Doing ecohydrology backward: Inferring wetland flow and hydroperiod from landscape patterns

Acharya, Subodh; Kaplan, D. A.; Jawitz, James W.; Cohen, Matthew J.

doi:10.1002/2017wr020516

Cited by 8 publications

(5 citation statements)

References 70 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…That landscape‐scale fluxes become most sensitive to patch configuration when water levels approach patch surfaces is consistent with other studies (Acharya et al, , ; Choi & Harvey, ; Kaplan et al, ). Choi and Harvey () identified how water level divides controls on large‐scale fluxes into two regimes, separated by a threshold coincident with patch surfaces: a high‐water regime, in which vegetative drag governs fluxes, and a low‐water regime, in which microtopography governs fluxes.…”

Section: Resultssupporting

confidence: 90%

How Important Is Connectivity for Surface Water Fluxes? A Generalized Expression for Flow Through Heterogeneous Landscapes

Larsen

Kaplan

2017

Geophysical Research Letters

Self Cite

View full text Add to dashboard Cite

How important is hydrologic connectivity for surface water fluxes through heterogeneous floodplains, deltas, and wetlands? While significant for management, this question remains poorly addressed. Here we adopt spatial resistance averaging, based on channel and patch configuration metrics quantifiable from aerial imagery, to produce an upscaled rate law for discharge. Our model suggests that patch coverage largely controls discharge sensitivity, with smaller effects from channel connectivity and vegetation patch fractal dimension. However, connectivity and patch configuration become increasingly important near the percolation threshold and at low water levels. These effects can establish positive feedbacks responsible for substantial flow change in evolving landscapes (14–36%, in our Everglades case study). Connectivity also interacts with other drivers; flow through poorly connected hydroscapes is less resilient to perturbations in other drivers. Finally, we found that flow through heterogeneous patches is alone sufficient to produce non‐Manning flow–depth relationships commonly observed in wetlands but previously attributed to depth‐varying roughness.

show abstract

Section: Resultssupporting

confidence: 90%

How Important Is Connectivity for Surface Water Fluxes? A Generalized Expression for Flow Through Heterogeneous Landscapes

Larsen

Kaplan

2017

Geophysical Research Letters

Self Cite

View full text Add to dashboard Cite

show abstract

“…Surface discharge is commonly approximated as a power function of stage (e.g., Acharya et al, 2017). This is well supported for BICY total wetlandscape discharge Q at channel station TR and wetland stages h i at five monitoring stations TR1, TR2, TR3, BCA16, and BCNPA14 ( Figure 2) for…”

Section: Stage-discharge Relationshipmentioning

confidence: 56%

Q \approx ({, \begin{array}{l} a {(\frac{h_{d, i}}{h_{italicref}})}^{c} for h_{d . i} \geq 0 \\ 0 for h_{d, i} \leq 0 \end{array})

where a = 500 m 3 /s and c = 2 are fitting parameters for an arbitrarily chosen reference value of h ref = 1 m. Detrended wetland stages h d,i are defined as

h_{d . i} = h_{i} - b_{i}

where b i = {3.78, 3.59, 2.58, 3.27, 2.60} m are individual fitting parameters (also found from Figure 2) for stations TR1, TR2, TR3, BCA16, and BCNPA14, respectively.…”

Section: Reduced‐complexity Wetlandscape‐scale Dynamic Modelmentioning

confidence: 99%

Local Storage Dynamics of Individual Wetlands Predict Wetlandscape Discharge

Klammler

Quintero

Jawitz

et al. 2020

Water Resources Research

Self Cite

View full text Add to dashboard Cite

Wetlands provide valuable hydrological, ecological, and biogeochemical functions, both alone and in combination with other elements comprising the wetlandscape. Understanding the processes and mechanisms that drive wetlandscape functions, as well as their sensitivity to natural and man-made alterations, requires a sound physical understanding of wetland hydrodynamics. Here, we develop and apply a single reservoir hydrologic model to a low-relief karst wetlandscape in southwest Florida (≈10 3 km 2 of Big Cypress National Preserve) using precipitation P and potential evapotranspiration PET as climatic drivers. This simple approach captures the dynamics of storage for individual wetlands across the entire wetlandscape and accurately predicts landscape discharge. Key model insights are the importance of depth-dependent extinction of evapotranspiration ET and the negligible effects of depth-dependent specific yield, the effects of which are diluted by landscape relief. We identify three phases of the wetlandscape hydrological regime: dry, wet-stagnant, and wet-flowing. The model allowed a simple steady-state analysis, which demonstrated the sudden seasonal shift between wet-stagnant and wet-flowing states, indicating a consistent threshold at P ≈ PET. Notably, stage data from any single wetland appears sufficient for accurate whole-landscape discharge prediction because of the relative homogeneity in timing and duration of local wetland hydrologic connectivity in this landscape. We also show that this method will be transferable to other wetlandscapes, where individual storage elements respond hydrologically synchronously, whereas model performance is expected to deteriorate for hydrologically more heterogeneous wetlandscapes.

show abstract

“…As discussed earlier, realizations generated by GANs are typically more realistic-looking and sharper to the human eye, even if the difference may not manifest in statistical metrics. In addition, hydrologists have long pursued the idea of "doing hydrology backward" (Acharya et al, 2017;Kirchner, 2009); that is, inferring the driving forces from the outcomes. There are many other water problems where we can observe consequences but not the forcings, e.g., surface water and groundwater contamination.…”

Section: Tackling Water Resources Challenges With the Help Of DLmentioning

confidence: 99%

A Transdisciplinary Review of Deep Learning Research and Its Relevance for Water Resources Scientists

Shen

2018

Water Resources Research

755

411

View full text Add to dashboard Cite

Deep learning (DL), a new generation of artificial neural network research, has transformed industries, daily lives, and various scientific disciplines in recent years. DL represents significant progress in the ability of neural networks to automatically engineer problem‐relevant features and capture highly complex data distributions. I argue that DL can help address several major new and old challenges facing research in water sciences such as interdisciplinarity, data discoverability, hydrologic scaling, equifinality, and needs for parameter regionalization. This review paper is intended to provide water resources scientists and hydrologists in particular with a simple technical overview, transdisciplinary progress update, and a source of inspiration about the relevance of DL to water. The review reveals that various physical and geoscientific disciplines have utilized DL to address data challenges, improve efficiency, and gain scientific insights. DL is especially suited for information extraction from image‐like data and sequential data. Techniques and experiences presented in other disciplines are of high relevance to water research. Meanwhile, less noticed is that DL may also serve as a scientific exploratory tool. A new area termed AI neuroscience, where scientists interpret the decision process of deep networks and derive insights, has been born. This budding subdiscipline has demonstrated methods including correlation‐based analysis, inversion of network‐extracted features, reduced‐order approximations by interpretable models, and attribution of network decisions to inputs. Moreover, DL can also use data to condition neurons that mimic problem‐specific fundamental organizing units, thus revealing emergent behaviors of these units. Vast opportunities exist for DL to propel advances in water sciences.

show abstract

Doing ecohydrology backward: Inferring wetland flow and hydroperiod from landscape patterns

Cited by 8 publications

References 70 publications

How Important Is Connectivity for Surface Water Fluxes? A Generalized Expression for Flow Through Heterogeneous Landscapes

How Important Is Connectivity for Surface Water Fluxes? A Generalized Expression for Flow Through Heterogeneous Landscapes

Local Storage Dynamics of Individual Wetlands Predict Wetlandscape Discharge

A Transdisciplinary Review of Deep Learning Research and Its Relevance for Water Resources Scientists

Contact Info

Product

Resources

About