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Forecasting river flow is an important stage in reservoir operation, urban water management, and water resource optimization. The goal of this research is to forecast daily river flows for two intermittent and ephemeral rivers. Based on the antecedent river flow, the forecasting approach used stochastic (AR, ARIMA, and SARIMA) and machine learning (ML) techniques. The ML methods consist of three shallow learning models (NARnet, OS-ELM, and NAR-GMDH) and three deep learning models (LSTM, CNN-GRU, and stacked-LSTM). The precision of all the models in the ephemeral river was higher the intermittent river considering both base flow (R2ave = 0.94 vs. 0.87) and peak flow. The study was also extended to forecasting peak river flow values, demonstrating in the superiority of deep learning (RMSEave = 7.6 m3/s) over the shallow (RMSEave = 8.9 m3/s) and stochastic (RMSEave = 9.1 m3/s). Applied models acted similarly in forecasting peak flow in both rivers due to substantial variations in the floods. Moreover, the results demonstrated that the deep learning group models surpass stochastic and shallow learning group models in terms of five evaluation criteria, including RMSE, MAE, mean bias error, correlation coefficient, and agreement index for both rivers. In general, the results indicate that the CNN-GRU outperforms the other models in terms of river flow forecasting and is suggested as a viable model for learning the complicated behavior of streamflow. [ABSTRACT FROM AUTHOR]

Analysis of the change in groundwater used as a drinking and irrigation water source is of critical importance in terms of monitoring aquifers, planning water resources, energy production, combating climate change, and agricultural production. Therefore, it is necessary to model groundwater level (GWL) fluctuations to monitor and predict groundwater storage. Artificial intelligence-based models in water resource management have become prevalent due to their proven success in hydrological studies. This study proposed a hybrid model that combines the artificial neural network (ANN) and the artificial bee colony optimization (ABC) algorithm, along with the ensemble empirical mode decomposition (EEMD) and the local mean decomposition (LMD) techniques, to model groundwater levels in Erzurum province, Türkiye. GWL estimation results were evaluated with mean square error (MSE), coefficient of determination (R2), and residual sum of squares (RSS) and visually with violin, scatter, and time series plot. The study results indicated that the EEMD-ABC-ANN hybrid model was superior to other models in estimating GWL, with R2 values ranging from 0.91 to 0.99 and MSE values ranging from 0.004 to 0.07. It has also been revealed that promising GWL predictions can be made with previous GWL data. [ABSTRACT FROM AUTHOR]

The expansion of urban areas contributes to the growth of impervious surfaces, leading to increased pollution and altering the configuration, composition, and context of land covers. This study employed machine learning methods (partial least square regressor and the Shapley Additive exPlanations) to explore the intricate relationships between urban expansion, land cover changes, and water quality in a watershed with a park and lake. To address this, we first evaluated the spatio-temporal variation of some physicochemical and microbiological water quality variables, generated yearly land cover maps of the basin adopting several machine learning classifiers, and computed the most suitable landscape metrics that better represent the land cover. The main results highlighted the importance of spatial arrangement and the size of the contributing watershed on water quality. Compact urban forms appeared to mitigate the impact on pollutants. This research provides valuable insights into the intricate relationship between landscape characteristics and water quality dynamics, informing targeted watershed management strategies aimed at mitigating pollution and ensuring the health and resilience of aquatic ecosystems. [ABSTRACT FROM AUTHOR]

A recent trend in hydroinformatics has been the growing number of data, models, and cyber tools, which are web accessible, each aiming to improve common research tasks in hydrology through web technologies. Coupling web-based models and tools holds great promise for an integrated environment that can facilitate community participation, collaboration, and scientific replication. There are many examples of server-side, hydroinformatics resource coupling, where a common standard serves as an interface. Yet, there are few, if any, examples of client-side resource coupling, particularly cases where a common specification is employed. Toward this end, we implemented the basic model interface (BMI) specification in the JavaScript programing language, the most widely used programing language on the web. By using BMI, we coupled two client-side hydrological applications (HydroLang and HLM-Web) to perform rainfall–runoff simulations of historical events with rainfall data and a client-side hydrological model as a case study demonstration. Through this process, we present how a common and often tedious task – the coupling of two independent web resources – can be made easier through the adoption of a common standard. Furthermore, applying the standard has facilitated a step toward the possibility of client-side ‘Model as a Service’ for hydrological models. HIGHLIGHTS We present the basic model interface (BMI) specification for the JavaScript programming language.; We present a comprehensive example of how BMI may be used as a common standard to couple client-side, hydroinformatics web resources.; For developers, BMI for JavaScript simplifies the effort needed to implement an Application Programing Interface for their resource.; For users, BMI for JavaScript accelerates learning and working with a new resource.;

Increasing extreme weather events pose significant challenges in hydrology, requiring tools for preparedness and prediction of intense rainfall impacts, especially flash floods. Current risk reduction measures for pluvial flood risk management rely on flood hazard maps, but inconsistencies in transregional standards that are used for risk assessment hinder cross-regional comparisons. While there are existing guidelines for the development of pluvial flood hazard maps, there is still a lack of holistic modelling systems that enable harmonised predictions of the impacts of heavy rainfall events. Furthermore, sensitive city data (e.g., critical infrastructure, sewer network) exist in many municipalities, which cannot be readily disclosed for modelling purposes. In this work, we propose an approach using distributed analytics to distribute computation commands to existing hydrodynamic models at different locations. In combination with harmonising model adapters, we enable the generation of harmonised pluvial flood hazard maps of different regions to tackle the inconsistencies and privacy concerns. We apply our approach to four adjacent urban areas in the Rhein-Sieg Kreis of North Rhine-Westphalia. Our results demonstrate the ability of our approach to produce cross-regional pluvial flood hazard maps, supporting disaster preparedness and management in regions prone to extreme weather events and flash floods. HIGHLIGHTS Decentralised flood modelling: Innovative approach using distributed analytics for harmonised pluvial flood hazard maps, addressing inconsistencies.; Improved risk assessment: Enables standardised cross-regional comparisons, enhancing disaster preparedness.; Real-world validation: Practical application in North Rhine-Westphalia validates the approach's feasibility and relevance for complex urban areas prone to flooding.;

Over the years, data-driven models have gained notable traction in water and environmental engineering. The adoption of these cutting-edge frameworks is still in progress in the grand scheme of things, yet for the most part, such attempts have been centered around the models themselves, and their internal computational architecture, that is, the model-centric approach. These endeavors can certainly pave the way for more tailor-fitted models capable of producing accurate results. However, such a perspective often neglects a fundamental assumption of these models, which is the importance of reliability, correctness, and accessibility of the data used in constructing them. This challenge arises from the prevalent model-centric paradigm of thinking in the field. An alternative approach, however, would prioritize placing data at the focal point, focusing on systematically enhancing current datasets and devising frameworks to improve data collection schemes. This suggests a paradigm shift toward more data-centric thinking in water and environmental engineering. Practically, this shift is not without challenges and necessitates smarter data collection rather than an excessive one. Equally important is the ethical and accurate collection of data, making it available to everyone while safeguarding the rights of individuals and other legal entities involved in the process. [ABSTRACT FROM AUTHOR]

This document is an editorial titled "Hydro-informatics for sustainable water management in agrosystems, volume II." It discusses the second volume of a research topic on hydroinformatics for sustainable water management in agriculture. The editorial highlights various topics covered in the volume, including filling water data gaps, managing drought impacts, studying soil compaction and hydrodynamic properties, improving irrigation systems efficiency, and modeling sediment concentrations and loads. The articles in the volume demonstrate the relevance of hydroinformatics in addressing farming system challenges and emphasize the need for socio-technical approaches to achieve sustainability in the agricultural sector. [Extracted from the article]

The sustainable management of water cycles is crucial in the context of climate change and global warming. It involves managing global, regional, and local water cycles—as well as urban, agricultural, and industrial water cycles—to conserve water resources and their relationships with energy, food, microclimates, biodiversity, ecosystem functioning, and anthropogenic activities. Hydrological modeling is indispensable for achieving this goal, as it is essential for water resources management and mitigation of natural disasters. In recent decades, the application of artificial intelligence (AI) techniques in hydrology and water resources management has made notable advances. In the face of hydro-geo-meteorological uncertainty, AI approaches have proven to be powerful tools for accurately modeling complex, non-linear hydrological processes and effectively utilizing various digital and imaging data sources, such as ground gauges, remote sensing tools, and in situ Internet of Things (IoTs). The thirteen research papers published in this Special Issue make significant contributions to long- and short-term hydrological modeling and water resources management under changing environments using AI techniques coupled with various analytics tools. These contributions, which cover hydrological forecasting, microclimate control, and climate adaptation, can promote hydrology research and direct policy making toward sustainable and integrated water resources management. [ABSTRACT FROM AUTHOR]

Drought monitoring and prediction have important roles in various aspects of hydrological studies. In the current research, the standardized precipitation index (SPI) was monitored and predicted in Peru between 1990 and 2015. The current study proposed a hybrid model, called ANN-FA, for SPI prediction in various time scales (SPI3, SPI6, SPI18, and SPI24). A state-of-the-art firefly algorithm (FA) has been documented as a powerful tool to support hydrological modeling issues. The ANN-FA uses an artificial neural network (ANN) which is coupled with FA for Lima SPI prediction via other stations. Through the intelligent utilization of SPI series from neighbors' stations as model inputs, the suggested approach might be used to forecast SPI at various time scales in a meteorological station with insufficient data. To conduct this, the SPI3, SPI6, SPI18, and SPI24 were modeled in Lima meteorological station using other meteorological stations' datasets in Peru. Various error criteria were employed to investigate the performance of the ANN-FA model. Results showed that the ANN-FA is an effective and promising approach for drought prediction and also a multi-station strategy is an effective strategy for SPI prediction in the meteorological station with a lack of data. The results of the current study showed that the ANN-FA approach can help to predict drought with the mean absolute error = 0.22, root mean square error = 0.29, the Pearson correlation coefficient = 0.94, and index of agreement = 0.97 at the testing phase of best estimation (SPI3). [ABSTRACT FROM AUTHOR]

Hydropower is currently one of the leading renewable energy sources in developing countries. Despite the benefits that it can provide, it also triggers significant environmental impacts, such as changes in the reservoirs' water quality. In quantifying those changes, dissolved oxygen (DO) is used as one of the water quality indicators and is the most used variable to quantify water quality and analyze water pollution. This paper aims to establish a relationship between water quality and hydrometeorological variables in tropical reservoirs to better estimate dissolved oxygen. Univariate and multivariate techniques were used to analyze temporal and spatial changes in watersheds to better select vital variables for the forecast model, such as Vector Autoregression (VAR). The results show that, for all monitoring stations, the water quality variables associated with the DO process are COD, BOD, and PO₄. Likewise, precipitation and flow discharge were the hydrometeorological parameters that had the most significant impact on DO. Also, the principal component analysis (PCA) allowed us to identify that the strength of the relationships between water quality and hydrometeorology changes depending on the location of the monitoring site. Finally, the implementation of a VAR model showed good performance metrics for dissolved oxygen predictions based on all analyses. [ABSTRACT FROM AUTHOR]

Evaporation is one of the main components of the hydrological cycle, and its estimation is crucial and important for water resources management issues. Access to a reliable estimator tool for evaporation simulation is important in arid and semi-arid areas such as Iran, which lose more than 70% of their received precipitation by evaporation. Current research employs the Bayesian Regularization (BR) and Scaled Conjugate Gradient (SCG) algorithms for training the Multilayer Perceptron (MLP) model (as MLP-BR and MLP-SCG) and comparing their performance with the Levenberg–Marquardt (LM) algorithm (as MLP-LM). For this purpose, 16 meteorological variables were used on a daily scale; including temperature (5 variables), air pressure (4 variables), and relative humidity (6 variables) as input data sets, and pan evaporation as the target variable of the MLP model. The surveys were conducted during the period of 2006–2021 in Fars Province in Iran, which is a semi-arid region and has many natural lakes. Various combinations of input-target pairs were tested by several learning algorithms, resulting in seven input scenarios: (1) temperature-based (T), (2) pressure-based (F), (3) humidity-based (RH), (4) temperature–pressure-based (T-F), (5) temperature–humidity-based (T-RH), (6) pressure–humidity-based (F-RH) and (7) temperature–pressure–humidity-based (T-F-RH). The results indicated the relative superiority of the three-component scenario of T-F-RH, and a considerable weakness in the single-component scenario of RH compared with others. The best performance with a root mean square error (RMSE) equal to 1.629 and 1.742 mm per day and a Wilmott Index (WI) equal to 0.957 and 0.949 (respectively for validation and test periods) belonged to the MLP-BR model. Additionally, the amount of R2 (greater than 84%), Nash-Sutcliff efficiency (greater than 0.8) and normalized RMSE (less than 0.1) all indicate the reliability of the estimates provided for the daily pan evaporation. In the comparison between the studied training algorithms, two algorithms, BR and SCG, in most cases, showed better performance than the powerful and common LM algorithm. The obtained results suggest that future researchers in this field consider BR and SCG training algorithms for the supervised training of MLP for the numerical estimation of pan evaporation by the MLP model. [ABSTRACT FROM AUTHOR]

The global increase in potato production and yield is expected to lead to increased irrigation needs and this has prompted concerns with respect to the sustainability of irrigation water sources, such as groundwater. The magnitude, and inter- and intra-annual variation, of the crop water requirements and irrigation needs for potato production together with their impact on aquifer storage in a temperate humid region (Prince Edward Island, Canada) were estimated by using long-term (i.e., 2010–2019) daily soil water content (SWC). The amount of supplemental irrigation required for the minimal irrigation scenario (SWC = 70% of field capacity; 0.7 FC) was relatively small (i.e., 17.0 mm); however, this increased significantly, to 85.2 and 189.6 mm, for the moderate (SWC = 0.8 FC) and extensive (SWC = 0.9 FC) irrigation scenarios, respectively. The water supply requirement for the growing season (GS) increased to 154.9 and 344.7 mm for a moderately efficient irrigation system (55% efficiency) for the SWC = 0.8 FC and SWC = 0.9 FC irrigation scenarios, respectively. Depending on the efficiency and the areal extent of the irrigation system, the irrigation water supply requirement can approach or exceed both the GS and annual groundwater recharge. The methodology developed in this research has been translated into a free online tool (SWIB—Soil Water Stress, Irrigation Requirement and Water Balance), which can be applied to other areas or crops where an estimation of soil water deficit and irrigation requirement is sought. [ABSTRACT FROM AUTHOR]

The agricultural sector is vulnerable to extreme phenomena such as droughts, particularly in arid and semi-arid environments and in regions where water infrastructure is limited. Devising preparedness plans, including means for efficient monitoring and timely identification of drought events, is essential for informed decision making on drought mitigation and water management, especially for the water-dependant agricultural sector. This paper presents the incorporation of two new drought indices, designed for agricultural drought identification, in Drought Indices Calculator (DrinC) software. These indices, namely the Agricultural Standardized Precipitation Index (aSPI) and the Effective Reconnaissance Drought Index (eRDI), require commonly available meteorological data, while they employ the concept of effective precipitation, taking into account the amount of water that contributes productively to plant development. The design principles of DrinC software leading to the proper use of the indices for agricultural drought assessment, including the selection of appropriate reference periods, calculation time steps and other related issues, are presented and discussed. The incorporation of aSPI and eRDI in DrinC enhances the applicability of the software towards timely agricultural drought characterisation and analysis, through a straightforward and comprehensible approach, particularly useful for operational purposes. [ABSTRACT FROM AUTHOR]

Hydroinformatics and water data science topics are increasingly common in university graduate settings through dedicated courses and programs as well as incorporation into traditional water science courses. The technical tools and techniques emphasized by hydroinformatics and water data science involve distinctive instructional styles, which may be facilitated by online formats and materials. In the broader hydrologic sciences, there has been a simultaneous push for instructors to develop, share, and reuse content and instructional modules, particularly as the COVID-19 pandemic necessitated a wide scale pivot to online instruction. The experiences of hydroinformatics and water data science instructors in the effectiveness of content formats, instructional tools and techniques, and key topics can inform educational practice not only for those subjects, but for water science generally. This paper reports the results of surveys and interviews with hydroinformatics and water data science instructors. We address the effectiveness of instructional tools, impacts of the pandemic on education, important hydroinformatics topics, and challenges and gaps in hydroinformatics education. Guided by lessons learned from the surveys and interviews and a review of existing online learning platforms, we developed four educational modules designed to address shared topics of interest and to demonstrate the effectiveness of available tools to help overcome identified challenges. The modules are community resources that can be incorporated into courses and modified to address specific class and institutional needs or different geographic locations. Our experience with module implementation can inform development of online educational resources, which will advance and enhance instruction for hydroinformatics and broader hydrologic sciences for which students increasingly need informatics experience and technical skills.

The National Water Model (NWM), a configuration of the Weather Research and Forecasting Hydrological model, operates as the United States' hydrological model. The NWM predicts streamflow at more than 2.7 million river reaches; and is a subject of growing attention in the hydrological modeling community. Large‐scale computationally distributed models such as the NWM, often require technical knowledge of, and access to, cluster‐based computing environments for model compilation and simulation. User‐friendly tools capable of setting up and running such models to adjust and explore their parameter space generally do not exist. Here we present the Dockerized Job Scheduler (DJS) a Python library that takes a service approach to modeling. The library is capable of (1) generating varied parameter sets and (2) orchestrating concurrent NWM simulations via Docker. DJS is designed to automate the deployment of varied parameter simulations and lower the model usage entrance barrier. In this paper, we use a case study to demonstrate its installation and usage. [ABSTRACT FROM AUTHOR]

This research investigated relationships between the most notable characteristics of end-use events, namely, event duration, volume, and intensity, in order to categorize water use as being indoor or outdoor. Three classification models were developed, calibrated, and compared using more than 200,000 household end-use events that were recorded independently in Australia and South Africa. The three methods were also compared to a practice-based limit classification scheme. The classification model presented in this paper correctly apportions ∼81% of the indoor end-use event volumes and ∼98% of the outdoor end-use event volumes, thus reinforcing the value of basic smart water meter data sets as a source of useful information for water demand management. [ABSTRACT FROM AUTHOR]

Drought monitoring and prediction have important roles in various aspects of hydrological studies. In the current research, the standardized precipitation index (SPI) was monitored and predicted in Peru between 1990 and 2015. The current study proposed a hybrid model, called ANN-FA, for SPI prediction in various time scales (SPI3, SPI6, SPI18, and SPI24). A state-of-the-art firefly algorithm (FA) has been documented as a powerful tool to support hydrological modeling issues. The ANN-FA uses an artificial neural network (ANN) which is coupled with FA for Lima SPI prediction via other stations. Through the intelligent utilization of SPI series from neighbors’ stations as model inputs, the suggested approach might be used to forecast SPI at various time scales in a meteorological station with insufficient data. To conduct this, the SPI3, SPI6, SPI18, and SPI24 were modeled in Lima meteorological station using other meteorological stations’ datasets in Peru. Various error criteria were employed to investigate the performance of the ANN-FA model. Results showed that the ANN-FA is an effective and promising approach for drought prediction and also a multi-station strategy is an effective strategy for SPI prediction in the meteorological station with a lack of data. The results of the current study showed that the ANN-FA approach can help to predict drought with the mean absolute error = 0.22, root mean square error = 0.29, the Pearson correlation coefficient = 0.94, and index of agreement = 0.97 at the testing phase of best estimation (SPI3).

Stormwater detention ponds are essential stormwater management solutions that regulate the urban catchment discharge towards streams. Their purposes are to reduce the hydraulic load to avoid stream erosion, as well as to minimize the degradation of the natural waterbody by direct discharge of pollutants. Currently, static controllers are widely implemented for detention pond outflow regulation in engineering practice, i.e., the outflow discharge is capped at a fixed value. Such a passive discharge setting fails to exploit the full potential of the overall water system, hence further improvements are needed. We apply formal methods to synthesize (i.e., derive automatically) optimal active controllers. We model the stormwater detention pond, including the urban catchment area and the rain forecasts with its uncertainty, as hybrid Markov decision processes. Subsequently, we use the tool Uppaal Stratego to synthesize using Q-learning a control strategy maximizing the retention time for pollutant sedimentation (optimality) while also minimizing the duration of emergency overflow in the detention pond (safety). These strategies are synthesized for both an off-line and on-line settings. Simulation results for an existing pond show that Uppaal Stratego can learn optimal strategies that significantly reduce emergency overflows. For off-line controllers, a scenario with low rain periods shows a 26% improvement of pollutant sedimentation with respect to static control, and a scenario with high rain periods shows a reduction of overflow probability of 10%–19% for static control to lower than 5%, while pollutant sedimentation has only declined by 7% compared to static-control. For on-line controllers, one scenario with heavy rain shows a 95% overflow duration reduction and a 29% pollutant sedimentation improvement compared to static control. [ABSTRACT FROM AUTHOR]

The agricultural sector is vulnerable to extreme phenomena such as droughts, particularly in arid and semi-arid environments and in regions where water infrastructure is limited. Devising preparedness plans, including means for efficient monitoring and timely identification of drought events, is essential for informed decision making on drought mitigation and water management, especially for the water-dependant agricultural sector. This paper presents the incorporation of two new drought indices, designed for agricultural drought identification, in Drought Indices Calculator (DrinC) software. These indices, namely the Agricultural Standardized Precipitation Index (aSPI) and the Effective Reconnaissance Drought Index (eRDI), require commonly available meteorological data, while they employ the concept of effective precipitation, taking into account the amount of water that contributes productively to plant development. The design principles of DrinC software leading to the proper use of the indices for agricultural drought assessment, including the selection of appropriate reference periods, calculation time steps and other related issues, are presented and discussed. The incorporation of aSPI and eRDI in DrinC enhances the applicability of the software towards timely agricultural drought characterisation and analysis, through a straightforward and comprehensible approach, particularly useful for operational purposes.

Rainfall nowcasting is critical for timely weather predictions and emergency responses, particularly in flood-prone areas. Existing models, while accurate, often require substantial computational resources. Addressing this challenge, our study introduces EfficientRainNet, a neural network that leverages mobile inverted residual linear bottleneck blocks for memory-efficient rainfall nowcasting. Our evaluation, conducted over the State of Iowa, demonstrates that EfficientRainNet achieves accuracy comparable to that of the widely adopted encoder-decoder convolutional GRUs, yet with a substantially reduced model complexity, possessing less than 6% of the trainable parameters of the encoder-decoder convolutional GRUs and less than 9% of those of the compared Small Attention UNet. This lightweight design opens the possibility for deployment on edge devices, offering a scalable and accessible solution for real-time rainfall prediction. The results suggest further potential for extending the application of EfficientRainNet across broader regions and varied climatic conditions, harnessing its computational efficiency for widespread climate monitoring and forecasting. • This study presents a resilient memory-wise efficient neural network architecture for rainfall nowcasting. • The neural network architecture this study presents was tested over a large basin in Iowa. • The study compared the proposed approach to state-of-the-art neural network approaches in the rainfall nowcasting literature. [ABSTRACT FROM AUTHOR]

We present HydroCompute, a high-performance client-side computational library specifically designed for web-based hydrological and environmental science applications. Leveraging state-of-the-art technologies in web-based scientific computing, the library facilitates both sequential and parallel simulations, optimizing computational efficiency. Employing multithreading via web workers, HydroCompute enables the porting and utilization of various engines, including WebGPU, Web Assembly, and native JavaScript code. Furthermore, the library supports local data transfers through peer-to-peer communication using WebRTC. The flexible architecture and open-source nature of HydroCompute provide effective data management and decision-making capabilities, allowing users to integrate their own code into the framework. To demonstrate the capabilities of the library, we conducted two case studies: a benchmarking study assessing the performance of different engines and a real-time data processing and analysis application for the state of Iowa. The results exemplify HydroCompute's potential to enhance computational efficiency and contribute to the interoperability and advancement of hydrological and environmental sciences. • HydroCompute is a web-based high-performance library designed specifically for hydrology and environmental sciences. • Developed to leverage local multithreading in both CPU and GPU, resulting in significantly performance improvements. • The library enables computational efficiency in both sequential and parallel simulations, catering to diverse modeling needs. • Using technologies such as Web Workers, WebAssembly, WebGPU, and WebRTC, the library facilitates efficient data manipulation. • Through the developed case studies, the library demonstrates its relevance and applicability in the field of hydrology. [ABSTRACT FROM AUTHOR]

Tethys Platform is an open source framework for developing web-based applications for Earth Observation data. Our experience shows that Tethys significantly lowers the barrier for cloud-based app development, simplifies the process of accessing scalable distributed cloud computing resources and leverages additional software for data and computationally intensive modeling. The Tethys software development kit allows users to create web apps for visualizing, analyzing, and modeling Earth Observation data. Tethys platform provides a collaborative environment for scientists to develop and deploy several Earth Observation web applications across multiple Tethys portals. We work in partnership with leading regional organizations world-wide to help developing countries use information provided by earth-observing satellites and geospatial technologies for managing climate risks and land use. This paper highlights the several Tethys portals and web applications that were developed as part of this effort. Implementation of the Tethys framework has significantly improved the Application Readiness Level metric for several NASA projects and the potential impact of Tethys to replicate and scale other applied science programs.

Hydrologic modeling can be used to aid in decision-making at the local scale. Developed countries usually have their own hydrologic models; however, developing countries often have limited hydrologic modeling capabilities due to factors such as the maintenance, computational costs, and technical capacity needed to run models. A global streamflow prediction system (GSPS) would help decrease vulnerabilities in developing countries and fill gaps in areas where no local models exist by providing extensive results that can be filtered for specific locations. However, large-scale forecasting systems come with their own challenges. These New hydroinformatic challenges can prevent these models from reaching their full potential of becoming useful in the decision making process. This article discusses these challenges along with the background leading to the development of a large-scale streamflow prediction system. In addition, we present a large-scale streamflow prediction system developed using the GloFAS-RAPID model. The developed model covers Africa, North America, South America, and South Asia. The results from this model are made available using a Hydrologic Modeling as a Service approach (HMaaS) as an answer to some of the discussed challenges. In contrast to the traditional modeling approach, which makes results available only to those with the resources necessary to run hydrologic models, the HMaaS approach makes results available using web services that can be accessed by anyone with an internet connection. Web applications and services for providing improved data accessibility, and addressing the discussed hydroinformamtic challenges are also presented. The HydroViewer app, a custom application to display model results and facilitate data consumption and integration at the local level is presented. We also conducted validation tests to ensure that model results are acceptable. Some of the countries where the presented services and applications have been tested include Argentina, Bangladesh, Colombia, Peru, Nepal, and the Dominican Republic. Overall, a HMaaS approach to operationalize a GSPS and provide meaningful and easily accessible results at the local level is provided with the potential to allow decision makers to focus on solving some of the most pressing water-related issues we face as a society.

The description of complex river environments requires interdisciplinary approaches to collect and manage manifold data types and sources. Deriving comprehensive knowledge from complex data sources is challenging and necessitates not only knowledge of environmental science but also statistics and Software engineering. This study introduces a relational database framed in an application called River Analyst for creating and managing river data with open-source standards (Python3 and Django). We conceptualize data models of river environments, which describe sediment characteristics and hydraulics related to hyporheic exchange. River Analyst enabled us to derive novel insights for restoring rivers affected by so-called riverbed clogging, notably, fine sediment infiltration in the hyporheic zone. The database analysis reveals that clogging is not a dominant control process when the fraction of fine sediment exceeds 50%–55%. In conclusion, the new Software holds promise for data-informed advancements in augmenting knowledge to restore ecologically functional hydro-environments. • A database app in Django enables scalable, centralized management of fluvial data. • Database scheme enhances data analysis through linked parametrical data. • Open-source framework can leverage data-driven decisions for river restoration. [ABSTRACT FROM AUTHOR]

Recent decades have witnessed a massive increase in the volume and quality of hydrologic data available to aid water resources decision makers, managers, and scientists. This has been accompanied by exponential growth in both desktop and cloud computing, as well as data storage capabilities. As a result, there are abundant opportunities to drastically change how water data is collected, managed, disseminated, and analyzed – which should ultimately have significant positive impacts on water science, engineering, and management. We are at the cusp of a new era in water data science which brings with it many exciting technological and scientific challenges and opportunities. Many of these challenges are alleviated and opportunities are multiplied when hydrology is viewed as a "team sport" rather than as an individual activity. These factors formed the motivation for the development of the HydroShare open-source software and the hydroshare.org operational system. This retrospective paper reviews a decade of HydroShare development and operation by presenting the general architecture, functionality, key contributions of the project to earth science and cyberinfrastructure research, current usage metrics, and future directions. • We present a review of the design and development of HydroShare for publishing water data and models. • The design and current functionality of this open source and open access software system is presented. • We review specific scientific and technical contributions of the project in both information science and modeling. • Current usage statistics and future directions of the system are discussed. [ABSTRACT FROM AUTHOR]