Dynamics of transmission losses in a large arid stream channel

Lange, Jens

doi:10.1016/j.jhydrol.2004.09.016

Cited by 116 publications

(101 citation statements)

References 16 publications

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“…For lower parts of the Diamantina River (further downstream from our study site) and nearby Cooper Creek, high transmission losses have also been reported (75-90%), which are higher than our results most likely due to wider floodplains, higher actual evaporation losses (from wider flow extents), longer water residence times and a lower gradient with increasing distance downstream (Costelloe et al, 2006;Knighton and Nanson, 1994). Water balance calculations based on gauge data are often blurred by unknown lateral inflows (Lange, 2005). We found (see Table 4) that the lateral inflow between available sparse gauging stations provided the highest uncertainty in water balance estimation.…”

Section: Water Balance and Transmission Loss Partitioningcontrasting

confidence: 56%

Where does all the water go? Partitioning water transmission losses in a data-sparse, multi-channel and low-gradient dryland river system using modelling and remote sensing

Jarihani

Larsen

Callow

et al. 2015

Journal of Hydrology

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Where does all the water go? Partitioning water transmission losses in a data-sparse, multi-channel and low-gradient dryland river system using modelling and remote sensing, Journal of Hydrology (2015), doi: http://dx.doi.org/10.1016/j.jhydrol. 2015.08.030 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. 1Where does all the water go? Partitioning water transmission losses in a data-sparse, multi-channel and low-gradient dryland river system using modelling and remote sensing and April 2012 along a 180 km reach of the Diamantina River in the Lake Eyre Basin, Australia.Transmission losses were found to be high, on average 46% of total inflow within 180 km reach segment or 7 GL/km (range: 4 to 10 GL/km). However, in 180 km reach, transmission losses vary non-linearly with flood discharge, with smaller flows resulting in higher losses (up to 68%), which diminish in higher flows (down to 24%) and in general there is a minor increase in losses with distance downstream. Partitioning these total losses into the major components shows that actual evaporation was the most significant component (21.6% of total inflow), followed by infiltration (13.2%) and terminal water storage (11.2%). Lateral inflow can be up to 200% of upstream inflow (mean = 86%) and is therefore a critical parameter in the water balance and 3 transmission loss calculations. This study shows that it is possible to constrain the water balance using hydrodynamic models in dryland river systems using remote sensing and simple field measurements to address the otherwise scarce availability of data. The results of this study also enable a better understanding of the water resources available for ecosystems in these unique multi-channel and large floodplain rivers. The combined modelling / remote sensing approach of this study can be applied elsewhere in the world to better understand the water balances and water transmission losses, important drivers of ecohydrological processes in dryland environments.

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Section: Water Balance and Transmission Loss Partitioningcontrasting

confidence: 56%

Where does all the water go? Partitioning water transmission losses in a data-sparse, multi-channel and low-gradient dryland river system using modelling and remote sensing

Jarihani

Larsen

Callow

et al. 2015

Journal of Hydrology

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“…There is a westeast gradient with decreasing fog frequency (Lancaster, 1984). Rainfall and groundwater availability are often the primary determinants of species distribution in the Namib Desert (Schachtschneider and February, 2010), with large trees confined to the eastern edge of the desert and along ephemeral water courses (Burke, 2006) where they access the shallow alluvial aquifers (Lange, 2005). The sampling sites were located between the Swakop and Kuiseb ephemeral rivers (Fig.…”

Section: Site Descriptionmentioning

confidence: 99%

Fog Spatial Distributions over the Central Namib Desert - An Isotope Approach

Kaseke

Tian

Wang

et al. 2018

Aerosol Air Qual. Res.

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Fog is a characteristic feature of the Namib Desert and is essential to life in this fog dependent system. It is often acknowledged that advective fog from the ocean is the dominant fog type over the Namib Desert fog-zone but recent evidence suggests that other fog types occur in this area. Knowledge of the existence and spatial distribution of different fog types will enhance the mechanistic understanding of fog formation and potential changes in this region, but such knowledge is limited in literature. In this study, we investigated fog spatial variations within the Namib Desert fog-zone by applying stable isotope (δ 18 O and δ 2 H) techniques to differentiate various fog types and identify their source waters. Isotope based results showed that at least three types of fog (advective, radiation and mixed) occurred in this region and what appears as a single fog event may include all three types. Results suggest that radiation fog was the dominant fog type during our study period. The results also suggest that advective fog (with Atlantic Ocean origins) either dissipated 30-50 km inland and the residual humidity combined with locally derived moisture to form mixed fog or advective fog incorporated local moisture along its trajectory inland resulting in mixed fog. Fog in the Namib Desert was consistently depleted in 18 O and 2 H compared to rainfall and this was attributed to sub-cloud evaporation of the rainfall as well as different sources of fog and rainfall. Sub-cloud evaporation led to enrichment of 18 O and 2 H in rainfall beyond that of the first stage condensate, fog. Advective fog is often considered the architect of the fog-zone in the Namib Desert, but our results demonstrated multiple dominant fog types during the study period, suggesting knowledge of both fog frequency and fog type is needed to better predict climate change impacts on the fog-zone.

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“…Therefore, the degree of aridity of a dryland river may be an indicator of potential model uncertainty and subsequent attainable predictability of the system. supply and channel-associated ecosystems (Goodrich et al, 2004;Blasch et al, 2004;Lange, 2005;Dagès et al, 2008;. The surface hydrological connectivity between dryland catchments and/or upstream and downstream reaches of dryland rivers occurs if and only if the runoff propagated into channels overcomes its transmission losses (based on Beven, 2002;Bracken and Croke, 2007).…”

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confidence: 99%

A channel transmission losses model for different dryland rivers

Costa

Bronstert

Araújo

2012

Hydrol. Earth Syst. Sci.

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Abstract. Channel transmission losses in drylands take place normally in extensive alluvial channels or streambeds underlain by fractured rocks. They can play an important role in streamflow rates, groundwater recharge, freshwater supply and channel-associated ecosystems. We aim to develop a process-oriented, semi-distributed channel transmission losses model, using process formulations which are suitable for data-scarce dryland environments and applicable to both hydraulically disconnected losing streams and hydraulically connected losing(/gaining) streams. This approach should be able to cover a large variation in climate and hydro-geologic controls, which are typically found in dryland regions of the Earth. Our model was first evaluated for a losing/gaining, hydraulically connected 30 km reach of the Middle Jaguaribe River (MJR), Ceará, Brazil, which drains a catchment area of 20 000 km 2 . Secondly, we applied it to a small losing, hydraulically disconnected 1.5 km channel reach in the Walnut Gulch Experimental Watershed (WGEW), Arizona, USA. The model was able to predict reliably the streamflow volume and peak for both case studies without using any parameter calibration procedure. We have shown that the evaluation of the hypotheses on the dominant hydrological processes was fundamental for reducing structural model uncertainties and improving the streamflow prediction. For instance, in the case of the large river reach (MJR), it was shown that both lateral stream-aquifer water fluxes and groundwater flow in the underlying alluvium parallel to the river course are necessary to predict streamflow volume and channel transmission losses, the former process being more relevant than the latter. Regarding model uncertainty, it was shown that the approaches, which were applied for the unsaturated zone processes (highly nonlinear with elaborate numerical solutions), are much more sensitive to parameter variability than those approaches which were used for the saturated zone (mathematically simple water budgeting in aquifer columns, including backwater effects). In case of the MJR-application, we have seen that structural uncertainties due to the limited knowledge of the subsurface saturated system interactions (i.e. groundwater coupling with channel water; possible groundwater flow parallel to the river) were more relevant than those related to the subsurface parameter variability. In case of the WEGW application we have seen that the non-linearity involved in the unsaturated flow processes in disconnected dryland river systems (controlled by the unsaturated zone) generally contain far more model uncertainties than do connected systems controlled by the saturated flow. Therefore, the degree of aridity of a dryland river may be an indicator of potential model uncertainty and subsequent attainable predictability of the system.

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Dynamics of transmission losses in a large arid stream channel

Cited by 116 publications

References 16 publications

Where does all the water go? Partitioning water transmission losses in a data-sparse, multi-channel and low-gradient dryland river system using modelling and remote sensing

Where does all the water go? Partitioning water transmission losses in a data-sparse, multi-channel and low-gradient dryland river system using modelling and remote sensing

Fog Spatial Distributions over the Central Namib Desert - An Isotope Approach

A channel transmission losses model for different dryland rivers

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