Gridded Soil Moisture Research Articles

Comparative assessment of gridded soil moisture data products (2000–2019) for drought detection: A study over Indian region

Comparative assessment of gridded soil moisture data products (2000–2019) for drought detection: A study over Indian region

The important role of reliable land surface model simulation in high-resolution multi-source soil moisture data fusion by machine learning

While machine learning (ML) is used to correct gridded soil moisture (SM) products by fusing in-situ observations, the contribution of land surface model (LSM)- and satellite-based SM products are yet to be validated over a large area, leading to imprudent adoption of SM product(s) for data fusion. In this study, single or multiple SM products from CSSPv2 LSM simulation, ERA5 and GLDASv2.1 reanalysis, and ESA-CCI satellite data with different resolutions are used to train ML models and generate daily SM estimates at 0.0625° resolution with in-situ measurements as target and relevant variables as auxiliary. Three widely used ML methods, namely Random Forest (RF), LightGBM, and XGBoost, were compared. Validations over independent in-situ stations during 2012–2017 showed the improvement of fusion products against their corresponding raw products, with KGE and CC increased by 87 % and 6 %, and RMSE decreased by 22 % for SM at surface and rootzone layers. Regionally, ML-based SM estimates improve mainly in southeast China. Merging three coarse-resolution SM datasets (i.e., ERA5, GLDASv2.1 and ESA-CCI) together with in-situ observations further increases KGE and CC by 15 % and 5 % against individual fusion products, but it still fails to outperform the individual high-resolution fusion product of ML/CSSPv2. Merging all four gridded SM products with in-situ data shows advantage against the ML/CSSPv2, with KGE and CC increased by 9 % and 7 %. The results are consistent by using different ML methods. This study suggests the importance of high-resolution LSM for SM data fusion, even with the emergence of ML approaches.

Comprehensive quality assessment of satellite- and model-based soil moisture products against the COSMOS network in Germany

Critical to the reliable application of gridded soil moisture products is a thorough assessment of their quality at spatially compatible scales. While previous studies have attempted to evaluate different soil moisture products, comprehensive assessments at the appropriate scale remain challenging and rare. This study explores the potential of the Cosmic-Ray Soil Moisture Observation System (COSMOS) in Germany for effectively mitigating the scale mismatch between soil moisture products and reference measurements. A newly released extensive COSMOS data set provides time series of hectare-scale soil moisture of the main root zone at different locations in Germany and offers a unique opportunity for a comprehensive quality assessment of 15 commonly-used coarse-scale soil moisture products. Those are either satellite-based (AMSR2 LPRM, ASCAT H115/H116, Sentinel-1 SSM, SMAP L3E, SMOS L3, ASCAT/Sentinel-1 SWI, SMAP/Sentinel-1 L2, CCI Combined, and NOAA SMOPS) or model-based (ERA5-Land, GLDAS-Noah, ASCAT H141/H142, GLEAM, SMAP L4, and SMOS L4). We compared the temporal dynamics of the soil moisture products against that of the COSMOS soil moisture estimates at 21 sites of different land cover types over six years (2015–2020), including the drought of 2018. We found that the model-based products generally yield a higher correlation (0.74) and lower unbiased root-mean-square differences (0.05m3m−3) than the satellite-based products (0.60 and 0.07m3m−3, respectively) against the COSMOS data in Germany. Notably, the application of the exponential filter significantly improves the performance of the products. Conversely, deseasonalized time series of all selected products demonstrate lower performances across all COSMOS sites. Most products show a considerable positive bias, which limits their usability for the assessment of absolute soil water storage. We also found that the land cover type, mean annual soil moisture, and vertical support have notable influences on the performance of the soil moisture products. Additionally, the performances of the soil moisture products show seasonal variations, such that both correlation and bias are highest during the summer season. This study highlights the strengths of COSMOS data as a robust reference for evaluating soil moisture products. Additionally, it provides insights on how to assess, interpret, and improve large-scale soil moisture products.

Can Hydrological Models Benefit From Using Global Soil Moisture, Evapotranspiration, and Runoff Products as Calibration Targets?

AbstractHydrological models are usually calibrated to in‐situ streamflow observations with reasonably long and uninterrupted records. This is challenging for poorly gage or ungaged basins where such information is not available. Even for gaged basins, the single‐objective calibration to gaged streamflow cannot guarantee reliable forecasts because, as has been documented elsewhere, the inverse problem is mathematically ill‐posed. Therefore, the inclusion of other observations, and the reproduction of other hydrological variables beyond streamflow, become critical components of accurate hydrological forecasting. In this study, six single‐ and multi‐objective model calibration schemes based on different combinations of gaged streamflow, global‐scale gridded soil moisture, actual evapotranspiration (ET), and runoff products are used for the calibration of a process‐based hydrological model for 20 catchments located within the Lake Michigan watershed, of the Laurentian Great Lakes. Results show that the addition of gridded soil moisture to gaged streamflow in model calibration improves the ET simulation performance for most of the catchments, leading to the overall best‐performing models. The monthly streamflow simulation performance for the experiments using gridded runoff products to inform the model is outperformed by those using the gaged streamflow, but the discrepancy is mitigated with increasing catchment scale. A new visualization method that effectively synthesizes model performance for the simulations of streamflow, soil moisture, and ET was also proposed. Based on the method, it is revealed that the streamflow simulation performance is relatively weak for baseflow‐dominated catchments; overall, the 20 catchment models simulate streamflow and ET better than soil moisture.

Soil moisture routing modeling of targeted biochar amendment in undulating topographies: an analysis of biochar's effects on streamflow

<abstract><p>The effect of biochar on hydrologic fluxes was estimated using a single hillslope version of a gridded soil moisture routing (SMR) model. Five grid cells were aligned linearly with varied slopes to simulate a small undulating hillslope with or without a restrictive layer beneath the soil profile. Biochar amendments (redwood sawdust and wheat straw biochar) at concentrations of 0%, 4%, and 7% were applied to the topmost grid-cell by mass of dry soil. Simulated streamflow hydrographs for restricted and non-restricted soil profiles were manually calibrated with measured Palouse River streamflow data. Evapotranspiration, percolation, lateral flow, baseflow, and streamflow were all modeled yearly. Two generally reported field capacities (FC) in literature at −6 and −33 kPa were considered to assess the effect of biochar. Field capacity considered at −6 kPa corresponds to higher moisture content, and hence higher moisture storage capacity between FC and permanent wilting point than at −33 kPa. At −6 kPa FC, biochar effectively increased evapotranspiration and reduced the lateral flow of the system. Increased soil porosity from biochar amendment enhanced the water holding capacity of the soil and plant available water. These mechanisms impacted the streamflow generated from the system indicating positive outcomes from biochar amendment in both restricted and non-restricted soil profiles. Biochar amendment showed an order of magnitude smaller effects with −33 kPa FC compared to −6 kPa FC; the increased porosity appeared to be less influential at lower field capacity values. Additionally, the results showed that the over-application of coarse biochar might negatively affect retaining soil moisture. These findings point to positive results for using biochar as a water management strategy if applied less than 7% in this study, but further exploration is needed to find the optimum level of biochar with different biochar and soil properties.</p></abstract>

A 1 km daily soil moisture dataset over China using in situ measurement and machine learning

Abstract. High-quality gridded soil moisture products are essential for many Earth system science applications, while the recent reanalysis and remote sensing soil moisture data are often available at coarse resolution and remote sensing data are only for the surface soil. Here, we present a 1 km resolution long-term dataset of soil moisture derived through machine learning trained by the in situ measurements of 1789 stations over China, named SMCI1.0 (Soil Moisture of China by in situ data, version 1.0). Random forest is used as a robust machine learning approach to predict soil moisture using ERA5-Land time series, leaf area index, land cover type, topography and soil properties as predictors. SMCI1.0 provides 10-layer soil moisture with 10 cm intervals up to 100 cm deep at daily resolution over the period 2000–2020. Using in situ soil moisture as the benchmark, two independent experiments were conducted to evaluate the estimation accuracy of SMCI1.0: year-to-year (ubRMSE ranges from 0.041 to 0.052 and R ranges from 0.883 to 0.919) and station-to-station experiments (ubRMSE ranges from 0.045 to 0.051 and R ranges from 0.866 to 0.893). SMCI1.0 generally has advantages over other gridded soil moisture products, including ERA5-Land, SMAP-L4, and SoMo.ml. However, the high errors of soil moisture are often located in the North China Monsoon Region. Overall, the highly accurate estimations of both the year-to-year and station-to-station experiments ensure the applicability of SMCI1.0 to study the spatial–temporal patterns. As SMCI1.0 is based on in situ data, it can be a useful complement to existing model-based and satellite-based soil moisture datasets for various hydrological, meteorological, and ecological analyses and models. The DOI link for the dataset is http://dx.doi.org/10.11888/Terre.tpdc.272415 (Shangguan et al., 2022).

Gridded maps of wetlands dynamics over mid-low latitudes for 1980–2020 based on TOPMODEL

Dynamics of global wetlands are closely linked to biodiversity conservation, hydrology, and greenhouse gas emissions. However, long-term time series of global wetland products are still lacking. Using a diagnostic model based on the TOPography-based hydrological MODEL (TOPMODEL), this study produced an ensemble of 28 gridded maps of monthly global/regional wetland extents (with more reliable estimates at mid-low latitudes) for 1980–2020 at 0.25° × 0.25° spatial resolution, calibrated with a combination of four observation-based wetland data and seven gridded soil moisture reanalysis datasets. The gridded dynamic maps of wetlands capture the spatial distributions, seasonal cycles, and interannual variabilities of observed wetland extent well, and also show a good agreement with independent satellite-based terrestrial water storage estimates over wetland areas. The long temporal coverage extending beyond the era of satellite datasets, the global coverage, and the opportunity to provide real-time updates from ongoing soil moisture data make these products helpful for various applications such as analyzing the wetland-related methane emission.

Changing spectral patterns of long‐term drought propensity in Iran through reliability–resilience–vulnerability‐based Drought Management Index

AbstractThis study aims to investigate the change in spectral patterns of droughts in Iran based on the concepts of reliability, resilience, and vulnerability (RRV) in the context of time. To achieve this goal, RRV values were first estimated based on monthly gridded soil moisture data for a period of 64 years (1955–2018) for the framework of Iran's political borders. The pairwise comparison of the values of the three variables RRV indicates a positive relationship between reliability–resilience and a negative relationship between reliability–vulnerability and resilience–vulnerability. In order to obtain the Joint Probability Distribution between reliability–vulnerability, Drought Management Index (DMI) for all the gridded points within Iran's political borders were calculated by fitting the Gaussian copula. According to this index, drought is an unfavourable climatic phenomenon that is associated with increased Vulnerability and decreased Reliability. The temporal and spatial changes of this index indicate that DMI values are always high for the south, east and centre of Iran and low for the western and northern half. To study the change in spectral patterns of droughts in Iran, the 64‐year period studied (1955–2018) was divided into two equal periods of 32 years. Then, using spectral analysis through Fourier transform method, the first three periodicities related to each period that had the highest powers were extracted. Finally, their periodicity was compared. The results indicated that the drought periodicities in the north‐west, north‐east, west, south‐west, and south of Iran have become longer, that is, their nature has been changed from shorter periodicities to longer ones. This change of nature in their first three periodicities has been sometimes from 2–10 years to 2–15 years, from 2–10 years to 3–30 years, and from 3–8 years to 8–15 years. However, the periodicities of droughts in the eastern, south‐eastern, and central Iran has changed from longer periodicities to shorter ones. It has been changed from 3–15 years to 2–6 years, from 3–15 years to 2–3 years, and from 3–30 years to 3–15 years.

Variability in soil moisture using AMSR-E product- A regional case study in the province of Marathwada division, India

ABSTRACT Soil moisture plays a crucial role in the assessment of weather patterns and analyzing the precipitation. The moisture analysis in the soil surface has been most efficient with passive remote sensing. The study attempts extraction of soil moisture and quantifying it with rainfall. The Marathwada division was selected as the study area, the region corresponds to low rainfall and is a drought-prone area. The data used were AMSR-E/Aqua Daily L3 gridded soil moisture and CHRS rainfall data, the daily product of 25 km×25 km resolution. The methodology was demonstrated for the years 2003, 2004, and 2005. A cumulative 122 AMSR-E daily ascending and descending scenes were retrieved and analyzed for the rainy season. The spatial analysis depicts peak soil moisture of 25% for the year 2005, greater than 20%, and 18% during 2003 and 2004, respectively. Peak moisture corresponding to all years is 20% in July compared to 13%, 17%, and 16% in June, August, and September, respectively. The temporal analysis revealed that during the onset of monsoon, soil moisture is 4% with 1.5 mm rainfall and it increases by 25% at peak rainfall of 39 mm. The soil moisture variation is in line with seasonal changes and rainfall variations.

Evaluation of gridded soil moisture products over varied land covers, climates, and soil textures using in situ measurements: A case study of Lake Urmia Basin

Evaluation of gridded soil moisture products over varied land covers, climates, and soil textures using in situ measurements: A case study of Lake Urmia Basin

Multi-Dimensional Drought Assessment in Abbay/Upper Blue Nile Basin: The Importance of Shared Management and Regional Coordination Efforts for Mitigation

Drought is one of the least understood and complex natural hazards often characterized by a significant decrease in water availability for a prolonged period. It can be manifested in one or more forms as meteorological, agricultural, hydrological, and/or socio-economic drought. The overarching objective of this study is to demonstrate and characterize the different forms of droughts and to assess the multidimensional nature of drought in the Abbay/ Upper Blue Nile River (UBN) basin and its national and regional scale implications. In this study, multiple drought indices derived from in situ and earth observation-based hydro-climatic variables were used. The meteorological drought was characterized using the Standardized Precipitation Index (SPI) computed from the earth observation-based gridded CHIRPS (Climate Hazards Group InfraRed Precipitation with Station) rainfall data. Agricultural and hydrological droughts were characterized by using the Soil Moisture Deficit Index (SMDI) and Standardized Runoff-discharge Index (SRI), respectively. The monthly time series of SMDI was derived from model-based gridded soil moisture and SRI from observed streamflow data from 1982 to 2019. The preliminary result illustrates the good performance of the drought indices in capturing the historic severe drought events (e.g., 1984 and 2002) and the spatial extents across the basin. The results further indicated that all forms of droughts (i.e., meteorological, agricultural, and hydrological) occurred concurrently in Abbay/Upper Blue Nile basin with a Pearson correlation coefficient ranges from 0.5 to 0.85 both Kiremt and annual aggregate periods. The concurrent nature of drought is leading to a multi-dimensional socio-economic crisis as indicated by rainfall, and soil moisture deficits, and drying of small streams. Multi-dimensional drought mitigation necessitates regional cooperation and watershed management to protect both the common water sources of the Abbay/Upper Blue Nile basin and the socio-economic activities of the society in the basin. This study also underlines the need for multi-scale drought monitoring and management practices in the basin.

A multi-sourced assessment of the spatiotemporal dynamics of soil moisture in the MARINE flash flood model

Abstract. The MARINE (Model of Anticipation of Runoff and INundations for Extreme events) hydrological model is a distributed model dedicated to flash flood simulation. Recent developments of the MARINE model are explored in this work. On one hand, transfers of water through the subsurface, formerly relying on water height, now take place in a homogeneous soil column based on the soil saturation degree (SSF model). On the other hand, the soil column is divided into two layers, which represent, respectively, the upper soil layer and the deep weathered rocks (SSF–DWF model). The aim of the present work is to assess the accuracy of these new representations for the simulation of soil moisture during flash flood events. An exploration of the various products available in the literature for soil moisture estimation is performed. The efficiency of the models for soil saturation degree simulation is estimated with respect to several products either at the local scale or spatially distributed: (i) the gridded soil moisture product provided by the operational modeling chain SAFRAN-ISBA-MODCOU; (ii) the gridded soil moisture product provided by the LDAS-Monde assimilation chain, which is based on the ISBA-A-gs land surface model and assimilating satellite derived data; (iii) the upper soil water content hourly measurements taken from the SMOSMANIA observation network; and (iv) the Soil Water Index provided by the Copernicus Global Land Service (CGLS), which is derived from Sentinel-1 C-SAR and ASCAT satellite data. The case study is performed over two French Mediterranean catchments impacted by flash flood events over the 2017–2019 period. The local comparison of the MARINE outputs with the SMOSMANIA measurements, as well as the comparison at the basin scale of the MARINE outputs with the gridded LDAS-Monde and CGLS data, lead to the following conclusion: both the dynamics and the amplitudes of the soil saturation degree simulated with the SSF and SSF–DWF models are better correlated with both the SMOSMANIA measurements and the LDAS-Monde data than the outputs of the base model. Finally, the soil saturation degree simulated by the two-layers model for the deep layer is compared to the soil saturation degree provided by the LDAS-Monde product at corresponding depths. In conclusion, the developments presented for the representation of subsurface flow in the MARINE model enhance the soil saturation degree simulation during flash floods with respect to both gridded data and local soil moisture measurements.

Triple Collocation Evaluation of In Situ Soil Moisture Observations from 1200+ Stations as part of the U.S. National Soil Moisture Network

AbstractSoil moisture is an important variable for numerous scientific disciplines, and therefore provision of accurate and timely soil moisture information is critical. Recent initiatives, such as the National Soil Moisture Network effort, have increased the spatial coverage and quality of soil moisture monitoring infrastructure across the contiguous United States. As a result, the foundation has been laid for a high-resolution, real-time gridded soil moisture product that leverages data from in situ networks, satellite platforms, and land surface models. An important precursor to this development is a comprehensive, national-scale assessment of in situ soil moisture data fidelity. Additionally, evaluation of the United States’s current in situ soil moisture monitoring infrastructure can provide a means toward more informed satellite and model calibration and validation. This study employs a triple collocation approach to evaluate the fidelity of in situ soil moisture observations from over 1200 stations across the contiguous United States. The primary goal of the study is to determine the monitoring stations that are best suited for 1) inclusion in national-scale soil moisture datasets, 2) deriving in situ–informed gridded soil moisture products, and 3) validating and benchmarking satellite and model soil moisture data. We find that 90% of the 1233 stations evaluated exhibit high spatial consistency with satellite remote sensing and land surface model soil moisture datasets. In situ error did not significantly vary by climate, soil type, or sensor technology, but instead was a function of station-specific properties such as land cover and station siting.

A Comparison of the U.S. Climate Reference Network Precipitation Data to the Parameter-Elevation Regressions on Independent Slopes Model (PRISM)

AbstractSince drought and excessive rainfall can have significant socioeconomic impacts, it is important to have accurate high-resolution gridded datasets that can help improve analysis and forecasting of these conditions. One such widely used dataset is the Parameter-Elevation Regressions on Independent Slopes Model (PRISM). PRISM uses a digital elevation model (DEM) to obtain gridded elevation analyses and then uses a regression analysis along with approximately 15 000 surface precipitation measurements to produce a 4-km resolution daily precipitation product over the conterminous United States. The U.S. Climate Reference Network (USCRN) consists of 114 stations that take highly accurate meteorological measurements across all regions of the United States. A comparison between the USCRN and PRISM was performed using data from 2006 to 2018. There were good comparisons between the two datasets across nearly all seasons and regions; most mean daily differences were <1 mm, with most absolute daily differences ~5 mm. The most general characteristics were for a net dry bias in the PRISM data in the Southwest and a net moist bias in the southern United States. Verifying the PRISM dataset provides us with confidence it can be used with estimates of evapotranspiration, high-resolution gridded soil properties, and vegetation datasets to produce a daily gridded soil moisture product for operational use in the analyses and prediction of drought and excessive soil moisture conditions.

Ensemble Downscaling of Soil Moisture Data Using BMA and ATPRK

Soil moisture is essential information for meteorological and hydrological analyses. To date, many efforts have been made to achieve the two goals for soil moisture data, i.e., the improvement of accuracy and resolution, which is very challenging. We presented an ensemble downscaling method for quality improvement of gridded soil moisture data in terms of the accuracy and the spatial resolution by the integration of BMA (Bayesian model averaging) and ATPRK (area-to-point regression kriging). In the experiments, the BMA ensemble showed a 22% better accuracy than the data sets from ESA CCI (European Space Agency-Climate Change Initiative), ERA5 (ECMWF Reanalysis 5), and GLDAS (Global Land Data Assimilation System) in terms of RMSE (root mean square error). Also, the ATPRK downscaling could enhance the spatial resolution from 0.25o to 0.05o while preserving the improved accuracy and the spatial pattern of the BMA ensemble, without under- or over-estimation. The quality-improved data sets can contribute to a variety of local and regional applications related to soil moisture, such as agriculture, forest, hydrology, and meteorology. Because the ensemble downscaling method can be applied to the other land surface variables such as temperature, humidity, precipitation, and evapotranspiration, it can be a viable option to complement the accuracy and the spatial resolution of satellite images and numerical models.

A new framework for tracking flash drought events in space and time

Flash drought is a space–time phenomenon with rapid intensification nature that poses a series of challenges for early warning systems and drought relief. Traditional works do not consider the space–time dynamic processes of flash droughts, unable to provide important information such as how fast an event spreads in space that hampers governors and stakeholders from making timely drought mitigation operations. Here we introduced a novel framework for tracking flash droughts that fully accounts of their dynamic space–time behavior, with focus upon the instantaneous intensification/recovery rate (IIR/IRR) and spatial propagation. Flash drought events were defined by a space–time coherent set of grids where pentad-scale standardized evapotranspiration deficit index is below a prescribed threshold. The drought development/recovery stage is identified in terms of intensity, and IIR/IRR between two consecutive drought patches is determined based on the variable motion relationship of speed-time process from the physics perspective. A single space–time drought event is extracted when the duration, averaged intensification rate and IIR all reach prescribed standards. Using daily meteorological station data and daily root zone (0–100 cm) gridded soil moisture data, the framework is demonstrated by analyzing the flash droughts in the Pearl River basin over China from 1960 to 2015. Results indicate that the framework can well capture space–time structure of flash droughts including the severity and dynamic spatial propagation. Most of the identified flash drought events last 5–6 pentads but affect over half of the basin, and the top seven events have affected over 90% of the basin which intensify in two pentads showing rapid intensification nature. Moreover, flash droughts are associated with precipitation, humidity, temperature, and sunshine duration. The framework is conducive to better understanding of flash drought processes that help guide effective monitoring and development of early warning systems.

Detecting Winter Wheat Irrigation Signals Using SMAP Gridded Soil Moisture Data

The southern part of the Hebei Province is one of China’s major crop-producing regions. Due to the continuous decline in groundwater level, agricultural water use is facing significant challenges. Precision agricultural irrigation management is undoubtedly an effective way to solve this problem. Based on multisource data (time series soil moisture active passive (SMAP) data, Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) and evapotranspiration (ET), and meteorological station precipitation), the irrigation signal (frequency, timing and area) is detected in the southern part of the Hebei Province. The SMAP data was processed by the 5-point moving average method to reduce the error caused by the uncertainty of the microwave data derived SM. Irrigation signals can be detected by removing the precipitation effect and setting the SM change threshold. Based on the validation results, the overall accuracy of the irrigation signal detection is 77.08%. Simultaneously, considering the spatial resolution limitation of SMAP pixels, the SMAP irrigation area was downscaled using the winter wheat area extracted from MODIS NDVI. The analytical results of 55 winter wheat samples (5 samples in a group) showed that winter wheat covered by one SMAP pixel had an 82.72% growth consistency in surface water irrigation period, which can indicate a downscaling effectiveness. According to the above statistical analysis, this paper considers that although the spatial resolution of SMAP data is insufficient, it can reflect the change of SM more sensitively. In areas where the crop pattern is relatively uniform, the introduction of high-resolution crop pattern distribution can be used not only to detect irrigation signals but also to validate the effectiveness of irrigation signal detection by analyzing crop growth consistency. Therefore, the downscaling results can indicate the true winter wheat irrigation timing, area and frequency in the study area.

Citizen observatory based soil moisture monitoring – the GROW example

GROW Observatory is a project funded under the European Union’s Horizon 2020 research and innovation program. Its aim is to establish a large scale (more than 20,000 participants), resilient and integrated ‘Citizen Observatory’ (CO) and community for environmental monitoring that is self-sustaining beyond the life of the project. This article describes how the initial framework and tools were developed to evolve, bring together and train such a community; raising interest, engaging participants, and educating to support reliable observations, measurements and documentation, and considerations with a special focus on the reliability of the resulting dataset for scientific purposes. The scientific purposes of GROW observatory are to test the data  quality and the spatial representativity of a citizen engagement driven spatial distribution as reliably inputs for soil moisture monitoring and to create timely series of gridded soil moisture products based on citizens’ observations using low cost soil moisture (SM) sensors, and to provide an extensive dataset of in situ soil moisture observations which can serve as a reference to validate satellite-based SM products and support the Copernicus in situ component. This article aims to showcase the initial steps of setting up such a monitoring network that has been reached at the mid-way point of the project’s funded period, focusing mainly on the design and development of the CO monitoring network.

Fuzzy-set-based real-time reservoir operation using genetic algorithm

ABSTRACT A short-term reservoir operation model is developed for irrigation wherein the soil moisture of crops at grids and the reservoir storage are considered as fuzzy sets. The distinguished main features of the model as compared to the former fuzzy short-term reservoir operation model are the consideration of real-coded genetic algorithm and the implementation of the satellite-based gridded soil moisture of crops. The water is released from the reservoir at 10-day time periods by maintaining storage balance and the available water for the irrigation is distributed between the crops at grids by considering ponding depth for paddy, soil moisture continuity and root depth of the crops. The irrigation area is discretized into a number of grids (1º × 1º) to consider the spatial variations of rainfall and soil moisture of the crops. The fuzzy short-term reservoir operation model is optimized by the real-coded genetic algorithm by minimizing soil moisture deficits using forecasted inflow and rainfalls at grids from the present to last periods. The fuzzy short-term reservoir operation model using genetic algorithm performs better as compared to the conceptual reservoir operation policy and is faster than the short-term reservoir operation model using dynamic programming.

An in-situ data based model to downscale radiometric satellite soil moisture products in the Upper Hunter Region of NSW, Australia

High spatial resolution soil moisture information is important for hydrological, climatic and agricultural applications. The lack of high resolution soil moisture data over large areas at the required accuracy is a major impediment for such applications. This study investigates the feasibility of downscaling satellite soil moisture products to 1 km resolution. This study was undertaken in the semi-arid Goulburn River Catchment, located in south-eastern Australia. The Soil Moisture Active Passive (SMAP)-Enhanced 9 km (L3SMP-E) and Soil Moisture and Ocean Salinity (SMOS) 25 km gridded (SMOS CATDS L3 SM 3-DAY) radiometric products were compared with in-situ soil moisture observations and a regression tree model was developed for downscaling based on thermal inertia theory. Observations from a long-term soil moisture monitoring network were employed to develop a regression tree model between the diurnal temperature difference and the daily mean soil moisture for soils with different clay content and vegetation greenness. Moderate-resolution Imaging Spectroradiometer (MODIS) land surface temperatures were used to estimate the soil moisture at high spatial resolution by disaggregating the satellite soil moisture products through the regression model. The downscaled SMAP-Enhanced 9 km and SMOS 25 km gridded soil moisture products showed unbiased root mean square errors (ubRMSE) of 0.07 and 0.05 cm3/cm3, respectively, against the in-situ data. These ubRMSEs include errors caused by measuring instrument and the scale mismatch between downscaled products and in-situ data. An RMSE of 0.07 cm3/cm3 was observed when comparing the downscaled soil moisture against the passive airborne L-band retrievals. The findings here auger well for the use of satellite remote sensing for the assessment of high resolution soil moisture.