Systemic change in the Rhine-Meuse basin: Quantifying and explaining parameters trends in the PCR-GLOBWB global hydrological model

In hydrological modelling, traditionally one calibration was performed over a certain calibration period before the model is used to study the hydrological system. This implies that a constant model structure and parameterization are assumed. However, if the catchment system is subject to changes that are not incorporated in the model, the parameter values found in a calibration period may not be optimal for other periods, which is called systemic change. The aim of this study was to identify systemic change and its possible causes with the PCR-GLOBWB hydrological model in the Rhine-Meuse basin, by performing a brute-force calibration for multiple periods for five calibration locations between 1901-2010. Systemic change was studied for the main model components, by selecting a key parameter from each component (minimum soil depth fraction, saturated hydraulic conductivity, groundwater recession coefficient, degree day factor, Manning's n). These parameters were calibrated for 10-year rolling periods between 1901-2010. The results showed that at the downstream locations, the changes in optimal parameter values were small, while at the upstream locations, the optimal values of most parameters changed considerably over the different rolling calibration periods, signifying systemic change. Especially the degree day factor showed large variations, varying over time between 0.5 and 2.5 times its default value at Basel and Maxau (upstream and middle part of the Rhine basin). Based on correlation analysis, it was found that climate change as well as changes in land use and river structure are possible causes of changes in optimal parameter values through time

Coupling a global glacier model to a global hydrological model prevents underestimation of glacier runoff

Global hydrological models have become a valuable tool for a range of global impact studies related to water resources. However, glacier parameterization is often simplistic or non-existent in global hydrological models. By contrast, global glacier models do represent complex glacier dynamics and glacier evolution, and as such, they hold the promise of better resolving glacier runoff estimates. In this study, we test the hypothesis that coupling a global glacier model with a global hydrological model leads to a more realistic glacier representation and, consequently, to improved runoff predictions in the global hydrological model. To this end, the Global Glacier Evolution Model (GloGEM) is coupled with the PCRaster GLOBal Water Balance model, version 2.0 (PCR-GLOBWB 2), using the eWaterCycle platform. For the period 2001–2012, the coupled model is evaluated against the uncoupled PCR-GLOBWB 2 in 25 large-scale (>50 000 km2), glacierized basins. The coupled model produces higher runoff estimates across all basins and throughout the melt season. In summer, the runoff differences range from 0.07 % for weakly glacier-influenced basins to 252 % for strongly glacier-influenced basins. The difference can primarily be explained by PCR-GLOBWB 2 not accounting for glacier flow and glacier mass loss, thereby causing an underestimation of glacier runoff. The coupled model performs better in reproducing basin runoff observations mostly in strongly glacier-influenced basins, which is where the coupling has the most impact. This study underlines the importance of glacier representation in global hydrological models and demonstrates the potential of coupling a global hydrological model with a global glacier model for better glacier representation and runoff predictions in glacierized basins

Evaluation of Groundwater Storage Variations Estimated from GRACE Data Assimilation and State-of-the-Art Land Surface Models in Australia and the North China Plain

The accurate knowledge of the groundwater storage variation (ΔGWS) is essential for reliable water resource assessment, particularly in arid and semi-arid environments (e.g., Australia, the North China Plain (NCP)) where water storage is significantly affected by human activities and spatiotemporal climate variations. The large-scale ΔGWS can be simulated from a land surface model (LSM), but the high model uncertainty is a major drawback that reduces the reliability of the estimates. The evaluation of the model estimate is then very important to assess its accuracy. To improve the model performance, the terrestrial water storage variation derived from the Gravity Recovery And Climate Experiment (GRACE) satellite mission is commonly assimilated into LSMs to enhance the accuracy of the ΔGWS estimate. This study assimilates GRACE data into the PCRaster Global Water Balance (PCR-GLOBWB) model. The GRACE data assimilation (DA) is developed based on the three-dimensional ensemble Kalman smoother (EnKS 3D), which considers the statistical correlation of all extents (spatial, temporal, vertical) in the DA process. The ΔGWS estimates from GRACE DA and four LSM simulations (PCR-GLOBWB, the Community Atmosphere Biosphere Land Exchange (CABLE), the Water Global Assessment and Prognosis Global Hydrology Model (WGHM), and World-Wide Water (W3)) are validated against the in situ groundwater data. The evaluation is conducted in terms of temporal correlation, seasonality, long-term trend, and detection of groundwater depletion. The GRACE DA estimate shows a significant improvement in all measures, notably the correlation coefficients (respect to the in situ data) are always higher than the values obtained from model simulations alone (e.g., ~0.15 greater in Australia, and ~0.1 greater in the NCP). GRACE DA also improves the estimation of groundwater depletion that the models cannot accurately capture due to the incorrect information of the groundwater demand (in, e.g., PCR-GLOBWB, WGHM) or the unavailability of a groundwater consumption routine (in, e.g., CABLE, W3). In addition, this study conducts the inter-comparison between four model simulations and reveals that PCR-GLOBWB and CABLE provide a more accurate ΔGWS estimate in Australia (subject to the calibrated parameter) while PCR-GLOBWB and WGHM are more accurate in the NCP (subject to the inclusion of anthropogenic factors). The analysis can be used to declare the status of the ΔGWS estimate, as well as itemize the possible improvements of the future model development.This work is funded by The University of Newcastle to support NASA’s GRACE and GRACE Follow-On projects as an international science team member to the missions

DynQual v1.0: A high-resolution global surface water quality model

Maintaining good surface water quality is crucial to protect ecosystem health and for safeguarding human water use activities. Yet, our quantitative understanding of surface water quality is mostly predicated upon observations at monitoring stations that are highly limited in space and fragmented across time. Physically-based models, based upon pollutant emissions and subsequent routing through the hydrological network, provide opportunities to overcome these shortcomings. To this end, we have developed the dynamical surface water quality model (DynQual) for simulating water temperature (Tw) and concentrations of total dissolved solids (TDS), biological oxygen demand (BOD) and fecal coliform (FC) with a daily timestep and at 5 arc-minute (~10 km) spatial resolution. Here, we describe the main components of this new global surface water quality model and evaluate model performance against in-situ water quality observations. Furthermore, we describe both the spatial patterns and temporal trends in TDS, BOD and FC concentrations for the period 1980&ndash;2019, also attributing the dominant contributing sectors. The model code is available open-source (https://github.com/UU-Hydro/DYNQUAL) and we provide global datasets of simulated hydrology, Tw, TDS, BOD and FC at 5 arc-minute resolution with a monthly timestep (https://doi.org/10.5281/zenodo.7139222). This data has potential to inform assessments in a broad range of fields, including ecological, human health and water scarcity studies.</p

Hyper-resolution PCR-GLOBWB: opportunities and challenges from refining model spatial resolution to 1km over the European continent

The quest for hydrological hyper-resolution modelling has been on-going for more than a decade. While global hydrological models (GHMs) have seen a reduction in grid size, they have thus far never been consistently applied at a hyper-resolution (<Combining double low line1km) at the large scale. Here, we present the first application of the GHM PCR-GLOBWB at 1km over Europe. We thoroughly evaluated simulated discharge, evaporation, soil moisture, and terrestrial water storage anomalies against long-term observations and subsequently compared results with the established 10 and 50km resolutions of PCR-GLOBWB. Subsequently, we could assess the added value of this first hyper-resolution version of PCR-GLOBWB and assess the scale dependencies of model and forcing resolution. Eventually, these insights can help us in understanding the current challenges and opportunities from hyper-resolution models and in formulating the model and data requirements for future improvements. We found that, for most variables, epistemic uncertainty is still large, and issues with scale commensurability exist with respect to the long-term yet coarse observations used. Merely for simulated discharge, we can confidently state that model output at hyper-resolution improves over coarser resolutions due to better representation of the river network at 1km. However, currently available observations are not yet widely available at hyper-resolution or lack a sufficiently long time series, which makes it difficult to assess the performance of the model for other variables at hyper-resolution. Here, additional model validation efforts are needed. On the model side, hyper-resolution applications require careful revisiting of model parameterization and possibly the implementation of more physical processes to be able to resemble the dynamics and spatial heterogeneity at 1km. With this first application of PCR-GLOBWB at 1km, we contribute to meeting the grand challenge of hyper-resolution modelling. Even though the model was only assessed at the continental scale, valuable insights could be gained which have global validity. As such, it should be seen as a modest milestone on a longer journey towards locally relevant model output. This, however, requires a community effort from domain experts, model developers, research software engineers, and data providers

GLOBGM v1.0: A parallel implementation of a 30arcsec PCR-GLOBWB-MODFLOW global-scale groundwater model

We discuss the various performance aspects of parallelizing our transient global-scale groundwater model at 30′′ resolution (30arcsec; °1/41km at the Equator) on large distributed memory parallel clusters. This model, referred to as GLOBGM, is the successor of our 5′ (5arcmin; °1/410km at the Equator) PCR-GLOBWB 2 (PCRaster Global Water Balance model) groundwater model, based on MODFLOW having two model layers. The current version of GLOBGM (v1.0) used in this study also has two model layers, is uncalibrated, and uses available 30′′ PCR-GLOBWB data. Increasing the model resolution from 5′ to 30′′ creates challenges, including increased runtime, memory usage, and data storage that exceed the capacity of a single computer. We show that our parallelization tackles these problems with relatively low parallel hardware requirements to meet the needs of users or modelers who do not have exclusive access to hundreds or thousands of nodes within a supercomputer. For our simulation, we use unstructured grids and a prototype version of MODFLOW 6 that we have parallelized using the message-passing interface. We construct independent unstructured grids with a total of 278 million active cells to cancel all redundant sea and land cells, while satisfying all necessary boundary conditions, and distribute them over three continental-scale groundwater models (168 million - Afro-Eurasia; 77 million - the Americas; 16 million - Australia) and one remaining model for the smaller islands (17 million). Each of the four groundwater models is partitioned into multiple non-overlapping submodels that are tightly coupled within the MODFLOW linear solver, where each submodel is uniquely assigned to one processor core, and associated submodel data are written in parallel during the pre-processing, using data tiles. For balancing the parallel workload in advance, we apply the widely used METIS graph partitioner in two ways: it is straightforwardly applied to all (lateral) model grid cells, and it is applied in an area-based manner to HydroBASINS catchments that are assigned to submodels for pre-sorting to a future coupling with surface water. We consider an experiment for simulating the years 1958-2015 with daily time steps and monthly input, including a 20-year spin-up, on the Dutch national supercomputer Snellius. Given that the serial simulation would require °1/44.5 months of runtime, we set a hypothetical target of a maximum of 16h of simulation runtime. We show that 12 nodes (32 cores per node; 384 cores in total) are sufficient to achieve this target, resulting in a speedup of 138 for the largest Afro-Eurasia model when using 7 nodes (224 cores) in parallel. A limited evaluation of the model output using the United States Geological Survey (USGS) National Water Information System (NWIS) head observations for the contiguous United States was conducted. This showed that increasing the resolution from 5′ to 30′′ results in a significant improvement with GLOBGM for the steady-state simulation when compared to the 5′ PCR-GLOBWB groundwater model. However, results for the transient simulation are quite similar, and there is much room for improvement. Monthly and multi-year total terrestrial water storage anomalies derived from the GLOBGM and PCR-GLOBWB models, however, compared favorably with observations from the GRACE satellite. For the next versions of GLOBGM, further improvements require a more detailed (hydro)geological schematization and better information on the locations, depths, and pumping rates of abstraction wells

Offshore fresh groundwater in coastal unconsolidated sediment systems as a potential fresh water source in the 21st century

Coastal areas worldwide are often densely populated and host regional agricultural and industrial hubs. Strict water quality requirements for agricultural, industrial and domestic use are regularly not satisfied by surface waters in coastal areas and consequently lead to over-exploitation of local fresh groundwater resources. Additional pressure by both climate change and population growth further intensifies the upcoming water stress and raise the urgency to search for new fresh water sources. In recent years, offshore fresh groundwater (OFG) reserves have been identified as such a potential water source. In this study, we quantify, for the first time, the global volume of OFG in unconsolidated coastal aquifers using numerical groundwater models. Our results confirm previously reported widespread presence of OFG along the global coastline. Furthermore, we find that these reserves are likely non-renewable resources mostly deposited during glacial periods when sea levels were substantially lower compared to current sea level. We estimate the total OFG volume in unconsolidated coastal aquifers to be approximately 1.06 0.2 million km3, which is roughly three times more than estimated previously and about 10% of all terrestrial fresh groundwater. With extensive active and inactive offshore oil pumping present in areas of large OFG reserves, they could be considered for temporary fresh groundwater exploration as part of a transition to sustainable water use in coastal areas on the long run

Improving global hydrological simulations through bias-correction and multi-model blending

There is an immediate need to develop accurate and reliable global hydrological forecasts in light of the future vulnerability to hydrological hazards and water scarcity under a changing climate. As a part of the World Meteorological Organization's (WMO) Global Hydrological Status and Outlook System (HydroSOS) initiative, we investigated different approaches to blending multi-model simulations for developing holistic operational global forecasts. The ULYSSES (mULti-model hYdrological SeaSonal prEdictionS system) dataset, to be published as “Global seasonal forecasts and reforecasts of river discharge and related hydrological variables ensemble from four state-of-the-art land surface and hydrological models” is used in this study. The first step for improving these forecasts is to investigate ways to improve the model simulations, as global models are not calibrated for local conditions. The analysis was performed over 119 different catchments worldwide for the baseline period of 1981–2019 for three variables: evapotranspiration, surface soil moisture and streamflow. This study evaluated blending approaches with a performance metric based (weighted) averaging of the multi-model simulations, using the catchment's Kling-Gupta Efficiency (KGE) for the variable to define the weight. Hydrological model simulations were also bias-corrected to improve the multi-model blending output. Weighted blending in conjunction with bias-correction provided the best improvement in performance for the catchments investigated. Applying modelled weights during blending original simulations improved performance over ungauged catchments. The results indicate that there is potential to successfully and easily implement the bias-corrected weighted blending approach to improve operational forecasts globally. This work can be used to improve water resources management and hydrological hazard mitigation, especially in data-sparse regions

Current wastewater treatment targets are insufficient to protect surface water quality

The quality of global water resources is increasingly strained by socio-economic developments and climate change, threatening both human livelihoods and ecosystem health. With inadequately managed wastewater being a key driver of deterioration, Sustainable Development Goal (SDG) 6.3 was established to halve the proportion of untreated wastewater discharged to the environment by 2030. Yet, the impact of achieving SDG6.3 on global ambient water quality is unknown. Addressing this knowledge gap, we develop a high-resolution surface water quality model for salinity as indicated by total dissolved solids, organic pollution as indicated by biological oxygen demand and pathogen pollution as indicated by fecal coliform. Our model includes a novel spatially-explicit approach to incorporate wastewater treatment practices, a key determinant of in-stream pollution. We show that achieving SDG6.3 reduces water pollution, but is still insufficient to improve ambient water quality to below key concentration thresholds in several world regions. Particularly in the developing world, reductions in pollutant loadings are locally effective but transmission of pollution from upstream areas still leads to water quality issues downstream. Our results highlight the need to go beyond the SDG-target for wastewater treatment in order to achieve the overarching goal of clean water for all