Evaluation of SMAP, SMOS-IC, FY3B, JAXA, and LPRM Soil Moisture Products over the Qinghai-Tibet Plateau and Its Surrounding Areas

© 2019 by the authors. High-quality and long time-series soil moisture (SM) data are increasingly required for the Qinghai-Tibet Plateau (QTP) to more accurately and effectively assess climate change. In this study, to evaluate the accuracy and effectiveness of SM data, five passive microwave remotely sensed SM products are collected over the QTP, including those from the soil moisture active passive (SMAP), soil moisture and ocean salinity INRA-CESBIO (SMOS-IC), Fengyun-3B microwave radiation image (FY3B), and two SM products derived from the advanced microwave scanning radiometer 2 (AMSR2). The two AMSR2 products are generated by the land parameter retrieval model (LPRM) and the Japan Aerospace Exploration Agency (JAXA) algorithm, respectively. The SM products are evaluated through a two-stage data comparison method. The first stage is direct validation at the grid scale. Five SM products are compared with corresponding in situ measurements at five in situ networks, including Heihe, Naqu, Pali, Maqu, and Ngari. Another stage is indirect validation at the regional scale, where the uncertainties of the data are quantified by using a three-cornered hat (TCH) method. The results at the regional scale indicate that soil moisture is underestimated by JAXA and overestimated by LPRM, some noise is contained in temporal variations in SMOS-IC, and FY3B has relatively low absolute accuracy. The uncertainty of SMAP is the lowest among the five products over the entire QTP. In the SM map composed by five SM products with the lowest pixel-level uncertainty, 66.64% of the area is covered by SMAP (JAXA: 19.39%, FY3B: 10.83%, LPRM: 2.11%, and SMOS-IC: 1.03%). This study reveals some of the reasons for the different performances of these five SM products, mainly from the perspective of the parameterization schemes of their corresponding retrieval algorithms. Specifically, the parameterization configurations and corresponding input datasets, including the land-surface temperature, the vegetation optical depth, and the soil dielectric mixing model are analyzed and discussed. This study provides quantitative evidence to better understand the uncertainties of SM products and explain errors that originate from the retrieval algorithms

Simulating carbon and water fluxes using a coupled process-based terrestrial biosphere model and joint assimilation of leaf area index and surface soil moisture

Reliable modeling of carbon and water fluxes is essential for understanding the terrestrial carbon and water cycles and informing policy strategies aimed at constraining carbon emissions and improving water use efficiency. We designed an assimilation framework (LPJ-Vegetation and soil moisture Joint Assimilation, or LPJ-VSJA) to improve gross primary production (GPP) and evapotranspiration (ET) estimates globally. The integrated model, LPJ-PM (LPJ-PT-JPLSM Model) as the underlying model, was coupled from the Lund–Potsdam–Jena Dynamic Global Vegetation Model (LPJ-DGVM version 3.01) and a hydrology module (i.e., the updated Priestley–Taylor Jet Propulsion Laboratory model, PT-JPLSM). Satellite-based soil moisture products derived from the Soil Moisture and Ocean Salinity (SMOS) and Soil Moisture Active and Passive (SMAP) and leaf area index (LAI) from the Global LAnd and Surface Satellite (GLASS) product were assimilated into LPJ-PM to improve GPP and ET simulations using a proper orthogonal decomposition (POD)-based ensemble four-dimensional variational assimilation method (PODEn4DVar). The joint assimilation framework LPJ-VSJA achieved the best model performance (with an R2 ( coefficient of determination) of 0.91 and 0.81 and an ubRMSD (unbiased root mean square deviation) reduced by 40.3 % and 29.9 % for GPP and ET, respectively, compared with those of LPJ-DGVM at the monthly scale). The GPP and ET resulting from the assimilation demonstrated a better performance in the arid and semi-arid regions (GPP: R2 = 0.73, ubRMSD = 1.05 g C m−2 d−1; ET: R2 = 0.73, ubRMSD = 0.61 mm d−1) than in the humid and sub-dry humid regions (GPP: R2 = 0.61, ubRMSD = 1.23 g C m−2 d−1; ET: R2 = 0.66; ubRMSD = 0.67 mm d−1). The ET simulated by LPJ-PM that assimilated SMAP or SMOS data had a slight difference, and the SMAP soil moisture data performed better than SMOS data. Our global simulation modeled by LPJ-VSJA was compared with several global GPP and ET products (e.g., GLASS GPP, GOSIF GPP, GLDAS ET, and GLEAM ET) using the triple collocation (TC) method. Our products, especially ET, exhibited advantages in the overall error distribution (estimated error (μ): 3.4 mm per month; estimated standard deviation of μ: 1.91 mm per month). Our research showed that the assimilation of multiple datasets could reduce model uncertainties, while the model performance differed across regions and plant functional types. Our assimilation framework (LPJ-VSJA) can improve the model simulation performance of daily GPP and ET globally, especially in water-limited regions.</p

The International Soil Moisture Network:Serving Earth system science for over a decade

In 2009, the International Soil Moisture Network (ISMN) was initiated as a community effort, funded by the European Space Agency, to serve as a centralised data hosting facility for globally available in situ soil moisture measurements (Dorigo et al., 2011b, a). The ISMN brings together in situ soil moisture measurements collected and freely shared by a multitude of organisations, harmonises them in terms of units and sampling rates, applies advanced quality control, and stores them in a database. Users can freely retrieve the data from this database through an online web portal (https://ismn.earth/en/, last access: 28 October 2021). Meanwhile, the ISMN has evolved into the primary in situ soil moisture reference database worldwide, as evidenced by more than 3000 active users and over 1000 scientific publications referencing the data sets provided by the network. As of July 2021, the ISMN now contains the data of 71 networks and 2842 stations located all over the globe, with a time period spanning from 1952 to the present. The number of networks and stations covered by the ISMN is still growing, and approximately 70 % of the data sets contained in the database continue to be updated on a regular or irregular basis. The main scope of this paper is to inform readers about the evolution of the ISMN over the past decade, including a description of network and data set updates and quality control procedures. A comprehensive review of the existing literature making use of ISMN data is also provided in order to identify current limitations in functionality and data usage and to shape priorities for the next decade of operations of this unique community-based data repository

Managing Water Resources in Large River Basins

Management of water resources in large rivers basins typically differs in important ways from management in smaller basins. While in smaller basins the focus of water resources management may be on project implementation, irrigation and drainage management, water use efficiency and flood operations; in larger basins, because of the greater complexity and competing interests, there is often a greater need for long-term strategic river basin planning across sectors and jurisdictions, and considering social, environmental, and economic outcomes. This puts a focus on sustainable development, including consumptive water use and non-consumptive water uses, such as inland navigation and hydropower. It also requires the consideration of hard or technical issues—data, modeling, infrastructure—as well as soft issues of governance, including legal frameworks, policies, institutions, and political economy. Rapidly evolving technologies could play a significant role in managing large basins. This Special Issue of Water traverses these hard and soft aspects of managing water resources in large river basins through a series of diverse case studies from across the globe that demonstrate recent advances in both technical and governance innovations in river basin management

Ground, Proximal, and Satellite Remote Sensing of Soil Moisture

Soil moisture (SM) is a key hydrologic state variable that is of significant importance for numerous Earth and environmental science applications that directly impact the global environment and human society. Potential applications include, but are not limited to, forecasting of weather and climate variability; prediction and monitoring of drought conditions; management and allocation of water resources; agricultural plant production and alleviation of famine; prevention of natural disasters such as wild fires, landslides, floods, and dust storms; or monitoring of ecosystem response to climate change. Because of the importance and wide‐ranging applicability of highly variable spatial and temporal SM information that links the water, energy, and carbon cycles, significant efforts and resources have been devoted in recent years to advance SM measurement and monitoring capabilities from the point to the global scales. This review encompasses recent advances and the state‐of‐the‐art of ground, proximal, and novel SM remote sensing techniques at various spatial and temporal scales and identifies critical future research needs and directions to further advance and optimize technology, analysis and retrieval methods, and the application of SM information to improve the understanding of critical zone moisture dynamics. Despite the impressive progress over the last decade, there are still many opportunities and needs to, for example, improve SM retrieval from remotely sensed optical, thermal, and microwave data and opportunities for novel applications of SM information for water resources management, sustainable environmental development, and food security

Agro-hydrological modelling of regional irrigation water demand

The irrigation sector accounts for over 70% of the total freshwater consumption in the world. Therefore, e cient management of irrigation water is essential to ensure water, food, energy and environmental securities in a sustainable manner; these securities are grand challenges of the 21st century. The main objective of this research is to evaluate the simulation of irrigation water demand at the catchment scale in order to develop improved tools for conducting quantitative planning and climate change studies. Irrigation water demand is mostly driven by soil moisture. It is a state variable which is used to trigger the irrigation in hydrological models. In this study, a hydrolgical model (Soil and Water Assessment Tool, SWAT) is evaluated for reliably simulating the spatial and temporal patterns of soil moisture at a catchment scale. The SWAT simulated soil moisture was compared with the indirect estimates of soil moisture from Landsat and Time-domain re ectometry (TDR). The results showed that the SWAT simulated soil moisture was comparable with the soil moisture estimated from Landsat and TDR. Secondly, the applicability of the SWAT model was tested for simulating stream ow, evapotranspiration (ET) and irrigation water demand for four di erent agro-climatic zones (Mediterranean, Subtropical monsoon, Humid, and Tropical). Two di erent irrigation scheduling techniques were used to simulate irrigation namely, soil water de cit and plant water demand. It was seen from the results that the SWAT simulated irrigation amounts under soil moisture irrigation scheduling technique were close to the irrigation statistics provided by the state. However, the irrigation amounts simulated under the plant water demand irrigation scheduling technique were underestimated. Additionally, the two reanalysis data were also used to check the data uncertainty in simulating irrigation water demand. SWAT model code was modi ed by incorporating modi ed root density distribution function and dynamic stress factor. The modi ed model was used to simulate irrigation and crop yield. It was tested against the irrigation and crop yield simulated by Soil Water Atmosphere Plant (SWAP) model and eld data (Hamerstorf, Lower Saxony, Germany). It was then validated for di erent catchments (Germany, India and Vietnam). The results showed that the SWAT simulated irrigation water demand in case of plant water demand is comparable with the amount simulated by the model under soil water de cit irrigation scheduling technique. This dissertation not only bridges the gap between the scales of soil moisture determination but also establishes a close connection with the actual observations and modelled soil moisture and irrigation amounts at the eld, regional and global studies in agricultural water management. Additionally, the studies about simulating irrigation water requirement in data-scarce areas must address data uncertainty when using reanalysis data. It was found that rainfall is not always the dominant variable in irrigation simulation. Therefore, it is worth checking and bias correct the other climate variables

Impact of fully coupled hydrology-atmosphere processes on atmosphere conditions: investigating the performance of the WRF-Hydro model in the Three River source region on the Tibetan Plateau, China

The newly developed WRF-Hydro model is a fully coupled atmospheric and hydrological processes model suitable for studying the intertwined atmospheric hydrological processes. This study utilizes the WRF-Hydro system on the Three-River source region. The Nash-Sutcliffe efficiency for the runoff simulation is 0.55 compared against the observed daily discharge amount of three stations. The coupled WRF-Hydro simulations are better than WRF in terms of six ground meteorological elements and turbulent heat flux, compared to the data from 14 meteorological stations located in the plateau residential area and two flux stations located around the lake. Although WRF-Hydro overestimates soil moisture, higher anomaly correlation coefficient scores (0.955 versus 0.941) were achieved. The time series of the basin average demonstrates that the hydrological module of WRF-hydro functions during the unfrozen period. The rainfall intensity and frequency simulated by WRF-Hydro are closer to global precipitation mission (GPM) data, attributed to higher convective available potential energy (CAPE) simulated by WRF-Hydro. The results emphasized the necessity of a fully coupled atmospheric-hydrological model when investigating land-atmosphere interactions on a complex topography and hydrology region

Emerging Hydro-Climatic Patterns, Teleconnections and Extreme Events in Changing World at Different Timescales

This Special Issue is expected to advance our understanding of these emerging patterns, teleconnections, and extreme events in a changing world for more accurate prediction or projection of their changes especially on different spatial–time scales