Field kits for testing the level of a toxicant in the environment are inherently less accurate than a laboratory instrument. Using a specific example, we argue here that kit measurements still have a key role to play when the spatial distribution of a toxicant is very heterogeneous. The context is provided by the groundwater arsenic problem in Bangladesh. We combine here two data sets, a blanket survey of 6595 wells over a 25 km2 based on laboratory measurements and 900 paired kit and laboratory measurements from the same area. We explore different hypothetical mitigation scenarios based on actual data that rely on households with a high-arsenic well switching to a nearby low-arsenic well. We show that the decline in average exposure to arsenic from relying on kit rather than laboratory data is modest in relation to the logistical and financial challenge of delivering exclusively laboratory data. Our analysis indicates that the 50 ug/L threshold used in Bangladesh to distinguish safe and unsafe wells, rather than the WHO guideline of 10 ug/L, is close to optimal in terms of average exposure reduction. We also show, however, that providing kit data at the maximum possible resolution rather than merely classifying wells as unsafe or safe would be even better. These findings are relevant as the government of Bangladesh is about to launch a new blanket testing campaign of millions of wells using field kits.
This evaluates the potential for a newly proposed non-linear subsurface flux equation to improve the performance of the hydrological Hillslope Link Model (HLM). The equation contains parameters that are functionally related to the hillslope steepness and the presence of tile drainage. As a result, the equation allows a better representation of hydrograph recession curves, hydrograph timing, and total runoff volume. The authors explore the new parameterization’s potential by comparing a set of diagnostic and prognostic setups in HLM. In the diagnostic approach, they configure 12 different scenarios with spatially uniform parameters over the state of Iowa. In the prognostic case, they use information from topographical maps and known locations of tile drainage to distribute parameter values. To assess performance improvements, they compare simulation results to streamflow observations during a 17-year period (2002–2018) at 140 U.S. Geological Survey (USGS) gauging stations. The operational setup of the HLM model used at the Iowa Flood Center (IFC) serves as a benchmark to quantify overall model improvement. In particular, the new equation provides better representation of recession curves and the total streamflow volumes. However, when comparing the diagnostic and prognostic setups, the authors find discrepancies in the spatial distribution of hillslope scale parameters. The results suggest that more work is required when using maps of physical attributes to parameterize hydrological models. The findings also demonstrate that the diagnostic approach is a useful strategy to evaluate models and assess changes in their formulations.
Induced polarization (IP) is increasingly applied for hydrological, environmental and agricultural purposes. Interpretation of IP data is based on understanding the relationship between the IP signature and the porous media property of interest. Mechanistic models on the IP phenomenon rely on the Poisson-Nernst-Plank equations, where diffusion and electromigration fluxes are the driving forces of charge transport and are directly related to IP. However, to our knowledge, the impact of advection flux on IP was not investigated experimentally and was not considered in any IP model. In this work, we measured the spectral IP (SIP) signature of porous media under varying flow conditions, in addition to developing and solving a model for SIP signature of porous media, which takes flow into consideration. The experimental and the model results demonstrate that as bulk velocity increases, polarization and relaxation time decrease. Using a numerical model, we established that fluid flow near the particle deforms the electrical double layer (EDL) structure, accounting for the observed reduction in polarization. We found a qualitative agreement between the model and the measurements. Still, the model overestimates the impact of flow rate on SIP signature, which we explain in terms of the flow boundary conditions. Overall, our results demonstrate the sensitivity of the SIP signature to fluid flow, highlighting the need to consider fluid velocity in the interpretation of the SIP signature of porous media, and opening an exciting new direction for noninvasive measurements of fluid flow at the EDL scale.
Does river topography have stage thresholds for maintaining fluvial landforms, and if so how can they be quantified? Geomorphic covariance structure analysis offers a novel, systematic framework for evaluating nested topographic patterns in river corridors. In this study, a threshold in mountain river stage was hypothesized to exist; above this stage landform structure is organized to be freely self-maintaining via flow convergence routing morphodynamics. A 13.2 km segment of the canyon-confined Yuba River, California, was studied using 2944 cross-sections. Geomorphic covariance structure analysis was carried out on a meter-resolution topographic model to test the hypothesis. A critical stage threshold governing flow convergence routing morphodynamics was evident in several metrics. Below this threshold, narrow/high “nozzle” and wide/low “oversized” landforms that are out-of-phase with flow convergence routing morphodynamics dominated (excluding “normal channel”), while above it wide/high “wide bar” and narrow/low “constricted pool” landforms consistent with the flow convergence mechanism were dominant. Three-level nesting of co-located base-bankfull-flood stage landforms was dictated by canyon confinement, with nozzle-nozzle-nozzle nesting as the top permutation, excluding normal channel. In conclusion, this study demonstrates a significantly different and highly effective approach to finding process-based fluvial thresholds that can complement pre-existing methods, such as estimating incipient sediment motion, to get at more powerful dynamics controlling fluvial landforms structure.
Due to its substantial role on the Earth’s biogeochemical cycles and human health, nitrogen is recognized as one of the major water quality indicators of Sustainable Development Goal 6.3.2. Quantifying these potential impacts in large spatial scales still appears to be a grand challenge because of the high computational demand required by the distributed physically based global models and their intensive data requirements for calibration and validation. The former prevents a comprehensive analysis of the full spectrum of the model behavior under different conditions, and the latter impinges on the reliability of model-based inference. To tackle this problem, we developed a data-driven model using a spatio-temporal Random Forest algorithm to predict levels of nitrogen at 0.5-degree spatial resolution from 1992 to 2010 across the world. Several variables representing livestock, climate, hydrology, topography, etc. have been selected as predictors. The response variable of interest was nitrate–nitrite, which is responsible for the high risk of infant methemoglobinemia. Our results indicate that changes in the nitrogen concentration is mainly driven by cattle and sheep population, fertilizer application, precipitation, and temperature variability, implying livestock population, climate change, and anthropogenic forces can be important risk factors for global water quality deterioration. Furthermore, using the predicted levels of nitrogen, we characterized large-scale water quality patterns, and thus identified a few major ‘hot spots’ of water quality. The proposed model can also help assess potential impacts of future scenarios (e.g., livestock production or land use change) on global water quality conditions for better development of effective policy strategies.
Parameter estimation is one of the most challenging tasks in large-scale distributed modeling, because of the high dimensionality of the parameter space. Relating model parameters to catchment/landscape characteristics reduces the number of parameters, enhances physical realism, and allows the transfer of hydrological model parameters in time and space. This study presents the first large-scale application of automatic parameter transfer function (TF) estimation for a complex hydrological model. The Function Space Optimization (FSO) method can automatically estimate TF structures and coefficients for distributed models. We apply FSO to the mesoscale Hydrologic Model (mHM, mhm-ufz.org), which is the only available distributed model that includes a priori defined TFs for all its parameters. FSO is used to estimate new TFs for the parameters “saturated hydraulic conductivity” and “field capacity”, which both influence a range of hydrological processes. The setup of mHM from a previous study serves as a benchmark. The estimated TFs resulted in predictions in 222 validation basins with a median NSE of 0.68, showing that even with 5 years of calibration data, high performance in ungauged basins can be achieved. The performance is similar to the benchmark results, showing that the automatic TFs can achieve comparable results to TFs that were developed over years using expert knowledge. In summary, the findings present a step towards automatic TF estimation of model parameters for distributed models.
We present herein a new basis for measuring river discharge in ungauged catchments. Surrogate runoff (SR) is created using remotely sensed data to compensate for the absence of ground streamflow measurements. Because of their widespread availability, remotely sensed SR products are attractive, with approaches such as satellite-derived measurement-calibration ratio (C/M ratio). However, the use of the C/M ratio suffers from its limited penetration through ground vegetation canopies. While a microwave signal with a longer wavelength has been used to enhance the penetration capability, the coarseness of the spatial resolution of the microwave signal offsets its improvement due to the inherent assumptions in the C/M ratio, i.e., selecting two contrasting pixels (i.e., measurement and calibration) at the same time. To address both issues, this study proposes a new SR formulation using a longer wavelength (L-band microwave) with a better assumption for handling coarse grids, whereby the temporal variability of dryness against the driest state in each grid is used. The performance of the new SR is assessed for 467 Australian Hydrologic Reference Station catchments. Results show considerable improvements in the Pearson linear correlation (R) between the proposed SR and streamflow: 44% of the study areas show R higher than 0.4 with the new approach, whereas only 13% of the study areas show R higher than 0.4 with the currently used alternative (C/M ratio derived from Ka-band microwave). Overall, the resulting SR is dramatically improved by using the newly designed SR approach with the L-band microwave signal.
Robust and reliable projections of future streamflow are essential to create more resilient water resources, and such projections must first be bias corrected. Standard bias correction techniques are applied over calendar-based time windows and leverage statistical relations between observed and simulated data to adjust a given simulated datapoint. Motivated by a desire to connect the statistical process of bias correction to the underlying dynamics in hydrologic models, we introduce a novel windowing technique for projected streamflow wherein data are windowed based on hydrograph-relative time, rather than Julian day. We refer to this method as ‘seasonally anchored’. Four existing bias correction methods, each using both the standard day-of-year and the novel windowing technique, are applied to daily streamflow simulations driven by 10 global climate models across a diverse subset of six watersheds in California to investigate how these methods alter the model climate change signals. Among the methods, only PresRat preserves projected annual streamflow changes, and does so for both windowing techniques. The seasonally anchored window PresRat reduces the ensemble bias by a factor of two compared to quantile mapping (Qmap), cumulative distribution function transform (CDFt), and equidistant quantile matching (EDCDFm) methods. For wet season flows, PresRat with seasonally anchored windowing best preserves the original model change over the entire distribution, particularly at the highest quantiles, and the other three methods show improved performance using the novel windowing method. Concerning temporal shifts in seasonality, PresRat and CDFt preserve the original model signals with both the novel and standard windowing methods.
Increasing wildfire and declining snowpacks in mountain regions threaten water availability. We combine satellite-based fire detection with snow seasonality classifications to examine fire activity in California’s seasonal and ephemeral snow areas. We find a nearly tenfold increase in fire activity during 2020 and 2021 compared to 2001-2019 as measured by satellite data. Accumulation season snow albedo declined 17-77% in two burned sites as measured by in-situ data relative to un-burned conditions, with greater declines associated with increased soil burn severity. By enhancing snowpack susceptibility to melt, decreased snow albedo drove mid-winter melt during a multi-week midwinter dry spell in 2022. Despite similar meteorological conditions in 2013 and 2022, which we link to persistent high pressure weather regimes, minimal melt occurred in 2013. Post-fire differences are confirmed with satellite measurements. Our findings suggest larger areas of California’s snowpack will be increasingly impacted by the compounding effects of dry spells and wildfire.
Hundreds of ancient palaeolake basins have been identified and catalogued on Mars, indicating the distribution and availability of liquid water as well as sites of astrobiological potential. Palaeolakes are widely distributed across the Noachian aged terrains of the southern highlands, but Arabia Terra hosts few documented palaeolakes and even fewer examples of open-basin palaeolakes. Here we present a detailed topographic and geomorphological study of a previously unknown set of seven open-basin palaeolakes adjacent to the planetary dichotomy in western Arabia Terra. High resolution topographic data were used to aid identification and characterisation of palaeolakes within subtle and irregular basins, revealing two palaeolake systems terminating at the dichotomy including a ~160 km chain of six palaeolakes connected by short valley segments. Analysis and correlation of multiple, temporally distinct palaeolake fill levels within each palaeolake basin indicate a complex and prolonged hydrological history during the Noachian. Drainage catchments and collapse features place this system in the context of regional hydrology and the history of the planetary dichotomy, showing evidence for the both groundwater sources and surface accumulation. Furthermore, the arrangement of large palaeolakes fed by far smaller palaeolakes, indicates a consistent flow of water through the system, buffered by reservoirs, rather than a catastrophic overflow of lakes cascading down through the system.
Transformation of rainfall to runoff is a complex hydrological phenomenon involving various interconnected processes. Besides, the distribution of rainfall and basin characteristics are not uniform across time and space leading to a poor understanding of the process. Hydrologists have been using various hydrological models to understand transformation of rainfall into runoff. Conceptual models developed in the 1960s represent various individual components of hydrological cycle via interconnected conceptual elements, thus model various aspects of the hydrological cycle. On the other hand, data-driven models such as Artificial Neural Networks (ANNs) are widely regarded as universal approximators due to their ability to model many complex problems. Very few studies reported the application of a widely used conceptual model, Sacramento Soil Moisture Accounting model (SAC-SMA), in the Indian river basins context. Considering that the hydrological cycle is very complex and may never be fully understood in detail, conceptual models like Sacramento Soil Moisture Accounting model (SAC-SMA) can be integrated with data-driven models which can take care of poorly described and understood aspects of hydrological modelling. In this study, a hybrid rainfall-runoff model was developed and applied over the Godavari river basin in India at multiple spatial scales for capturing the spatial variations in model inputs and catchment charateristics.The hybrid model by virtue of the semi-distributed configuaration and addition of ANN component led to improved simulations of streamflow in comparison to the standalone SAC-SMA model.
A common viewpoint across the Earth science community is that global soil moisture estimates from satellite L-band (1.4 GHz) measurements represent moisture only in the shallow soil layers (0-5 cm) and are of limited value for studying global terrestrial ecosystems because plants use water from deeper rootzones. Here, we argue that such a viewpoint is flawed for two reasons. First, microwave soil emission theory and statistical considerations of vertically correlated soil moisture information together indicate that L-band measurements are typically representative of soil moisture within at least the top 15-25 cm, or 3-5 times deeper than commonly thought. Second, in reviewing isotopic tracer field studies of plant water uptake, we find a global prevalence of vegetation that primarily draws moisture from these upper soil layers. This is especially true for grasslands and croplands covering more than a third of global vegetated surfaces. While shrub and tree species tend to draw deeper soil moisture, these plants often still preferentially or seasonally draw water from the upper soil layers. Therefore, L-band satellite soil moisture estimates are more relevant to global vegetation water uptake than commonly appreciated, and we encourage their application across terrestrial hydrosphere and biosphere studies.
With the intensification of climate change and population growth, human-water relationships (HWR) have changed from the simple utilization of water resources to changing the spatial distributions and distribution proportions of water resources through formulating corresponding policies, such as Chinese ecological civilization policy. However, the impact of the ecological civilization policy on the evolution of HWR is still unclear. Here, taking the 600-year old “Tunpu” area as a typical study area, this research analyses the evolution of HWR over different space and time spans based on the Remote Sensing Hydrological Station (RSHS) technology, an improved water balance formula and the transition theory. The results show that at the village scale, the water cycle structure of a typical village has remained stable, and deforestation has increased the proportion of runoff to precipitation by 10.62%. At the basin scale, due to land use/cover changes and precipitation fluctuations, the trend of the runoff changes from slowly decreasing to accelerated increases, with change rate increasing from -0.073×104 m3·a-1 in the Ming Dynasty (1470-1636) to 30.946×104 m3·a-1 in the China stage (1949-2020). HWR have developed from the initial balanced resource-rich period to the unbalanced extensive-development period and have finally changed into a rebalancing period under the influence of the ecological civilization policy. Four stages of HWR are as follows: predevelopment (1470-1685), take off (1685-1912), acceleration (1912-2000) and rebalancing (2000-2020). This research indicates that the ecological civilization policy can rebuild HWR, and it is expected to provide enlightenment for future construction of the ecological civilization.
Grid-based spatially distributed hydrological modeling became feasible along with advances in watershed routing scheme, remote sensing technology, and computing resources. Such modeling is expected to be common in routine hydrological analysis and watershed management planning as it can maximize the use of spatial information and provide the detailed picture of transport processes. However, the heavy computational requirement and resulting long running time are still barriers that prevent the spatially detailed modeling practices from being employed widely, particularly in a fine-resolution large-scale study. Parallelizing computational tasks has been successful in mitigating the difficulty. We propose a noble way to improve the simulation efficiency of direct runoff transport processes by carefully grouping watershed areas based on the time-area routing scheme. The proposed parallelization method was applied to simulating the runoff routing processes of three watersheds draining the areas of 3.79 km2, 133.59 km2, and 2,800 km2 respectively at a 30-m resolution. Results demonstrated that the new method could substantially improve the computational efficiency of the time-area routing method with common computing resources. The efficiency of the parallelization scheme was not limited by the hierarchical relationship between upstream and downstream catchments along flow paths, which could be possible with the Lagrangian flow tracking strategy of the time-area routing method.
This mass balance study was intended to provide up-to-date information about the water quality of the headwater streams draining to the Mohican and Walhonding rivers. This data will be used to define target locations for conservation practices, including agricultural and stormwater management practices. During the study, 124 sites were sampled twice in 2021: during spring high-flow conditions (May) and fall low-flow conditions (August).
While more hydrological data is being generated than ever before, the power of modelling this collected information is not fully realized unless it is of high quality, especially considering hydrological data from sensor networks, which is often errant due to the possibility of malfunction or non-conducive environmental conditions. Fluctuations or errors are difficult to predict, identify, and interpret. Manual models of quality assurance are not designed for managing datasets with continuous timeseries or spatially extensive coverage, resulting in time- consuming models that rely on humanmade decision making and lack statistical inference. This research hypothesizes that the stochasticity of rainfall and deterministic properties of flow can be used in concert to create a more characteristic quality assurance model for high-resolution environmental data. An automated implementation of this model is presented herein with the application of two use-cases, which maintains statistical integrity and circumvents biases and potential for user error of manual frameworks.
Not only are reservoir managers and aquatic scientists concerned with the environmental effects of water quality, civil engineers must also consider water quality to comply with regulations in the construction of new reservoirs, or in making structural and operational modifications to existing reservoirs. This study establishes a machine learning approach for predicting Carlson’s Trophic State Index (CTSI), which is a frequently used metric of water quality in reservoirs. Data collected over ten years (1995-2016) from the stations at 20 reservoirs in Taiwan were preprocessed as the input for the modeling system. Four well-known artificial intelligence (AI) techniques, ANN (Artificial Neural Network), SVM (Support Vector Machine), CART (Classification And Regression Technique), and LR (Linear Regression), were used to analyze in baseline and ensemble scenarios. Moreover, one variation of support vector machine was integrated with a metaheuristic optimization algorithm to develop a hybrid AI model. The comprehensive comparison demonstrated that the ensemble ANN model, based on tiering method, is more accurate than the other single, ensemble, and hybrid models. The novelty of this study is providing a new approach of AI models, reducing the complexity of measuring three traditional parameters of CTSI formula, as an alternative to the conventional approach to predicting CTSI. This work contributes to the improvement of water quality management by providing a versatile technique that offers diverse predictive methods to meet the specific requirements of practitioners.
Throughout the last years, there is an increasing interest of the geoscientific community in using terrestrial gravimetry as an integrative and non-invasive method for observing mass change and mass redistribution in the environment due geophysical processes. The nature of the observed processes and the need for nearby data collection often require the gravimeters to be installed at remote field sites. In contrast to classical deployment at permanent observatory sites, this often is a challenge because there are three main requirements to be fulfilled for continuous high-quality operation of the gravimeter: electrical power, stable site conditions, and data connection. Whereas the latter can usually be accomplished by wireless solutions, the second requirement is more demanding as it requires an adequate design of a gravimeter enclosure and a stable pillar, while the first requirement so far has been practically insolvable in the absence of a power line. Here, we present the prototype of a mobile field container for gravity monitoring that fulfils all above requirements: the gPhone-solar-cube. The container consists of a cubic steel container as used by ships and trucks with edge length of about 2 meters. We optimized all components to host a continuously operating gPhoneX. Components include temperature shielding, ventilation, solar panels, power management and monitoring, storage batteries and an integrated backup generator to guarantee self-sufficient power supply, data loggers and wireless data transfer components. Furthermore we developed a new type of gravimeter pillar which is simple to install and to remove, without connection to the container floor to avoid vibration transfer. The pillar is large enough to accommodate two CG-6 field gravimeters, next to the gPhoneX. Other instruments integrated are a complete weather station and a cosmic ray neutron probe. The gPhone-solar-cube has been installed in the Ore mountains, Germany, as a continuously operating gravity reference station for time-lapse field surveys with CG-6 gravimeters to assess water storage changes in the course of heavy precipitation events. After 6 months of field operation, all requirements concerning data transmission, remote access, energy consumption, pillar stability and reliable gravity data were continuously met.