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The Hydrologic Engineering Center-Hydrologic Modeling System (HEC-HMS) model can be applied to simulate hydrological processes. This model has been widely used in recent days because it is openly accessible to the public. The search result from this study identifies 180 cases discussing the model applications in Indonesian watersheds in peer-reviewed journal articles and conference papers. The model has been used in Indonesia since 2005, but the highest application is in 2020 with 42 articles. In the context of application distribution, Java Island ranks first place with 123 scientific publications. However, the HEC-HMS model is predominantly used to simulate the "Small" and "Very Small" watershed size categories. The availability of insufficient field data is huge obstacle for hydrological modeling in Indonesia. Furthermore, the model may be extended to address the issues in Indonesia's eastern watersheds. Moreover, the model also can be used to assess the impact of climate change on streamflow. [ABSTRACT FROM AUTHOR]

Losses from catastrophic floods are driving intense efforts to increase preparedness and improve response to disastrous flood events by providing early warnings. Yet accurate flood forecasting remains a challenge due to uncertainty in modeling, calibrating, and validating a useful early warning system. This paper presents the Requisitely Simple (ReqSim) flood forecasting system that includes key variables and processes of basin hydrology and atmospheric forcing in a data-driven modeling framework. The simplicity of the modeling structure and data requirements of the system allows for customization and implementation in any medium to large rain-fed river basin globally, provided there are water level or discharge measurements at the forecast locations. The proposed system's efficacy is demonstrated in this paper through providing useful forecasts for various river basins around the world. This include 3–10-day forecasts for the Ganges and Brahmaputra rivers in South Asia, 2–3-day forecast for the Amur and Yangtze rivers in East Asia, 5–10-day forecasts for the Niger, Congo and Zambezi rivers in West and Central Africa, 6–8-day forecasts for the Danube River in Europe, 2–5-day forecasts for the Parana River in South America, and 2–7-day forecasts for the Mississippi, Missouri, Ohio, and Arkansas rivers in the USA. The study also quantifies the effect of basin size, topography, hydrometeorology, and river flow controls on forecast accuracy and lead times. Results indicate that ReqSim's forecasts perform better in river systems with moderate slopes, high flow persistence, and less flow controls. The simple structure, minimal data requirements, ease of operation, and useful operational accuracy make ReqSim an attractive option for effective real-time flood forecasting in medium and large river basins worldwide. [ABSTRACT FROM AUTHOR]

The rivers of High Mountain Asia provide freshwater to around 2 billion people. However, precipitation, the main driver of river flow, is still poorly understood due to limited direct measurements in this area. Existing tools to interpolate these measurements or downscale and bias-correct precipitation models have several limitations. To overcome these challenges, this paper uses a probabilistic machine learning approach called Multi-Fidelity Gaussian Processes (MFGPs) to downscale ERA5 climate reanalysis. The method is first validated by downscaling ERA5 precipitation data over data-rich Europe and then data-sparse Upper Beas and Sutlej River Basins in the Himalayas. We find that MFGPs are simpler to implement and more applicable to smaller datasets than other state-of-the-art machine learning models. MFGPs are also able to quantify and narrow the uncertainty associated with the precipitation estimates, which is especially needed over ungauged areas, and can be used to estimate the likelihood of extreme events that lead to floods or droughts. Over the Upper Beas and Sutlej River Basins, the precipitation estimates from the MFGP model are similar to or more accurate than available gridded precipitation products (APHRODITE, TRMM, CRU, bias-corrected WRF). The MFGP model and APHRODITE annual mean precipitation estimates generally agree with each other for this region. The MFGP model predicting slightly higher average precipitation and variance. However, more significant spatial deviations between the MFGP model and APHRODITE over this region appear during the summer monsoon. The MFGP model also presents a more effective spatial resolution of precipitation, generating more structure at finer scales than ERA5 and APHRODITE. MFGP precipitation estimates for the Upper Beas and Sutlej Basins between 1980 and 2013 at a 0.0625° resolution (approx. 9 km) are jointly published with this paper. [ABSTRACT FROM AUTHOR]

Small intermountain river basins are most suitable for developing new methods to estimate water balance due to their well-defined catchment boundaries, relatively rapid runoff processes, and accessible landscapes for study. In general terms, dissecting the hydrograph of a small mountain river requires calibration of the flow model against multi-year data sets, including (a) glacier mass balance and snow water content, (b) radiation balance calculation, (c) estimation of the groundwater contribution, and (d) water discharge measurements. The minimum primary data set is limited to the precipitation and temperature distributions at the catchment. This approach postulates that the conditions for the formation of all components of river flow are known in advance. It is reduced to calculating the dynamic balance between precipitation (input part) and runoff, ablation, and evaporation (output part). In practice, accurately accounting for the inflow and outflow components of the balance, as well as the impact of regulating reservoirs, can be a challenging task that requires significant effort and expense, even for the extensively researched catchments. Our studies indicate the potential benefits of an approach based on one-time, but detailed, observations of stable isotope composition, temperature, and water chemistry, in addition to standard datasets. This paper presents the results of the 2022–2023 work conducted in the basin of the small mountain river Ala-Archa, located on the northern slope of the Kyrgyz Range in Tien-Shan, which was chosen as an example due to its well-studied nature. Our approach could identify previously unknown factors of flow formation and assess the time and effectiveness of work in similar conditions. [ABSTRACT FROM AUTHOR]

In our original paper (Nat Hazards 76:327-346, . doi:), we analyzed hydrological droughts based on the minimum values of the streamflow drought index at different time scales using L-moments. In this paper, we highlight the importance of the magnitude and type of selected drought index for regional frequency analysis and discuss the validity of results only for station clusters considered in response to the commentary by Bahmani and Khorsandi (Nat Hazards, . doi:). [ABSTRACT FROM AUTHOR]

A series of numerical experiments were conducted to test the connection between streamflow hydrographs at the outlet of large watersheds and the time series of hillslope-scale runoff yield. We used a distributed hydrological routing model that discretizes a large watershed (∼ 17 000 km 2) into small hillslope units (∼ 0.1 km 2) and applied distinct surface runoff time series to each unit that deliver the same volume of water into the river network. The numerical simulations show that distinct runoff delivery time series at the hillslope scale result in indistinguishable streamflow hydrographs at large scales. This limitation is imposed by space-time averaging of input flows into the river network that are draining the landscape. The results of the simulations presented in this paper show that, under very general conditions of streamflow routing (i.e., nonlinear variable velocities in space and time), the streamflow hydrographs at the outlet of basins with Horton–Strahler (H–S) order 5 or above (larger than 100 km 2 in our setup) contain very little information about the temporal variability of runoff production at the hillslope scale and therefore the processes from which they originate. In addition, our results indicate that the rate of convergence to a common hydrograph shape at larger scales (above H–S order 5) is directly proportional to how different the input signals are to each other at the hillslope scale. We conclude that the ability of a hydrological model to replicate outlet hydrographs does not imply that a correct and meaningful description of small-scale rainfall–runoff processes has been provided. Furthermore, our results provide context for other studies that demonstrate how the physics of runoff generation cannot be inferred from output signals in commonly used hydrological models. [ABSTRACT FROM AUTHOR]

The hydrological assessment of the Vistula River basin in the near future will be a key element in the development of strategies to adapt agriculture to climate change. The Vistula River basin covers 61% of Poland's area (190,062 km2) and is mainly used for agricultural production. The aim of this study is to assess the water balance of the Vistula River basin from the perspective of 2050 based on the analysis of two climate scenarios, RCP 4.5 and RCP 8.5, and the three climate models ICHEC-EC-EARTH_KNMI-RACMO22E (A), ICHEC-EC-EARTH_DMI-HIRHAM5 (B), and ICHEC-EC-EARTH_SMHI-RCA4 (C). This paper presents the steps in the development of the SWAT model and the results of the hydrological analysis of the Vistula catchment. Calibration and validation of the model were carried out using the SUFI-2 algorithm in the SWAT-CUP programme for 2013–2018. The data used to calibrate the SWAT model are monthly flow measurements [m3/s] from the measurement station in Tczew, located near the estuary of the Vistula basin to the Baltic Sea. The summary result of the work is the results of modelling the flow of the Vistula River catchment for different climate scenarios in the 2020–2050 perspective. The average annual precipitation for all projections in 2021–2030, 2031–2040, and 2041–2050 will be higher by up to 22% (763 mm) (RCP 8.5.C for 2041–2050) compared to the 2013–2018 simulation years (624 mm). The average annual temperature for most climate projections for 2021–2030 will fall to as low as 8.7 °C (RCP 4.5.B) compared to the 2013–2018 simulation period (9.2 °C). In contrast, for all projections in 2031–2040 and 2041–2050, the average annual temperature will increase to as much as 10.3 °C (RCP 8.5.C). The simulation results for the climate projections (2020–2050) indicate that there are no clear trends of change in the water management of the Vistula River basin for the coming decades. According to scenarios RCP 4.5.A, RCP 8.5.A, and RCP 8.5.B, the annual sums of potential evapotranspiration show a slight downward trend. On the other hand, for the RCP 8.5.C and RCP 4.5.C projections and the climate change scenario RCP 4.5.B, the results obtained show a slight upward trend in the annual sum of potential evapotranspiration. For the overall evapotranspiration and potential evapotranspiration assessment for all climate projections analysed, the annual evapotranspiration total shows a clear increase compared to the 2013–2018 baseline period. The average annual actual evapotranspiration for all projections in 2021–2030, 2031–2040, and 2041–2050 will increase up to 467 mm (RCP 4.5.A—2021–2030) compared to the 2013–2018 simulation period of 401 mm. The average annual potential evapotranspiration for all projections in 2021–2030, 2031–2040, and 2041–2050 will increase up to 755 mm (RCP 8.5.C—2031–2040) compared to the 2013–2018 simulation period—616 mm. The analysis of the total runoff in all climate models for the RCP 4.5 scenario shows that the annual average total runoff tends to decrease. The results of the simulations carried out for the RCP 8.5 scenario, which are generally characterised by an increase in total runoff in subsequent years, are different. When analysing annual total runoff on a regional basis, it appears that for most of the climate projections analysed (except for the RCP 8.5.A scenario), annual runoff will be lower, especially in the lowlands in the central part of the Vistula basin. In regions where the increase in precipitation is greatest in the north-western and southern basins, higher total runoff should be expected. The analysis of the total runoff in all climate models for the RCP 4.5 scenario shows that the annual average total runoff tends to decrease. The results of the simulations carried out for the RCP 8.5 scenario, which are generally characterised by an increase in total runoff in subsequent years, are different. When analysing annual total runoff on a regional basis, it appears that for most of the climate projections analysed (except for the RCP 8.5.A scenario), annual runoff will be lower, especially in the lowlands in the central part of the Vistula basin. In regions where the increase in precipitation is greatest in the north-western and southern basins, higher total runoff should be expected. [ABSTRACT FROM AUTHOR]

From the Rocky Mountains in the US to the Sea of Cortez in Mexico, the Colorado River is one of the most managed and monitored rivers in the world. As decreasing river flows from drought and climate change continue to impact the Colorado River Basin, the US federal government, seven US states, and Mexico have signed agreements that would reduce deliveries, including a Drought Contingency Plan (DCP), an agreement among the US states and the Bureau of Reclamation, and Minute 323 (a treaty agreement between the US and Mexico). As part of the DCP, the US Bureau of Reclamation (Reclamation) committed to increasing conservation efforts to augment storage in Lake Mead. This paper presents an analysis of Reclamation's Decree Accounting Reports that record water deliveries and conservation savings from 1964 to 2019. Overall, this analysis illustrates that (1) the agency has implemented significant conservation actions over the last three decades, (2) the agency has responded to increased accounting needs as dictated by national and international management agreements, (3) some actions have resulted in environmental costs, and (4) future actions could be designed to avoid environmental harms while also augmenting Lake Mead levels. As climate change continues to constrain Colorado River water supply, detailed accounting may help reveal areas for potential efficiencies or demonstrate where the greatest levels of savings have been reached while ensuring that environmental and social benefits are preserved. Over the last 100 years, humans have changed the path and personality of the Colorado River by distributing water to cities, communities, and agricultural fields to support over 40 million people. Throughout the twentieth century, the US federal government, seven US, and Mexico signed agreements enabling development of Colorado River water. By the early 21st century, as it became evident that water demands and climate change were reducing available supplies, these same governments signed agreements to reduce pressure on the river. All these agreements have, in part, been tracked by the US federal agency that manages the river, the Bureau of Reclamation (Reclamation), through Decree Accounting Reports. Since 1964, these reports have recorded how water is or is not accounted for that might be important to the future of the river. This paper examines those reports and reveals four main findings: (1) Reclamation has implemented numerous water conservation projects, (2) they have responded to new management agreements, (3) some of their actions have impacted the environment, and (4) there are proactive ways they can reduce environmental impacts and bolster water supplies in the future. [ABSTRACT FROM AUTHOR]

In recent years, a new discipline known as Explainable Artificial Intelligence (XAI) has emerged, which has followed the growing trend experienced by Artificial Intelligence over the last decades. There are, however, important gaps in the adoption of XAI in hydrology research, in terms of application studies in the southern hemisphere, or in studies associated with snowmelt-driven streamflow prediction in arid regions, to mention a few. This paper seeks to contribute to filling these knowledge gaps through the application of XAI techniques in snowmelt-driven streamflow prediction in a basin located in the arid region of north-central Chile in South America. For this, two prediction models were built using the Random Forest algorithm, for one and four months in advance. The models show good prediction performance in the training set for one (RMSE:1.33, R2: 0.94, MAE:0.55) and four (RMSE: 5.67, R2:0.94, MAE: 1.51) months in advance. The selected interpretation techniques (importance of the variable, partial dependence plot, accumulated local effects plot, Shapley values and local interpretable model-agnostic explanations) show that hydrometeorological variables in the vicinity of the basin are more important than climate variables and this occurs both for the dataset level and for the months with the lowest streamflow records. The importance of the XAI approach adopted in this study is discussed in terms of its contribution to the understanding of hydrological processes, as well as its role in high-stakes decision-making. [ABSTRACT FROM AUTHOR]

Estimating peak flow for a catchment is commonly undertaken using the design event method; however, this method does not allow for the understanding of uncertainty in the result. This research first presents a simplified method of fragments approach to rainfall disaggregation that ignores the need to consider seasonality, offering a greater diversity in storm patterns within the resulting sub-daily rainfall. By simulating 20 iterations of the disaggregated sub-daily rainfall within a calibrated continuous simulation hydrologic model, we were able to produce multiple long series of streamflow at the outlet of the catchment. With these data, we investigated the use of both the annual maximum and peaks over threshold approaches to flood frequency analysis and found that for a 1-in-100-year annual exceedance probability peak flow, the peaks over threshold method (333 m3/s ± 50 m3/s) was significantly less uncertain than the annual maximum method (427 m3/s ± 100 m3/s). For the 1-in-100-year annual exceedance probability, the median peak flow from the peaks over threshold method (333 m3/s) produced an outcome comparable to the design event method peak flow (328 m3/s), indicating that this research offers an alternative approach to estimating peak flow, with the additional benefit of understanding the uncertainty in the estimation. Finally, this paper highlighted the impact that length and period of streamflow has on peak flow estimation and noted that previous assumptions around the minimum length of gauged streamflow required for flood frequency analysis may not be appropriate in particular catchments. [ABSTRACT FROM AUTHOR]

With the booming big data techniques, large-sample hydrological analysis on streamflow regime is becoming feasible, which could derive robust conclusions on hydrological processes from a big-picture perspective. However, there is a lack of a comprehensive global large-sample dataset for components of the streamflow regime yet. This paper presents a new time series dataset on global streamflow indices calculated from daily streamflow records after data quality control. The dataset contains 79 indices over seven major components of streamflow regime (i.e., magnitude, frequency, duration, changing rate, timing, variability, and recession) of 41 263 river reaches globally on yearly and multiyear scales. Streamflow indices values until 2022 are covered in the dataset. Time span of the time series dataset is from 1806 to 2022 with an average length of 36 years. Compared to existing global datasets, this global dataset covers more stations and more indices, especially those characterizing the frequency, duration, changing rate, and recession of streamflow regime. With the dataset, research on streamflow regime will become easier without spending time handling raw streamflow records. This comprehensive dataset will be a valuable resource to the hydrology community to facilitate a wide range of studies, such as studies of hydrological behaviour of a catchment, streamflow regime prediction in data-scarce regions, as well as variations in streamflow regime from a global perspective. The dataset can be accessed at 10.57760/sciencedb.07227 (Chen et al., 2023a). [ABSTRACT FROM AUTHOR]

Spatio-temporal models are widely used in many research areas from ecology to epidemiology. However, a limited number of computational tools are available for modeling river network datasets in space and time. In this paper, we introduce the R package SSNbayes for fitting Bayesian spatio-temporal models and making predictions on branching stream networks. SSNbayes provides a linear regression framework with multiple options for incorporating spatial and temporal autocorrelation. Spatial dependence is captured using stream distance and flow connectivity while temporal autocorrelation is modelled using vector autoregression approaches. SSNbayes provides the functionality to make predictions across the whole network, compute exceedance probabilities, and other probabilistic estimates, such as the proportion of suitable habitat. We illustrate the functionality of the package using a stream temperature dataset collected in the Clearwater River Basin, USA. [ABSTRACT FROM AUTHOR]

The pressure on freshwater resources is leading to diminishing flows in some of the critical river systems across the globe. India is no exception, and this is mainly because of water withdrawal for irrigation, which is often to the tune of 70% to 80% of the lean season flows, with some proportion for domestic and industrial use. While graduating from the concept of environmental flows and its assessment methodologies in India, the water-managers, the researchers and the conservationists are now moving towards answering the next question, if the rivers are to be revived, where will the water come from, especially in the case of over-allocated rivers, including the River Ganga. While the logical way is to look at the biggest user of water, i.e., irrigation, it remains to be seen whether the irrigation water savings will actually lead to enhancing flows in a river, complementing the efforts towards maintaining e-flows in rivers, or whether it will lead to more area under agriculture, bring changes in cropping patterns towards more water-intensive crops or result in something else. This is a growing debate across the globe, where India is no exception, and there has been a wide range of opinions in this regard. This paper discusses the process, findings and lessons from a joint initiative involving farmers, the Uttar Pradesh state Irrigation and Water Resources Department, Bijnor District Administration and a conservation organisation, WWF, to enhance flows in a sub-tributary, called the Karula River, which is part of the Ganga River system. Another objective of this paper is to look at the scalability and replicability of similar approaches in other irrigation command areas to benefit nearby river systems in general. Under this initiative, the team attempted to enhance flows in the river Karula by routing the saved water from irrigation supplies in a canal commanded area. This saving of water is being achieved due to supply-side and demand-side measures that are being adopted in the project area. With the objective of ensuring the sustainability of the initiative, efforts are made to form an institutional arrangement, through which this initiative can be sustained beyond the project support. [ABSTRACT FROM AUTHOR]

Streamflow in semiarid areas, especially in North China, was rapidly decreasing, which made it important to analyze the characteristics and influencing factors of streamflow. Using the hydro-meteorological data series of 1961–2017 in the upper Yongding River Basin (UYRB) (including the Yang River Basin (YRB) and Sanggan River Basin (SRB)), spatio-temporal variation characteristics of air temperature, precipitation, and potential evapotranspiration (E0) were analyzed. The results showed that precipitation has no significant trend; the temperature showed a significant increase of 0.1–0.5 °C per decade; E0 showed a significant decrease of approximately −2 mm/10yr (in 18 stations); the estimated rates of streamflow change were −7 and −8 mm/10yr for SRB and YRB. As for spatial distribution, the YRB presented a higher E0 value than the SRB; the mountain areas had more precipitation than the plain areas. The change points of streamflow occurred in 1982 and 2003. Both the Budyko and the DMC methods were used to evaluate the impacts of climate change and human activities on the mean annual streamflow. In variation stage I (1983~2003), impacts of human activities account for 90.6% and 62.7% of the mean annual streamflow changes in YRB and SRB, respectively. In variation stage II (2004~2017), the percentages were 99.5% and 93.5%, respectively. It is also noted that the first change point in streamflow was indeed at the beginning of China's land reform, when the farmers could manage their reallocated lands and, therefore, there was an increase in agricultural water consumption. The second change point coincided with "Capital Water Resources Planning", including water conservation projects and irrigation district construction programs. In general, human activities were mainly responsible for the significant decline in the annual streamflow of UYRB. This paper will provide valuable results for water resources planning and give guidance on the construction of water conservation function areas and ecological environment support areas in the capital. [ABSTRACT FROM AUTHOR]

Calibration and regionalization are two key tasks in hydrological modelling, and the availability of increased data and computing resources has enhanced our ability to complete these tasks. This paper addresses both tasks, i.e. (i) it proposes novel model structure-based calibration strategies with the goal to improve estimation of high streamflows, and (ii) it explores the association of soil hydraulic properties with optimal parameters to estimate streamflows in ungauged catchments. Both tasks are demonstrated by employing a conceptual hydrological model, Sacramento Soil Moisture Accounting (SAC-SMA), on multiple catchments in the Narmada River Basin. The initial calibrated model exhibited good model performance, with high flows being underestimated. Seven calibration strategies via revising parameter space are explored, and improvement in high flow performance is observed at nine out of 12 catchments. Estimated streamflows based on soil hydraulic parameters demonstrate regionalization potential. The results indicate successful application of the proposed methods which are transferable to other basins. [ABSTRACT FROM AUTHOR]

In this paper, we analyse the uncertainty and parameter sensitivity of a conceptual water quality model, based on a travel time distribution (TTD) approach, simulating electrical conductivity (EC) in the Duck River, Northwest Tasmania, Australia for a 2‐year period. Dynamic TTDs of stream water were estimated using the StorAge Selection (SAS) approach, which was coupled with two alternate methods to model stream water EC: (1) a solute‐balance approach and (2) a water age‐based approach. Uncertainty analysis using the Differential Evaluation Adoptive Metropolis (DREAM) algorithm showed that: 1. parameter uncertainty was a small contribution to the overall uncertainty; 2. most uncertainty was related to input data uncertainty and model structure; 3. slightly lower total error was obtained in the water age‐based model than the solute‐balance model; 4. using time‐variant SAS functions reduced the model uncertainty markedly, which likely reflects the effect of dynamic hydrological conditions over the year affecting the relative importance of different flow pathways over time. Model parameter sensitivity analysis using the Variogram Analysis of Response Surfaces (VARS‐TOOL) framework found that parameters directly related to the EC concentration were most sensitive. In the solute‐balance model, the rainfall concentration Crain and in the age‐based model, the parameter controlling the rate of change of EC with age (λ) were the most sensitive parameter. Model parameters controlling the age mixes of both evapotranspiration and streamflow water fluxes (i.e., the SAS function parameters) were influential for the solute‐balance model. Little change in parameter sensitivity over time was found for the age‐based concentration relationship; however, the parameter sensitivity was quite dynamic over time for the solute‐balance approach. The overarching outcomes provide water quality modellers, engineers and managers greater insight into catchment functioning and its dependence on hydrological conditions. [ABSTRACT FROM AUTHOR]

Partitioning methods such as cluster analysis are advantageous in pooling catchments into hydrometric similar regions. They help overcome data shortage in ungauged catchments, which is a common problem in Sud Mediterranean zones. Without accurate forecasts, it is difficult to assess and manage water resources efficiently this situation won't be of any assistance to hydrology decision-makers. This paper illustrates a Tunisian application case, that aims to pool catchments with a hierarchical clustering algorithm (HCA) based on distances calculated in multidimensional physiographical and hydrometric space. The homogeneity of generated clusters is checked by the silhouette index. Then the distances efficiencies are compared. Nineteen semi-arid Tunisian catchments monitored since 1992 are studied. Twelve physiographical attributes, nine rainfall and streamflow signatures are considered in the HCA with two clusters. Correlation distance provides the most homogeneous clusters. Statistically the: percentage of area affected by anti-erosive practices, percentage of forest cover and catchment area are the most discriminating attributes. However, hydrometrical signatures appear to be irrelevant. These partitions highlight two different hydrological behaviors that must support forecasting. Results are promising in the Sud-Mediterranean case, where the shortage of hydrometrical data is an ongoing problem. They have the advantage of enabling hydrologic forecasting without requiring heavy information. [ABSTRACT FROM AUTHOR]

Rapid population growth, industrialization, and agricultural activities have impacted water resources in the arid and semi-arid areas of Somalia. The Lower Juba region in Somalia has been the most affected region. Therefore, an analysis of the hydrological patterns is essential. This paper assesses streamflow and evapotranspiration in the Wabiga Juba basin in Somalia using a hydrological simulation model, namely, the water evaluation and planning (WEAP) system via the soil moisture method. The datasets included 53 (average precipitation) and 13 (streamflow) year periods from two meteorological stations. The estimated values for potential evapotranspiration (11,921.98 to 20,775.39 MCM) were higher than the actual evapotranspiration (4904.10 to 8242.72 MCM) by 50 to 79.5%, respectively. The annual streamflow in Juba Dolow and runoff proportion of the Wabiga Juba River was estimated to be 10% of the annual precipitation. Most of the surface runoff occurred in April (47%), May (31%), October (5%), and November (14%). The streamflow variation responded to the pattern of precipitation. The model performance achieved a Nash–Sutcliffe model efficiency (NSE) coefficient of 0.71, coefficient of determination (R2) of 0.91, and percent bias (PBIAS) of 14%. The WEAP model of the Wabiga Juba basin is a baseline study for water resource management in Somalia to mitigate water shortage impacts due to limited water resources. [ABSTRACT FROM AUTHOR]

Groundwater exploitations in semi‐arid regions result in significant declines of groundwater levels and streamflow and even trigger seasonal streamflow drying up, which may bring adverse impacts on riparian ecosystems. It is critical to explore future streamflow changes caused by variable climate scenarios and groundwater exploitation intensities; however, a comprehensive assessment of the streamflow drying up frequency remains lacking. Furthermore, the thresholds of controlling factors beyond which streamflow drying up occurs have rarely been explored. Here we develop an integrated surface water‐groundwater model for the Taoer River Basin (TRB), a typical semi‐arid watershed in northeast China. We apply the model to multiple future scenarios to reveal the frequency of potential streamflow drying up and explore the thresholds of controlling factors of streamflow drying up based on a decision tree method. We find that the groundwater level in the lower region of the TRB (LTRB) is hard to recover to the riverbed elevation and the streamflow drying up frequency is still high unless the groundwater exploitation is reduced by 50%. However, this leads to a competition between the ecological and economic objectives. Our results also show that the streamflow drying up frequency in the LTRB is very sensitive to the upstream water inflow that can be regulated through the reservoir operation or inter‐basin water diversion, indicating the importance of a basin scale strategic management. The findings of this paper can help policy makers sustainably exploit water resources in semi‐arid regions. [ABSTRACT FROM AUTHOR]

This paper details the development and evaluation of the enhanced future FLows and Groundwater (eFLaG) dataset of nationally consistent hydrological projections for the UK, based on the latest UK Climate Projections (UKCP18). The projections are derived from a range of hydrological models. For river flows, multiple models (Grid-to-Grid, PDM (Probability Distributed Model) and GR (Génie Rural; both four- and six-parameter versions, GR4J and GR6J)) are used to provide an indication of hydrological model uncertainty. For groundwater, two models are used, a groundwater level model (AquiMod) and a groundwater recharge model (ZOODRM: zooming object-oriented distributed-recharge model). A 12-member ensemble of transient projections of present and future (up to 2080) daily river flows, groundwater levels and groundwater recharge was produced using bias-corrected data from the UKCP18 regional (12 km) climate ensemble. Projections are provided for 200 river catchments, 54 groundwater level boreholes and 558 groundwater bodies, all sampling across the diverse hydrological and geological conditions of the UK. An evaluation was carried out to appraise the quality of hydrological model simulations against observations and also to appraise the reliability of hydrological models driven by the regional climate model (RCM) ensemble in terms of their capacity to reproduce hydrological regimes in the current period. The dataset was originally conceived as a prototype climate service for drought planning for the UK water sector and so has been developed with drought, low river flow and low groundwater level applications as the primary objectives. The evaluation metrics show that river flows and groundwater levels are, for the majority of catchments and boreholes, well simulated across the flow and level regime, meaning that the eFLaG dataset could be applied to a wider range of water resources research and management contexts, pending a full evaluation for the designated purpose. Only a single climate model and one emissions scenario are used, so any applications should ideally contextualise the outcomes with other climate model–scenario combinations. The dataset can be accessed in Hannaford et al. (2022): 10.5285/1bb90673-ad37-4679-90b9-0126109639a9. [ABSTRACT FROM AUTHOR]

Hydrological modelling of small mountainous catchments is particularly challenging because of the high spatio-temporal resolution required for the meteorological forcings. In-situ measurements of precipitation are typically scarce in these remote areas, particularly at high elevations. Precipitation reanalyses propose different alternative forcings for the simulation of streamflow using hydrological models. In this paper, we evaluate the performances of two hydrological models representing some of the key processes for small mountainous catchments, using different meteorological products with a fine spatial and temporal resolution. The evaluation is performed on 55 small catchments of the Northern French Alps. While the simulated streamflows are adequately reproduced for most of the configurations, these evaluations emphasize the added value of radar measurements, in particular for the reproduction of flood events. However, these better performances are only obtained because the hydrological models correct the underestimations of accumulated amounts (e.g. annual) from the radar data in high-elevation areas. [ABSTRACT FROM AUTHOR]

The assessment of seasonal streamflow forecasting is essential for appropriate water resource management. A suitable seasonal forecasting system requires the evaluation of both numerical weather prediction (NWP) and hydrological models to represent the atmospheric and hydrological processes and conditions in a specific region. In this paper, we evaluated the ECMWF-SEAS5 precipitation product with four hydrological models to represent seasonal streamflow forecasts performed at hydropower plants in the Legal Amazon region. The adopted models included GR4J, HYMOD, HBV, and SMAP, which were calibrated on a daily scale for the period from 2014 to 2019 and validated for the period from 2005 to 2013. The seasonal streamflow forecasts were obtained for the period from 2017 to 2019 by considering a daily scale streamflow simulation comprising an ensemble with 51 members of forecasts, starting on the first day of every month up to 7 months ahead. For each forecast, the corresponding monthly streamflow time series was estimated. A post-processing procedure based on the adjustment of an autoregressive model for the residuals was applied to correct the bias of seasonal streamflow forecasts. Hence, for the calibration and validation period, the results show that the HBV model provides better results to represent the hydrological conditions at each hydropower plant, presenting NSE and NSElog values greater than 0.8 and 0.9, respectively, during the calibration stage. However, the SMAP model achieves a better performance with NSE values of up to 0.5 for the raw forecasts. In addition, the bias correction displayed a significant improvement in the forecasts for all hydrological models, specifically for the representation of streamflow during dry periods, significantly reducing the variability of the residuals. [ABSTRACT FROM AUTHOR]

Precipitation (P) gauge undercatch (PUC) is an important source of error when using observed meteorological datasets for hydrologic modeling studies in regions with cold and windy winters. Preliminary simulations using the Variable Infiltration Capacity (VIC) hydrological model forced with different meteorological datasets showed significant underprediction of simulated streamflow throughout the domain. A new hybrid gridded meteorological dataset at 1/16° resolution based on observed station data was assembled over the U.S. Midwest and Great Lakes region from 1915 to 2021 at a daily time step. Correction of primary station data using existing techniques is generally difficult or infeasible in the United States due to missing station metadata and lack of local wind speed (WS) measurements. We developed and tested several different postprocessing adjustment techniques using regridded WS obtained from the NCEP–NCAR reanalysis. The most effective approach corrected rain or mixed P using WS alone, and P as snow using a regressed snow-to-P ratio from a group of wind-shielded reference stations (to account for different and generally unknown snow measurement techniques). The PUC-corrected gridded products were validated against high-quality shielded stations and corrected Global Historical Climatology Network stations with in situ WS, showing good overall agreement. Observed monthly streamflow at 40 river basins was also compared to hydrologic model simulations forced by datasets with and without PUC corrections. The best PUC-corrected dataset produced improvements in streamflow simulations in at least 80% of the streamflow locations for three validation metrics (r2, Nash–Sutcliff efficiency, bias in the mean), demonstrating its value for hydrometeorological studies in the greater Midwest region. Significance Statement: Many applications in hydrology require in situ precipitation (P) measurements, which are known to have a systematic low bias due to the effects of wind, also known as precipitation undercatch (PUC). Addressing PUC is problematic in the United States due to limited access to detailed station metadata (SMD) and local wind speed (WS) measurements. In this paper we develop a set of procedures to create gridded precipitation datasets for the U.S. Midwest region that incorporate corrections for PUC without needing either (i) detailed SMD or (ii) local WS measurements. Among other tests, results in 40 test basins throughout the Midwest show substantial improvements in simulated streamflow in 32 out of 40 basins when PUC corrections are included in meteorological driving datasets. [ABSTRACT FROM AUTHOR]

This paper presents an evaluation of the parameter sensitivity of a process-based model at the global scale using large-sample data. The analysis was carried out using the HYdrological Prediction of the Environment (HYPE) model, for which soil and snow parameters were evaluated using 187 river flow gauges spread worldwide. As a result, 6 out of 12 soil parameters and 7 out of 10 snow parameters were found to be sensitive. Taking advantage of the global dataset, an additional analysis was used to investigate links between catchment characteristics and parameter sensitivity. Different patterns of sensitivity were observed for different Köppen climate classes, which indicates that parameter regionalization would benefit from calibration based on climate zones. This numerical sensitivity method was compared with the judgement of a set of expert HYPE modellers to understand how numerical results compare with modellers' experience. [ABSTRACT FROM AUTHOR]

Besides existing challenges to water management under scarcity in Africa, climate change has the potential to impose additional pressures on water resources. This study aims at reviewing and identifying the climate change signals presented in 61 peer reviewed papers with respect to seven largest African river basins, viz. Nile, Senegal, Volta, Niger, Congo, Zambezi and Limpopo. Thirty percent of the reviewed papers focussed on the Nile basin while only 13% reported on the Congo basin. The climate change signals were segregated into decreasing, increasing and uncertain trends. Results stated the overwhelming tendency for using unmitigated future emission pathways. The use of ensemble models superiors the individual and synthetic scenarios. While projected temperature showed robust increasing trends, rainfall dominated by uncertain trends. The hydrological impacts were assessed through 19 hydrological models with accepted considerations of land uses at 52% of the cases. Modellers managed to understand and build robust rainfall-runoff relationships in Zambezi, Limpopo, Senegal and partially in Niger but failed in Congo and Nile. Climate change impacts on runoff, stream flows and evapotranspiration should be interpreted cautiously. Further researches are recommended to incorporate the effects of human interventions (e.g. dams) and land use changes in climate change-hydrological modelling. [ABSTRACT FROM AUTHOR]

The need for and the use of different data assimilation techniques to improve the quality of streamflow forecast is now well established. In this paper, the goal is to demonstrate the power of a new class of methods known as the forward sensitivity method (FSM) which is based on the temporal evolution of model sensitivities with respect to the control variables consisting of initial conditions and parameters. FSM operates in two phases: the first phase provides a simple algorithm for placing observations at or near where the square of forward sensitivities attains their maximum values. Using only this selected subset of observations in a weighted least squares method, the second phase then provides an estimate of the unknown elements of the control variables. In this paper, FSM based assimilation is applied to a simple class of two parameter model in a medium‐sized agriculture dominant watershed lying in the Krishna River Basin, India. Four assimilation scenarios were tested to determine the effect of assimilating only sensitive observations as well as the impact of temporally evolving initial condition sensitivity. Sensitivity results showed that observations during the monsoon time alone are enough for assimilation purposes, which has helped in reducing the computational time greatly. Assimilation and forecast results also indicated that the scenarios which assimilated only sensitive observations are better in estimating daily streamflow. From the obtained results, it is concluded that FSM based assimilation has significant potential to improve the streamflow simulations, especially in places where data availability remains a major challenge. Key Points: The potential of assimilating only sensitive soil moisture observations on streamflow forecast was demonstrated using a novel forward sensitivity method (FSM) methodImpact of temporally evolving sensitivity of initial condition on hydrological model performance during assimilation was determinedThe sensitivity‐based soil moisture assimilation was effective up to 45 lead days during streamflow forecast [ABSTRACT FROM AUTHOR]

Model calibration has always been one major challenge in the hydrological community. Flood scaling properties (FS) are often used to estimate the flood quantiles for data-scarce catchments based on the statistical relationship between flood peak and contributing areas. This paper investigates the potential of applying FS and multivariate flood scaling properties [multiple linear regression (MLR)] as constraints in model calibration. Based on the assumption that the scaling property of flood exists in four study catchments in northern China, eight calibration scenarios are designed with adopting different combinations of traditional indicators and FS or MLR as objective functions. The performance of the proposed method is verified by employing a distributed hydrological model, namely, the Soil and Water Assessment Tool (SWAT) model. The results indicate that reasonable performance could be obtained in FS with fewer requirements of observed streamflow data, exhibiting better simulation of flood peaks than the Nash-Sutcliffe efficiency coefficient calibration scenario. The observed streamflow data or regional flood information are required in the MLR calibration scenario to identify the dominant catchment descriptors, and MLR achieves better performance on catchment interior points, especially for the events with uneven distribution of rainfall. On account of the improved performance on hydrographs and flood frequency curve at the watershed outlet, adopting the statistical indicators and flood scaling property simultaneously as model constraints is suggested. The proposed methodology enhances the physical connection of flood peak among sub-basins and considers watershed actual conditions and climatic characteristics for each flood event, facilitating a new calibration approach for both gauged catchments and data-scarce catchments. [ABSTRACT FROM AUTHOR]

In recent years, deep learning (DL) approaches have been proven effective in addressing high nonlinear relationships within complex systems. Although various scientific studies have primarily focused on optimizing model architecture and enhancing its computational efficiency through hybridization, they have neglected the crucial aspect of selecting appropriate predictor variables (PV) and their influence on the model's predictions. In this paper, a new multi-phase stochastic DL model is designed with a feature selection framework for monthly streamflow prediction. The multi-phase hybrid MCMC–BC–BiLSTM–BiGRU model is based on bidirectional long short-term memory (BiLSTM) and bidirectional gated recurrent unit (BiGRU) integrated with Markov Chain Monte Carlo (MCMC) based bivariate copulas (BC). First, Ant Colony Optimization (ACO) algorithm was employed for screening optimal PV, and then significant antecedent streamflow was coupled with it to generate optimized input combinations. In addition, 25 different MCMC–BC models were adopted for each input combination to determine the dependency of previous month's streamflow on current and future streamflow. Finally, best-fitted MCMC–BC model was integrated with hybrid BILSTM–BiGRU model to predict streamflow at nine different catchments in the Victoria region of the Upper Murray Basin (UMB), Australia. The experimental results show that the multi-phase hybrid MCMC–BC–BiLSTM–BiGRU model showed remarkable performance in comparison to its benchmark counterparts with respect to different robust statistical error metrics. Therefore, this study demonstrates that stochastic DL techniques can be effectively employed as a promising alternative predictive tool for upstream discharge predictions with high consistency and accuracy, specifically when a statistically significant relationship with the antecedent streamflow exists. [ABSTRACT FROM AUTHOR]

Baseflow was proven to be the most unpredictable component of streamflow through various research. However, the recent method for estimating baseflow is due to the development of theoretical and computational techniques. This paper attempted to develop a fully automated baseflow separation system based on a recursive digital filter with an optimization algorithm for the single separation. Most of the previous baseflow separation methods use a single set of a parameter and BFImax (the maximum value of baseflow index), which can underestimate or overestimate the baseflow; however, the system developed in this study estimates multiple optimized a parameters using seasonality and flow conditions and uses them for BFImax calculation and baseflow separation. This system derived baseflow results in better understanding of watershed and streamflow tendency characteristics. This study developed a Web‐based Hydrograph Analysis Tool 2020 with a user‐friendly interface and new separation method regarding the seasonality and flow conditions with a fully automated python module to optimize a parameters and BFImax. The application to the two area show diverse parameter sets and different baseflow according to seasonality and flow conditions representing the flow characteristics. This study could be a fundamental tool for detailed watershed management decisions regarding water security in the dry season or environmental water for aquatic ecosystems. [ABSTRACT FROM AUTHOR]

Extended hydrometric (water level and flow) records are presented for eight Irish catchments subject to arterial drainage. The procedures employed to collect and process historical data, extend flow records and compile key metadata and information about each gauging station are described. Procedures are developed to handle data quality issues related to hydrometric practices and equipment malfunction and to quality assure rescued data using quality codes that complement modern hydrometric practices. The workflow developed will assist other hydrometric data rescue efforts and minimize subjectivity during the rescue process. The newly extended records represent the longest continuous river flow series available in Ireland, extending to the commencement of formal hydrometric monitoring in the country in 1940. The resultant data sets add 150 years of daily data across eight stations and will provide a key new resource for hydrological studies into the impacts of arterial drainage and flow nonstationarity. [ABSTRACT FROM AUTHOR]

This paper aims to improve the Soil and Water Assessment Tool (SWAT) model performance across the Major River Basins in Madagascar (MRBM), specifically for SWAT simulation in the Manambolo, Onilahy, Mananara, and Mandrare basins. A multi-gauge calibration was carried out to compare the performance of SWAT + Toolbox, and R-SWAT, SWAT + Editor Hard calibration on a monthly time step for the periods 1982-1999. We found that the SWAT + model generated greater surface runoff, while the SWAT model resulted in higher groundwater flow in both CSFR and CHIRPS datasets. It has been demonstrated that the SWAT + Toolbox had more potential in calibrating runoff across the MRBM compared to R-SWAT. Calibration in both methods led to a reduction in surface runoff, percolation, water yield, and curve number but increased the lateral flow, evapotranspiration (ET), and groundwater flow. The results showed that the multi-gauge calibrations did not significantly enhance simulation performance in the MRBM compared to single-site calibration. The performance of the SWAT + model for runoff simulation within the SWAT + Toolbox and R-SWAT was unsatisfactory for most basins (NSE,0) except for Betsiboka, Mahavavy, Tsiribihina, Mangoro, and Mangoky basins (NSE = 0.40-0.70; R² = 0.45-0.80, PBIAS≤ +25), whether considering the CHIRPS or CSFR datasets. Further study is still required to address this issue. [ABSTRACT FROM AUTHOR]

Stochastic methods have been typically used for the design and operations of hydraulic infrastructure. They allow decision makers to evaluate existing or new infrastructure under different possible scenarios, giving them the flexibility and tools needed in decision making. In this paper, we present a novel stochastic streamflow simulation approach able to replicate both temporal and spatial dependencies from the original data in a multi‐site basin context. The proposed model is a multi‐site extension of the modified Fractional Gaussian Noise (mFGN) model which is well‐known to be efficient to maintain periodic correlation for several time lags, but presents shortcomings in preserving the spatial correlation. Our method, called Weighted‐mFGN (WmFGN), incorporates spatial dependency into streamflows simulated with mFGN by relying on the Cholesky decomposition of the spatial correlation matrix of the historical streamflow records. As the order in which the decomposition steps are performed (temporal then spatial, or vice‐versa) affects the performance in terms of preserving the temporal and spatial correlation, our method searches for an optimal convex combination of the resulting correlation matrices. The result is a Pareto‐curve that indicates the optimal weights of the convex combination depending on the importance given by the user to spatial and temporal correlations. The model is applied to a number of river basins in Chile, where the results show that the WmFGN approach maintains the qualities of the single‐site mFGN, while significantly improving spatial correlation. Key Points: Weighted modified Fractional Gaussian Noise model improves spatial correlations without significantly sacrificing temporal correlationThe method searches for an optimal convex combination of the spatial and temporal correlation matrices according on the user's priorityOur results in several Chilean basins demonstrate that Weighted‐modified Fractional Gaussian Noise (WmFGN) represents a significant improvement over the mFGN in preserving correlations [ABSTRACT FROM AUTHOR]

Catchments characteristics, such as geomorphology, geology, soil, land use, and climatic variables, play an important role in total stream flow responses, a critical resource for people and the environment. Most of the previous literatures were applied a conventional statistical regression model to assess the relationship between landscape-climate descriptors, and streamflow and PET. However, a conventional statistical regression model didn't consider dependence of explanatory variables that were collected or extracted across both space and time. This paper investigated the impacts of landscape attributes and climate variables on catchment scale temporal variation of total streamflow and spatio-temporal variation of potential evapotranspiration (PET) in the Mille catchment using multiple linear regression techniques, and the importance of this study was to test spatial autocorrelation in the spatial regression model which is required to properly assess and quantify the relationship between hydrological regime response components and Landscape-climate descriptors in a catchment with topographically complex, and high spatio-temporal climatic variation like in our case study area, the Mille catchment. Statistical regression analysis revealed significant relationships between streamflow and climate variables, especially with rainfall. Mean maximum temperature is the most dominant factor controlling temporal variation of potential evapotranspiration at a monthly scale, whereas NDVI is the most significant factor that controls the spatial variability of PET. The multiple regression model shows that 91.1% of temporal variation in streamflow was accounted for rainfall, whereas, 96.6% and 78.4% of temporal and spatial variation in potential evapotranspiration was accounted for in maximum temperature and NDVI, respectively. Methods also can be applied to catchments with similar landscape attributes and climate variables. [ABSTRACT FROM AUTHOR]

Rivers are a source of life. However, these past years, the sustainability of rivers around the world has been threatened by urbanization, industrialization, and rapid development. These activities put pressure on waterway systems and rivers, leading to the emergence of riverine litter. The lack of understanding of the migration of litter in rivers can affect the decision-making efficiency of policymakers when it comes to riverine litter management. Therefore, the primary objective of this study is to evaluate the travel time for different densities of Floatable Litter (FL) in rivers influenced by river flow velocity. The density of the selected FL was manually determined by assessing litter characteristics of mass, volume, and size. The Sg. Berkelah river in Pahang, Malaysia was used as the simulation medium for the sampling of FL travel time utilizing the extrapolation of the Time of Travel (ToT) approach. The ToT technique utilizes a 30 m distance. The sampling was done under three different river flow velocity conditions. In this study, a correlation analysis between these two variables was carried out and evaluated. The results revealed that magazine papers possess the fastest FL travel time, with T = 43.93 s when v = 0.230 m/s, while cloth possesses the slowest FL travel time, with T = 204.90 s when v = 0.167 m/s. The travel time of FL is not influenced by low-class density, p < 1000 kg/m3, but by other factors that have a stronger influence on the travel time of horizontally migrated litter. This study provides a basic understanding and overview of FL migrating characteristics in rivers for further reference by local authorities for litter monitoring and also future riverine litter studies. [ABSTRACT FROM AUTHOR]

Nature-based solutions (NBSs), a sustainable landscape restoration approach, covers rebuilding ecological functionality and integrity in a watershed. The objective of restoration with NBSs is to revitalize the ecosystems to provide and sustain multiple services. Therefore, it is more than just planting trees or rewetting wetlands. NBSs can provide effective landscape restoration and management tools but should be applied on a methodological basis to get the full benefits. The methodological basis includes the type and nature of NBS, the application principles, and the tools to assess the efficiency of the set of NBS applied. However, most literature on NBSs is theoretical, while practitioners need applicable guidance. In this paper, we reviewed the latest literature on the NBSs and tried to connect the theory with some practical examples. We also underlined that NBSs applied in landscape restoration should relate to watershed processes since streamflow and/or stream quality are significant performance indicators. The NBSs should strengthen the resiliency towards multiple stressors and disturbances in a landscape. The widespread stressors in Türkiye landscapes are related to water balance that compares precipitation and evapotranspiration. Therefore, we suggest Budyko theory application in evaluating NBS options instead of typical climate models. Overall, this paper defines NBSs, provides examples, discusses possible methodologies, and comes up with some practical conclusions. The points we discuss are the resiliency assessment approach, scale, and location of the application, identifying the problems in a watershed through adequate quantitative indicators, and setting up the thresholds planned to be achieved. [ABSTRACT FROM AUTHOR]

Although hydraulic groundwater (GW) theory has been recognized as a promising tool for understanding the role of the aquifer(s) in the surface‐subsurface hydrologic cycle, the integrated modeling community still lacks a proper hydrologic structure to apply the well‐studied theory to large‐scale hydrologic predictions. This study aims to present a novel hydrologic structure that enables the Boussinesq equation‐based depiction of the bidirectional stream‐hillslope processes for applying hydraulic GW theory to large‐scale model configurations. We integrated the BE3S's (Hong et al., 2020, https://doi.org/10.1029/2020wr027571) representation scheme of the catchment‐scale stream‐hillslope continuum into the National Water Model (NWM) and applied the modified NWM (i.e., the NWM‐BE3S) to three major basins in Texas (i.e., the Trinity, Brazos, and Colorado River basins). Since the NWM currently relies on a single reservoir model for baseflow simulation, we used the Boussinesq aquifer as an alternative subsurface hydrology routine and evaluated its predictive skill and efficacy. We identified that the implemented Boussinesq aquifer(s) in the NWM‐BE3S yielded noticeable improvements in predicting streamflow for aquifers that exhibited higher nonlinearities in the observed recessions. The varying degree of improvements in streamflow predictions per the recession nonlinearities demonstrated not only (a) the algorithmic enhancement of subsurface hydrology (physics) but also (b) the applicability of the Boussinesq theory‐based depiction of the stream‐hillslope two‐way continuum. We diagnosed each stream's state based on the bidirectional stream‐hillslope exchanges and identified the dominant processes (i.e., river infiltration or baseflow) that were represented spatially in the NWM‐BE3S. Plain Language Summary: Streamflow is a critical land hydrologic component to manage water resources and the health of the associated ecosystem. While the water cycle between the stream and hillslope is the key process to accurately simulating streamflow, most currently used hydrologic/land surface models lack a theoretical basis to characterize the catchment‐scale groundwater to depict the stream‐hillslope water cycle. In this paper, we presented a new structure NWM‐BE3S that enables the Boussinesq approximation‐based characterization of the catchment(s) for improving the predictability of streamflow. The NWM‐BE3S was developed by integrating a recent numerical scheme BE3S (Hong et al., 2020, https://doi.org/10.1029/2020wr027571) into the WRF‐Hydro National Water Model (NWM) configuration as an alternative subsurface routing routine. The NWM‐BE3S was tested against streamflow observations from three major basins in Texas (i.e., the Trinity, Brazos, and Colorado River basins) to ensure the applicability of the Boussinesq‐based stream‐hillslope continuum scheme. We identified theory‐consistent improvements in the streamflow predictions from the NWM‐BE3S framework compared to the original NWM. Based on the two‐way fluxes modeled from the hydraulically continuous catchment(s) in the NWM‐BE3S, moreover, about 10% of the river reaches in the three basins were identified as losing streams during the evaluation period. Key Points: The Boussinesq theory‐based stream‐hillslope two‐way hydrologic interactions were newly introduced in the National Water Model (NWM)The Boussinesq aquifer yielded improved streamflow predictions than the single bucket model as the nonlinearity of recession increasesGaining or losing reaches in three Texas major basins showed improved predictions using the modified NWM [ABSTRACT FROM AUTHOR]

In the present paper, we propose a new approach for monthly streamflow prediction based on the extreme learning machine (ELM) and the metaheuristic bat algorithm (Bat-ELM). The performance of the Bat-ELM was compared to that of ELM, support vector regression (SVR), Gaussian process regression (GPR), multilayer perceptron neural network (MLPNN), and generalized regression neural network (GRNN). The proposed models were applied using data from three hydrometric stations located in the Cheliff Basin, Algeria. The results showed that the Bat-ELM was more satisfactory than the standalone models. The Bat-ELM achieved the highest numerical performance with correlation coefficient and Nash-Sutcliffe efficiency ranging from 0.927 to 0.973 and from 0.846 to 0.944, respectively, much higher than the respective values obtained using the MLPNN, GRNN, SVR, GPR and ELM approaches. The obtained results demonstrate that the Bat-ELM is an interesting alternative algorithm for predicting high and extreme streamflow. [ABSTRACT FROM AUTHOR]

Snowfall, snowpack, and snowmelt are among the processes with the greatest influence on the water cycle in mountainous watersheds. Hydrological models may be significantly biased if snow estimations are inaccurate. However, the unavailability of in situ snow data with enough spatiotemporal resolution limits the application of spatially distributed models in snow-fed watersheds. This obliges numerous modellers to reduce their attention to the snowpack and its effect on water distribution, particularly when a portion of the watershed is predominately covered by snow. This research demonstrates the added value of remotely sensed snow cover products from the Moderate Resolution Imaging Spectroradiometer (MODIS) in evaluating the performance of hydrological models to estimate seasonal snow dynamics and discharge. The Soil and Water Assessment Tool (SWAT) model was used in this work to simulate discharge and snow processes in the Oued El Abid snow-dominated watershed. The model was calibrated and validated on a daily basis, for a long period (1981–2015), using four discharge-gauging stations. A spatially varied approach (snow parameters are varied spatially) and a lumped approach (snow parameters are unique across the whole watershed) have been compared. Remote sensing data provided by MODIS enabled the evaluation of the snow processes simulated by the SWAT model. Results illustrate that SWAT model discharge simulations were satisfactory to good according to the statistical criteria. In addition, the model was able to reasonably estimate the snow-covered area when comparing it to the MODIS daily snow cover product. When allowing snow parameters to vary spatially, SWAT model results were more consistent with the observed streamflow and the MODIS snow-covered area (MODIS-SCA). This paper provides an example of how hydrological modelling using SWAT and snow coverage products by remote sensing may be used together to examine seasonal snow cover and snow dynamics in the High Atlas watershed. [ABSTRACT FROM AUTHOR]

Climate change and its impact on surface runoff in the upper Blue Nile basin and sub-basins have been widely studied in future climate projections. However, the impact on extreme flow events of rivers is barely investigated discretely. In this paper, the change in temperature and rainfall under the Representative Concentration Pathway (RCP) highest emission scenario (RCP 8.5) and its impact on the high flow and low flow simulated by the Soil and Water Assessment Tool (SWAT 2012) in major watersheds of the Lake Tana Basin has been evaluated by comparing the baseline period (1971–2000) with the 2020s (2011–2040), 2050s (2041–2070), and 2080s (2071–2100). The high flows of watersheds were selected by the Annual Maximum Series (AMS) model, whereas the low-flow watersheds were selected by the 7-day sustained mean annual minimum flow method. The result showed that the highest change in maximum temperature ranged from 2.93 °C to 5.17 °C in monthly time scales in the 2080s. The increment in minimum temperature is also more prominent in the 2080s and it is expected to rise by 4.75 °C. Inter-annual variability of the change in rainfall has shown increasing and decreasing patterns. The highest increments are expected by 22.37%, 25.58%, and 29.75% in the 2020s, 2050s, and 2080s, respectively, whereas the projected highest decrease in rainfall dictates the decrease of 6.42%, 7.11%, and 9.26% in 2020s, 2050s, and 2080s, respectively. Due to changes in temperature and rainfall, the low flow is likely to decrease by 8.39%, 8.33%, 6.21%, and 5.02% in Ribb, Gumara, Megech, and Gilgel Abay watersheds, respectively, whereas the high flow of Gilgel Abay, Megech, Gumara, and Ribb watersheds are expected to increase by 13.94%, 12.16%, 10.90%, and 10.24%, respectively, every 30 years. [ABSTRACT FROM AUTHOR]

This paper addresses the nexus of climate change and variability, soil moisture and surface runoff over the Lake Baikal catchment. Water level and distribution of dissolved and suspended matter over Lake Baikal are strongly affected by river inflow during rain-driven floods. In this study, we evaluate river flow changes at 44 streamflow gauges as well as related precipitation, evaporation, potential evaporation and soil moisture obtained from the ERA5-Land dataset. Based on Sen's slope trend estimator, Mann–Kendall non-parametric test, and using dominance analysis, we estimated the influence of meteorological parameters on river flow during 1979–2019. We found a significant correlation between the precipitation elasticity of river flow and catchment characteristics. Half of the gauges in the eastern part of the Selenga River basin showed a significant decreasing trend of average and maximum river flow (up to −2.9%/year). No changes in the central volume date of flood flow have been found. The reduction in rainfall amount explains more than 60% of runoff decrease. A decrease in evaporation is observed in areas where precipitation decrease is higher than 0.8%/year. Catchments, where the precipitation trends are not as substantial, are associated with increasing evaporation as a result of the increasing potential evaporation. Negative precipitation trends are accompanied by negative trends of soil moisture. Finally, the study reveals the sensitivity of catchments with steep slopes located in humid areas to precipitation change. [ABSTRACT FROM AUTHOR]

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Understanding the effects of global change and human activities on water supplies depends greatly on surface water dynamics. A comprehensive examination of the hydroclimatic variations at the transboundary level is essential for the development of any adaptation or mitigation plans to deal with the negative effects of climate change. This research paper examines the hydroclimatic factors that contribute to the desiccation of the Doosti Dam's basin in the transboundary area using multisensor satellite data from the Google Earth Engine (GEE) platform. The Mann–Kendall and Sens slope estimator test was applied to the satellite datasets to analyse the spatial and temporal variation of the hydroclimate variables and their trend over the transboundary area for 18 years from 2004 to 2021 (as the dam began operating in 2005). Statistical analysis results showed decreasing trends in temperature and an increase in rainfall with respect to station-observed available data. Evapotranspiration and irrigated area development followed the increasing pattern and a slight decrease in snow cover. The results confirmed a large expansion of the irrigated area, especially during the winter growing season. The increase in irrigated cultivated areas during both winter and summer seasons is possibly the main reason for the diversion of water to meet the irrigation requirements of the developed agriculture areas. The approach followed in this study could be applied to any location around the globe to evaluate the hydrological conditions and spatiotemporal changes in response to climate change, trend analysis and human activities. [ABSTRACT FROM AUTHOR]

Dominant hydrological processes of a catchment could shift due to a changing climate. This climate-induced hydrological nonstationarity could affect the reliability of future runoff projection developed using a hydrological model calibrated for the historical period as the model or parameters may no longer be suitable under a different future hydroclimate. This paper explores whether competing parameterization approaches proposed to account for hydrological nonstationarity could improve the robustness of future runoff projection compared to the traditional approach where the model is calibrated targeting overall model performance over the entire historical period. The modeling experiments are carried out using climate and streamflow datasets from southeastern Australia, which has experienced a long drought and exhibited noticeable hydrological nonstationarity. The results show that robust multicriteria calibration based on the Pareto front can provide a more consistent model performance over contrasting hydroclimate conditions, but at a slight expense of increased bias over the entire historical period compared to the traditional approach. However, the robust calibration does not necessarily result in a more reliable projection of future runoff. This is because the systematic bias in any parameterization approach would propagate from the historical period to the future period and would largely be cancelled out when estimating the relative runoff change. Ensemble simulations combining results from different parameterization considerations could produce a more inclusive range of future runoff projection as it covers the uncertainties due to model parameterization. [ABSTRACT FROM AUTHOR]

Reliable urban flood modeling is highly demanded in emergency response, risk management, and urban planning related to urban flooding. In this paper, the Storm Water Management Model (SWMM) is adapted to simulate urban rainfall‐runoff and pipe drainage processes within the Dominant river tracing‐Routing Integrated with VIC Environment (DRIVE) model which accounts for natural river basin runoff generation and routing processes. The integrated DRIVE‐SWMM model (referred to as DRIVE‐Urban) allows to explicitly delineate the mass‐energy interactions between urban drainage system (e.g., pipes and dikes) and river networks. This presents a further step model development for accurate urban flooding prediction which is lacking in existing urban flood models and traditional hydrological models. The validity of the DRIVE‐Urban model is evaluated for three case studies in Haikou City, China, with camera observations of street inundation during typhoon landfalls and heavy rainfall events. The results show that the DRIVE‐Urban model successfully captures 62%, 69%, and 77% of the total observed inundated road‐sections for the three cases respectively. The third case study with severe flooding situation shows that the DRIVE‐Urban performance is further improved when given reliable river and tidal level information, indicating the importance of integrating river‐basin with urban hydrological and hydraulic modeling. Plain Language Summary: This study mainly focuses on the development of an urban flood model (Dominant river tracing‐Routing Integrated with VIC Environment, DRIVE‐Urban), which couples the river‐basin and urban hydrological modeling, with a great potential for large‐scale real‐time urban flood monitoring and forecasting through the current Global Flood Monitoring System. Two inundation algorithms are developed within the DRIVE‐Urban model. One is for the river inundation caused urban flooding, and the other one is for the urban inundation caused by manhole overflow or excessive runoff drainage over the city land surface. Multi‐source observations including the closed‐circuit television video, citizen photos and municipal reports are collected for model validation. The results show that the DRIVE‐Urban model simulates well the different type of urban inundations at the 5‐m high spatial resolution with a promising hit success (i.e., POD >60%) according to three case studies. In particular, the results for the typhoon Kalmaegi case show that the model performance can be improved significantly by coupling the upstream river flow simulation with the urban runoff‐routing process, which validates our strategy of including the upstream river‐basin hydrological process in the urban flood modeling. Key Points: The Dominant river tracing‐Routing Integrated with VIC Environment (DRIVE)‐Urban flood model is developed by coupling river basin and urban hydrological and hydraulic processTwo 2D inundation algorithms are implemented within the model to simulate the manhole inundating and river overflow flooding respectivelyThe validity of the DRIVE‐Urban model is well established through three case studies with observations during typhoon and rainfall events [ABSTRACT FROM AUTHOR]

This paper uses hindcasts (1981–2010) to investigate the sources of skill in seasonal hydrological forecasts for Europe. The hindcasts were produced with WUSHP (Wageningen University Seamless Hydrological Prediction system). Skill was identified in a companion paper. In WUSHP, hydrological processes are simulated by running the Variable Infiltration Capacity (VIC) hydrological model forced with an ensemble of bias-corrected output from the seasonal forecast system 4 (S4) of the European Centre for Medium-Range Weather Forecasts (ECMWF). We first analysed the meteorological forcing. The precipitation forecasts contain considerable skill for the first lead month but hardly any significant skill at longer lead times. Seasonal forecasts of temperature have more skill. Skill in summer temperature is related to climate change and is more or less independent of lead time. Skill in February and March is unrelated to climate change. Different sources of skill in hydro-meteorological variables were isolated with a suite of specific hydrological hindcasts akin to ensemble streamflow prediction (ESP). These hindcasts show that in Europe, initial conditions of soil moisture (SM) form the dominant source of skill in run-off. From April to July, initial conditions of snow contribute significantly to the skill. Some remarkable skill features are due to indirect effects, i.e. skill due to forcing or initial conditions of snow and soil moisture at an earlier stage is stored in the hydrological state (snow and/or soil moisture) of a later stage, which then contributes to persistence of skill. Skill in evapotranspiration (ET) originates mostly in the meteorological forcing. For run-off we also compared the full hindcasts (with S4 forcing) with two types of ESP (or ESP-like) hindcasts (with identical forcing for all years). Beyond the second lead month, the full hindcasts are less skilful than the ESP (or ESP-like) hindcasts, because inter-annual variations in the S4 forcing consist mainly of noise which enhances degradation of the skill. [ABSTRACT FROM AUTHOR]

Multiyear climate predictions provide climate outlooks from years to a decade in advance. As multiyear temperature predictions become more mainstream and skillful, guidance is needed to assist practitioners who wish to explore this maturing field. This paper demonstrates the process and considerations of incorporating multiyear temperature predictions into water resources planning. Multiyear temperature predictions from the Community Earth System Model Decadal Prediction Large Ensemble are presented as discrete and probabilistic products and are used to force two common hydrologic modeling approaches: conceptual and empirical. The approaches are demonstrated to simulate streamflow in the upper Colorado River basin watershed in Colorado, where diagnostics show that increasing temperatures are associated with decreasing streamflows. Using temperature information for lead years 2-6, two analyses are performed: (i) a retrospective hindcast for the climatological period (1981-2010) and (ii) a blind forecast for 2011-15. For the retrospective hindcast, including temperature information improved the percent error as compared with climatology. For the blind forecast, the multiyear temperature prediction for warming was skillful, but the corresponding multiyear average streamflow predictions from both approaches were counterintuitive: with the predicted warming, the multiyear average streamflow was predicted to be lower than the climatological mean; however, the observed multiyear average streamflow was higher than the climatological mean. This was due to above-average precipitation during the prediction time frame, particularly for one of the years. With that year removed, the multiyear streamflow average became lower than the climatological mean. Temperature provides a marginal source of streamflow predictability, but there will be substantial uncertainty until prediction skill for year-to-year climate variability, especially for precipitation, increases. [ABSTRACT FROM AUTHOR]

Event-based estimates of the design flood in ungauged catchments are normally based on a single catchment response time parameter expressed as either the time of concentration (TC), lag time (TL) and/or time to peak (TP). In small, gauged catchments, a simplified convolution process between a single observed hyetograph and hydrograph is generally used to estimate these time parameters. In medium to large heterogeneous, gauged catchments, such a simplification is neither practical nor applicable, given that the variable antecedent soil moisture status resulting from previous rainfall events and spatially non-uniform rainfall hyetographs can result in multi-peaked hydrographs. In ungauged catchments, time parameters are estimated using either empirical or hydraulic methods. In South Africa (SA), unfortunately, the majority of the empirical methods recommended for general use were developed and verified in catchments ≤ 0.45 km² without using any local data. This paper presents the further development and verification of the streamflow-based approach developed by Gericke (2016) to estimate observed TP values and to derive a regional empirical TP equation in Primary Drainage Region X, SA. A semi-automated hydrograph analysis tool was developed to extract and analyse complete hydrographs for time parameter estimation using primary streamflow data from 51 flowgauging sites. The observed TP values were estimated using three methods: (i) duration of total net rise of a multi-peaked hydrograph, (ii) triangular-shaped direct runoff hydrograph approximations, and (iii) linear catchment response functions. The combined use of these methods incorporated the high variability of eventbased time parameters, and Method (iii), in conjunction with an ensemble-event approach sampled from the time parameter distributions, should replace the event-based approaches to enable the improved calibration of empirical time parameter equations. The conceptual approach used to derive the regional empirical TP equation should also be adopted when regional equations need to be derived at a national scale in SA. [ABSTRACT FROM AUTHOR]

Natural streamflow data is required in many hydrological applications. However, many basins are located in data-scarce regions or are impacted by human construction and activities. In this paper, we explore three machine learning algorithms, namely artificial neural networks, random forest and light gradient boosting machine, to simultaneously estimate all the parameters of the coupled modèle du Génie Rural à 4 paramètres Journaliers (GR4J) and snow accounting routine called CemaNeige model. A database of 675 basins in the USA and Quebec is used to train and test ensembles. After using the estimated parameters in GR4J, the resulting naturalized streamflow series are compared with those obtained by the established drainage area ratio and spatial proximity transfer methods in 11 test basins. The results indicate that the machine learning algorithms outperform the drainage area ratio and spatial proximity transfer methods. Among machine learning algorithms, random forests obtain lower (better) continuous ranked probability scores than the other methods for 10 out of 11 test basins. [ABSTRACT FROM AUTHOR]