Evaluation of aquifer parameters at regional scale by spectral analysis of discharge time series

2020 ◽  
Author(s):  
Mariaines Di Dato ◽  
Rohini Kumar ◽  
Estanislao Pujades ◽  
Timo Houben ◽  
Sabine Attinger
Keyword(s):  
Time Series ◽  
Spectral Analysis ◽  
Characteristic Time ◽  
Large Scale ◽  
Multiple Scales ◽  
Regional Scale ◽  
Hydrologic Model ◽  
Aquifer Parameters ◽  
Basin Scale ◽  
Linear Reservoir

<p>River stream is the result of several complex processes operating at basin scale. Therefore, the river catchment can be conceptualized as a series of interlinked compartments, which are characterized by their own response time to a rainfall event. Each compartment generates a flow component, such as the direct runoff, the interflow and the baseflow. The latter, typically generating from groundwater, is the slower portion of stream flow and plays a key role in studying the hydrological droughts.</p><p>In many catchment or large-scale hydrologic models, the groundwater dynamics are typically described by a linear reservoir model, which depends on the state of the reservoir and the parameter, known as recession coefficient or characteristic time. The characteristic time can be considered as the time needed until an aquifer reacts to a certain perturbation. So far, the characteristic time has been estimated by analyzing the slope of the recession (discharge) curve. However, as this method assumes that the recharge is zero within the basin, it may lead to inaccurate estimate when such a hypothesis is not fulfilled in reality.</p><p>The present work proposes to infer the characteristic time by using a stochastic approach based on spectral analysis. The catchment aquifer can be viewed as a filter, which modifies an input signal (e.g., rainfall or recharge) into an output signal (e.g., the baseflow or the hydraulic head). Since the transfer function, namely the ratio between the spectrum of baseflow and the spectrum of recharge, is dependent on the aquifer characteristics, it can be used to infer the aquifer parameters. In particular, the characteristic time is evaluated by fitting the spectrum and the variance of the measured baseflow with the analytical stochastic solutions for the linear reservoir. We compare six different methods for hydrograph separation, thereby highlighting a systematic uncertainty in determining the characteristic time due to the choice of filter used. To reduce the uncertainty in fitting, we will use the mesoscale Hydrological Model (mHM) (Samaniego et al., 2010; Kumar et al., 2013) to generate realistic time series for recharge. We apply the spectral analysis method to several river basins in Germany, with the goal to define a regionalization rule for characteristic time.</p><p> </p><p>References:</p><ul><li>Samaniego L., R. Kumar, S. Attinger (2010): Multiscale parameter regionalization of a grid-based hydrologic model at the mesoscale. Water Resour. Res., 46, W05523, doi:10.1029/2008WR007327.</li> <li>Kumar, R., L. Samaniego, and S. Attinger (2013): Implications of distributed hydrologic model parameterization on water fluxes at multiple scales and locations, Water Resour. Res., 49, doi:10.1029/2012WR012195</li> </ul>

scholarly journals Improving runoff estimates from regional climate models: a performance analysis in Spain

2012 ◽  
Vol 16 (6) ◽  
pp. 1709-1723 ◽  
Author(s):  
D. González-Zeas ◽  
L. Garrote ◽  
A. Iglesias ◽  
A. Sordo-Ward
Keyword(s):  
Time Series ◽  
Large Scale ◽  
Climate Models ◽  
Regional Climate ◽  
Aridity Index ◽  
Regional Climate Models ◽  
Hydrologic Model ◽  
Direct Runoff ◽  
Interpolation Methods ◽  
Basin Scale

Abstract. An important step to assess water availability is to have monthly time series representative of the current situation. In this context, a simple methodology is presented for application in large-scale studies in regions where a properly calibrated hydrologic model is not available, using the output variables simulated by regional climate models (RCMs) of the European project PRUDENCE under current climate conditions (period 1961–1990). The methodology compares different interpolation methods and alternatives to generate annual times series that minimise the bias with respect to observed values. The objective is to identify the best alternative to obtain bias-corrected, monthly runoff time series from the output of RCM simulations. This study uses information from 338 basins in Spain that cover the entire mainland territory and whose observed values of natural runoff have been estimated by the distributed hydrological model SIMPA. Four interpolation methods for downscaling runoff to the basin scale from 10 RCMs are compared with emphasis on the ability of each method to reproduce the observed behaviour of this variable. The alternatives consider the use of the direct runoff of the RCMs and the mean annual runoff calculated using five functional forms of the aridity index, defined as the ratio between potential evapotranspiration and precipitation. In addition, the comparison with respect to the global runoff reference of the UNH/GRDC dataset is evaluated, as a contrast of the "best estimator" of current runoff on a large scale. Results show that the bias is minimised using the direct original interpolation method and the best alternative for bias correction of the monthly direct runoff time series of RCMs is the UNH/GRDC dataset, although the formula proposed by Schreiber (1904) also gives good results.

scholarly journals Improving runoff estimates from regional climate models: a performance analysis in Spain

2012 ◽  
Vol 9 (1) ◽  
pp. 175-214
Author(s):  
D. González-Zeas ◽  
L. Garrote ◽  
A. Iglesias ◽  
A. Sordo-Ward
Keyword(s):  
Time Series ◽  
Large Scale ◽  
Climate Models ◽  
Regional Climate ◽  
Regional Climate Models ◽  
Hydrologic Model ◽  
Direct Runoff ◽  
Interpolation Methods ◽  
Basin Scale ◽  
The Impact

Abstract. An important aspect to assess the impact of climate change on water availability is to have monthly time series representative of the current situation. In this context, a simple methodology is presented for application in large-scale studies in regions where a properly calibrated hydrologic model is not available, using the output variables simulated by regional climate models (RCMs) of the European project PRUDENCE under current climate conditions (period 1961–1990). The methodology compares different interpolation methods and alternatives to generate annual times series that minimize the bias with respect to observed values. The objective is to identify the best alternative to obtain bias-corrected, monthly runoff time series from the output of RCM simulations. This study uses information from 338 basins in Spain that cover the entire mainland territory and whose observed values of naturalised runoff have been estimated by the distributed hydrological model SIMPA. Four interpolation methods for downscaling runoff to the basin scale from 10 RCMs are compared with emphasis on the ability of each method to reproduce the observed behavior of this variable. The alternatives consider the use of the direct runoff of the RCMs and the mean annual runoff calculated using five functional forms of the aridity index, defined as the ratio between potential evaporation and precipitation. In addition, the comparison with respect to the global runoff reference of the UNH/GRDC dataset is evaluated, as a contrast of the "best estimator" of current runoff on a large scale. Results show that the bias is minimised using the direct original interpolation method and the best alternative for bias correction of the monthly direct runoff time series of RCMs is the UNH/GRDC dataset, although the formula proposed by Schreiber also gives good results.

scholarly journals Improved representation of groundwater at a regional scale – coupling of mesocale Hydrologic Model (mHM) with OpeneGeoSys (OGS)

2017 ◽  
Author(s):  
Miao Jing ◽  
Falk Heße ◽  
Wenqing Wang ◽  
Thomas Fischer ◽  
Marc Walther ◽  
...  
Keyword(s):  
Time Series ◽  
Groundwater Flow ◽  
Groundwater Recharge ◽  
Large Scale ◽  
Groundwater Modeling ◽  
Coupled Model ◽  
Hydrologic Model ◽  
Source Modeling ◽  
Prediction Ability ◽  
Groundwater Head

Abstract. Most of the current large scale hydrological models do not contain a physically-based groundwater flow component. The main difficulties in large-scale groundwater modeling include the efficient representation of unsaturated zone flow, the characterization of dynamic groundwater-surface water interaction and the numerical stability while preserving complex physical processes and high resolution. To address these problems, we propose a highly-scalable coupled hydrologic and groundwater model (mHM#OGS) based on the integration of two open-source modeling codes: the mesoscale hydrologic Model (mHM) and the finite element simulator OpenGeoSys (OGS). mHM#OGS is coupled using a boundary condition-based coupling scheme that dynamically links the surface and subsurface parts. Nested time stepping allows smaller time steps for typically faster surface runoff routing in mHM and larger time steps for slower subsurface flow in OGS. mHM#OGS features the coupling interface which can transfer the groundwater recharge and river baseflow rate between mHM and OpenGeoSys. Verification of the coupled model was conducted using the time-series of observed streamflow and groundwater levels. Moreover, we force the transient model using groundwater recharge in two scenarios: (1) spatially variable recharge based on the mHM simulations, and (2) spatially homogeneous groundwater recharge. The modeling result in first scenario has a slightly higher correlation with groundwater head time-series, which further validates the plausibility of spatial groundwater recharge distribution calculated by mHM in the mesocale. The statistical analysis of model predictions shows a promising prediction ability of the model. The offline coupling method implemented here can reproduce reasonable groundwater head time series while keep a desired level of detail in the subsurface model structure with little surplus in computational cost. Our exemplary calculations show that the coupled model mHM#OGS can be a valuable tool to assess the effects of variability in land surface heterogeneity, meteorological, topographical forces and geological zonation on the groundwater flow dynamics.

scholarly journals InSAR Greece with Parallelized Persistent Scatterer Interferometry: A National Ground Motion Service for Big Copernicus Sentinel-1 Data

2020 ◽  
Vol 12 (19) ◽  
pp. 3207
Author(s):  
Ioannis Papoutsis ◽  
Charalampos Kontoes ◽  
Stavroula Alatza ◽  
Alexis Apostolakis ◽  
Constantinos Loupasakis
Keyword(s):  
Time Series ◽  
Ground Motion ◽  
Large Scale ◽  
Persistent Scatterer Interferometry ◽  
Persistent Scatterers ◽  
Processing Chain ◽  
Persistent Scatterer ◽  
Basin Scale ◽  
Wide Range ◽  
The Impact

Advances in synthetic aperture radar (SAR) interferometry have enabled the seamless monitoring of the Earth’s crust deformation. The dense archive of the Sentinel-1 Copernicus mission provides unprecedented spatial and temporal coverage; however, time-series analysis of such big data volumes requires high computational efficiency. We present a parallelized-PSI (P-PSI), a novel, parallelized, and end-to-end processing chain for the fully automated assessment of line-of-sight ground velocities through persistent scatterer interferometry (PSI), tailored to scale to the vast multitemporal archive of Sentinel-1 data. P-PSI is designed to transparently access different and complementary Sentinel-1 repositories, and download the appropriate datasets for PSI. To make it efficient for large-scale applications, we re-engineered and parallelized interferogram creation and multitemporal interferometric processing, and introduced distributed implementations to best use computing cores and provide resourceful storage management. We propose a new algorithm to further enhance the processing efficiency, which establishes a non-uniform patch grid considering land use, based on the expected number of persistent scatterers. P-PSI achieves an overall speed-up by a factor of five for a full Sentinel-1 frame for processing in a 20-core server. The processing chain is tested on a large-scale project to calculate and monitor deformation patterns over the entire extent of the Greek territory—our own Interferometric SAR (InSAR) Greece project. Time-series InSAR analysis was performed on volumes of about 12 TB input data corresponding to more than 760 Single Look Complex Sentinel-1A and B images mostly covering mainland Greece in the period of 2015–2019. InSAR Greece provides detailed ground motion information on more than 12 million distinct locations, providing completely new insights into the impact of geophysical and anthropogenic activities at this geographic scale. This new information is critical to enhancing our understanding of the underlying mechanisms, providing valuable input into risk assessment models. We showcase this through the identification of various characteristic geohazard locations in Greece and discuss their criticality. The selected geohazard locations, among a thousand, cover a wide range of catastrophic events including landslides, land subsidence, and structural failures of various scales, ranging from a few hundredths of square meters up to the basin scale. The study enriches the large catalog of geophysical related phenomena maintained by the GeObservatory portal of the Center of Earth Observation Research and Satellite Remote Sensing BEYOND of the National Observatory of Athens for the opening of new knowledge to the wider scientific community.

The performance of LSTM models from basin to continental scales

2020 ◽  
Author(s):  
Frederik Kratzert ◽  
Daniel Klotz ◽  
Günter Klambauer ◽  
Grey Nearing ◽  
Sepp Hochreiter
Keyword(s):  
Machine Learning ◽  
Large Scale ◽  
Short Term Memory ◽  
Regional Scale ◽  
Data Driven ◽  
Hydrological Models ◽  
Short Term ◽  
Term Memory ◽  
Basin Scale ◽  
Long Short Term Memory

<p>Simulation accuracy among traditional hydrological models usually degrades significantly when going from single basin to regional scale. Hydrological models perform best when calibrated for specific basins, and do worse when a regional calibration scheme is used. </p><p>One reason for this is that these models do not (have to) learn hydrological processes from data. Rather, they have a predefined model structure and only a handful of parameters adapt to specific basins. This often yields less-than-optimal parameter values when the loss is not determined by a single basin, but by many through regional calibration.</p><p>The opposite is true for data driven approaches where models tend to get better with more and diverse training data. We examine whether this holds true when modeling rainfall-runoff processes with deep learning, or if, like their process-based counterparts, data-driven hydrological models degrade when going from basin to regional scale.</p><p>Recently, Kratzert et al. (2018) showed that the Long Short-Term Memory network (LSTM), a special type of recurrent neural network, achieves comparable performance to the SAC-SMA at basin scale. In follow up work Kratzert et al. (2019a) trained a single LSTM for hundreds of basins in the continental US, which outperformed a set of hydrological models significantly, even compared to basin-calibrated hydrological models. On average, a single LSTM is even better in out-of-sample predictions (ungauged) compared to the SAC-SMA in-sample (gauged) or US National Water Model (Kratzert et al. 2019b).</p><p>LSTM-based approaches usually involve tuning a large number of hyperparameters, such as the number of neurons, number of layers, and learning rate, that are critical for the predictive performance. Therefore, large-scale hyperparameter search has to be performed to obtain a proficient LSTM network.  </p><p>However, in the abovementioned studies, hyperparameter optimization was not conducted at large scale and e.g. in Kratzert et al. (2018) the same network hyperparameters were used in all basins, instead of tuning hyperparameters for each basin separately. It is yet unclear whether LSTMs follow the same trend of traditional hydrological models to degrade performance from basin to regional scale. </p><p>In the current study, we performed a computational expensive, basin-specific hyperparameter search to explore how site-specific LSTMs differ in performance compared to regionally calibrated LSTMs. We compared our results to the mHM and VIC models, once calibrated per-basin and once using an MPR regionalization scheme. These benchmark models were calibrated individual research groups, to eliminate bias in our study. We analyse whether differences in basin-specific vs regional model performance can be linked to basin attributes or data set characteristics.</p><p>References:</p><p>Kratzert, F., Klotz, D., Brenner, C., Schulz, K., and Herrnegger, M.: Rainfall–runoff modelling using Long Short-Term Memory (LSTM) networks, Hydrol. Earth Syst. Sci., 22, 6005–6022, https://doi.org/10.5194/hess-22-6005-2018, 2018. </p><p>Kratzert, F., Klotz, D., Shalev, G., Klambauer, G., Hochreiter, S., and Nearing, G.: Towards learning universal, regional, and local hydrological behaviors via machine learning applied to large-sample datasets, Hydrol. Earth Syst. Sci., 23, 5089–5110, https://doi.org/10.5194/hess-23-5089-2019, 2019a. </p><p>Kratzert, F., Klotz, D., Herrnegger, M., Sampson, A. K., Hochreiter, S., & Nearing, G. S.: Toward improved predictions in ungauged basins: Exploiting the power of machine learning. Water Resources Research, 55. https://doi.org/10.1029/2019WR026065, 2019b.</p>

scholarly journals Potential of Multiway PLS (N-PLS) Regression Method to Analyse Time-Series of Multispectral Images: A Case Study in Agriculture

2022 ◽  
Vol 14 (1) ◽  
pp. 216
Author(s):  
Eva Lopez-Fornieles ◽  
Guilhem Brunel ◽  
Florian Rancon ◽  
Belal Gaci ◽  
Maxime Metz ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Heat Wave ◽  
Large Scale ◽  
Regional Scale ◽  
Pls Regression ◽  
Wave Impact ◽  
Sensing Applications ◽  
Sentinel 2

Recent literature reflects the substantial progress in combining spatial, temporal and spectral capacities for remote sensing applications. As a result, new issues are arising, such as the need for methodologies that can process simultaneously the different dimensions of satellite information. This paper presents PLS regression extended to three-way data in order to integrate multiwavelengths as variables measured at several dates (time-series) and locations with Sentinel-2 at a regional scale. Considering that the multi-collinearity problem is present in remote sensing time-series to estimate one response variable and that the dataset is multidimensional, a multiway partial least squares (N-PLS) regression approach may be relevant to relate image information to ground variables of interest. N-PLS is an extension of the ordinary PLS regression algorithm where the bilinear model of predictors is replaced by a multilinear model. This paper presents a case study within the context of agriculture, conducted on a time-series of Sentinel-2 images covering regional scale scenes of southern France impacted by the heat wave episode that occurred on 28 June 2019. The model has been developed based on available heat wave impact data for 107 vineyard blocks in the Languedoc-Roussillon region and multispectral time-series predictor data for the period May to August 2019. The results validated the effectiveness of the proposed N-PLS method in estimating yield loss from spectral and temporal attributes. The performance of the model was evaluated by the R2 obtained on the prediction set (0.661), and the root mean square of error (RMSE), which was 10.7%. Limitations of the approach when dealing with time-series of large-scale images which represent a source of challenges are discussed; however, the N–PLS regression seems to be a suitable choice for analysing complex multispectral imagery data with different spectral domains and with a clear temporal evolution, such as an extreme weather event.

scholarly journals Thresholds definition in MOD13Q1 and VGT-S10 time series for coffee crop area estimation in Triângulo Mineiro/Alto Paranaíba

2019 ◽  
Vol 3 (2) ◽  
pp. 1-10
Author(s):  
Michel Eustáquio Dantas Chaves ◽  
Elizabeth Ferreira ◽  
Antonio Augusto Aguilar Dantas
Keyword(s):  
Land Use ◽  
Time Series ◽  
Large Scale ◽  
Agricultural Research ◽  
Regional Scale ◽  
Vegetation Indices ◽  
Seasonal Development ◽  
Land Use Classification ◽  
Crop Area ◽  
The Time Domain

In the last decades, remote sensing application in agricultural research has intensified to evaluate phenological cycles. Vegetation indices time series have been used to obtain information about the seasonal development of agricultural vegetation on a large scale. The multitemporal approach increases the gain of information coming from orbital images, an important factor for analysis of its spatial distribution. The objective of this study was to test the application of vegetation indices of the MODIS and SPOT-VEGETATION sensors to estimate the areas destined for coffee crops in the Triângulo Mineiro/Alto Paranaíba mesoregion. The results show that the vegetation indices NDVI and EVI of the product MOD13Q1 were more adequate for the estimation of land use over the time domain, especially NDVI. The best minimum threshold varies between 0.39 - 0.42 and the best maximum threshold varies between 0.71 - 0.74. The contribution of this work is that these thresholds can serve as subsidies for land use classification studies on a regional scale and for estimating areas for planting.

scholarly journals Validation of the Accuracy of Different Precipitation Datasets over Tianshan Mountainous Area

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Chuancheng Zhao ◽  
Shuxia Yao ◽  
Shiqiang Zhang ◽  
Haidong Han ◽  
Qiudong Zhao ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Satellite Remote Sensing ◽  
Large Scale ◽  
Regional Scale ◽  
Precipitation Amount ◽  
Rain Gauge ◽  
Small Scale ◽  
Accurate Information ◽  
Mountainous Area ◽  
Basin Scale

Precipitation is one of the important water supplies in the arid and semiarid regions of northwestern China, playing a vital role in maintaining the fragile ecosystem. In remote mountainous area, it is difficult to obtain an accurate and reliable spatialization of the precipitation amount at the regional scale due to the inaccessibility, the sparsity of observation stations, and the complexity of relationships between precipitation and topography. Furthermore, accurate precipitation is important driven data for hydrological models to assess the water balance and water resource for hydrologists. Therefore, the use of satellite remote sensing becomes an important means over mountainous area. Precipitation datasets based on station data or pure satellite data have been increasingly available in spite of several weaknesses. This paper evaluates the usefulness of three precipitation datasets including TRMM 3B43_V6, 3B43_V7, and Asian Precipitation Highly Resolved Observational Data Integration Towards Evaluation with rain gauge data over Tianshan mountainous area where precipitation data is scarce. The results suggest that precipitation measurements only provided accurate information on a small scale, while the satellite remote sensing of precipitation had obvious advantages in basin scale or large scale especially over remote mountainous area.

Defining bounding surfaces within and between eolian and non-eolian deposits, Lower Jurassic Navajo Sandstone, Moab Area, Utah, U.S.A.: Implications for subdividing erg system strata

2021 ◽  
Vol 91 (12) ◽  
pp. 1275-1304
Author(s):  
Stephen T. Hasiotis ◽  
Marjorie A. Chan ◽  
Judith Totman Parrish
Keyword(s):  
Large Scale ◽  
Multiple Scales ◽  
Complex Surface ◽  
Lower Jurassic ◽  
Composite Surface ◽  
Independent Sequence ◽  
Navajo Sandstone ◽  
Basin Scale ◽  
Eolian Deposits ◽  
Bounding Surfaces

ABSTRACT A model-independent, sequence stratigraphic approach is used to define bounding surfaces in the Navajo Sandstone in order to identify an architectural hierarchy of genetically related sedimentary packages and the surfaces that bound them across multiple scales of both eolian and non-eolian components of an erg system. Seven bounding surfaces and eight depositional units are defined, from small to large scale. A lamina-deviation surface bounds wedge- and tabular-shaped sets of laminae and/or laminasets, separating those that have different angle orientations on the dune slipface. A bed-deviation surface bounds a succession of beds (crossbeds) that lie at different angles or orientations to bedding above, below, or adjacent to it. A bedset-deviation surface is curved, inclined, and/or wavy and irregular that bounds bedsets and their internal stratification patterns; that is, bed-deviation surfaces, and lamina-deviation surfaces. A simple surface is gently inclined with or without small, concave or convex segments that bound beds and bedsets. A composite surface is horizontal with or without concave, curved, or irregular portions of that surface. A complex surface is laterally extensive (∼ 1–10+ km) that regionally bounds and truncates underlying conterminous and interfingered eolian and non-eolian strata. An amalgamated surface is a regionally extensive (∼ 10 to 100s km) mappable unconformity, merged unconformities, and their laterally equivalent conformable surface that can exhibit local to regional pedogenic modification, lags, and significant (meters to 10s m) paleotopographic relief. The genetically related sedimentary packages typically bounded by like or higher-rank surfaces are defined as laminae, laminasets, bed, bedsets, and simple, composite, complex, and amalgamated units. Field relationships of strata and surfaces are key to reconstructing the interactions between eolian and non-eolian deposits and the processes they represent at the local, regional, and basin scale. This classification scheme can be applied to erg-system strata to fully integrate changes in diverse facies within and between contiguous deposits.

scholarly journals Examining regional groundwater–surface water dynamics using an integrated hydrologic model of the San Joaquin River basin

2017 ◽  
Vol 21 (2) ◽  
pp. 923-947 ◽  
Author(s):  
James M. Gilbert ◽  
Reed M. Maxwell
Keyword(s):  
Surface Water ◽  
River Basin ◽  
Regional Scale ◽  
Power Spectra ◽  
Central Valley ◽  
Hydrologic Model ◽  
Water Dynamics ◽  
Irrigated Agriculture ◽  
San Joaquin River ◽  
Basin Scale

Abstract. Widespread irrigated agriculture and a growing population depend on the complex hydrology of the San Joaquin River basin in California. The challenge of managing this complex hydrology hinges, in part, on understanding and quantifying how processes interact to support the groundwater and surface water systems. Here, we use the integrated hydrologic platform ParFlow-CLM to simulate hourly 1 km gridded hydrology over 1 year to study un-impacted groundwater–surface water dynamics in the basin. Comparisons of simulated results to observations show the model accurately captures important regional-scale partitioning of water among streamflow, evapotranspiration (ET), snow, and subsurface storage. Analysis of this simulated Central Valley groundwater system reveals the seasonal cycle of recharge and discharge as well as the role of the small but temporally constant portion of groundwater recharge that comes from the mountain block. Considering uncertainty in mountain block hydraulic conductivity, model results suggest this component accounts for 7–23 % of total Central Valley recharge. A simulated surface water budget guides a hydrograph decomposition that quantifies the temporally variable contribution of local runoff, valley rim inflows, storage, and groundwater to streamflow across the Central Valley. Power spectra of hydrograph components suggest interactions with groundwater across the valley act to increase longer-term correlation in San Joaquin River outflows. Finally, model results reveal hysteresis in the relationship between basin streamflow and groundwater contributions to flow. Using hourly model results, we interpret the hysteretic cycle to be a result of daily-scale fluctuations from precipitation and ET superimposed on seasonal and basin-scale recharge and discharge.

Sign in / Sign up

Export Citation Format

Share Document