Characterisation of Hydrological Response to Rainfall at Multi Spatio-Temporal Scales in Savannas of Semi-Arid Australia

Jarihani, Ben; Sidle, Roy C.; Bartley, Rebecca; Roth, Christian; Wilkinson, Scott N.

doi:10.3390/w9070540

Cited by 27 publications

(20 citation statements)

References 58 publications

(95 reference statements)

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“…Relationships between indicators of AMC and flow activation times were weaker, compared with those with rainfall intensity. Although several studies in semiarid areas have reported strong relationships between AMC and OF characteristics in general (e.g., Brocca, Melone, & Moramarco, ; Li & Chibber, ) as well as seasonally (e.g., Castillo et al, ; Jarihani, Sidle, Bartley, Roth, & Wilkinson, ), such was not consistently the case here. This is likely because OF occurred primarily as HOF at our site.…”

Section: Discussioncontrasting

confidence: 75%

Hydroclimatic controls on runoff activation in an artificially drained, near‐level vertisolic clay landscape in a Prairie climate

Kokulan

Macrae

Ali

et al. 2018

Hydrological Processes

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Water quality problems are frequently influenced by hydrological processes, particularly in landscapes in which land drainage has been modified. The expansion of agricultural tile drainage in the Northern Great Plains of North America is occurring, yet is controversial due to persistent water quality problems such as eutrophication. Runoff-generating mechanisms inNorth American tile-drained landscapes in vertisolic soils have not been investigated but are important for understanding the impacts of tile drainage on water quantity and quality. This study evaluated the role of climate drivers on the activation of overland (OF) and tile (TF) flow and groundwater flow responses (GWT) on tile-drained and nontile-drained farm fields in Southern Manitoba, Canada. The response times of different flow paths (OF, TF, and GWT)were compared for 23 hydrological events (April-September 2015, 2016) to infer dominant runoff generation processes. Runoff responses (all pathways) were more rapid following higher intensity rainfall. Subsurface responses were hastened by wetter antecedent conditions in spring and delayed by the seasonal soil-ice layer. The activation of OF did not differ between the tiled and nontiled fields, suggesting that tile drains do little to reduce the occurrence of OF in this landscape. Rapid vertical preferential flow into tiles via preferential flow pathways was uncommon at our site, and the soil profile instead wet up from the top down. These conclusions have implications for the expansion of tile drainage and the impact of such an expansion on hydrological and biogeochemical processes in agricultural landscapes.

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Section: Discussioncontrasting

confidence: 75%

Hydroclimatic controls on runoff activation in an artificially drained, near‐level vertisolic clay landscape in a Prairie climate

Kokulan

Macrae

Ali

et al. 2018

Hydrological Processes

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“…The climate is dry-tropical with two distinct seasons; a summer wet season between October and March, and a dry season between April and September [86]. The mean-annual rainfall (1900-2012) is 686 mm year −1 , but exhibits high interannual variability linked to the phase of the Southern Oscillation, with drought conditions coinciding with El Niño/Southern Oscillation (ENSO), and wetter periods accompanying anti-ENSO (La Niña) events [87,88]. Vegetation is generally characterised by a discontinuous upper stratum of Eucalyptus spp.…”

Section: Study Sitementioning

confidence: 99%

“…conditions coinciding with El Niño/Southern Oscillation (ENSO), and wetter periods accompanying anti-ENSO (La Niña) events [87,88]. Vegetation is generally characterised by a discontinuous upper stratum of Eucalyptus spp.…”

Section: Study Sitementioning

confidence: 99%

Assessment of UAV and Ground-Based Structure from Motion with Multi-View Stereo Photogrammetry in a Gullied Savanna Catchment

Koci

Jarihani

Leon

et al. 2017

IJGI

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Abstract:Structure from Motion with Multi-View Stereo photogrammetry (SfM-MVS) is increasingly used in geoscience investigations, but has not been thoroughly tested in gullied savanna systems. The aim of this study was to test the accuracy of topographic models derived from aerial (via Unmanned Aerial Vehicle, 'UAV') and ground-based (via handheld digital camera, 'ground') SfM-MVS in modelling hillslope gully systems in a dry-tropical savanna, and to assess the strengths and limitations of the approach at a hillslope scale and an individual gully scale. UAV surveys covered three separate hillslope gully systems (with areas of 0.412-0.715 km 2 ), while ground surveys assessed individual gullies within the broader systems (with areas of 350-750 m 2 ). SfM-MVS topographic models, including Digital Surface Models (DSM) and dense point clouds, were compared against RTK-GPS point data and a pre-existing airborne LiDAR Digital Elevation Model (DEM). Results indicate that UAV SfM-MVS can deliver topographic models with a resolution and accuracy suitable to define gully systems at a hillslope scale (e.g., approximately 0.1 m resolution with 0.4-1.2 m elevation error), while ground-based SfM-MVS is more capable of quantifying gully morphology (e.g., approximately 0.01 m resolution with 0.04-0.1 m elevation error). Despite difficulties in reconstructing vegetated surfaces, uncertainty as to optimal survey and processing designs, and high computational demands, this study has demonstrated great potential for SfM-MVS to be used as a cost-effective tool to aid in the mapping, modelling and management of hillslope gully systems at different scales, in savanna landscapes and elsewhere.

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“…Overall, results showed that for both catchments, including rainfall of the previous months increased model accuracy compared to just using rainfall of the same month. This shows the importance of antecedent soil moisture in the runoff generation process [68]. Additionally, including runoff and rainfall together as input data slightly improved model performance for both catchments ( Table 4), indicating that only using rainfall is insufficient to precisely estimate runoff, while combining this with antecedent runoff improves predictions [59].…”

Section: Different Patterns Of Hydro-climate Datamentioning

confidence: 88%

Rainfall-Runoff Modelling Using Hydrological Connectivity Index and Artificial Neural Network Approach

Asadi

Shahedi

Jarihani

et al. 2019

Water

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The input selection process for data-driven rainfall-runoff models is critical because input vectors determine the structure of the model and, hence, can influence model results.Here, hydro-geomorphic and biophysical time series inputs, including Normalized Difference Vegetation Index (NDVI) and Index of Connectivity (IC; a type of hydrological connectivity index), in addition to climatic and hydrologic inputs were assessed. Selected inputs were used to develop Artificial Neural Networks (ANNs) in the Haughton River catchment and the Calliope River catchment, Queensland, Australia. Results show that incorporating IC as a hydro-geomorphic parameter and remote sensing NDVI as a biophysical parameter, together with rainfall and runoff as hydro-climatic parameters, can improve ANN model performance compared to ANN models using only hydro-climatic parameters. Comparisons amongst different input patterns showed that IC inputs can contribute to further improvement in model performance, than NDVI inputs. Overall, ANN model simulations showed that using IC along with hydro-climatic inputs noticeably improved model performance in both catchments, especially in the Calliope catchment. This improvement is indicated by a slight increase (9.77% and 11.25%) in the Nash-Sutcliffe efficiency and noticeable decrease (24.43% and 37.89%) in the root mean squared error of monthly runoff from Haughton River and Calliope River, respectively. Here, we demonstrate the significant effect of hydro-geomorphic and biophysical time series inputs for estimating monthly runoff using ANN data-driven models, which are valuable for water resources planning and management.2 of 20 models in the simulation of R-R non-linear process is more popular than conceptual models [5]. Data-driven models attempt to find a relationship between input and output parameters without considering the physical process [1]. Artificial Neural Networks (ANNs) are a type of data-driven model with a flexible mathematical structure which includes both linear and nonlinear concepts which operate within a dynamic input-output system [6].In the past several decades, there has been substantial growth in application of ANNs for R-R modelling [7][8][9][10][11][12][13][14][15] where which ANNs have been compared to other methods, including traditional statistical methods, conceptual models and other artificial intelligence models. Result of these studies have shown that ANN is more accurate than conventional and traditional statistical methods. For example, the ANN approach predicted more accurate runoff in the Mississippi River basin than the conventional autoregressive moving average with exogenous inputs (ARMAX) approach in [16]. Birikundavyi et al. [17] have demonstrated that streamflow forecasting using ANN models has better performance than conceptual model (PREVIS) and the conventional autoregressive model coupled with a Kalman filter (ARMAX-KF) models. Additionally, comparison of ANNs with other artificial intelligence models (e.g., support vector machine (SVM), genetic pr...

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Characterisation of Hydrological Response to Rainfall at Multi Spatio-Temporal Scales in Savannas of Semi-Arid Australia

Cited by 27 publications

References 58 publications

Hydroclimatic controls on runoff activation in an artificially drained, near‐level vertisolic clay landscape in a Prairie climate

Hydroclimatic controls on runoff activation in an artificially drained, near‐level vertisolic clay landscape in a Prairie climate

Assessment of UAV and Ground-Based Structure from Motion with Multi-View Stereo Photogrammetry in a Gullied Savanna Catchment

Rainfall-Runoff Modelling Using Hydrological Connectivity Index and Artificial Neural Network Approach

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