Global Hydrological Models Research Articles

The analysis on groundwater storage variations from GRACE/GRACE-FO in recent 20 years driven by influencing factors and prediction in Shandong Province, China

Monitoring and predicting the regional groundwater storage (GWS) fluctuation is an essential support for effectively managing water resources. Therefore, taking Shandong Province as an example, the data from Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On (GRACE-FO) is used to invert GWS fluctuation from January 2003 to December 2022 together with Watergap Global Hydrological Model (WGHM), in-situ groundwater volume and level data. The spatio-temporal characteristics are decomposed using Independent Components Analysis (ICA), and the impact factors, such as precipitation and human activities, which are also analyzed. To predict the short-time changes of GWS, the Support Vector Machines (SVM) is adopted together with three commonly used methods Long Short-Term Memory (LSTM), Singular Spectrum Analysis (SSA), Auto-Regressive Moving Average Model (ARMA), as the comparison. The results show that: (1) The loss intensity of western GWS is significantly greater than those in coastal areas. From 2003 to 2006, GWS increased sharply; during 2007 to 2014, there exists a loss rate − 5.80 ± 2.28 mm/a of GWS; the linear trend of GWS change is − 5.39 ± 3.65 mm/a from 2015 to 2022, may be mainly due to the effect of South-to-North Water Diversion Project. The correlation coefficient between GRACE and WGHM is 0.67, which is consistent with in-situ groundwater volume and level. (2) The GWS has higher positive correlation with monthly Global Precipitation Climatology Project (GPCP) considering time delay after moving average, which has the similar energy spectrum depending on Continuous Wavelet Transform (CWT) method. In addition, the influencing facotrs on annual GWS fluctuation are analyzed, the correlation coefficient between GWS and in-situ data including the consumption of groundwater mining, farmland irrigation is 0.80, 0.71, respectively. (3) For the GWS prediction, SVM method is adopted to analyze, three training samples with 180, 204 and 228 months are established with the goodness-of-fit all higher than 0.97. The correlation coefficients are 0.56, 0.75, 0.68; RMSE is 5.26, 4.42, 5.65 mm; NSE is 0.28, 0.43, 0.36, respectively. The performance of SVM model is better than the other methods for the short-term prediction.

Global‐Scale Groundwater Recharge Modeling Is Improved by Tuning Against Ground‐Based Estimates for Karst and Non‐Karst Areas

AbstractQuantification of groundwater recharge is highly important for understanding freshwater systems and sustainable management of both groundwater and surface water bodies, but it is very uncertain. To improve the global‐scale simulation of diffuse groundwater recharge (GWR), we developed, for the first time in global hydrological modeling, an approach for explicitly simulating GWR in karst areas, based on the World Karst Aquifer Map and ground‐based estimates of karst GWR. Ground‐based estimates of both karst and non‐karst GWR from 5,600 locations were aggregated to produce estimates for 816 globally distributed 0.5° grid cells, including 64 representing karst GWR. Applying WaterGAP2.2e, we found that karst GWR is best approximated by total runoff from land (without soil overflow and urban runoff). The GWR algorithm for non‐karst GWR was tuned against ground‐based estimates for 422 non‐karst grid cells outside of Australia. While simulated GWR tends to underestimate GWR in non‐karst areas with mean annual ground‐based GWR > 200 mm/yr, it overestimates national GWR in six out of seven European countries. With an increase in the coefficient for the discharge from groundwater to surface water bodies, the updated GWR algorithm results in an improved fit of simulated streamflow to observations, including low flows. Global mean annual GWR and thus renewable groundwater resources (without Greenland and Antarctica) are estimated to be about one‐half of the total renewable water resources, or 21,000 km3/yr, which is over 50% higher than the standard model estimate. 20% of global GWR is generated in karst areas, which cover about 11% of the world's land surface.

Global Evaluation of Simulated High and Low Flows from 23 Macroscale Models

Abstract Macroscale hydrological/land surface models are important tools for assessing historical and predicting future characteristics of extreme hydrological events, yet quantitative understandings of how these large-scale models perform in simulating extreme hydrological characteristics remain limited. Here we evaluate simulated high and low flows from 23 macroscale models within three modeling experiments (i.e., 14 climate models from CMIP6, 6 global hydrological models from ISIMIP2a, and 3 land surface models from GLDAS) against observation in 633 unimpaired catchments globally over 1971–2010. Our findings reveal limitations in simulating extreme flow characteristics by these models. Specifically, we find that (i) most models overestimate high-flow magnitudes (bias range: from +15% to +70%) and underestimate low-flow magnitudes (bias range: from −80% to −20%); (ii) interannual variability in high and low flows is reasonably reproduced by ISIMIP2a and GLDAS models but poorly reproduced by CMIP6 models; (iii) no model consistently replicates the observed trend direction in high and low flows in over two-thirds of the catchments, and most models overestimate high-flow trends and underestimate low-flow trends; and (iv) CMIP6 and GLDAS models show timing biases, with early high flows and late low flows, while ISIMIP2a models exhibit the opposite pattern. Furthermore, all models performed better in more humid environments and noncold regions, with model structure and parameterization contributing more to uncertainties than climatic forcings. Overall, our results demonstrate that extreme flow characteristics simulated from current state-of-the-art macroscale models still contain large uncertainties and provide important guidance regarding the robustness of assessing extreme hydrometeorological events based on these modeling outputs. Significance Statement Macroscale hydrological and land surface models represent crucial tools for assessing historical trends and making predictions about future hydrological changes. Nevertheless, our current understanding of the quantitative performance of these large-scale models in simulating extreme hydrological characteristics remains limited. Here, we evaluate simulated high and low flows from 23 state-of-the-art macroscale models against observation in 633 unimpaired catchments globally over 1971–2010. Our results reveal important limitations in the extreme flow characteristics simulated from these models and provide important guidance regarding the robustness of assessing extreme hydrometeorological events based on these modeling outputs. The model evaluation performed herein serves as a pivotal, offering valuable insights to inform the development of the next generation of macroscale hydrological and land surface models.

Scaling from global to regional river flow with global hydrological models: Choice matters

High-resolution global flow discharge datasets are critical to understand the terrestrial water cycle and river ecosystem services. Recently, a number of global hydrological models have been developed to capture global river flow dynamics at locally relevant scales. However, the relative accuracy of these simulated flow discharges is largely unknown. In this paper, using the largest observed streamflow datasets from > 26,000 gauging stations, we evaluated the performance of monthly and sub-monthly flow variation (1980–2014) generated from five major global hydrological models, Catchment-based Macro-scale Floodplain model (CaMa-Flood), Global Flood Awareness System (GloFAS-reanalysis), Global Reach-level Flood Reanalysis (GRFR), PCRaster GLOBal Water Balance model (PCR-GLOBWB), and Water Global Assessment and Prognosis (WaterGAP). Significant spatial heterogeneity in model performance was identified. We found that estimates by all five discharge datasets have a relatively high correlation with the observations (medians > 0.6). But none of them showed adequate correlations between simulated and observed flows for systems in Southwest or Central U.S.A., South Africa, or South Australia. Across all models, overestimation of monthly mean flow was more common than underestimation and high flows at a sub-monthly scale were more accurately characterized than low flows. Performance of all models was poor in Africa and Oceania. No single model performed universally best at all gauging stations, but GRFR showed the best estimate at around 40 % of the locations. None of the models provided satisfying estimates for rivers and streams in arid regions. We also found a significant negative effect of flow regulation on the predictive accuracy of all models. Our study highlights the importance of thorough evaluation of global discharge datasets before their local and regional application and provides guidance for future enhancement of global hydrological models.

Historical and projected future runoff over the Mekong River basin

Abstract. The Mekong River (MR) crosses the borders and connects six countries, including China, Myanmar, Laos, Thailand, Cambodia, and Vietnam. It provides critical water resources and supports natural and agricultural ecosystems, socioeconomic development, and the livelihoods of the people living in this region. Understanding changes in the runoff of this important international river under projected climate change is critical for water resource management and climate change adaptation planning. However, research on long-term runoff dynamics for the MR and the underlying drivers of runoff variability remains scarce. Here, we analyse historical runoff variations from 1971 to 2020 based on runoff gauge data collected from eight hydrological stations along the MR. With these runoff data, we then evaluate the runoff simulation performance of five global hydrological models (GHMs) forced by four global climate models (GCMs) under the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP). Furthermore, based on the best simulation combination, we quantify the impact of future climate change on river runoff changes in the MR. The result shows that the annual runoff in the MR has not changed significantly in the past 5 decades, while the establishment of dams and reservoirs in the basin visibly affected the annual runoff distribution. The ensemble-averaged result of the Water Global Assessment and Prognosis version 2 (WaterGAP2; i.e. GHM) forced by four GCMs has the best runoff simulation performance. Under Representative Concentration Pathways (RCPs; i.e. RCP2.6, RCP6.0 and RCP8.5), the runoff of the MR is projected to increase significantly (p<0.05); e.g. 3.81 ± 3.47 m3s-1a-1 (9 ± 8 % increase in 100 years) at the upper reach under RCP2.6 and 16.36 ± 12.44 m3s-1a-1 (13 ± 10 % increase in 100 years) at the lower reach under RCP6.0. In particular, under the RCP6.0 scenario, the increase in annual runoff is most pronounced in the middle and lower reaches, due to increased precipitation and snowmelt. Under the RCP8.5 scenario, the runoff distribution in different seasons varies obviously, increasing the risk of flooding in the wet season and drought in the dry season.

Improving river routing algorithms to efficiently implement canal water diversion schemes in global hydrological models

Improving river routing algorithms to efficiently implement canal water diversion schemes in global hydrological models

Machine learning assessment of hydrological model performance under localized water storage changes through downscaling

The coarse spatial resolution of the Gravity Recovery and Climate Experiment (GRACE) data has limited its application in the management of local-scale water resources. To address this limitation, we developed a new downscaling approach using predictors from regional and global hydrological models for a 15-year period (2002–2017) and tested it in the northern Great Artesian Basin, Australia. We used four different machine learning algorithms (support vector machine, partial least squares, gaussian process and random forest) to downscale the original GRACE estimate of 0.5° to a spatial grain size of 0.1° (global) and 0.05° (regional). This was based on precipitation, evapotranspiration and runoff estimates from the Famine Early Warning Systems Network Land Data Assimilation System (FLDAS) and Australian Water Outlook (AWO) hydrological models, respectively. The downscaled products were validated using 42 in-situ precipitation observations spread across the test region. We further evaluated which of the downscaled products best mimicked local-scale hydrology using a range of statistical metrics. Our results showed that regional hydrological models best characterized the dynamics of local scale hydrology (rainfall v. downscaled product), and the gaussian process regression algorithm made the best predictions for both models. The correlation coefficients for the raw values varied from 0.45 to 0.49 while that of the standardized values varied from 0.46 to 0.52 with the random forest model providing the best fitting for the regional-based products. The regional downscaling approach employed in this study may be readily integrated into local water resources planning programs.

Deriving the Terrestrial Water Storage Anomaly From GRACE Spherical Harmonic Coefficients Using a Convolutional Neural Network

AbstractTerrestrial water storage anomaly (TWSA), derived from Gravity Recovery and Climate Experiment (GRACE) satellites, has been widely used in hydrology studies. The inversion is commonly achieved by truncating and filtering spherical harmonic coefficients (SHC), whereby the result is characterized by leakage error and low resolution. It remains unclear whether machine learning methods can help resolve this challenging issue. In this study, we present a convolutional neural network (CNN) approach to correct TWSA from GRACE SHC by leveraging the knowledge of the leakage effect determined from global hydrological models (GHMs) and land surface models (LSMs). The CNN approach is implemented in three representative regions in China, that is, the human‐impacted Haihe River Basin, the nature‐impacted Yangtze River Basin and the model‐limited Tibetan Plateau. The results show the following: (a) The recovery performance of CNN at the basin scale is better than that at the grid scale, and the grid‐scale recovery is significantly influenced by the spatial heterogeneity of TWSA and the input GHM/LSMs; (b) The more accurate the GHM/LSMs used for training, the better the recovery performance of CNN; and (c) The trained model retains comparable performance in deriving the TWSA time series from GRACE SHC when compared to that derived from other methods (i.e., scaling factor and mass concentration solutions) with average r = 0.90 and RMSE = 21.50 mm. This study highlights the potential of machine learning to supplement conventional correction methods when deriving the TWSA from GRACE SHC by utilizing signal restoration knowledge learned from multiple accurate GHMs/LSMs.

Quantifying the drivers of terrestrial drought and water stress impacts on carbon uptake in China

Droughts are one of the most damaging weather-related hazards and can have detrimental impacts on ecosystem carbon uptake. However, little is known about the physical mechanisms underlying drought evolution as well as their potential effects on the carbon budget under historical and future climates. Here, we first quantify the impacts of water stress on carbon uptake under climate change in China. While heat and water stress play a crucial role in carbon uptake, the effect of ecosystem complexity is also significant. Then, we employ a machine learning model to explore the driving mechanisms of droughts, which are identified through the depletion of terrestrial water storage (TWS). Our results indicate that TWS droughts tend to be governed by atmospheric dryness, with precipitation, relative humidity (RH) and temperature playing dominant roles in drought evolution across most land areas. Precipitation and RH control moisture supply and demand, while rising temperature signifies increasing evaporative demand and enhanced evapotranspiration, leading to soil moisture depletion and reduced surface runoff, thereby intensifying drought. Further, by combining satellite data, field measurements, six global hydrological models, a global land surface model and a dynamic vegetation model, we find that water and heat stress have negative impacts on gross primary productivity (GPP), total ecosystem respiration (TER) and net ecosystem productivity (NEP), under both current and future climates. By the end of the 21st century (2071–2100), drought occurrence is projected to increase by sixfold over more than 60 % of land areas, leading to disproportionate negative impacts on carbon assimilation. Negative anomalies of NEP under drought stress are projected to decline from -0.09 g·m−2·day−1 (historical period) to −0.16 g·m−2·day−1(future period) under SSP370, with even more severe effects on future carbon assimilation under higher emission pathways. Our results suggest that more severe drought conditions might challenge ecosystem sustainability, and highlight the necessity of improving ecosystem resilience to climate warming.

Assessing robustness in global hydrological predictions by comparing modelling and Earth observations

ABSTRACTHydrological modelling to support hypotheses on Earth system boundaries or the accelerating water crisis is nowadays done at the global scale, with difficulties associated to model uncertainties. Here we bring a robustness analysis of internal model variables as an additional tool for model evaluation using data from six Earth observation products and the global catchment model World-Wide HYPE in a comparative study. The assessment shows that: (i) variables have high agreement in mid-latitude temperate regions; (ii) the variables with higher agreement, and associated with good model performance in streamflow, were actual evapotranspiration, fractional snow cover and snow water equivalent; and (iii) changes in total water storage showed very poor agreement, probably due to an insufficient number of aquifers in the model set-up. We propose this procedure as a standard complementary method in global hydrological modelling, highlighting the importance of justifying models before using them for scenario analysis or water accounting.

Corrigendum: Spatiotemporal features of the soil moisture across Northwest China using remote sensing data, reanalysis data, and global hydrological model

Corrigendum: Spatiotemporal features of the soil moisture across Northwest China using remote sensing data, reanalysis data, and global hydrological model

Assessment of pluri-annual and decadal changes in terrestrial water storage predicted by global hydrological models in comparison with the GRACE satellite gravity mission

Abstract. The GRACE (Gravity Recovery And Climate Experiment) satellite gravity mission enables global monitoring of the mass transport within the Earth's system, leading to unprecedented advances in our understanding of the global water cycle in a changing climate. This study focuses on the quantification of changes in terrestrial water storage with respect to the temporal average based on an ensemble of GRACE solutions and two global hydrological models. Significant changes in terrestrial water storage are detected at pluri-annual and decadal timescales in GRACE satellite gravity data that are generally underestimated by global hydrological models though consistent with precipitation. The largest differences (more than 20 cm in equivalent water height) are observed in South America (Amazon, São Francisco and Paraná River basins) and tropical Africa (Congo, Zambezi and Okavango River basins). Smaller but significant (a few centimetres) differences are observed worldwide. While the origin of such differences is unknown, part of it is likely to be climate-related and at least partially due to inaccurate predictions of hydrological models. Pluri-annual to decadal changes in the terrestrial water cycle may indeed be overlooked in global hydrological models due to inaccurate meteorological forcing (e.g. precipitation), unresolved groundwater processes, anthropogenic influences, changing vegetation cover and limited calibration/validation datasets. Significant differences between GRACE satellite measurements and hydrological model predictions have been identified, quantified and characterised in the present study. Efforts must be made to better understand the gap between methods at both pluri-annual and decadal timescales, which challenges the use of global hydrological models for the prediction of the evolution of water resources in changing climate conditions.

Groundwater Storage Variations in the Main Karoo Aquifer Estimated Using GRACE and GPS

The Gravity Recovery and Climate Experiment (GRACE) provided valuable insights into variations in Groundwater Storage (GWS). However, the sensitivity of utilizing Global Positioning System (GPS) time series displacement data for detecting changes in GWS remains a subject of ongoing discussion. In order to estimate the spatiotemporal GWS, we selected a vertical displacement from 65 GPS stations located in the Main Karoo Aquifer (MKA). We performed total water storage (TWS) inversion on GPS vertical displacement components; after that, we deducted surface water components based on the Global Land Data Assimilation System (GLDAS) from January 2013 to December 2021. Additionally, for validation, we compared our GWS estimates with the GRACE-derived GWS and observed GWS values derived from the WaterGAP Global Hydrology Model (WGHM) compartments. We discovered that the TWS and GWS trends derived from GPS and GRACE exhibited similar behaviors with trend values overestimated by GRACE and WGHM. Our findings demonstrate relatively typical behavior between GPS and GRACE in the first and second principal component behaviors (PCs) and empirical orthogonal function (EOF) loadings (or spatial patterns). With a contribution of 71.83% to GPS-derived GWS variability and 68.92% to GRACE-derived GWS variability, EOF-1 is a relatively potent factor. For Principal Components PC1 and PC2, the GRACE and GPS PCs have correlation coefficients of 0.75 and 0.84, respectively. Finally, with higher temporal resolution, GPS can perform the same task as GRACE in hydrological applications. In addition, GPS can add important and valuable information to assess regional GWS change.

Improving the quantification of climate change hazards by hydrological models: a simple ensemble approach for considering the uncertain effect of vegetation response to climate change on potential evapotranspiration

Abstract. Almost no hydrological model takes into account that changes in evapotranspiration are affected by how vegetation responds to changing CO2 and climate. This severely limits their ability to quantify the impact of climate change on evapotranspiration and, thus, water resources. As the simulation of vegetation responses is both complex and very uncertain, we recommend a simple approach to considering (in climate change impact studies with hydrological models) the uncertainty that the vegetation response causes with respect to the estimation of future potential evapotranspiration (PET). To quantify this uncertainty in a simple manner, we propose running the hydrological model in two variants: with its standard PET approach and with a modified approach to compute PET. In the case of PET equations containing stomatal conductance, the modified approach can be implemented by adjusting the conductance. We introduce a modified approach for hydrological models that computes PET as a function of net radiation and temperature only, i.e., with the Priestley–Taylor (PT) equation. The new PT-MA approach is based on the work of Milly and Dunne (2016) (MD), who compared the change in non-water-stressed actual evapotranspiration (NWSAET) as computed by an ensemble of global climate models (GCMs), which simulate vegetation response as well as interactions between the atmosphere and the land surface, with various methods to compute PET change. Based on this comparison, MD proposed estimating the impact of climate change on PET as a function of only the change in net energy input at the land surface. PT-MA retains the impact of temperature on daily to interannual as well as spatial PET variations but removes the impact of the long-term temperature trend on PET such that long-term changes in future PET are driven by changes in net radiation only. We implemented PT-MA in the global hydrological model WaterGAP 2.2d and computed daily time series of PET between 1901 and 2099 using the bias-adjusted output of four GCMs. Increases in GCM-derived NWSAET between the end of the 20th and the end of the 21st century for Representative Concentration Pathway 8.5 (RCP8.5) are simulated well by WaterGAP if PT-MA is applied but are severely overestimated with the standard PT method. Application of PT-MA in WaterGAP results in smaller future decreases or larger future increases in renewable water resources (expressed as the variable RWR) compared with the standard PT method, except in a small number of grid cells where increased inflow from upstream areas due to increased upstream runoff leads to enhanced evapotranspiration from surface water bodies or irrigated fields. On about 20 % of the global land area, PT-MA leads to an increase in RWR that is more than 20 % higher than in the case of standard PT, while on more than 10 % of the global land area, the projected RWR decrease is reduced by more than 20 %. While the modified approach to compute PET is likely to avoid the overestimation of future drying in many if not most regions, the vegetation response in other regions may be such that the application of the standard PET leads to more likely changes in PET. As these regions cannot be identified with certainty, the proposed ensemble approach with two hydrological model variants serves to represent the uncertainty in hydrological changes due to the vegetation response to climate change that is not represented in the model.

Bias correcting discharge simulations from the GEOGloWS global hydrologic model

The Group on Earth Observations (GEO) Global Water Sustainability (GEOGloWS) hydrologic model provides global river discharge hindcasts and daily forecasts at approximately one million subbasins worldwide. The model is meant to sustainably provide discharge data during emergency situations and to underdeveloped countries which do not have sufficient local capacity. The primary model error is biased flow magnitudes which reduce the usefulness of the results. We applied a revised implementation of the SABER bias correction method to correct GEOGloWS model results. SABER uses a combination of watershed clustering with machine learning, geospatial analysis, and statistics to generalize bias patterns in gauged basins so they can also be applied to ungauged basins. We validated the bias corrected data created using the improved SABER method at 12,965 gauges globally and showed that this method reduced the mean error at 90% of gauges. We present an analysis of the improved SABER method using several metrics including mean error, root mean squared error, and Kling Gupta Efficiency. We found that the GEOGloWS model is usually biased high but our results indicate a reduction in the bias of the GEOGloWS model worldwide. We evaluate the varied performance of the bias correction procedure and significance of the improvements which vary based on stream order the watershed classifications derived in our analysis. We provide guidance on the use of bias corrected global data to local scale applications and discuss implications for the GEOGloWS model in the future.

Enhancing the representation of water management in global hydrological models

Abstract. This study enhances an existing global hydrological model (GHM), Xanthos, by adding a new water management module that distinguishes between the operational characteristics of irrigation, hydropower, and flood control reservoirs. We remapped reservoirs in the Global Reservoir and Dam (GRanD) database to the 0.5∘ spatial resolution in Xanthos so that a single lumped reservoir exists per grid cell, which yielded 3790 large reservoirs. We implemented unique operation rules for each reservoir type, based on their primary purposes. In particular, hydropower reservoirs have been treated as flood control reservoirs in previous GHM studies, while here, we determined the operation rules for hydropower reservoirs via optimization that maximizes long-term hydropower production. We conducted global simulations using the enhanced Xanthos and validated monthly streamflow for 91 large river basins, where high-quality observed streamflow data were available. A total of 1878 (296 hydropower, 486 irrigation, and 1096 flood control and others) out of the 3790 reservoirs are located in the 91 basins and are part of our reported results. The Kling–Gupta efficiency (KGE) value (after adding the new water management) is ≥ 0.5 and ≥ 0.0 in 39 and 81 basins, respectively. After adding the new water management module, model performance improved for 75 out of 91 basins and worsened for only 7. To measure the relative difference between explicitly representing hydropower reservoirs and representing hydropower reservoirs as flood control reservoirs (as is commonly done in other GHMs), we use the normalized root mean square error (NRMSE) and the coefficient of determination (R2). Out of the 296 hydropower reservoirs, the NRMSE is > 0.25 (i.e., considering 0.25 to represent a moderate difference) for over 44 % of the 296 reservoirs when comparing both the simulated reservoir releases and storage time series between the two simulations. We suggest that correctly representing hydropower reservoirs in GHMs could have important implications for our understanding and management of freshwater resource challenges at regional-to-global scales. This enhanced global water management modeling framework will allow the analysis of future global reservoir development and management from a coupled human–earth system perspective.

Estimating the Impacts of Ungauged Reservoirs Using Publicly Available Streamflow Simulations and Satellite Remote Sensing

On the Korean Peninsula, the Imjin River is a transboundary river that flows from North Korea into South Korea. Therefore, human intervention activities in the upstream region can have a substantial impact on the downstream region of South Korea. In addition to climate impacts, there are increasing concerns regarding upstream man-made activities, particularly the operation of the Hwanggang dam located in the territory of North Korea. This study explored the feasibility of using the publicly available global hydrological model and satellite remote sensing imagery for monitoring reservoir dynamics and assessing their impacts on downstream hydrology. “Naturalized” streamflow simulation was obtained from the Group on Earth Observation (GEO) Global Water Sustainability (GEOGloWS) European Centre for Medium-Range Weather Forecasts (ECMWF) Streamflow Services (GESS) model. To correct the biases of the GESS-based streamflow simulations, we employed quantile mapping using the observed streamflow from a nearby location. This method significantly reduced volume and variability biases by up to 5 times on both daily and monthly scales. Nevertheless, its effectiveness in improving temporal correlation on a daily scale in small catchments remained constrained. For the reservoir storage changes in the Hwanggang dam, we combined multiple remote sensing imagery, particularly cloud-free optical images of Landsat-8, Sentinel-2, and snow-free Sentinel-1, with the area–elevation–volume (AEV) curves derived from the Shuttle Radar Topography Mission (SRTM) Digital Elevation Model (DEM). In assessing its hydrological impacts, the study found that overall impacts within the downstream catchment in Pilseung bridge of South Korea were generally less significant compared to the upstream Hwanggang catchment. However, there was a higher probability of experiencing water shortages during wet months due to the upstream dam’s operations. The study highlights the potential benefits of utilizing the publicly available hydrological model and satellite remote sensing imagery to supplement decision makers with important information for the effective management of the transboundary river basin in ungauged regions.

Assessing groundwater level variation in Aspas alluvial aquifer using satellite-based GRACE data combined with global (GLDAS) and locally-parametrized (SWAT) hydrological models

Assessing groundwater level variation in Aspas alluvial aquifer using satellite-based GRACE data combined with global (GLDAS) and locally-parametrized (SWAT) hydrological models

Introducing the Idea of Classifying Sets of Permanent GNSS Stations as Benchmarks for Hydrogeodesy

AbstractWe propose a novel approach to classify sets of Global Navigation Satellite System (GNSS) permanent stations as benchmarks for hydrogeodesy. Benchmarks are trusted sets of GNSS stations whose displacements are classified as significantly and positively correlated with hydrospheric changes and identified in a three temporal‐scales: short‐term, seasonal and long‐term. We use 63 vertical displacement time series processed at the Nevada Geodetic Laboratory for the period 1998–2021 from stations located within Amazon basin and show that estimates of trends and annual signals, including the annual phase maximum, are very coherent with water surface levels provided by altimetry missions. We compute vertical displacements from Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow‐On gravity missions and predict those also from Global Land Water Storage (GLWS) v2.0 data set which values are produced by assimilation of GRACE into WaterGAP Global Hydrological Model (WGHM). We divide vertical displacements from the three data sets into the pre‐defined temporal‐scales of short‐term, seasonal and long‐term, using non‐parametric wavelet analysis. For each temporal‐scale, correlation coefficients are computed between GNSS‐measured and GRACE‐derived/GLWS‐predicted displacements. We present the benefits of applying high‐resolution GRACE‐assimilating hydrology model to benchmark GNSS stations, which are particularly evident when using spherical harmonic coefficients higher than 120. Their increase causes the number of stations included in the benchmarks to rise by up to 15% for short‐term. Benchmarking allows hydrogeodesy to take advantage of a broader set of GNSS stations that were previously omitted, such as earthquake‐affected sites and those where a possible poroelastic response is observed.

Global streamflow modelling using process-informed machine learning

Abstract We present a novel hybrid framework that incorporates information from the process-based global hydrological model PCR-GLOBWB, to reduce prediction errors in streamflow simulations. In addition to catchment attributes and meteorological data, our methodology employs simulated streamflow and state variables from PCR-GLOBWB as predictors of observed river discharge. These outputs are used in a random forest, trained on a global database of streamflow measurements, to improve estimates of simulated river discharge across the globe. PCR-GLOBWB was run for the years 1979–2019 at 30 arcmin and its inputs and outputs were upscaled from daily to monthly time steps. A single random forest model was trained with these state variables, meteorological data and catchment attributes, as predictors of observed streamflow at 2,286 stations worldwide. Model performance was evaluated using Kling–Gupta efficiency (KGE). Results based on cross-validation show that the model is capable of discerning between a variety of hydroclimatic conditions and river flow dynamics, improving KGE of PCR-GLOBWB simulations at more than 80% of testing locations and increasing median KGE from −0.03 in uncalibrated runs to 0.51 after post-processing. Performance boosts are usually independent of the availability of streamflow data, making our method a potential candidate in addressing prediction in poorly gauged and ungauged basins.