Hydrological Attributes Research Articles

Linking the Flow Regime of Papyrus‐Dominated Wetlands to Biologically Relevant Hydrologic Attributes

ABSTRACTThe dominant plant species in many African wetlands is Cyperus papyrus. Its adaptation to saturated and low‐oxygen conditions and its dense structure and height provide breeding and feeding grounds for unique flora and fauna. As a keystone species adapted to local hydrology, the flooding regime of papyrus offers the full range of hydrologic conditions and events essential to ecosystem health. However, no study has attempted to link papyrus wetlands' flow regimes to their biologically relevant hydrologic attributes. This study assesses hydrologic alterations of a papyrus wetland's flow regime due to rice irrigation. We develop a conceptual ecological model linking papyrus to hydrologic attributes to determine the consequences of changed environmental flow components (EFCs) on papyrus as a habitat. We find that agricultural water management considerably alters the magnitude, duration, timing and rate of change of EFCs, which could affect productivity (seed dispersal, germination and establishment; rhizome spreading; papyrus distribution across transects; and dispersal of floating mats) in papyrus wetlands. However, the effect on the papyrus wetlands' natural pulsed regime is negligible when the ratio of irrigated area to catchment area is no greater than 1:150. Overall, a better understanding of the threats of water diversion for agriculture is made by linking papyrus' flow regimes to biologically relevant hydrologic attributes. Knowledge of the roles of the various EFCs could provide opportunities for conserving and protecting papyrus wetlands, especially for systems at risk of altered flows.

Hydrological classification of mine pit lakes using modelling experiments

With the growing global prevalence of open-pit mining activities, there is an increasing necessity for sustainable mine life cycle plans with an early outlook towards mine closure. A major consideration in mine closure planning is the potential formation of lakes in the mine void and how these “pit lakes” can be managed to minimise risks and, if possible, create benefits. Understanding the long-term interactions between pit lakes, groundwater, and surface water systems is essential for that purpose. While numerous site-specific studies have been undertaken, there have been no studies that aim to provide a general, broadly applicable understanding of how pit lake hydrology relates to geographical context, and no efforts to hydrologically classify pit lakes using geographical criteria. This research employs an integrated generic pit lake water balance model to examine mine pit lake interactions with the surrounding surface water and groundwater. Simulating 243 scenarios, the influence of five input factors (climate, hydraulic conductivity, regional groundwater level, catchment area and pit slope) on pit lake behaviour up to 6000 years beyond the closure of the mine was considered. The assessment focused on four pit lake hydrological attributes once the system reached equilibrium: water level, time to equilibrium, the fraction of days where the pit recharges groundwater, and the fraction of days where there is surface overflow from the lake. All scenarios were assigned to one of five hydrological classes based on the interactions between the pit lake and the surrounding surface water and groundwater. Our findings show that, in many contexts, general data on climate type and subsurface hydraulic conductivity can yield reliable predictions of a pit lake's long-term hydrological classification without having to develop a detailed, site-specific pit lake model. The classification needs improvements in non-arid climates where inter-annual variation in rainfall is pronounced. The pit lake classification is particularly valuable for first-pass risk assessments to determine whether site-specific modelling is required.

Multi-fidelity deep neural network with Monte Carlo dropout technique for uncertainty-aware risk recognition of backward erosion piping in dikes

Backward erosion piping (BEP) is an increasingly critical failure mechanism in dike systems, often triggered by floods resulting from extreme rainfall events, which are exacerbated by the ongoing shifts in climate patterns. This study embarks on a nuanced examination of BEP, a phenomenon that substantially undermines flood control and emergency management endeavors. Addressing the complexities of BEP incidents and the consequential variable impact on dike infrastructure, we have incorporated Monte Carlo dropout techniques within a multi-fidelity deep learning framework to enhance predictive accuracy and provide an uncertainty-aware assessment of BEP risk. Drawing on 16 sets of physical model experiments emulating dike structures from the lower Yangtze River basin in China, we conducted a detailed study of the BEP evolution mechanism under various hydrological scenarios intensified by climate change. These experiments allowed us to observe the initiation and progression of BEP in different conditions. The development of a structural equation model quantifies the effects of several critical factors—including those exacerbated by climatic variability—on BEP dynamics. Seven key factors were identified as influential to BEP risk levels, integrating distinct BEP traits, hydrological attributes, and dike engineering conditions. A Monte Carlo dropout-enhanced multi-fidelity deep neural network (MFDNN) was crafted, synthesizing low-fidelity experimental data with high-fidelity field case studies, to construct an advanced model for BEP risk level identification. Compared against four sophisticated machine learning models, our MFDNN demonstrated superior performance, effectively blending experimental insights with real-world occurrences. The proposed model emerges as a scientifically robust and pragmatic tool for delineating BEP risk levels in dike systems, providing vital guidance for categorizing BEP incidents and forging targeted, climate-informed emergency response strategies.

Integrating geoenvironmental and socioenvironmental analyses for flood vulnerability assessment in the Kullu Valley, Himachal Pradesh, India

The escalating impacts of climate change necessitate a focused analysis of flood vulnerability in ecologically fragile and densely inhabited mountain areas, particularly in the Himalayan region. This study concentrates on the Kullu Valley within the Beas Basin, Himachal Pradesh, India, a locale characterized by its intricate terrain and dynamic climatic conditions. Employing an integrated approach of using satellite imagery, Geographic Information System (GIS), and field surveys, the research evaluates the region's topographical and hydrological attributes alongside land use patterns to discern flood vulnerabilities. Key findings from this study include, the identification of zones with elevated runoff potential, high-risk settlements, and vulnerable agricultural lands. The complex interplay of steep slopes and intricate stream networks exacerbates the susceptibility to flash floods in the region. The study's socio-environmental analysis resulted in the delineation of five zones, each exhibiting distinct adaptive capacities, exposures, and sensitivities. Particularly, Zones 5 and 4 emerged as highly vulnerable due to dense settlements and economic dependence on agriculture and tourism. This study suggests implementing a 100-m buffer zone along the river and monitoring the use of Common Property Resource (CPR) lands to mitigate flood risks. Our recommendations extend to enhancing emergency response protocols, improving disaster management team preparedness, improving communication and transport infrastructures, and ensuring medical facility accessibility. Overall, this research offers a comprehensive perspective on flood risk factors in mountainous landscapes, serving as a guide for policymakers and disaster management authorities in formulating resilient and sustainable flood mitigation strategies.

Assessing Hydropower Potential in Nepal's Sunkoshi River Basin: An Integrated GIS and SWAT Hydrological Modeling Approach.

This study assessed the hydropower potential of a mountain watershed within the Sunkoshi River basin in Sindhupalchok, Nepal, utilizing geographic information systems (GIS) and the soil and water assessment tool (SWAT) hydrological model. Topographical, soil, land use, meteorological, and discharge data were employed to assess the study area for the appropriateness of hydropower generation. SWAT was utilized to delineate the Sunkoshi basin into 23 distinct subbasins and involved the creation of a detailed river network, incorporating various hydrological attributes including stream links, stream order, stream length, and slope gradient. After that, it was employed to simulate river discharges within these subbasins. The Sequential Uncertainty Fitting Version 2 (SUFI-2) algorithm, integrated within the SWAT Calibration and Uncertainty Program (SWAT-CUP), was employed to calibrate and validate the model. This step involved the adjustment of 25 selected parameters to enhance the model's accuracy and reliability in representing the hydrological processes of the Sunkoshi basin. Model performance was assessed utilizing three well-established efficiency criteria: coefficient of determination (R2 = 0.79), Nash-Sutcliffe efficiency (NSE = 0.73), and percent bias (PBIAS = 17.59). The study identified 36 sites across streams of order 3, 4, and 5 as having potential for hydropower generation. The hydropower potential at each identified site was evaluated using estimated stream flow and topographical head at various probability of exceedance (PoE) levels (40%, 45%, 50%, and 60%). The aggregate hydropower potential of the basin was quantified, yielding a potential of 371.30 MW at a 40% PoE. The findings suggest that an integrated approach combining SWAT-based hydrological modeling within a GIS can accurately assess a river basin's hydropower potential and provide insights into further evaluation of the comprehensive environmental assessment of the fragile Himalayan watersheds.

Hydrochemical characterization of the natural Tamda Lake and its environs (Middle Atlas, Morocco)

The Middle Atlas region, distinguished by its diverse natural landscapes, hosts approximately forty lakes, each formed through unique geological processes, predominantly tectonic movements and karst phenomena. This research focuses on a specific case study of a lake formed by a landslide within the northern Middle Atlas. Employing both field and laboratory water analysis techniques, this study meticulously evaluates the physico-chemical parameters of the lake's water and its adjacent aquatic systems. The primary objective is to delineate the physico-chemical quality of the water in Tamda Lake and to elucidate its hydrological attributes, thereby contributing to the broader understanding of limnological dynamics in geologically diverse terrains. This investigation not only sheds light on the physico-chemical status and hydrological behavior of Tamda Lake but also enhances our comprehension of lake formation processes in the Middle Atlas, offering insights into the environmental and geological factors influencing water quality in naturally occurring lakes.

Physical-hydric attributes in areas under forest-pasture conversion in southern Amazon, Brazil

Description of the subject. The increasing occupation and exploitation of the Amazon has resulted in the increased conversion of forest areas into agricultural land or pasture. This often has negative impacts on soil and water dynamics. Therefore, there is a need for comparative studies between forest areas and those undergoing pasture conversion, in order to provide information to mitigate the impacts caused. Objectives. This study aimed to evaluate the physical and hydrological attributes of areas where forest has been replaced by pasture, located in the southern Amazon, Brazil. Method. The study areas are located in Humaitá, in the southern Amazon. For this study, five pasture areas, one natural pasture area, and one forest area were selected. A transect was established in each area containing fifteen sampling points. Mini trenches were opened at each point to collect soil samples (disturbed and undisturbed) in the 0.00-0.10 and 0.10-0.20 m depth. The samples collected in clods were dried in the shade and then crushed to obtain fine air dried soil. The volumetric rings (undisturbed samples) were saturated on the tension table and finally subjected to a penetrograph. Soil attributes such as sand, silt, clay, macroporosity, microporosity, total porosity, volumetric soil water content, and soil penetration resistance were also determined in the laboratory. The data obtained was tabulated and analyzed using descriptive statistics, a test of means to group the different environments, correlation analysis, and principal component analysis using multivariate analysis. Results. The results indicate that the conversion from forest to pasture harmed soil penetration resistance, volumetric soil water content, total porosity, and soil macroporosity. Pearson correlation in the first depth (0.00-0.10 m) revealed that sand content negatively correlated with microporosity, total porosity, and silt but no correlation with the other attributes. The soil penetration resistance showed a negative correlation with macroporosity, total porosity, and volumetric soil water content, suggesting a direct relationship where changes in the volumes of macroporosity, total porosity, and volumetric soil water content can significantly affect the soil penetration resistance. In the 0.10-0.20 m depth, the soil penetration resistance correlated negatively with macroporosity and positively with microporosity. The total porosity, correlated positively with microporosity and macroporosity and negatively with sand content. In this depth, macroporosity showed no correlation with microporosity. Conclusions. The forest and natural grassland areas stood out for having higher volumetric soil water content, microporosity, and total porosity, while the pasture areas were characterized by high penetration resistance. Amazonia, soil, environmental impact assessment, soil quality

Adapting reservoir operation to climate change in regions with long-term hydrologic persistence

Large-scale climate variability patterns such as El Niño-Southern Oscillation (ENSO) influence the hydrology and hence affect the management of water resources in numerous regions around the globe. The presence of multiyear drought and wet periods is already challenging as these long, extreme, events tend to stress water resources systems much more than multiple, isolated, ones. This manuscript presents a variant of a hydrologically-driven approach to assess the performance of large-scale water resources systems in regions where the long-term persistence that characterizes the flow regime is likely to be affected by climate change. This approach comprises several steps including the construction of a large ensemble of hydrological projections which are bias-corrected in the frequency domain to account for the long-term persistence; the clustering of these projections based on hydrologic attributes to identify likely alterations of the flow regime; and the use of an optimization model to derive allocation policies tailored to identified alterations of the flow regime. The proposed approach is tested on the Senegal River basin which has experienced multiyear dry, normal, and wet periods in the past. The analysis of allocation policies highlights the relevance of climate-tailored policies in adapting to climate change, with climate tailored policies yielding moderate gains under the most extreme alterations, while they remain meaningful under more moderate ones.

Coupled sink and flow accumulation analyses with single flow direction and multiple flow direction algorithms

Abstract A static flood analysis (SFA) toolset is implemented with the purpose of performing simplified, event-based flood inundation modeling. The simulation process is divided into two steps: a topographic sink analysis of the terrain followed by flow accumulation (FA) of runoff volumes. Both procedures are coupled to account for sink storage effects. The sink analysis procedure determines the morphology of present sinks that are utilized during FA to calculate the sink storage by solving a mass balance, whereby inflows are captured in each sink according to their capacity and overflows are routed further downstream. Each sink is enriched with a set of hydrological attributes such as the total inflow, overflow, and flood depth. The flood depth is then utilized to determine the flood extent and flood depths of each sink. Two main options are made available for the FA procedure: either a single flow direction (SFD) or a novel formulation of the multiple flow direction (MFD) algorithm. Both methods were compared in terms of their accuracy with results from the TELEMAC-2D finite-volume solver for an urban inundation model. Both methods showed good agreement when compared with the validation results, with the MFD method performing marginally better than the SFD method.

Aerial characterization of surface depressions in urban watersheds

Digital elevation models (DEM) are one of the most fundamental inputs for hydrological modeling. It has been a common practice to remove all surface depressions in a DEM as they are assumed to be data errors. The emerging technology of unmanned aircraft systems (UAS) provides an opportunity to re-examine this assumption at the hyperspatial resolution. This study was the first attempt to characterize small surface depressions in urban environments using UAS imagery. Using an urban area in south Texas as the study site, UAS flights were conducted to yield hybrid DEMs at the resolution of 8–14 cm, coupled with comprehensive ground truth collection. Surface depressions identified from the UAS DEMs were first corrected based on the vertical accuracy of DEMs and then validated through field surveys, with comparisons to two existing LiDAR DEMs (1-m and 10-m). The hydrological impacts of different DEM-derived estimates of catchment depression storage were examined using the Curve Number method across different design storms. Results show that the UAS DEMs outperformed the LiDAR DEMs in describing the microtopographic control of urban overland flow and associated hydrological connectivity across built and natural features. The 8-cm UAS DEM revealed 926% more depression storage than the 10-m LiDAR DEM. This demonstrates a compelling correlation between increasing DEM resolution and enhanced quantification of depression volume. Consequently, the increased depression storage reduced surface runoff by 41% under a two-year design storm and 13% under a 200-year design storm. The results suggest a strong relationship between the DEM resolution and the derived depression estimates, aligning with the fractal nature of watershed systems. Also, the results indicate that the centimeter-level UAS DEMs were not immune from problems. They could yield fake depressions caused by factors such as vegetation, temporary street objects, and underground sewer pipes. The findings of this study suggest the need to quantify the relationships between DEM resolution and associated hydrological attributes and develop new digital drainage analysis algorithms that could effectively incorporate UAS data into urban hydrological modeling.

Fractal characteristics of the middle reach of the Paraná River floodplain during extreme hydrological events

AbstractThe hydrological events that occur on floodplains are largely defined by precipitation and runoff inputs interacting with their geomorphological characteristics. In the middle Paraná River (MPR), hydrological events have traditionally been studied using water level time series and topographic charts or satellite images of certain floodplain sectors, using the same scale throughout the study area. Different zones of the floodplain were integrated using fractal dimension (FD) to better understand hydrological processes. FD is a useful tool used to characterize the geomorphological complexity of river networks in watersheds. The box‐counting method was applied in the MPR floodplain. The result showed that as water levels increase, FD decreases, thereby indicating the direct relation with the hydrological attributes of the floodplain (e.g., duration, magnitude, recurrence of the flood event, and connectivity). This relation constitutes the basis of a new approach of the analysis of hydrological events and their effect on the MPR floodplain. This research provides fundamental information obtained from two hydrological stages and different spatial scales which may complement the method designed to accurately understand the hydrogeomorphological processes that operate on the Paraná River floodplain. It provides a simple parameter that can be used to evaluate the role that each floodplain section plays in attenuating the hydrograph recorded at each gauge analyzed along the river. This information will be useful in the implementation of management policies directed to the mitigation of future hydrological threats generated by alterations in the hydrological regime due to hydroclimatic variations or watershed alterations. The results further strengthen the basis for integrated sustainable planning of the main channel of the Paraná River, its floodplain, and the resources provided by this ecosystem.

Ecohydrological metrics derived from multispectral images to characterize surface water in an intermittent river

Accurately describing the hydrology of intermittent rivers is a critical step in improving our understanding and management of freshwater ecosystems. Traditional approaches such as using gauged discharge data provide little information once flow ceases and no insight into the location, morphology, or persistence of river pools. However, multispectral images can be used to describe surface water, characterize hydrology, and provide insight into ecological functioning. A multispectral approach is highly cost-effective and well suited to remote intermittent rivers with little or no gauging infrastructure. Here, we develop an algorithm to extract hydrological attributes (i.e., pool area, length, perimeter, and mean width) from multispectral imagery (Sentinel-2) and use these attributes to create a suite of ecologically relevant hydrological metrics. We describechanges in attributes and metrics in a large lowland intermittent river as it transitions from wet to dry over a four-year period. We also describe temporal changes in attributes and metrics among five river sections with contrasting hydrological persistence and fragmentation.Our algorithm successfully identified surface water in the main channel and the adjacent floodplain, the centerline of pools, and their upstream and downstream ends. Metrics proved effective at describing seasonal patterns in hydrology; revealing how the size, complexity, and elongation of surface water features (e.g., pools) decreased as the study river transitioned from wet to dry and how fragmentation increased. Metrics also successfully differentiated the river sections with varied hydrological persistence. Ecohydrological metrics derived from multispectral imagery have the potential to provide meaningful insights into riverine morphology, resilience, and ecological functioning. Our spatial approach represents a significant advancement in the ability to characterize and manage intermittent rivers, which are increasingly threatened by water resource development and a drying climate.

On the application of rainfall projections from a convection-permitting climate model to lumped catchment models

Climate change is predicted to increase rainfall intensity in tropical regions. Convection permitting (CP) climate models have been developed to address deficiencies in conventional climate models that use parameterised convection. However, to date, precipitation projections from CP climate models have not been used in conjunction with hydrological models to explore potential impacts of explicit modelling of convective rainfall on river flows in the tropics. Here we apply the outputs of a continental scale CP climate model as inputs to lumped rainfall-runoff models in Africa for the first time. Applied to five catchments in the Lake Victoria Basin, we show that the CP climate model produces greater river flows than an equivalent model using parameterised convection in both the current and future (c. 2100) climate. However, the location of the catchments near to Lake Victoria results in limited changes in extreme rainfall and river flows relative to changes in mean rainfall and river flows. Application of CP model rainfall data from an area where rainfall extremes change more than the change in mean rainfall to the rainfall-runoff model does not result in significant changes in river flows. Instead, this is shown to be a result of the rainfall-runoff model structure and parameterisation, which we posit is due to large-scale storage in the catchments associated with wetland cover, that buffers the impact of rainfall extremes. Based on an assessment of hydrological attributes (wetland coverage, water table depth, topography, precipitation, evapotranspiration and river flow) using global-scale datasets for the catchments in this research, this buffering may be extensive across humid regions. Application of CP climate model data to lumped catchment models in these areas are unlikely to result in significant increases in extreme river flows relative to increases in mean flows.

Characterization of Subsurface Hydrogeological Structures With Convolutional Conditional Neural Processes on Limited Training Data

AbstractOne of the main issues in the application of statistical‐learning‐based methods to the characterization of hydrological phenomena is the complex parameterization of the high‐quality reconstruction. The difficulty of obtaining enough training data and generating multiple‐scale structures hinder the applications of deep‐learning‐based methods in hydrogeological modeling. Hydrogeological modeling problems can be regarded as stochastic processes, and we propose a novel hydrogeological modeling approach based on convolutional conditional neural processes (GM‐ConvCNPs), a meta‐learning approach for dealing with stochastic processes. In this work, GM‐ConvCNP is used to reconstruct the entire spatial structures of subsurface hydrological attributes and channels from a limited amount of conditioning data. To achieve 3‐D characterization of subsurface structures, 3‐D GM‐ConvCNP is proposed according to the spatial distribution characteristics of 3‐D conditioning data. Compared to other deep‐learning‐based methods, we use a small amount of training data and obtain positive model generalization capabilities. Four data sets of categorical and continuous hydrogeological structures are exploited in the experiments. Various validation tests including variograms, connectivity functions, and multi‐dimensional scaling (MDS) map are used to evaluate the quality of generated realizations. The proposed approach is able to significantly reduce training consumption and improve the performance of realizations compared to a set of different benchmark tests. The experiments confirm that the GM‐ConvCNP model can extract heterogeneous patterns by using meta‐learning from limited training data and reconstruct multiple‐scale hydrogeological structures. A case study demonstrates that our method can be applied to characterize multiple‐source hydrological variables.

Assessing the spatial variability of saturated soil hydraulic conductivity at the watershed scale using the sequential Gaussian co-simulation method

Accurate spatial characterization of saturated soil hydraulic conductivity (KSat) is vital for modeling hydrological processes. An effective representation of KSat by field sampling is very difficult on a watershed scale, due the heterogeneity of landscape characteristics for wide land surfaces. To improve the estimation of spatial distributions of KSat at this scale, Sequential Gaussian Simulation (SGS) and Sequential Gaussian Co-Simulation (SGCS) algorithms were applied. The study was conducted in a 70 ha watershed, with 178 sampling points. In addition to KSat the database is composed of Bulk Density (BD), Total Porosity (TP), Macroporosity (Mac), Microporosity (Mic) and Organic Carbon (OC). The Land Use (LU) was also quantified as a regionalized variable. A principal component analysis generated additional two regionalized variables (PC1 and PC2). The simulations were evaluated by faithfully reproducing the stochastic and spatial characteristics of the KSat original data. To estimate the KSat, a univariate SGS was run first. The reproduction of the KSat histograms was satisfactory, while the semivariograms of the simulated fields underestimated the sill. The SGCS was run using five different secondary variables: BD; Mac; LU; PC1 and PC2. All SGCS satisfactorily represented the original KSat histogram, with a slight overestimation for some simulated fields. The semivariogram was very well reproduced for SGCS, being an indication that the process was quite efficient for all secondary variables. The smallest uncertainties were identified for the SGCS that used BD and Mac as secondary variables while the greatest uncertainties were associated with the SGS. The results of this study demonstrate that soil hydrological attributes as BD and Mac are a reliable auxiliary dataset to improve the spatial assessment of KSat by SGCS at watershed scale.

Reliability assessment of the Lattice-Boltzmann Method for modeling and quantification of hydrological attributes of porous media from microtomography images

Simulation of flow at the pore scale is of paramount importance to characterize the behavior of a fluid in a porous medium. The pore scale simulations allow us to investigate pore-scale mechanisms governing field-scale flow attributes. One of the big challenges in the field of pore-scale simulation is reliable modeling of the no-slip boundary condition, which in the lattice Boltzmann method is usually defined by the bounce-back boundary condition. In the present study, the effect of the magic parameter value on the simulation results was investigated by considering the inherent error in micro-CT images, and finally, the results were compared with the Navier-Stokes method ones. Furthermore, determining the amplitude of the error due to improper image resolution is one of the challenges in simulating micro-CT images, which by defining the magic parameter, analyzing its behavior and effect on results, a proper criterion to estimate the error amplitude of flow characteristics due to the micro-CT imaging is presented. In this study, based on the interpolation between different angles of the boundaries, an ideal magic parameter Λ = 0.21 was presented, which can simulate complex geometries with the least error in terms of the accuracy of the bounce-back boundary condition. It can also be found that for reasonable values of the magic parameter, the error amplitude due to the magic parameter is a subset of the imaging error amplitude. That is D∧⊆DCT. A reasonable value for the magic parameter depends on the number of nodes in the pores, but the range 0.01 <Λ <1.0 is highly reliable, and the range 0.01 <Λ <2.0 is also recommended in networks with a very high number of nodes per unit diameter of the pore. At these intervals, the magic parameter error is always less than the imaging error. Moreover, by defining the ideal boundary in micro-CT images, the results of LBM and NSE methods are compared. The results reveal that the permeability value obtained by the NSE method is always slightly less than the value obtained from the LBM in the magic parameter 0.21 under the same conditions; however, both responses fall within the imaging error range. However, the LBM results at Λ = 0.21 were closer to the ideal and median boundary results in micro-CT images. Finally, the comparison of the numerical results with the experimental ones illustrated that Considering the response that fits the ideal imaging boundary as the most optimal response that can be achieved in the LBM method by choosing Λ = 0.21 to some extent is a logical and appropriate idea.

Compost Amendment Impact on Soil Physical Quality Estimated from Hysteretic Water Retention Curve

Capacity-based indicators of soil physical quality (SPQ) and pore distribution parameters were proposed to assess the effects of compost amendment but their determination was limited to desorption water retention experiments. This study also considered the pore size distribution obtained from adsorption experiments to establish the effectiveness of compost amendment in modifying the physical and hydrological attributes of a sandy loam soil. Repacked soil samples with different compost to soil ratios, r, were subjected to a wetting–drying cycle, and the water retention data were fit to the van Genuchten model to obtain the pore volume distribution functions. The soil bulk density was minimally affected by the wetting–drying cycle but a significant negative correlation with r was obtained. The sorption process involved larger and more heterogeneous pores than the desorption one thus resulting in an estimation of the air capacity SPQ indicators (Pmac and AC) that were higher for the wetting–water retention curve (WWRC) than the drying one (DWRC). The opposite result was found for the water storage SPQ indicators (PAWC and RFC). In general, SPQ indicators and pore distribution parameters were generally outside the optimal range but estimates from the DWRC were closer to the reference values. The water entry potential increased and the air entry potential decreased with an increase in the compost rate. Significant correlations were found between the SPQ indicators estimated from the DWRC and r but the same result was not obtained for the WWRC. It was concluded that compost addition could trigger positive effects on soil hydrological processes and agronomic service as both water infiltration during wetting and water storage during drying are favored. However, the effectiveness of the sorption process for evaluating the physical quality of soils needs further investigation.

Suitable Habitat Dynamics of Wintering Geese in a Large Floodplain Wetland: Insights from Flood Duration

The relationship between hydrological variation and the habitat use of waterbirds in wetland complexes is a significant field of ecological research. Quantification of the relationships between wetland hydrological attributes and waterbirds distribution is critical for the success of waterbird conservation. In this study, flood duration (FD) derived from synthetic aperture radar (SAR) imagery was combined with geese GPS tracking data to quantify the optimal FD thresholds for identifying geese habitats. Based on the thresholds, we defined the suitable habitats of wintering geese and investigated the difference in the spatial distribution pattern of habitat from 2018 to 2020 in Poyang Lake, China. We also considered the role of sub-lakes in habitat protection. The results showed that the area of suitable habitats for wintering geese decreased in both dry and wet years, and the range of optimal FD threshold was wider in normal years than in both dry and wet years. The proportion of suitable habitats per unit area was greater in the sub-lakes than in the whole Poyang Lake. We concluded that FD indices extracted from SAR data are valuable for reflecting the influence of the pattern of hydrological variation on waterbird distribution and for the protection and rational use of wetland ecosystems.

A hydrologically-driven approach to climate change adaptation for multipurpose multireservoir systems

Climate change can significantly affect water systems with negative impacts on many facets of society and ecosystems. Therefore, significant attention must be devoted to the development of efficient adaptation strategies. More specifically, the reoperation of water resources systems to keep the overall performance within acceptable limits should be prioritized to avoid, or at least delay as much as possible, costly infrastructural investments. This manuscript presents a hydrologically-driven approach to support the reoperation of multipurpose multireservoir systems. The approach is organized around 1) the use of a large ensemble of GCM hydro-climate projections to drive a climate stress test; 2) the bottom-up clustering of those hydrologic projections based on hydrologic attributes that are both relevant to the region of interest and interpretable by the operators; and finally, 3) the identification of adaptation measures for each cluster after developing a one-way coupling of an optimization model with a simulation model. The climate impact assessment is illustrated with the multipurpose multireservoir system of the Lievre River basin in Quebec (Canada). Results show that cluster-specific, adapted, operating rules can improve the performance of the system and reveal its operational flexibility with respect to the different operating objectives.

Application of Different Modelling Methods to Arbitrate Various Hydrological Attributes Using CMORPH and TRMM Satellite Data in Upper Omo-Gibe Basin of Ethiopia

Rainfall is a basic input parameter for hydrological modelling that exerts a great influence on the dependability of hydrological simulations. Limited availability of accurate and reliable precipitation data in Abelti watershed of Omo Gibe basin of Ethiopia coerces to use satellite rainfall data to design watershed management practices. The primary objective of this research is to find a better output by comparing and evaluating Climate Prediction Centre Morphing techniques (CMORPH) and Tropical Rainfall Measuring Mission (TRMM). Satellite precipitation products (SPPs) and inputs were incorporated to simulate stream flow. Sensitivity and uncertainty analysis, calibration, and validation of the model were conducted using Soil and Water Assessment Tool (SWAT), Calibration and Uncertainty Program 2012 (SWAT-CUP-2012), particularly the Sequential/Uncertainty Fitting (SUFI-2) algorithm for all rainfall inputs independently. The calibration and validation period was taken as 2003–2010 and 2011–2018, respectively. On the basis of the modelling results of SWAT and uncertainty analysis, TRRM relatively performed well than that of CMORPH. The result illustrated that the SWAT model thoroughly predicted the catchment runoff simulation for all SPPs. However, TRMM-based simulations capture the shape of the observed stream flow hydrograph, and there was slight under and overestimation of the stream flow volume simulated SPPs followed by the reduction of model performance statistics. Bias-corrected satellite rainfall-based simulations significantly improved the model performance as well as the volume of stream flow simulated. The detail study exhibited that the in situ-based simulation outperformed satellite products in terms of the objective functions in the study area.