Spatial distribution of soil moisture over 6 and 30cm depth, Mahurangi river catchment, New Zealand

Integration of satellite-based data with hydrological modelling was generally conducted via data assimilation or model calibration, and both approaches can enhance streamflow predictions. In this study, we assessed the feasibility of another approach that uses satellite-based soil moisture data to directly estimate the parameter β to represent the degree of the spatial distribution of soil moisture storage capacity in the semi-distributed Hymod model. The impact of using historical root-zone soil moisture data from the Soil Moisture Active Passive (SMAP) mission on the prior estimation of the parameter β was explored. Two different ways to incorporate the root-zone soil moisture data to estimate the parameter β are proposed, i.e., one is to derive a priori distribution of β , and the other is to derive a fixed value for β . The simulations of the Hymod models employing the two ways to estimate β are compared with the results produced by the original model, i.e., the one without employing satellite-based data to estimate the parameter β , at three study catchments (the Upper Hanjiang River catchment, the Xiangjiang River catchment, and the Ganjiang River catchment). The results illustrate that the two ways to incorporate the SMAP root-zone soil moisture data in order to predetermine the parameter β of the semi-distributed Hymod model both perform well in simulating streamflow during the calibration period, and a slight improvement was found during the validation period. Notably, deriving a fixed β value from satellite soil moisture data can provide better performance for ungauged catchments despite reducing the model freedom degrees due to fixing the β value. It is concluded that the robustness of the Hymod model in predicting the streamflow can be improved when the spatial information of satellite-based soil moisture data is utilized to estimate the parameter β .

Download Full-text

Predictive modelling and ground validation of the spatial distribution of the New Zealand long-tailed bat (Chalinolobus tuberculatus)

Biological Conservation ◽

10.1016/j.biocon.2006.04.016 ◽

2006 ◽

Vol 132 (2) ◽

pp. 211-221 ◽

Cited By ~ 29

Author(s):

Glen J. Greaves ◽

Renaud Mathieu ◽

Philip J. Seddon

Keyword(s):

Spatial Distribution ◽

New Zealand ◽

Predictive Modelling ◽

Ground Validation

Download Full-text

Spatial Distribution of Lichens in Metrosideros excelsa in Northern New Zealand Urban Forests

Diversity ◽

10.3390/d13040170 ◽

2021 ◽

Vol 13 (4) ◽

pp. 170

Author(s):

Gladys N. Benitez ◽

Glenn D. Aguilar ◽

Dan Blanchon

Keyword(s):

Spatial Distribution ◽

Species Richness ◽

New Zealand ◽

Lichen Species ◽

Positive Correlation ◽

Breast Height ◽

Lichen Community ◽

The North ◽

The City ◽

Lichen Flora

The spatial distribution of corticolous lichens on the iconic New Zealand pōhutukawa (Metrosideros excelsa) tree was investigated from a survey of urban parks and forests across the city of Auckland in the North Island of New Zealand. Lichens were identified from ten randomly selected trees at 20 sampling sites, with 10 sites classified as coastal and another 10 as inland sites. Lichen data were correlated with distance from sea, distance from major roads, distance from native forests, mean tree DBH (diameter at breast height) and the seven-year average of measured NO2 over the area. A total of 33 lichen species were found with coastal sites harboring significantly higher average lichen species per tree as well as higher site species richness. We found mild hotspots in two sites for average lichen species per tree and another two separate sites for species richness, with all hotspots at the coast. A positive correlation between lichen species richness and DBH was found. Sites in coastal locations were more similar to each other in terms of lichen community composition than they were to adjacent inland sites and some species were only found at coastal sites. The average number of lichen species per tree was negatively correlated with distance from the coast, suggesting that the characteristic lichen flora found on pōhutukawa may be reliant on coastal microclimates. There were no correlations with distance from major roads, and a slight positive correlation between NO2 levels and average lichen species per tree.

Download Full-text

SHETRAN and HEC HMS Model Evaluation for Runoff and Soil Moisture Simulation in the Jičinka River Catchment (Czech Republic)

Water ◽

10.3390/w13060872 ◽

2021 ◽

Vol 13 (6) ◽

pp. 872

Author(s):

Vesna Đukić ◽

Ranka Erić

Keyword(s):

Soil Moisture ◽

Czech Republic ◽

Hydrological Model ◽

Flash Flood ◽

Model Performance ◽

Rainfall Runoff ◽

The Czech Republic ◽

River Catchment ◽

Hydrological Variable ◽

Model Structures

Due to the improvement of computation power, in recent decades considerable progress has been made in the development of complex hydrological models. On the other hand, simple conceptual models have also been advanced. Previous studies on rainfall–runoff models have shown that model performance depends very much on the model structure. The purpose of this study is to determine whether the use of a complex hydrological model leads to more accurate results or not and to analyze whether some model structures are more efficient than others. Different configurations of the two models of different complexity, the Système Hydrologique Européen TRANsport (SHETRAN) and Hydrologic Modeling System (HEC-HMS), were compared and evaluated in simulating flash flood runoff for the small (75.9 km2) Jičinka River catchment in the Czech Republic. The two models were compared with respect to runoff simulations at the catchment outlet and soil moisture simulations within the catchment. The results indicate that the more complex SHETRAN model outperforms the simpler HEC HMS model in case of runoff, but not for soil moisture. It can be concluded that the models with higher complexity do not necessarily provide better model performance, and that the reliability of hydrological model simulations can vary depending on the hydrological variable under consideration.

Download Full-text

Aboveground Biomass of Reaumuria soongorica Shrub Effect on Soil Moisture and Nutrient Spatial Distribution in Arid and Semi-Arid Region

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.726-731.3803 ◽

2013 ◽

Vol 726-731 ◽

pp. 3803-3806

Author(s):

Bing Ru Liu ◽

Jun Long Yang

Keyword(s):

Spatial Distribution ◽

Soil Moisture ◽

Moisture Content ◽

Aboveground Biomass ◽

Soil Moisture Content ◽

Plant Biomass ◽

Soil Layer ◽

Soil Nutrient ◽

Desert Plant ◽

Important Species

In order to revel aboveground biomass of R. soongorica shrub effect on soil moisture and nutrients spatial distribution, and explore mechanism of the changes of soil moisture and nutrients, soil moisture content, pH, soil organic carbon (SOC) and total nitrogen (TN) at three soil layers (0-10cm,10-20cm, and 20-40cm) along five plant biomass gradients of R. soongorica were investigated. The results showed that soil moisture content increased with depth under the same plant biomass, and increased with plant biomass. Soil nutrient properties were evidently influenced with plant biomass, while decreased with depth. SOC and TN were highest in the top soil layer (0-10 cm), but TN of 10-20cm layer has no significant differences (P < 0.05). Moreover, soil nutrient contents were accumulated very slowly. These suggests that the requirement to soil organic matter is not so high and could be adapted well to the desert and barren soil, and the desert plant R. soongorica could be acted as an important species to restore vegetation and ameliorate the eco-environment.

Download Full-text

Estimation of Synthetic Aperture Radar (SAR) soil moisture with the use of fractal roughness

10.5194/egusphere-egu21-3203 ◽

2021 ◽

Author(s):

Ju Hyoung Lee ◽

Notarnicola Claudia ◽

Jeff Walker

Keyword(s):

Spatial Distribution ◽

Soil Moisture ◽

Synthetic Aperture Radar ◽

Synthetic Aperture ◽

Accurate Estimation ◽

Distribution Data ◽

Fractal Surface ◽

Fractal Method ◽

Local Point ◽

Aperture Radar

<p>To estimate surface soil moisture from Sentinel-1 backscattering, accurate estimation of soil roughness is a key. However, it is usually error source, due to complexity of surface heterogeneity. This study investigates the fractal methods that takes multi-scale roughness into account. Fractal models are widely recognized as one of the best approaches to depict soil roughness of natural system. Unlike the conventional approach of fractal method that uses local roughness measured in the field or Digital Elevation Model information seldom considering a stochastic characteristic of soil surface, fractal surface is generated with the roughness spatially inverted from Synthetic Aperture Radar (SAR) backscatter. Assuming that the land surface in study site is on small to intermediate scales, pseudo-roughness is spatially estimated by modelling SAR roughness with the one-sided power-law spectrum. In addition, it is assumed that both multiple and single scales of roughness affect SAR backscatter in an integrative way. For validation, soil moisture is retrieved with this time-varying roughness. Based upon local validation and cost minimization, as compared with an inversion approach of surface scattering models (Integral Equation Model), a fractal method seems geometrically more sensible than an inversion, based upon a spatial distribution and a priori knowledge in the field. Although inverted roughness is used as an input, fractal model does not reproduce the same roughness. Results will show local point validation, fractal surface, and estimation of coefficients, and various spatial distribution data. This study will be useful for future satellite missions such as NASA-ISRO SAR mission.</p>

Download Full-text

Preliminary investigation of emerging suburban landsliding in Gisborne, New Zealand

Quarterly Journal of Engineering Geology and Hydrogeology ◽

10.1144/qjegh2021-087 ◽

2022 ◽

pp. qjegh2021-087

Author(s):

Matthew E. Cook ◽

Martin S. Brook ◽

Jon Tunnicliffe ◽

Murry Cave ◽

Noah P. Gulick

Keyword(s):

Soil Moisture ◽

New Zealand ◽

High Intensity ◽

Preliminary Investigation ◽

Rainfall Event ◽

Slope Instability ◽

Antecedent Soil Moisture ◽

Field Surveys ◽

Coastal City ◽

The North

Recently uplifted, soft Pleistocene sediments in northern New Zealand are particularly vulnerable to landsliding because they are often underlain by less permeable, clay-rich Neogene mudstone/siltstone rocks. Typically, instability is rainfall-induced, often due to a high intensity rainfall event from extra-tropical cyclones, following wetter months when antecedent soil moisture has increased. Using remote sensing, field surveys and laboratory testing, we report on some emerging slope instability hazards in the eastern suburbs of the coastal city of Gisborne, on the North Island. Retrogressive failure of the main landslide (at Wallis Road) is ongoing and has already led to the abandonment of one home, while an adjacent landslide (at Titirangi Drive) appears to be in an incipient phase of failure. The Wallis Road landslide has been particularly active from mid-2017, with slumping of the headscarp area transitioning to a constrained mudflow downslope, which then descends a cliff before terminating on the beach. In contrast, the incipient Titirangi Drive landslide at present displays much more subtle effects of deformation. While activity at both landslides appears to be linked to rainfall-induced increases in soil moisture, this is due to the effects of prolonged periods of rainfall rather than the passage of high intensity cyclonic storms.

Download Full-text

Remotely sensed latent heat fluxes for improving model predictions of soil moisture: a case study

Hydrology and Earth System Sciences Discussions ◽

10.5194/hessd-7-6179-2010 ◽

2010 ◽

Vol 7 (4) ◽

pp. 6179-6205

Author(s):

J. M. Schuurmans ◽

F. C. van Geer ◽

M. F. P. Bierkens

Keyword(s):

Spatial Distribution ◽

Soil Moisture ◽

Data Assimilation ◽

Latent Heat ◽

Satellite Data ◽

Hydrological Model ◽

Remotely Sensed ◽

Heat Fluxes ◽

Model Errors ◽

Small Catchment

Abstract. This paper investigates whether the use of remotely sensed latent heat fluxes improves the accuracy of spatially-distributed soil moisture predictions by a hydrological model. By using real data we aim to show the potential and limitations in practice. We use (i) satellite data of both ASTER and MODIS for the same two days in the summer of 2006 that, in association with the Surface Energy Balance Algorithm for Land (SEBAL), provides us the spatial distribution of daily ETact and (ii) an operational physically based distributed (25 m×25 m) hydrological model of a small catchment (70 km2) in The Netherlands that simulates the water flow in both the unsaturated and saturated zone. Firstly, model outcomes of ETact are compared to the processed satellite data. Secondly, we perform data assimilation that updates the modelled soil moisture. We show that remotely sensed ETact is useful in hydrological modelling for two reasons. Firstly, in the procedure of model calibration: comparison of modeled and remotely sensed ETact together with the outcomes of our data assimilation procedure points out potential model errors (both conceptual and flux-related). Secondly, assimilation of remotely sensed ETact results in a realistic spatial adjustment of soil moisture, except for the area with forest and deep groundwater levels. As both ASTER and MODIS images were available for the same days, this study provides also an excellent opportunity to compare the worth of these two satellite sources. It is shown that, although ASTER provides much better insight in the spatial distribution of ETact due to its higher spatial resolution than MODIS, they appeared in this study just as useful.

Download Full-text