Hydrological Modeling and Engineering: Managing Risk and Uncertainties

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. Full article

Loess-paleosol bluffs can be unstable, but in the course of urbanization, houses may be built in such locations to take advantage of the view. One factor affecting the stability of such bluffs is water, the role of which in mass movements is well established. In this study, the connection of mass movements to meteorological conditions, such as rainfall and subsequent water level changes, was researched using new statistical methods. The periodicity of the water level of the Danube was analyzed using wavelet spectrum analyses, while changepoint analysis was used to determine variations in the quantity of precipitation. These results were compared to the chronology of six mass movements in Kulcs, Hungary. This study also focused on the changes in geochemical properties of loess in different weather conditions (dry periods, wet periods, and flooding). The results showed that only two mass movements were connected to hydrological conditions, and in the other case human activity and geochemical changes may have been factors. The results of geochemical models created using PHREEQC showed calcite and kaolinite precipitation, and albite and dolomite dissolution as the main mineral changes over the course of a year. Albite was found to dissolve only in wet periods, and kaolinite precipitation was significant during flood periods. Full article

Distributed hydrological modeling has increased its popularity in the community, leading to the development of multiple models with different approaches. However, the rapid growth has also opened a gap between models, interfaces, and advanced users. User interfaces help to set up and pre-process steps. Nevertheless, they also limit the implementation of more complex experiments. This work presents the Watershed Modeling Framework (WMF) as a step forward in closing the interface–usage gap. WMF is a Fortran-Python module designed to provide tools to perform hydrological analysis and modeling that conceptualizes the watershed as an object with a defined topology, properties, and functions. WMF has a built-in hydrological model, geomorphological analysis functions, and a QGIS plugin. WMF interacts with other popular Python modules, making it dynamic and expandible. In this work, we describe the structure of WMF and its capabilities. We also provide some examples of its implementation and discuss its future development. Full article

A discharge uncertainty envelope is presented that provides an observation error model for data assimilation (DA) using discharge observations derived from measurement of stage using a rating curve. It uniquely represents the rating curve representation error, which is due to scale and process incompatibility between the rating curve hydrodynamic model and “true” discharge, within the observation error model. Ensemble methods, specifically, the iterative ensemble smoother (IES) algorithms in PEST++, provide the DA framework for this observation error model. The purpose of the uncertainty envelope is to describe prior observation uncertainty for ensemble methods of DA. Envelope implementation goals are (1) limiting the spread of the envelope to avoid conditioning to extreme parameter values and producing posterior parameter distributions with increased variance, and (2) incorporating a representative degree of observation uncertainty to avoid overfitting, which will introduce bias into posterior parameter estimates and predicted model outcomes. The expected uncertainty envelope is flow regime dependent and is delineated using stochastic, statistical methods before undertaking history matching with IES. Analysis of the goodness-of-fit between stochastically estimated “true” discharge and observed discharge provides criteria for the selection of best-fit parameter ensembles from IES results. Full article

A proper understanding of hydrological processes is vital for water resource assessment, management, and conservation at a local, national, and global scale. The role of hydrological models is critically important in rarely studied ungauged catchments including of Kobo-Golina, in the Danakil basin of Ethiopia. The main objective of this research is to model the hydrology of the Kobo-Golina catchment using the completely restructured SWAT (SWAT+) model. Validated reanalysis river flow from the Global Flood Awareness System (GloFAS) and actual evapotranspiration (AET) from Moderate Resolution Imaging Spectroradiometer (MODIS) were used for single and multi-variable calibration. It is found that the multi-variable calibration scenario reasonably attained the minimum satisfactory performance limit for both variables (NSE = 0.67, R² = 0.68, PBias = −9.68%, and RSR = 0.57 for calibration of GloFAS flow; and NSE = 0.56, R² = 0.63, RSR = 0.66, PBias = 3.86 for calibration of MODIS AET). The model simulation showed that evapotranspiration accounts for 47% of the input water while surface runoff, lateral flow, and groundwater recharge account for 30%, 1.53%, and 21.4%, respectively. The simulated mean annual streamflow at the Basin outlet is 10.6 m³/s. The monthly low flow occurs in June with a median flow of 1.43 m³/s and a coefficient of dispersion of 0.67. High flows occur in August, with a median flow of 16.55 m³/s and a coefficient of dispersion of 1.55. The spatial distribution of simulated runoff was depicted as being higher in the floodplains and along the riparian/drainage lines, whereas upland areas showed lower runoff. The maximum monthly recharge occurs in September with a recharge value of 78.2 mm. The findings of the study suggested that both surface water harvesting and groundwater exploitation can be sought in floodplain areas while conserving the uplands. Full article

Soil moisture is an essential hydrological variable for a suite of hydrological applications. Its spatio-temporal variability can be estimated using satellite remote sensing (e.g., SMOS and SMAP) and in-situ measurements. However, both have their own strengths and limitations. For example, remote sensing has the strength of maintaining the spatial variability of near-surface soil moisture, while in-situ measurements are accurate and preserve the dynamics range of soil moisture at both surface and larger depths. Hence, this study is aimed at (1) merging the strength of SMAP with in-situ measurements and (2) exploring the effectiveness of merged SMAP/in-situ soil moisture in improving ensemble streamflow forecasts. The conditional merging technique was adopted to merge the SMAP-enhanced soil moisture (9 km) and its downscaled version (1 km) separately with the in-situ soil moisture collected over the au Saumon watershed, a 1025 km² watershed located in Eastern Canada. The random forest machine learning technique was used for downscaling of the near-surface SMAP-enhanced soil moisture to 1 km resolution, whereas the exponential filter was used for vertical extrapolation of the SMAP near-surface soil moisture. A simple data assimilation technique known as direct insertion was used to update the topsoil layer of a physically-based distributed hydrological model with four soil moisture products: (1) the merged SMAP/in-situ soil moisture at 9 and 1 km resolutions; (2) the original SMAP-enhanced (9 km), (3) downscaled SMAP-enhanced (1 km), and (4) interpolated in-situ surface soil moisture. In addition, the vertically extrapolated merged SMAP/in-situ soil moisture and subsurface (rootzone) in-situ soil moisture were used to update the intermediate layer of the model. Results indicate that downscaling of the SMAP-enhanced soil moisture to 1 km resolution improved the spatial variability of soil moisture while maintaining the spatial pattern of its original counterpart. Similarly, merging of the SMAP with in- situ soil moisture preserved the dynamic range of in-situ soil moisture and maintained the spatial heterogeneity of SMAP soil moisture. Updating of the top layer of the model with the 1 km merged SMAP/in-situ soil moisture improved the ensemble streamflow forecast compared to the model updated with either the SMAP-enhanced or in-situ soil moisture alone. On the other hand, updating the top and intermediate layers of the model with surface and vertically extrapolated SMAP/in-situ soil moisture, respectively, did not further improve the accuracy of the ensemble streamflow forecast. Overall, this study demonstrated the potential of merging the SMAP and in-situ soil moisture for streamflow forecast. Full article

Wetlands are valuable natural capital and sensitive ecosystems facing significant risks from anthropogenic and climatic stressors. An assessment of the environmental risk levels for wetlands’ dynamic ecosystems can provide a better understanding of their current ecosystem health and functions. Different levels of environmental risk are defined by considering the categories of risk and the probability and severity of each in the environment. Determining environmental risk levels provides a general overview of ecosystem function. This mechanism increases the visibility of risk levels and their values in three distinct states (i.e., low, moderate, and high) associated with ecosystem function. The Bayesian belief network (BBN) is a novel tool for determining environmental risk levels and monitoring the effectiveness of environmental planning and management measures in reducing the levels of risk. This study develops a robust methodological framework for determining the overall level of risks based on a combination of varied environmental risk factors using the BBN model. The proposed model is adopted for a case study of Shadegan International Wetlands (SIWs), which consist of a series of Ramsar wetlands in the southwest of Iran with international ecological significance. A comprehensive list of parameters and variables contributing to the environmental risk for the wetlands and their relationships were identified through a review of literature and expert judgment to develop an influence diagram. The BBN model is adopted for the case study location by determining the states of variables in the network and filling the probability distribution tables. The environmental risk levels for the SIWs are determined based on the results obtained at the output node of the BBN. A sensitivity analysis is performed for the BBN model. We proposed model-informed management strategies for wetland risk control. According to the BBN model results, the SIWs ecosystems are under threat from a high level of environmental risk. Prolonged drought has been identified as the primary contributor to the SIWs’ environmental risk levels. Full article

Permeable Pavement (PP) models are valuable tools for studying the implementation of PPs in urban environments. However, the runoff simulated by traditional models such as the Curve Number (CN) is different from that created with PP models, as infiltration is computed differently. However, many investigations compare the runoff created by both models to extract broader conclusions without considering how the two models are related. Hence, this research explores the relation between runoff simulated by one general model, selecting the widespread CN model as a baseline, and the PP model provided in the Storm Water Management Model (SWMM). Correlation was set using the hydrograph created with the CN in a single event as a baseline and obtaining the best pavement permeability value from the PP model by calibration. The influence of storm depth, pavement slope, catchment shape, and PP type was also analysed. Calibration was conducted based on the Nash–Sutcliffe coefficient, but peak and volume performances were also studied. The results show that it is possible to link runoff hydrographs computed with the PP model to those created with the CN method, although that relation is not useful for the entire CN range. That relation is practical for CNs higher than 88 and shall be helpful for urban planners and researchers to compare several pervious/impervious scenarios in urban drainage models more robustly. One direct application is to compare the runoff computed by both models without changing the method that simulates runoff. It shall be enough to change a unique parameter that can be linked to a certain imperviousness by the CN. Full article

The use of data-driven surrogate models to produce deterministic flood inundation maps in a timely manner has been investigated and proposed as an additional component for flood early warning systems. This study explores the potential of such surrogate models to forecast multiple inundation maps in order to generate probabilistic outputs and assesses the impact of including quantitative precipitation forecasts (QPFs) in the set of predictors. The use of a $k$-fold approach for training an ensemble of flood inundation surrogate models that replicate the behavior of a physics-based hydraulic model is proposed. The models are used to forecast the inundation maps resulting from three out-of-the-dataset intense rainfall events both using and not using QPFs as a predictor, and the outputs are compared against the maps produced by a physics-based hydrodynamic model. The results show that the $k$-fold ensemble approach has the potential to capture the uncertainties related to the process of surrogating a hydrodynamic model. Results also indicate that the inclusion of the QPFs has the potential to increase the sharpness, with the tread-off also increasing the bias of the forecasts issued for lead times longer than 2 h. Full article

With the rapid development of meteorological models, numerical weather prediction is increasingly used in flood forecasting and reservoir regulation, but its forecasting ability is limited by the large amount of uncertainty from meteorological systems. In this paper, a new, hybrid framework is developed to improve numerical precipitation forecasting by combining the multimodel ensemble and probabilistic postprocessing methods. The results show that the multimodel ensemble method used in this paper is an efficient way to reduce prediction errors, especially missing alarm errors. In a comparison of the probabilistic postprocessors based the generalized Bayesian model (GBM) and bivariate probabilistic model (BPM), the GBM shows better performance from the aspects of indicators and is more suitable for real-time applications. Meanwhile, the assessment of probabilistic results shows that the skill of probabilistic precipitation forecasts is related to the quality of their inputs. According to these results, a new hybrid framework is proposed by taking the results from multimodel ensemble as the input of probabilistic postprocessor. Compared to using the raw numerical in GBM, the hybrid framework improves the accuracy, sharpness, reliability, and resolution ability from different lead times by 2–13%, 1–22%, and 0–12% respectively, especially when the lead time is less than 4 d, the improvement can reach 9–13%, 10–22%, and 5–12% respectively. In conclusion, the hybrid two-step framework can provide a more skillful precipitation forecast, which can be useful for flood forecasting and reservoir regulation. Full article

Hydrological modeling is an important tool for water resources management, providing a feasible solution to represent the main hydrological processes and predict future streamflow regimes. The literature presents a set of hydrological models commonly used to represent the rainfall-runoff process in watersheds with different meteorological and geomorphological characteristics. The response of such models could differ significantly for a single precipitation event, given the uncertainties associated with the input data, parameters, and model structure. In this way, a correct hydrological representation of a watershed should include the evaluation of different hydrological models. This study explores the use and performance of five hydrological models to represent daily streamflow regimes at six hydropower plants located in the Tocantins river basin (Brazil). The adopted models include the GR4J, HYMOD, HBV, SMAP, and MGB-IPH. The evaluation of each model was elaborated considering the calibration (2014–2019) and validation period (2005–2010) using observed data of precipitation and climatological variables. Deterministic metrics and statistical tests were used to measure the performance of each model. For the calibration stage, results show that all models achieved a satisfactory performance with NSE values greater than 0.6. For the validation stage, only the MGB-IPH model present a good performance with NSE values greater than 0.7. A bias correction procedure were applied to correct the simulated data of conceptual models. However, the statistical tests exposed that only the MGB-IPH model could preserve the main statistical properties of the observed data. Thus, this study discusses and presents some limitations of the lumped model to represent daily streamflows in large-scale river basins (>50,000 km${}^2$). Full article

Quantifying uncertainties in water resource prediction in data-scarce regions is essential for resource development. We use globally available datasets of precipitation and potential evapotranspiration for the regionalization of model parameters in the data-scarce regions of Ethiopia. A regional model was developed based on 14 gauged catchments. Three possible parameter sets were tested for regionalization: (1) the best calibration parameters, (2) the best validation parameter set derived from behavioral parameters during the validation period, and (3) the stable parameter sets. Weighted multiple linear regression was applied by assigning more weight to identifiable parameters, using a novel leave-one-out cross-validation technique for evaluation and uncertainty quantification. The regionalized parameter sets were applied to the remaining 35 ungauged catchments in the Ethiopian Rift Valley Lake Basin (RVLB) to provide regional water balance estimations. The monthly calibration of the gauged catchments resulted in Nash Sutcliffe Efficiencies (NSE) ranging from 0.53 to 0.86. The regionalization approach provides acceptable regional model performances with a median NSE of 0.63. The results showed that, other than the commonly used best-calibrated parameters, the stable parameter sets provide the most robust estimates of regionalized parameters. As this approach is model-independent and the input data used are available globally, it can be applied to any other data-scarce region. Full article

Interactions among society, water resources, and environment systems have become increasingly prominent with the progressively far-reaching impact of human activities. Therefore, this paper aims to construct a co-evolution model to establish the mutual feedback relationship among society, water resources, and environment from the perspective of socio-hydrology. Firstly, social factors such as environmental sensitivity, environmental protection awareness, and technological level are introduced to this model to describe the coevolutionary trajectory of society, water resources and environment subsystems. Then, this model is implemented in 11 provincial administrative regions in the Yangtze River Economic Belt, and the degree of coordination of their coupling is evaluated. Results show that the water-use efficiency of each provincial administrative region in the Yangtze River Economic Belt gradually increases during the forecast period. The coupling-coordinated degree of each provincial administrative region of the Yangtze River Economic Belt has greatly improved during the 14th Five-Year Plan period, reflecting that policy support has played a significant role in the coordinated development of the Yangtze River Economic Belt. The dynamic fluctuation process of environmental sensitivity effectively depicts the co-evolution process of the coupling system, which provides a reference for the subsequent exploration and cognition of the human-water coevolutionary mechanism. Full article

With widespread ongoing urbanization and as climate change continues, the importance of protecting the water quality of streams and lakes is intensifying. However, while many water quality constituents in lakes and rivers are of overall interest, water temperature is a ‘key’ variable as temperature influences mixing within a waterbody, influences the acceptability of the habitat for flora and fauna, and serves as a guide to the general health of a stream. To enable the assessment, a physics-based, deterministic hydraulic and heat-balance modeling procedure using the combination of MIKE SHE, MIKE HYDRO and ECO Lab is described to assess heat transfer magnitudes in portions of the Credit River, Ontario. Changes in instream temperature regimes are examined, including both frequency and spatial extent, providing insights into the impacts of urbanization in terms of seasonal temperature shifts arising from land use changes. For flow and temperature regimes, Nash–Sutcliffe model efficiency coefficient (NSE) values of 0.49 and 0.955 were achieved, respectively, for current threshold conditions. Durations of temperature increases from threshold levels indicate that land use changes from current agriculture conditions to urbanization may change stream water temperatures for 9% of the time by 1 °C, and 2% of the time by 2 °C for distances of 1000 m downstream, because of land use change from agriculture to low-density urbanization, and for 20% of the time by 1 °C, and 4% of the time by 2 °C at distances of 1000 m downstream with land use change to high-density urbanization. With climate change RCP 4.5 Scenario in 2050 (Base, for a Wet Year—2017), the continuous amount of time the stream water temperature remains at elevated temperatures of more than 3 °C (from 5000 m to 25,607 m from the most upstream point of Fletchers Creek) for a distance of 20,000 m is more than 13 h. These elevations in temperature may have serious implications for flora and fauna in the creek, particularly impacting the cold-water and mixed-water fish species. Full article

This study quantifies some of the potential impacts of climate change and nutrient pollution to identify the most important factors on water quality changes and algal blooms in the study region. Three variables, air temperature and streamflow, representing climate change, and nutrient runoff, were varied in eight hypothetical scenarios to determine their impact on water quality and algal blooms by the calibrated and validated water quality model QUAL2K. Water quality was assessed by the concentrations of dissolved oxygen, total nitrogen, and phosphorus. Algal blooms were identified by phytoplankton concentration. An increase in air temperature of up to 2 °C resulted in an average increase of 3% in water temperature and 4.79% in phytoplankton concentration, and an average decrease of 0.48% in dissolved-oxygen concentration. Projected decreases in streamflow not only made the above phenomenon more significant but also significantly increased the concentration of total nitrogen, total phosphorus, and phytoplankton with the same pollution inputs. Under climate change, the biggest cause of concern for estuarine water quality is reduced streamflow due to decreases in rainfall. Water quality improvement is possible by regulating the concentration of non-point-source pollution discharge. By reducing nutrient runoff, the total nitrogen and phosphorus concentrations were also reduced, resulting in a significant increase in the dissolved oxygen concentration. This study highlights the most significant factors for managing water quality in estuaries subject to climate change. Full article

Soil moisture (SM) is a crucial driving variable for the global land surface-atmosphere water and energy cycle. There are now many satellite-based SM products available internationally and it is necessary to consider all available SM products under the same context for comprehensive assessment and inter-comparisons at the global scale. Moreover, product performances varying with dynamic environmental factors, especially those closely related to retrieval algorithms, were less investigated. Therefore, this study evaluated and identified the relative strengths of nine mainstream satellite-based SM products derived from the Advanced Microwave Scanning Radiometer 2 (AMSR2), Chinese Fengyun-3B (FY3B), the Soil Moisture Active Passive (SMAP), the Soil Moisture and Ocean Salinity (SMOS), and the European Space Agency (ESA) Climate Change Initiative (CCI) by using the Pearson correlation coefficient (R), R of SM seasonal anomalies (Ranom), unbiased Root Mean Square Error (ubRMSE), and bias metrics against ground observations from the International Soil Moisture Network (ISMN), as well as the Global Land Data Assimilation System (GLDAS) Noah model simulations, overall and under three dynamic (Land Surface Temperature (LST), SM, and Vegetation Optical Depth (VOD)) conditions. Results showed that the SMOS-INRA-CESBIO (IC) product outperformed the SMOSL3 product in most cases, especially in Australia, but it exhibited greater variability and higher random errors in Asia. ESA CCI products outperformed other products in capturing the spatial dynamics of SM seasonal anomalies and produced significantly high accuracy in croplands. Although the Chinese FY3B presented poor skills in most cases, it had a good ability to capture the temporal dynamics of the original SM and SM seasonal anomalies in most regions of central Africa. Under various land cover types, with the changes in LST, SM, and VOD, different products exhibited distinctly dynamic error characteristics. Generally, all products tended to overestimate the low in-situ SM content but underestimate the high in-situ SM content. It is expected that these findings can provide guidance and references for product improvement and application promotions in water exchange and land surface energy cycle. Full article

The sequence of reservoir operations has a profound influence on the regulation and storage capacity of reservoir groups to effectively utilise the natural water inflow and external water transfer in the basin, especially for reservoir groups with water supply tasks. This study establishes the reservoir operation sequence (ROS) of four reservoir group modes, aiming at national economic and ecological water consumption, constructs a model of ROS-based multi-objective ecological operation of the reservoir group, and uses the particle swarm optimisation (PSO) method to optimise the solution. Analysing the results of the three schemes in two scenarios at the Yinma River Basin (YRB) indicates that after the Central Jilin Water Supply Project is put into operation, not only will the production and living water be effectively improved, but also the ecological water in the basin. Then, we compared the optimisation results of different water supply sequences in series and parallel reservoirs, which illustrates that the ROS of the four modes formulated in this research is the optimal water supply sequence. Full article

Soil and water loss in karst areas seriously restricts the sustainable development of karst ecosystems, the economy and society in southwest China, which has been a concern of, and studied by, many scholars in China and abroad. Soil and water loss has a great influence on the evolution of rocky desertification, groundwater quality, drought and flood disasters in karst regions. This paper aimed to provide a review of studies of slope hydrological processes in soil and water loss. In this paper, 322 related articles retrieved from the Web of Science database and CNKI database were systematically reviewed. Firstly, a quantitative study was conducted to analyze the annual number, countries and research progress of the published literature. Secondly, the main progress and achievements of slope hydrology and soil erosion control technology were classified and summarized according to theoretical research, mechanism research, technology research and technical demonstration. Finally, the key problems and future research prospects are put forward, starting with the existing technology, in order to find more suitable soil and water loss control measures in karst regions and achieve economic and ecological benefits. Full article

The processes of sediment particle movement were studied through numerical simulation using a coupled method with focus on discussing the characteristics of sliding or rolling and saltation sediment particles, respectively. Turbulent flow was simulated using large eddy simulation (LES). The sediment particle was simulated using the combined finite-discrete element method (FDEM). The interaction forces of turbulent flow and sediment particle were calculated using the immersed boundary method (IBM). It indicated that the collisions of saltating particle with low concentration increase the saltation length and flight time. In response, sediment particle velocity also increases. The particle angular velocity is largest at the takeoff moment, and decreases gradually in the saltation progress. The drag and lift forces near the bed are large, and away from the bed decrease and trend to be a stable value, gradually. From the relative magnitudes of the drag and lift forces, the lift force plays a more important role than the drag force in the sediment saltation. The relative magnitudes of drag and lift forces influence the incident and takeoff angles. The sediment transport rate calculated based on the characteristics of saltation sediment particles is overestimated, ignoring the effect of sliding or rolling sediment particles and inter-particle collisions. Full article

As flood damage is becoming more frequent and severe worldwide, efficient flood control of dams has been highlighted. The purpose of the study is to establish a simple method for dam operators to estimate the flood control capacity and predischarge level required for flood response. The cumulative probability distribution (CDF) pair with the same return period for 12 major dams located in South Korea were estimated using the frequency matching method. A Ratio of Storage volume to Flood inflow (RSF) concept was suggested and applied for each dam, and they were classified into three types: Linear, Estranged, and Vague according to the water storage characteristics. Using the method presented in this study, we suggested the required amount of flood control capacity and target water level for each dam. The results demonstrated that there is no linear relationship between flood and storage of dam when the ratio of watershed area to a storage capacity of the dam is 2.0 or more, or the ratio of watershed area to flood control capacity is 20.0 or more. Further, it was found that the RSF value is affected by the initial water level of the dam when a high flood inflow was observed for Estranged and Vague types. It is expected that the method presented in this study can be basic information for performing predischarge for flood control in numerous dams. Full article

Evaluation and modeling of soil water infiltration are essential to all aspects of water resources management and the design of hydraulic structures. Nonetheless, research focused on experimental studies of infiltration rates in arid and semi-arid regions under unknown boundary conditions remains minimal. This paper investigates the characteristics of the spatial variability of infiltration over a semi-arid rural basin of Algeria. The experiments were conducted using a portable double-ring infiltrometer filled at an equal volume of approximately 100 L of water for each of the 25 catchment locations. Soil moisture contents at the proximity of each test location were evaluated in the laboratory as per the standard NF P94–050 protocol. The experimental results are used to produce the catchment infiltration curves using three statistically fitted infiltration models, namely Horton, Kostiakov, and Philip models. The reliability of the models was assessed using four performance criteria. The statistical regressions of the fitted models suggest that the Horton model is the most suitable to assess the infiltration rate over the catchment with mean coefficients of Nash = 0.963, CC = 0.985, RMSE = 1.839 (cm/h), and Bias = 0.241. The superiority of the Horton model suggests that the initial and final infiltration rates, primarily affected by soil type, initial soil moistures, and land cover, are important predictors of the modeling process over the Madjez Ressoul catchment. The results also infer that the applicability of other models to the different types of undeveloped soils in the study area requires advanced field investigations. This finding will support the understanding of the hydrologic processes over semi-arid basins, especially in advising crop irrigation schemes and methods and managing the recurring flood and drought over the country. Full article

The Ca Mau peninsula (CMP) is a key economic region in southern Vietnam. In recent decades, the high demand for water has increased the exploitation of groundwater, thus lowering the groundwater level and leading to risks of degradation, depletion, and land subsidence, as well as salinity intrusion in the groundwater of the whole Mekong Delta region. By using a finite element groundwater model with boundary expansion to the sea, we updated the latest data on hydrogeological profiles, groundwater levels, and exploitation. The basic model setup covers seven aquifers and seven aquitards. It is determined that the inflow along the coastline to the mainland is 39% of the total inflow. The exploitation of the study area in 2019 was 567,364 m³/day. The most exploited aquifers are the upper-middle Pleistocene (qp_2–3) and the middle Pliocene (n₂²), accounting for 63.7% and 24.6%, respectively; the least exploited aquifers are the upper Pleistocene and the upper Miocene, accounting for 0.35% and 0.02%, respectively. In the deeper aquifers, qp_2–3 and n₂², the change in storage is negative due to the high exploitation rate, leading to a decline in the reserves of these aquifers. These groundwater model results are the calculations of groundwater reserves from the coast to the mainland in the entire system of aquifers in the CMP. This makes groundwater decision managers, stakeholders, and others more efficient in sustainable water resources planning in the CMP and Mekong Delta (MKD). Full article

Most old urban areas of China have a dense population, severely indurated underlying surface, and highly developed underground space. Those increase the waterlogging risk and obstruct the stormwater management in old urban areas. To propose an appropriate engineering strategy for low impact development (LID) transformation in an urban area, a simulation was carried out by storm water management model (SWMM) in this project. Bioretention cells, permeable pavements, and green roofs were selected according to the study area surface. Runoff control performance of single LID control and combined schemes were compared. Results illustrate that only 50.21% of roofs can build green roofs in urban areas with dense populations, and the runoff control performance of green roofs is unsatisfactory, while bioretention cells and permeable pavements can effectively mitigate runoff caused by storms with a recurrence period less than 10 years, and combined LID controls can obtain better runoff control performance with less construction area. Those outcomes screened out the LID controls suitable for application in densely populated old urban areas and put forward reasonable engineering practice strategies. This study provides guidance and reference for the LID transformation in the densely populated old urban area. Full article

In semiarid to arid regions of the western U.S., river flow availability and variability are highly subject to shifts in snow accumulation and ablation in alpine watersheds. This study aims to examine how shifts in snowmelt rate (SMR) and snow continuity, an indicator of the consistent existence of snow on the ground, affect snow-driven streamflow dynamics in three alpine watersheds in the U.S. Great Basin. To achieve this end, the coupled hydro-ecological simulation system (CHESS) is used to simulate river flow dynamics, and multiple snow metrics are calculated to quantify the variation of SMR and snow continuity, the latter of which is measured by snow persistence (SP), snow residence time (SRT), and snow season length (SSL). Then, a new approach is proposed to partition streamflow into snow-driven and rain-driven streamflow. The statistical analyses indicate that the three alpine watersheds experienced a downward trend in SP, SRT, SSL, and SMR during the study period of 1990–2016 due to regional warming. As a result, the decrease in SMR and the decline in snow continuity shifted the occurrence day of 25% and 50% of the snow-driven cumulative discharge, as well as peak discharge, toward an earlier occurrence. Moreover, the magnitudes of snow-driven annual streamflow, summer baseflow, and peak discharge also decreased due to the declined snow continuity and the reduced SMR. Overall, by using multiple snow and flow metrics, and by partitioning streamflow into snow-driven and rain-driven flow via the newly proposed approach, we found that SMR and snow continuity determine the streamflow hydrographs and magnitudes in the three alpine watersheds. Given that warming can significantly affect snow dynamics in alpine watersheds in semiarid to arid regions, this has important implications for water resource management in the snow-dominated region when facing future climate warming. Full article

The effect of stage–discharge (H-Q) data uncertainty on the predictions of a MIKE SHE-based distributed model was assessed by conditioning the analysis of model predictions at the outlet of a medium-size catchment and two internal gauging stations. The hydrological modelling was carried out through a combined deterministic–stochastic protocol based on Monte Carlo simulations. The approach considered to account for discharge uncertainty was statistically rather simple and based on (i) estimating the H-Q data uncertainty using prediction bands associated with rating curves; (ii) redefining the traditional concept of residuals to characterise model performance under H-Q data uncertainty conditions; and (iii) calculating a global model performance measure for all gauging stations in the framework of a multi-site (MS) test. The study revealed significant discharge data uncertainties on the order of 3 m³ s⁻¹ for the outlet station and 1.1 m³ s⁻¹ for the internal stations. In general, the consideration of the H-Q data uncertainty and the application of the MS-test resulted in remarkably better parameterisations of the model capable of simulating a particular peak event that otherwise was overestimated. The proposed model evaluation approach under discharge uncertainty is applicable to modelling conditions differing from the ones used in this study, as long as data uncertainty measures are available. Full article

Riparian ecosystems fundamentally depend on groundwater, and accurate estimations of groundwater evapotranspiration (ET_G) are important for understanding ecosystem functionality and managing regional water resources. Over the past several decades, various methods have been proposed to estimate groundwater evapotranspiration based on water table fluctuations. However, the majority of methods cannot resolve sub-daily variations in ET_G. In this study, we proposed a new hydraulic theory-based ET_G estimation method at a sub-daily time scale. To evaluate its performance, we employed a variety of measurements (i.e., water table levels, latent heat flux and soil water contents) at a riparian forest (T. ramosissima) in Northwest China from 25 July to 10 October in 2017. The results indicated that the proposed method can successfully estimate ET_G at both sub-daily (R² = 0.75) and daily (R² = 0.88) time scales, but the variations in the specific yield under different water table conditions should be carefully taken into account. In addition, we investigated the seasonal variations in water uptake source of the riparian plant, and found that it had strong plasticity in water usage during the study period. That is, it consumed approximately equal amounts of soil water and groundwater when soil moisture was available, and tended to consume more groundwater for survival as the soil moisture was depleted. To verify the seasonal patterns of the water uptake of the riparian forest, systematic isotope-based studies are needed in future study. Full article

Natural disasters, including droughts and floods, have caused huge losses to mankind. Hydrological modelling is an indispensable tool for obtaining a better understanding of hydrological processes. The DEM-based routing methods, which are widely used in the distributed hydrological models, are sensitive to both the DEM resolution and the computational cell size. Too little work has been devoted to the joint effects of DEM resolution and computational cell size on the routing methods. This study aims to study the joint effects of those two factors on discharge simulation performance with two representative routing methods. The selected methods are the improved aggregated network-response function routing method (I-NRF) and the Liner-reservoir-routing method (LRR). Those two routing methods are combined with two runoff generation models to simulate the discharge. The discharge simulation performance is evaluated under the cross combination of four DEM resolutions (i.e., 90 m, 250 m, 500 m, and 1000 m) and fifty-six computational cell sizes (ranging from 5 arc-min to 60 arc-min). Eleven years of hydroclimatic data from the Jianxi basin (2000–2010) and the Shizhenjie basin (1983–1993) in China are used. The results show that the effects of the DEM resolution and the computational cell size are different on the I-NRF method and the LRR method. The computational cell size has nearly no influence on the performance of the I-NRF methods, while the DEM resolution does. On the contrary, the LRR discharge simulation performance decreases with oscillating values as the computational cell size increases, but is hardly affected by the DEM resolution. Furthermore, the joint effects of the DEM resolution and the computational cell size can be ignored for both routing methods. The results of this study will help to establish the appropriate DEM resolution, computational cell size, and routing method when researchers build hydrological models to predict future disasters. Full article