While previous model sensitivity studies have mainly focused on discerning the soil moisture-precipitation feedback processes over the Indian region, the present study investigates the impact of soil moisture-temperature (SM-T) coupling on the temperature extremes (ExT) using the high-resolution (~60 km) model simulations. These simulations include the control and soil moisture (SM) sensitivity experiments (DRY-SM and WET-SM) initialized by perturbing (decreasing/increasing) SM from the historical (HIST: 1951-2010) and future 4K warming (FUT: 2051-2100) control runs. The analysis identifies the transitional regions of north-central India (NCI) as the hotspot of strong SM-T coupling. Over NCI, the HIST experiment shows an occurrence of 4-5 extreme events per year, with an average duration of 5-6 days per event and intensity exceeding 46oC. Whereas, FUT estimates indicate relatively severe, long-lasting, and more frequent extreme events. The SM sensitivity experiments reveal the significant influence of SM-T coupling on the ExT over NCI in both historical and future climates. We find that the DRY-SM results in significant enhancement of frequency, duration and intensity of ExT, in contrast to WET-SM. We note that the difference between DRY-SM and WET-SM 50-year return value of the block maxima GEV fit can reach upto 1.25oC and 3oC for historical and future climate, respectively. The enhanced (reduced) extreme temperature conditions in DRY-SM (WET-SM) simulation are caused by the intensification (abridgement) of sensible heat flux by limiting (intensifying) available total energy for evaporative cooling due to faster (slower) dissipation of positive soil moisture anomalies (also called as soil moisture memory). In addition, the influence of SM on ExT over NCI is found to be larger during the post-monsoon season as compared to the pre-monsoon and monsoon seasons.
Mesoscale soil moisture heterogeneity can locally amplify humid heat
UK Centre for Ecology and Hydrology（英国生态与水文学中心）；School of Earth and Environment, University of Leeds（利兹大学地球与环境学院）；National Centre for Earth Observation（国家地球观测中心）；Centre for Environmental Modelling and Computation, University of Leeds（利兹大学环境建模与计算中心）；Met Office（英国气象局）
Machine learning (ML) has emerged as a novel tool for generating large-scale land surface data in recent years. ML can learn the relationship between input and target, e.g. meteorological variables and in-situ soil moisture, and then estimate soil moisture across space and time, independently of prior physics-based knowledge. Here we develop a high-resolution (0.1°) daily soil moisture dataset in Europe (SoMo.ml-EU) using Long Short-Term Memory trained with in-situ measurements. The resulting dataset covers three vertical layers and the period 2003-2020. Compared to its previous version with a…
Max Planck Institute for Biogeochemistry（马克斯·普朗克生物地球化学研究所）
While soil moisture information is essential for a wide range of hydrologic and climate applications, spatially-continuous soil moisture data is only available from satellite observations or model simulations. Here we present a global, long-term dataset of soil moisture generated from in-situ measurements using machine learning, SoMo.ml. We train a Long Short-Term Memory (LSTM) model to extrapolate daily soil moisture dynamics in space and in time, based on in-situ data collected from more than 1,000 stations across the globe. SoMo.ml provides multi-layer soil moisture data (0-10 cm, 10-30 cm,…
The interaction between the Earths surface and the atmosphere plays a key role in the initiation of cumulus convection. Over the land surface, a necessary boundary condition to consider for resolving land-atmosphere interactions is soil moisture. The aim in the study is twofold. One, through object oriented and traditional verification techniques determine how higher resolution soil moisture initial conditions influences the prediction of the location and timing of convective initiation (CI) within a convective permitting, operational NWP model over South Africa. Two, to study the modelled CI-…
In paddy field, monitoring soil moisture is required for irrigation scheduling and water resource allocation, management and planning. The current study proposes an Artificial Neural Networks (ANN) model to estimate soil moisture in paddy field with limited meteorological data. Dynamic of ANN model was adopted to estimate soil moisture with the inputs of reference evapotranspiration (ETo) and precipitation. ETo was firstly estimated using the maximum, average and minimum values of air temperature as the inputs of model. The models were performed under different weather conditions between the t…
Soil moisture (SM) is a key state variable of the hydrological cycle, needed to monitor the effects of a changing climate on natural resources. Soil moisture is highly variable in space and time, presenting seasonalities, anomalies and long-term trends, but also, and important nonlinear behaviours. Here, we introduce a novel fast and nonlinear complex PCA method to analyze the spatio-temporal patterns of the Earth's surface SM. We use global SM estimates acquired during the period 2010-2017 by ESA's SMOS mission. Our approach unveils both time and space modes, trends and periodicities unlike s…
Remote sensing of soil moisture and vegetation water content from space often requires underdetermined inversion of a zeroth-order approximation of the forward radiative transfer equation in L-band---known as the $τ$-$ω$ model. This paper shows that the least-squares (LS) inversion of the model is not strictly convex due to its saddle point structure. It is demonstrated that the widely used unconstrained damped least-squares (DLS) inversion of the model could lead to biased and physically unrealistic retrievals---chiefly because of the existing preferential solution spaces that are characteriz…
Soil moisture (SM) is referred to as a finite amount of water molecules within the pore spaces and it is a crucial parameter of Hydro-Meteorological processes. The behaviour of soil moisture water changes spatially and temporally in response to topography, soil characteristics, and climate[1]. Soil moisture is overseen by various hydro-meteorological factors that vary vertically with depth, laterally across terrestrial shapes, and temporarily in feedback to the climate. The precise monitoring and quantification of high-resolution surface and subsurface soil moisture observations are very impor…
Microwave remote sensors mounted on center pivot irrigation systems provide a feasible approach to obtain soil moisture information, in the form of water content maps, for the implementation of closed-loop irrigation. Major challenges such as significant time delays in the soil moisture measurements, the inability of the sensors to provide soil moisture information in instances where the center pivot is stationary, and the inability of the sensors to provide soil moisture information in the root zone reduce the usability of the water content maps in the effective implementation of closed-loop …
Warming-driven rise in soil moisture entropy signals destabilization of the Asian Water Tower
Beijing Normal University（北京师范大学）；Technical University of Munich（慕尼黑工业大学）；Potsdam Institute for Climate Impact Research（波茨坦气候影响研究所）；Chinese Academy of Meteorological Sciences（中国气象科学研究院）；Tsinghua University（清华大学）；Kunming University of Science and Technology（昆明理工大学）
The proper determination of soil moisture on different scales is important for applications in a variety of fields. We aim to develop a high-level soil moisture product with high temporal and spatial resolution by assimilating the multilayer soil moisture product SCATSAR-SWI (Scatterometer Synthetic Aperture Radar Soil Water Index) into the surface model SURFEX. In addition, we probe the impact of the findings on the Numerical Weather Prediction (NWP) in Austria. The data assimilation system consists of the NWP model AROME and the SURFEX Offline Data Assimilation, which provide atmospheric for…
Soil thermal conductivity is an important physical parameter in modeling land surface processes. Previous studies on evaluations of parameterization schemes of soil thermal conductivity are mostly based on specific experimental conditions or local soil samples, and their recommendations may not be the optimal schemes for land surface model (LSMs). In this work, seven highly recommended soil thermal conductivity schemes are evaluated for their applicability in LSMs. With the consideration of both scheme estimations and land process simulations by incorporation into the Common Land Model, the Ba…
