Defining Homogeneous Drought Zones Based on Soil Moisture across Japan and Teleconnections with Large-Scale Climate Signals

2021 ◽  
Author(s):  
Ke Shi ◽  
Yoshiya Touge ◽  
So Kazama
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Large Scale ◽  
Southern Oscillation ◽  
Wavelet Coherence ◽  
The Arctic ◽  
Surface Model ◽  
Drought Prediction ◽  
Climate Signals ◽  
Wavelet Coherence Analysis

Abstract Droughts are widespread disasters worldwide and are concurrently influenced by multiple large-scale climate signals. This is particularly true over Japan, where drought has strong heterogeneity due to multiple factors such as monsoon, topography, and ocean circulations. Regional heterogeneity poses challenges for drought prediction and management. To overcome this difficulty, this study provides a comprehensive analysis of teleconnection between climate signals and homogeneous drought zones over Japan. First, droughts are characterized by simulated soil moisture from land surface model during 1958-2012. The Mclust toolkit, distinct empirical orthogonal function, and wavelet coherence analysis are used, respectively, to investigate the homogeneous drought zone, principal component of each homogeneous zone, and teleconnection between climate signals and drought. Results indicate that nine homogeneous drought zones with different characteristics are defined and quantified. Among these nine zones, zone-1 is dominated by extreme drought events. Zone-2 and zone-6 are typical representatives of spring droughts, while zone-7 is wet for most of the period. The Hokkaido region is divided into wetter zone-4 and drier zone-9. Zone-3, zone-5 and zone-8 are distinguished by the topography. The analyses also reveal almost nine zones have a high level of homogeneity, with more than 60% explained variance. Also, these nine zones are dominated by different large-scale climate signals: the Arctic Oscillation has the strongest impact on zone-1, zone-7, and zone-8; the influence of the North Atlantic Oscillation on zone-3, zone-4, and zone-6 is significant; zone-2 and zone-9 are both dominated by the Pacific Decadal Oscillation; El Niño-Southern Oscillation dominates zone-5. The results will be valuable for drought management and drought prevention.

scholarly journals Using data assimilation to optimize pedotransfer functions using large-scale in-situ soil moisture observations

2020 ◽  
Author(s):  
Elizabeth Cooper ◽  
Eleanor Blyth ◽  
Hollie Cooper ◽  
Rich Ellis ◽  
Ewan Pinnington ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Data Assimilation ◽  
Land Surface ◽  
Large Scale ◽  
Pedotransfer Functions ◽  
Surface Model ◽  
Climate Projections ◽  
Land Surface Models ◽  
Surface Models

Abstract. Soil moisture predictions from land surface models are important in hydrological, ecological and meteorological applications. In recent years the availability of wide-area soil-moisture measurements has increased, but few studies have combined model-based soil moisture predictions with in-situ observations beyond the point scale. Here we show that we can markedly improve soil moisture estimates from the JULES land surface model using field scale observations and data assimilation techniques. Rather than directly updating soil moisture estimates towards observed values, we optimize constants in the underlying pedotransfer functions, which relate soil texture to JULES soil physics parameters. In this way we generate a single set of newly calibrated pedotransfer functions based on observations from a number of UK sites with different soil textures. We demonstrate that calibrating a pedotransfer function in this way can improve the performance of land surface models, leading to the potential for better flood, drought and climate projections.

scholarly journals Toward a Surface Soil Moisture Product at High Spatiotemporal Resolution: Temporally Interpolated, Spatially Disaggregated SMOS Data

2018 ◽  
Vol 19 (1) ◽  
pp. 183-200 ◽  
Author(s):  
Y. Malbéteau ◽  
O. Merlin ◽  
G. Balsamo ◽  
S. Er-Raki ◽  
S. Khabba ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Temporal Resolution ◽  
Land Surface ◽  
Large Scale ◽  
Surface Soil ◽  
Land Surface Model ◽  
Open Loop ◽  
Surface Model ◽  
Surface Soil Moisture ◽  
Spatiotemporal Resolution

Abstract High spatial and temporal resolution surface soil moisture is required for most hydrological and agricultural applications. The recently developed Disaggregation based on Physical and Theoretical Scale Change (DisPATCh) algorithm provides 1-km-resolution surface soil moisture by downscaling the 40-km Soil Moisture Ocean Salinity (SMOS) soil moisture using Moderate Resolution Imaging Spectroradiometer (MODIS) data. However, the temporal resolution of DisPATCh data is constrained by the temporal resolution of SMOS (a global coverage every 3 days) and further limited by gaps in MODIS images due to cloud cover. This paper proposes an approach to overcome these limitations based on the assimilation of the 1-km-resolution DisPATCh data into a simple dynamic soil model forced by (inaccurate) precipitation data. The performance of the approach was assessed using ground measurements of surface soil moisture in the Yanco area in Australia and the Tensift-Haouz region in Morocco during 2014. It was found that the analyzed daily 1-km-resolution surface soil moisture compared slightly better to in situ data for all sites than the original disaggregated soil moisture products. Over the entire year, assimilation increased the correlation coefficient between estimated soil moisture and ground measurements from 0.53 to 0.70, whereas the mean unbiased RMSE (ubRMSE) slightly decreased from 0.07 to 0.06 m3 m−3 compared to the open-loop force–restore model. The proposed assimilation scheme has significant potential for large-scale applications over semiarid areas, since the method is based on data available at the global scale together with a parsimonious land surface model.

scholarly journals Assimilation of SMOS brightness temperature into a large-scale distributed conceptual hydrological model

2019 ◽  
Author(s):  
Renaud Hostache ◽  
Dominik Rains ◽  
Kaniska Mallick ◽  
Marco Chini ◽  
Ramona Pelich ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Brightness Temperature ◽  
Land Surface ◽  
Large Scale ◽  
Hydrological Model ◽  
Land Surface Model ◽  
Surface Model ◽  
Meteorological Model ◽  
Temperature Observations

Abstract. The main objective of this study is to investigate how brightness temperature observations from satellite microwave sensors may help in reducing errors and uncertainties in soil moisture simulations with a large-scale conceptual hydro-meteorological model. In particular, we use as forcings the ERA-Interim public dataset and we couple the CMEM radiative transfer model with a hydro-meteorological model enabling therefore soil moisture and SMOS-like brightness temperature simulations. The hydro-meteorological model is configured using recent developments of the SUPERFLEX framework, which enables tailoring the model structure to the specific needs of the application as well as to data availability and computational requirements. In this case, the model spatial resolution is adapted to the spatial grid of the satellite data, and the soil stratification is tailored to the satellite datasets to be assimilated and the forcing data. The hydrological model is first calibrated using a sample of SMOS brightness temperature observations (period 2010–2011). Next, SMOS-derived brightness temperature observations are sequentially assimilated into the coupled SUPERFLEX-CMEM model (period 2010–2015). For this experiment, a Local Ensemble Transform Kalman Filter is used and the meteorological forcings (ERA interim-based rainfall, air and soil temperature) are perturbed to generate a background ensemble. Each time a SMOS observation is available, the SUPERFLEX state variables related to the water content in the various soil layers are updated and the model simulations are resumed until the next SMOS observation becomes available. Our empirical results show that the SUPERFLEX-CMEM modelling chain is capable of predicting soil moisture at a performance level similar to that obtained for the same study area and with a quasi-identical experimental set up using the CLM land surface model. This shows that a simple model, when carefully calibrated, can yield performance level similar to that of a much more complex model. The correlation between simulated and in situ observed soil moisture ranges from 0.62 to 0.72. The assimilation of SMOS brightness temperature observation into the SUPERFLEX-CMEM modelling chain improves the correlation between predicted and in situ observed soil moisture by 0.03 on average showing improvements similar to those obtained using the CLM land surface model.

scholarly journals Role of Subsurface Physics in the Assimilation of Surface Soil Moisture Observations

2009 ◽  
Vol 10 (6) ◽  
pp. 1534-1547 ◽  
Author(s):  
Sujay V. Kumar ◽  
Rolf H. Reichle ◽  
Randal D. Koster ◽  
Wade T. Crow ◽  
Christa D. Peters-Lidard
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Large Scale ◽  
Surface Soil ◽  
Root Zone ◽  
Regional Climate ◽  
Surface Model ◽  
Surface Soil Moisture ◽  
Community Land Model ◽  
Assimilation System

Abstract Root-zone soil moisture controls the land–atmosphere exchange of water and energy, and exhibits memory that may be useful for climate prediction at monthly scales. Assimilation of satellite-based surface soil moisture observations into a land surface model is an effective way to estimate large-scale root-zone soil moisture. The propagation of surface information into deeper soil layers depends on the model-specific representation of subsurface physics that is used in the assimilation system. In a suite of experiments, synthetic surface soil moisture observations are assimilated into four different models [Catchment, Mosaic, Noah, and Community Land Model (CLM)] using the ensemble Kalman filter. The authors demonstrate that identical twin experiments significantly overestimate the information that can be obtained from the assimilation of surface soil moisture observations. The second key result indicates that the potential of surface soil moisture assimilation to improve root-zone information is higher when the surface–root zone coupling is stronger. The experiments also suggest that (faced with unknown true subsurface physics) overestimating surface–root zone coupling in the assimilation system provides more robust skill improvements in the root zone compared with underestimating the coupling. When CLM is excluded from the analysis, the skill improvements from using models with different vertical coupling strengths are comparable for different subsurface truths. Last, the skill improvements through assimilation were found to be sensitive to the regional climate and soil types.

scholarly journals Dual state/rainfall correction via soil moisture assimilation for improved streamflow simulation: evaluation of a large-scale implementation with Soil Moisture Active Passive (SMAP) satellite data

2020 ◽  
Vol 24 (2) ◽  
pp. 615-631 ◽  
Author(s):  
Yixin Mao ◽  
Wade T. Crow ◽  
Bart Nijssen
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Large Scale ◽  
Land Surface Model ◽  
Systematic Errors ◽  
Red River ◽  
Future Research ◽  
Surface Model ◽  
Random Errors ◽  
Rainfall Estimates

Abstract. Soil moisture (SM) measurements contain information about both pre-storm hydrologic states and within-storm rainfall estimates, both of which are required inputs for event-based streamflow simulations. In this study, an existing dual state/rainfall correction system is extended and implemented in the 605 000 km2 Arkansas–Red River basin with a semi-distributed land surface model. The Soil Moisture Active Passive (SMAP) satellite surface SM retrievals are assimilated to simultaneously correct antecedent SM states in the model and rainfall estimates from the Global Precipitation Measurement (GPM) mission. While the GPM rainfall is corrected slightly to moderately, especially for larger events, the correction is smaller than that reported in past studies due primarily to the improved baseline quality of the new GPM satellite product. In addition, rainfall correction is poorer in regions with dense biomass due to lower SMAP quality. Nevertheless, SMAP-based dual state/rainfall correction is shown to generally improve streamflow estimates, as shown by comparisons with streamflow observations across eight Arkansas–Red River sub-basins. However, more substantial streamflow correction is limited by significant systematic errors present in model-based streamflow estimates that are uncorrectable via standard data assimilation techniques aimed solely at zero-mean random errors. These findings suggest that more substantial streamflow correction will likely require better quality SM observations as well as future research efforts aimed at reducing systematic errors in hydrologic systems.

Hyper-resolution land surface modeling enables 30-m SMAP-based soil moisture at continental scales

2020 ◽  
Author(s):  
Noemi Vergopolan ◽  
Nathaniel W. Chaney ◽  
Hylke E. Beck ◽  
Ming Pan ◽  
Justin Sheffield ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Brightness Temperature ◽  
Spatial Resolution ◽  
Land Surface ◽  
Large Scale ◽  
Radiative Transfer Model ◽  
Surface Model ◽  
Transfer Model ◽  
Field Scale ◽  
Remotely Sensed Data

<p>Accurate and detailed soil moisture information is essential for, among other things, irrigation, drought and flood prediction, water resources management, and field-scale (i.e., tens of m) decision making. Microwave-based satellite remote sensing offers unique opportunities for the large-scale monitoring of soil moisture at frequent temporal intervals. However, the utility of these satellite products is limited by the large footprint of the microwave sensors. Several downscaling techniques based on high-resolution remotely sensed data proxies have been proposed (1 km to 100 m). Although these techniques yield aesthetically pleasing maps, by neglecting how the water and energy fluxes physically interact with the landscape, these approaches often fail to provide soil moisture estimates that are hydrologically consistent.</p><p>This work introduces a state-of-the-art framework that combines a process-based hyper-resolution land surface model (LSM), a radiative transfer model (RTM), and a Bayesian scheme to merge and downscale coarse resolution brightness temperature to a 30-m spatial resolution. The framework is based on HydroBlocks, an LSM that solves the field-scale spatial heterogeneity of land surface processes through interacting hydrologic response units (HRUs). We demonstrate this framework by coupling HydroBlocks with the Tau-Omega RTM used in the Soil Moisture Active Passive (SMAP) mission and subsequently merging the HydroBlocks-RTM and the SMAP L3-enhanced brightness temperature at the HRU scale. This allows for hydrologically consistent SMAP-based soil moisture retrievals at an unprecedented 30-m spatial resolution over continental domains. </p><p>We applied this framework to obtain 30-m SMAP-based soil moisture retrievals over the contiguous United States (2015-2018). When evaluated against sparse and dense in-situ soil moisture networks, the 30-m soil moisture retrievals showed substantial improvements in performance at field and watershed scales, outperforming both the SMAP L3-enhanced and the SMAP L4 soil moisture products. This work leads the way towards hydrologically consistent field-scale soil moisture retrievals and highlights the value of hyper-resolution modeling to bridge the gap between coarse-scale satellite retrievals and field-scale hydrological applications. </p>

scholarly journals Soil Moisture Initialization Error and Subgrid Variability of Precipitation in Seasonal Streamflow Forecasting

2014 ◽  
Vol 15 (1) ◽  
pp. 69-88 ◽  
Author(s):  
Randal D. Koster ◽  
Gregory K. Walker ◽  
Sarith P. P. Mahanama ◽  
Rolf H. Reichle
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Large Scale ◽  
Forecast Skill ◽  
Surface Model ◽  
Streamflow Forecast ◽  
Continental Scale ◽  
Streamflow Forecasts ◽  
Seasonal Streamflow ◽  
Soil Moisture Initialization

Abstract Offline simulations over the conterminous United States (CONUS) with a land surface model are used to address two issues relevant to the forecasting of large-scale seasonal streamflow: (i) the extent to which errors in soil moisture initialization degrade streamflow forecasts, and (ii) the extent to which a realistic increase in the spatial resolution of forecasted precipitation would improve streamflow forecasts. The addition of error to a soil moisture initialization field is found to lead to a nearly proportional reduction in large-scale seasonal streamflow forecast skill. The linearity of the response allows the determination of a lower bound for the increase in streamflow forecast skill achievable through improved soil moisture estimation, for example, through the assimilation of satellite-based soil moisture measurements. An increase in the resolution of precipitation is found to have an impact on large-scale seasonal streamflow forecasts only when evaporation variance is significant relative to precipitation variance. This condition is met only in the western half of the CONUS domain. Taken together, the two studies demonstrate the utility of a continental-scale land surface–modeling system as a tool for addressing the science of hydrological prediction.

scholarly journals Sensitivity of the regional European boreal climate to changes in surface properties resulting from structural vegetation perturbations

2015 ◽  
Vol 12 (10) ◽  
pp. 3071-3087 ◽  
Author(s):  
J. H. Rydsaa ◽  
F. Stordal ◽  
L. M. Tallaksen
Keyword(s):  
Land Surface ◽  
Large Scale ◽  
Structural Changes ◽  
Mixed Forest ◽  
Surface Fluxes ◽  
Heat Fluxes ◽  
Energy Partitioning ◽  
The Arctic ◽  
Surface Model ◽  
Near Surface

Abstract. Amplified warming at high latitudes over the past few decades has led to changes in the boreal and Arctic climate system such as structural changes in high-latitude ecosystems and soil moisture properties. These changes trigger land–atmosphere feedbacks through altered energy partitioning in response to changes in albedo and surface water fluxes. Local-scale changes in the Arctic and boreal zones may propagate to affect large-scale climatic features. In this study, MODIS land surface data are used with the Weather Research and Forecasting model (WRF V3.5.1) and Noah land surface model (LSM), in a series of experiments to investigate the sensitivity of the overlying atmosphere to perturbations in the structural vegetation in the northern European boreal ecosystem. Emphasis is placed on surface energy partitioning and near-surface atmospheric variables, and their response to observed and anticipated land cover changes. We find that perturbations simulating northward migration of evergreen needleleaf forest into tundra regions cause an increase in latent rather than sensible heat fluxes during the summer season. Shrub expansion in tundra areas has only small effects on surface fluxes. Perturbations simulating the northward migration of mixed forest across the present southern border of the boreal forest, have largely opposite effects on the summer latent heat flux, i.e., they lead to a decrease and act to moderate the overall mean regional effects of structural vegetation changes on the near-surface atmosphere.

scholarly journals Impacts of the Soil Water Transfer Parameterization on the Simulation of Evapotranspiration over a 14-Year Mediterranean Crop Succession

2018 ◽  
Vol 19 (1) ◽  
pp. 3-25 ◽  
Author(s):  
S. Garrigues ◽  
A. Boone ◽  
B. Decharme ◽  
A. Olioso ◽  
C. Albergel ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Soil Water ◽  
Land Surface ◽  
Large Scale ◽  
Field Capacity ◽  
Water Transfer ◽  
Soil Parameters ◽  
Surface Model ◽  
Crop Succession

Abstract This paper presents a comparison of two water transfer schemes implemented in land surface models: a three-layer bulk reservoir model based on the force–restore scheme (FR) and a multilayer soil diffusion scheme (DIF) relying on explicit mass-diffusive equations and a root profile. The performances of each model at simulating evapotranspiration (ET) over a 14-yr Mediterranean crop succession are compared when the standard pedotransfer estimates versus the in situ values of the soil parameters are used. The Interactions between Soil, Biosphere, and Atmosphere (ISBA) generic land surface model is employed. When the pedotransfer estimates of the soil parameters are used, the best performance scores are obtained with DIF. DIF provides more accurate simulations of soil evaporation and gravitational drainage. It is less sensitive to errors in the soil parameters compared to FR, which is strongly driven by the soil moisture at field capacity. When the in situ soil parameters are used, the performance of the FR simulations surpasses those of DIF. The use of the proper maximum available water content for the plant removes the bias in ET and soil moisture over the crop cycle with FR, while soil water stress is simulated too early and the transpiration is underestimated with DIF. Increasing the values of the root extinction coefficient and the proportion of homogeneous root distribution slightly improves the DIF performance scores. Spatiotemporal uncertainties in the soil parameters generate smaller uncertainties in ET simulated with DIF compared to FR, which highlights the robustness of DIF for large-scale applications.

scholarly journals Effect of Soil Moisture on Diurnal Convection and Precipitation in Large-Eddy Simulations

2017 ◽  
Vol 18 (7) ◽  
pp. 1885-1903 ◽  
Author(s):  
Guido Cioni ◽  
Cathy Hohenegger
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Large Scale ◽  
Land Surface Model ◽  
Surface Model ◽  
Total Precipitation ◽  
Atmospheric Conditions ◽  
Large Eddy Simulation Model ◽  
Large Eddy ◽  
Strong Scaling

Abstract A determination of the sign and magnitude of the soil moisture–precipitation feedback relies either on observations, where synoptic variability is difficult to isolate, or on model simulations, which suffer from biases mainly related to poorly resolved convection. In this study, a large-eddy simulation model with a resolution of 250 m is coupled to a land surface model and several idealized experiments mimicking the full diurnal cycle of convection are performed, starting from different spatially homogeneous soil moisture conditions. The goal is to determine under which conditions drier soils may produce more precipitation than wetter ones. The methodology of previous conceptual studies that have quantified the likelihood of convection to be triggered over wet or dry soils is followed but includes the production of precipitation. Although convection can be triggered earlier over dry soils than over wet soils under certain atmospheric conditions, total precipitation is found to always decrease over dry soils. By splitting the total precipitation into its magnitude and duration component, it is found that the magnitude strongly correlates with surface latent heat flux, hence implying a wet soil advantage. Because of this strong scaling, changes in precipitation duration caused by differences in convection triggering are not able to overcompensate for the lack of evaporation over dry soils. These results are further validated using two additional atmospheric soundings and a series of perturbed experiments that consider cloud radiative effects, as well as the effect of large-scale forcing, winds, and plants on the soil moisture–precipitation coupling.

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