The Eastern Nile Basin (ENB) countries of Egypt, Sudan, South Sudan, and Ethiopia are subject to pronounced water, energy, and food (WEF) insecurity problems. There is a need to manage the WEF nexus to meet rapidly increasing demands, but this is extremely challenging due to resource scarcity and climate change. If countries that rely on shared transboundary water resources have contradictory WEF plans, that could diminish the expected outcomes, both nationally and regionally. Egypt as the downstream Nile country is concerned about ongoing and future developments upstream, which could exacerbate Egypt’s water scarcity and affect its ability to meet its WEF objectives. In this context, we introduce a multi-model WEF framework that simulates the ENB’s water resources, food production, and hydropower generation systems. The models were calibrated and validated for the period 1983-2016, then utilized to project a wide range of future development plans, up to 2050, using four performance measures to evaluate the WEF nexus. A thematic pathway for regional development that showed high potential for mutual benefits was identified. Results indicate that the ENB countries could be nearly food self-sufficient before 2050 and generate an additional 42000 GWh/yr of hydropower, with minimal impacts on Egypt’s water scarcity problems. The WEF planning outcomes for the region are sensitive to climate change, but, if social drivers can be managed (e.g., by lowered population growth rates) despite the difficulties involved, climate change impacts on WEF security could be less severe.

Quantitative precipitation forecasting in numerical weather prediction (NWP) models rely on physical parameterization schemes. However, these schemes involve considerable uncertainties due to limited knowledge of the mechanisms involved in the precipitating process, ultimately leading to degraded precipitation forecasting skills. To address this issue, our study proposes using a Swin-Transformer based deep learning (DL) model to quantitatively map fundamental variables solved by NWP models to precipitation maps. Our results show that the DL model effectively extracts features over meteorological variables, leading to improved precipitation skill scores of 21.7%, 60.5%, and 45.5% for light rain, moderate rain, and heavy rain, respectively, on an hourly basis. We also evaluate two case studies under different driven synoptic conditions and show promising results in estimating heavy precipitation during strong convective precipitation events. Overall, the proposed DL model can provide a vital reference for capturing precipitation-triggering mechanisms and enhancing precipitation forecasting skills. Additionally, we discuss the sensitivities of the fundamental meteorological variables used in this study, training strategies, and performance limitations.

Existing models for estimating hyporheic oxygen mass transfer often require numerous parameters related to flow, bed, and channel characteristics, which are frequently unavailable. We performed a meta-analysis on existing dataset, enhanced with high Reynolds number cases from a validated Computational Fluid Dynamics model, to identify key parameters influencing effective diffusivity at the sediment water interface. We applied multiple linear regression to generate empirical models for predicting eddy diffusivity. To simplify this, we developed two single-parameter models using either a roughness or permeability-based Reynolds number. These models were validated against existing models and literature data. The model using roughness Reynolds number is easy to use and can provide an estimate of the oxygen transfer coefficient, particularly in scenarios where detailed bed characteristics such as permeability might not be readily available.

Turbidity limits light availability in many glacier-fed lakes and reservoirs, with far-reaching ecological consequences. We use field observations and hydrodynamic modelling to examine the physical processes affecting turbidity in the epilimnion of a glacier-fed hydroelectric reservoir in response to changes in reservoir operations (e.g. water level, inflows and withdrawals), and to natural processes (e.g. particle settling, internal seiching and upwelling). The combination of cold inflows and deep outlets leads to plunging inflows and the isolation of the epilimnion; this isolation, along with particle settling, results in a remarkable clearing of the epilimnion during summer. We simulate a wide range of scenarios based on 46 years of historical flows. We find that the water level and inflow rate in spring control epilimnetic turbidity at the beginning of summer, and this turbidity is a primary determinant of the turbidity and light penetration for the rest of the summer. Turbidity during summer is also impacted by wind-driven thermocline motions. We examine these motions using wave characteristics diagrams and two-dimensional spectra and identify the period and wavelength of the two dominant wave modes: the fundamental internal seiche (\(\approx\) 4 days) and diurnally-forced waves. Occasionally, internal motions are large enough to upwell turbid metalimnetic water to the free surface at the upstream end of the reservoir. These upwelling events coincide with peaks in the inverse of the Wedderburn number. Pulses of upwelled water are advected downstream, setting up a longitudinal turbidity gradient.

Stochastic Watershed Models (SWMs) are an important innovation in hydrologic modeling that propagate uncertainty into model predictions by adding samples of model error to deterministic simulations. A growing body of work shows that univariate SWMs effectively reduce bias in hydrologic simulations, especially at the upper and lower flow quantiles. This has important implications for short term forecasting and the estimation of design events for long term planning. However, the application of SWMs in a regional context across many sites is underexplored. Streamflow across nearby sites is highly correlated, and so too are hydrologic model errors. Further, in arid and semi-arid regions streamflow can be intermittent, but SWMs rarely model zero flows at one site, let alone correlated intermittency across sites. In this technical note, we contribute a multisite SWM that captures univariate attributes of model error (heteroscedasticity, autocorrelation, non-normality, conditional bias), as well as multisite attributes of model error (cross-correlated error magnitude and persistence). The SWM also incorporates a multisite, auto-logistic regression model to account for multisite persistence in streamflow intermittency. The model is applied and tested in a case study that spans 14 watersheds in the Sacramento, San Joaquin, and Tulare basins in California. We find that the multisite SWM is able to better reproduce regional low and high flow events and design statistics as compared to a single-site SWM applied independently to all locations.

Central Asia (CA) is experiencing rapid warming, leading to more Extreme precipitation events (EPEs). However, the anticipated changes in cropland and population exposure to EPEs are still unexplored. In this study, projected changes in EPEs characteristics, as well as cropland and population exposure from EPEs are quantified using global climate model simulations. Our findings reveal a significant increase in the exposure of cropland and population to extreme precipitation over time. Specifically, under the high-emission SSP5-8.5 future pathway, the amount, frequency, intensity, and spatial extent of extreme precipitation in CA are projected to considerably amplify, particularly in the high mountain regions. Under the SSP5-8.5 scenario, cropland exposure in CA increases by 46.4%, with a total cropland exposure of approximately 190.7 million km² expected between 2021 and 2100. Additionally, under the SSP3-7.0 scenario, population exposure in CA increases by 92.6%, resulting in a total population exposure of about 48.1 billion person-days during the same period. The future maximum centers of exposure are concentrated over northern Kazakhstan and the tri-border region of Tajikistan, Kyrgyzstan, and Uzbekistan. Notably, the climate effect is more dominant than the other effects, whereas changes in population effect contribute to the total change in population exposure. Given the heterogeneous distribution of population and cropland in CA, it is imperative for the countries in the region to implement effective measures that harness extreme precipitation and cope with the impacts of these extreme climate events.

In recent years, the Sample Analysis at Mars (SAM) instrument on board the Mars Science Laboratory (MSL) Curiosity rover has detected methane variations in the atmosphere at Gale crater. Methane concentrations appear to fluctuate seasonally as well as sub-diurnally, which is difficult to reconcile with an as-yet-unknown transport mechanism delivering the gas from underground to the atmosphere. To potentially explain the fluctuations, we consider barometrically-induced transport of methane from an underground source to the surface, modulated by temperature-dependent adsorption. The subsurface fractured-rock seepage model is coupled to a simplified atmospheric mixing model to provide insights on the pattern of atmospheric methane concentrations in response to transient surface methane emissions, as well as to predict sub-diurnal variation in methane abundance for the northern summer period, which is a candidate time frame for Curiosity’s potentially final sampling campaign. The best-performing scenarios indicate a significant, short-lived methane pulse just prior to sunrise, the detection of which by SAM-TLS would be a potential indicator of the contribution of barometric pumping to Mars’ atmospheric methane variations.

Montane snowpack is a vital source of water supply in the Western United States. However, the future of snow in these regions in a changing climate is uncertain. Here, we use a large-ensemble approach to evaluate the consistency across 124 statistically downscaled snow water equivilent (SWE) projections between end-of-century (2076 – 2095) and early 21st century (2106 – 2035) periods. Comparisons were performed on dates corresponding with the end of winter (15 April) and spring snowmelt (15 May) in five western US montane domains. By benchmarking SWE climate change signals using the disparity between snow projections, we identified relationships between SWE projections that were repeatable across each domain, but shifted in elevation. In low to mid-elevations, 15 April average projected decreases to SWE of 48% or larger were greater than the disparity between models. Despite this, a significant portion of 15 April SWE volume (39 – 93%) existed in higher elevation regions where the disparities between snow projections exceeded the projected changes to SWE. Results also found that 15 April and 15 May projections were strongly correlated (r 0.82), suggesting that improvements to the spread and certainty of 15 April SWE projections would translate to improvements in later dates. The results of this study show that large-ensemble approaches can be used to measure coherence between snow projections and identify both 1) the highest-confidence changes to future snow water resources, and 2) the locations and periods where and when improvements to snow projections would most benefit future snow projections.

Climate change and nitrogen (N) pollution are altering biogeochemical and ecohydrological processes in dryland watersheds, increasing N export, and threatening water quality. While simulation models are useful for projecting how N export will change in the future, most models ignore biogeochemical “hotspots” that develop in drylands as moist microsites become hydrologically disconnected from plant roots when soils dry out. These hotspots enable N to accumulate over dry periods and rapidly flush to streams when soils wet up. To better project future N export, we developed a framework for representing hotspots using the ecohydrological model RHESSys. We then conducted a series of virtual experiments to understand how uncertainties in model structure and parameters influence N export. Modeled export was sensitive to the abundance of hotspots in a watershed, increasing linearly and then reaching an asymptote with increasing hotspot abundance. Peak streamflow N was also sensitive to a soil moisture threshold at which subsurface flow from hotspots reestablished, allowing N to be transferred to streams; it increased and then decreased with an increasing threshold value. Finally, N export was generally higher when water diffused out of hotspots slowly. In a case study, we found that when hotspots were modeled explicitly, peak streamflow nitrate export increased by 29%, enabling us to better capture the timing and magnitude of N losses observed in the field. This modeling framework can improve projections of N export in watersheds where hotspots play an increasingly important role in water quality.

A document by Robert Ladwig. Click on the document to view its contents.

Underground wastewater injection into deep reservoirs confronts complex hydrogeologic conditions potentially leading to induced seismicity. We focus on the geologic units of the Arbuckle Group carbonates in Oklahoma that are frequently used for disposing of produced water associated with oil and gas production. Subsurface pressures and fluid flow figure prominently in most explanations for induced seismicity and are important in evaluations of future storage potential of the Arbuckle. To understand subsurface pressure conditions within the Arbuckle we monitored the water levels in 15 inactive wells. The wells were monitored at 30-second intervals, with eight wells monitored since September 2016, and an additional seven from July 2017. All of the wells were monitored until early March 2020. Since 2016, 13 of the 15 wells showed a net decrease in well level (a.k.a. hydraulic head), proportional to near-borehole fluid pressure. The pressure patterns observed in each well vary from one another, and some wells display a gradual decrease in pressure over time, some have a rapid decrease, and others show irregular changes. The well pressures respond to Earth tides and injections into nearby wells as well as response to distal and proximal seismic waves, though responses vary considerably between wells. Moreover, there appears to be a threshold injection rate above which pressure changes level off. The data illustrate that Arbuckle hydrogeology is a multi-scale, temporally dynamic system, with regional heterogeneity of porosity and permeability. These dynamics exert important controls on seismic hazard and storage capacity estimates.

The value of seasonal streamflow forecasts for the hydropower industry has long been assessed by considering metrics related to hydropower availability. However, this approach overlooks the role played by hydropower dams within the power grid, therefore providing a myopic view of how forecasts could improve the operations of large-scale power systems. With the aim of understanding how the value of streamflow forecasts penetrates through the power grid, we developed a coupled-water energy model that is subject to reservoir inflow forecasts with different levels of accuracy. We implement the modelling framework on a real-world case study based on the Cambodian grid, which relies on hydropower, coal, oil, and imports from neighboring countries. In particular, we evaluate the performance in terms of metrics selected from both the reservoir and power systems, including available and dispatched hydropower, power production costs, CO2 emissions, and transmission line congestion. Through this framework, we demonstrate that streamflow forecasts can positively impact the operations of hydro-dominated power systems, especially during the transition from wet to dry seasons. Moreover, we show that the value largely varies with the specific metric of performance at hand as well as the level of operational integration between water and power systems.

Two-phase fluid flow in fractured porous media impacts many natural and industrial processes but our understanding of flow dynamics in these systems is constrained by difficulties measuring the flow in the interacting fracture and porous media. We present a novel experimental system that allows quantitative visualization of the air and water phases in a single analog fractured porous medium. The fracture system consists of a sintered-glass porous plate in contact with an impermeable glass plate. A reservoir connected to the porous plate allows control of pore pressure within the porous medium. The fracture fills and drains through the porous matrix and flow manifolds along two edges of the fracture. The fracture is mounted in an imaging system that includes a controlled light-emitting diode (LED) panel and a charge-coupled-device (CCD) camera. Flow and pressure are controlled and monitored by a computer during experiments. To demonstrate this system, we carried out a series of cyclic drainage and imbibition experiments in fractures bounded by porous media with different pore-size distributions in the porous matrix. Images of the drainage process demonstrate that the air-water distribution within the fracture evolves differently than has been observed in non-porous fractured systems. Specifically, we observed limited trapping of water within the fracture during drainage. Conversely, during imbibition, because air cannot exit through the porous matrix, significant regions of air became entrapped once pathways to the fracture boundaries became water filled. The differences in phase evolution led to substantial differences in the evolution of estimated relative permeability with saturation.

The complex interactions among soil, vegetation, and site hydrologic conditions driven by precipitation and tidal cycles control biogeochemical transformations and bi-directional exchange of carbon and nutrients across the terrestrial-aquatic interfaces (TAIs) in the coastal regions. This study uses a highly mechanistic model, ATS-PFLOTRAN, to explore how these interactions impact the material exchanges and carbon and nitrogen cycling along a TAI transect in the Chesapeake Bay region that spans zones of open water, coastal wetland and upland forest. Several simulation scenarios are designed to parse the effects of the individual controlling factors and the sensitivity of carbon cycling to reaction constants derived from laboratory experiments. Our simulations revealed a hot zone for carbon cycling under the coastal wetland and the transition zones between the wetland and the upland. Evapotranspiration is found to enhance the exchange fluxes between the surface and subsurface domains, resulting in higher dissolved oxygen concentration in the TAI. The transport of organic carbon decomposed from leaves provides additional source of organic carbon for the aerobic respiration and denitrification processes in the TAI, while the variability in reaction rates mediated by microbial activities plays a dominant role in controlling the heterogeneity and dynamics of the simulated redox conditions. This modeling-focused exploratory study enabled us to better understand the complex interactions of various system components at the TAIs that control the hydro-biogeochemical processes, which is an important step towards representing coastal ecosystems in larger-scale Earth system models.

Seasonal snowpack in the Western United States (WUS) is vital for meeting summer hydrological demands, reducing the intensity and frequency of wildfires, and supporting snow-tourism economies. While the frequency and severity of snow droughts (SD) are expected to increase under continued global warming, the uncertainty from internal climate variability remains challenging to quantify. Using a 30-member large ensemble from a state-of-the-art global climate model, the Seamless System for Prediction and EArth System Research (SPEAR), and an observations-based dataset, we find WUS SD changes are already significant. By 2100, SPEAR projects SDs to be nearly 9 times more frequent under shared socioeconomic pathway 5-8.5 (SSP5-8.5) and 5 times more frequent under SSP2-4.5. By investigating the influence of the two primary drivers of SD, temperature and precipitation amount, we find the average WUS SD will become warmer and wetter. To assess how these changes affect future summer water availability, we track April 15th snowpack across WUS watersheds, finding differences in the onset time of a “no-snow” threshold between regions and large internal variability within the ensemble that are both on the order of decades. For example, under SSP5-8.5, SPEAR projects California could experience no-snow anywhere between 2058 and 2096, while in the Pacific Northwest, the earliest transition happens in 2091. We attribute the inter-regional uncertainty to differences in the regions’ mean winter temperature and the intra-regional uncertainty to irreducible internal climate variability. This analysis indicates that internal climate variability will remain a significant source of uncertainty for WUS hydrology through 2100.

With ongoing climate change and more frequent high flows and droughts, it becomes inevitable to understand potentially altered catchment processes under changing climatic conditions. Water age metrics such as median transit times and young water fractions are useful variables to understand the process dynamics of catchments and the release of solutes to the streams. This study, based on extensive high-frequency stable isotope data, unravels the changing contribution of different water ages to stream water in six heterogeneous catchments, located in the Harz mountains and the adjacent northern lowlands in Central Germany. Fractions of water up to 7 days old (Fyw7), comparable with water from recent precipitation events, and fractions of water up to 60 days old (Fyw60) were simulated by the tran-SAS model. As Fyw7 and Fyw60 were sensitive to discharge, an integrated analysis of high and low flows was conducted. This revealed an increasing contribution of young water for increasing discharge, with larger contributions of young water during wet spells compared to dry spells. Considering the seasons, young water fractions increased in summer and autumn, which indicates higher contributions of young water after prolonged dry conditions. Moreover, the relationship between catchment characteristics and the water age metrics revealed an increasing amount of young water with increasing agricultural area, while the amount of young water decreased with increasing grassland proportion. By combining transit time modelling with high-frequency isotopic signatures in contrasting sub-catchments in Central Germany, our study extends the understanding of hydrological processes under high and low flow conditions.

A document by Eleonora Dallan. Click on the document to view its contents.

Hydrological conditions (i.e., high-flow versus low-flow) in peatland drainage streams influence both the quantity of dissolved organic carbon (DOC) exports and dissolved organic matter (DOM) composition. Yet, our knowledge on DOM fate after exports from the peatland remains limited while this highly reactive component sustains emissions and exports of carbon dioxide (CO2) from streams through degradation processes. The present study demonstrates the relationships between DOM composition evolution and catchment hydrological conditions along a 3 km long headwater stream running through a boreal peatland, from its source to the outlet. Our results show that hydrological conditions significantly influenced DOM composition evolution along the stream. DOM exported during high-flow conditions presented a composition similar to peat porewater in terms of DOC:DON ratio and aromaticity, but a lower average molecular weight, indicating preferential exports of low molecular weight DOM recently produced in the acrotelm. The DOM composition changed little along the stream during high-flow as it was rapidly flushed downstream. During low-flow conditions, DOM composition evolved along the stream in contrast to high-flow with a strong increase in DOM aromaticity and molecular weight along the stream. These changes were significantly correlated to the water residence time in the stream and to the estimated proportion of mineralized DOC to total DOC flux exported at the stream outlet. These results highlight the importance of hydrological conditions on DOM dynamics as DOM was locally mineralized during low-flow conditions, when DOC exports were low, while mineralization processes happened downstream under high-flow conditions which favored important DOC exports.

Martian Global Dust Storms (GDS) can significantly affect the water cycle in the lower atmosphere (0-40 km). We compare the evolution of water vapor abundances, dust opacity and surface temperatures in the Southern Polar Region (SPR) during GDS years of MY25, MY28 and MY34 relative to years without GDS. During all GDS years, the vapor abundances decrease in the lower atmosphere in the SPR following the storm. Our results suggest that this decrease could be the result of vapor moving to higher altitudes and not being available for poleward transport in the lower atmosphere.

Peatlands are sources of the bioaccumulating neurotoxin methylmercury (MeHg) and linked to adverse health outcomes, yet the impact of climate change and reductions in atmospheric pollutants on mercury (Hg) export from peatlands are highly uncertain. Here, we present the response in annual flow-weighted concentrations (FWC) and yields of total-Hg (THg) and MeHg to cleaner air and climate change using an unprecedented hydroclimatic (55-years; streamflow, air temperature, precipitation, regional and peatland water tables), depositional chemistry (21-years; Hg and major ions concentration and total mass), and streamwater chemistry (~17-years; THg, MeHg, major ions, total organic carbon, and pH) datasets from a reference peatland catchment in the north central USA. Over the hydroclimatic record, annual mean air temperature increased by ~1.8 ℃, decreasing baseflow and, subsequently, the efficiency that precipitation was converted to streamwater runoff (runoff ratio). Concurrently, precipitation-based deposition of sulfate and Hg declined, where wet Hg deposition rates declined to near pre-industrial levels. Annual MeHg FWC was positively correlated mean annual air temperatures (p=0.03, r=0.51), annual runoff ratio (p<0.0001, r=0.76), and wet Hg deposition concentration (p<0.0001, r=0.79). Over the study period, decreasing wet Hg deposition concentration and annual runoff ratios counterbalanced increased peatland MeHg production due to higher air temperatures, leading to an overall decline in streamwater MeHg FWC. Climate change and cleaner air were responsible for 0.51 and 0.32 of the variability in MeHg FWC, respectively. Streamwater MeHg export may continue to decrease only if declines in runoff ratio and wet Hg deposition concentration persistently outpace increased air temperature.

Himalayan rivers are prone for drying subject to continuous receding of glacial meltwater and groundwater supply. The present study is conducted in the region of West Kameng District of Arunachal Pradesh, India, covering drainage basin of 2 tributaries of Kameng river, viz., Tenga & Dirang-Bichom. Here we used stable isotopic technique to identify the sources and quantify their contribution to the river flow i.e. from the headwater to the flood plain region. A Local Water line (LWL) for the region during the dry period is defined based on observation which relate δD = (8.1 ± 0.3)×δ 18O + (11.6 ± 2.5‰). This equation is established from analysis of river water sampled collected during March 2021. This LWL is identical to the local meteoric water line generated using precipitation isotope data on samples collected between April – October 2007 from a station at Mawlong, Meghalaya, India i.e. δD = (8.1 ± 0.1) ×δ 18O + (11.8 ± 0.9) ‰. The d-excess values from the two set observations are similar at 11.2± 1.8 ‰ and 11.3± 2.7‰, respectively, implying that the rainwater which feed the shallow groundwater is a major component of the river water during dry season. Further, we compared present observation with other studies on the surface water composition in other Himalayan River systems and documented an elevation effect on stable isotopes in river water. Spatial observations on isotopic signature in other rivers originating from the Himalaya documented a maximum altitude effect in the North-Western Himalaya and subdued effect with progressive moisture recycling process on approaching biosphere dominated region of Eastern Himalaya.

SPAtial EFficiency (SPAEF) metric is one of the most thoroughly metrics in hydrologic community. In this study, our aim is to improve SPAEF by replacing the histogram match component with other statistical indices, i.e. kurtosis and earth mover’s distance, or by adding a fourth or fifth component such as kurtosis and skewness. The existing spatial metrics i.e. SPAtial Efficiency (SPAEF), Structural Similarity (SSIM) and Spatial Pattern Efficiency Metric (SPEM) were compared with newly proposed metrics to assess their converging performance. The mesoscale Hydrologic Model (mHM) of the Moselle River is used to simulate streamflow (Q) and actual evapotranspiration (AET). The two-source energy balance (TSEB) AET during the growing season is used as monthly reference maps to calculate the spatial performance of the model. The Moderate Resolution Imaging Spectroradiometer (MODIS) based Leaf area index (LAI) is utilized by the mHM via pedo-transfer functions and multi-scale parameter regionalization approach to scale the potential ET. In addition to the real monthly AET maps, we also tested these metrics using a synthetic true AET map simulated with a known parameter set for a randomly selected day. The results demonstrate that the newly developed four-component metric i.e. SPAtial Hybrid 4 (SPAH4) slightly outperform conventional three-component metric i.e. SPAEF (3% better). However, SPAH4 significantly outperforms the other existing metrics i.e. 40% better than SSIM and 50% better than SPEM. We believe that other fields such as remote sensing, change detection, function space optimization and image processing can also benefit from SPAH4.

Satellite remote sensing is commonly used to observe the hydrologic cycle at spatial scales ranging from river basins to the globe. Yet it remains difficult to obtain a balanced water budget using remote sensing data, which highlights the errors and uncertainties in earth observation (EO) data. Various methods have been proposed to correct EO datasets to make them more coherent, so that they result in a more balanced water budget. This study aimed to improve estimates of water budget components (precipitation, evapotranspiration, runoff, and total water storage change) at the global scale using the methods of optimal interpolation (OI) and neural network (NN) modeling. We trained a set of NNs on a set of 1,358 river basins and validated them on an independent set of 340 basins and in-situ observations of evapotranspiration and river discharge. We extended the models to make pixel-scale predictions in 0.5° grid cells for near-global coverage. Calibrated datasets result in lower water budget residuals in validation basins: the mean and standard deviation of the imbalance is 11 ± 44 mm/mo when calculated with uncorrected EO data and 0.03 ± 24 mm/mo after calibration by the NN models. This study suggests to data producers where corrections should be made to the EO datasets, and demonstrates the benefits of physically-driven NN models for studying the hydrologic cycle at the global scale.

Below the equator, the preeminent tourist attraction in the late nineteenth century was the White Terraces of Lake Rotomahana in New Zealand.