Soil Water Index Research Articles

Assessment of SMADI and SWDI agricultural drought indices using remotely sensed root zone soil moisture

Abstract. The increasing frequency of drought events has expanded the research interest in drought monitoring. In this regard, remote sensing is a useful tool to globally mapping the agricultural drought. While this type of drought is directly linked to the availability of root zone soil moisture (RZSM) for plants growth, current satellite soil moisture observations only characterize the water content of the surface soil layer (0–5 cm). In this study, two soil moisture-based agricultural drought indices were obtained at a weekly rate from June 2010 to December 2016, using RZSM estimations at 1 km from the Soil Moisture and Ocean Salinity (SMOS) satellite, instead of surface soil moisture (SSM). The RZSM was estimated by applying the Soil Water Index (SWI) model to the SMOS SSM. The Soil Moisture Agricultural Drought Index (SMADI) and the Soil Water Deficit Index (SWDI) were assessed over the Castilla y León region (Spain) at 1 km spatial resolution. They were compared with the Atmospheric Water Deficit (AWD) and the Crop Moisture Index (CMI), both computed at different weather stations distributed over the study area. The level of agreement was analyzed through statistical correlation. Results showed that the use of RZSM does not influence the characterization of drought, both for SMADI and SWDI.

Dynamic Modeling of Sediment Budget in Shihmen Reservoir Watershed in Taiwan

Qualifying sediment dynamic in a reservoir watershed is essential for water resource management. This study proposed an integrated model of Grid-based Sediment Production and Transport Model (GSPTM) at watershed scale to evaluate the dynamic of sediment production and transport in the Shihmen Reservoir watershed in Taiwan. The GSPTM integrates several models, revealing landslide susceptibility and processes of rainfall–runoff, sediment production from landslide and soil erosion, debris flow and mass movement, and sediment transport. For modeling rainfall–runoff process, the tanks model gives surface runoff volume and soil water index as a hydrological parameter for a logistic regression-based landslide susceptibility model. Then, applying landslide model with a scaling relation of volume and area predicts landslide occurrence. The Universal Soil Loss Equation is then used for calculating soil erosion volume. Finally, incorporating runoff-routing algorithm and the Hunt’s model achieves the dynamical modeling of sediment transport. The landslide module was calibrated using a well-documented inventory during 10 heavy rainfall or typhoon events since 2004. A simulation of Typhoon Morakot event was performed to evaluate model’s performance. The results show the simulation agrees with the tendency of runoff and sediment discharge evolution with an acceptable overestimation of peak runoff, and predicts more precise sediment discharge than rating methods do. In addition, with clear distribution of sediment mass trapped in the mountainous area, the GSPTM also showed a sediment delivery ratio of 30% to quantify how much mass produced by landslide and soil erosion is still trapped in mountainous area. The GSPTM is verified to be useful and capable of modeling the dynamic of sediment production and transport at watershed level, and can provide useful information for sustainable development of Shihmen Reservoir watershed.

Assessment of Root Zone Soil Moisture Estimations from SMAP, SMOS and MODIS Observations

In this study, six satellite-based root zone soil moisture (RZSM) estimates from March 2015 to December 2016 were evaluated both temporally and spatially. The first two were the Soil Moisture Active Passive (SMAP) and the Soil Moisture and Ocean Salinity (SMOS) L4 RZSM products. The other four were obtained through the Soil Water Index (SWI) approach, which embedded surface soil moisture (SSM). The SMOS-Barcelona Expert Center (BEC) L4 SSM product and the apparent thermal inertia (ATI)-derived SSM from the Moderate Resolution Imaging Spectroradiometer (MODIS) data were used as SSM datasets. In the temporal analysis, the RZSM estimates were compared to in situ RZSM from 14 stations of the Soil Moisture Measurements Station Network of the University of Salamanca (REMEDHUS). Regarding the spatial assessment, the resulting RZSM maps of the Iberian Peninsula were compared between them. All RZSM values followed the temporal evolution of the ground-based measurements well, although SMOS and MODIS showed underestimation while SMAP displayed overestimation. The good results obtained from MODIS ATI are notable, notwithstanding they were not estimated through microwave radiometry. A very high agreement was found in terms of spatial patterns for the whole Iberian Peninsula except for the extreme north area, which is dominated by high mountains and dense forests.

An early warning criterion for the prediction of snowmelt-induced soil slope failures in seasonally cold regions

In Hokkaido, Japan, soil slope failures occur frequently during the snow melting season. These slope failures are triggered by the excess amount of water derived from snowmelt and rainfall. For the prediction of snowmelt-induced soil slope failures in seasonally cold regions, an early warning criterion is required. The existing Japanese early warning criteria for sediment disasters, i.e., the relationship between the 60-min cumulative rainfall and the Soil Water Index (SWI), the effective rainfall index etc., consider the influence of rainfall and the time-dependent random moisture of the soil. However, these criteria do not consider the soil moisture contributed by the snowmelt water. In this study, therefore, the applicability of the existing early warning criteria to predict snowmelt-induced soil slope failures is examined. An empirical method to quantify the amount of snowmelt water is presented. Various scenarios of conceptual soil slope failures are studied using numerical simulations under different magnitudes of rainfall and snowmelt water. As a result, a revision is introduced for the SWI and the effective rainfall index, adding the amount of snowmelt water to that of rainfall, and slope failure scenarios are studied. Based on the results, a new early warning criterion, the Effective Precipitation (EP) index, is introduced. It is found that the new failure criterion performs well for the prediction of snowmelt-induced soil slope failures.

Evaluating Consistency between the Remotely Sensed Soil Moisture and the Hydrological Model-Simulated Soil Moisture in the Qujiang Catchment of China

Accurate soil moisture estimation plays a crucial role in agricultural management and hydrological studies. Considering the scarcity of direct in-situ measurements, it is important to evaluate the consistency of soil moisture data acquired in indirect ways, including both satellite products and simulation values obtained via hydrological models. In this study, two types of high spatial-resolution remotely sensed values, namely the surface soil moisture (SSM) and the profile soil water index (SWI), are estimated from each of the ASCAT-A, ASCAT-B, SMAP and SMOS microwave satellites. They are compared with two groups of model-simulated daily soil moisture values, which are obtained by implementing the lumped Xinanjiang (XAJ) model and the DEM-based distributed hydrological model (DDRM) across the Qujiang catchment, located in southwest China. The results indicate that for each satellite product, SWI values always show closer agreement with model-simulated soil moisture values than SSM values, and SWI values estimated from ASCAT products perform best in terms of correlation coefficient with the model-simulated soil moisture, at around 0.8 on average, followed by the SMAP product, which shows a correlation coefficient of 0.48 on average, but the SMOS product shows poor performance. This evaluation of consistency provides useful information on their systematic differences and suggests subsequent studies to ensure their reconciliation in long-term records.

Effects of land use changes on soil water conservation in Hainan Island, China

In tropical areas, a large number of natural forests have been transformed into other plantations, which affected the water conservation function of terrestrial ecosystems. In order to clari-fy the effects of land use changes on soil water conservation function, we selected four typical land use types in the central mountainous region of Hainan Island, i.e., natural forests with stand age greater than 100 years (VF), secondary forests with stand age of 10 years (SF), areca plantations with stand age of 12 years (AF) and rubber plantations with stand age of 35 years (RF). The effects of land use change on soil water holding capacity and water conservation (presented by soil water index, SWI) were assessed. The results showed that, compared with VF, the soil water holding capacity index of other land types decreased in the top soil layer (0-10 cm). AF had the lowest soil water holding capacity in all soil layers. Soil water content and maximum water holding capacity were significantly related to canopy density, soil organic matter and soil bulk density, which indicated that canopy density, soil organic matter and compactness were important factors influencing soil water holding capacity. Compared to VF, soil water conservation of SF, AF and RF were reduced by 27.7%, 54.3% and 11.5%, respectively. The change of soil water conservation was inconsistent in different soil layers. Vegetation canopy density, soil organic matter and soil bulk density explained 83.3% of the variance of soil water conservation. It was suggested that land use conversion had significantly altered soil water holding capacity and water conservation function. RF could keep the soil water better than AF in the research area. Increasing soil organic matter and reducing soil compaction would be helpful to improve soil water holding capacity and water conservation function in land management.

Comparison of Three Methods for Vertical Extrapolation of Soil Moisture in Oklahoma

Core Ideas Exponential filter performs better than LR and ANN for SWI vertical extrapolation. Adding meteorological variables does not improve model accuracy. Soil properties can improve the linear regression model. ExpF and general LR are two potential general methods for SWI extrapolation. Soil moisture monitoring networks can provide real‐time and accurate soil moisture measurements; however, missing values and the lack of unified measurement depths across different networks impedes soil moisture applications at regional and national scales. Therefore, methods for vertical extrapolation of soil moisture, i.e., using shallow soil moisture measurements to estimate deeper soil moisture, are needed for standardizing measurements to a set of common depths. This study compared three methods, artificial neural network (ANN), linear regression (LR), and exponential filter (ExpF), for vertical extrapolation of soil moisture using data from the Oklahoma Mesonet. Based on our analysis of intra‐annual variations in soil moisture, we divided each year into two seasons, warm and cool. Our results demonstrate that all methods perform better in the warm season than in the cool season, especially at deeper depths. The Kling–Gupta efficiency was used to assess the performance of each method. All methods had similar performance for near‐surface extrapolation of soil moisture (top 25 cm). Although the accuracy of all models tended to decrease with depth, the ExpF outperformed the other methods at deeper depths. The soil water index (SWI) is preferred over volumetric water content as input to the ExpF. Incorporating air temperature and an antecedent precipitation index into the ANN and LR methods did not significantly improve their accuracy. We demonstrated that both ExpF and general LR can be used for SWI extrapolation at sites where only surface soil moisture data are available. Soil properties may be useful for further improving the accuracy of the general LR method.

A quality assessment of the soil water index by the propagation of ASCAT soil moisture error estimates through an exponential filter

ABSTRACTSoil moisture data from remote-sensing measurements are used extensively and error characterization is critical in assessing their utility for specific applications. In hydrological monitoring and forecast applications, an estimate of the root-zone soil moisture is often required, and the exponential filter constitutes a common approach to obtaining a soil water index (SWI), i.e. the profile soil moisture from the remotely-sensed surface states. Advanced Scatterometer (ASCAT) surface soil moisture (SSM) time series include error estimates as ancillary data, however their effects are rarely considered in SWI computations. In this study, we introduced a simple error propagation scheme in parallel to exponential filter computation, aimed at estimating a SWI noise, which implicitly takes into account both errors and availability of the input SSM data actually used for each single SWI estimation. Integrating a control on SWI noise within quality check procedures was analysed by comparing in situ measurements available at different depths, up to 60 cm, for the period 2007–2014 in 10 stations located in Italy. The capacity of ASCAT-derived SWI to capture the same processes observed in the root-zone by in situ sensors, was evaluated by computing standard statistics (the correlation coefficient r and the root mean square difference [RMSD]) for different ‘a priori’ data masking procedures on SSM data, based on several indicators (processing flag, orbit direction, SSM noise) available for the ASCAT product. The effects of using the information contained in SSM noise and in the proposed SWI noise were also investigated. Removing data according to ‘a posteriori’ control on SWI noise rather than a priori masking SSM data, with the exponential filter T value and the percentage of removed data being equal, was found to generally improve the statistics of the resulting SWI time series. In addition, evaluating T on the SWI time series screened according to the SWI noise, outperforms other approaches analysed in almost every test case.

The value of remotely sensed surface soil moisture for model calibration using SWAT

AbstractRemotely sensed (RS) data can add value to a hydrological model calibration. Among this, RS soil moisture (SM) data have mostly been assimilated into conceptual hydrological models using various transformed variable or indices. In this study, raw RS surface SM is used as a calibration variable in the Soil and Water Assessment Tool model. This means the SM values were not transformed into another variable (e.g., soil water index and root zone SM index). Using a nested catchment, calibration based only on RS SM and optimizing model parameters sensitive to SM using particle swarm optimization improved variations in streamflow predictions at some of the gauging stations compared to the uncalibrated model. This highlighted part of the catchments where the SM signal directly influenced the flow distribution. Additionally, highlighted high and low flow signals were mostly influenced. The seasonal breakdown indicates that the SM signal is more useful for calibrating in wetter seasons and in areas with higher variations in elevation. The results identified that calibration only on RS SM improved the general rainfall–runoff response simulation by introducing delays but cannot correct the overall routing effect. Furthermore, catchment characteristics (e.g., land use, elevation, soil types, and precipitation) regulating SM variation in different seasons highlighted by the model calibration are identified. This provides further opportunities to improve model parameterization.

Multilevel Drought Hazard Assessment under Climate Change Scenarios in Semi-Arid Regions—A Case Study of the Karkheh River Basin in Iran

Studies using Drought Hazard Indices (DHIs) have been performed at various scales, but few studies associated DHIs of different drought types with climate change scenarios. To highlight the regional differences in droughts at meteorological, hydrological, and agricultural levels, we utilized historic and future DHIs derived from the Standardized Precipitation Index (SPI), Standardized Runoff Index (SRI), and Standardized Soil Water Index (SSWI), respectively. To calculate SPI, SRI, and SSWI, we used a calibrated Soil and Water Assessment Tool (SWAT) for the Karkheh River Basin (KRB) in Iran. Five bias-corrected Global Circulation Models (GCMs) under two Intergovernmental Panel on Climate Change (IPCC) scenarios projected future climate. For each drought type, we aggregated drought severity and occurrence probability rate of each index into a unique DHI. Five historic droughts were identified with different characteristics in each type. Future projections indicated a higher probability of severe and extreme drought intensities for all three types. The duration and frequency of droughts were predicted to decrease in precipitation-based SPI. However, due to the impact of rising temperature, the duration and frequency of SRI and SSWI were predicted to intensify. The DHI maps of KRB illustrated the highest agricultural drought exposures. Our analyses provide a comprehensive way to monitor multilevel droughts complementing the existing approaches.

Runoff modeling of the Mara River using satellite observed soil moisture and rainfall

Hydrological models are essential in water resources management. However modeling in poorly gauged catchments is a big challenge. Recent studies have shown that satellite based hydrological and meteorological data has the potential of being part of the solution towards overcoming this challenge. In this study, we modify the conceptual lumped rainfall-runoff model by Meier et al. (2011) to simulate the runoff of the Mara River basin. The model is developed based on the relationships found between satellite observed soil moisture and rainfall and the measured runoff. It uses the satellite observed rainfall as the prime forcing, and the soil moisture to separate the fast surface runoff and slow base flow contributions. The soil moisture and rainfall products used in this research are the Advanced Scatterometer Soil Water Index (ASCAT SWI) and Tropical Rainfall Measurement Mission (TRMM) 3B42 v7 respectively. The performance of the model is evaluated for three sub-catchments defined by the Mara mines, Nyangores and Amala gauging stations along the Mara River. The Pearson correlation (r) for Mara mines Nyangores and Amala during calibration and (validation) were 0.54 (0.77), 0.67 (0.74), 0.125 (0.48) respectively. The model showed great potential for simulating dry season runoff, but needs further improvement to be able to reliably simulate wet season runoff. Nevertheless, this study demonstrates the potential role operational satellite based soil moisture and rainfall products can play in quantifying the available water resources particularly in the many un-gauged river basins across Africa.Keywords—ASCAT SWI, hydrological modeling, rainfall, runoff, soil moisture, TRMM 3B42 v7

Runoff modeling of the Mara River using satellite observed soil moisture and rainfall

Hydrological models are essential in water resources management. However modeling in poorly gauged catchments is a big challenge. Recent studies have shown that satellite based hydrological and meteorological data has the potential of being part of the solution towards overcoming this challenge. In this study, we modify the conceptual lumped rainfall-runoff model by Meier et al. (2011) to simulate the runoff of the Mara River basin. The model is developed based on the relationships found between satellite observed soil moisture and rainfall and the measured runoff. It uses the satellite observed rainfall as the prime forcing, and the soil moisture to separate the fast surface runoff and slow base flow contributions. The soil moisture and rainfall products used in this research are the Advanced Scatterometer Soil Water Index (ASCAT SWI) and Tropical Rainfall Measurement Mission (TRMM) 3B42 v7 respectively. The performance of the model is evaluated for three sub-catchments defined by the Mara mines, Nyangores and Amala gauging stations along the Mara River. The Pearson correlation (r) for Mara mines Nyangores and Amala during calibration and (validation) were 0.54 (0.77), 0.67 (0.74), 0.125 (0.48) respectively. The model showed great potential for simulating dry season runoff, but needs further improvement to be able to reliably simulate wet season runoff. Nevertheless, this study demonstrates the potential role operational satellite based soil moisture and rainfall products can play in quantifying the available water resources particularly in the many un-gauged river basins across Africa.Keywords—ASCAT SWI, hydrological modeling, rainfall, runoff, soil moisture, TRMM 3B42 v7

Robust Initial Wetness Condition Framework of an Event-Based Rainfall–Runoff Model Using Remotely Sensed Soil Moisture

Runoff prediction in limited-data areas is vital for hydrological applications, such as the design of infrastructure and flood defenses, runoff forecasting, and water management. Rainfall–runoff models may be useful for simulation of runoff generation, particularly event-based models, which offer a practical modeling scheme because of their simplicity. However, there is a need to reduce the uncertainties related to the estimation of the initial wetness condition (IWC) prior to a rainfall event. Soil moisture is one of the most important variables in rainfall–runoff modeling, and remotely sensed soil moisture is recognized as an effective way to improve the accuracy of runoff prediction. In this study, the IWC was evaluated based on remotely sensed soil moisture by using the Soil Conservation Service-Curve Number (SCS-CN) method, which is one of the representative event-based models used for reducing the uncertainty of runoff prediction. Four proxy variables for the IWC were determined from the measurements of total rainfall depth (API5), ground-based soil moisture (SSMinsitu), remotely sensed surface soil moisture (SSM), and soil water index (SWI) provided by the advanced scatterometer (ASCAT). To obtain a robust IWC framework, this study consists of two main parts: the validation of remotely sensed soil moisture, and the evaluation of runoff prediction using four proxy variables with a set of rainfall–runoff events in the East Asian monsoon region. The results showed an acceptable agreement between remotely sensed soil moisture (SSM and SWI) and ground based soil moisture data (SSMinsitu). In the proxy variable analysis, the SWI indicated the optimal value among the proposed proxy variables. In the runoff prediction analysis considering various infiltration conditions, the SSM and SWI proxy variables significantly reduced the runoff prediction error as compared with API5 by 60% and 66%, respectively. Moreover, the proposed IWC framework with remotely sensed soil moisture indicates an improved Nash–Sutcliffe efficiency from 0.48 to 0.74 for the four catchments in the Korean Peninsula. It can be concluded that the SCS-CN method extended with remotely sensed soil moisture for reducing uncertainty in the runoff prediction and the proxy variables obtained from the soil moisture data provided by the ASCAT can be useful in enhancing the accuracy of runoff prediction over a range of spatial scales.

Analyzing rainfall-induced mass movements in Taiwan using the soil water index

This study applied the soil water index (SWI), which can represent the conceptual soil water contents as influenced by present and antecedent rainfall, for analyzing rainfall-induced mass movements in Taiwan. The SWI has been used in Japan for nationwide mass movement warnings. This study examined whether the SWI can be also applied to Taiwan, which has a climatic condition and high-relief topography similar to Japan. We used data for mass movements for 2006–2012 (n = 263) for the main analyses and those for 2013 (n = 19) for verification. The SWI values before the rainfall events that triggered mass movements were used as the indicator of the antecedent rainfall condition. We found that when SWI values before rainfall events increased from 35, the upper threshold of rainfall conditions needed for triggering mass movements significantly decreased. The mass movements in 2013 support this finding. We classified rainfall conditions for triggering mass movements into two types, short duration–high intensity (SH) and long duration–low intensity (LL), based on a principal component analysis (PCA). The SH type is associated with a rapid increase in SWI, and the LL type is associated with a gradual rise and subsequent constancy of SWI except in some extremely long rainfall events. Based on this result, we modeled the general trend of the time series changes in SWI for the two types, which was verified using the mass movements in 2013.

Root-zone plant available water estimation using the SMOS-derived soil water index

Currently, there are several space missions capable of measuring surface soil moisture, owing to the relevance of this variable in meteorology, hydrology and agriculture. However, the Plant Available Water (PAW), which in some fields of application could be more important than the soil moisture itself, cannot be directly measured by remote sensing. Considering the root zone as the first 50cm of the soil, in this study, the PAW at 25cm and 50cm and integrated between 0 and 50cm of soil depth was estimated using the surface soil moisture provided by the Soil Moisture Ocean Salinity (SMOS) mission. For this purpose, the Soil Water Index (SWI) has been used as a proxy of the root-zone soil moisture, involving the selection of an optimal T (Topt), which can be interpreted as a characteristic soil water travel time. In this research, several tests using the correlation coefficient (R), the Nash-Sutcliffe score (NS), several error estimators and bias as predictor metrics were applied to obtain the Topt, making a comprehensive study of the T parameter. After analyzing the results, some differences were found between the Topt obtained using R and NS as decision metrics, and that obtained using the errors and bias, but the SWI showed good results as an estimator of the root-zone soil moisture. This index showed good agreement, with an R between 0.60 and 0.88.The method was tested from January 2010 to December 2014, using the database of the Soil Moisture Measurements Stations Network of the University of Salamanca (REMEDHUS) in Spain. The PAW estimation showed good agreement with the in situ measurements, following closely the dry-downs and wetting-up events, with R ranging between 0.60 and 0.92, and error values lower than 0.05 m3m−3. A slight underestimation was observed for both the PAW and root-zone soil moisture at the different depths; this could be explained by the underestimation pattern observed with the SMOS L2 soil moisture product, in line with previous studies. Nevertheless, the results obtained in this research showed an encouraging improvement of the PAW estimation. Despite the need for more research on this issue, the results of this study show that this methodology can be useful for applications of great interest in agriculture and hydrology.

Impacts of Re-Vegetation on Surface Soil Moisture over the Chinese Loess Plateau Based on Remote Sensing Datasets

A large-scale re-vegetation supported by the Grain for Green Project (GGP) has greatly changed local eco-hydrological systems, with an impact on soil moisture conditions for the Chinese Loess Plateau. It is important to know how, exactly, re-vegetation influences soil moisture conditions, which not only crucially constrain growth and distribution of vegetation, and hence, further re-vegetation, but also determine the degree of soil desiccation and, thus, erosion risk in the region. In this study, three eco-environmental factors, which are Soil Water Index (SWI), the Normalized Difference Vegetation Index (NDVI), and precipitation, were used to investigate the response of soil moisture in the one-meter layer of top soil to the re-vegetation during the GGP. SWI was estimated based on the backscatter coefficient produced by the European Remote Sensing Satellite (ERS-1/2) and Meteorological Operational satellite program (MetOp), while NDVI was derived from SPOT imageries. Two separate periods, which are 1998–2000 and 2008–2010, were selected to examine the spatiotemporal pattern of the chosen eco-environmental factors. It has been shown that the amount of precipitation in 1998–2000 was close to that of 2008–2010 (the difference being 13.10 mm). From 1998–2000 to 2008–2010, the average annual NDVI increased for 80.99%, while the SWI decreased for 72.64% of the area on the Loess Plateau. The average NDVI over the Loess Plateau increased rapidly by 17.76% after the 10-year GGP project. However, the average SWI decreased by 4.37% for two-thirds of the area. More specifically, 57.65% of the area on the Loess Plateau experienced an increased NDVI and decreased SWI, 23.34% of the area had an increased NDVI and SWI. NDVI and SWI decreased simultaneously for 14.99% of the area, and the decreased NDVI and increased SWI occurred at the same time for 4.02% of the area. These results indicate that re-vegetation, human activities, and climate change have impacts on soil moisture. However, re-vegetation, which consumes a large quantity of soil water, may be the major factor for soil moisture change in most areas of the Loess Plateau. It is, therefore, suggested that Soil Moisture Content (SMC) should be kept in mind when carrying out re-vegetation in China’s arid and semi-arid regions.

토양함수지수를 이용한 국가 산사태 예보체계 구축 및 운영

The objective of this study was to describe the background and methodology of the current operational national landslide forecast system in Republic of Korea and analyze its operational results in the past two years. Soil water index and tank model were employed as the national landslide forecast system with a goal of pre-1-hour forecast for landslide likelihood in a national scale. The tank model was parameterized with the past rainfall data inducing landslides in each divided eleven region after considering rainfall and geological characteristics in the whole country. The national landslide forecast system have provided local administrations with the information of predicted warnings calculated with observed and predicted rainfall data by Korea Weather Administration in a hour since 2013, and they issue landslide warnings including landslide watch and alert based on the information. As results of its two-year-operation, 420 and 248 predicted warnings were calculated in 2013 and 2014, respectively. But, 33 of 420 in 2013 and 17 of 248 in 2014 were not finally issued in reality, accounting for 7.9% and 6.9%, respectively. Also, predicted warnings were not calculated for 36 of 72 landslides occurred in 2014 compared to only one out of 212 landslides in 2013. Overall, improvement of input rainfall data accuracy, regular revision of tank model parameters, and relevant adjustment of landslide warning issuing level were suggested to improve the efficiency of the national landslide forecast system.

Numerical Simulation of the Topographical Change in Korea Mountain Area by Intense Rainfall and Consequential Debris Flow

The objectives of this study are to simulate the topographical changes associated with rainfall and the consequential debris flow using terrestrial LiDAR (Light Detection And Ranging). Three rainfall events between July 9 and July 14, 2009, triggered a number of debris flows at Jecheon County in Korea. Rain fell at a rate of 64 mm/h, producing 400 mm of total accumulation during this period. Tank simulation model for SWI (Soil Water Index) estimated the water stored beneath the ground and debris flow occurrence in study area. For the LiDAR (Light Detection and Ranging) survey, the terrestrial laser scanning system RIEGL LMS-Z390i consists of an accurate and fast 3D scanner, associated RTK GPS system. The DEM derived from LiDAR enabled the debris flow to be mapped and analyzed in great detail. The estimated affected area and erosion/deposition volumes by debris flow were compared with two-dimensional numerical simulation. The simulation results were sufficiently in good agreement with the debris flow track, and a success rate of over 90% was achieved with a simulation time of 300 s. A comparison of the simulated and surveyed results based on deposition volume yields a success rate of over 97% with 350 s of simulation time.

50-yr recurrence interval for hourly precipitation and Soil Water Index over the Japanese archipelago

50-yr recurrence interval for hourly precipitation and Soil Water Index over the Japanese archipelago

Validation Study of Active Microwave Soil Moisture Products in Korea and Brazil

Soil moisture is a key component of the hydrology meteorology, climatology, and agriculture management in terms of interaction between land surface and atmosphere. Understanding energy and water cycles processes is needed to accurately monitor spatial and temporal variability of soil moisture. In this study, we conducted validation study of active microwave satellite soil moisture data by comparing and evaluating with in-situ soil moisture measured from South Korea and Brazil. The satellite data are derived from the advanced Scatterometer (ASCAT) on board the Metop (VV polarization C-band to 5.255 GHz) satellite, called as Soil Water Index (SWI). We will focus on growing season from May through September in 2012, to avoid any effect on soil moisture data quality due to the severe wintry weather. The comparison results of this validation task were expressed in three statistical values: Correlation coefficient (R), Bias and Root Mean Square Error (RMSE). The results showed that the ASCAT SWI products have reliable agreements with ground measurements in Korea and Brazil. Thus, the lack of data on soil moisture for the Brazilian territory, with the level of accuracy and the geographic extent necessary for studies on a regional scale, limited understanding of the interaction between soil moisture and the atmosphere. On the other hand, even South Korea is a country of smaller dimensions they have a range of 50 sampling stations of in-situ measurements. Therefore, it is possible to do the validation of remote sensing data with an unquestionable quality, even if this study we will only be 5 stations with the highest values of correlation coefficient. In this paper, the main purpose is to assess the accuracy of soil moisture estimates from the ASCAT sensor comparing them with in-situ data for two different regions. Therefore, a recursive exponential filter formulation which allows to estimate the SWI (Soil Water Index) from intermittent SSM measurements (8) is used to compare the quality of the data between Brazil and South Korea.