Correlating Global Precipitation Measurement satellite data with karst spring hydrographs for rapid catchment delineation

Longenecker, Jake; Bechtel, Timothy; Chen, Zhao; Goldscheider, Nico; Liesch, Tanja; Walter, Robert C.

doi:10.1002/2017gl073790

Cited by 11 publications

(12 citation statements)

References 13 publications

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“…Groundwater basins are often incongruent with river basins (e.g., Schaller & Fan, ), and topography alone offers incomplete guidance, particularly in karst terrain (Ford & Williams, ; Worthington et al, ) where the cave and conduit network can reach >500 km (e.g., the Mammoth Cave System in Kentucky; Groves & Meiman, ) through which groundwater moves as fast as rivers. Recent work including karstic structures significantly altered the modeled hydrologic partitioning (Hartmann et al, , ; Longenecker et al, ; Rahman & Rosolem, ). The karst example illustrates that many of the improvements in large‐scale models would be futile without acknowledging such first‐order geologic controls on groundwater flow.…”

Section: Representing Hillslope Hydrology In Esmsmentioning

confidence: 99%

Hillslope Hydrology in Global Change Research and Earth System Modeling

Fan

Clark

Lawrence

et al. 2019

Water Resources Research

328

319

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Earth System Models (ESMs) are essential tools for understanding and predicting global change, but they cannot explicitly resolve hillslope-scale terrain structures that fundamentally organize water, energy, and biogeochemical stores and fluxes at subgrid scales. Here we bring together hydrologists, Critical Zone scientists, and ESM developers, to explore how hillslope structures may modulate ESM grid-level water, energy, and biogeochemical fluxes. In contrast to the one-dimensional (1-D), 2-to 3-m deep, and free-draining soil hydrology in most ESM land models, we hypothesize that 3-D, lateral ridge-to-valley flow through shallow and deep paths and insolation contrasts between sunny and shady slopes are the top two globally quantifiable organizers of water and energy (and vegetation) within an ESM grid cell. We hypothesize that these two processes are likely to impact ESM predictions where (and when) water and/or energy are limiting. We further hypothesize that, if implemented in ESM land models, these processes will increase simulated continental water storage and residence time, buffering terrestrial ecosystems against seasonal and interannual droughts. We explore efficient ways to capture these mechanisms in ESMs and identify critical knowledge gaps preventing us from scaling up hillslope to global processes. One such gap is our extremely limited knowledge of the subsurface, where water is stored (supporting vegetation) and released to stream baseflow (supporting aquatic ecosystems). We conclude with a set of organizing

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Section: Representing Hillslope Hydrology In Esmsmentioning

confidence: 99%

Hillslope Hydrology in Global Change Research and Earth System Modeling

Fan

Clark

Lawrence

et al. 2019

Water Resources Research

328

319

View full text Add to dashboard Cite

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“…Where no information about the catchment is available at all, an approximate localization is advantageous as a first step towards an exact delineation, since it facilitates the application of more elaborate methods like tracer tests. There has already been an attempt by Longenecker et al (2017) to semiautomatically derive approximate catchment boundaries by correlating karst spring discharge events with global precipitation measurement (GPM) gridded data (NASA, 2016). The authors were able to achieve reasonable results with their method but also noticed that they could not replace conventional methods.…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, we investigate the potential of this approach to identifying the approximate catchment location based on a modified spatial input sensitivity analysis from Anderson and Radić (2022). Deriving recharge areas based on rainfall-discharge event correlation, as previously done by Longenecker et al (2017), requires (i) heterogeneous rainfall at catchment scale, (ii) precipitation data with sufficient spatial resolution that capture this heterogeneity and (iii) a karst system without too much dampening of the precipitation signals. These requirements hold for our proposed methodology as well, but a potential advantage of ANNs is their nonlinearity, which may better capture the nonlinear relationships between rainfall and discharge.…”

Section: Introductionmentioning

confidence: 99%

Karst spring discharge modeling based on deep learning using spatially distributed input data

Wünsch

Liesch

Cinkus

et al. 2022

Hydrol. Earth Syst. Sci.

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Abstract. Despite many existing approaches, modeling karst water resources remains challenging as conventional approaches usually heavily rely on distinct system knowledge. Artificial neural networks (ANNs), however, require only little prior knowledge to automatically establish an input–output relationship. For ANN modeling in karst, the temporal and spatial data availability is often an important constraint, as usually no or few climate stations are located within or near karst spring catchments. Hence, spatial coverage is often not satisfactory and can result in substantial uncertainties about the true conditions in the catchment, leading to lower model performance. To overcome these problems, we apply convolutional neural networks (CNNs) to simulate karst spring discharge and to directly learn from spatially distributed climate input data (combined 2D–1D CNNs). We investigate three karst spring catchments in the Alpine and Mediterranean region with different meteorological–hydrological characteristics and hydrodynamic system properties. We compare the proposed approach both to existing modeling studies in these regions and to our own 1D CNN models that are conventionally trained with climate station input data. Our results show that all the models are excellently suited to modeling karst spring discharge (NSE: 0.73–0.87, KGE: 0.63–0.86) and can compete with the simulation results of existing approaches in the respective areas. The 2D models show a better fit than the 1D models in two of three cases and automatically learn to focus on the relevant areas of the input domain. By performing a spatial input sensitivity analysis, we can further show their usefulness in localizing the position of karst catchments.

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“…Understanding karst aquifer response to a changing climate is critical to the 20–25% of the global population dependent on karst aquifers as a water source (Ford & Williams, ; Hartmann et al, ). However, the complex nature of karst aquifers, combined with surface water basins that often do not correspond with the associated groundwater basin (Kačaroğlu, ; Longenecker et al, ), inhibits estimation of current water availability and makes the resilience of these aquifers difficult to predict. As summarized within Hartmann et al (), many studies have attempted to quantify the storage and model the hydrologic connectivity within karst aquifers, but large quantities of hydrogeologic information and tracer studies are required to obtain these estimates and significant predictive uncertainty remains.…”

Section: Introductionmentioning

confidence: 99%

Stream Centric Methods for Determining Groundwater Contributions in Karst Mountain Watersheds

Neilson

Tennant

Stout

et al. 2018

Water Resources Research

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Climate change influences on mountain hydrology are uncertain but likely to be mediated by variability in subsurface hydrologic residence times and flow paths. The heterogeneity of karst aquifers adds complexity in assessing the resiliency of these water sources to perturbation, suggesting a clear need to quantify contributions from and losses to these aquifers. Here we develop a stream centric method that combines mass and flow balances to quantify net and gross gains and losses at different spatial scales. We then extend these methods to differentiate between karst conduit and matrix contributions from the aquifer. In the Logan River watershed in Northern Utah we found significant amounts of the river water repeatedly gained and then lost through a 35‐km study reach. Further, the direction and amount of water exchanged varied over space, time, and discharge. Streamflow was dominated by discharge of karst conduit groundwater after spring runoff with increasing, yet still small, fractions of matrix water later in the summer. These findings were combined with geologic information, prior subsurface dye tracing, and chemical sampling to provide additional lines of evidence that repeated groundwater exchanges are likely occurring and river flows are highly dependent on karst aquifer recharge and discharge. Given the large population dependent on karst aquifers throughout the world, there is a continued need to develop simple methods, like those presented here, for determining the resiliency of karst groundwater resources.

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Correlating Global Precipitation Measurement satellite data with karst spring hydrographs for rapid catchment delineation

Cited by 11 publications

References 13 publications

Hillslope Hydrology in Global Change Research and Earth System Modeling

Hillslope Hydrology in Global Change Research and Earth System Modeling

Karst spring discharge modeling based on deep learning using spatially distributed input data

Stream Centric Methods for Determining Groundwater Contributions in Karst Mountain Watersheds

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