Soil Water Index Research Articles

An assessment of remotely sensed surface and root zone soil moisture through active and passive sensors in northeast Asia

Active and passive microwave remote sensing techniques provide an effective way to observe soil moisture contents. We validated Advanced Scatterometer (ASCAT) and Advanced Microwave Scanning Radiometer — Earth Observing System (AMSR-E) sensor products using estimations from nine different stations located in the Korean peninsula, in northeast Asia from May 1 to September 30, 2010. The results of the surface soil moisture (SSM) products showed a reasonable agreement with the average correlation coefficient (R) values of 0.39, 0.42, and 0.53 for the National Snow and Ice Data Centre (NSIDC), Vrije Universiteit Amsterdam — National Aeronautics and Space Administration (VUA-NASA) AMSR-E, and ASCAT SSM datasets, respectively. The root zone soil moisture (RZSM) products, derived using the NSIDC soil water index (SWI), the United States Department of Agriculture (USDA) AMSR-E, and the ASCAT SWI datasets showed relatively high R values of 0.47, 0.72, and 0.75, respectively, with in situ soil moisture at a depth of 20cm. In particular, AMSR-E USDA RZSM data show best agreements with in-situ data at 20cm, among the four depths (10, 20, 30, and 50cm). In this study, the ASCAT SSM and SWI were rescaled based on the porosity and the effective saturation according to soil texture. Renormalized soil moisture products using three renormalization methods: the linear regression correction (REG), average–standard deviation (μ−σ), and cumulative distribution function (CDF) provided an improvement in biases and RMSEs, with SSM (SWI) RMSEs of 0.04 (0.02), 0.05 (0.03), and 0.05 (0.03)m3/m3 for REG, μ−σ, and CDF matching, respectively. A Taylor diagram was used to assess the accuracy of four satellite soil moisture products with in situ data on a plot. Based on these results, ASCAT soil moisture products were potentially proven to be more appropriate than AMSR-E products in northeast Asia. Remotely sensed soil moisture datasets from passive (AMSR-E) and active (ASCAT) sensors are beneficial to operational hydrological investigations and water management activities.

Estimation of soil moisture content from the spectral reflectance of bare soils in the 0.4-2.5 µm domain.

This work aims to compare the performance of new methods to estimate the Soil Moisture Content (SMC) of bare soils from their spectral signatures in the reflective domain (0.4–2.5 μm) in comparison with widely used spectral indices like Normalized Soil Moisture Index (NSMI) and Water Index SOIL (WISOIL). Indeed, these reference spectral indices use wavelengths located in the water vapour absorption bands and their performance are thus very sensitive to the quality of the atmospheric compensation. To reduce these limitations, two new spectral indices are proposed which wavelengths are defined using the determination matrix tool by taking into account the atmospheric transmission: Normalized Index of Nswir domain for Smc estimatiOn from Linear correlation (NINSOL) and Normalized Index of Nswir domain for Smc estimatiOn from Non linear correlation (NINSON). These spectral indices are completed by two new methods based on the global shape of the soil spectral signatures. These methods are the Inverse Soil semi-Empirical Reflectance model (ISER), using the inversion of an existing empirical soil model simulating the soil spectral reflectance according to soil moisture content for a given soil class, and the convex envelope model, linking the area between the envelope and the spectral signature to the SMC. All these methods are compared using a reference database built with 32 soil samples and composed of 190 spectral signatures with five or six soil moisture contents. Half of the database is used for the calibration stage and the remaining to evaluate the performance of the SMC estimation methods. The results show that the four new methods lead to similar or better performance than the one obtained by the reference indices. The RMSE is ranging from 3.8% to 6.2% and the coefficient of determination R2 varies between 0.74 and 0.91 with the best performance obtained with the ISER model. In a second step, simulated spectral radiances at the sensor level are used to analyse the sensitivity of these methods to the sensor spectral resolution and the water vapour content knowledge. The spectral signatures of the database are then used to simulate the signal at the top of atmosphere with a radiative transfer model and to compute the integrated incident signal representing the spectral radiance measurements of the HYMAP airborne hyperspectral instrument. The sensor radiances are then corrected from the atmosphere by an atmospheric compensation tool to retrieve the surface reflectances. The SMC estimation methods are then applied on the retrieve spectral reflectances. The adaptation of the spectral index wavelengths to the HyMap sensor spectral bands and the application of the convex envelope and ISER models to boarder spectral bands lead to an error on the SMC estimation. The best performance is then obtained with the ISER model (RMSE of 2.9% and R2 of 0.96) while the four other methods lead to quite similar RMSE (from 6.4% to 7.8%) and R2 (between 0.79 and 0.83) values. In the atmosphere compensation processing, an error on the water vapour content is introduced. The most robust methods to water vapour content variations are WISOIL, NINSON, NINSOL and ISER model. The convex envelope model and NSMI index require an accurate estimation of the water vapour content in the atmosphere.

Analysis of Rainfall Effect to Slope Stability in Ulu Klang, Malaysia

Malaysia is a country that is located near the equator line with tropical climates which receives high abundant rainfall, averaging 2,400mm annually. This makes Malaysia prone to the landslide events as rainfall is one of the main triggering factors that can cause landslide. Landslides in Malaysia are mainly attributed to frequent and prolonged rainfalls, in many cases associated with monsoon rainfalls. Of these, Ulu Klang area has received the most exposure. The area has constantly hit by fatal landslides since December 1993. This paper is aimed to investigate the correlation between the effective working rainfall and soil water index (SWI) methods with the landslide events in Ulu Klang, Malaysia. In this study 15 landslide events that occurred in Ulu Klang areas between years 1993 to 2012 were investigated and analyzed using rainfall threshold based on effective working rainfall and soil water index (SWI) methods. The analysis results showed that these methods are significant tools that can be used to identify the rainfall critical threshold of landslide event.

Probable Hourly Precipitation and Soil Water Index for 50-yr Recurrence Interval over the Japanese Archipelago

We produced a preliminary high-resolution probable hourly precipitation (mm h−1) and probable Soil Water Index (SWI) for a 50-yr recurrence interval over the Japanese archipelago from 5-km grid-cell Radar/Raingauge-Analyzed Precipitation (R/A) with a 26-yr time series (1988-2013). To date, the selection and applicability of a probability density function such as the Gumbel distribution (GUM) or the generalized extreme value distribution (GEV) has been investigated mainly from a statistical perspective, whereas this study examines them from the viewpoint of disaster prevention. Results show detailed spatial patterns of the probable hourly precipitation and SWI in Japan with ranges of 17.0-158.0 (±1.1-51.1) mm h−1 and 82.1-638.6 (±3.7-93.0), respectively, by spatial resolution of 5-km grid-cell. The probable hourly precipitation by GUM is less than that of GEV with a shape parameter of less than 0.0. Our results demonstrate that R/A-based probable hourly precipitation and SWI by GUM, which has robust estimation, is appropriate for landslide hazard assessment. There results revealed the potential and limitation of estimating the probable precipitation from the R/A dataset. Further study should verify the R/A dataset and the probable precipitations with accumulation of R/A.

Mapping of coupling hot spots of satellite derived latent heat flux in indian agro-climatic regions

Abstract. This study focuses on the understanding and mapping of coupling hotspots of LE versus terrestrial and meteorological parameters. Single source surface energy balance model was used to derive surface energy balance parameters. Agro climatic region wise monthly information of terrestrial, energy balance and meteorological parameters were derived during June- September from decadal analysis of MODIS data (2003–2012) over India (68–100°E, 5–40°N) at 5 km spatial resolution. Information on rainfall was obtained from gridded rainfall data (1° × 1° spatial resolution) from Indian Meteorological Department (IMD). The spatiotemporal variability of the parameters such as rainfall, evapotranspiration (ET), evaporative fraction (EF), soil water index (SWI), land surface temperature (LST) and air temperature (Ta) showed strong influence on seasonal LE fluctuation. LE showed positive linear coupling with ET (0.52

Assessment of heavy rainfall-induced disaster potential based on an ensemble simulation of Typhoon Talas (2011) with controlled track and intensity

Abstract. Typhoon Talas (2011) caused extensive damage through landslides on the Kii Peninsula, Japan, in September 2011. The purpose of the present study is to quantitatively describe the potential for the occurrence of heavy rainfall-induced disasters if the typhoon track perturbs slightly or the typhoon intensifies. Regarding to the consideration of the track displacement of the typhoon, a procedure is proposed to generate different typhoon tracks perturbed from the original track of the typhoon. In this procedure, the position of a typhoon is artificially shifted at a certain time before landing in a physically consistent manner by applying potential vorticity inversion (PVI) methodology. After relocating the typhoon, the subsequent progress is simulated by a mesoscale meteorological model. Using the output, which consists of a set of realizations having different typhoon tracks, the worst-case scenario is discussed in terms of the soil water index (SWI) of the Kii Peninsula. The SWI is an indicator of the amount of water in soil that represents the hazard of landslide disasters. The maximum spatially averaged SWI is 1.10 times as large as that from the original typhoon track. Regarding the consideration of severer typhoon, the same method is used, but the intensity of the potential vorticity of a typhoon is artificially modified at the position instead of relocating potential vorticity to a different place. The maximum spatially averaged SWI is 1.28 times as large as that of the original typhoon intensity.

Comparison of Meteorological, Hydrological and Agricultural Drought Responses to Climate Change and Uncertainty Assessment

A comparison study of meteorological, hydrological and agricultural drought responses to climate change resulting from different General Circulation Models (GCMs), emission scenarios and hydrological models is presented. Drought variations from 1961–2000 to 2061–2100 in Huai River basin above Bengbu station in China are investigated. Meteorological drought is recognized by the Standardized Precipitation Index (SPI) while hydrological drought and agricultural drought are indexed with a similar standardized procedure by the Standardized Runoff Index (SRI) and Standardized Soil Water Index (SSWI). The results generally approve that hydrological and agricultural drought could still pose greater threats to local water resources management in the future, even with a more steady background to meteorological drought. However, the various drought responses to climate change indicate that uncertainty arises in the propagation of drought from meteorological to hydrological and agricultural systems with respect to alternative climates. The uncertainty in hydrological model structure, as well as the uncertainties in GCM and emission scenario, are aggregated to the results and lead to much wider variations in hydrological and agricultural drought characteristics. Our results also reveal that the selection of hydrological models can induce fundamental differences in drought simulations, and the role of hydrological model uncertainty may become dominating among the three uncertainty sources while recognizing frequency of extreme drought and maximum drought duration.

Validation of the ASCAT Soil Water Index using in situ data from the International Soil Moisture Network

Soil moisture is an essential climate variable and a key parameter in hydrology, meteorology and agriculture. Surface Soil Moisture (SSM) can be estimated from measurements taken by ASCAT onboard Metop-A and have been successfully validated by several studies. Profile soil moisture, while equally important, cannot be directly measured by remote sensing but must be modeled. The Soil Water Index (SWI) product developed for near real time applications within the framework of the GMES project geoland2 aims to provide such a modeled profile estimate using satellite data as input. It is produced from ASCAT SSM estimates using a two-layer water balance model which describes the relationship between surface and profile soil moisture as a function of time. It provides daily global data about moisture conditions for eight characteristic time lengths representing different depths.The objective of this work was to assess the overall quality of the SWI data. Furthermore we tested the assumptions of the used water balance model and checked if ancillary information about topography, water fraction and noise information are useful for identifying observations of questionable quality. SWI data from January 1st 2007 until the end of 2011 was compared to in situ soil moisture data from 664 stations belonging to 23 observation networks which are available through the International Soil Moisture Network (ISMN). These stations delivered 2081 time series at different depths which were compared to the SWI values.The average of the significant Pearson correlation coefficients was 0.54 while being greater than 0.5 for 64.4% of all time series. It was found that the characteristic time length showing the highest correlation increases with in situ observation depth, thus confirming the SWI model assumptions. Relationships of the correlation coefficients with topographic complexity, water fraction, in situ observation depth, and soil moisture noise were found.

Calibrating a large-extent high-resolution coupled groundwater-land surface model using soil moisture and discharge data

[1] We explore the possibility of using remotely sensed soil moisture data and in situ discharge observations to calibrate a large-extent hydrological model. The model used is PCR-GLOBWB-MOD, which is a physically based and fully coupled groundwater-land surface model operating at a daily basis and having a resolution of 30 arc sec (about 1 km at the equator). As a test bed, we use the combined Rhine-Meuse basin (total area: about 200,000 km2), where there are 4250 point-scale observed groundwater head time series that are used to verify the model results. Calibration is performed by simulating 3045 model runs with varying parameter values affecting groundwater head dynamics. The simulation results of all runs are evaluated against the remotely sensed soil moisture time series of SWI (Soil Water Index) and field discharge data. The former is derived from European Remote Sensing scatterometers and provides estimates of the first meter profile soil moisture content at 30 arc min resolution (50 km at the equator). From the evaluation of these runs, we then introduce a stepwise calibration approach that considers stream discharge first, then soil moisture, and finally verify the resulting simulation to groundwater head observations. Our results indicate that the remotely sensed soil moisture data can be used for the calibration of upper soil hydraulic conductivities determining simulated groundwater recharge of the model. However, discharge data should be included to obtain full calibration of the coupled model, specifically to constrain aquifer transmissivities and runoff-infiltration partitioning processes. The stepwise approach introduced in this study, using both discharge and soil moisture data, can calibrate both discharge and soil moisture, as well as predicting groundwater head dynamics with acceptable accuracy. As our approach to parameterize and calibrate the model uses globally available data sets only, it opens up the possibility to set up large-extent coupled groundwater-land surface models in other basins or even globally.

Enhancing the Water Stress Factors for Simulation of Corn in RZWQM2

Enhancement of agricultural system models for more accurate simulations of the water stress response of crops can improve their application under limited water management. Currently, the crop system model RZWQM2 uses a ratio of potential root water uptake (supply) to potential transpiration (demand) as a water stress factor (WSDef) that modulates plant growth processes. We tested two progressive modifications of the WSDef (WSI1 and WSI2) in the DSSAT‐CSM‐CERES‐Maize (Version 4.0) module embedded within the RZWQM2 model for simulating the response of corn (Zea mays L.) to different levels of water and compared them with the use of WSDef. The WSI1 was a modification of the SWFAC (Soil Water FACtor) for photosynthesis‐related processes in RZWQM2 using the daily potential root water uptake (TRWUP) calculated by the Nimah and Hanks approach. The WSI2 was WSI1 with terms accounting for stress due to additional heating of the canopy from unused energy of potential soil evaporation in both the supply and demand terms of the WSI1. These factors were evaluated using the data for corn grain yield, biomass, soil water, and leaf area index (LAI) derived from canopy cover data from multiple water‐level experiments conducted at Greeley, CO, from 2008 to 2011, irrigated and rainfed corn at Akron, CO, and irrigated corn at Gainesville, FL, on different soil types. Overall, the stress factors WSI1 and WSI2 were found to be superior to WSDef in simulations of grain yield, biomass, and LAI in all three experiments. Further, in general, WSI2 simulations of the crop were either comparable to or more accurate than WSI1 simulations in most of the crop seasons simulated in this study. The stress factor WSI2 has been incorporated in the RZWQM2 for simulating corn.

A Simple Water Balance Model of Rubber Tree Plantations under Different Evaporative Demand Regimes

Drought constraints and transpiration of rubber (Hevea brasiliensis) plantations under different evaporative demand regimes were assessed by the simple water balance model. A lump water model, BILJOU (BILan hydrique JOUrnalier), is the daily water balance model. This model requires daily potential evapotranspiration (ETo) and rainfall as input climatic data, also requires site and stand parameters are maximum extractable soil water and leaf area index (LAI). The study was carried out two sites; namely Songkhla and Chachoengsao province, Thailand, traditional and new plantation area, respectively. The calibration of this model was done with sap flow measurements. Soil water derived by tensiometer for Songkhla and soil sampling for Chachoengsao were used to validate the model. Under non limiting soil water and full canopy, transpiration of rubber was influenced by evaporative demand. Consistently, under limited soil water represented as threshold of relative extractable water (REWc < 0.4), transpiration was influenced by REW. In the new plantation area; Chachoengsao, drought constraints were evident annually from the beginning of senescence until the new accomplished flushing; December to June. However, at Songkhla site, transpiration and soil water was mainly driven by evaporative demand. And the limitation of soil water represented shortly during the plateau stage of LAI.

Using ERS spaceborne microwave soil moisture observations to predict groundwater head in space and time

The study presented in this paper is to investigate the possibility of using spaceborne remote sensing data for groundwater head prediction. Remotely-sensed soil moisture time series of SWI (Soil Water Index) derived from ERS (European Remote Sensing) scatterometers are used to predict groundwater head dynamics in the Rhine–Meuse basin, where over four thousand observed groundwater head time series are available. Our study consists of three evolving research steps. First, the correlation between observed time series of groundwater head and SWI is investigated. Second, SWI time series are used as input to a transfer-function noise (TFN) model for temporal prediction (forecasts) of groundwater heads. Third, TFN models with spatially interpolated parameters are used with SWI time series for spatio-temporal prediction of groundwater heads. Here, HAND (Height Above Nearest Drainage) as derived from a digital elevation model is used as auxiliary information. Results show that the correlation between SWI and groundwater head time series is apparent, particularly in areas with shallow groundwater, and that correlation increases when a time-lag is taken into account. Temporal predictions with TFN models reproduce observed groundwater head time series well at locations with shallow groundwater, but results are poor for locations with deep groundwater. The spatio-temporal prediction method is not able to estimate the absolute value of groundwater heads. However, head variation in terms of timing and amplitude is predicted reasonably well, in particular in areas with shallow groundwater. This suggests that, once a groundwater model is suitably calibrated, remotely sensed soil moisture data could be used to improve groundwater prediction in an operational data-assimilation framework.

Dinámica de la humedad del suelo en una cuenca agrícola del sector central de la cuenca del Duero

In this work the soil moisture dynamics was estudied in a small agricultural catchment in the central sector of the Duero basin. The experience took place in the 1 km2 Villamor experimental catchment, located in the southeastern part of the Zamora province. The evolution of the soil water contenr and its availability for plants was analyzed from the data measured in eleven soil profiles equipped with TDR (Time Domain Reflectometry) probes at different depths, between 2002 and 2006. The soil moisture data recorded in Villamor which were compared against the data simultaneously registered in the Network of Soil Moisture Measurement Stations (REMEDHUS) in the agricultural region of La Guarena over 1300 km2. This comparison allowed to assess the repre-sentativeness of the experimental catchment as an expression of the hydrological dynamics of a territory larger of which is part. The results of the study carried out in Villamor have shown the seasonal water content changes in the soil, as it is expected under Mediterranean conditions. However, these changes were much more attenuated than those observed in other nearby areas. The use of the Soil Water Index (IDH) allows the analysis in terms of availability of water for the plants and enables the comparison between different areas. IDH data have shown much more reduced water deficit conditions of what might be expected, given the semi-arid conditions in the area. The comparison with series of IDH of two forested basins and the identification of deficit situations much more intense in them, leads to reflection on the very prominent role of vegetation cover in the hydrological response of certain territories.

Assessment of Drought in Agricultural Regions of Slovakia Using Soil Water Dynamics Simulation

Assessment of agronomic drought occurrence and severity in agricultural regions of Slovakia is presented in the paper. Drought severity assessment is based on the soil water dynamics simulation by agroecological model Daisy. Daily meteorological data from the years 1961-2012 from 31 localities were used in simulations. Criteria for the drought occurrence were 1) available soil water content below 50% of available water capacity, 2) soil water content below long-term average soil water content and 3) duration of continuous drought for fifteen or more days. Standardized index of daily available soil water content was used for drought severity classification. According to the index the drought is categorized into four degrees of severity from mild to extreme drought. Cumulative sum of available soil water index was used to drought quantification throughout its duration. Normal climate period 1961- 1990 was chosen as reference period to enable historical comparison of drought severity as well as climate change impacts. Extreme drought of the largest spatial extent was identified in 1990. Extreme drought occurred regionally in the southwest Slovakia in 1978 and in the southeast Slovakia in 1986, respectively.

Soil Moisture Estimation in Alpine Catchments through Modeling and Satellite Observations

Soil moisture is widely recognized as a state variable governing the mass and energy balance between the land surface and the atmosphere. For that, its knowledge is of upmost importance for many applications including flood and landslide prediction. In alpine catchments, soil moisture estimation is a very difficult task, because of complex topography, high vegetation density, and presence of snow and outcrops. In this study, the possibility to estimate soil moisture for these areas by using modeled and satellite data is investigated. Specifically, an updated version of a soil water balance model, which takes the snowmelt process into account, is employed. Moreover, satellite‐derived soil moisture observations obtained by the Advanced SCATterometer (ASCAT) sensor onboard the MetOp satellite are tested by considering two products: the Surface Soil Moisture (SSM) and the Soil Water Index (SWI). The latter is obtained through the application of an exponential filter and it is aimed to reduce the differences in the layer depth of in situ measurements (10 cm) and satellite data (∼2–3 cm). Quality‐checked in situ soil moisture measurements collected at four continuous monitoring sites in Valle d'Aosta (North Italy) are used to test the accuracy of modeled and satellite estimates. Notwithstanding the above issues, results indicated the potential not only of modeling approaches but also, unexpectedly, of satellite data to retrieve soil moisture in high elevation regions (>1000 m a.s.l.). Indeed, by estimating correctly the snowmelt contribution, the agreement between modeled and observed data is quite good, with correlation coefficient values, r, in the range 0.795–0.940. In addition, the ASCAT‐derived SWI product provides satisfactorily results with r = 0.635–0.869. Based on these findings, in situ, modeled, and satellite soil moisture data will be used for improving flood and landslide risk prediction at the Valle d'Aosta Functional center to improve the Civil Protection Alert System.

Using a mathematical framework to examine physiological changes in winter wheat after livestock grazing: 1. Model derivation and coefficient calibration

Existing crop-livestock models can largely be separated into two groups: those that simulate crop growth and grain yield with no grazing, and those that simulate pasture production during and after grazing but do not simulate grain yield. The purpose of this paper was to develop a crop-grazing model (hereafter, ‘WHTGRAZ’) capable of realistically simulating biophysical phenomena of grazed wheat crops in rainfed environments. This derivation revealed important physiological distinctions between typical crop simulation and changes in growth that occurred as a result of grazing, such as delayed phenological development.WHTGRAZ was based on the crop model SUCROS2, with modifications. The Penman approach to evapotranspiration simulation was adapted to the FAO Penman–Monteith method. Dry matter (DM) accumulation was modified from photosynthesis to radiation-use efficiency. Specific leaf area was modelled as a function of crop ontogeny, and rules for dead leaf decomposition and trampling of shoot DM were also added. Phenological delays induced by defoliation – and consequences thereof – were identified as a key determinant of growth after grazing, and were modelled as a function of temperature and the rate of shoot DM removal. Parameters were calibrated using measurements of phenology, soil water, shoot DM, and leaf area index from field experiments using two winter wheat cultivars and four grazing treatments.Two important physiological insights were gained from the derivation. First, accurate prediction (within one SEM) of shoot DM removal during grazing is a necessary prerequisite for accurate simulation of post-grazing shoot DM accumulation. Reasonable quantification of green leaf removal and trampling by livestock is needed because green leaf area governs light interception and growth in WHTGRAZ. Second, grazing-induced delays to phenological development cause greater proportions of shoot DM to be allocated to leaves during late vegetative development, decreasing the fraction of shoot DM that would otherwise be partitioned to stems. Delayed phenology of grazed crops also causes greater proportions of kernel DM to be sourced from post-anthesis photosynthesis, as opposed to DM retranslocated from stems.

Improving Landslide Forecasting Using ASCAT-Derived Soil Moisture Data: A Case Study of the Torgiovannetto Landslide in Central Italy

Predicting the spatial and temporal occurrence of rainfall triggered landslides represents an important scientific and operational issue due to the high threat that they pose to human life and property. This study investigates the relationship between rainfall, soil moisture conditions and landslide movement by using recorded movements of a rock slope located in central Italy, the Torgiovannetto landslide. This landslide is a very large rock slide, threatening county and state roads. Data acquired by a network of extensometers and a meteorological station clearly indicate that the movements of the unstable wedge, first detected in 2003, are still proceeding and the alternate phases of quiescence and reactivation are associated with rainfall patterns. By using a multiple linear regression approach, the opening of the tension cracks (as recorded by the extensometers) as a function of rainfall and soil moisture conditions prior the occurrence of rainfall, are predicted for the period 2007–2009. Specifically, soil moisture indicators are obtained through the Soil Water Index, SWI, a product derived by the Advanced SCATterometer (ASCAT) on board the MetOp (Meteorological Operational) satellite and by an Antecedent Precipitation Index, API. Results indicate that the regression performance (in terms of correlation coefficient, r) significantly enhances if an indicator of the soil moisture conditions is included. Specifically, r is equal to 0.40 when only rainfall is used as a predictor variable and increases to r = 0.68 and r = 0.85 if the API and the SWI are used respectively. Therefore, the coarse spatial resolution (25 km) of satellite data notwithstanding, the ASCAT SWI is found to be very useful for the prediction of landslide movements on a local scale. These findings, although valid for a specific area, present new opportunities for the effective use of satellite-derived soil moisture estimates to improve landslide forecasting.

Catastrophic Landslide Disasters Triggered by Record-Breaking Rainfall in Japan: Their Accurate Detection with Normalized Soil Water Index in the Kii Peninsula for the Year 2011

Catastrophic Landslide Disasters Triggered by Record-Breaking Rainfall in Japan: Their Accurate Detection with Normalized Soil Water Index in the Kii Peninsula for the Year 2011

개념적인 토양수분 모의모형을 이용한 미래 극한 가뭄사상의 시공간적 해석

본 연구에서는 현재 및 미래기후에서의 가뭄심도-영향면적-지속기간 곡선의 비교를 통하여 극한 가뭄사상의 시공간적 거동에 대한 기후변화의 영향을 살펴보았다. 미래의 기온상승경향을 고려하기 위하여 표준토양수분지수가 제안된다. 표준토양수분지수는 일평균기온과 일강수량 자료가 반영된 개념적인 토양수분모형으로부터 산출된다. 미래기후는 CGCM3.1-T63과 CSIRO-MK3.0으로부터 획득되었다. 분석 결과 CGCM3.1-T63 및 CSIRO-MK3.0 모두 미래가뭄이 현재보다 더 극심해질 것으로 예측되었다. 이에 따라 현재의 수자원 공급 시스템에 대한 기후변화 취약성 평가가 시급함을 제시하고 있다. In this study, the impact of climate change on the spatio-temporal behavior of extreme drought events is investigated by comparing drought severity-area-duration curves under present and future climate conditions. In order to consider future increasing trends in air temperature, the standardized soil water index is introduced. The standardized soil water index is calculated using a conceptual soil water model forced by daily air temperature and daily precipitation data. In our climate-change impact experiments, the future climate is based on two GCMs(CGCM3.1-T63 and CSIRO-MK3.0). As a result, in both cases of CGCM3.1-T63 and CSIRO-MK3.0 the future drought risk is likely to increase. Such results indicate that a climate change vulnerability assessment for present water resources supply system is urgent.

Climate change impact on meteorological, agricultural, and hydrological drought in central Illinois

This paper investigates the impact of climate change on drought by addressing two questions: (1) How reliable is the assessment of climate change impact on drought based on state‐of‐the‐art climate change projections and downscaling techniques? and (2) Will the impact be at the same level from meteorological, agricultural, and hydrologic perspectives? Regional climate change projections based on dynamical downscaling through regional climate models (RCMs) are used to assess drought frequency, intensity, and duration, and the impact propagation from meteorological to agricultural to hydrological systems. The impact on a meteorological drought index (standardized precipitation index, SPI) is first assessed on the basis of daily climate inputs from RCMs driven by three general circulation models (GCMs). Two periods and two emission scenarios, i.e., 1991–2000 and 2091–2100 under B1 and A1Fi for Parallel Climate Model (PCM), 1990–1999 and 2090–2099 under A1B and A1Fi for Community Climate System Model, version 3.0 (CCSM3), 1980–1989 and 2090–2099 under B2 and A2 for Hadley Centre CGCM (HadCM3), are undertaken and dynamically downscaled through the RCMs. The climate projections are fed to a calibrated hydro‐agronomic model at the watershed scale in Central Illinois, and agricultural drought indexed by the standardized soil water index (SSWI) and hydrological drought by the standardized runoff index (SRI) and crop yield impacts are assessed. SSWI, in particular with extreme droughts, is more sensitive to climate change than either SPI or SRI. The climate change impact on drought in terms of intensity, frequency, and duration grows from meteorological to agricultural to hydrological drought, especially for CCSM3‐RCM. Significant changes of SSWI and SRI are found because of the temperature increase and precipitation decrease during the crop season, as well as the nonlinear hydrological response to precipitation and temperature change.