Hydrology, Volume 9, Issue 10 (October 2022) – 25 articles

The development of a computationally efficient machine learning-based framework to understand the underlying causes for biases in climate model simulated fields is presented in this study. The framework consists of a two-step approach, with the first step involving the development of a Random Forest (RF) model, trained on observed data of the climate variable of interest and related predictors. The second step involves emulations of the climate variable of interest with the RF model developed in step one by replacing the observed predictors with those from the climate model one at a time. The assumption is that comparing these emulations with that of a reference emulation driven by all observed predictors can shed light on the contribution of respective predictor biases to the biases in the climate model simulation. The proposed framework is used to understand the biases in the Global Environmental Multiscale (GEM) model simulated surface soil moisture (SSM) for the April–September period, over a domain covering part of north-east Canada. The grid cell-based RF model, trained on daily SSM and related climate predictors (water availability, 2 m temperature, relative humidity, snowmelt, maximum snow water equivalent) from the fifth generation European Centre for Medium-Range Weather Forecasts reanalysis (ERA5), demonstrates great skill in emulating SSM, with root mean square error of 0.036. Comparison of the five RF emulations based on GEM predictors with that based on ERA5 predictors suggests that the biases in the mean April–September SSM can be attributed mainly to biases in three predictors: water availability, 2 m temperature and relative humidity. The regions where these predictors contribute to biases in SSM are mostly collocated with the regions where they are shown to be the among the top three influential predictors through the predictor importance analysis, i.e., 2 m temperature in the southern part of the domain, relative humidity in the northern part of the domain and water availability over rest of the domain. The framework, without having to undertake expensive simulations with the climate model, thus successfully identifies the main causes for SSM biases, albeit with slightly reduced skill for heavily perturbed simulations. Furthermore, identification of the causes for biases, by informing targeted climate model improvements, can lead to additional reductions in computational costs. Full article

This study aimed at the analysis and modelling of the groundwater temperature at the water table in different regions of Slovakia. In the first part, the analysis of the long-term trends of air and soil/ground temperature to a depth of 10 m is presented. The average annual soil/groundwater temperatures at different depths were the same but lower than the annual average air temperature by about 0.8 °C. The long-term trend analysis of the air temperature and soil temperature at a depth of up to 10 m in Slovakia showed that the air and soil/ground water temperature have risen by 0.6 and 0.5 °C, respectively, per decade over the past 30 years. The second part of the study aimed at modelling the daily groundwater temperatures at depths of 0.6–15 m below the surface. The simple groundwater temperature model was constructed based on a one-dimensional differential Fourier heat conduction equation. The given model can be used to estimate future groundwater temperature trends using regional air temperature projections calculated for different greenhouse gas emission scenarios. Full article

The study attempts to monitor geomorphological changes (e.g., erosion/deposition) with innovative tools at a typical Mediterranean torrent. The torrent’s geomorphological conditions are studied for an entire affected stream reach. The investigation utilizes two different environments/point views: (a) traditional terrestrial and (b) innovative aerial. The traditional methods include erosion pins at streambanks and field cross-section measurements of the stream channel. For the innovative methods, utilizing an unmanned aerial vehicle, in order to monitor the geomorphologic changes in the entire reach during different days over the last 3 years (2020–2022), there was a total of six flights. The results from innovative methods showcase the episodic nature of stream channel changes since erosion and deposition were captured during the different monitoring periods. Even during one flight in one cross-section, the stream bed and two banks in many cases experienced different changes. The significant erosion and deposition recorded showcase the disequilibrium in the torrent. In addition, the impact of the anthropogenic structure (Irish bridge) is evident, since upstream, more substantial deposition was recorded compared to downstream. The similarity of the results between the innovative method and the traditional methods indicates the method’s effectiveness and the potential usefulness in using UAV images for stream bank and bed monitoring. One of the biggest advantages is the ability to monitor the entire reach at substantially lower costs and time compared to the traditional methods. Still, more testing needs to be conducted in different stream and river environments to better refine the method in order to be adopted by land and water managers to be used for stream and river monitoring. Full article

Hydrologic models are indispensable tools for water resource planning and management. Accurate model predictions are critical for better water resource development and management decisions. Single-site model calibration and calibrating a watershed model at the watershed outlet are commonly adopted strategies. In the present study, for the first time, a multi-site calibration for the Soil and Water Assessment Tool (SWAT) in the Kelani River Basin with a catchment area of about 2340 km² was carried out. The SWAT model was calibrated at five streamflow gauging stations, Deraniyagala, Kithulgala, Holombuwa, Glencourse, and Hanwella, with drainage areas of 183, 383, 155, 1463, and 1782 km², respectively, using three distinct calibration strategies. These strategies were, utilizing (1) data from downstream and (2) data from upstream, both categorized here as single-site calibration, and (3) data from downstream and upstream (multi-site calibration). Considering the performance of the model during the calibration period, which was examined using the statistical indices R² and NSE, the model performance at Holombuwa was upgraded from “good” to “very good” with the multi-site calibration technique. Simultaneously, the PBIAS at Hanwella and Kithulgala improved from “unsatisfactory” to “satisfactory” and “satisfactory” to “good” model performance, while the RSR improved from “good” to “very good” model performance at Deraniyagala, indicating the innovative multi-site calibration approach demonstrated a significant improvement in the results. Hence, this study will provide valuable insights for hydrological modelers to determine the most appropriate calibration strategy for their large-scale watersheds, considering the spatial variation of the watershed characteristics, thereby reducing the uncertainty in hydrologic predictions. Full article

Drinking water systems’ energy footprints depend mostly on the source, quality, and volume of water supply, but also on local temperature and precipitation, both of which are changing with the global climate. From a previous survey, we develop an equation for modeling relative changes in U.S. water utilities’ annual energy use, in which their energy use increases with temperature and decreases with precipitation. To demonstrate, we insert gridded projections from three scenarios in the EPA’s Climate Resilience Evaluation and Awareness Tool (CREAT) and compare 2035 and 2060 periods with a 1981–2010 baseline. Averaged over the continental United States, the 2060 central scenario projects 2.7 °C warmer temperatures and 2.9 cm more annual precipitation. For the same water demand, we estimate that these conditions will cause U.S. water systems’ energy use to change by −0.7% to 13.7% depending on the location (average 8.5% across all grid cells). Warming accounts for a general increase, and local changes in precipitation can add to or subtract from it. We present maps showing the spatial variability for each scenario. Water systems are essential infrastructure that support sustainable communities, and the analysis underscores their needs for energy management, renewable energy, water conservation, and climate change resilience. Full article

Sediment load in streams is known as both a carrier and a potential source of contaminants, while sediment deposition can alter stream flow, stage and morphology, and thereby has broad impacts on stream hydrology, aquatic life, and recreation activity. For vast amounts of watersheds around the world, sparse daily measured sediment data may exist, but continuous and multi-year daily measured sediment data are largely unavailable because of time-consuming and budget constraint for measurements. However, when developing total maximum daily load (TMDL) and calibrating/validating watershed models for sediments, such continuous and multi-year datasets are inevitably required. This study extended the flow-weighted method, developed by Ouyang (Ouyang, Y. Environ. Monit. Assess. 193, 422 (2021)) to predict the continuous and multi-year daily sediment loads based on sparse, limited, and discontinuous measured data. This daily sediment load gap-filling tool was validated using measured data from six different US Geological Survey (USGS) gage stations across US. Results showed that the flow-weighted method well predicted daily sediment loads when a good linear correlation existed between measured seasonal sediment loads and measured seasonal stream discharges, which is a prerequisite to apply the flow-weighted method. Five out of six selected USGS gage stations used in this study met this prerequisite. The flow-weighted method (along with an example R script for implementing the method) is a useful tool for filling the daily sediment load gaps. Full article

Rising land surface temperature (LST), urban heat island (UHI) effects, and stress on surface-, processed-, potable-, and ground-water resources are some undesirable ecological changes due to rapid urbanization. Treating and reusing city-generated wastewater for maintaining urban green spaces (UGS) helps in reducing/preventing groundwater extraction, ensuring sufficient supply of potable water, and bringing down LST. However, the benefits of reusing treated wastewater in UGS for enhancing regulatory ecosystem services (RES) and ushering in a circular economy are yet to be realized. In view of these, the transportation costs of treated wastewater for irrigating the UGS of Panaji city—proposed to be developed as a smart city—were assessed. Field surveys were conducted at seven gardens/parks to collect the primary data on vegetation type (ground cover, hedge plants, and trees) and their daily water requirement. As the main focus of this study, a cost–benefit analysis of (a) drawing the groundwater using borewells versus use of treated wastewater from the city’s STP, and (b) two modes of treated wastewater transport: water tankers vs. pipeline was performed. Our analyses suggest that the copiously available 14 MLD treated wastewater from the STP, which meets all the safety standards, is far in excess of the current requirement of 6.24 MLD for watering the vegetation in all 17 parks/gardens in the city. Pipeline is an efficient (less energy, labor, and time) and economical (~47% more than water that is tanker-based) transportation mode. By utilizing the otherwise unused treated wastewater, which is processed at a cost of over USD half a million annually, the RES offered by the use of treated wastewater are (a) partially curtailing a combined loss of ~16 MLD due to the extraction of groundwater plus evapotranspiration (@8.86 mm d⁻¹) from Panaji city’s 1.86 km² UGS, and (b) reduction in LST ~3–4 °C in all of Panaji city. In addition, with the proficient and sustainable management of UGS and the meeting of many UNSDGs, the enhanced vegetation growth plus elevated carbon sequestration rates in the UGS are possible through the reuse of treated wastewater. Full article

Climate change, urbanization, and a growing population have led to a rapid increase in groundwater (GW) use. As a result, monitoring groundwater changes is essential for water managers and decision-makers. Due to the lack of reliable and insufficient in situ information, remote sensing and hydrological models may be counted as alternative sources to assess GW storage changes on regional and global scales. However, often, these hydrological models have a low spatial resolution for water-related applications on a small scale. Therefore, the main purpose of this study is to downscale the GW storage anomaly (GWSA) of the WaterGAP Global Hydrology Model (WGHM) from a coarse (0.5 degrees) to a finer spatial resolution (0.1 degrees) using fine spatial resolution auxiliary datasets (0.1 degrees), such as evaporation (E), surface (SRO), subsurface runoff (SSRO), snow depth (SD), and volumetric soil water (SWVL), from the ERA5-Land model, as well as the global precipitation (Pre) measurement (GPM-IMERG) product. The Qazvin Plain in central Iran was selected as the case study region, as it faces a severe decline in GW resources. Different statistical regression models were tested for the GWSA downscaling to find the most suitable method. Moreover, since different water budget components (such as precipitation or storage) are known to have temporal lead or lag relative to each other, the approach also incorporates a time shift factor. The most suitable regression model with the highest skill score during the training-validation was selected and applied to predict the final 0.1-degree GWSA. The downscaled results showed high agreement with the in situ groundwater levels over the Qazvin Plain on both interannual and monthly time scales, with a correlation coefficient of 0.989 and 0.62, respectively. Moreover, the downscaled product represents clear proof that the developed downscaling technique is able to learn from high-resolution auxiliary data to capture GWSA features at a higher spatial resolution. The major benefit of the proposed method lies in the utilization of only the auxiliary data that are available with global coverage and are free of charge, while not requiring in situ GW records for training or prediction. Therefore, the proposed downscaling technique can potentially be applied at a global scale and to aquifers in other geographical regions. Full article

This study of the Quantitative Estimation Precipitation (QEP) of rainfall, detected by two Meteorology Radars over Chi Basin, North-east Thailand, used data from the Thai Meteorological Department (TMD). The rainfall data from 129 rain gauge stations in the Chi Basin area, covering a period of two years, was also used. The study methodology consists of: firstly, deriving the QPE between radar and rainfall based on meteorological observations using the Marshall Palmer Stratiform, the Summer Deep Convection, and Regression Model and calibrating with rain gauge station data; secondly, Bias Correction using statistical method; thirdly, determining spatial variation using three methods, namely Kriging, Inverse Distance Weight (IDW), and the Minimum Curvature Method. The results of the study demonstrated the accuracy of estimating precipitation using meteorological radar. Estimated precipitation compared against an equivalent of 2 years of rain station measurement had a probability of detection (POD) of 0.927, where a value of 1 indicated perfect agreement, demonstrating the effectiveness of the method used to calibrate the radar data. The bias correction method gave high accuracy compared with measured rainfall. Furthermore, of the spatial estimation of rainfall methods, the Kriging methodology showed the best fit between estimation of rainfall distribution and measured rainfall distribution. Therefore, the results of this study showed that the rainfall estimation, using data from a meteorology radar, has good accuracy and can be useful, especially in areas where it is not possible to install and operate rainfall measurement stations, such as in heavily forested areas and/or in steep terrain. Additionally, good accuracy rainfall data derived from radar data can be integrated with other data used for water management and natural disasters for applications to reduce economic losses, as well as losses of life and property. Full article

Understanding the changes in climate and land use/land cover (LULC) over time is important for developing policies for minimizing the socio-economic impacts of riverine floods. The present study evaluates the influence of hydro-climatic factors and anthropogenic practices related to LULC on floods in the Kelani River Basin (KRB) in Sri Lanka. The gauge-based daily precipitation, monthly mean temperature, daily discharges, and water levels at sub-basin/basin outlets, and both surveyed and remotely sensed inundation areas were used for this analysis. Flood characteristics in terms of mean, maximum, and number of peaks were estimated by applying the peak over threshold (POT) method. Nonparametric tests were also used to identify the climatic trends. In addition, LULC maps were generated over the years 1988–2017 using Landsat images. It is observed that the flood intensities and frequencies in the KRB have increased over the years. However, Deraniyagala and Norwood sub-basins have converted to dry due to the decrease in precipitation, whereas Kithulgala, Holombuwa, Glencourse, and Hanwella showed an increase in precipitation. A significant variation in atmospheric temperature was not observed. Furthermore, the LULC has mostly changed from vegetation/barren land to built-up in many parts of the basin. Simple correlation and partial correlation analysis showed that flood frequency and inundation areas have a significant correlation with LULC and hydro-climatic factors, especially precipitation over time. The results of this research will therefore be useful for policy makers and environmental specialists to understand the relationship of flood frequencies with the anthropogenic influences on LULC and climatic factors. Full article

This study strives to utilize WEF resources for the sustainable development of the city, with respect to future climate change. Two diffusion scenarios of Rcp8.5 and Rcp2.6 from the 5th Assessment Report by the IPCC, with the output of the HADGEM2 model were used and the city of Borujerd, Iran was chosen as the case study. The urban morphological dataset was calculated using ArcGIS. Furthermore, the water requirement of some crops (apples, grapes, lettuce and vegetables with leaves) is estimated with the NETWAT and CROPWAT models. This output indicates that in the next period, an approximate 2.25 °C change will take place in the temperature and the rainfall will change between 20–40%. Adopting a WEF Nexus, this study suggests that an urban centralized agriculture will provide 21.3% of the local demand for fruit and a significant amount of the local demand for vegetables. The water reused for urban agricultural irrigation purposes and 3.6% of the freshwater resource demand and sewage cycling can be supplied by harvesting rainwater. Water treatment and recycling can also provide 60.74% of the city’s current water demand. Furthermore, the production of biogas from human sewage and urban wastewater can save 32.4% of the current electricity, on a monthly basis. Full article

Reservoir expansion is commonly considered an adaptation strategy to attenuate water shortage conditions. In many locations in the United States, there are ongoing discussions about the effectiveness and feasibility of reservoir expansion with regard to the growing drought conditions and a consequent significant decrease in surface water. This study investigates if the expansion of the existing Unites States reservoirs should be still considered an effective and adequate management solution to cope with water shortages. To this end, we have defined three reservoir expansion metrics to assess the efficiency, feasibility, and usefulness of increasing the storage capacity of 304 reservoirs across the conterminous United States (CONUS). The efficiency metric is defined as the ratio of reservoir average storage to maximum active storage. The feasibility metric is defined as the ratio of reservoir average annual inflow to maximum active storage and the usefulness metric is described as the ratio of the reservoir average annual excess inflow (average annual inflow–maximum active storage) to the average intensity of water shortages. The finding indicates that most reservoirs in Colorado and Utah currently have high or very high efficiency metrics meaning that these reservoirs are, on average, more than half full while most reservoirs in Texas have low or medium efficiency metrics indicating that these reservoirs are, on average, less than half full. Additionally, the feasibility metrics indicate that reservoir expansion in most western and southern states may not be fruitful because the average annual inflow to reservoirs is less than their maximum active storage over the historical period. Nevertheless, the usefulness metrics show that reservoir expansion can be a useful adaptation strategy to mitigate or attenuate water shortages for some reservoirs in California and Colorado while it cannot considerably decrease the intensity of water shortages in Texas. Findings from this study highlight the utility of the assessment of reservoir expansion at a regional scale considering both available freshwater as an input to reservoirs and the potential water shortage conditions as the main trigger. Full article

Propagation of pluvial floods in urban areas, occurring with return time periods of few years, can be well solved using dual models accounting for the mutual relationship between the water level in the streets and the discharges inside the sewer pipes. The extended WEC-flood model (EWEC), based on the use of unstructured triangular meshes and a diffusive formulation of the momentum equations in both the 2D and the 1D lower domains, is presented along with its novelty, limits, and advantages. The model is then applied to a small computational domain in the Palermo area, where only some ‘hard’ data given by one rain gauge has been used for calibration and validation, along with other ‘soft’ data like yes/no surcharge observations and water depths available from photos and interviews. Model input data are mainly geometrical parameters, and calibration parameters are restricted only to average Manning coefficients. In the test case a very good validation has been obtained of three historical events using the EWEC model, with only one average Manning coefficient calibrated using other two historical events. Full article

Assessing fish habitat suitability in a data-scarce tidal river is often challenging due to the absence of continuous water quantity and quality records. This study is comprised of an intensive field study on a 42 km reach which recorded bathymetry and physical water quality parameters (pH, electroconductivity, dissolved oxygen, and total dissolved solids) testing and corresponding water levels and velocity. Frequent water sampling was carried out on 17 out of 90 locations for laboratory water quality tests. Based on this, an interpolation technique, i.e., Inverse Distance Weighted (IDW), generates a map in a Geographic Information System (GIS) environment using ArcGIS software to determine the river water quality parameters. Additionally, a hydrodynamic model study was conducted to simulate hydraulic parameters using Delft3D software followed by a water quality distribution. During validation, the Delft3D-simulated water quality could reasonably mimic most field data, and GIS featured dissolved oxygen. The overall water quality distribution showed a lower dissolved oxygen level (~3 mg/L) in the industrial zone compared to the other two zones during the study period. On the other hand, these validated hydraulic properties were applied in the Physical Habitat Simulation Model (PHABSIM) set up to conduct the hydraulic habitat suitability for Labeo rohita (Rohu fish). Thus, the validated model could represent the details of habitat suitability in the studied river for future decision support systems, and this study envisaged applying it to other similar rivers. Full article

This paper presents research regarding the assessment of the hydrogeological system of the River Nestos Western Delta, NE Greece, during the period of 2019. The procedure included the collection and analysis of relevant hydrological and hydrogeological data concerning the aquifer system of the study area. Specifically, groundwater level measurements and sampling were carried out in a monitoring well network in the shallow unconfined and the deep confined aquifers of the study area, respectively; and surface water sampling was conducted from the River Nestos at selected locations in each of the main drainage canals, as well as in lagoons of the study area; followed by analysis and processing of the relevant chemical analyses results. Finally, piezometric, hydrochemical maps and diagrams were constructed to augment the evaluation of results and the assessment of the system. The present study contributes to the development and management of water resources in the River Nestos Delta area by providing insight into the hydrodynamic and hydrochemical status of the system based on comprehensive contemporary data that can support and justify the compilation of realistic measurements. The conjunctive management of the surface and groundwater in the study area can improve the quantitative and qualitative characteristics of the water. The water level in piezometric maps varies from −4 m up to 16 m for both time periods (May 2019 and October 2019). Moreover, the maximum values of EC are 2700 μS/cm and 2390 μS/cm for the confined and unconfined aquifer systems, respectively. The maximum values of Cl⁻ concentrations are 573.89 mg/L for the confined aquifer system and 514.73 mg/L for the unconfined aquifer system for both time periods (May 2019 and October 2019). Full article

Accurate streamflow prediction is significant when developing water resource management and planning, forecasting floods, and mitigating flood damage. This research developed a novel methodology that involves data pre-processing and an artificial neural network (ANN) optimised with the coefficient-based particle swarm optimisation and chaotic gravitational search algorithm (CPSOCGSA-ANN) to forecast the monthly water streamflow. The monthly streamflow data of the Tigris River at Amarah City, Iraq, from 2010 to 2020, were used to build and evaluate the suggested methodology. The performance of CPSOCGSA was compared with the slim mold algorithm (SMA) and marine predator algorithm (MPA). The principal findings of this research are that data pre-processing effectively improves the data quality and determines the optimum predictor scenario. The hybrid CPSOCGSA-ANN outperformed both the SMA-ANN and MPA-ANN algorithms. The suggested methodology offered accurate results with a coefficient of determination of 0.91, and 100% of the data were scattered between the agreement limits of the Bland–Altman diagram. The research results represent a further step toward developing hybrid models in hydrology applications. Full article

Given the widespread presence of non-native vegetation in urban and Mediterranean watersheds, it is important to evaluate how these sensitive ecosystems will respond to activities to manage and restore native vegetation conditions. This research focuses on Del Cerro, a tributary of the San Diego River in California, where non-native vegetation dominates the riparian zone, creating flooding and fire hazards. Field data were collected in 2018 to 2021 and consisted of water depth, streamflow, and stream temperature. Our data set also captured baseline conditions in the floodplain before and after the removal of burned non-native vegetation in November 2020. Observed changes in hydrologic and geomorphic conditions were used to parameterize and calibrate a two-dimensional hydraulic model to simulate urban floodplain hydraulics after vegetation removal. We utilized the U.S. Army Corps of Engineers’ Hydrologic Engineering Center River Assessment System (HEC-RAS) model to simulate the influence of canopy loss and vegetation disturbance and to assess the impacts of vegetation removal on stream restoration. We simulated streamflow, water depth, and flood extent for two scenarios: (1) 2019; pre-restoration where non-native vegetation dominated the riparian area, and (2) 2021; post-restoration following the removal of non-native vegetation and canopy. Flooding after restoration in 2021 was more frequent compared to 2019. We also observed similar flood extents and peak streamflow for storm events that accumulated half the amount of precipitation as pre-restoration conditions. Our results provide insight into the responses of small urban stream reaches to the removal of invasive vegetation and canopy cover. Full article

During recent decades, the South-Central part of Chile has shown strong vulnerability due to the effects of land use change (LUC). The interaction of these changes with local hydrology has not been adequately investigated and is poorly understood, especially in mountainous areas under irrigated agriculture. We applied the SWAT + agrohydrological model to study the effects of LUC on hydrological fluxes in the Longaví catchment, Maule region, South-Central Chile. Land use maps (LUMs) from 1997, 2009, and 2016 were used in conjunction with a 41-year (1979–2019) hydro-meteorological series of daily observations as forcing data. The dominant changes in land use during the study period relate to agriculture, shrublands, forestry of exotic species, and urban sprawl. First, the LUM of 1997 was used for model setup, sensitivity analysis, calibration, and validation. Second, the impact of LUC documented through LUMs 2009 and 2016 was analyzed. Our analysis clearly reveals that the overall water balance and internal moisture redistribution in the Longaví catchment have been considerably affected by decreases in precipitation, changes in land use and water use practices. Unless a comprehensive regulatory system is introduced that addresses current climatic conditions and territorial use, it is likely that the decrease in water resources will persist and worsen through climate changes. Full article

Increasing demand for water for agricultural use within the Dougherty Plain of the southeastern United States has depleted surface water bodies. In karstic landscapes, such as the Dougherty Plain in southwest Georgia where the linkages between surface and ground waters are close, there is a need to understand the physical characteristics of the subsurface that allow these close linkages. Having a better understanding of the subsurface characteristics will aid numerical modeling efforts that underpin policy decisions and economic analyses. Two common features on this karstic landscape are draws and geographically isolated wetlands. Using LiDAR, aerial imagery, and ground-penetrating radar, this study investigates the subsurface characteristics of a draw and a series of geographically isolated wetlands. GPR reflections indicative of karst features are laterally continuous and connect the landscape to the nearby Ichawaynochaway Creek. The identification of the size and scale of the laterally continuous karstic features will guide the implementation of groundwater models used to determine irrigation and forest restoration programs while minimizing the impacts of water use on surface streams and the ecosystems. Full article

In most developing countries, biophysical data are scarce, which hinders evidence-based watershed planning and management. To use the scarce data for resource development applications, special techniques are required. Thus, the primary goal of this study was to estimate sediment yield and identify erosion hotspot areas of the Andasa watershed with limited sediment concentration records. The hydrological simulation used meteorological, hydrological, suspended sediment concentration, 12.5 m Digital Elevation Model (DEM), 250 m resolution African Soil Information Service (AfSIS) soil, and 30 m resolution land-cover data. Using the limited sediment concentration data, a sediment rating curve was developed to estimate the sediment yield from discharge. The physical-based Soil and Water Assessment Tool (SWAT) model was employed to simulate streamflow and sediment yield in a monthly time step. The result shows that SWAT predicted streamflow with a coefficient of determination (R²) of 0.88 and 0.81, Nash–Sutcliffe Efficiency (NSE) of 0.88 and 0.80, and percent of bias (PBIAS) of 6.4 and 9.9 during calibration and validation periods, respectively. Similarly, during calibration and validation, the model predicted the sediment yield with R² of 0.79 and 0.71, NSE of 0.72 and 0.66, and PBIAS of 2.7 and −8.6, respectively. According to the calibrated model result in the period 1992–2020, the mean annual sediment yield of the watershed was estimated as 17.9 t ha⁻¹yr⁻¹. Spatially, around 22% of the Andassa watershed was severely eroded, and more than half of the watershed (55%) was moderately eroded. The remaining 23% of the watershed was free of erosion risk. Therefore, the findings suggests that applying the sediment rating curve equation, in conjunction with hydrological and sediment modeling, can be used to estimate sediment yield and identify erosion hotspot areas in data-scarce regions of the Upper Blue Nile Basin in particular, and the Ethiopian highlands in general with similar environmental settings. Full article

Streamflow measurements provide valuable hydrological information but, at the same time, are difficult to obtain. For this reason, discharge records of regular intervals are usually obtained indirectly by a stage–discharge rating curve, which establishes a relation between measured water levels to volumetric rate of flow. Rating curves are difficult to develop because they require simultaneous measurements of discharge and stage over a wide range of stages. Furthermore, the shear forces generated during flood events often change the streambed shape and roughness. As a result, over long periods, the stage–discharge measurements are likely to form clusters to which different stage–discharge rating curves apply. For the identification of these clusters, various robust statistical approaches have been suggested by researchers, which, however, have not become popular among practitioners because of their complexity. Alternatively, various researchers have employed machine learning approaches. These approaches, though motivated by the time-dependent nature of the rating curves, handle the data as of stationary origin. In this study, we examine the advantages of a very simple technique: use time as one of the machine learning model inputs. This approach was tested in three real-world case studies against a statistical method and the results indicated its potential value in the development of a simple tool for rating curves suitable for practitioners. Full article

Arid and semi-arid regions typically lack high-resolution river gauging data causing difficulties in understanding rainfall-runoff patterns. A common predictive method for discharge estimation within ungauged catchments is regional flood frequency estimation (RFFE), deriving peak discharge estimates from similar, gauged catchments and applying them to the catchment of interest. The majority of RFFE equations are developed for larger catchments where flow events may be larger and of greater interest. We test a series of RFFE methods derived for the Pilbara region, applying them to new ungauged small catchments under 10 km². Rainfall values are derived from a guideline Australian design rainfall database, Australian Rainfall and Runoff 2019 (ARR2019) which was recently updated with an additional 30 years of rainfall data. RFFE equations are compared to a direct rainfall model to evaluate their performance within small catchments, identifying key limitations and considerations when modelling small headwater catchments. Full article

In this article, we present the use of the coupled land surface model and groundwater flow model SWAT-MODFLOW with the decision support tool WEAP (Water Evaluation and Planning software) to predict future surface-water abstraction scenarios in a complex river basin under conditions of climate change. The modelling framework is applied to the Dee River catchment in Wales, United Kingdom. Regarding hydrology, the coupled model improves overall water balance and low-streamflow conditions compared with a stand-alone SWAT model. The calibrated SWAT-MODFLOW is employed with high-resolution climate model data from the UKCP18 project with the future scenario of RCP85 from 2020 to 2040. Then, water supply results from SWAT-MODFLOW are fed into WEAP as input for the river reach in the downstream region of the river basin. This system is utilized to create various future scenarios of the surface-water abstraction of public water supply in the downstream region—maximum licensed withdraw, 50% authorized abstractions, monthly time series with 1% increases in water use, and maximum water withdraw per year based on historical records repeated every year with 1% increases in water use—to estimate the unmet demands and streamflow requirement. This modelling approach can be used in other river basins to manage scenarios of supply and demand. Full article

The Po River Basin (PRB) is Italy’s largest river system and provides a vital water supply source for varying demands, including agriculture, energy (hydropower), and water supply. The current (2022) drought has been associated with low winter–early spring (2021–2022) snow accumulation in higher elevations (European Alps) and a lack of late spring–early summer (2022) precipitation, resulting in deficit PRB streamflow. Many local scientists are now estimating a 50- to 100-year (return period) drought for 2022. Given the importance of this river system, information about past (paleo) drought and pluvial periods would provide important information to water managers and planners. Annual streamflow data were obtained for thirteen gauges that were spatially located across the PRB. The Old World Drought Atlas (OWDA) provides annual June–July–August (JJA) self-calibrating Palmer Drought Severity Index (scPDSI) data for 5414 grid points across Europe from 0 to 2012 AD. In lieu of tree-ring chronologies, this dataset was used as a proxy to reconstruct PRB regional streamflow. Singular value decomposition (SVD) was applied to PRB streamflow gauges and gridded scPDSI data for two periods of record, referred to as the short period of record (SPOR), 1980 to 2012 (33 years), and the long period of record (LPOR), 1967 to 2012 (46 years). SVD serves as both a data reduction technique, identifying significant scPDSI grid points within the selected 450 km search radius, and develops a single vector that represents the regional PRB streamflow variability. Due to the high intercorrelations of PRB streamflow gauges, the SVD-generated PRB regional streamflow vector was used as the dependent variable in regression models for both the SPOR and LPOR, while the significant scPDSI grid points (cells) identified by SVD were used as the independent variables. This resulted in two highly skillful regional reconstructions of PRB streamflow from 0 to 2012. Multiple drought and pluvial periods were identified in the paleo record that exceed those observed in the recent historical record, and several of these droughts aligned with paleo streamflow reconstructions of neighboring European watersheds. Future research will utilize the PRB reconstructions to quantify the current (2022) drought, providing a first-time paleo-perspective of drought frequency in the watershed. Full article

There have been conflicting findings on hydrological dynamics in tropical montane cloud forests (TMCFs)—attributed to differences in climate, altitude, topography, and vegetation. We contribute another observation-based comparison between a TMCF (8.53 ha; 1906 m.a.s.l.) and a tropical lowland rainforest (TLRF) (5.33 ha; 484 m.a.s.l.) catchment in equatorial Sabah, Malaysian Borneo. In each catchment, a 90° v-notch weir was established at the stream’s outlet and instrumented with a water-level datalogger that records data at 10-min intervals (converted to discharge). A nearby meteorological station records rainfall at the same 10-min intervals via a tipping bucket rain gauge connected to a datalogger. Over five years, 91 and 73 storm hydrographs from a TMCF and a TLRF, respectively, were extracted and compared. Various hydrograph metrices relating to discharge and flashiness were compared between the TMCF and TLRF while controlling for event rainfall, rainfall intensity, and antecedent moisture. Compared to the TLRF, storm-event runoff in the TMCF was up to 169% higher, reflecting the saturated conditions and tendency for direct runoff. Instantaneous peak discharge was also higher (up to 6.6x higher) in the TMCF. However, despite high moisture and steep topography, stream responsiveness towards rainfall input was lower in the TMCF, which we hypothesise was due to its wide and short catchment dimensions. Baseflow was significantly correlated with API20, API10, and API7. Overall, we found that the TMCF had higher runoff, but higher moisture condition alone may not be sufficient to govern flashiness. Full article