An Improved Triple Collocation Analysis Algorithm for Decomposing Autocorrelated and White Soil Moisture Retrieval Errors

Estimation of high-resolution precipitation using downscaled satellite soil moisture and SM2RAIN approach

The European Space Agency's Climate Change Initiative (ESA CCI) soil moisture could provide long-time microwave-retrieved soil moisture data but is limited to regional applications due to the low resolution (25 km). A new method of downscaling ESA CCI soil moisture to 1 km is presented in this study. First, the soil and vegetation component temperatures (SVCT) were estimated using MODIS land surface temperature and normalized difference vegetation index (NDVI) data. Following this, the relationship between ESA CCI soil moisture and 1-km SVCT was constructed based on the negative linear relationship between the temperature vegetation dryness index (TVDI) and soil moisture. The dry and wet lines used to estimate TVDI need not to be obtained in the method. The coefficients were obtained directly from 25-km ESA CCI soil moisture and 1-km SVCT by the upscaling algorithm of soil moisture. The method was applied to the Naqu area on the Tibetan Plateau. Downscaled soil moisture was validated with ground measurements collected at five sites within the soil moisture/temperature monitoring network on the central Tibetan Plateau from May to October 2014. The results show that the trend of the time series of the downscaled soil moisture is similar to the ground measurements during this period, and the root-mean-square error is 0.0568 m³/m³. The method is suitable for the condition with an NDVI higher than 0.4. The key points of the approach are to obtain SVCT and the relationship between soil moisture and SVCT.

To better understand error and spatial variability sources of soil moisture simulated with land surface models, observed and simulated values of soil moisture (using offline simulations with the Noah land surface model with four soil layers and approximately 1-km horizontal grid spacing) were compared. This comparison between observed and modeled daily values of soil moisture was performed over the Lower Mississippi Delta region during summer–fall months 2004–06. The Noah simulations covered the 2.5° × 2.5° latitude–longitude domain and were forced by the North American Land Data Assimilation System (NLDAS) atmospheric forcing fields. Hourly soil moisture measurements and other data, including local meteorological and soil physical properties data from 12 Soil Climate Analysis Network (SCAN) sites, were used. The results show that both the observed and simulated level of soil moisture depend critically on the specified–sampled soil texture. Soil types with a relatively high observed clay content (more than 50% of weight) retain more water as a result of low water diffusivity than silty–sandy soils with 20% or less clay, provided that other conditions are the same. This fact is in agreement with previous studies and implies a strong soil texture control (through related hydraulic parameters) on the accuracy of simulated soil moisture. Sensitivity tests using the Noah model were performed to assess the effect of using the hydraulic parameters related to the site-specific soil texture on soil moisture quality. Indeed, at some SCAN sites, the errors (root-mean-square difference and bias) were reduced. Simulated soil moisture showed at least a 50% reduction when the site-specific soil texture was used in Noah simulations compared to those derived from the State Soil Geographic (STATSGO) data. The most significant improvement of simulated soil moisture was observed within the top 0–10 cm layer where an original positive bias (an excessive wetness) was almost eliminated. Meanwhile, excessive dryness (negative soil moisture bias), which was dominant within the second and third model layers, was also reduced. These improvements are expected to be valid at sites/regions with low (<0.3) vegetation fraction.

Accurate quantitative precipitation forecasts are essential for water resources management, agriculture, and other applications. Soil moisture is among the most important boundary conditions affecting the overlying atmosphere. It can affect precipitation either directly via evapotranspiration, affecting the moisture supply to the atmosphere, or indirectly, changing sensible heat fluxes, which can affect the structure of storms and their associated distributions of precipitation. In this study we investigate the impact of soil moisture uncertainty on simulated precipitation over the conterminous United States (CONUS) with the Weather Research and Forecasting model. We selected two 30‐day periods to conduct simulation scenarios with 3‐km horizontal grid spacing over the CONUS, where the different simulations were identical except for their initial soil moisture and temperature states which were derived from three different products: Global Forecast System analyses, the National Center for Atmospheric Research (NCAR) continuously cycling ensemble Kalman filter (EnKF) data assimilation system, and the North American Land Data Assimilation System (NLDAS‐2). Results indicate different characteristics of the soil moisture–precipitation relationship during the cold and warm periods and under different climate and meteorological conditions. Better representation of initial soil moisture does not consistently provide an improvement in the simulation of precipitation. Our results suggest that soil moisture affects both the magnitude and spatial distribution of precipitation. The impact of soil moisture on precipitation is more pronounced under drier soil and strong thermal and weak synoptic forcing, while in areas with complex terrain the soil moisture‐precipitation coupling becomes weaker.

Soil moisture observations from seven observational networks (spanning portions of seven states) with different biome and climate conditions were used in this study to evaluate multimodel simulated soil moisture products. The four land surface models, including Noah, Mosaic, Sacramento soil moisture accounting (SAC), and the Variable Infiltration Capacity model (VIC), were run within phase 2 of the North American Land Data Assimilation System (NLDAS-2), with a ⅛° spatial resolution and hourly temporal resolution. Hundreds of sites in Alabama, Colorado, Michigan, Nebraska, Oklahoma, West Texas, and Utah were used to evaluate simulated soil moisture in the 0–10-, 10–40-, and 40–100-cm soil layers. Soil moisture was spatially averaged in each state to reduce noise. In general, the four models captured broad features (e.g., seasonal variation) of soil moisture variations in all three soil layers in seven states, except for the 10–40-cm soil layer in West Texas and the 40–100-cm soil layer in Alabama, where the anomaly correlations are weak. Overall, Mosaic, SAC, and the ensemble mean have the highest simulation skill and VIC has the lowest simulation skill. The results show that Noah and VIC are wetter than the observations while Mosaic and SAC are drier than the observations, mostly likely because of systematic errors in model evapotranspiration.

SUMMARY Crustal deformation caused by hydrological processes has long been detected using space geodetic techniques, yet questions remain about the relative contributions of surface water and groundwater to the geodetic signals in different regions. Here, we investigate forward models of elastic loading deformation caused by a variety of water-storage changes within the Great Lakes region, including fluctuations in lake-water volume, soil moisture and snow load. We use lake-level data from the Great Lakes Environmental Research Laboratory, soil-moisture content from the North American Land Data Assimilation System (NLDAS), snow load from the Snow Data Assimilation System (SNODAS) and background hydrological load at the global scale from Gravity Recovery and Climate Experiment (GRACE). We compare the modelled surface deformation with estimates of hydrological loading deformation inferred from Global Navigation Satellite System (GNSS) measurements. We find that seasonal deformation measured by GNSS is dominated by regional-scale hydrological loading based on strong correlations with the modelled loading displacements. The mean correlation coefficient for the study network is 0.56. The correlation coefficients vary spatially within the study region and exceed 0.9 at some stations near to the Great Lakes. We assess the relative contribution of each individual hydrological component to the total integrated hydrological load. We find that soil moisture consistently explains the largest percentage (27–69 per cent) of the total vertical loading deformation for 87 per cent of GNSS stations in the Great Lakes region. Snow loading and soil moisture contribute relatively equally in the northern reaches of the study area (e.g. Canadian shield, northern Superior basin). Lake loading accounts for about 10–25 per cent of the total loading signal in the immediate vicinity of the lakes. We also investigate the sensitivities of the surface loading displacements to three different Earth models, including two with lateral variations in structure. The structural variations considered here have limited impact (<0.2 mm) on the predicted hydrological loading displacements and could be neglected at the current level of observational precision.

A relationship between the likelihood of wildfires and various drought metrics (soil moisture-based fire potential indices) were examined over the southern part of Mississippi. The following three indices were tested and used to simulate spatial and temporal wildfire probability changes: (1) the accumulated difference between daily precipitation and potential evapotranspiration (P - E); (2) simulated moisture content of the top 10 cm of soil; and (3) the Keetch-Byram Drought Index (KBDI). These indices were estimated from gridded meterological data and Mosaic-simulated soil moisture data available from the North American Land Data Assimilation System (NLDAS-2). The relationships between normalized fire potential index deviations and the probability of at least one fire occurring during the following five consecutive days were evaluated using a 23-year (1986-2008) forest fire record for an evenly spaced grid (0.25° x 0.25°) across the state of Mississippi's coastal plain. Two periods were selected and examined (January-mid June and mid September-December). There was good agreement between the observed and logistic model-fitted fire probabilities over the study area during both seasons. The fire potential indices based on the top 10 cm soil moisture and KBDI had the largest impact on wildfire odds, increasing it by almost 2 times in response to each unit change of the corresponding fire potential index during January-mid-June period and by nearly 1.5 times during mid-September-December. These results suggest that soil moisture-based fire potential indices are good indicators of fire occurrence probability across this region.

Soil moisture is vital for studying hydrological processes, vegetation growth, and climate change. Recently, Global Navigation Satellite System-Reflectometry (GNSS-R) technology has shown great potential for soil moisture monitoring. However, most existing GNSS-R algorithms rely empirically on multiple land surface parameters, which increases model complexity and the risk of overfitting. This study investigates underlying surface influencing factors for soil moisture retrieval and developes a new soil moisture retrieval with considering the factors that influence the relationship between GNSS-R measurements and surface soil moisture, using Cyclone Global Navigation Satellite System (CYGNSS) data in the middle and lower reaches of the Yangtze River (23.5°N-35°N, 110°E-123°E). Monthly CYGNSS reflectivity and SMAP soil moisture data from 2019 to 2024 are analyzed with consideration of influencing factors such as elevation, slope, biomass, land use, tree height, and Normalized Difference Vegetation Index (NDVI). Results show the following key findings: (1) Reflectivity exhibits a significant correlation with soil moisture, with 84.05% displaying a positive correlation. (2) Over the six-year period from 2019 to 2024, the correlation between SMAP soil moisture and CYGNSS reflectivity shows clear seasonal variability, with lower correlations in spring and autumn and higher correlations in summer and winter, peaking in summer. (3) Geodetector analysis revealed that land use had the most significant effect on soil moisture in the single-factor analysis, while biomass and reflectivity had the strongest interaction in the two-factor analysis, characterized by nonlinear enhancement. (4) A retrieval scheme was designed based on the influencing factors, and its accuracy was validated using the eXtreme Gradient Boosting (XGBoost) machine learning algorithm. The root mean square error (RMSE) of the soil moisture retrieval using all selected features was 0.0526 cm<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup>/cm<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup>. The major feature group achieved an RMSE of 0.0527 cm<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup>/cm<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup>, while the minor feature group yielded an RMSE of 0.0611 cm<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup>/cm<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup>. Spatial RMSE analysis further demonstrated the effectiveness of Geodetector in guiding feature selection. The retrieval results were further validated using the SSM V2.0 high-resolution soil moisture product, which has been extensively evaluated against in situ measurements, demonstrating strong consistency and confirming the robustness and practical applicability of the proposed method.

Time series of soil moisture (SM) data in the Qinghai–Tibet plateau (QTP) covering a period longer than one decade are important for understanding the dynamics of land surface–atmosphere feedbacks in the global climate system. However, most existing SM products have a relatively short time series or show low performance over the challenging terrain of the QTP. In order to improve the spaceborne monitoring in this area, this study presents a random forest (RF) method to rebuild a high-accuracy SM product over the QTP from 19 June 2002 to 31 March 2015 by adopting the advanced microwave scanning radiometer for earth observing system (AMSR-E), and the advanced microwave scanning radiometer 2 (AMSR2), and tracking brightness temperatures with latitude and longitude using the International Geosphere–Biospheres Programme (IGBP) classification data, the digital elevation model (DEM) and the day of the year (DOY) as spatial predictors. Brightness temperature products (from frequencies 10.7 GHz, 18.7 GHz and 36.5 GHz) of AMSR2 were used to train the random forest model on two years of Soil Moisture Active Passive (SMAP) SM data. The simulated SM values were compared with third year SMAP data and in situ stations. The results show that the RF model has high reliability as compared to SMAP, with a high correlation (R = 0.95) and low values of root mean square error (RMSE = 0.03 m3/m3) and mean absolute percent error (MAPE = 19%). Moreover, the random forest soil moisture (RFSM) results agree well with the data from five in situ networks, with mean values of R = 0.75, RMSE = 0.06 m3/m3, and bias = −0.03 m3/m3 over the whole year and R = 0.70, RMSE = 0.07 m3/m3, and bias = −0.05 m3/m3 during the unfrozen seasons. In order to test its performance throughout the whole region of QTP, the three-cornered hat (TCH) method based on removing common signals from observations and then calculating the uncertainties is applied. The results indicate that RFSM has the smallest relative error in 56% of the region, and it performs best relative to the Japan Aerospace Exploration Agency (JAXA), Global Land Data Assimilation System (GLDAS), and European Space Agency’s Climate Change Initiative (ESA CCI) project. The spatial distribution shows that RFSM has a similar spatial trend as GLDAS and ESA CCI, but RFSM exhibits a more distinct spatial distribution and responds to precipitation more effectively than GLDAS and ESA CCI. Moreover, a trend analysis shows that the temporal variation of RFSM agrees well with precipitation and LST (land surface temperature), with a dry trend in most regions of QTP and a wet trend in few north, southeast and southwest regions of QTP. In conclusion, a spatiotemporally continuous SM product with a high accuracy over the QTP was obtained.

Prolonged duration and increased severity of agricultural droughts during 1978 to 2016 detected by ESA CCI SM in the humid Yunnan Province, Southwest China

A multivariate approach for statistical assessments of compound extremes

<para xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> We examine the Naval Research Laboratory (NRL) blended satellite (NRL-Blend) High-Resolution Precipitation Product (HRPP) as a proxy for a Global Precipitation Mission (GPM)-era HRPP by using the NRL-Blend for precipitation forcing in land surface models (LSM). We use the existing (late 2008) constellation of low Earth orbiting (LEO) microwave-based satellite platforms as a baseline to examine the impact of omitting several satellite and sensor types from future GPM-era HRPPs. A response of 1-m soil water content (SWC) to different precipitation forcing represented by six NRL-Blend satellite/sensor omission scenarios was investigated using simulations over the central United States with the Noah and Mosaic land surface models (LSM). The LSMs were integrated over a domain encompassing the Arkansas-Red River basin, using the North American Land Data Assimilation System (NLDAS) atmospheric forcing (except for precipitation). Both spatial and temporal statistical properties of the SWC response were examined. Both LSMs predicted a rather consistent geographical response of the 1-m SWC to different precipitation inputs, having positive/negative SWC monthly mean anomalies in western/eastern parts of the domain. The biggest impact was due to the omission of either the crosstrack microwave sounders, or the morning local time crossing satellites. On the other hand, omission of afternoon local time crossing satellites in the NRL-Blend resulted in the smallest impact upon the soil moisture simulated both with the Noah and Mosaic models. Although the relative magnitude of these SWC changes is small, these results suggest the importance of the crosstrack microwave sounders for future GPM constellations. </para>

This paper investigates surface temperature variables as they relate to passive microwave-derived surface freeze/thaw (FT) state and assesses the accuracy of such FT products relative to surface temperature. Utilizing retrievals from the soil moisture active/passive (SMAP), advanced microwave scanning radiometer, and special sensor microwave imager instruments, surface FT records have previously been derived globally. Moderate Resolution Imaging Spectroradiometer skin temperature, North American Land Data Assimilation System (NLDAS) skin, 0–10-cm soil layer, and 2-m air temperatures are compared to the various FT state products (FTSPs) by defining the threshold for FT state transitions at 0 °C. This paper utilizes the 2015–2016 overlap period in FT records within the NLDAS domain. Spatial variability of classification accuracy (CA) is then investigated over the study area. A proportional differencing method also enables the identification of biases between FTSPs and surface temperature variables. Additionally, by analyzing probability distribution functions of FTSPs associated with temperature values, we assess the distribution of temperature variables as they relate to FT classifications. Classification agreement is shown to vary with sensor configuration, seasonality, and retrieval time. Air temperature is found to have the highest CAs across FTSPs (81%–91%), while NLDAS soil exhibits a close relationship to FTSPs, especially in regard to SMAP products. Finally, ascending ( <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p.m.</small> ) retrievals are shown to be increasingly linked to the selected temperature parameters as compared to descending ( <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a.m.</small> ) observations. This paper contributes to an improved understanding of current FTSPs and will benefit efforts to enhance future FT products.

Accurate rainfall-runoff modelling is particularly challenging due to complex nonlinear relationships between various factors such as rainfall characteristics, soil properties, land use, and temporal lags. Recently, with improvements to computation systems and resources, data-driven models have shown good performances for runoff forecasting. However, the relative performance of common data-driven models using small temporal resolutions is still unclear. This study presents an application of data-driven models using artificial neural network, support vector regression and long-short term memory approaches and distributed forcing data for runoff predictions between 2010 and 2019 in the Russian River basin, California, USA. These models were used to predict hourly runoff with 1–6 h of lead time using precipitation, soil moisture, baseflow and land surface temperature datasets provided from the North American Land Data Assimilation System. The predicted results were evaluated in terms of seasonal and event-based performance using various statistical metrics. The results showed that the long-short term memory and support vector regression models outperforms artificial neural network model for hourly runoff forecasting, and the predictive performance of the models was greater during the wet seasons compared to the dry seasons. In addition, a comparison of the data-driven model results with the National Water Model, a fully distributed physical-based hydrologic model, showed that the long-short term memory and support vector regression models provide comparable performance. The results demonstrate that data-driven models for hourly runoff forecasting are sufficiently predictive and useful in areas where observation systems are not available.

Soil moisture (SM) products presently available in permafrost regions, especially on the Qinghai–Tibet Plateau (QTP), hardly meet the demands of evaluating and modeling climatic, hydrological, and ecological processes, due to their significant bias and low spatial resolution. This study developed an algorithm to generate high-spatial-resolution SM during the thawing season using Sentinel-1 (S1) and Sentinel-2 (S2) temporal data in the permafrost environment. This algorithm utilizes the seasonal backscatter differences to reduce the effect of surface roughness and uses the normalized difference vegetation index (NDVI) and the normalized difference moisture index (NDMI) to characterize vegetation contribution. Then, the SM map with a grid spacing of 50 m × 50 m in the hinterland of the QTP with an area of 505 km × 246 km was generated. The results were independently validated based on in situ data from active layer monitoring sites. It shows that this algorithm can retrieve SM well in the study area. The coefficient of determination (R2) and root-mean-square error (RMSE) are 0.82 and 0.06 m3/m3, respectively. This study analyzed the SM distribution of different vegetation types: the alpine swamp meadow had the largest SM of 0.26 m3/m3, followed by the alpine meadow (0.23), alpine steppe (0.2), and alpine desert (0.16), taking the Tuotuo River basin as an example. We also found a significantly negative correlation between the coefficient of variation (CV) and SM in the permafrost area, and the variability of SM is higher in drier environments and lower in wetter environments. The comparison with ERA5-Land, GLDAS, and ESA CCI showed that the proposed method can provide more spatial details and achieve better performance in permafrost areas on QTP. The results also indicated that the developed algorithm has the potential to be applied in the entire permafrost regions on the QTP.