Large-scale Hydrological Models Research Articles

Developing water supply reservoir operating rules for large-scale hydrological modelling

Developing water supply reservoir operating rules for large-scale hydrological modelling

GloRESatE: A dataset for global rainfall erosivity derived from multi-source data

Numerous hydrological applications, such as soil erosion estimation, water resource management, and rain driven damage assessment, demand accurate and reliable rainfall erosivity data. However, the scarcity of gauge rainfall records and the inherent uncertainty in satellite and reanalysis-based rainfall datasets limit rainfall erosivity assessment globally. Here, we present a new global rainfall erosivity dataset (0.1° × 0.1° spatial resolution) integrating satellite (CMORPH and IMERG) and reanalysis (ERA5-Land) derived rainfall erosivity estimates with gauge rainfall erosivity observations collected from approximately 6,200 locations across the globe. We used a machine learning-based Gaussian Process Regression (GPR) model to assimilate multi-source rainfall erosivity estimates alongside geoclimatic covariates to prepare a unified high-resolution mean annual rainfall erosivity product. It has been shown that the proposed rainfall erosivity product performs well during cross-validation with gauge records and inter-comparison with the existing global rainfall erosivity datasets. Furthermore, this dataset offers a new global rainfall erosivity perspective, addressing the limitations of existing datasets and facilitating large-scale hydrological modelling and soil erosion assessments.

Pywr-DRB: An open-source Python model for water availability and drought risk assessment in the Delaware River Basin

The Delaware River Basin (DRB) in the Mid-Atlantic region of the United States is an institutionally complex water resources system that provides drinking water for 13.5 million people, plus water for energy, industry, recreation, and ecosystems. This paper introduces Pywr-DRB, an open-source Python model exploring the impacts of reservoir operations, transbasin diversions, and minimum flow targets on water availability and drought risk in the DRB. Pywr-DRB draws on streamflow estimates from emerging data resources, bridging advances in large-scale hydrologic modeling with an improved representation of the basin's evolving water infrastructure and management institutions. Our detailed model diagnostic assessment demonstrates that Pywr-DRB provides substantial improvements over sole use of hydrologic models in capturing the DRB's dynamics. We also explore how water management alters model-derived risk estimates for low flows and water demand shortfalls. Our approach to diagnostic benchmarking and water systems modeling is broadly applicable to other major basins.

A large-scale hydrological and hydrodynamic coupled model for flow routing in the Yangtze-Dongting system

In lowland alluvial river systems consisting of mainstream, distributaries, and floodplain lakes, the flow dynamics are characterized by changing backwater effects and seasonal water storage and release, and the bed morphology is unstable due to frequent sediment deposition and erosion. Hence, models that can accurately and efficiently simulate hydraulic interactions with low topographic data demands are required for such water systems. In this study, we present a coupled flow routing model for the Yangtze River-Dongting Lake system by coupling a 1D hydrodynamic model for the mainstream with hydrological models for distributaries and the lake. A new approach is proposed to generate a set of rating curves to represent the combined impacts of the inflow and outlet stage on the lake storage volume, thereby an innovative hydrological routing method fully considering backwater effects is established. Additionally, an implicit fully coupled algorithm is presented to integrate the hydrodynamic and hydrological models, which demonstrates ideal numerical stability and high computational efficiency. The coupled model is applied to flow routing in the Yangtze-Dongting system, with the Nash-Sutcliffe efficiency coefficients above 0.960, and most of the mean absolute error below 0.4 m (1000 m3/s) for stage (discharge). The model is also used to evaluate the impact of topographic changes during 1998–2016 on the flow process. The results indicate that the mainstream stage has decreased notably, while the reduction of the storage capacity of the lake is not yet remarkable. For the model proposed in this paper, only hydrological data and mainstream topographic data are necessary during model development, calibration, and application. In the context of continuous channel/lake bed adjustment in the middle reach of the Yangtze River Basin, the coupled model provides an effective tool for quantitatively evaluating the hydrological regime and flood process of a river system at a full scale.

When ancient numerical demons meet physics-informed machine learning: adjoint-based gradients for implicit differentiable modeling

Abstract. Recent advances in differentiable modeling, a genre of physics-informed machine learning that trains neural networks (NNs) together with process-based equations, have shown promise in enhancing hydrological models' accuracy, interpretability, and knowledge-discovery potential. Current differentiable models are efficient for NN-based parameter regionalization, but the simple explicit numerical schemes paired with sequential calculations (operator splitting) can incur numerical errors whose impacts on models' representation power and learned parameters are not clear. Implicit schemes, however, cannot rely on automatic differentiation to calculate gradients due to potential issues of gradient vanishing and memory demand. Here we propose a “discretize-then-optimize” adjoint method to enable differentiable implicit numerical schemes for the first time for large-scale hydrological modeling. The adjoint model demonstrates comprehensively improved performance, with Kling–Gupta efficiency coefficients, peak-flow and low-flow metrics, and evapotranspiration that moderately surpass the already-competitive explicit model. Therefore, the previous sequential-calculation approach had a detrimental impact on the model's ability to represent hydrological dynamics. Furthermore, with a structural update that describes capillary rise, the adjoint model can better describe baseflow in arid regions and also produce low flows that outperform even pure machine learning methods such as long short-term memory networks. The adjoint model rectified some parameter distortions but did not alter spatial parameter distributions, demonstrating the robustness of regionalized parameterization. Despite higher computational expenses and modest improvements, the adjoint model's success removes the barrier for complex implicit schemes to enrich differentiable modeling in hydrology.

Ensemble hydrological predictions at an intraseasonal scale through a statistical–dynamical downscaling approach over southwestern Amazonia

ABSTRACT We developed and analyzed the performance of an ensemble forecasting system for the Madeira River basin, the largest sub-basin of the Amazon, with forecasts up to 30 days under different hydrometeorological conditions. We used outputs from the regional Eta model of precipitation and global climatological data as inputs to a large-scale hydrological model. Bias correction of precipitation through quantile mapping significantly improved the results, achieving a hit rate >70%. The system demonstrated the ability to discriminate between high, medium, and low flow conditions. Forecast performance is better for larger catchment areas. This system is expected to increase decision-making efficiency for flood and drought situations in the largest Amazon tributary.

Remote Sensing and Modeling of the Cryosphere in High Mountain Asia: A Multidisciplinary Review

Over the past decades, the cryosphere has changed significantly in High Mountain Asia (HMA), leading to multiple natural hazards such as rock–ice avalanches, glacier collapse, debris flows, landslides, and glacial lake outburst floods (GLOFs). Monitoring cryosphere change and evaluating its hydrological effects are essential for studying climate change, the hydrological cycle, water resource management, and natural disaster mitigation and prevention. However, knowledge gaps, data uncertainties, and other substantial challenges limit comprehensive research in climate–cryosphere–hydrology–hazard systems. To address this, we provide an up-to-date, comprehensive, multidisciplinary review of remote sensing techniques in cryosphere studies, demonstrating primary methodologies for delineating glaciers and measuring geodetic glacier mass balance change, glacier thickness, glacier motion or ice velocity, snow extent and water equivalent, frozen ground or frozen soil, lake ice, and glacier-related hazards. The principal results and data achievements are summarized, including URL links for available products and related data platforms. We then describe the main challenges for cryosphere monitoring using satellite-based datasets. Among these challenges, the most significant limitations in accurate data inversion from remotely sensed data are attributed to the high uncertainties and inconsistent estimations due to rough terrain, the various techniques employed, data variability across the same regions (e.g., glacier mass balance change, snow depth retrieval, and the active layer thickness of frozen ground), and poor-quality optical images due to cloudy weather. The paucity of ground observations and validations with few long-term, continuous datasets also limits the utilization of satellite-based cryosphere studies and large-scale hydrological models. Lastly, we address potential breakthroughs in future studies, i.e., (1) outlining debris-covered glacier margins explicitly involving glacier areas in rough mountain shadows, (2) developing highly accurate snow depth retrieval methods by establishing a microwave emission model of snowpack in mountainous regions, (3) advancing techniques for subsurface complex freeze–thaw process observations from space, (4) filling knowledge gaps on scattering mechanisms varying with surface features (e.g., lake ice thickness and varying snow features on lake ice), and (5) improving and cross-verifying the data retrieval accuracy by combining different remote sensing techniques and physical models using machine learning methods and assimilation of multiple high-temporal-resolution datasets from multiple platforms. This comprehensive, multidisciplinary review highlights cryospheric studies incorporating spaceborne observations and hydrological models from diversified techniques/methodologies (e.g., multi-spectral optical data with thermal bands, SAR, InSAR, passive microwave, and altimetry), providing a valuable reference for what scientists have achieved in cryosphere change research and its hydrological effects on the Third Pole.

Benchmarking multimodel terrestrial water storage seasonal cycle against Gravity Recovery and Climate Experiment (GRACE) observations over major global river basins

Abstract. The increasing reliance on global models for evaluating climate- and human-induced impacts on the hydrological cycle underscores the importance of assessing the models' reliability. Hydrological models provide valuable data on ungauged river basins or basins with limited gauge networks. The objective of this study was to evaluate the reliability of 13 global models using the Gravity Recovery and Climate Experiment (GRACE) satellite's total water storage (TWS) seasonal cycle for 29 river basins in different climate zones. Results show that the simulated seasonal total water storage change (TWSC) does not compare well with GRACE even in basins within the same climate zone. The models overestimated the seasonal peak in most boreal basins and underestimated it in tropical, arid, and temperate zones. In cold basins, the modeled phase of TWSC precedes that of GRACE by up to 2–3 months. However, it lagged behind that of GRACE by 1 month over temperate and arid to semi-arid basins. The phase agreement between GRACE and the models was good in the tropical zone. In some basins with major underlying aquifers, those models that incorporate groundwater simulations provide a better representation of the water storage dynamics. With the findings and analysis of our study, we concluded that R2 (Water Resource Reanalysis tier 2 forced with Multi-Source Weighted Ensemble Precipitation (MSWEP) dataset) models with optimized parameterizations have a better correlation with GRACE than the reverse scenario (R1 models are Water Resource Reanalysis tier 1 and tier 2 forced with the ERA-Interim (WFDEI) meteorological reanalysis dataset). This signifies an enhancement in the predictive capability of models regarding the variability of TWSC. The seasonal peak, amplitude, and phase difference analyses in this study provide new insights into the future improvement of large-scale hydrological models and TWS investigations.

Large-Scale Hydrological Models and Transboundary River Basins

Large-scale hydrological modeling is an emerging approach in river hydrology, especially in regions with limited available data. This research focuses on evaluating the performance of two well-known large-scale hydrological models, namely E-HYPE and LISFLOOD, for the five transboundary rivers of Greece. For this purpose, discharge time series at the rivers’ outlets from both models are compared with observed datasets wherever possible. The comparison is conducted using well-established statistical measures, namely, coefficient of determination, Percent Bias, Nash–Sutcliffe Efficiency, Root-Mean-Square Error, and Kling–Gupta Efficiency. Subsequently, the hydrological models’ time series are bias corrected through scaling factor, linear regression, delta change, and quantile mapping methods, respectively. The outputs are then re-evaluated against observations using the same statistical measures. The results demonstrate that neither of the large-scale hydrological models consistently outperformed the other, as one model performed better in some of the basins while the other excelled in the remaining cases. The bias-correction process identifies linear regression and quantile mapping as the most suitable methods for the case study basins. Additionally, the research assesses the influence of upstream waters on the rivers’ water budget. The research highlights the significance of large-scale models in transboundary hydrology, presents a methodological approach for their applicability in any river basin on a global scale, and underscores the usefulness of the outputs in cooperative management of international waters.

Synthesis of historical reservoir operations from 1980 to 2020 for the evaluation of reservoir representation in large-scale hydrologic models

Abstract. All the major river systems in the contiguous United States (CONUS) (and many in the world) are impacted by dams, yet reservoir operations remain difficult to quantify and model due to a lack of data. Reservoir operation data are often inaccessible or distributed across many local operating agencies, making the acquisition and processing of data records quite time-consuming. As a result, large-scale models often rely on simple parameterizations for assumed reservoir operations and have a very limited ability to evaluate how well these approaches match actual historical operations. Here, we use the first national dataset of historical reservoir operations in the CONUS domain, ResOpsUS, to analyze reservoir storage trends and operations in more than 600 major reservoirs across the US. Our results show clear regional differences in reservoir operations. In the eastern US, which is dominated by flood control storage, we see storage peaks in the winter months with sharper decreases in the operational range (i.e., the difference between monthly maximum and minimum storage) in the summer. In the more arid western US where storage is predominantly for irrigation, we find that storage peaks during the spring and summer with increases in the operational range during the summer months. The Lower Colorado region is an outlier because its seasonal storage dynamics more closely mirrored those of flood control basins, yet the region is classified as arid, and most reservoirs have irrigation uses. Consistent with previous studies, we show that average annual reservoir storage has decreased over the past 40 years, although our analyses show a much smaller decrease than previous work. The reservoir operation characterizations presented here can be used directly for development or evaluation of reservoir operations and their derived parameters in large-scale models. We also evaluate how well historical operations match common assumptions that are often applied in large-scale reservoir parameterizations. For example, we find that 100 dams have maximum storage values greater than the reported reservoir capacity from the Global Reservoirs and Dams database (GRanD). Finally, we show that operational ranges have been increasing over time in more arid regions and decreasing in more humid regions, pointing to the need for operating policies which are not solely based on static values.

The use of GRDC gauging stations for calibrating large-scale hydrological models

Abstract. The Global Runoff Data Centre (GRDC) provides time series of observed discharges and information on hydrometric stations that are valuable for calibrating and validating the results of hydrological models. We address a common issue in large-scale hydrology that has not been satisfactorily solved, though investigated several times. To compare simulated and observed discharge, grid-based hydrological models must fit reported station locations to the resolution-dependent gridded river network. We introduce an intersection-over-union ratio approach to selected station locations on a coarser grid scale, reducing the errors in assigning stations to the correct upstream basin. We update the 10-year-old database of watershed boundaries with additional stations based on a high-resolution (3 arcsec) river network and provide source codes and high- and low-resolution watershed boundaries to easily select stations for calibration/validation of hydrological models. The dataset is stored on Zenodo with the associated DOI: https://doi.org/10.5281/zenodo.6906577 (Burek and Smilovic, 2022).

Evaluation of gridded precipitation data in water availability modeling in CONUS

Water availability estimates are affected by the quality of data used. In this study precipitation data from meteorological forcing datasets specifically compiled for large-scale hydrological modeling are evaluated over the Conterminous United States (CONUS). The performance of the datasets was evaluated using Pearson Correlation (r), Nash Sutcliff Efficiency (NSE), Percent Bias (PBIAS), and Kling Gupta Efficiency (KGE) criterion. Three observation-based gridded precipitation data from the Inter-Sectoral Impact Model Intercomparison Project – ISIMIP (EartH2Observe, WFDEI and ERA-Interim data Merged and Bias-corrected for ISIMIP (EWEMBI), WATCH Forcing Data methodologyapplied to ERA-Interim data (WFDEI), and WFDE5 over land merged with ERA5 over the ocean (W5E5)) and one regional dataset (GRIDMET) were compared. The datasets were compared using more than 3300 gage precipitation observations. For grid-level comparisons, the Climate Prediction Center's Unified gage-based gridded precipitation dataset (CPC) was used as a reference dataset after verification with gage observations. The grid-to-grid comparison was made on daily, monthly, annual, and seasonal time steps. Grid runoff and discharge were simulated using the Community Water Model (CWatM) model. The flow estimations using the datasets were compared with CPC estimations and with more than 900 discharge observations distributed over the nine climate regions of CONUS. Furthermore, the per capita water availability was estimated and compared. In comparison with gage precipitation observation, CPC had superior performance, followed by GRIDMET. CPC resulted in daily KGE, NSE, and r above 0.5, and PBIAS within 25 % in 79 %, 69 %, 87 %, and 90 % of the stations, respectively. Grid-to-grid precipitation comparisons resulted in overall comparable performance between the datasets. In grid discharge estimation, GRIDMET (median KGE = 0.68) had the highest correlation with CPC. WFDEI, W5E5, and EWEMBI show close performance to each other in estimating grid discharge with median KGE values of 0.43, 0.25, and 0.45 respectively. The performance of the datasets as compared to observed stream flow gages also showed comparable performance with above zero KGE in 73, 56, 71, 60, and 70 stations by GRIDMET, EWEMBI, W5E5, and WFDEI, and CPC respectively. In estimating water availability per capita, W5E5 showed an overestimation as compared to ISMIP datasets. The estimations from CPC and GRIDMET were close to each other. This study serves as a valuable resource for guiding the selection of a suitable dataset for large-scale modeling over the CONUS. By providing performance information for the compared datasets, it assists in making informed data selection and contributes to our understanding of how the selection of input datasets affect water availability modeling.

Hydrological simulation of ungauged basins via forcing by large‐scale hydrology models

AbstractThe established watershed's hydrologic simulation process, involving the calibration and validation of a model through spatiotemporal streamflow observations, has recently been adapted to encompass ungauged watersheds. The research introduces a methodology for simulating ungauged hydrosystems, suggesting the calibration and validation of regional rainfall‐runoff models using large‐scale hydrological models (LSMs) in the absence of in situ streamflow measurements. For doing so, LSM's 30‐years simulated discharges at various basins and subbasins of the case study River Basin District are assessed for their accuracy against the limited available observations. Subsequently, the rectified time series tunes the optimal parametrization of a regional lumped hydrologic model that is triggered by precipitation and temperature data from a partially dense meteorological network. The validated regional model is then applied in selective ungauged neighbouring subbasins employing the regionalization concept used in LSMs, that is in basins with similar physiographic characteristics to those where the regional model was set up. The performance of the research is assessed using statistical metrics. In cases where observations are available, the results show very satisfactory indexes (e.g., coefficient of determination: 0.654 < R2 < 0.949, Kling‐Gupta efficiency: 0.759 < KGE <0.790, Nash–Sutcliffe efficiency: 0.649 < NSE <0.745 for all the cases). In completely ungauged basins and in an order of magnitude index, the comparison of the simulated discharges with those from the LSM also indicates the satisfactory performance of the proposed approach. To sum up, the methodology underscores the alternate use of large and regional scale hydrological modelling coupling as an asset across various basin scales globally, offering reliable water quantity information.

Impact of diverse configuration in multivariate bias correction methods on large-scale hydrological modelling under climate change

For climate change impact studies, bias correction methods are widely utilized to improve climate model outputs by correcting the representation of climate variables. In addition to the univariate bias correction methods, many multivariate bias correction methods have been proposed, using diverse mathematical concepts in their individual designs. Although several attempts have been made to evaluate the performance of the multivariate bias correction methods, there is no exploration in the impact of diverse configurations to target multivariate dependencies in the multivariate bias correction methods particularly for hydrological modeling. This study tackles this research gap by developing an inter-comparison framework that assesses how the different configurations affect the climate and hydrological simulations. To achieve the research goal, the impact of three configurations (inter-variable, inter-spatial, and full-dimensional dependence configurations) is evaluated on multiple basins in a national-scale domain over South Korea. Results show that although most methods in the three different configurations capably correct the statistics, some instabilities are also observed when a high-dimensional dependence configuration is used, informing that some multivariate bias correction methods may not be compatible with high-dimensional configurations. For hydrological modeling, the configuration definitions bring about distinct differences in streamflow realizations. Only some of the inter-variable and full-dimensional configurations perform better or similarly to univariate bias correction methods. Finally, we demonstrate the importance of selecting bias correction methods while addressing the necessity for further improvement.

A high-resolution, 3D groundwater-surface water simulation of the contiguous US: Advances in the integrated ParFlow CONUS 2.0 modeling platform

Large-scale, high-resolution hydrologic modeling is an important tool to address questions of water quantity, availability, and potential recharge. Continental-to-Global scale models, particularly those that include groundwater, are growing in number. However, many of these approaches simplify aspects of the system and the connections between surface water and groundwater. The ParFlow CONUS modeling platform is a large-scale, hyper-resolution, hydrologic model that relies on the integrated solution of 3D partial differential equations that describe groundwater, soil, and 2D surface water flow. The prior version, ParFlow CONUS 1.0, was the first large-scale hydrologic model that included an explicit treatment of lateral groundwater flow for the contiguous US (CONUS). Here, we present the ParFlow CONUS 2.0 integrated hydrologic model. This model extends to the coastlines and contributing basins for CONUS and is consistent with the NOAA National Water Model. Here we document the roughly five years of technical development of this platform, present steady-state simulation results, rigorously compare these results to the prior ParFlow CONUS 1.0 simulations, and evaluate the model performance based on observations. Simulated water table depth and streamflow were evaluated using more than 635 K observations from USGS monitoring wells, other compiled groundwater datasets, and NHD and USGS streamflow gauges. Our results demonstrate improvement in both groundwater and surface water simulations over the prior generation model for all USGS Hydrologic Unit Code (HUC) basins. These results suggest that this current generation hydrologic model has good to excellent streamflow performance over the entire CONUS, with almost half of the HUC subbasins exhibiting excellent performance based on normalized root-square error (RSR). These results suggest that the current generation model approaches good performance for water table depth over the CONUS, a metric not usually compared directly at all in large-scale studies, with good-to-excellent performance exhibited over some HUC regions. We also delineate two regions that influence model performance, one where microtopography around streams dominates (D2), and another where a mix of subsurface heterogeneity and topographic gradients dominate (D1). Improvements in topography from CONUS1 to CONUS2 generally result in better streamflow and water table depth performances. Advancements in the subsurface depth and structure also produce better water table estimates.

Combining large-scale and regional hydrological forecasts using simple methods

The development and expanded application of large-scale hydrological models has produced forecasts that often overlap with more targeted, regional hydrological forecasts. Here the possibility is explored for using simple methods to combine forecasts from a large-scale model, the Great Lakes portion of the National Surface and River Prediction System (NSRPS), and a regional system, the Système de Prévision Hydrologique (SPH) which covers southern Quebec, to improve regional forecasts. Outputs from the two forecasting systems are combined using multiple methods, including the simple mean, a weighted average in which the weights are optimized using the Kling-Gupta Efficiency (KGE), the Reduced Continuous Ranked Probability Score (RCRPS), and Ignorance Score (IGN) as cost functions, and weights calculated from the residual errors of the models. Bayesian Model Averaging (BMA) is also used to combine the probabilistic forecasts from both systems. The results show that it is possible to improve regional hydrological forecasts by using simple weighted combinations with forecasts from the large-scale system, even though the regional system performs clearly better. Performance is assessed via many well-known metrics, such as Nash-Sutcliffe Efficiency (NSE), KGE, RCRPS, and IGN. Results are averaged over 40 gauging stations and analyzed at lead times from 3 to 120 h. Improvements in all criteria for lead times over 60 h are observed, and there is no loss in performance at any lead times. Finally, the methods are used in a leave-one-out setup containing 29 validation basins to simulate performance on ungauged basins. The performance gain for ungauged basins is similar to that of the gauged basins, demonstrating that these simple methods can also improve forecasts in more remote territories where no gauging is available.

An assessment of the present hydroclimatic regime of the Madeira River basin using climate and hydrological models

ABSTRACT Southwestern Amazonia has low spatial coverage of hydro-meteorological data, making climate and hydrological models essential tools. Thus, it is necessary to evaluate these models to verify their suitability for future climate change assessments. We evaluate the ability of a regional climate model (Eta) forced with three Earth system models and a large-scale hydrological model (MGB) to represent hydroclimatic variables for the current climate in the Madeira River basin. Different hydro-meteorological databases are used to evaluate the systematic errors of the coupled model system. The simulation results show that the Eta model underestimates (overestimates) precipitation over the basin during the rainy (dry) season. The hydrological processes simulated by the MGB are considered satisfactory, although the model has some limitations with respect to the timing of floods and their removal in different discharge regimes.

Guidance on large scale hydrologic model calibration with isotope tracers

Standard hydrologic model evaluation and calibration approaches focus on the accurate simulation of streamflow, disregarding internal process simulations. Stable isotope tracers can provide additional information on water sources, and process flux and storage, which can be used to inform model calibration. This study assesses the added value of isotope data in comparison to current best-practice flow-only calibration methods and evaluates the merits and limitations of isotope simulation performance metrics for the purposes of hydrological model calibration. Following several years of regular isotope sampling and measurement, an isotope-enabled process-based hydrologic model was tested on a large watershed in western Canada (Athabasca River), which allowed model calibration using global sensitivity analyses, Monte Carlo simulations, and multi-objective optimizations. Isotope tracer data were found to improve both process and streamflow component identifiability and produced some minor improvement in individual parameter value identifiability. Calibrating to optimize both flow and isotope simulation performance produced better flow simulation ensembles, with improved observation capture and validation performance, relative to calibrating to optimize flow simulations alone. Using an isotope simulation performance metric which includes timing error as a secondary optimization objective led to more robust streamflow modeling, even in mesoscale watersheds with limited isotope observation datasets.

Impact of local versus global datasets on hydrological responses in Mahanadi River basin in India

ABSTRACT Global environmental changes are likely to have a large impact on water resources across the developing countries. However, most of these countries suffer from acute shortage of local data. New high-resolution global products are available, which can be integrated with large-scale hydrological models. Most of these global products, however, are rarely evaluated in developing regions. We propose a thorough evaluation of five new global products in Mahanadi River basin in India. We employ the variable infiltration capacity (VIC) model over the basin and perform model experiments to directly evaluate the impacts of the specific combinations of local and global datasets. Results suggest that the reference experiment, which uses all local datasets, most closely represents the observed discharge. However, some of the global datasets could be used as a viable alternative to local observations in this river basin and potentially in nearby basins where there is a lack of observations.

Toward Large‐Scale Soil Moisture Monitoring Using Rail‐Based Cosmic Ray Neutron Sensing

AbstractCosmic ray neutron sensing (CRNS) has become a promising method for soil water content (SWC) monitoring. Stationary CRNS offers hectare‐scale average SWC measurements at fixed locations maintenance‐free and continuous in time, while car‐borne CRNS roving can reveal spatial SWC patterns at medium scales, but only on certain survey days. The novel concept of a permanent mobile CRNS system on rails promises to combine the advantages of both methods, while its technical implementation, data processing and interpretation raised a new level of complexity. This study introduced a fully automatic CRNS rail‐borne system as the first of its kind, installed within the locomotive of a cargo train. Data recorded from September 2021 to July 2022 along an ∼9 km railway segment were analyzed, as repeatedly used by the train, supported by local SWC measurements (soil samples and dielectric methods), car‐borne and stationary CRNS. The results revealed consistent spatial SWC patterns and temporary variation along the track at a daily resolution. The observed variability was mostly related to surface features, seasonal dynamics and different responses of the railway segments to wetting and drying periods, while some variations were related to measurement uncertainties. The achieved medium scale of SWC mapping could support large scale hydrological modeling and detection of environmental risks, such as droughts and wildfires. Hence, rail‐borne CRNS has the chance to become a central tool of continuous SWC monitoring for larger scales (≤10‐km), with the additional benefit of providing root‐zone soil moisture, potentially even in sub‐daily resolution.