Hydroclimatic Data Research Articles

Reconstructing suspended sediment concentrations in the Mekong River Basin via semi-supervised-based deep neural networks

The Mekong River Basin (MRB) is crucial for the livelihoods of over 60 million people across six Southeast Asian countries. Understanding long-term sediment changes is crucial for management and contingency plans, but the sediment concentration data in the MRB are extremely sporadic, making analysis challenging. This study focuses on reconstructing long-term suspended sediment concentration (SSC) data using a novel semi-supervised machine learning (ML) model. The key idea of this approach is to exploit abundant available hydroclimate data to reduce training overfitting rather than solely relying on sediment concentration data, thus enhancing the accuracy of the employed ML models. Extensive experiments on daily hydroclimate and SSC data obtained from 1979 to 2019 at the three main stations (i.e., Chiang Saen, Nong Khai, and Mukdahan) are conducted to demonstrate the superior performance of the proposed method compared to the state-of-the-art supervised techniques (i.e., Random Forest, XGBoost, CatBoost, MLP, CNN, and LSTM), and surpasses existing semi-supervised methods (i.e., CoReg, ⊓ Model, ICT, and Mean Teacher). This approach is the first semi-supervised method to reconstruct sediment data in the field and has the potential for broader application in other river systems.

Drivers of Spatiotemporal Patterns of Riparian Forest NDVI Along a Hydroclimatic Gradient

ABSTRACTIn the context of rising global temperatures and their impact on weather patterns and water cycles, understanding the relationship between vegetation and hydroclimatic forcing is critical. Riparian forests are highly vulnerable to hydroclimatic variability, which can significantly affect water availability in the soil on which they primarily depend. Along large rivers, hydroclimatic forcings can vary, resulting in different vegetative responses depending on the local climatic context and site conditions. To explore this, we studied riparian forest greenness along a 512‐km river corridor with a 3° latitudinal gradient, analysing the relative contributions of climate (latitude, season, temperature, precipitation) and local hydrological conditions (groundwater). Here, we show that riparian forests along a latitudinal gradient respond differently to hydroclimatic controls, with vegetative dynamics that can be attenuated or accentuated by local site conditions. We combined Sentinel‐2 satellite Normalised Difference Vegetation Index (NDVI) data over seven years (2016–2022) with hydroclimatic data to examine riparian forest greenness responses to latitudinal, seasonal and interannual hydroclimatic variability. We found contrasting hydroclimatic controls across the latitudinal gradient, with the northernmost sites predominantly controlled by temperature, while those further south are limited by water availability. In addition, we identified temperature as the primary driver of NDVI throughout the growing season, either positively or negatively. Late season precipitation and high phreatic water availability positively influenced NDVI, emphasising the role of local conditions in governing riparian forest resilience. This study enhances understanding of climate controls on riparian tree greenness, which is critical for designing effective landscape‐scale riparian ecosystem management and restoration strategies.

Impacts of Climate Change on Groundwater in the Al-Badan Sub-Catchment, Palestine: Analyzing Historical Data and Future Scenarios

Climate change is significantly impacting water resources, especially in arid regions. This study evaluates its effects on groundwater in the Al-Badan sub-catchment, Palestine, by analyzing hydroclimatic data from 1990 to 2020 and the future predicted climate change scenarios. Using the Mann-Kendall test and Sen’s slope estimator, a significant annual decline in annual precipitation of 125 mm and a temperature increase of 1.84 °C were observed, resulting in a spring discharge reduction of 1.2 MCM. Multiple linear regression analysis showed that a 10% increase in precipitation correlates with a 5% discharge increase, while a 1 °C rise in temperature results in a 2.3% discharge decrease. Future scenarios indicate significant changes: for 2040–2060, RCP2.6 forecasts average precipitation of 334.5 mm with temperatures at 18.5 °C, resulting in spring discharge of about 4.6 MCM. In contrast, RCP4.5 and RCP8.5 predict reductions in precipitation to 307.2 mm and 311.2 mm, respectively, with temperatures rising to 18.9 °C and 19.3 °C, leading to discharge declines to 4.2 MCM and 4.0 MCM. For 2080–2100, RCP2.6 anticipates 335.8 mm of precipitation and temperatures rising to 19.5 °C, resulting in average discharge of 4.5 MCM. RCP4.5 and RCP8.5 predict further declines in precipitation and discharge, underscoring the need for effective water management strategies.

Delineation of groundwater potential zones using multi-criteria analysis (AHP), frequency ratios (RF), remote sensing and GIS: a case study of the Batcham municipality (west Cameroon)

Groundwater resource management and development should be done sustainably, which calls for accurate assessment based on new methods and scientific principles. There is an increasing demand for potable water supplies, to meet the woefully inadequate domestic water needs in the Batcham municipality (west of Cameroun). The dearth of knowledge on sustainable management of water resources is to blame for this predicament. As integrated methodologies are not used in groundwater exploration programs, the current problem has gotten worse. Innovative scientific principles of groundwater resource exploration are required to effectively counteract the non-rationalization of water and the problem of unacceptable failure rates in drilled water borehole development. This will improve the appropriate and sustainable use of these resources. Therefore, the goal of this research is to map groundwater potential zones by utilizing readily available datasets and remote sensing (RS), GIS, Frequency Ratios (FR), and multi-criteria analysis (AHP) techniques. The database used is made up of geological data, hydroclimatic data, well data, and satellite images. Each of the methods used is based on the study of the relationships between the factors controlling the groundwater potential. Among these factors, ten (10) were retained based on previous work; these are slope, elevation, land use, land cover, lineament density, drainage density, topographic wetness index, curvature, geology, rainfall, and the static water level. The AHP allowed, according to the user's expertise, to assign weights to each factor controlling the water potential. The FR made it possible to study the relationships linking each variable with high flow rates. The thematic layers obtained were integrated into a GIS environment to generate the groundwater potential map. Four classes of groundwater potential have been identified: bad, moderate, good, and excellent. The bad classes represent 8.32% and 11.21%; moderate 36.57% and 28.70%; good 43.55% and 38.93%; and excellent 11.56% and 21.16%, for the AHP and FR models, respectively. The overlying of wells with high flow rates on areas of excellent groundwater potential shows perfect concordance, which validated the final map. Likewise, the resulting map was validated by sensitivity curve analysis, and the receiver operating characteristic (ROC) curve from which result accuracy was obtained is 70.63% and 80.28% for the AHP method and FR method, respectively. Finally, the FR model outperformed the AHP model in terms of performance and it is possible to use these dependable models to describe groundwater potential zones in the other regions of Cameroon.

Holocene hydroclimate synthesis of the Aegean: Diverging patterns, dry periods and implications for climate-society interactions

The Aegean region has a wealth of archeological data originating from a long and rich human history and is frequently used as a testbed for climate-society interactions at different time scales. Here, we summarize and synthesize 31 proxy records derived from lake and wetland sediments, marine sediments, and speleothems reflecting hydroclimatic conditions over the last 10,000 years in the Aegean. To enable comparison and numerical analyses, we convert each record into z-scores and average values into centurial and decadal bins. The long-term hydroclimatic trajectory is best described by individual trends in three sub-regions that periodically converge and diverge. The results from our analyses highlight the complex long- and short-term spatio-temporal patterns of the hydroclimate with periods of wetter and drier conditions. Statistical analyses of the last 3500 years, the most data-rich period, corroborate the complex climate picture. Three periods that appear dry in the regional averages, at 7250–6650 BP, 3350–2750 BP, and 1200–700 BP, highlight spatio-temporal variability and underpin the importance of local data when comparing hydroclimate data with archeological information. Considering the complexities and uncertainties in hydroclimate records, we advocate for careful consideration when utilizing these in studies of climate-society interactions, including transparency regarding their uncertainties and the relevance of each record to a respective archeological site. We have produced a freely available, simplified dataset that can be used by those interested in studying the region, as well as maps displaying climatic conditions during each century.

What controls hydrology? An assessment across the contiguous United States through an interpretable machine learning approach

Machine learning (ML) is increasingly perceived as a futuristic, superior data-driven approach to scientific discovery. It has already demonstrated remarkable performance in forecasting and prediction, yet its role in enhancing our understanding of hydrological processes remains underexplored. Traditional hydrological interpretations have relied heavily on model-dependent interpretation methods, focusing on the predictive accuracy of ML model predictions. Since hydrological models are built on a collection of assumptions and simplifications, model-dependent approaches might suffer from limited model realism, adequacy, accuracy, and equifinality issues. To address this gap, this study provides an ML approach that works in a model-independent context, working directly on hydroclimatic data collected through monitoring systems.We apply our model-independent interpretation approach to a carefully designed set of hydrologic data collected across the contiguous United States to address the following questions: (1) What are the primary controls of runoff-generation mechanisms, and how can such controls be attributed to catchment properties? (2) How and under what circumstances can the history of climate variables, such as precipitation, be a surrogate for present-time state variables, such as soil moisture and snowpack? We show that the ML approach aids in distinguishing catchments characterized by strong overland flow, interflow, or baseflow components and those primarily driven by rainfall, snowmelt, or a mix thereof. We further show that typical surrogate variables used in hydrology may come short in representing the dynamics of catchments that exhibit a complex interplay of rain and snow.

Coupling higher-order probability weighted moments with norming constants method for non-stationary annual maximum flood frequency analysis

Non-stationary flood frequency analysis (NFFA) is essential for reducing the risk of hydrologic engineering design and operation, especially when done under the severe influences of climate change and strong human activities. Although different types of methods have been proposed, NFFA is still a challenging task due to the complex characteristics of hydroclimatic data and weaknesses of diverse methods. In this article, a new method, called HPWM-NCM, is proposed for NFFA. The method uses the higher-order probability weighted moments (HPWM) method to accurately estimate parameters of flood data, and then applies the norming constants method (NCM) to precisely calculate the norming constants and further do NFFA. Results of both Monte-Carlo experiments and observed hydrologic data at 62 stations in the Rhine River basin illustrated that HPWM-NCM significantly improved the NFFA results compared to conventional NCM and the probability weighted moments method, as evaluated by overall fitting bias, residuals’ correlation, and residuals’ maximum error. Results highlighted the importance of using the information in high quantiles (rather than low quantiles) of flood data for NFFA, which is the key of the proposed method and ensures its superiority. Overall, the advantages of HPWM-NCM for non-stationary flood frequency analysis were confirmed, and the method has the potential for wide use in hydrologic and climate sciences.

Unpredicted ecosystem response to compound human impacts in a European river

Climate change elevates the threat of compound heat and drought events, with their ecological and socioeconomic impacts exacerbated by human ecosystem alterations such as eutrophication, salinization, and river engineering. Here, we study how multiple stressors produced an environmental disaster in a large European river, the Oder River, where a toxic bloom of the brackish-water planktonic haptophyte Prymnesium parvum (the “golden algae”) killed approximately 1000 metric tons of fish and most mussels and snails. We uncovered the complexity of this event using hydroclimatic data, remote sensing, cell counts, hydrochemical and toxin analyses, and genetics. After incubation in impounded upstream channels with drastically elevated concentrations of salts and nutrients, only a critical combination of chronic salt and nutrient pollution, acute high water temperatures, and low river discharge during a heatwave enabled the riverine mass proliferation of B-type P. parvum along a 500 km river section. The dramatic losses of large filter feeders and the spreading of vegetative cells and resting stages make the system more susceptible to new harmful algal blooms. Our findings show that global warming, water use intensification, and chronic ecosystem pollution could increase likelihood and severity of such compound ecoclimatic events, necessitating consideration in future impact models.

Deep Learning for Flash Drought Detection: A Case Study in Northeastern Brazil

Flash droughts (FDs) pose significant challenges for accurate detection due to their short duration. Conventional drought monitoring methods have difficultly capturing this rapidly intensifying phenomenon accurately. Machine learning models are increasingly useful for detecting droughts after training the models with data. Northeastern Brazil (NEB) has been a hot spot for FD events with significant ecological damage in recent years. This research introduces a novel 2D convolutional neural network (CNN) designed to identify spatial FDs in historical simulations based on multiple environmental factors and thresholds as inputs. Our model, trained with hydro-climatic data, provides a probabilistic drought detection map across northeastern Brazil (NEB) in 2012 as its output. Additionally, we examine future changes in FDs using the Coupled Model Intercomparison Project Phase 6 (CMIP6) driven by outputs from Shared Socioeconomic Pathways (SSPs) under the SSP5-8.5 scenario of 2024–2050. Our results demonstrate that the proposed spatial FD-detecting model based on 2D CNN architecture and the methodology for robust learning show promise for regional comprehensive FD monitoring. Finally, considerable spatial variability of FDs across NEB was observed during 2012 and 2024–2050, which was particularly evident in the São Francisco River Basin. This research significantly contributes to advancing our understanding of flash droughts, offering critical insights for informed water resource management and bolstering resilience against the impacts of flash droughts.

PyCoSMoS: An advanced toolbox for simulating real-world hydroclimatic data

Simulation models are a fundamental tool for investigating hydrological processes and for water resource management. In this study, we introduce PyCoSMoS, a Python toolbox that enables researchers to simulate observed univariate time series mimicking hydroclimatic processes. This toolbox preserves arbitrary marginal distribution and autocorrelation functions, while significantly reducing computational burden. PyCoSMoS is built upon the mixed-Uniform CoSMoS method recently proposed by Papalexiou et al. (2023). The toolbox is designed to minimize the user’s input, requiring only observed time series, marginal distribution, correlation function, and the number of lags. The output provides both visual and quantitative comparisons between the observed and simulated time series. We evaluate the performance of the package using various synthetic case studies and the results demonstrate satisfactory accuracy. Furthermore, we apply the toolbox to three real case studies: precipitation, temperature, and relative humidity, for which the toolbox can successfully simulate the observed time series in each case.

Simbi: historical hydro-meteorological time series and signatures for 24 catchments in Haiti

Abstract. Haiti, a Caribbean country, is highly vulnerable to hydroclimatic hazards due to heavy rainfall, which is partly linked to tropical cyclones. Additionally, its steep slopes generate flash floods, particularly in small catchments. Moreover, the hydrology of this region remains poorly understood and understudied. Unfortunately, there is no accessible database for the scientific community to use in this country. To fill this gap, hydroclimatic data were collected to create the first historical database in Haiti. This database, called Simbi (guardian of rivers, freshwater, and rain in Haitian mythology), includes 156 monthly rainfall series over the period 1905–2005, 59 daily rainfall series over the period 1920–1940, 70 daily streamflow series, and 23 monthly temperature series, not necessarily continuous, over the period 1920–1940. It also provides simulated streamflow series over the period 1920–1940 using the GR2M and GR4J rainfall–runoff models for 24 catchments and 49 attributes covering a wide range of topographic, climatic, geological, land use, hydrogeological, and hydrological signature indices. Simbi is the first open-access hydro-meteorological dataset for Haiti and will contribute to a better knowledge of hydrological risk in Haiti. Several sources of uncertainty associated with Simbi are acknowledged, including data quality (historical data), digitisation of paper archives, identification of relevant rain gauges, and rainfall–runoff models. It is important to consider these uncertainties when using Simbi. The database will be regularly updated to include additional historical data that will be digitised in the future. It will thus contribute toward better knowledge of the hydrology of Haitian catchments and will enable the implementation of various hydrological calculations useful for designing structures or flow forecasting. Simbi is an open-access database and is available for download at https://doi.org/10.23708/02POK6 (Bathelemy et al., 2023).

Evaluation of satellite precipitation products for real-time extreme river flow modeling in data scarce regions

Abstract. Inadequacy of spatio-temporal hydro-climatic data limits the efficacy of hazard monitoring and disaster risk reduction activities in disaster-prone areas. Various satellite missions are recently providing climate data, but prior evaluation and enhancement of these data are necessary for a reliable application. In this study, we conducted performance evaluation and enhancement of three real-time satellite precipitation products (SPPs) (GSMaP, GPM-IMERG, and PERSIANN) for flood modeling in the Blue Nile basin. The bias correction improved the original SPPs, with the largest improvement being for factors generated from 10 d mean data. Flood event hydrograph indicated satisfactory results of error metrics on the devastating flood event of 2012. Employing reliable physical–based distributed hydrologic models provided longer lead time and high-accuracy flood simulation. Furthermore, the results indicate that integrating available initial observed precipitation data improved the efficiency of SPPs simulation, and hence are applicable in operational flood monitoring.

Groundwater Potential Assessment in the Rural Loulouni Municipality (Southern Mali): Decision Tree Approach

Groundwater Potential Assessment in the Rural Loulouni Municipality (Southern Mali): Decision Tree Approach

Climate variability and river flow changes in Wundanyi sub-catchment of Taita hills, Kenya

<p>Wundanyi sub-catchment of Taita hills is experiencing a high rate of deforestation due to the conversion of all its original forestland to agriculture and settlement during the last century. The landscape dynamics coupled with rainfall fluctuations in these critical ecosystems may significantly affect water resource distribution and food security in Taita Taveta County and its environs. This study aimed to establish the trends of selected hydroclimatic variables in Wundanyi sub-catchment from 1970 to 2030 and their specific and combined effects on surface runoff and streamflow in the same period. The analysis was based on statistical trend analysis and dynamic landscape modelling using both historical and primary hydroclimatic data from Wundanyi and Voi weather stations. Results show highly variable mean seasonal and annual values of temperature, rainfall, runoff and discharge in both Wundanyi and Voi weather stations. Increasing mean temperatures and rainfall were observed during the long dry season (JJAS), while decreasing seasonal discharges were observed during both the JJAS dry season and the OND short rainy season. These anomalies were pronounced in 1980–1981, 1986–1987 and 1992–1993, probably due to both global and local environmental changes affecting Taita Hills in general and Wundanyi sub-catchment in particular. The predicted effects of rainfall fluctuation were supported by declining surface runoff of 1.3% during JJAS, and an increase of 0.8% during the OND, with similar effects on river discharges. The combined effects of climate variability and Land use and cover changes (LUCC) on surface runoff were estimated to an increase of 200 mm during JJAS and 370 mm during OND, and while river discharges increased by 2.37 m<sup>3</sup>/s and 1.93 m<sup>3</sup>/s during JJAS and OND, respectively. Consequently, natural forest covers have significant control effects on surface runoff and can boost river discharges amid diverse agricultural cropping practices. Hence, crop diversification, agroforestry, and soil and water conservation structures are recommended to maintain effective control of LUCC on hydrological processes going on in Wundanyi sub-catchment.</p>

Dynamic Bayesian-Network-Based Approach to Enhance the Performance of Monthly Streamflow Prediction Considering Nonstationarity

In recognizing the pervasive nonstationarity of hydrometeorological variables, a paradigm shift towards alternative analytical methodologies is imperative for refining hydroclimatic data modeling and prediction. We introduce a novel approach leveraging nonstationary Graphical Modeling and Bayesian Networks (NGM-BNs) tailored for hydrometeorological applications. Demonstrated through monthly streamflow forecasting in the Kashgar River Basin of China, our method illuminates the temporal evolution of network relationships, underscoring the dynamism inherent in both input variables and modeling parameters. The key to our approach is identifying the most suitable time horizon (MST) for model updates, which is intricately problem-specific and crucial for peak performance. This methodology not only unveils changing predictor significance across varying flow conditions but also elucidates the fluctuating temporal links between variables, especially under the lens of climate change, for instance, the growing impact of snowmelt on the Kashgar Basin’s streamflow. Compared to stationary counterparts, our nonstationary Bayesian framework excels in capturing extreme events by adeptly accommodating temporal shifts, outperforming traditional models including both stationary and nonstationary variants of Support Vector Regression (SVR) and Adaptive Neuro-Fuzzy Inference Systems (ANFIS).

Hydroclimatic Trends and Streamflow Response to Recent Climate Change: An Application of Discrete Wavelet Transform and Hydrological Modeling in the Passaic River Basin, New Jersey, USA

The exigency of the current climate crisis demands a more comprehensive approach to addressing location-specific climate impacts. In the Passaic River Basin (PRB), two bodies of research—hydroclimatic trend detection and hydrological modeling—have been conducted with the aim of revealing the basin’s hydroclimate patterns as well as the hydrologic response to recent climate change. In a rather novel application of the wavelet transform tool, we sidelined the frequently used Mann–Kendal (MK) trend test, to identify the hidden monotonic trends in the inherently noisy hydroclimatic data. By this approach, the use of MK trend test directly on the raw data, whose results are almost always ambiguous and statistically insignificant in respect of precipitation data, for instance, no longer poses a challenge to the reliability of trend results. Our results showed that, whereas trends in temperature and precipitation are increasing in the PRB, streamflow trends are decreasing. Based on results from the hydrological modeling, streamflow is more sensitive to actual evapotranspiration (ET) than it is to precipitation. In periods spanning decades with sufficient water availability, energy governs actual evapotranspiration rates, rendering streamflow more sensitive to increases in precipitation. Conversely, during meteorologically stressed decades, water availability dictates actual evapotranspiration, consequently amplifying streamflow sensitivity to fluctuations in actual evapotranspiration. We found that the choice of baseline condition constitutes an important source of uncertainty in the sensitivities of streamflow to precipitation and evapotranspiration changes and should routinely be considered in any climate impact assessment.

Prolonged drought periods over the last four decades increase flood intensity in southern Africa

In semi-arid sub-Saharan Africa, climate change and the intensification of human activities have altered the hydrological balance and modified the recurrence of extreme hydroclimatic events, such as droughts and floods. The geomorphological heterogeneity of river catchments across the region, the variable human pressure, and the lack of continuous hydroclimatic data preclude the definition of proper mitigation strategies, with a direct effect on the sustainability of rural communities.Here, for the first time in Africa, we characterize hydrological extreme events using a multidisciplinary approach that includes sedimentary data from dams. We focus on the Limpopo River basin to evaluate which factors control flood magnitude since the 1970. Extreme flood events were identified across the basin in 1988–89, 1995–96, 1999–2000, 2003–04, 2010–11, 2013–14 and 2016–17. The statistical analysis of sedimentary flood records revealed a dramatic increase in their magnitude over the studied period. A positive correlation between maximum river flow and antecedent prolonged drought conditions was found in South Africa and Mozambique. Most importantly, since 1980, we observed the likely decoupling of extreme floods from the magnitude of La Niña events, suggesting that the natural interannual variability driven by El Niño-Southern Oscillation (ENSO) has been disrupted by climate changes and human activities.

Climate, water, hydropower, wind speed and wind energy potential resources assessments using weather time series data, downscaled regional circulation models: A case study for Mono River Basin in the Gulf of Guinea region

Hydropower and wind energy are important renewable energies source in Gulf Guinea region, but they are sensitive to climate change, because the changing climate may alter atmospheric and hydrological conditions. In this study, firstly we analyzed the historical hydro-climate data in connection with changes in Mono river of Gulf Guinea region discharge and their associated impacts on hydropower generation and assessed the future hydropower availability in different climate scenarios. Secondly, statistical methods are used to examine past and future wind speed (WS) and wind energy potential. We used historical data (1981–2010) obtained from the National Meteorology Agency in Togo and Benin and a multi-model ensemble for future projections data (2021–2050) of eight selected Regional Climate Models (RCMs) under Representative Concentration Pathway (RCP) under scenarios 4.5 and 8.5. We found in the recent past years that the temperature and evaporation increase during dry season, while flows and hydropower generation decreased. The multi-model ensemble used have announced that in general, the decreasing trend of the rainfall and increasing trend of temperature is projected to be higher in RCP 8.5 than RCP 4.5, in the near future (2021–2050). Overall, the annual hydropower potential the hydropower will decrease between 8 and 19.3% under the RCP4.5 and 9 and 29% under the RCP8.5 while the monthly hydropower potential is projected to decrease by 4–41% under the RCP4.5 and by 6–47% under the RCP8.5 in the same period. Concerning the wind speed and the potential wind power, the observed results show that both the Weibull and Rayleigh approximations provided a better fit for the wind speed data. The projected of wind speed and wind power density (2021–2050) at an annual timescale is similar to the monthly timescale of wind speed and wind power density at 90 m height. The multi-model ensemble indicate that projected wind speeds (WS) are between 5 and 10 m/s under the RCP4.5 and, between 3 and 12 m/s under the RCP8.5 while wind power density is projected by 15–250 w/m2, and by 13–357 w/m2 under the RCP4.5 and under the RCP8.5, respectively. South and North of Mono basin, have higher WPD and are promising for installing wind turbines for small-scale power generation.

Case study of continental‐scale hydrologic modeling's ability to predict daily streamflow percentiles for regulatory application

AbstractRegulatory practitioners use hydroclimatic data to provide context to observations typically collected through field site visits and aerial imagery analysis. In the absence of site‐specific data, regulatory practitioners must use proxy hydroclimatic data and models to assess a stream's hydroclimatology. One intent of current‐generation continental‐scale hydrologic models is to provide such hydrologic context to ungaged watersheds. In this study, the ability of two state‐of‐the‐art, operational, continental‐scale hydrologic modeling frameworks, the National Water Model and the Group on Earth Observation Global Water Sustainability (GEOGloWS) European Centre for Medium‐Range Weather Forecasts (ECMWF) Streamflow Model, to produce daily streamflow percentiles and categorical estimates of the streamflow normalcy was examined. The modeled streamflow percentiles were compared to observed daily streamflow percentiles at four United States Geological Survey stream gages. The model's performance was then compared to a baseline assessment methodology, the Antecedent Precipitation Tool. Results indicated that, when compared to baseline assessment techniques, the accuracy of the National Water Model (NWM) or GEOGloWS ECMWF Streamflow Model was greater than the accuracy of the baseline assessment methodology at four stream gage locations. The NWM performed best at three of the four gages. This work highlighted a novel application of current‐generation continental‐scale hydrologic models.

Spatiotemporal management of solar, wind and hydropower across continental Europe

Weather climate fluctuations cause large variations in renewable electricity production, which requires substantial amounts of energy storage to overcome energy drought periods. Based on daily hydroclimatic data and information about renewable power systems covering Europe, here we quantify the complementarity in the solar-wind-hydro energy components of the continental climate system. We show that the spatiotemporal management of renewable electricity production over Europe can induce a virtual energy storage gain that is several times larger than the available energy storage capacity in hydropower reservoirs. The potential electricity production matches the consumption by spatiotemporal management of suitable shares of solar and wind power complemented with the present hydropower. While the mixed renewable energy potential varies less than anticipated at the continental scale, utilization of the complementarity requires new continental electrical transmission lines and stable international trade. We highlight that management models need to consider incentives beyond national boundaries to appropriately benefit from continental climate conditions.