Hydrological Community Research Articles

Foreseen impact of climate change on temporary ponds located along a latitudinal gradient in Morocco

ABSTRACT Wetlands have been declining worldwide, with an estimated 64–71% loss over the last century. Climate change is increasing pressure on wetlands, affecting their hydrology, and Mediterranean temporary ponds could be particularly vulnerable to these changes. We studied the expected changes in hydrology and plant community according to climatic change scenarios in 18 temporary ponds distributed in 6 areas along a latitudinal and climatic gradient in Morocco (750 km; 4° latitudinal gradient). Richness of pond species was significantly correlated with water level and pond hydroperiod and showed a strong decline from north to south along with increased water stress. Using the Mar-O-Sel software (mar-o-sel.net), we simulated the future evolution of water balance and wetland hydroperiod (duration and frequency of flooding) in these ponds for mid- and late-21st century. Climate projections were estimated based on 2 Representative Concentration Pathway (RCP) scenarios assuming a stabilization (RCP4.5) or increase (RCP8.5) in greenhouse gas emissions. The mean water deficit of the ponds currently ranges between −1380 and −1812 mm/yr and would increase by 16–67% in 2100 under the RCP8.5 scenarios. Pond richness is expected to decrease by 8–15 species in 2100, depending on area, as a result of shorter projected hydroperiods. Severity of change was not related to location of the ponds along the latitudinal (aridity) gradient, but rather to the interaction between the size of their catchment area and the thickness of the permeable soil layer.

A Near Real-Time Hydrological Information System for the Upper Danube Basin

The multi-national catchment of the Upper Danube covers an area of more than 100,000 km2 and is of great ecological and economic value. Its hydrological states (e.g., runoff conditions, snow cover states or groundwater levels) affect fresh-water supply, agriculture, hydropower, transport and many other sectors. The timely knowledge of the current status is therefore of importance to decision makers from administration or practice but also the interested public. Therefore, a web-based, near real-time hydrological information system was conceptualized and developed for the Upper Danube upstream of Vienna (Upper Danube HIS), utilizing ERA5 reanalysis data (ERA5) and hydrological simulations provided by the semi-distributed hydrological model COSERO. The ERA5 reanalysis data led to comparatively high simulation performance for a total of 65 subbasins with a median NSE and KGE of 0.69 and 0.81 in the parameter calibration and 0.63 and 0.75 in the validation period. The Upper Danube HIS was implemented within the R programming environment as a web application based on the Shiny framework. This enables an intuitive, interactive access to the system. It offers various capabilities for a hydrometeorological analysis of the 65 subbasins of the Upper Danube basin, inter alia, a method for the identification of hydrometeorological droughts. This proof of concept and system underlines how valuable information can be obtained from freely accessible data and by the means of open source software and is made available to the hydrological community, water managers and the public.

Peatland microbial community responses to plant functional group and drought are depth-dependent.

Peatlands store one-third of Earth's soil carbon, the stability of which is uncertain due to climate change-driven shifts in hydrology and vegetation, and consequent impacts on microbial communities that mediate decomposition. Peatland carbon cycling varies over steep physicochemical gradients characterizing vertical peat profiles. However, it is unclear how drought-mediated changes in plant functional groups (PFGs) and water table (WT) levels affect microbial communities at different depths. We combined a multiyear mesocosm experiment with community sequencing across a 70-cm depth gradient, to test the hypotheses that vascular PFGs (Ericaceae vs. sedges) and WT (high vs. low) structure peatland microbial communities in depth-dependent ways. Several key results emerged. (i) Both fungal and prokaryote (bacteria and archaea) community structure shifted with WT and PFG manipulation, but fungi were much more sensitive to PFG whereas prokaryotes were much more sensitive to WT. (ii) PFG effects were largely driven by Ericaceae, although sedge effects were evident in specific cases (e.g., methanotrophs). (iii) Treatment effects varied with depth: the influence of PFG was strongest in shallow peat (0-10, 10-20cm), whereas WT effects were strongest at the surface and middle depths (0-10, 30-40cm), and all treatment effects waned in the deepest peat (60-70cm). Our results underline the depth-dependent and taxon-specific ways that plant communities and hydrologic variability shape peatland microbial communities, pointing to the importance of understanding how these factors integrate across soil profiles when examining peatland responses to climate change.

Synergistic Calibration of a Hydrological Model Using Discharge and Remotely Sensed Soil Moisture in the Paraná River Basin

Hydrological models are useful tools for water resources studies, yet their calibration is still a challenge, especially if aiming at improved estimates of multiple components of the water cycle. This has led the hydrologic community to look for ways to constrain models with multiple variables. Remote sensing estimates of soil moisture are very promising in this sense, especially in large areas for which field observations may be unevenly distributed. However, the use of such data to calibrate hydrological models in a synergistic way is still not well understood, especially in tropical humid areas such as those found in South America. Here, we perform multiple scenarios of multiobjective model optimization with in situ discharge and the SMOS L4 root zone soil moisture product for the Upper Paraná River Basin in South America (drainage area > 900,000 km²), for which discharge data for 136 river gauges are used. An additional scenario is used to compare the relative impacts of using all river gauges and a small subset containing nine gauges only. Across the basin, the joint calibration (CAL-DS) using discharge and soil moisture leads to improved precision and accuracy for both variables. The discharges estimated by CAL-DS (median KGE improvement for discharge was 0.14) are as accurate as those obtained with the calibration with discharge only (median equal to 0.14), while the CAL-DS soil moisture retrieval is practically as accurate (median KGE improvement for soil moisture was 0.11) as that estimated using the calibration with soil moisture only (median equal to 0.13). Nonetheless, the individual calibration with discharge rates is not able to retrieve satisfactory soil moisture estimates, and vice versa. These results show the complementarity between these two variables in the model calibration and highlight the benefits of considering multiple variables in the calibration framework. It is also shown that, by considering only nine gauges instead of 136 in the model optimization, the model is able to estimate reasonable discharge and soil moisture, although relatively less accurately and with less precision than for the entire dataset. In summary, this study shows that, for poorly gauged tropical basins, the joint calibration of SMOS soil moisture and a few in situ discharge gauges is capable of providing reasonable discharge and soil moisture estimates basin-wide and is more preferable than performing only a discharge-oriented optimization process.

Variance decomposition of forecasted sediment transport in a lowland watershed using global climate model ensembles

Forecasting change in sediment transport using global climate model ensembles is under-developed in the hydrology community due to a lack of knowledge regarding the variance structure of predictions. We investigate the uncertainty of forecast sediment transport from sources, including global climate model realizations, global climate model ensemble design, forecasted hydrologic inputs, and hydrologic modeling parameterizations. We then forecast sediment transport for the gently rolling watershed in Kentucky, USA. Contrary to past research forecasting hydrology with global climate models, hydrologic model parameterization was the most significant source of variance impacting forecasted sediment transport variables. Forecast of sediment transport responses shows the propagation of uncertainty from the hydrologic model parameterization was over two times greater than the uncertainty from the selected global climate model realizations. This result emphasizes researchers focused on forecasting sediment transport with global climate models may need to give as much, or more, consideration to their water–sediment linkages as climate-water–sediment linkages. Hydrologic inputs from climate change, including forecast precipitation, temperature, relative humidity, solar radiation, and wind speed, impacted sediment transport. Considering changes in precipitation and temperature alone under-predicts streamflow and sediment transport by 49% and 35%, respectively, compared to including all meteorological variables inputs. Variance introduced across the three different global climate model ensembles was a relatively small source of variance impacting forecasted streamflow or sediment yield. The results suggest a quantitative effort by the researcher to design the global climate model ensemble by considering representativeness, historical performance, and independency will lead to robust results. Ensemble average forecasts forecast streamflow and sediment yield to increase by 19% and 14%, respectively, for the lowland watershed. The sediment transport forecasts reflect the shear-limited landscape of the watershed and the transport-limited conditions in the stream channel.

Visualization Workflow for Quantifying Parameter Sensitivities and Uncertainties for Hydrologic Models

AbstractFrom regional to continental scales, hydrologic processes are represented by modular modeling frameworks dependent on input datasets and parameter sets representing physical attributes. The hydrologic community needs a common procedure to evaluate model output based on parameter sensitivities and uncertainties compared to performance metrics (i.e., objective functions), especially for large parameter sets. We developed a reproducible workflow for evaluating hydrologic models to objectively analyze model outputs as a function of parameter choice using numerical and visualization techniques. Our workflow was implemented on three separate case studies, each with a different hydrologic model, and the results can be reproduced and visualized from a community github code repository. Model parameter sensitivity was evaluated using several global sensitivity indices and Bayesian theory. Uncertainty in parameter spaces was quantified to highlight the impact of unreliable input data on model output. Model parameter sensitivities and uncertainties were evaluated numerically and visually to provide a comprehensive perspective on their impacts on model output. For each case study, we provided a summary and interpretation of the workflow results. Our workflow can be integrated into hydrologic modeling frameworks for objective modular model and parameter set evaluations based on a data‐driven approach appropriate for model selection.

Advances in Catchment Science through Integrated Hydrological Modelling and Monitoring

Environmental research is rapidly evolving toward an integration of different disciplines, and this is also reflected in hydrology and the hydrological modelling community [...]

An open web‐based module developed to advance data‐driven hydrologic process learning

AbstractThe era of ‘big data’ promises to provide new hydrologic insights, and open web‐based platforms are being developed and adopted by the hydrologic science community to harness these datasets and data services. This shift accompanies advances in hydrology education and the growth of web‐based hydrology learning modules, but their capacity to utilize emerging open platforms and data services to enhance student learning through data‐driven activities remains largely untapped. Given that generic equations may not easily translate into local or regional solutions, teaching students to explore how well models or equations work in particular settings or to answer specific problems using real data is essential. This article introduces an open web‐based module developed to advance data‐driven hydrologic process learning, targeting upper level undergraduate and early graduate students in hydrology and engineering. The module was developed and deployed on the HydroLearn open educational platform, which provides a formal pedagogical structure for developing effective problem‐based learning activities. We found that data‐driven learning activities utilizing collaborative open web platforms like CUAHSI HydroShare and JupyterHub to store and run computational notebooks allowed students to access and work with datasets for systems of personal interest and promoted critical evaluation of results and assumptions. Initial student feedback was generally positive, but also highlighted challenges including trouble‐shooting and future‐proofing difficulties and some resistance to programming and new software. Opportunities to further enhance hydrology learning include better articulating the benefits of coding and open web platforms upfront, incorporating additional user‐support tools, and focusing methods and questions on implementing and adapting notebooks to explore fundamental processes rather than tools and syntax. The profound shift in the field of hydrology toward big data, open data services and reproducible research practices requires hydrology instructors to rethink traditional content delivery and focus instruction on harnessing these datasets and practices in the preparation of future hydrologists and engineers.

Knowledge gaps in our perceptual model of Great Britain's hydrology

AbstractThere is a no lack of significant open questions in the field of hydrology. How will hydrological connectivity between freshwater bodies be altered by future human alterations to the hydrological cycle? Where does water go when it rains? Or what is the future space–time variability of flood and drought events? However, the answers to these questions will vary with location due to the specific and often poorly understood local boundary conditions and system properties that control the functional behaviour of a catchment or any other hydrologic control volume. We suggest that an open, shared and evolving perceptual model of a region's hydrology is critical to tailor our science questions, as it would be for any other study domain from the plot to the continental scale. In this opinion piece, we begin to discuss the elements of and point out some knowledge gaps in the perceptual model of the terrestrial water cycle of Great Britain. We discuss six major knowledge gaps and propose four key ways to reduce them. While the specific knowledge gaps in our perceptual model do not necessarily transfer to other places, we believe that the development of such perceptual models should be at the core of the debate for all hydrologic communities, and we encourage others to have a similar debate for their hydrologic domain.

Continental Hydrologic Intercomparison Project, Phase 1: A Large‐Scale Hydrologic Model Comparison Over the Continental United States

AbstractHigh‐resolution, coupled, process‐based hydrology models, in which subsurface, land‐surface, and energy budget processes are represented, have been applied at the basin‐scale to ask a wide range of water science questions. Recently, these models have been developed at continental scales with applications in operational flood forecasting, hydrologic prediction, and process representation. As use of large‐scale model configurations increases, it is exceedingly important to have a common method for performance evaluation and validation, particularly given challenges associated with accurately representing large domains. Here, we present phase 1 of a comparison project for continental‐scale, high‐resolution, processed‐based hydrologic models entitled the Continental Hydrologic Intercomparison Project (CHIP). The first phase of CHIP is based on past Earth System Model intercomparisons and comprised of a two‐model proof of concept comparing the ParFlow‐CONUS hydrologic model, version 1.0 and a NOAA US National Water Model configuration of WRF‐Hydro, version 1.2. The objectives of CHIP phase 1 are: (a) describe model physics and components, (b) design an experiment to ensure a fair comparison, and (b) assess simulated streamflow with observations to better understand model bias. To our knowledge, this is the first comparison of continental‐scale, high‐resolution, physics‐based models which incorporate lateral subsurface flow. This model intercomparison is an initial step toward a continued effort to unravel process, parameter, and formulation differences in current large‐scale hydrologic models and to engage the hydrology community in improving hydrology model configuration and process representation.

Achieving high-quality probabilistic predictions from hydrological models calibrated with a wide range of objective functions

Probabilistic predictions of streamflow are important in many environmental modelling applications. These predictions are often constructed using hydrological models combined with residual error models to describe predictive uncertainty. However, many objective functions commonly used to calibrate hydrological models make (implicit) assumptions that do not match the well-known heteroscedasticity and skewness of residual errors. Using a daily-scale hydrological model, we demonstrate that the use of such ‘inconsistent’ objective functions in combination with a commonly used residual error model introduces spurious flow dependencies in the mean of residual errors, which in turn leads to poor-quality probabilistic predictions. We then use residual error diagnostics to develop a simple enhanced error model, where the error mean depends linearly on the (transformed) simulated streamflow. The enhanced error model is compared with a commonly used reference error model that assumes a zero error mean. The empirical case study employs 54 perennial Australian catchments, the hydrological model GR4J, 9 common objective functions and 4 performance metrics (reliability, precision, volumetric bias and errors in the flow duration curve). The enhanced error model overcomes the loss of performance caused by inconsistent objective functions and achieves probabilistic predictions comparable to those obtained using consistent objective functions. These findings hold for all objective functions used in the case study. The enhanced residual error model is expected to facilitate the adoption of probabilistic predictions in the hydrological modelling community. In particular, it can be used to achieve high-quality probabilistic predictions from hydrological models calibrated with a wide range of common objective functions.

Machine learning for hydrologic sciences: An introductory overview

AbstractThe hydrologic community has experienced a surge in interest in machine learning in recent years. This interest is primarily driven by rapidly growing hydrologic data repositories, as well as success of machine learning in various academic and commercial applications, now possible due to increasing accessibility to enabling hardware and software. This overview is intended for readers new to the field of machine learning. It provides a non‐technical introduction, placed within a historical context, to commonly used machine learning algorithms and deep learning architectures. Applications in hydrologic sciences are summarized next, with a focus on recent studies. They include the detection of patterns and events such as land use change, approximation of hydrologic variables and processes such as rainfall‐runoff modeling, and mining relationships among variables for identifying controlling factors. The use of machine learning is also discussed in the context of integrated with process‐based modeling for parameterization, surrogate modeling, and bias correction. Finally, the article highlights challenges of extrapolating robustness, physical interpretability, and small sample size in hydrologic applications.This article is categorized under: Science of Water

Performance of the IMERG Precipitation Products over High-latitudes Region of Finland

Highly accurate and real-time estimation of precipitation over large areas remains a fundamental challenge for the hydrological and meteorological community. This is primarily attributed to the high heterogeneity of precipitation across temporal and spatial scales. Rapid developments in remote sensing technologies have made the quantitative measurement of precipitation by satellite sensors a significant data source. The Global Precipitation Measurement (GPM) mission makes precipitation data with high temporal and spatial resolutions available to different users. The objective of this study is to evaluate the accuracy of Integrated Multi-satellite Retrievals for GPM (IMERG) V06 (Early, Late, and Final) satellite precipitation products (SPPs) at high latitudes. Ground-based observation data across Finland were used as a reference and compared with IMERG data from 2014 to 2019. Three aspects were evaluated: the spatial coverage of the satellite estimates over Finland; the accuracy of the satellite estimates at various temporal scales (half-hourly, daily, and monthly); and the variation in the performance of SPPs over different spatial regions. The results showed that IMERG SPPs can be used with high confidence over Southern, Eastern, and Western Finland. These SPPs can be used with caution over the region of the historical province of Oulu but are not recommended for higher latitudes over Lapland. In general, the IMERG-Final SPP performed the best, and it is recommended for use because of its low number of errors and high correlation with ground observation. Furthermore, this SPP can be used to complement or substitute ground precipitation measurements in ungauged and poorly gauged regions in Southern Finland.

Role of hydrological model structure in the assimilation of soil moisture for streamflow prediction

Soil moisture data assimilation (SMDA) has found growing application in the hydrological research community because of readily available satellite-based soil moisture (SM) data. Several past studies have explored the capability to assimilate observed SM in hydrological model to enhance streamflow prediction. However, the impact of the conceptual hydrological model (CHM) structure on the SMDA in streamflow prediction has not been investigated yet. In this study, to understand the CHM structure's role, we used three different CHMs for the SMDA: dynamic Budyko (DB), Génie Rural à 4 paramètres Journalier (GR4J), and Probability Distributed Model (PDM) model. The SM obtained from Global Land Data Assimilation System (GLDAS) was assimilated using Ensemble Kalman Filter (EnKF) for 43 Model Parameter Estimation Experiment (MOPEX) basins. The GR4J model was found to be performed best from calibration and validation results, and the DB model was found to be performed least. The results show that the performance of the DB model improved during assimilation compared to its open-loop version for all the 43 basins. However, deterioration in model performance was observed for the GR4J and PDM model during assimilation for all basins except a few. The assimilated model performance was evaluated in respect of Assimilation Efficiency (AE) and ranged from 2.56 to 81.24%, −70.9 to 17.65%, and −71.38 to 24.86%, respectively, for the DB, GR4J, and PDM model. Further, we hypothesized that if the GLDAS SM is better than the model-simulated SM, then improvement in the model performance is observed due to the SMDA. The coefficient of determination (r2) between the SM simulated by all the three models without assimilation and the GLDAS SM was found for all the basins. Results indicated that the GR4J and PDM model captured SM better than the DB model. Moreover, to strengthen this hypothesis, we run the GR4J and PDM model deterministically, considering the daily GLDAS SM as the initial condition for streamflow simulation instead of the SM simulated by these models for the basins where deterioration in model performance was observed. For all the basins, it is found that the GR4J and PDM model performance deteriorated. Even though the GLDAS SM is used as every day's initial condition to simulate the streamflow, no improvement of model performance is observed. In general, our study underlines the significance of the CHM structure in the SMDA exercise.

Experiments of an IoT-based wireless sensor network for flood monitoring in Colima, Mexico

Abstract Urban flooding is one of the major issues in many parts of the world, and its management is often challenging. One of the challenges highlighted by the hydrology and related communities is the need for more open data and monitoring of floods in space and time. In this paper, we present the development phases and experiments of an Internet of Things (IoT)-based wireless sensor network for hydrometeorological data collection and flood monitoring for the urban area of Colima-Villa de Álvarez in Mexico. The network is designed to collect fluvial water level, soil moisture and weather parameters that are transferred to the server and to a web application in real-time using IoT Message Queuing Telemetry Transport protocol over 3G and Wi-Fi networks. The network is tested during three different events of tropical storms that occurred over the area of Colima during the 2019 tropical cyclones season. The results show the ability of the smart water network to collect real-time hydrometeorological information during extreme events associated with tropical storms. The technology used for data transmission and acquisition made it possible to collect information at critical times for the city. Additionally, the data collected provided essential information for implementing and calibrating hydrological models and hydraulic models to generate flood inundation maps and identify critical infrastructure.

Estimation of daily hydrological mass changes using continuous GNSS measurements in mainland China

Surface displacements measured by the Global Navigation Satellite System (GNSS) integrate the elastic response of the solid Earth to regional and local hydrologic loading signals and provide an opportunity for near-real-time monitoring of terrestrial water storage variations. Here, we estimate the spectral information of hydrology-induced vertical surface deformation based on spherical Slepian basis functions and recover the daily changes in continental water storage over mainland China from January 2010 to December 2019. Our inversion results, depending on a sparsely-distributed GNSS network, indicate the largest seasonal fluctuation of hydrological mass loads in southwestern China. The GNSS-inferred equivalent water height has an annual amplitude of up to 314 mm, which is greater than 243 mm from the Gravity Recovery and Climate Experiment (GRACE) and 150 mm from the Global Land Data Assimilation System (GLDAS). We also find notable seasonal water oscillations of 153–166 mm in the middle and lower Yangtze River Basin. This feature appears in the GNSS inversion model and GRACE Mascon solutions but is invisible in the GLDAS model, mostly indicating the existence of significant changes in surface water storage that is unmodeled in the GLDAS model. There is board agreement in the water estimates derived from multiple data sets in South China, in contrast to weak temporal coherence of various water estimates in North China. We also demonstrate the ability of GNSS for tracking the drought and wet periods, which can serve as an independent means to characterize hydrological extremes. Our study emphasizes that a sparse GNSS network can still be considered as a complementary tool to map large-scale spatiotemporal variations of water storage and benefit the hydrological community for assessing water storage changes and hydrological dynamics.

Zaraa Uul: An archaeological record of Pleistocene-Holocene palaeoecology in the Gobi Desert.

Environmentally-based archaeological research at Zaraa Uul, including zooarchaeology, phytolith analysis, and radiocarbon dating, is the first of its kind in Mongolia and presents critical new insight on the relationship between periods of occupational intensity and climatic amelioration from the earliest anatomically modern humans to the adoption of pastoralism. The palaeoenvironmental and faunal record of Zaraa Uul show that Early-Middle Holocene hydrology and species distributions were distinct from all other periods of human occupation. Holocene hunter-gatherers inhabited an ecosystem characterized by extensive marshes, riparian shrub and arboreal vegetation along the hill slopes and drainages. The exploitation of species associated with riparian and wetland settings supports the hypothesis of, but suggests an earlier timing for, oasis-based logistical foraging during the Early-Middle Holocene of arid Northeast Asia. The onset of wetter conditions at 8500 cal BP agrees with other regional studies, but multiple lines of evidence present the first integrated field- and laboratory-based record of human-environment relationships in arid East Asia during the Holocene Climatic Optimum. We compare it to Late Pleistocene climatic amelioration, and highlight specific responses of the hydrological, vegetative and faunal communities to climate change in arid Northeast Asia.

Planktonic Metabolism and Microbial Carbon Substrate Utilization in Response to Inundation in Semi‐Arid Floodplain Wetlands

AbstractIn semi‐arid floodplain wetlands, planktonic metabolism, a balance of gross primary productivity of phytoplankton (GPP) and planktonic respiration (PR), may be affected by hydrology, geomorphology, carbon, and microbial communities. Understanding relationships between these factors helps develop effective water and land management plans for these wetlands. In this study, variations in planktonic metabolism and microbial carbon substrate utilization were investigated in the semi‐arid floodplain wetlands of the Macquarie Marshes, south‐eastern Australia, by comparing three geomorphologically distinct zones with different historical inundation frequencies (zones 1: moderate, 2: low, and 3: very low). Floodplain soil samples collected from each zone were inundated artificially in controlled laboratory conditions. GPP and PR were determined using biological oxygen demand methods. Biolog EcoPlates™ were used to assess microbial utilization of carbon substrate. Among the zones examined, zone 1 had the highest GPP and lowest PR, while zone 3 had the lowest GPP and highest PR. GPP/PR showed zones 1 and 2 autotrophic, and zone 3 heterotrophic. EcoPlates showed the highest and lowest microbial activity in zone 3 and zone 1 respectively, indicating a strong link between microbial carbon substrate utilization and PR for heterotrophy in the rarely inundated zone. Inundation of drier wetland zones may lead to a large pulse in PR, at least for a short period of time, potentially leading to anoxic water events. When making decisions for environmental water allocations, the antecedent hydro‐geomorphological and microbial condition of the targeted wetlands should be considered to help predict expected aquatic ecosystem responses.

What Role Does Hydrological Science Play in the Age of Machine Learning?

AbstractThis paper is derived from a keynote talk given at the Google's 2020 Flood Forecasting Meets Machine Learning Workshop. Recent experiments applying deep learning to rainfall‐runoff simulation indicate that there is significantly more information in large‐scale hydrological data sets than hydrologists have been able to translate into theory or models. While there is a growing interest in machine learning in the hydrological sciences community, in many ways, our community still holds deeply subjective and nonevidence‐based preferences for models based on a certain type of “process understanding” that has historically not translated into accurate theory, models, or predictions. This commentary is a call to action for the hydrology community to focus on developing a quantitative understanding of where and when hydrological process understanding is valuable in a modeling discipline increasingly dominated by machine learning. We offer some potential perspectives and preliminary examples about how this might be accomplished.

Statistical Postprocessing for Weather Forecasts: Review, Challenges, and Avenues in a Big Data World

AbstractStatistical postprocessing techniques are nowadays key components of the forecasting suites in many national meteorological services (NMS), with, for most of them, the objective of correcting the impact of different types of errors on the forecasts. The final aim is to provide optimal, automated, seamless forecasts for end users. Many techniques are now flourishing in the statistical, meteorological, climatological, hydrological, and engineering communities. The methods range in complexity from simple bias corrections to very sophisticated distribution-adjusting techniques that incorporate correlations among the prognostic variables. The paper is an attempt to summarize the main activities going on in this area from theoretical developments to operational applications, with a focus on the current challenges and potential avenues in the field. Among these challenges is the shift in NMS toward running ensemble numerical weather prediction (NWP) systems at the kilometer scale that produce very large datasets and require high-density high-quality observations, the necessity to preserve space–time correlation of high-dimensional corrected fields, the need to reduce the impact of model changes affecting the parameters of the corrections, the necessity for techniques to merge different types of forecasts and ensembles with different behaviors, and finally the ability to transfer research on statistical postprocessing to operations. Potential new avenues are also discussed.