The defining characteristic of non-perennial rivers is the presence of period of no-flow presenting variable duration and spatial extent. This variability in the flow regime influences the distribution and productivity of vegetation communities, especially riparian vegetation. Non-perennial rivers are often overlooked by society, water managers and ecologists, due to their episodic hydrology, which together with poor existing ecological knowledge means decisions are made without complete environmental system understanding potentially leading to dangerous modifications. Among non-perennial rivers, ephemeral rivers are those that only transport flow after rains are received in the catchment resulting in usually short periods of flow. Drylands and their ephemeral streams are suffering from the influence of many local and global changes, such as agricultural activities, river course modifications, human settlement and climate changes. Anthropogenic activities tend to alter the natural dynamics of these environments, modifying the episodic hydrology to a regime more desirable for human occupation. However, these activities strongly disrupt the flow dynamics, compromising both the flow and flood pulses, vegetation cycles and the maintenance of aquatic habitat. This work focuses on the processing of airborne LiDAR and analysis of satellite fractional cover data to expand the knowledge on vegetation distribution and dynamics in non-perennial rivers. Cooper Creek, located in dryland Australia provides good opportunities to learn important lessons for biodiversity protection and water management. The integrated use of LiDAR data and Landsat Fractional Cover data enhanced our understanding of vegetation and landscape co-functioning by providing more insights of the vegetation dynamics. However, it is necessary to think of effective methods to meaningfully combine these data with contrasting spatial and temporal characteristics: LiDAR data had very high spatial resolution (1 m) acquired at a specific time with Landsat Fractional Cover having coarser spatial resolutions (25 m) being acquired frequently (every 16 days since the mid-1980s).

There are three main hydrological states for temporary streams: dry streambed, standing water and flowing water. Ecologically, the changes between these states can alter habitat conditions, and create hot moments for biochemical processes. Hydrologically, these changes can cause stream network expansion and contraction, which influence flow paths and travel times, and thus (sub) surface water quality and quantity. It is, therefore, important to quantify the spatiotemporal dynamics of the hydrological state of temporary streams, and identify factors that control these dynamics. However, only a few studies have collected high spatiotemporal resolution state data and most of these studies only distinguish between two states: dry and wet (which is often interpreted as flowing water). Therefore, in our study aimed at quantifying the dynamics of the temporary streams in the Studibach catchment, we collected high spatiotemporal resolution data of the three main hydrological states for temporary streams. The Studibach catchment is a 0.12 km² pre-Alpine headwater catchment in Switzerland. The state data was collected using a network of 30 custom-designed multi-sensor monitoring systems. For every monitoring location, the state data was used to determine: (1) the permanence of the three hydrological states, and (2) the wetness thresholds (soil moisture, and rainfall amount and intensity) for state changes. The spatial variability in hydrological state permanence and wetness thresholds were compared to topographic, landuse and channel characteristics. The results show that the standing water state is common, underlining the importance to differentiate between standing water and flowing water states. The rainfall amount thresholds were found to depend on antecedent wetness conditions, and the soil moisture thresholds on the rainfall intensity. The spatial variability in hydrological state permanence and wetness thresholds can be explained by accumulated area, except for in a fault zone between two major types of Flysch bedrock.

Temporary streams (i.e. streams that temporarily cease to flow) are characterized by strongly heterogeneous flow patterns, from rivers that episodically experience droughts to flashy streams that only flow after rainfall events, and can be found in a wide range of climatic settings, from the most arid regions of the world to very humid areas. Temporary streams provide an invaluable contribution to riverine ecosystems, as they host unique habitats that are capable of sustaining a high biodiversity by continuously shifting between lotic, lentic and terrestrial conditions. Several recent studies investigated temporary streams in the recent years, yet the availabile observational datasets about event-based or seasonal network dynamics are still very limited. Empirical or conceptual models are usually employed for assessing the main physical drivers of network dynamics in each specific study site.
In this contribution we present the Stream Length Duration Curve (SLDC), a novel theoretical tool that can efficiently summarize catchment-scale dynamics of the active length, providing an objective way to quantify network dynamics. The SLDC presents different features for streams with different patterns of network activity, such as seasonal vs ephemeral streams, therefore facilitating the identification of the underlying hydrological processes and possibly aiding the assessment of the potential variability of other related processes, such as nutrient transport and greenhouse gas emissions. The SLDC is here applied to a set of Italian headwater catchments characterized by different climates and geologies, where data about temporal changes in the configuration of the flowing stream are available, providing a clue for the characterization of emergent temporal and spatial patterns of network dynamics. The Stream Length Duration Curve can facilitate comparisons across different catchments and time periods, possibly enabling and objective classification of temporary streams.

Water-limited regions in the south of Australia have been affected by water table rise resulting in dryland salinity following deforestation for agricultural land. Plantations were established in the 1990s and early 2000s to counteract dryland salinity; however, a long drought between late 1990s and 2009 caused a lowering of the water table across several catchments, with plantations now posing concerns for water resources because of their high transpiration rates. The water balance of two catchments with intermittent flow was estimated. The two catchments are located in southwestern Victoria, with one dominated by a Eucalyptus globulus plantation and the other by grazing pasture. The catchments were instrumented to measure precipitation, groundwater levels and streamflow since about 2010. In addition, latent heat over the pasture was measured to estimate evapotranspiration (ET) since 2015, and, in a total of two years, sap flow sensors were used to estimate transpiration (T) in the plantation. The estimated ET from water balance components were comparable to the measurements. ET accounted for most of the precipitation in the plantation, even exceeding it in drier years, while for pasture ET was around 90% of precipitation. Although the plantation has higher ET rates than the pasture, the difference is smaller than often found in other regions. The precipitation variability and temperature seem largely responsible to the pasture ET; in contrast, tree plantation appears to sustain T throughout the year. Rainfall in the wet season not only triggers tree transpiration in higher parts of the catchment, but also streamflow in both catchments, with higher flow in the pasture than in plantation. However, the difference in streamflow may be due to contrasting geology, topography and hydraulic conductivity of the two catchments, rather than land use.

In recent decades, characterization of isotope signature (δ¹⁸O and δD)  of groundwater-surface water in a Himalayas catchment is critical to define the sources of moisture and for developing an advance isotope-elevation relationship for paleoelevation reconstructions. In this study, stable isotope datasets are generated from the ninety-six water samples collected from the Indus River, tributaries, streams, glaciers, groundwater, lakes, hot springs, and pond water during the summer season. The Indus River and its major tributaries (such as the Zanskar, Nubra, and Shyok rivers) are characterized by relatively lower δ¹⁸O values, whereas TangTse and other small streams contributing to the Indus are relatively enriched in δ¹⁸O. The δ¹⁸O values of river water vary from -15.7‰ to -6.9‰ with a general enrichment in δ¹⁸O from source to sink. The obtained d‐excess and δ¹⁸O values suggest that the main source of moisture in the studied region is mainly from westerlies and southwest monsoons. Thus, Subcloud evaporation and local moisture recycling play a little role in supplying moisture to the precipitation.  Further, two-component mixing reveals that the Mediterranean sea contributing maximum share (72±12%) of moisture, and the remaining coming from the SW monsoon. Isotopic signature (δ¹⁸O) of tributary-stream and its surrounding regions tends to a best-fit second‐order polynomial relationship between δ¹⁸O and elevation over  3500 masl.  Moreover, the δ¹⁸O elevation gradient of −2‰/km is also founded. This relationship will be highly useful and can be applied to other global rivers for paleo-elevation reconstruction.

To mitigate the negative impacts of increasingly occurring rainfall-induced disasters and contribute to the development of Early Warning Systems, real-time forecasting or nowcasting models are needed to predict extreme river water levels and discharges. This study introduces hydrometeorological tools and methods for the future development of real-time forecasting of river water levels and discharges for use in Early Warning Systems and associated flash flood disaster prevention in the Chugoku region of Japan. The Cell Distributed Runoff Model version 3.1.1 (CDRM) hydrological model calibrated by the Shuffled Complex Evolution optimization method developed at the University of Arizona (SCE-UA) was applied for the river mouth hydrograph calculations using two methods, 5 Calibrated Parameters Method (Troselj et al., 2017, Trošelj and Lee, 2021) and newly introduced 7 Calibrated Parameters Method. The hydrological parameter calibration was conducted for seven first-class rivers on seven the most extreme historical heavy rainfall events (JMA) in the Chugoku region of Japan. Then, the calibrated parameter sets from the six past events were applied with MSM deterministic forecasted rainfall data from the Heavy Rainfall Event of July 2018 to project and validate river mouth hydrographs of all seven rivers. The mean ensembles of river discharges from the six validation cases for every 168 hourly time steps from 3^rd to 9^th July 2018 were compared for validation. The NSE accuracy metrics of the river mouth hydrograph across multiple rivers were satisfactory high (NSE > 0.7) starting from forecast of 6^th July at 9 a.m., which is more than 10 hours before occurrence of the peak river discharges. Based on these results and discussions, we conclude that our proposed methodology can be used for accurate future hydrometeorological real-time forecasting of river discharge hydrographs in the region ahead of flash flood events and thus contribute to the development of Early Warning Systems.

Global warming caused by an increase in greenhouse gases not only changes the regional hydrological cycle, but also increases climate variability, resulting in an increase in the frequency and intensity of extreme climate events. Coupled Model Intercomparison Project Phase 5 (CMIP5) climate change scenario data, which was used in the IPCC's AR5 report, have been used in establishing national climate change adaptation measures after a downscale process. Downscaled climate change scenario data are not only used for modeling in various sector, but also used as a climate exposure factor for vulnerability assessment. Thus it has been used to select spatial priorities of vulnerable areas in the Korean Peninsula. Recently, CMIP6 data based on the Shared Socioeconomic Pathways (SSP) scenario reflecting socioeconomic factors has been released, and this needs to be used as the scientific basis for establishing national climate change adaptation measures in the future. In this study, CMIP6-based downscaled data of 3km resolution were produced using the 11 Global Climate Models (GCMs) using the same procedure as for the production of CMIP5 downscaled data. Finally, the temporal and spatial characteristics of extreme climate index used for vulnerability assessment were evaluated using downscaled data based on CMIP5 and CMIP6 scenarios.

Global warming can intensify the hydrological cycle and lead to more frequent and intense precipitation events around the world. This has become a serious issue for many cities where unprecedented floods due to heavy precipitation have been frequently observed in recent years. How to increase city resilience to floods caused by heavy precipitation under climate change has become one of the major challenges for urban planners and engineering practitioners. Here, we propose a new model to assess urban flooding resilience to heavy precipitation. The model is capable of reflecting the frequent inflow and outflow interactions among grid cells and capturing the rapid generation of surface runoff in urban areas during heavy rainfall. It also accounts for typical characteristics of urban areas, such as large-scale impermeable surfaces and urban drainage systems. In addition, the model uses both surface elevation and instantaneous surface water depth of all grid cells to dynamically determine the directions of horizontal inflow and outflow during each time step of model simulation. This enables the model to capture the reverse-flow phenomenon which is commonly seen in flat urban areas during heavy storms. The proposed model can be used for both real-time urban flood prediction and long-term flood-resilient urban planning in the context of climate change.

Fluvial flooding has been responsible for floods events causing billions of dollars in damage across Canada. Currently, fluvial flooding is often snowmelt-driven and occurs mostly in spring. In a warmer climate, increasing rainfall and changing snowmelt rates could lead to significant shifts in flood-generating mechanisms. Here, projected changes to flood-generating mechanisms in terms of the relative contribution of snowmelt and rainfall are assessed across Canada, based on an ensemble of transient climate change simulations performed using a state-of-the-art regional climate model. Changes to flood-generating mechanisms are assessed for both a late 21st century, high-emissions scenario, and in a 2 °C global warming context. Under 2 °C of global warming, the relative contribution of snowmelt and rainfall to streamflow peaks is projected to remain close to current climate, despite slightly increased rainfall contribution. In contrast, following a high-emissions scenario leads to widespread increases to rainfall contribution and the emergence of hotspots of change in currently snowmelt-dominated regions across Canada. In addition, several regions in southern Canada would be projected to become rainfall dominated. Thus, exceeding the 2 °C threshold implies an increased likelihood that adaptation measures would be required, highlighting the benefits of climate change mitigation.

Assessing the impacts of climate change on hydrological systems requires accurate downscaled climate projections. In the past two decades, various statistical and machine-learning techniques have been developed and tested for climate downscaling; however, there is no consensus regarding which technique is the most reliable for climate downscaling and hydrological impact assessment. In this study, an advanced machine-learning technique, Long Short-Term Memory (LSTM) neural network, is used to build multi-model ensembles for downscaling climate projections from a wide ranges of global and regional climate models, and its performance is compared with a number of traditional statistical and machine-learning methods, such as ensemble average, linear regression, Multi-layer Perceptron, Time-lagged Feed-forward Neural Network, and Nonlinear Auto-regression Network with exogenous inputs. The downscaling input consists of temperature and precipitation projections provided by regional climate models, such as CanRCM4, CRCM5, RCA4, and HIRHAM5, and the output is observation data collected from meteorological stations. Performance of the developed LSTM ensemble is evaluated for two case studies in Canada and China. The downscaled climate projections are further used to assess the hydrological impacts in the southwestern mountainous area in China, with the assist of a fully distributed hydrological model, MIKE SHE. The results can support future applications of LSTM neural networks and other similar data-driven techniques for climate downscaling and hydrological impact assessment.

Climate change has been recognized as having a profound impact on hydrologic processes. Consequently, there is an urgent need to assess this impact on the extreme rainfalls (ERs) for hydraulic structure design. The key challenge is how to develop the linkage between global-scale climate change information and the ERs at a given local site of interest. In addition, several existing approaches have been proposed to establish this linkage at “gaged” sites but very few methods are available for linking climate change projections to the ERs at an “ungaged” location where rainfall record is limited or unavailable. Therefore, the main objective of this study is to propose an innovative approach that could be used for constructing reliable IDF relations at an ungaged location in consideration of climate change projection uncertainty given by different climate models. The proposed approach consists of a regional-to-point downscaling to link daily regional rainfalls to daily extreme rainfalls at a given ungaged site and a temporal downscaling using the scale-invariance GEV model to link daily-to-sub-daily extreme rainfall distributions at the same location. The feasibility and accuracy of the proposed approach were assessed using the 25-km regional rainfall data that have been downscaled by NASA from the climate simulations of 21 different Global Climate Models, and the observed extreme rainfall data from a network of 69 raingages located in Ontario region (Canada). The jackknife technique was used to represent the ungaged site conditions. Results of this illustrative application have indicated the feasibility and accuracy of the proposed approach.

There exists an urgent need to assess the possible impacts of climate change on the extreme rainfall Intensity-Duration-Frequency (IDF) relations in general and on the design storm in particular for improving the design of urban water infrastructure in the context of a changing climate. At present, the derivation of IDF relations in the context of climate change at a location of interest has been recognized as one of the most challenging tasks in current engineering practices. The main challenge is how to establish the linkages between the climate projections given by Global/Regional Climate Models at global/regional scales and the observed extreme rainfalls at a given local site or at many sites concurrently over an urban catchment area. If these linkages could be established, then the projected climate change conditions given by climate models could be used to predict the resulting changes of local extreme rainfalls and related runoff characteristics.  Consequently, innovative downscaling approaches are needed in the modeling extreme rainfall (ER) processes over a wide range of temporal and spatial scales for climate change impact and adaptation studies in urban areas. Therefore, the overall objective of the present paper is to provide an overview of some recent progress and shortcomings in the modeling of extreme rainfall processes in a changing climate from both theoretical and practical viewpoints. In particular, the main focus of this paper is on the recently published technical guide by the Canadian Standards Association (CSA PLUS 4013:19) entitled “Development, interpretation, and use of rainfall intensity-duration-frequency (IDF) information: Guideline for Canadian water resources practitioners” to provide some guidance to water professionals in Canada on how to consider the climate change information in the design of urban water infrastructure.

The stable water isotopes are a widespread tool to trace physical and chemical processes in hydrology research. With a substantial amount of water samples for analysis, the throughput becomes critical on the premise that the best precision of the analyzer is maintained. Here, we will present two new methodologies for the Picarro L2130-i Cavity Ring-Down Spectroscopy (CRDS) water isotope analyzer that allow improving the throughput with no compromise of data quality. With respective optimization on memory reduction and sample analysis, Picarro Express Method allows to measure up to 50 samples per day while maintaining excellent precision. This method alone corresponds to nearly doubling the throughput compared to the standard Picarro methodology. Picarro Survey Method utilizes ultrafast injections (~ 900 injections per day) and enables sorting samples for the minimal isotopic difference between adjacent samples. It will save injections for memory reduction and thus improves the throughput further. We will discuss different measurement strategies to increase the throughput for routine water isotope analysis, without sacrificing precision. The improved methodologies do not require any hardware changes and are solely based on modifications of the injection procedure.

In the era of integrated water management and climate change, the government plans to expand groundwater monitoring network in South Korea. New monitoring sites are determined based on the existence of long-term trend in groundwater levels. For the determination of trend existence, multilayer perceptron as an artificial neural network is used by using 14 input variables (related to topographic slope, distance to stream, land use, soil, hydrogeology, and density of well), which are extracted using GIS technology, and 1 output variable (long-term trend of groundwater levels) of the existing monitoring sites. Current 521 monitoring wells are used to develop this MLP model. For the whole region of South Korea, 98,667 positions of 1 km interval are tested with the MLP model and the candidates for new monitoring site and their priorities are proposed based on the prediction of trend existence of groundwater levels. 814 sites are newly proposed and totally 1475 wells will be installed in 2045 in this country. This number will be efficient for systematic groundwater management with the density of 1.5 per 100 km². The accuracy of the MLP model for a prediction of groundwater future characteristics may not be perfect but acceptable to select a new site because their prediction accuracy is 83.7% Acknowledgements: This work was supported by the National Research Foundation of Korea and the Ministry of Science and ICT (No. NRF-2019R1A2C1088085).

Fracture size is a critical parameter in characterizing fracture rocks’ hydrogeological properties. However, unbiased fracture size estimates cannot be obtained from in-situ fracture mapping due to censoring, truncation, and orientation biases. The Power-law distribution has been favored as the representative probability density function (pdf) of fracture size. Theoretically, an exponent D and a minimum fracture size x₀ are required to define the Power-law pdf, f_X(x). Unfortunately, x₀ is impossible to be determined in the field due to the truncation effect. In this study, we proposed two different estimates of x₀ based on fracture trace data collected from outcrops.Our analysis started by deriving the pdf of trace length exposed on a sampling plane, f_T(t), in which the mean fracture radius R was used as a parameter. The resulting f_T(t) has a similar form to f_X(x)  but replacing D  by D-1. A point estimate of x₀ was derived from f_T(t). On the other hand, we have also derived a confidence interval of x₀  based on the central limit theorem and the sample mean of outcrop trace length. We have applied the above analysis to sample data collected from three sampling windows deployed along the southeastern coastline of Kinmen, a granitic island to the west of Taiwan. However, sample data was significantly subjected to the censoring effect because large fractures tend to submerge into the ocean. Kilometer-scale surface lineaments were also included in the data analysis. All the sample data have confirmed that fracture size follows the Power-law distribution with an estimate of D as 2.6. The point estimate of x₀ was calculated as 0.57 m, which agrees with the upper limit of the confidence interval of x₀. Thus, a consistent estimate of  x₀ can be obtained from outcrop fracture trace samples.

The Loess Plateau in China is deeply affected by the East Asian monsoon, with high variability of rainfall. The ecological environment on the Loess Plateau is extremely fragile and natural disasters occur frequently. The traditional hydrology-ecology-human-land process on the Loess Plateau is undergoing profound changes, and the traditional research methods are not enough to answer a series of new phenomena and problems faced by the Loess Plateau in the new era. Therefore, this study takes the thicket ecological system in the Loess Plateau gully region as the research object, and uses the eddy covariance technique to observe the flux of water and energy. We observed the land surface energy flux and meteorological data in the thicket ecosystem of Chunhua Ecohydrological Experiment Watershed on the Loess Plateau gully region in 2019-2020. In the thicket ecosystem, the changes in energy flux and the distribution characteristics of the energy component are analyzed. We calculate the energy closure and Bowen ratio and explore the correlation of latent heat and sensible heat flux to the environmental factors. The results will improve the understanding of the land-atmosphere water and heat exchange mechanism in thicket ecosystem in the gully region of the Loess Plateau.

The riverbed changes by erosion, transport, and deposition depending on the flow rate and flow of sedimentation, and the consequences of the riverbed change are shown in the form of channels (single, meandering, braided, etc.) and the structure of habitats (riffle and pool, wetlands, backwater, etc.). Although the river is linked to the spatial and temporal scales, the restoration project of the river is mainly focused on the construction of the habitat structures. However, in order to manage ecological restoration projects, the river management should be carried out in linking with the flow rate or sediment movement and habitat structure with species diversity. This study propose the framework by linking flow regime (discharge or sediment) with geomorphology (habitat structure) and ecology (species diversity). Prior to hydraulic modeling, the relationship between river species monitoring results and diversity of habitat structure used for river health assessment is analyzed first, and then hydraulic modeling is conducted. The Nays2D hydraulic modeling is used to examine the riverbed changes, and the hydraulic modeling results are interpreted as the habitat structure distribution. The subjects of this study are species data, aerial photographs, and topographical data (DEM and cross-sectional data) of the Guem River basin.This work was supported by the National Research Foundation of Korea (NRF-2019R1I1A1A01060142).

The Han River is the water source area of the South-to-North Water Diversion Project and the “Han River to Wei River Water Project” in China. The Han River is the largest tributary of the Yangtze River. There are several large reservoirs in the Han River basin, which undertake the task of water supply and power generation. In recent years, under the influence of human activities, the coupling effect of human and water in the upper reach of the Han River is obvious. On the analysis of the socio-ecological-hydrological changes in southern Shaanxi, Han River, a benchmark model of socio-ecological-hydrological of Han River in the southern Shaanxi was constructed in this study. Sensitive parameters were selected and we set four development modes: natural continuation model, economic development model, green development model and industrial adjustment model. Taking 2018 as the base year, the evolution process of social eco-hydrology in the upper reach of the Han River in the future is studied under four scenarios from 2019 to 2045. The simulation results show that the industrial adjustment model and green development model are relative superiority to other plans, natural continuation model is the second, and the economic development model were the most unfavorable to the coordinated development of water resources and economy. Meanwhile, according to the development scenarios, some policy suggestions were put forward, such as reducing industrial water quota, adjusting industrial structure properly, promoting the development of tertiary industry, reducing irrigation quota, and strengthening the reclamation of reserve land resources.

Sayogawa River is located in Nishiharima, Hyogo, Japan. It is a mountainous river and the catchment area is approx. 191 km². The river experienced severe flooding in August 2009 by Typhoon Etau. The number of death and missing was 18 and 2 at the flooding. The peak discharge attained approx.1400 m³/s at Enkouji of the Sayogawa main river. The 24 hour rainfall at Sayo rainfall observatory was 327 mm. The discharge hydrograph of the flooding at Enkouji was first reproduced by two rainfall-runoff models, a storage function and a distributed rainfall-runoff/flood-inundation model. Then, the climate change effect was investigated using a dataset called d4PDF (database for Policy Decision-making for Future climate change).Specifically, hourly rainfall data of d4PDF for the present (1951-2011) and future (2051-2111) climate was used. In the analysis, the bias of the original d4PDF was corrected with a Dual-Window method (Watanabe et al., 2018). The annual maximum 24 hour rainfall was calculated with the bias corrected data and plotted using a Cunnane formula. GEV distribution was fitted to the plots by Cunnane formula. Then the change of the shape of the GEV distribution between the present and future climate was analyzed. Afterwards, the hourly rainfall hyetograph of the annual maximum 24 hour rainfall was given to the two rainfall-runoff models. The discharges simulated were also fitted to GEV distribution. The change of the shapes of GEV between the present and future was also analyzed. Specifically, difference by multi rainfall-runoff models were investigated in detail. The details will be presented at the virtual conference.

The purpose of this study is to economically assess the flood risk of enterprises in Toyama City and its surrounding cities and villages, which are located in a flood-prone area.
Damage to firms caused by flooding not only directly causes damage by shutting down operations, but also indirectly affects the operations of related firms and becomes a major barrier to regional recovery and reconstruction in the form of disruption of distribution networks and supply chains.
Japanese companies have generally developed business continuity plans (BCP) and disaster prevention plans for earthquakes. Therefore, the rate of developing BCPs for water-related disasters is low.
In this study, as a basic study for the formulation of BCPs for water-related disasters, we conducted an analysis to clarify the risk of flood inundation in the event of a water-related disaster for companies based in the study area.
The datasets used in this study are: maps of the estimated flooding areas of the Jinzu River, Joganji River, Kumano River, Ida River, and Nishiha River at the designed scale (L1) and the assumed maximum scale (L2); data from the Economic Census Activity Survey; and corporate data sold by the Hokuriku Economic Research Institute and Tokyo Shoko Research.
After processing the data to match the spatial resolution of the Economic Census, we compared the different enterprise data on a single mesh.
As a result of this study, 16,284 establishments and 358,064 employees in L1 and 19,111 establishments and 420,226 employees in L2 were located in the designed inundation area of Toyama City. In addition, the total sales revenue of the companies in the inundation risk area was estimated to be 1,966.4 billion yen in L1 and 2,156.5 billion yen in L2.
The risk of inundation by a large river in Toyama City was assessed from an economic perspective.

The metalloids arsenic (As) and antimony (Sb), owing to similar chemical speciation and bond coordination, are both strongly chalcophilic toxic elements that most commonly occur in association with hydrothermal ore deposits and geothermal systems. The environmental mobility and toxicity of both elements is controlled by microbiological biotransformations, which have direct implications for responses aimed at managing As and/or Sb contaminated sites. The oxidized chemical species of these elements, serve as electron acceptors for microbial respiration under anoxic conditions, where As(V) reduction to As(III) serve as a detoxification strategy for numerous phylogenetically diverse prokaryotes. Few studies have been directed toward the isolation and characterization of bacteria from natural settings that reduce Sb(V), and no previous studies have reported an analogous detoxification mechanism which involves enzymatic Sb(V) reduction. We have isolated a robust Sb-resistant and Sb(V)-reducing bacterium (JABI-1) from lake sediments collected from Warner Valley, OR. JABI-1 can grow by fermenting amino acids  in the presence of notably high Sb concentrations (up to 15mM) while reducing Sb(V) to Sb(III), driving precipitation of amorphous Sb₂O₃ (senarmontite), or alternately Sb₂S₃ (stibnite) when sulfide is present.  This organism is also highly As-resistant and possesses the arsC gene which encodes for reductive As(V) resistance. JABI-1 did not attain higher cell density with the presence of either Sb or As compared to in their absence, suggesting that the observed reduction is a detoxification strategy.  qPCR amplification of arsC gene products demonstrated that this gene is expressed during growth of JABI-1 in the presence of Sb(V) providing the first evidence that the arsC gene may also confer resistance for Sb.  This report adds to the current knowledge concerning microbiological interactions with Sb and provides new evidence suggesting arsC, may detoxify Sb.

Over the past decade, there  have  been  increasing  interests  in  the  use  of  natural coagulants as an environmentally friendly alternative to chemical coagulants in coagulation-flocculation process. The current study investigated the effectiveness of two natural coagulants (wheat starch and dolomite stone powder) for treatment of wastewater from blast furnace 3 of the Isfahan Steel Company in Central Iran. A factorial design was applied to determine the effect of selected factors (pH, coagulant dosage and type of coagulant) on the pollutant reduction. It was found that all factors and their interactions exhibited a significant effect on turbidity removal when applying the extracted natural coagulants in a standard coagulation process. The highest percentage of turbidity reduction (98%) was observed at pH 7 with a coagulant dosage of 60 mg/l for the wheat starch. In addition, the highest percentage of turbidity removal (98.1%) using dolomite was measured at pH 7 with a coagulant dose of 30 mg/l. Wheat starch and dolomite were not effective in removing of EC, TDS and TS from wastewater (less than 20 %). Additionally, the chemical/natural coagulants combinations were effective in turbidity removal. The highest decrease in turbidity of wastewater (96%) was detected when combination of chemical and natural coagulants used at both natural pH and pH 7. However, combination of chemical/natural coagulants were not effective for EC removal from wastewater (less than 10 %). The optimal coagulant dosage and optimal pH were determined to be 3 mg/l and natural pH of wastewater for all chemical/natural coagulants combinations.  Overall, the results of this study provided the potential use of dolomite powder and wheat starch as primary or partial natural coagulants in the steel industry wastewater treatment process to decrease the negative contributions of conventional chemical coagulants.