Global Catchment Research Articles

Large-sample detection of reservoir impacts on flow regime alteration through improved paired-catchment approach

The 20th century witnessed a substantial surge in global reservoir construction, representing a significant milestone and resulting in a profound anthropogenic alteration of flow regimes. Despite this surge, a comprehensive large-sample quantification of reservoir impacts on flow regime alteration has been impeded by limitations in statistical and modeling methodologies. Here we present an improved paired-catchment method tailored to discern the impact of reservoirs on flow regime within the context of climate change. Employing this method, we identified and analyzed 177 paired catchments from a selection of over 12,000 global catchments. Our findings highlight the effectiveness of the improved method, demonstrating that reservoirs predominantly alleviate high flows while enhancing low flows. This serves to moderate the annual periodicity of streamflow signals while amplifying their low-frequency components. Furthermore, predetermined water releases play a significant role in diminishing the seasonality of low flows and reducing the sensitivity of runoff to precipitation. Additionally, the extent of flow regime alteration caused by reservoirs is closely linked to their functionalities and regulatory capacity. For example, reservoirs designed for irrigation and water supply show minimal enhancement of low flow values, while the magnitude of reservoir-induced changes rises with higher regulatory capacity. The improved method can be further employed in the future to investigate the impact of reservoirs on extreme hydrological events such as droughts and floods. It emerges as a crucial scientific tool for effective water resource management and the mitigation of water-related disasters in the Anthropocene era.

Assessment of land use/ land cover change derived catchment hydrologic response: An integrated parsimonious hydrological modeling and alteration analysis based approach

Land use/land cover (LULC) change, often a consequence of natural or anthropogenic drivers, plays a decisive role in governing global catchment dynamics, and subsequent impact on regional hydrology. Insight into the complex relationship between the drivers of LULC change and catchment hydrology is of utmost importance to decision makers. Contemplating the dynamic rainfall-runoff response of the Indian catchments, this study proposes an integrated modeling-based approach to identify the drivers and relative contribution to catchment hydrology. The proposed approach was evaluated in the tropical climate Nagavali River Basin (NRB) (9512 km2) of India. The Soil and Water Assessment Tool (SWAT) hydrological model, which uses daily-scale rainfall, temperature, wind speed, relative humidity, solar radiation, and streamflow information was integrated with the Indicators of Hydrologic Alteration (IHA) technique to characterize the plausible changes in the flow regime of the NRB. Subsequently, the Partial Least Squares Regression (PLSR) based modeling analysis was performed to quantify the relative contribution of individual LULC components on the catchment water balance. The outcomes of the study revealed that forest land has been significantly converted to agricultural land (45–59%) across the NRB resulting in mean annual streamflow increase of 3.57 m3/s during the monsoon season. The affinity between land use class and streamflow revealed that barren land (CN = 83–87) exhibits the maximum positive response to streamflow followed by the built-up land (CN = 89–91) and fallow land (CN = 88–93). The period 1985–1995 experienced an increased ET scenario (911–1050 mm), while the recent period (2005–2020) experienced reduced ET scenario owing to conversion of forest to agricultural land. Certainly, the study endorses adopting the developed methodology for understanding the complex land use and catchment-scale hydrologic interactions across global-scales for early watershed management planning.

Stationarity of High‐ and Low‐Flows Under Climate Change and Human Interventions Across Global Catchments

AbstractThe assumption of stationarity is fundamental for predicting future hydrologic changes based on historical data. Here we present the first global‐scale, observation‐based assessment of long‐term stationarity in annual streamflow extremes (i.e., maximum and minimum monthly streamflow, or Qmax and Qmin). Observational evidence from 11,069 catchments worldwide reveal that Qmax and Qmin series remain stationary in approximately 93% and 67% of catchments exclusively influenced by climate change, respectively, indicating that climate change alone has not disrupted stationarity in annual streamflow extremes. In contrast, these proportions decrease to 76% and 44% for catchments subjected to direct human interventions, with the prevalence of non‐stationary Qmax and Qmin series generally increasing in tandem with the degree of human interventions. These findings provide valuable quantitative insights into the extent of hydrologic extreme alterations caused by human activities and emphasize the need for adaptive measures to mitigate direct human impacts on the hydrological system.

Global scale identification of catchments phosphorus source shifts with urbanization: A phosphate oxygen isotope and Bayesian mixing model approach

Different scenarios of urban expansion can influence the dynamic characteristics of catchments in terms of phosphorus (P). It is important to identify the changes in P sources that occur during the process of urbanization to develop targeted policies for managing P in catchments. However, there is a knowledge gap in quantifying the variations of potential P sources associated with urbanization. By combining phosphate oxygen isotopes from global catchments with a Bayesian model and the urbanization process, we demonstrate that the characteristics of potential P sources (such as fertilizers, urban wastewater, faeces, and bedrock) change as urban areas expand. Our results indicate that using phosphate oxygen isotopes in conjunction with a Bayesian model provides direct evidence of the proportions of potential P sources. We classify catchment P loadings into three stages based on shifts in potential P sources during urban expansion. During the initial stage of urbanization (urban areas < 1.5 %), urban domestic and industrial wastewater are the main contributors to P loadings in catchments. In the mid-term acceleration stage (1.5 % ≤ urban areas < 3.5 %), efforts to improve wastewater treatment significantly reduce wastewater P input, but the increase in fertilizer P input offsets this reduction in sewage-derived P. In the high-level urbanization stage (urban areas ≥ 3.5 %), the proportions of the four potential P sources tend to stabilize. Remote areas bear the burden of excessive P loadings to meet the growing food demand and improved diets resulting from the increasing urban population. Our findings support the development of strategies for water quality management that better consider the driving forces of urbanization on catchment P loadings.

Global evapotranspiration models and their performance at different spatial scales: Contrasting a latitudinal gradient against global catchments

Actual evapotranspiration (AET) is a key variable in the global water balance, driving agricultural production and ecosystem health. It is a complex hydrologic process that depends on vegetation, climate, and available water conditions. Different moderate resolution global AET models have been developed to quantify water resources at large scales. In this work we evaluate five of these products, including MODIS, PML, SSEBop, TerraClimate, and a Synthesis AET using point and catchment-scale datasets based on flux towers. We also contrast water balance changes with total water storage (TWS) products. These comparisons cover different radiation and precipitation regimes over catchments around the world and along a strong climatic gradient in north-central Chile. We rank the models, contrast TWS datasets, and study differences related to scale in validation and the effect of rainfall and radiation on simulated values. Additionally, we use a Budyko framework to evaluate the AET products in terms of their agreement with expected water budgets. At different evaluation scales, AET estimates and observations agreed reasonably well, with the largest mean R2 of about 0.7 and errors of approximately 15% of the magnitude of the observed variables. MODIS and Synthesis AET had the highest R2 at the point (0.62) and at the catchment scales (0.71 and 0.59 for regional and global catchments), respectively, but were closely followed by PML. PML and TerraClimate led to the lowest magnitude errors at the point (RMSE = 0.78 mm day−1) and catchment scales (mean RMSE = 1.5 mm day−1), respectively. The rainfall gradient is reflected in a performance gradient. PML, MODIS, and TerraClimate gave consistent behaviour based on the Budyko curve, with a few arid catchments exceeding the water limit. The major conclusion is that remotely sensed AET outperforms flux tower AET extrapolation for water balance calculations at the catchment scale, which means that errors in satellite-based AET products tend to cancel out at larger spatial scales, which makes them viable alternatives for regional water balance studies. However, flux data integrated into AET models, such as the FluxCom model, leads to the lowest errors. The assimilation and downscaling of Gravity Recovery and Climate Experiment (GRACE) into the Global Land Data Assimilation System (GLDAS) leads to an improvement in regional results compared with other TWS products.

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.

Optimal Postprocessing Strategies With LSTM for Global Streamflow Prediction in Ungauged Basins

AbstractStreamflow prediction in ungauged basins (PUB) is challenging, and Long Short‐Term Memory (LSTM) is widely used to for such predictions, owing to its excellent migration performance. Traditional LSTM forced by meteorological data and catchment attribute data barely highlight the optimum data integration strategy for LSTM and its migration from data‐rich basins to ungauged ones. In this study, we experimented with 1,897 global catchments and found that LSTM‐corrected Global Hydrological Models (GHMs) outperformed uncorrected GHMs, improving the median Nash‐Sutcliff efficiency (NSE) from 0.03 to 0.66. Notably, there was a large gap between traditional LSTM modeling in ungauged basins and autoregressive modeling in data‐rich basins, and GHM‐forced LSTM were an effective way to close this gap in ungauged basins. The spatial heterogeneity of the performance of GHM‐forced LSTM was mainly influenced by three metrics (dryness, the leaf area index and latitude), which described the hydrological similarity among catchments. Weaker hydrological similarity among continental catchments results in larger variability in GHM‐forced LSTM, with the best performance in Siberia (NSE, 0.54) and the worst in North America (NSE, 0.10). However, the migration performance of GHM‐forced LSTM was significantly improved (NSE, 0.63) in ungauged basins when hydrological similarity was considered. This study stressed the advantages of GHM‐forced LSTM and due significance should be attached to hydrological similarities among catchments to improve hydrological prediction in ungauged catchments.

Predicting streamflow with LSTM networks using global datasets

Streamflow predictions remain a challenge for poorly gauged and ungauged catchments. Recent research has shown that deep learning methods based on Long Short-Term Memory (LSTM) cells outperform process-based hydrological models for rainfall-runoff modeling, opening new possibilities for prediction in ungauged basins (PUB). These studies usually feature local datasets for model development, while predictions in ungauged basins at a global scale require training on global datasets. In this study, we develop LSTM models for over 500 catchments from the CAMELS-US data base using global ERA5 meteorological forcing and global catchment characteristics retrieved with the HydroMT tool. Comparison against an LSTM trained with local datasets shows that, while the latter generally yields superior performances due to the higher spatial resolution meteorological forcing (overall median daily NSE 0.54 vs. 0.71), training with ERA5 results in higher NSE in most catchments of Western and North-Western US (median daily NSE of 0.83 vs. 0.78). No significant changes in performance occur when substituting local with global data sources for deriving the catchment characteristics. These results encourage further research to develop LSTM models for worldwide predictions of streamflow in ungauged basins using available global datasets. Promising directions include training the models with streamflow data from different regions of the world and with higher quality meteorological forcing.

Intensification of Global Hydrological Droughts Under Anthropogenic Climate Warming

AbstractAnthropogenic climate warming is expected to accelerate the hydrological cycle with significant consequences for hydrological droughts. However, a systematic understanding of climate warming impacts on the global hydrological droughts and their driving mechanisms is still lacking. Here, we integrate bias‐corrected climate experiments, multiple hydrological models (HYs), and a multivariate analysis of variance (ANOVA) with a machine learning modeling framework, to examine the evolving frequency and multivariate characteristics of hydrological droughts and their mechanisms under climate warming for 6,688 catchments in the five principal Köppen‐Geiger climate zones. Results show that the total frequency of hydrological droughts is likely to stay unchanged while extreme hydrological droughts (e.g., events with a 30 yr joint return period, JRP) are projected to occur more frequently across the 21st century. The historical 30 yr JRP events assessed during the historical baseline period of 1985–2014 could become twice as frequent over ∼60% of global catchments by 2071–2100 under the middle and high emission scenarios (ESs). Climate uncertainty (i.e., from global climate models and ESs) is the major source of uncertainty over temperate and tropical catchments, versus HY uncertainty in arid catchments with locally complex runoff regimes. Our machine learning framework indicates that precipitation stress controls the development of historical droughts over ∼87% of global catchments. However, with climate warming, air temperature variations are expected to become the new primary driver of droughts in high‐latitude cold catchments. This study highlights an increasing risk of global extreme hydrological droughts with warming and suggests that rising temperatures in high latitudes may lead to more extreme hydrological droughts.

How well do the multi-satellite and atmospheric reanalysis products perform in hydrological modelling

Multi-satellite, gauge-based and atmospheric reanalysis precipitation and temperature datasets with increasing spatiotemporal resolution are proliferating in recent decades. Their estimation accuracy has been evaluated in several areas, but their applications in global hydrological monitoring and modelling have been poorly understood so far. Here, we focus on the performance of the recently released Multi-Source Weighted-Ensemble Precipitation, version 2 (MSWEP V2) and the atmospheric reanalysis ERA5 temperature in modelling daily river discharge over 10,596 global catchments. Four lumped models including XAJ, GR4J, HMETS, and HBV are employed to take different model conceptual schemes into consideration. Moreover, different streamflow conditions including the peak timing and volumes of both high and low flows are evaluated to reveal further dynamics. The results show that: (a) the MSWEP V2 and ERA5 combination yields satisfactory performance in global streamflow simulation with at least one candidate model exhibiting Kling-Gupta efficiency (KGE) > 0.5 at both calibration and validation periods for each catchment; (b) better simulation skills are achieved in temperate, cold, polar, and tropical Köppen-Geiger climate zones, whereas the arid zone has poorer performance; (c) the XAJ, GR4J, and HMETS models with different degree of realisms have their own merits in simulating both daily mean flow and wet flow conditions but perform less satisfactorily in low flow conditions, while the HBV exhibits consistently poor performance in various flow conditions over most catchments. Our findings highlight the importance of selecting appropriate hydrological models by considering both climatic and geophysical conditions and targeting streamflow metrics, and suggest the potential of using multi-satellite and atmospheric reanalysis datasets as observed alternatives for quasi-global hydrological modelling.

Shift in precipitation-streamflow relationship induced by multi-year drought across global catchments

Increases in the intensity and frequency of droughts affected by climate changes induce greater uncertainty in precipitation (P) and streamflow (Q) relationship (P–Q relationship). Here, alteration in P–Q relations were assessed resulted from multi-year drought (≥7 years), lag and amplification effects were analyzed between meteorological and hydrological droughts, and then hydrological legacy induced by droughts were presented using the leaf area index (LAI) and the Horton index (ratio of watershed ET to catchment wetness) in different arid regions of 1210 selected catchments across global catchments. Results show that reduced P causes lower Q in arid regions, while tends to induce a higher Q in humid regions than expected. Generally, the severity and intensity were amplified in hydrological drought compared with its triggering meteorological drought. Interestingly, Q reduction was more likely to be induced by meteorological drought in more arid climates, while more likely to be recovered from Q deficit than meteorological drought in the humid regions. Unexpected, vegetation, stimulated by prolonged meteorological and hydrological droughts, tended to maintain higher LAI and subsequently resulted in a lower Horton index, especially in the humid regions. Combined with a traditional bucket model, a conceptual model was developed to elucidate threshold switching characteristics during the drought propagation, and deduced that vegetation played a vital role in partitioning of P and regulating how the catchment coped with climate changes. These new understanding of the hydrological legacy of meteorological drought provides important insights into hydrological mechanisms and the ability of ecology to regulate hydrological processes.

Climate and vegetation seasonality play comparable roles in water partitioning within the Budyko framework

Climate and vegetation changes are main factors influencing water partitioning. However, their impacts on water partitioning are still not well understood. To revisit the interactions among climate, vegetation and water partitioning, this study introduces the climate seasonality (CS) and vegetation seasonality (VS) into the Budyko framework, by using a statistical approach combining the three-parameter Log-logistic probability distribution and multiple linear regression, based on the monthly 〈P-ET0〉 (precipitation (P) minus potential evapotranspiration (ET0)) and Normalized Difference Vegetation Index (NDVI). This method is then validated and applied in 86 global catchments to quantify the contribution of climate and vegetation changes to evapotranspiration (ET). Results show that this model can predict well (mean R2 = 0.81 ± 0.05, P < 0.05) the parameter n, a parameter reflecting the interactions between land surface and atmosphere in the Budyko framework. More importantly, CS and VS jointly control the variation in parameter n, and in average jointly contribute more (mean relative contribution of 57.1 ± 2.3 %) than the changes in P and ET0 (mean of 42.9 ± 2.3%) to ET. These indicate that CS and VS are at least as important as the changes in P and ET0 in affecting water balance. Furthermore, the VS contributes slightly lower (mean of 24.9 ± 0.3%) than the CS does (mean of 32.2 ± 2.3%), implying that CS and VS play comparable roles in water partitioning. These findings would be valuable for water resources management and assessing the impacts of climate and land surface changes on water balance in a gradually changing environment.

Parameter regionalization of the FLEX-Global hydrological model

Global hydrological models (GHMs) are important tools for addressing worldwide change-related water resource problems from a global perspective. However, the development of these models has long been hindered by their low accuracy. In order to improve the streamflow simulation accuracy of GHMs, we developed a GHM—the FLEX-Global—based on the regionalization of hydrological model parameters. The FLEX-Global model is primarily based on the framework of the FLEX hydrological model coupled with the HAND-based Storage Capacity curve (HSC) runoff generation module to calculate net rainfall, and uses the global river-routing CaMa-Flood model to calculate river network routing. This new model allows for streamflow simulation at a spatial resolution of 0.5°×0.5° and a temporal resolution of 1 day in global catchments. To validate FLEX-Global accuracy, the FLEX-Global-simulated streamflow of 26 major rivers distributed in five different climate zones was compared with the observed data from the Global Runoff Data Center (GRDC). Next, the model performance of FLEX-Global was further verified by comparing it with that of seven existing GHMs with varying accuracy in the five climate zones. Multi-metric evaluation indicated that the streamflow simulation accuracy was improved by the FLEX-Global model with regionalized parameters, especially in the tropical and dry climate zones. This newly-developed GHM with regionalized parameters can provide scientific support for the assessment of climate change impact, optimization of global water resource mangement, simulation of Earth’s multi-sphere coupling, and implementation of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP).

Hydrological effects of change in vegetation components across global catchments

As an important component of the terrestrial system, vegetation plays a pivotal role in hydrological processes and water yield. Many studies have examined hydrological effects of changes in total vegetation coverage and density, whereas the impact of change in vegetation components (i.e., proportion of different vegetation types) is rarely considered and little known. Actually, even though the changes in total vegetation coverage and density are similar, the differences in vegetation components alteration among catchments can be pronounced, and thus result in distinct hydrological consequence. Here, using a state-of-the-art dataset of vegetation composition including tree canopy (TC) cover, short vegetation (SV) cover and bare ground (BG) cover, we quantitatively assess the hydrological consequences of vegetation components change in 76 large catchments globally under the water balance framework. We find SV is apparently the most sensitive vegetation component for runoff change, which is reinforced in regions with a drier climate. Attributing water yield change shows that runoff variations caused by TC change are usually offset by those caused by SV change. In typical afforested catchments across the world, the loss of SV can alleviate 39.3–108.7% runoff decrease due to the expansion of TC and the contraction of BG. Our results imply that hydrological sensitivity is much diverse among vegetation types, and trade-offs exist among the hydrological effects of different vegetation components. These previously unrevealed findings suggest that more vegetation properties (including vegetation composition) should be accounted when evaluating the impact of vegetation change on regional water resources.

Assessing the Steady‐State Assumption in Water Balance Calculation Across Global Catchments

AbstractIt has long been assumed that over a sufficiently long period of time, changes in catchment water storage (ΔS) are a relatively minor term compared to other fluxes and can be neglected in the catchment water balance equation. However, the validity of this fundamental assumption has rarely been tested, and the associated uncertainties in water balance calculations remain unknown. Here, we use long‐term (1982–2011) observations of monthly streamflow (Q) and precipitation (P) for 1,057 global unimpaired catchments, combined with four independent evapotranspiration (E) estimates to infer ΔS and to provide a global assessment of the steady‐state assumption in catchment water balance calculations. Results show that when the threshold for steady state is set to 5% of the mean monthly P, ~70% of the catchments attain steady state within 10 years while ~6% of the catchments fail to reach a steady state even after 30 years. The time needed for a catchment to reach steady state (τs) shows a close relationship with climatic aridity and vegetation coverage, with arid/semiarid and sparsely vegetated catchments generally having a longer τs. Additionally, increasing snowfall fraction also increases τs. The imbalance (ewb) caused by ignoring ΔS decreases as averaging period for water balance calculations increases as expected. For a typical 10‐year averaging period, ewb accounts for ~7% of P in arid, but that decreases to ~3% of P in humid catchments. These results suggest that catchment properties should be considered when applying the steady‐state assumption and call for caution when ignoring ΔS in arid/semiarid regions.

Land use drives nitrous oxide dynamics in estuaries on regional and global scales

AbstractUrban and agricultural development of coastal catchments is known to increase dissolved nitrogen inputs into estuaries; however, much less is known about how land use influences the production of the powerful greenhouse gas nitrous oxide (N2O). Here, we assess dissolved N2O dynamics in four nearby estuaries across a regional land use gradient and summarize the literature to put the observations into global perspective. During summer dry conditions, N2O saturation ranged from 131.4% ± 45.0% in the most pristine system (28% modified) to 198.6% ± 52.3% within the most modified urban system (91% modified). The N2O saturation in the wetter winter campaign was higher and more variable than the summer dry campaign (range 84.7–677.7%) likely due to direct transport of N2O into the estuaries from catchment runoff and/or produced through denitrification fueled by high nitrate inputs. During both seasons, N2O was lowest in areas adjacent to fringing mangroves and highest in upstream fresh/saltwater mixing areas. Coupling our results with previously published N2O data from 50 estuarine systems worldwide revealed that estuarine N2O increases concomitantly with catchment modification, dissolved inorganic nitrogen availability, and decreasing oxygen concentrations. Based on these results, a 1% increase in anthropogenic modification to global catchments (i.e., agricultural development and/or urbanization) may increase estuarine N2O saturation by 2.6% ± 1.2%. These findings indicate that future estuarine N2O emissions are likely to increase as anthropogenic modification of coastal catchments intensifies.

Global catchment modelling using World-Wide HYPE (WWH), open data, and stepwise parameter estimation

Abstract. Recent advancements in catchment hydrology (such as understanding catchment similarity, accessing new data sources, and refining methods for parameter constraints) make it possible to apply catchment models for ungauged basins over large domains. Here we present a cutting-edge case study applying catchment-modelling techniques with evaluation against river flow at the global scale for the first time. The modelling procedure was challenging but doable, and even the first model version showed better performance than traditional gridded global models of river flow. We used the open-source code of the HYPE model and applied it for &gt;130 000 catchments (with an average resolution of 1000 km2), delineated to cover the Earth's landmass (except Antarctica). The catchments were characterized using 20 open databases on physiographical variables, to account for spatial and temporal variability of the global freshwater resources, based on exchange with the atmosphere (e.g. precipitation and evapotranspiration) and related budgets in all compartments of the land (e.g. soil, rivers, lakes, glaciers, and floodplains), including water stocks, residence times, and the pathways between various compartments. Global parameter values were estimated using a stepwise approach for groups of parameters regulating specific processes and catchment characteristics in representative gauged catchments. Daily and monthly time series (&gt;10 years) from 5338 gauges of river flow across the globe were used for model evaluation (half for calibration and half for independent validation), resulting in a median monthly KGE of 0.4. However, the World-Wide HYPE (WWH) model shows large variation in model performance, both between geographical domains and between various flow signatures. The model performs best (KGE &gt;0.6) in the eastern USA, Europe, South-East Asia, and Japan, as well as in parts of Russia, Canada, and South America. The model shows overall good potential to capture flow signatures of monthly high flows, spatial variability of high flows, duration of low flows, and constancy of daily flow. Nevertheless, there remains large potential for model improvements, and we suggest both redoing the parameter estimation and reconsidering parts of the model structure for the next WWH version. This first model version clearly indicates challenges in large-scale modelling, usefulness of open data, and current gaps in process understanding. However, we also found that catchment modelling techniques can contribute to advance global hydrological predictions. Setting up a global catchment model has to be a long-term commitment as it demands many iterations; this paper shows a first version, which will be subjected to continuous model refinements in the future. WWH is currently shared with regional/local modellers to appreciate local knowledge.

A proportionality-based multi-scale catchment water balance model and its global verification

Unifying the description of catchment hydrological partitioning across spatial and temporal scales has long been an important research topic within the hydrological community. As an effort to that, here we develop a conceptual water balance model based on the proportionality hypothesis (denoted PWBM) and for the first time, test the proportionality hypothesis using hydrologic observations across global catchments at varying time scales. The PWBM has a transparent and parsimonious model structure with only two model parameters, and requires precipitation, potential evapotranspiration and leaf area index as model inputs. To test PWBM, we apply the model to simulate streamflow (Q), evapotranspiration (E) and storage change (ΔS) in 22 large basins and 255 small basins globally and find an overall good model performance in reproducing all three water balance components at the daily (only in small basins), monthly, seasonal, annual and mean-annual time scales. Compared with three other conceptual hydrological models developed for specific time scales, the PWBM substantially outperforms the SCS model at the daily scale and the ABCD model at the monthly scale and performs similarly as the long-standing Budyko model at the mean-annual scale. In summary, our study verifies the robustness of PWBM and offers convincing evidence regarding the applicability of the proportionality hypothesis for enhanced hydrological system understanding at multiple spatial and temporal scales.

Exploring the value of machine learning for weighted multi-model combination of an ensemble of global hydrological models

This study presents a novel application of machine learning to deliver optimised, multi-model combinations (MMCs) of Global Hydrological Model (GHM) simulations. We exemplify the approach using runoff simulations from five GHMs across 40 large global catchments. The benchmarked, median performance gain of the MMC solutions is 45% compared to the best performing GHM and exceeds 100% when compared to the ensemble mean (EM). The performance gain offered by MMC suggests that future multi-model applications consider reporting MMCs, alongside the EM and intermodal range, to provide end-users of GHM ensembles with a better contextualised estimate of runoff. Importantly, the study highlights the difficulty of interpreting complex, non-linear MMC solutions in physical terms. This indicates that a pragmatic approach to future MMC studies based on machine learning methods is required, in which the allowable solution complexity is carefully constrained.

Slavery in a Remote but Global Place: the British East India Company and Bencoolen, 1685-1825

Histories of the British East India Company usually ignore the company’s use of slave labor. Records from its factory at Bencoolen in Sumatra provide an opportunity to examine company attitudes and policies toward its chattel work force in greater detail. These sources reveal that the company drew slaves from a global catchment area to satisfy the demand for labor in its far-flung commercial empire, shed light on policies and practices regarding the treatment of company slaves, and illustrate the company’s role in the development of increasingly interconnected free and forced labor trades during the late eighteenth and early nineteenth centuries. The Bencoolen case study also highlights the need to examine colonial migrant labor systems in the Indian Ocean and maritime Asia worlds in more fully developed contexts.