Geology Reports⌕ Search

Geology topics

Benjamin B. Mirus

Publications and source records attributed to Benjamin B. Mirus.

At least 37 records · Page 2Linked to original sources
USGS2023

Mapping landslide susceptibility over large regions with limited data

Landslide susceptibility maps indicate the spatial distribution of landslide likelihood. Modeling susceptibility over large or diverse terrains remains a challenge due to the sparsity of landslide data (mapped extent of known landslides) and the variability in triggering conditions. Several different data sampling strategies of landslide locations used to train a susceptibility model are used to mitigate this challenge. However, to our knowledge, no study has systematically evaluated how different sampling strategies alter a model's predictor effects (i.e., how a predictor value influences the susceptibility output) critical to explaining differences in model outputs. Here, we introduce a statistical framework that examines the variation in predictor effects and the model accuracy (measured using receiver operator characteristics) to highlight why certain sampling strategies are more effective than others. Specifically, we apply our framework to an array of logistic regression models trained on landslide inventories collected at sub-regional scales over four terrains across the United States. Results show significant variations in predictor effects depending on the inventory used to train the models. The inconsistent predictor effects cause low accuracies when testing models on inventories outside the domain of the training data. Grouping test and training sets according to physiographic and ecological characteristics, which are thought to share similar triggering mechanisms, does not improve model accuracy. We also show that using limited landslide data distributed uniformly over the entire modeling domain is better than using dense but spatially isolated data to train a model for applications over large regions.

California, Kentucky, North Carolina, West Virgini↗
USGS2023

Prolonged influence of urbanization on landslide susceptibility

Landslides pose a threat to life and infrastructure and are influenced by anthropogenic modifications associated with land development. These modifications can affect susceptibility to landslides, and thus quantifying their influence on landslide occurrence can help design sustainable development efforts. Although landslide susceptibility has been shown to increase following urban expansion, the long-lasting effect of urbanization on landslide susceptibility remains largely unquantified. Hence, susceptibility maps developed based on inventories from non-urbanized areas may incorrectly evaluate the hazard in urbanized areas. To quantify this effect, we analyzed a landslide inventory from southwestern Pennsylvania, where the pulse of urbanization occurred more than a decade before the inventory was created. Using road density as a proxy for urbanization, the study area was divided into urbanized and non-urbanized areas. Susceptibility patterns were computed using statistical analyses of a post-urbanization landslide inventory together with maps of topographic, land cover, and geologic factors. A pre-urbanization landslide inventory was used as a control. Our findings indicate that urbanization has a decades-long effect on landslide susceptibility, where urbanized areas are generally more susceptible to landslides. In urbanized areas landslides are strongly associated with distance from roads and topographic curvature, whereas in non-urbanized landslides are strongly associated with stratigraphic formation and distance from streams. The consistent differences in susceptibility patterns between urbanized and non-urbanized areas indicate that urbanization has a long-lasting effect on landslide susceptibility and that susceptibility estimates should be made separately for these different environments to account for the persistent influence of urbanization.

Pennsylvania↗
USGS2022

National strategy for landslide loss reduction

Executive Summary Landslide hazards are present in all 50 States and most U.S. territories, and they affect lives, property, infrastructure, and the environment. Landslides are the downslope move­ment of earth materials under the force of gravity. They can occur without any obvious trigger. Widespread or severe land­slide events are often driven by such hazards as hurricanes, earthquakes, volcanic eruptions, heavy rain events, flooding, and wildfires. Landslides can also cause their own cascading consequences, such as the spread of hazardous materials or the creation of devastating local tsunamis. This strategy document describes goals and strategic actions of a comprehensive strategy to meet key challenges to reducing the Nation’s risk from landslide hazards equitably and effectively. The document follows the direction of the National Landslide Preparedness Act (Public Law 116–323) by presenting a strategy for addressing landslide hazards, including risk reduction and response. The act directs the Department of the Interior to establish a program that will work with State, Tribal, and local governments as well as with academia, the private sector, community-based groups, and nonprofit organizations to identify landslide hazards and risk and improve communication, coordination, and emergency preparedness, with the objective of reducing landslide losses. As the only Federal program dedicated to landslide hazard science, the U.S. Geological Survey’s Landslide Hazards Program will lead and coordinate many of the efforts described in this strategy document. Landslide hazard risk reduction must be undertaken collectively and collaboratively across the Federal Government. This strategy document will provide a framework for the creation of an interagency management plan that describes the programs, projects, workforce, and budgets required to carry out the national strategy. The strategy outlined in this document presents a vision of how to equitably produce, communicate, and apply landslide data and science to support a broad range of land management, infrastructure, planning, and emergency response decisions. These decisions are made by a variety of actors, including private and nonprofit landholders; State, Tribal, territorial, city, and county planners; emergency managers; engineers; infrastructure managers; Federal agencies and their partners; and community leaders and individuals. Supporting those decisions and reducing the Nation’s vulnerability to landslides requires overcoming three main challenges: (1) gaps in basic information needed to describe and understand landslide occurrence and societal risk, (2) difficulty in accurately mapping and forecasting landslide hazards, and (3) communication and coordination among the many jurisdictions and sectors that have responsi­bility for and interest in reducing landslide losses. To address those challenges, this strategy document puts forward a series of strategic actions to achieve four goals: Assess: Decision makers have access to detailed, nationwide, and contextually relevant information on land­slide hazard and risk. Coordinate: Landslide hazard mitigation, preparedness, response, and recovery efforts are coordinated across Federal, State, Tribal, territorial, and local levels. Plan: Communities and land managers are prepared and able to plan for landslide hazards. Respond: Landslide surveillance, warnings, and responses to events are effective, efficient, equitable, coopera­tive, and data-driven to protect lives, property, infrastructure, and the environment. These strategic actions focus on expanding the knowl­edge of societal risk posed by landslides as well as better understanding of where, when, and why they occur. They focus on applying that knowledge to support landslide risk reduction efforts and decisions, including the establishment of new advisory, coordination, and working groups focused on landslide hazard and risk. They take into account that supporting landslide loss reduction decisions also requires new guidance, tools, and training codeveloped with the entities, organizations, and individuals faced with making those decisions. Finally, they address actions needed to support and expand landslide warning information and improve the technical response to landslide emergencies.

Open-File Report↗
USGS2022

Constructing a large-scale landslide database across heterogeneous environments using task-specific model updates

Preparation and mitigation efforts for widespread landslide hazards can be aided by a large-scale, well-labeled landslide inventory with high location accuracy. Recent smallscale studies for pixel-wise labeling of potential landslide areas in remotely-sensed images using deep learning (DL) showed potential but were based on data from very small, homogeneous regions with unproven model transferability. In this paper we consider a more realistic and practical setting for large-scale heterogeneous landslide data collection and DL-based labeling. In this setting, remotely sensed images are collected sequentially in temporal batches, where each batch focuses on images from a particular ecoregion, but different batches can focus on different ecoregions with distinct landscape characteristics. For such a scenario, we study the following questions: (1) How well do DL models trained in homogeneous regions perform when they are transferred to different ecoregions, (2) Does increasing the spatial coverage in the data improve model performance in a given ecoregion (even when the extra data do not come from the ecoregion), and (3) Can a landslide pixel labeling model be incrementally updated with new data, but without access to the old data and without losing performance on the old data (so that researchers can share models obtained from proprietary datasets)' We address these questions by extending the Learning without Forgetting framework, which is used for incremental training of image classification models, to the setting of incremental training of semantic segmentation models (e.g., identifying all landslide pixels in an image). We call the resulting extension Task-Specific Model Updates (TSMU). TSMU semantic segmentation framework consists of an encoder shared by all ecoregions to capture the similarities between them, and ecoregion-specific decoders to capture the nuances of each ecoregion. This framework is continually updated using a threestage training procedure for each new addition of an ecoregion without having to revisit data from old ecoregions and without losing performance on them.

IEEE Journal of Selected Topics in Applied Earth O↗
USGS2021

Clays are not created equal: How clay mineral type affects soil parameterization

Clay minerals dominate the soil colloidal fraction and its specific surface area. Differences among clay mineral types significantly influence their effects on soil hydrological and mechanical behavior. Presently, the soil clay content is used to parameterize soil hydraulic and mechanical properties (SHMP) for land surface models while disregarding the type of clay mineral. This undifferentiated use of clay leads to inconsistent parameterization, particularly between tropical and temperate soils, as shown herein. We capitalize on recent global maps of clay minerals that exhibit strong climatic and spatial segregation of active and inactive clays to consider spatially resolved clay mineral types in SHMP estimation. Clay mineral-informed pedotransfer functions and machine learning algorithms trained with datasets including different clay types and soil structure formation processes improve SHMP representation regionally with broad implications for hydrological and geomechanical Earth surface processes.

Geophysical Research Letters↗
USGS2021

HydroMet: A new code for automated objective optimization of hydrometeorological thresholds for landslide initiation

Landslide detection and warning systems are important tools for mitigation of potential hazards in landslide prone areas. Traditionally, warning systems for shallow landslides have been informed by rainfall intensity-duration thresholds. More recent advances have introduced the concept of hydrometeorological thresholds that are informed not only by rainfall, but also by subsurface hydrological measurements. Previously, hydrometeorological thresholds have been shown to improve capabilities for forecasting shallow landslides, and they may ultimately be adapted to more generalized landslide forecasting. We present HydroMet, a code developed in Python by the U.S. Geological Survey, which allows users to guide the automated estimation of hydrometeorological thresholds for a site or area of interest, with the flexibility to select preferred threshold variables for the antecedent hydrologic conditions and the triggering meteorological conditions. Users can import hydrologic time-series data, including rainfall, soil-water content, and pore-water pressure, along with the times of known landslide occurrences, and then conduct objective optimization of warning thresholds using receiver operating characteristics. HydroMet presents many additional options, including selecting the threshold formula, the timescale of possible threshold variables, and the skill statistics used for optimization. Users can develop dual-stage thresholds for watch and warning alerts, with a lower, risk-averse threshold to avoid missed alarms and a less conservative threshold to minimize false alarms. Users may also choose to split their inventory data into calibration and evaluation subsets to independently evaluate the performance of optimized thresholds. We present output and applications of HydroMet using monitoring data from landslide-prone areas in the U.S. to demonstrate its utility and ability to produce thresholds with limited missed and false alarms for informing the next generation of reliable landslide warning systems.

Water↗
USGS2021

Numerical analysis of the effect of subgrid variability in a physically based hydrological model on runoff, soil moisture, and slope stability

In coarse resolution hydrological modeling we face the problem of subgrid variability, the effects of which are difficult to express and are often hidden in the parameterization and calibration. We present a numerical experiment with the physically based hydrological model ParFlow‐CLM with which we quantify the effect of subgrid heterogeneities in headwater catchments within the cell size typically used for regional hydrological applications. We simulate homogeneous domains and domains with subgrid heterogeneities in topography or soil thickness for two climates and soil types. The presence of side slope is the main error source, leading to large underestimation of runoff, and marginally also of evapotranspiration. The spatial distribution of soil saturation in the presence of subgrid variability in topography also leads to underestimation of landslide risk. Soil thickness is the second influential subgrid property, affecting soil moisture distribution and surface runoff formation. Results are consistent for the climates and the soil types considered. The topographic wetness index approach is tested as a way to downscale soil moisture simulations within the domain. Although this method is successful in reproducing some spatial variability and patterns, it fails when the coarse grid mean soil saturation is inaccurate or subgrid topography does not represent subsurface flow paths accurately. We conclude that ignoring subgrid variability in topography and soil thickness in coarse‐scale hydrological models may lead locally to underestimation of runoff and slope instability. Users of such models should be aware of these biases and consider ways to include subgrid effects in coarse‐scale hydrological predictions.

Water Resources Research↗
USGS2021

Rapid-response unsaturated zone hydrology: Small-scale data, small-scale theory, big problems

The unsaturated zone (UZ) extends across the Earth’s terrestrial surface and is central to many problems related to land and water resource management. Flow of water through the UZ is typically thought to be slow and diffusive, such that it could attenuate fluxes and dampen variability between atmospheric inputs and underlying aquifer systems. This would reduce water resource vulnerability to contaminants and water-related hazards. Reducing or negating that effect, however, spatially concentrated and rapid flow and transport through the unsaturated zone is surprisingly common and becoming more so with the increasing frequency and magnitude of extreme hydroclimatic events. Arising from the wide range in the rates and complex modes of nonlinear flow processes, these effects are among the most poorly characterized hydrologic phenomena. Issues of scale present additional difficulties. Equations representing unsaturated processes have been developed and tested on the basis of field and laboratory measurements typically made at scales from pore size to plot size. In contrast, related problems of significant interest to society, including floods, aquifer recharge, landslides, and groundwater contamination, range from watershed to regional scales. The disparity between the scale of our understanding and the scale of interest for societal problems has spurred application of these model equations at increasingly coarse resolutions over larger areas than can be justified by existing measurements or theory. This mismatch in scales requires an assumption that spatially averaging slow diffusive flow and rapid preferential flow can effectively represent the influence of both processes across vast areas. Given the currently inadequate recognition and quantitative characterization of focused and rapid processes in unsaturated flow, these phenomena are critically in need of expanded attention and effort.

Frontiers in Earth Science↗
USGS2021

Incorporating the effects of complex soil layering and thickness local variability into distributed landslide susceptibility assessments

Incorporating the influence of soil layering and local variability into the parameterizations of physics-based numerical models for distributed landslide susceptibility assessments remains a challenge. Typical applications employ substantial simplifications including homogeneous soil units and soil-hydraulic properties assigned based only on average textural classifications; the potential impact of these assumptions is usually disregarded. We present a multi-scale approach for parameterizing the distributed Transient Rainfall Infiltration and Grid-Based Regional Slope-Stability (TRIGRS) model that accounts for site-specific spatial variations in both soil thickness and complex layering properties by defining homogeneous soil properties that vary spatially for each model grid cell. These effective properties allow TRIGRS to accurately simulate the timing and distribution of slope failures without any modification of the model structure. We implemented this approach for the carbonate ridge of Sarno Mountains (southern Italy) whose slopes are mantled by complex layered soils of pyroclastic origin. The urbanized foot slopes enveloping these mountains are among the most landslide-prone areas of Italy and have been subjected to repeated occurrences of damaging and deadly rainfall-induced flow-type shallow landslides. At this scope, a primary local-scale application of TRIGRS was calibrated on physics-based rainfall thresholds, previously determined by a coupled VS2D (version 1.3) hydrological modeling and slope stability analysis. Subsequently, by taking into account the spatial distribution of soil thickness and vertical heterogeneity of soil hydrological and mechanical properties, a distributed assessment of landslide hazard was carried out by means of TRIGRS. The combination of these approaches led to the spatial assessment of landslide hazard under different hypothetical rainfall intensities and antecedent hydrological conditions. This approach to parameterizing TRIGRS can be adapted to other spatially variable soil layering and thickness to improve hazard assessments.

Mount Vesuvius↗
USGS2021

Evaluation of techniques for mitigating snowmelt infiltration-induced landsliding in a highway embankment

Infiltration-induced landslides threaten transportation infrastructure around the world, and impose both direct costs through repair and remediation work and indirect costs through lost economic activity. Therefore, finding the most cost-effective techniques to mitigate slope failures that can impact critical infrastructure links is desirable. The Straight Creek landslide, which affects a segment of Interstate 70 in Summit County, Colorado (USA), has experienced seasonal failure driven by rapid springtime snowmelt infiltration since the early 1970s, allowing changes in its stability to be studied. Past studies have established that seasonal failure is driven by pore-water pressure increase caused by the rapid infiltration of snowmelt and the hydraulic conductivity contrast between upper slope materials and the highway embankment. Two remediation designs have been applied to the site, including lightweight caissons beneath the highway surface in 2011 and 2012, and horizontal drains near the slide toe in 2012. The effects of the lightweight caissons and horizontal drains, as well as an alternative drain design that would extend into the hillslope above the highway embankment, are evaluated within a rigorous hydro-mechanical simulation framework along with a method to generate a field of local factor of safety. Model results show that the effect of the lightweight caissons on the factor of safety is no more than 1% during times of critical instability, as they do not affect the seasonal changes in hydrology that cause destabilizing decreases in effective stress along the failure surface. Horizontal drains are intended to reduce pore-water pressures, but the location of existing drains limit their efficacy due to the low hydraulic conductivity of subsurface materials underneath the highway. Model results indicate that these drains are only partially responsible for a reduction in movement rate since their installation, which is also due to lower annual cumulative snowmelt infiltration levels since 2012. Results also show that an alternative drain design could result in increased stability during critical periods by intercepting downslope subsurface flow before it arrives at the hydraulic conductivity contrast at the embankment.

Colorado↗
USGS2021

In-situ monitoring of infiltration-induced instability of I-70 embankment west of the Eisenhower-Johnson Memorial Tunnels, phase III

A new methodology that uses recent advances in unsaturated soil mechanics and hydrology was developed and tested. The approach consists of using soil suction and moisture content field information in the prediction of the likelihood of landslide movement. The testing ground was an active landslide on I-70 west of the Eisenhower/Johnson Memorial Tunnels. A joint effort between Colorado School of Mines, CDOT, and USGS performed detailed site characterization, set up and calibrated a hydro-mechanical model of the site based on seven years of field data, and performed a stability analysis of the slope. Results indicate that consecutive years of high or low infiltration have a compounding effect so that the slope stability is influenced by the preceding years. Additionally, a new drainage system is proposed based on analysis of the current horizontal drains.

Colorado↗
USGS2020

Hillslopes in humid-tropical climates aren’t always wet: Implications for hydrologic response and landslide initiation in Puerto Rico, USA

The devastating impacts of the widespread flooding and landsliding in Puerto Rico following the September 2017 landfall of Hurricane Maria highlight the increasingly extreme atmospheric disturbances and enhanced hazard potential in mountainous humid‐tropical climate zones. Long‐standing conceptual models for hydrologically driven hazards in Puerto Rico posit that hillslope soils remain wet throughout the year, and therefore, that antecedent soil wetness imposes a negligible effect on hazard potential. Our post‐Maria in situ hillslope hydrologic observations, however, indicate that while some slopes remain wet throughout the year, others exhibit appreciable seasonal and intra‐storm subsurface drainage. Therefore, we evaluated the performance of hydro‐meteorological (soil wetness and rainfall) versus intensity‐duration (rainfall only) hillslope hydrologic response thresholds that identify the onset of positive pore‐water pressure, a predisposing factor for widespread slope instability in this region. Our analyses also consider the role of soil‐water storage and infiltration rates on runoff generation, which are relevant factors for flooding hazards. We found that the hydro‐meteorological thresholds outperformed intensity‐duration thresholds for a seasonally wet, coarse‐grained soil, although they did not outperform intensity‐duration thresholds for a perennially wet, fine‐grained soil. These end‐member soils types may also produce radically different stormflow responses, with subsurface flow being more common for the coarse‐grained soils underlain by intrusive rocks versus infiltration excess and/or saturation excess for the fine‐grained soils underlain by volcaniclastic rocks. We conclude that variability in soil‐hydraulic properties, as opposed to climate zone, is the dominant factor that controls runoff generation mechanisms and modulates the relative importance of antecedent soil wetness for our hillslope hydrologic response thresholds.

Puerto Rico↗
USGS2020

Deep Learning as a tool to forecast hydrologic response for landslide-prone hillslopes

Empirical thresholds for landslide warning systems have benefitted from the incorporation of soil‐hydrologic monitoring data, but the mechanistic basis for their predictive capabilities is limited. Although physically based hydrologic models can accurately simulate changes in soil moisture and pore pressure that promote landslides, their utility is restricted by high computational costs and nonunique parameterization issues. We construct a deep learning model using soil moisture, pore pressure, and rainfall monitoring data acquired from landslide‐prone hillslopes in Oregon, USA, to predict the timing and magnitude of hydrologic response at multiple soil depths for 36‐hr intervals. We find that observation records as short as 6 months are sufficient for accurate predictions, and our model captures hydrologic response for high‐intensity rainfall events even when those storm types are excluded from model training. We conclude that machine learning can provide an accurate and computationally efficient alternative to empirical methods or physical modeling for landslide hazard warning.

Oregon↗
USGS2020

Community for data integration 2018 funded project report

The U.S. Geological Survey Community for Data Integration annually funds small projects focusing on data integration for interdisciplinary research, innovative data management, and demonstration of new technologies. This report provides a summary of the 10 projects funded in fiscal year 2018, outlining their goals, activities, and accomplishments.

Open-File Report↗
USGS2020

Landslides across the United States: Occurrence, susceptibility, and data limitations

Detailed information about landslide occurrence is the foundation for advancing process understanding, susceptibility mapping, and risk reduction. Despite the recent revolution in digital elevation data and remote sensing technologies, landslide mapping remains resource intensive. Consequently, a modern, comprehensive map of landslide occurrence across the United States (USA) has not been compiled. As a first step toward this goal, we present a national-scale compilation of existing, publicly available landslide inventories. This geodatabase can be downloaded in its entirety or viewed through an online, searchable map, with parsimonious attributes and direct links to the contributing sources with additional details. The mapped spatial pattern and concentration of landslides are consistent with prior characterization of susceptibility within the conterminous USA, with some notable exceptions on the West Coast. Although the database is evolving and known to be incomplete in many regions, it confirms that landslides do occur across the country, thus highlighting the importance of our national-scale assessment. The map illustrates regions where high-quality mapping has occurred and, in contrast, where additional resources could improve confidence in landslide characterization. For example, borders between states and other jurisdictions are quite apparent, indicating the variation in approaches to data collection by different agencies and disparity between the resources dedicated to landslide characterization. Further investigations are needed to better assess susceptibility and to determine whether regions with high relief and steep topography, but without mapped landslides, require further landslide inventory mapping. Overall, this map provides a new resource for accessing information about known landslides across the USA.

Landslides↗
USGS2020

The future of landslides’ past—A framework for assessing consecutive landsliding systems

Landslides often happen where they have already occurred in the past. The potential of landslides to reduce or enhance conditions for further landsliding has long been recognized and has often been reported, but the mechanisms and spatial and temporal scales of these processes have previously received little specific attention. Despite a preponderance of qualitative and anecdotal evidence, there has been limited analysis. As a result, there is little consensus on the meaning of terms such as landslide repetition, recurrence, and reactivation. This source of confusion is evident when such terms are also used to describe systems where landsliding is prevalent but unrelated to landslide history. Recent findings, partly based on a rare multi-temporal landslide inventory for an area in Italy, show that the impacts of earlier landslides affect a substantial fraction of landslides, that landslides following earlier landslides differ from those that do not, and that accounting for the effect of previous landslides can improve susceptibility assessments. These findings await confirmation in other landslide prone landscapes but show that consecutive landsliding deserves more attention, which requires consistent terminology. No such terminology is presently available, and we therefore propose it in this manuscript. We use the term 'uncorrelated landsliding' to describe situations where landslides are common, but where a correlation with environmental variables such as terrain steepness is not implied. We propose 'correlated landsliding' to describe situations where landslides are common and correlations with environmental variables exist, and 'path-dependent landsliding' to describe situations where causal relations exist between consecutive landslides, for instance when landslides occur at the scarp of previous landslides. These are situations where past landslides impact future landslides. Within the path-dependent category, we distinguish three subcategories based on the spatial distance between earlier and later landslides: 'reactivation' or 'continuation' if essentially the same material recommences or continues to slide, 'local activation' if an earlier slide causes changes in a local hillslope that cause a later slide, and 'remote activation' if an earlier slide causes changes elsewhere in the landscape that cause a later landslide. We use this proposed set of terms to outline some prominent knowledge gaps and potential research questions.

Landslides↗
USGS2020

Temporal and spatial variability of shallow soil moisture across four planar hillslopes on a tropical ocean island, San Cristóbal, Galápagos

Study Region: This paper provides a summary of findings from temporal and spatial studies of soil water content on planar hillslopes across the equatorial island of San Cristóbal, Galápagos (Ecuador). Study Focus: Soil water content (SWC) was measured to generate temporal and spatial records to determine seasonal variation and to investigate how the behavior of surface and near-surface root-zone soil water may support island-wide hydrogeology models. SWC probes were installed at four weather stations in a climosequence to generate a temporal record and spatial surveys of shallow SWC across the selected sites were completed during wet and dry seasons. Temporal differences in SWC were driven by seasonal variations in rainfall and evapotranspiration, while spatial variability remained high during both wet and dry seasons. Unsaturated hydraulic conductivity determined by mini-disk infiltrometers was highly variable across the slopes, as were other hydrologic variables. New Hydrological Insights for the Region: The high heterogeneity of soil water and hydrologic characteristics provides a means to explain why little runoff is observed at the study sites: soils do not saturate uniformly across hillslopes, allowing for runoff generated in one part of the hillslope to be conducted into the soil in adjacent parts of the hillslope. The lack of connected surface runoff helps explain how water enters the groundwater system of the island.

Journal of Hydrology: Regional Studies↗