We use seismic ambient noise recorded by dense ocean bottom nodes (OBNs) in the Gorgon gas field, Western Australia, to compute time-lapse seafloor models of shear-wave velocity. The extracted hourly cross-correlation (CC) functions in the frequency band 0.1 – 1 Hz contain mainly Scholte waves with very high signal to noise ratio. We observe temporal velocity variations (dv/v) at the order of 0.1% with a peak velocity change of 0.8% averaged from all station pairs, from the conventional time-lapse analysis with the assumption of a spatially homogeneous dv/v. With a high-resolution reference (baseline) model from full waveform inversion of Scholte waves, we present an elastic wave equation based double-difference inversion (EW-DD) method, using arrival time differences between the reference and time-lapsed Scholte waves, for mapping temporally varying dv/v in the heterogeneous subsurface. The time-lapse velocity models reveal increasing/decreasing patterns of shear-wave velocity in agreement with those from the conventional analysis. The velocity variation exhibits a ~24-hour cycling pattern, which appears to be inversely correlated with sea level height, possibly associated with dilatant effects for porous, low-velocity shallow seafloor and rising pore pressure with higher sea level. This study demonstrates the feasibility of using dense passive seismic surveys for quantitative monitoring of subsurface property changes in the horizontal and depth domain.

Global Hydrological and Land Surface Models (GHM/LSMs) embody numerous interacting predictors and equations, complicating the diagnosis of primary hydrological relationships. We propose a model diagnostic approach based on Random Forest feature importance to detect the input variables that most influence simulated hydrological processes. We analyzed the JULES, ORCHIDEE, HTESSEL, SURFEX and PCR-GLOBWB models for the relative importance of precipitation, climate, soil, land cover and topographic slope as predictors of simulated average evaporation, runoff, and surface and subsurface runoffs. The machine learning model could reproduce GHM/LSMs outputs with a coefficient of determination over 0.85 in all cases and often considerably better. The GHM/LSMs agreed precipitation, climate and land cover share equal importance for evaporation prediction, and mean precipitation is the most important predictor of runoff. However, the GHM/LSMs disagreed on which features determine surface and subsurface runoff processes, especially with regards to the relative importance of soil texture and topographic slope.

In the tropical Pacific, weak ventilation and intense microbial respiration at depth give rise to a low dissolved oxygen (O2) environment that is thought to be ventilated primarily by the equatorial current system (ECS). The role of mesoscale eddies and diapycnal mixing as potential pathways of O2 supply in this region, however, remains poorly known due to sparse observations and coarse model resolution. Using an eddy resolving simulation of ocean circulation and biogeochemistry, we assess the contribution of these processes to the O2 budget balance and find that turbulent mixing of O2 and its modulation by mesoscale eddies contribute substantially to the replenishment of O2 in the upper equatorial Pacific thermocline, complementing the advective supply of O2 by the ECS and meridional circulation at depth. These transport processes are strongly sensitive to seasonal forcing by the wind, with elevated mixing of O2 into the upper thermocline during summer and fall when the vertical shear of the lateral flow and eddy kinetic energy are intensified. The tight link between eddy activity and the downward mixing of O2 arises from the modulation of equatorial turbulence by Tropical Instability Waves via their eddy impacts on the vertical shear. This interaction of ocean processes across scales sustains a local pathway of O2 delivery into the equatorial Pacific interior and highlights the need for adequate observations and model representation of turbulent mixing and mesoscale processes for understanding and predicting the fate of the tropical Pacific O2 content in a warmer and more stratified ocean.

Water level drawdowns are increasingly common in lakes and reservoirs worldwide as a result of both climate change and water management. Drawdowns can have direct effects on physical properties of a waterbody (e.g., by altering stratification and light dynamics), and can also have emergent effects on the waterbody’s biology and chemistry. However, the emergent effects of drawdown remain poorly characterized in small, thermally-stratified reservoirs, which are common in the landscape. Here, we intensively monitored a small eutrophic reservoir for two years, including before, during, and after a month-long drawdown that reduced total reservoir volume by 36%. Our study aimed to quantify the effects of water level change on reservoir physical, chemical, and biological properties. During drawdown, stratification strength (maximum buoyancy frequency) and surface phosphate concentrations both increased, contributing to a substantial surface phytoplankton bloom. The peak in phytoplankton biomass was followed by cascading changes in surface water chemistry, with sequential peaks in dissolved organic carbon, dissolved carbon dioxide, and ammonium concentrations that reflect biogeochemical processes associated with bloom degradation. Dissolved oxygen concentrations substantially decreased in the surface waters during drawdown (to 41% saturation), which was associated with increased iron and manganese concentrations. Combined, our results illustrate how changes in water level can have emergent effects on coupled physical, chemical, and biological processes. As climate change and water management continue to increase the frequency of drawdowns in lakes worldwide, our results highlight the importance of characterizing how water level variability can alter complex in-lake ecosystem processes, thereby affecting water quality.

The moving solar terminator (ST) generates atmospheric disturbances, broadly termed solar terminator waves (STWs). Despite theoretically recurring daily, STWs remain poorly understood, partially due to measurement challenges near the ST. Analyzing Michelson Interferometer for Global High-resolution Thermospheric Imaging (MIGHTI) data from NASA’s Ionospheric Connection Explorer (ICON) observatory, we present observations of STW signatures in thermospheric neutral winds, including the first meridional wind signatures. Seasonal analysis reveals STWs are most prominent during solstices, when they intersect the ST about $\sim20^\circ$ from the equator in the winter hemisphere and are inclined at a $\sim40^\circ$ angle to the ST. We also provide the first observed STW altitude profiles, revealing large vertical wavelengths above 200 km. Comparing these observations to four different models suggests the STWs likely originate directly or indirectly from waves from below 97 km. STWs may play an under-recognized role in the daily variability of the thermosphere-ionosphere system, warranting further study.

A document by Hongxiang Yan. Click on the document to view its contents.

A document by Sergio León-Ríos. Click on the document to view its contents.

Observation and measurements of ice structure and deformation made in tunnels excavated into the margin of Taylor Glacier reveal a complex, rapidly deforming basal ice sequence. Displacement measurements in the basal ice, which is at a temperature of -18°C, together with the occurrence of cavities and slickenslides, suggests that sliding occurs at structural discontinuities within the basal zone although we cannot rule out the possibility of rapid deformation in thin zones of high shear. Strain measurements show that the highest strain rates occur in ice with average debris concentrations of 26% followed by ice with debris concentrations of around 12%. The lowest strain rates occur in clean ice that has very low debris concentrations (<0.02%). Deformation within the basal ice sequence is dominated by simple shear but disrupted by folding which results in shortening of the debris-bearing ice followed by attenuation of the folds due to progressive simple shear which generates predominantly laminar basal ice structures. About 60% of glacier surface velocity can be attributed to deformation within the 4.5 m thick sequence of basal ice that was monitored for this study, and 15% of motion can be attributed to sliding. The combination of high debris concentrations and high strain rates in the debris-bearing ice means that material transported in the basal ice is exposed to a high rates of abrasion which produces heavily striated and facetted clasts typical of temperate glaciers even though the basal ice is at a temperature of -18°C.

Cloud microphysics is a critical aspect of the Earth’s climate system, which involves processes at the nano- and micrometer scales of droplets and ice particles. In climate modeling, cloud microphysics is commonly represented by bulk models, which contain simplified process rates that require calibration. This study presents a framework for calibrating warm-rain bulk schemes using high-fidelity super-droplet simulations that provide a more accurate and physically based representation of cloud and precipitation processes. The calibration framework employs ensemble Kalman methods including ensemble Kalman inversion (EKI) and unscented Kalman inversion (UKI) to calibrate bulk microphysics schemes with probabilistic super-droplet simulations. We demonstrate the framework’s effectiveness by calibrating a single-moment bulk scheme, resulting in a reduction of data-model mismatch by more than $75\%$ compared to the model with initial parameters. Thus, this study demonstrates a powerful tool for enhancing the accuracy of bulk microphysics schemes in atmospheric models and improving climate modeling.

Following the 3rd release of the “Emerging PV reports” , the best achievements in the performance of emerging photovoltaic (e-PV) devices in diverse e-PV research subjects are summarized, as reported in peer-reviewed articles in academic journals since August 2022. Updated graphs, tables and analyses are provided with several performance parameters, such as power conversion efficiency, open-circuit voltage, short-circuit current density, fill factor, light utilization efficiency, and stability test energy yield. These parameters are presented as a function of the photovoltaic bandgap energy and the average visible transmittance for each technology and application, and are put into perspective using, for example, the detailed balance efficiency limit. The 4th installment of the “Emerging PV reports” discusses the “PV emergence” classification with respect to the “PV technology generations” and “PV research waves” and highlights the latest device performance progress in multijunction and flexible photovoltaics. Additionally, Dale-Scarpulla’s plots of efficiency-effort in terms of cumulative academic publication count are also introduced.

To improve the predictive capability of ecosystem biogeochemical models (EBMs), we discuss the feasibility of formulating biogeochemical processes using physical rules that have underpinned the many successes in computational physics and chemistry. We argue that the currently popular empirically based modeling approaches, such as multiplicative empirical response functions and the law of the minimum, will not lead to EBM formulations that can be continuously refined to incorporate improved mechanistic understanding and empirical observations of biogeochemical processes. As an alternative to these empirical models, we propose to formulate EBMs using established physical rules widely used in computational physics and chemistry. Through several examples, we demonstrate how mathematical representations derived from physical rules can improve understanding of relevant biogeochemical processes and enable more effective communication between modelers, observationalists, and experimentalists regarding essential questions, such as what measurements are needed to meaningfully inform models and how can models generate new process-level hypotheses to test in empirical studies?

A document by Xiang Yang. Click on the document to view its contents.

Mineral dust is one of the most abundant atmospheric aerosol species and has various far-reaching effects on the climate system and adverse impacts on air quality. Satellite observations can provide spatio-temporal information on dust emission and transport pathways. However, satellite observations of dust plumes are frequently obscured by clouds. We use a method based on established, machine-learning-based image in-painting techniques to restore the spatial extent of dust plumes for the first time. We train an artificial neural net (ANN) on modern reanalysis data paired with satellite-derived cloud masks. The trained ANN is applied to gray-scaled and cloud-masked false-color daytime images for dust aerosols from 2021 and 2022, obtained from the SEVIRI instrument onboard the Meteosat Second Generation satellite. We find up to 15 \% of summertime observations in West Africa and 10 \% of summertime observations in Nubia by satellite images miss dust events due to cloud cover. The diurnal and seasonal patterns in the reconstructed dust occurrence frequency are consistent with known dust emission and transport processes. We use the new dust-plume data to validate the operational forecasts provided by the WMO Dust Regional Center in Barcelona from a novel perspective. The comparison elucidates often similar dust plume patterns in the forecasts and the satellite-based reconstruction, but the latter computation is substantially faster. Our proposed reconstruction provides a new opportunity for validating dust aerosol transport in numerical weather models and Earth system models. It can be adapted to other aerosol species and trace gases.

Atmospheric nitrogen (N) deposition and climate change are transforming the way N moves through dryland watersheds. For example, N deposition is increasing N export to streams, which may be exacerbated by changes in the magnitude, timing, and intensity of precipitation (i.e., the precipitation regime). While deposition controls the amount of N entering a watershed, the precipitation regime influences rates of internal cycling; when and where soil N, plant roots, and microbes are hydrologically connected; how quickly plants and microbes assimilate N; and rates of denitrification, runoff, and leaching. We used the ecohydrological model RHESSys to investigate (1) how N dynamics differ between N-limited and N-saturated conditions in a dryland watershed, and (2) how total precipitation and its intra-annual intermittency (i.e., the time between storms in a year), interannual intermittency (i.e., the duration of dry months across multiple years), and interannual variability (i.e., variance in the amount of precipitation among years) modify N dynamics. Streamflow N export was more sensitive to increasing intermittency and variability in N-limited vs. N-saturated model scenarios, particularly when total precipitation was lower—the opposite was true for denitrification. N export and denitrification increased or decreased the most with increasing interannual intermittency compared to other changes in precipitation timing. This suggests that under future climate change, prolonged droughts that are followed by more intense storms may pose a major threat to water quality in dryland watersheds.

Estuaries in the northern California current system (NCCS) experience seasonally reversing wind stress, which is expected to impact the origin and properties of shelf water which enters NCCS estuaries (’shelf inflow’). Wind stress has been shown to affect the source of shelf inflow by driving alongshelf currents. However, the effects of wind-driven Ekman dynamics and shelf currents from larger-scale forcing on shelf inflow have yet to be explored. Variations in shelf inflow to the Salish Sea and the Columbia River estuary, two large NCCS estuarine systems, were studied using a realistic hydrodynamic model. The paths and source of shelf water were identified using particles released on the shelf. Particles were released every two weeks of 2017 and tracked for sixty days. Shelf inflow was identified as particles that crossed the estuary mouths. Mean wind stress during each release was compared with initial horizontal and vertical positions and physical properties of shelf inflow particles. For both the Salish Sea and the Columbia River estuary, upwelling-favorable wind stress was correlated with a shelf inflow source north of the estuary mouth. Depth was not correlated with wind stress for either estuary, but relative depth (depth scaled by isobath) increased during upwelling-favorable winds for both. Properties of inflow changed from cold and fresh during upwelling to warm and salty during downwelling, reflecting seasonal changes in NCCS shelf waters. These results may be extended to predict the source and properties of shelf inflow to estuaries in other regions with known wind or shelf current patterns.

A new class of vortices has been observed in the stratosphere after several extreme wildfires (Canada 2017, Australia 2020) and volcanic eruptions (Raikoke 2019). They are long-lived coherent plumes of aerosols and combustion/volcanic compounds confined within mesoscale (100s to 1000 km diameter) anticyclones. Due to their anomalous composition, these ascending anticyclonically-trapped plumes (ATPs) generate significant radiative forcing and diabatically-driven vertical motions. The present paper investigates the fundamental processes shaping the dynamics of ATPs from two complementary approaches: analytically in a potential vorticity (PV) perspective and using idealized but more complete numerical simulations with the Weather Research and Forecast (WRF) model. We adapt the axisymmetric Eliassen balanced vortex model, introduced as a prototype for tropical cyclones, to the case of a vortical flow forced by a diabatically-active Lagrangian tracer.  Invoking an extended PV impermeability theorem, it is first clarified that ATP formation is consubstantial to the large injection of mass into the stratified flow at extratropical latitude. We also prove that vertically self-translating, strictly zero-PV ellipsoidal anticyclonic plumes with uniform tracer constitute an exact solution of the governing equations, thus accommodating the joint ascent of tracer and PV in ATPs. The numerical simulations reveal that finite-PV plumes with distributed tracer evolve into a vertically asymmetrical structure featuring a tracer and anticyclonic PV front followed by a tracer tail where cyclonic PV develops. Switching to potential radius-potential temperature coordinate, the dynamics reduces to that of a comb of 1-dimensional Burgers’equation for the tracer, supplemented by a slave equation for PV. By virtue of the symmetry of the problem when neglecting the background density gradient, cooled vortices undergo a similar evolution during their early subsidence, a situation which may apply to the 2022 Hunga Tonga-Hunga-Ha’apai volcanic plume. Finally, the impact of the initial conditions are discussed.

We reprocessed and interpreted Chang’E-4 Lunar Penetrating Radar (LPR) data collected until 14th February 2023, exploiting a new Deep Learning-based algorithm to automatically extract reflectors from a processed radar dataset. The results are in terms of horizon probability and have been interpreted by integrating signal attribute analysis with orbital imagery. The approach provides more objective results by minimizing the subjectivity of data interpretation allowing to link radar reflectors to their geological context and surface structures. For the first time, we imaged dipping layers and at least 20 shallow buried crateriform structures within the regolith using LPR data. We further recognized four deeper structures similar to craters, locating ejecta deposits related to a crater rim crossed by the rover path and visible in satellite image data.

Climate risk management relies on accurate predictions of key climate variations such as El Niño-Southern Oscillation (ENSO), but the skill of ENSO predictions has recently plateaued or even degraded. Here we analyze the North American Multi-Model Ensemble (NMME) and estimate how the seasonal prediction of ENSO may benefit from reducing initial prediction errors. An analysis of predictable signals and system noises identifies a high-predictability regime and a low-predictability regime. The latter corresponds to the spring predictability barrier and is related to a rapid drop in the signal-to-noise ratio, which is caused by the comparably strong dampening of predictable signals. Reducing first-month prediction errors (FPEs) will likely reduce root-mean-square errors of the ENSO prediction. As a conservative estimate, halving the FPEs may extend the NMME’s skill by one to two months. Importantly, this study identifies the regions where reducing FPE is the most effective. Unlike the predictions initialized after the boreal spring, the March-initialized predictions of the wintertime ENSO will likely benefit the most from FPE reductions in the tropical Northwest Pacific. An opportunistic thought experiment suggests the buoy observation changes during 1995–2020 may have contributed to FPEs associated with large cold biases (>1K) in some El Niño-year predictions. While data availability prevented in-depth analyses of physical processes, the findings suggest that prioritizing modeling and observation in certain regions can improve climate predictions cost-effectively. The analytical framework here is applicable to other climate processes, thus holding wide potential for benefiting climate predictions.

Our understanding of tree stem methane (CH4) emissions is evolving rapidly. Few studies have combined seasonal measurements of soil, water and tree stem CH4 emissions from forested wetlands, inhibiting our capacity to constrain the tree stem CH4 flux contribution to total wetland CH4 flux. Here we present annual data from a subtropical freshwater Melaleuca quinquenervia wetland forest, spanning an elevational topo-gradient (Lower, Transitional and Upper zones). Eight field-campaigns captured an annual hydrological flood-dry-flood cycle, measuring stem fluxes on 30 trees, from four stem heights, and up to 30 adjacent soil or water CH4 fluxes per campaign. Tree stem CH4 fluxes ranged several orders of magnitude between hydrological seasons and topo-gradient zones, spanning from small CH4 uptake to ~203 mmol m-2 d-1. Soil CH4 fluxes were similarly dynamic and shifted from maximal CH4 emission (saturated soil) to uptake (dry soil). In Lower and Transitional zones respectively, tree stem CH4 contribution to the net ecosystem flux was greatest during flooded conditions (49.9 and 70.2 %) but less important during dry periods (3.1 and 28.2 %). Minor tree stem emissions from the Upper elevation zone still offset the Upper zone CH4 soil sink capacity by ~51% during dry conditions. Water table height was the strongest driver of tree stem CH4 fluxes, however tree emissions peaked once the soil was inundated and did not increase with further water depth. This study highlights the importance of quantifying the wetland tree stem CH4 emissions pathway as an important and seasonally oscillating component of wetland CH4 budgets.

The Sudan-Sahel region has long been vulnerable to environmental change. However, the intensification of global warming has led to unprecedented challenges that require a detailed understanding of climate change for this region. This study analyzes the impacts of climate change for Burkina Faso using eleven climate indices that are highly relevant to Sudan-Sahelian societies. The full ensemble of statistically downscaled NEX-GDDP-CMIP6 models (25 km) is used to determine the projected changes for the near (2031-2060) and far future (2071-2100) compared to the reference period (1985-2014) for different SSPs. Validation of the climate models against state-of-the-art reference data (CHIRPS and ERA5) shows reasonable performance for the main climate variables with some biases. Under the SSP5-8.5, Burkina Faso is projected to experience a substantial temperature increase of more than 4.3°C by the end of the century. Rainfall amount is projected to increase by 30% under the SSP5-8.5, with the rainy season starting earlier and lasting longer. This could increase water availability for rainfed agriculture but is offset by a 20% increase in evapotranspiration. The country could be at increased risk of flooding and heavy rainfall in all SSPs and future periods. Due to the pronounced temperature increase, heat stress, discomfort, and cooling degree days are expected to strongly increase under the SSP8.5 scenarios, especially in the western and northern parts. Under the SSP1-2.6 and SSP5-8.5, the projected changes are much lower for the country. Thus, timely implementation of climate change mitigation measures can significantly reduce climate change impacts for this vulnerable region.

Saturated hydraulic conductivity (Ks) is a crucial parameter that influences water flow in saturated soils, with applications in various fields such as surface water runoff, soil erosion, drainage, and solute transport. However, accurate estimation of Ks is challenging due to temporal and spatial uncertainties. This study addresses the knowledge gap regarding the long-term behaviour of Ks in sandy soils with less than 10% fine particles. The research investigates the changes in Ks over a long period of constant head tests and examines the factors influencing its variation. Two sandy samples were tested using a hydraulic conductivity cell, and the hydraulic head and discharge were recorded for over 50 days. The results show a general decline in Ks throughout the test, except for brief periods of increase. Furthermore, the relationship between flow rate and hydraulic head gradient does not follow the expected linear correlation from Darcy’s law, highlighting the complex nature of sandy soil hydraulic conductivity. The investigation of soil properties in three different sections of the samples before and after the tests revealed a decrease in the percentage of fine particles and a shift in specific gravity from the bottom to the top of the sample, suggesting particle migration along the flow direction. Factors such as clogging by fine particles and pore pressure variation contribute to the changes in Ks. The implications of this study have far-reaching effects on various geotechnical engineering applications. These include groundwater remediation, geotechnical stability analysis, and drainage system design.

This study investigates land loss and coastal inundation in Louisiana's Barataria Basin, a region highly susceptible to anthropogenic pressures and natural factors like land subsidence, sea-level rise, and tidal dynamics. Using high-resolution Digital Elevation Models (DEM) and water level data from the Coastal Reference Monitoring System (CRMS) stations, we analyzed changes in land area and water levels between 2007 and 2022. The attenuation coefficient magnitude of tidal intrusion, which quantifies tidal amplitude reduction as a function of landward distance from the coastline, exhibited a persistent decrease from 2007 to 2022 for O1 and K1 (the dominant tidal constituents), with an accumulated decrease of nearly 20%, signaling enhanced hydrological connectivity across the region. We also projected land area for historic years and predicted it for future years up to 2075, based on a range of displacement rates to account for uncertainties in vertical land motion. Our analyses predict that, in the absence of human intervention, the significance of tidal variations in influencing land loss will escalate; by 2045, the land area estimated based on Mean Higher High Water (MHHW) will constitute approximately 65% of the land area estimated using Mean Sea Level (MSL). Our findings underline the importance of considering the compound effects of subsidence, sea-level rise, and tidal dynamics in future land loss mapping and flood risk assessments. 

Diagnosing and predicting evaporation through satellite-based surface energy balance (SEB) and land surface models (LSMs) is challenging due to the non-linear responses of aerodynamic (ga) and stomatal conductance (gcs) to the coalition of soil and atmospheric drought. Despite a soaring popularity in refining gcs formulation in the LSMs by introducing a link between soil-plant hydraulics and gcs, the utility of gcs has been surprisingly overlooked in SEB models due to the overriding emphasis on eliminating ga uncertainties and the lack of coordination between these two different modeling communities. Therefore, a persistent challenge is to understand the reasons for divergent evaporation estimates from different models during strong soil-atmospheric drought. Here we present a virtual reality experiment over two contrasting European forest sites to understand the apparent sensitivity of the two critical conductances and evaporative fluxes to a water-stress factor (b-factor) in conjunction with land surface temperature (soil drought proxy) and vapor pressure deficit (atmospheric drought proxy) by using a non-parametric diagnostic model (Surface Temperature Initiated Closure, STIC1.2) and a prognostic model (Community Land Model, CLM5.0). Results revealed the b-factor and different functional forms of the two conductances to be a significant predictor of divergent response of the conductances to soil and atmospheric drought, which subsequently propagated in the evaporative flux estimates between STIC1.2 and CLM5.0. This analysis reaffirms the need for consensus on theory and models that capture the sensitivity of the biophysical conductances to the complex coalition of soil and atmospheric drought for better evaporation prediction.

To estimate groundwater flow and transport, lumped conceptual models are widely used due to their simplicity and parsimony - but these models are calibration reliant as their parameters are unquantifiable through measurements. To eliminate this inconvenience, we tried to express these conceptual parameters in terms of hydrodynamic aquifer properties to give lumped models a forward modelling potential. The most generic form of a lumped model representing groundwater is a unit consisting of a linear reservoir connected to a dead storage aiding extra dilution, or a combination of several such units mixing in calibrated fractions. We used one such standard two-store model as our test model, which was previously nicely calibrated on the groundwater flow and transport behaviour of a French agricultural catchment. Then using a standard finite element code, we generated synthetic Dupuit-Forchheimer box aquifers and calibrated their hydrodynamic parameters to exactly match the test model’s behaviour (concentration, age etc). The optimized aquifer parameters were then compared with conceptual parameters to find clear physical equivalence and mathematical correlation - we observed that the recession behaviour depends on the conductivity, fillable porosity, and length of the catchment whereas the mixing behaviour depends on the total porosity and mean aquifer thickness. We also noticed that for a two-store lumped model, faster and slower store represents differences only in porosities making it rather a dual porosity system. We ended with outlining a clear technique on using lumped models to run forward simulations in ungauged catchments where valid measurements of hydrodynamic parameters are available.