray Neutron Sensing: Integration with land surface modelling using data assimilation for improved field-scale soil moisture estimates

2020 ◽  
Author(s):  
Amol Patil ◽  
Benjamin Fersch ◽  
Harrie-Jan Hendricks-Franssen ◽  
Harald Kunstmann
Keyword(s):  
Soil Moisture ◽  
Data Assimilation ◽  
Spatial Resolution ◽  
Land Surface ◽  
Root Zone ◽  
Cosmic Ray ◽  
Soil Parameters ◽  
Surface Model ◽  
Field Scale ◽  
Spatio Temporal

<p>Soil moisture is a key variable in atmospheric modelling to resolve the partitioning of net radiation into sensible and latent heat fluxes. Therefore, high resolution spatio-temporal soil moisture estimation is getting growing attention in this decade. The recent developments to observe soil moisture at field scale (170 to 250 m spatial resolution) using Cosmic Ray Neutron Sensing (CRNS) technique has created new opportunities to better resolve land surface atmospheric interactions; however, many challenges remain such as spatial resolution mismatch and estimation uncertainties. Our study couples the Noah-MP land surface model to the Data Assimilation Research Testbed (DART) for assimilating CRN intensities to update model soil moisture. For evaluation, the spatially distributed Noah-MP was set up to simulate the land surface variables at 1 km horizontal resolution for the Rott and Ammer catchments in southern Germany. The study site comprises the TERENO-preAlpine observatory with five CRNS stations and additional CRNS measurements for summer 2019 operated by our Cosmic Sense research group. We adjusted the soil parametrization in Noah-MP to allow the usage of EU soil data along with Mualem-van Genuchten soil hydraulic parameters. We use independent observations from extensive soil moisture sensor network (SoilNet) within the vicinity of CRNS sensors for validation. Our detailed synthetic and real data experiments are evaluated for the analysis of the spatio-temporal changes in updated root zone soil moisture and for implications on the energy balance component of Noah-MP. Furthermore, we present possibilities to estimate root zone soil parameters within the data assimilation framework to enhance standalone model performance.</p>

scholarly journals Root-zone soil moisture estimation using data-driven methods

2014 ◽  
Vol 50 (4) ◽  
pp. 2946-2962 ◽  
Author(s):  
Kurt C. Kornelsen ◽  
Paulin Coulibaly
Keyword(s):  
Soil Moisture ◽  
Root Zone ◽  
Data Driven ◽  
Using Data ◽  
Moisture Estimation

scholarly journals Soil moisture observation in a forested headwater catchment: combining a dense cosmic-ray neutron sensor network with roving and hydrogravimetry at the TERENO site Wüstebach

2021 ◽  
Author(s):  
Maik Heistermann ◽  
Heye Bogena ◽  
Till Francke ◽  
Andreas Güntner ◽  
Jannis Jakobi ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Sensor Network ◽  
Root Zone ◽  
Water Storage ◽  
Cosmic Ray ◽  
Water Dynamics ◽  
Catchment Scale ◽  
Small Catchment ◽  
Hill Slope ◽  
The Hill

Abstract. Cosmic Ray Neutron Sensing (CRNS) has become an effective method to measure soil moisture at a horizontal scale of hundreds of meters and a depth of decimeters. Recent studies proposed to operate CRNS in a network with overlapping footprints in order to cover root-zone water dynamics at the small catchment scale, and, at the same time, to represent spatial heterogeneity. In a joint field campaign from September to November 2020 (JFC-2020), five German research institutions deployed 15 CRNS sensors in the 0.4 km2 Wüstebach catchment (Eifel mountains, Germany). The catchment is dominantly forested (but includes a substantial fraction of open vegetation), and features a topographically distinct watershed. In addition to the dense CRNS coverage, the campaign featured a unique combination of additional instruments and techniques: hydro-gravimetry (to detect water storage dynamics also below the root zone); ground-based and, for the first time, airborne CRNS roving; an extensive wireless soil sensor network, supplemented by manual measurements; and six weighable lysimeters. Together with comprehensive data from the long-term local research infrastructure, the published dataset (available at https://doi.org/10.23728/b2share.afb20a34a6ac429ca6b759238d842765) will be a valuable asset in various research contexts: to advance the retrieval of landscape water storage from CRNS, wireless soil sensor networks, or hydrogravimetry; to identify scale-specific combinations of sensors and methods to represent soil moisture variability; to improve the understanding and simulation of land-atmosphere exchange as well as hydrological and hydrogeological processes at the hill-slope and the catchment scale; and to support the retrieval soil water content from airborne and spaceborne remote sensing platforms.

Field scale root zone soil moisture estimation by coupling cosmic-ray neutron sensor with soil moisture sensors

2020 ◽  
Author(s):  
Hami Said ◽  
Georg Weltin ◽  
Lee Kheng Heng ◽  
Trenton Franz ◽  
Emil Fulajtar ◽  
...  
Keyword(s):  
Water Management ◽  
Soil Moisture ◽  
Root Zone ◽  
Cosmic Ray ◽  
Agricultural Water ◽  
Field Scale ◽  
Agricultural Water Management ◽  
Soil Moisture Sensors ◽  
Major Disadvantage ◽  
Filter Approach

<p>Since it has become clear that climate change is having a major impact on water availability for agriculture and crop productivity, an accurate estimation of field-scale root-zone soil moisture (RZSM) is essential for improved agricultural water management. The Cosmic Ray Neutron Sensor (CRNS) has recently been used for field-scale soil moisture (SM) monitoring in large areas and is a credible and robust technique. Like other remote or proximal sensing techniques, the CRNS provides only SM data in the near surface. One of the challenges and needs is to extend the vertical footprint of the CRNS to the root zone of major crops. This can be achieved by coupling the CRNS measurements with conventional methods for soil moisture measurements, which provide information on soil moisture for whole rooting depth.</p><p>The objective of this poster presentation is to estimate field-scale RZSM by correlating the CRNS information with that from soil moisture sensors that provide soil moisture data for the whole root depth. In this study, the Drill and Drop probes which provide continuous profile soil moisture were selected. The RZSM estimate was calculated using an exponential filter approach.</p><p>Winter Wheat cropped fields in Rutzendorf, Marchfeld region (Austria) were instrumented with a CRNS and Drill & Drop probes. An exponential filter approach was applied on the CRNS and Drill and drop sensor data to characterize the RZSM. The preliminary results indicate the ability of the merging framework procedure to improve field-scale RZSM in real-time. This study demonstrated how to combine the advantages of CRNS nuclear technique (especially the large footprint and good representativeness of obtained data) with the advantages of conventional methods (providing data for whole soil profile) and overcome the shortcoming of both methods (the lack of information in the deeper part of soil profile being the major disadvantage of CRNS and the spatial limitation and low representativeness of point data being the major disadvantage of conventional capacitance sensors). This approach can be very helpful for improving agricultural water management.</p>

scholarly journals Analysis of the linearised observation operator in a soil moisture and temperature analysis scheme

2015 ◽  
Vol 2 (1) ◽  
pp. 505-535 ◽  
Author(s):  
I. Dharssi ◽  
B. Candy ◽  
P. Steinle
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Surface Soil ◽  
Root Zone ◽  
Land Surface Model ◽  
Surface Model ◽  
Surface Soil Moisture ◽  
Soil Layers ◽  
Model Soil ◽  
Screen Level

Abstract. Several weather forecasting agencies have developed advanced land data assimilation systems that, in principle, can analyse any model land variable. Such systems can make use of a wide variety of observation types, such as screen level (2 m above the surface) observations and satellite based measurements of surface soil moisture and skin temperature. Indirect measurements can be used and information propagated from the surface into the deeper soil layers. A key component of the system is the calculation of the linearised observation operator matrix (Jacobian matrix) which describes the link between the observations and the land surface model variables. The elements of the Jacobian matrix (Jacobians) are estimated using finite difference by performing short model forecasts with perturbed initial conditions. The calculated Jacobians show that there can be strong coupling between the screen level and the soil. The coupling between the screen level and surface soil moisture is found to be due to a number of processes including bare soil evaporation, soil thermal conductivity as well as transpiration by plants. Therefore, there is significant coupling both during the day and at night. The coupling between the screen level and root-zone soil moisture is primarily through transpiration by plants. Therefore the coupling is only significant during the day and the vertical variation of the coupling is modulated by the vegetation root depths. The calculated Jacobians that link screen level temperature to model soil temperature are found to be largest for the topmost model soil layer and become very small for the lower soil layers. These values are largest during the night and generally positive in value. It is found that the Jacobians that link observations of surface soil moisture to model soil moisture are strongly affected by the soil hydraulic conductivity. Generally, for the Joint UK Land Environment Simulator (JULES) land surface model, the coupling between the surface and root zone soil moisture is weak. Finally, the Jacobians linking observations of skin temperature to model soil temperature and moisture are calculated. These Jacobians are found to have a similar spatial pattern to the Jacobians for observations of screen level temperature. Analysis is also performed of the sensitivity of the calculated Jacobians to the magnitude of the perturbations used.

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

2009 ◽  
Vol 6 (4) ◽  
pp. 5537-5563 ◽  
Author(s):  
H. Makurira ◽  
H. H. G. Savenije ◽  
S. Uhlenbrook
Keyword(s):  
Soil Moisture ◽  
Water Balance ◽  
Root Zone ◽  
Crop Growth ◽  
Deep Percolation ◽  
Field Scale ◽  
Dry Spell ◽  
Water Partitioning ◽  
Cultivation Techniques ◽  
Storage Structures

Abstract. Smallholder rainfed farming systems generally realise sub-optimal crop yields which are largely attributed to dry spell occurrences during crop growth stages. However, with improved farming practices, it seems possible to significantly 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). Alternative cultivation techniques such as runoff harvesting and in-field micro-storage structures are compared. These techniques aim to reduce soil and nutrient loss from the field but, more importantly, promote in-field infiltration and water retention. Water balance components have been observed in order to study water partitioning processes under different cultivation techniques. 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. It appears that about 50% of the diverted water leaves the root zone through deep percolation. Modelling shows 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. Conclusions from the research are that the innovations 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. It is also concluded that there is more scope for efficient utilisation of the diverted runoff water if storage structures could be installed to regulate water flow to the root zone when required.

scholarly journals Assimilation of ASCAT near-surface soil moisture into the SIM hydrological model over France

2011 ◽  
Vol 15 (12) ◽  
pp. 3829-3841 ◽  
Author(s):  
C. Draper ◽  
J.-F. Mahfouf ◽  
J.-C. Calvet ◽  
E. Martin ◽  
W. Wagner
Keyword(s):  
Soil Moisture ◽  
Real Time ◽  
Land Surface ◽  
Hydrological Model ◽  
Surface Soil ◽  
Root Zone ◽  
Surface Soil Moisture ◽  
Near Surface ◽  
Model Soil ◽  
Dry Bias

Abstract. This study examines whether the assimilation of remotely sensed near-surface soil moisture observations might benefit an operational hydrological model, specifically Météo-France's SAFRAN-ISBA-MODCOU (SIM) model. Soil moisture data derived from ASCAT backscatter observations are assimilated into SIM using a Simplified Extended Kalman Filter (SEKF) over 3.5 years. The benefit of the assimilation is tested by comparison to a delayed cut-off version of SIM, in which the land surface is forced with more accurate atmospheric analyses, due to the availability of additional atmospheric observations after the near-real time data cut-off. However, comparing the near-real time and delayed cut-off SIM models revealed that the main difference between them is a dry bias in the near-real time precipitation forcing, which resulted in a dry bias in the root-zone soil moisture and associated surface moisture flux forecasts. While assimilating the ASCAT data did reduce the root-zone soil moisture dry bias (by nearly 50%), this was more likely due to a bias within the SEKF, than due to the assimilation having accurately responded to the precipitation errors. Several improvements to the assimilation are identified to address this, and a bias-aware strategy is suggested for explicitly correcting the model bias. However, in this experiment the moisture added by the SEKF was quickly lost from the model surface due to the enhanced surface fluxes (particularly drainage) induced by the wetter soil moisture states. Consequently, by the end of each winter, during which frozen conditions prevent the ASCAT data from being assimilated, the model land surface had returned to its original (dry-biased) climate. This highlights that it would be more effective to address the precipitation bias directly, than to correct it by constraining the model soil moisture through data assimilation.

scholarly journals Hydrotropic Root Behavior in Water-saving Cultivation: a High-resolution Monitoring Study of Soil Water Dynamics

2021 ◽  
Author(s):  
Qichen Li ◽  
Toshiaki Sugihara ◽  
Sakae Shibusawa ◽  
Minzan Li
Keyword(s):  
Soil Moisture ◽  
High Resolution ◽  
Water Absorption ◽  
Soil Water ◽  
Root Zone ◽  
Water Dynamics ◽  
Individual Plant ◽  
Boundary Area ◽  
Photosynthetic Activities ◽  
Wet Zone

Abstract PurposeSubsurface irrigation has been confirmed to have high water use efficiency (WUE) due to it irrigating only the crop root zone. This study investigated hydrotropic root behavior when a wet zone was produced around the roots by subsurface irrigation to clarify the dynamics of soil water content in the wet zone caused by water absorption of the growing plant. ResultsWe conducted a feasibility study of a high-resolution soil moisture sensing prototype and gathered data to analyze hydrotropism and plant water absorption activity. We applied signal processing, high pass filtering, and Fast Fourier Transform (FFT) to the acquired high-resolution soil moisture data. The results showed distinct fluctuation of moisture at the boundary area, which indicated plant’s biological rhythm of photosynthetic activities. We also quantified root distribution inside and outside the wet zone and observed the shape of the root system from the cross-section of the wet zone. The results show that hydrotropism restricted most of the roots to the inside of the wet zone. Furthermore, root hydrotropic response is nonuniform for all roots of an individual plant. ConclusionsThe results suggest a new method to study hydrotropic root behavior and plant photosynthetic activities. We assumed a mechanical, push-and-pull model of water dynamics at the wetting front and the root mass accumulated by hydrotropism is an important system parameter. To further evaluate a plant’s hydrotropic performance, it is necessary to use stochastic analysis and/or a non-deterministic approach.

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