Soil moisture plays a key role in controlling land-atmosphere interactions, with implications for water resources, agriculture, climate, and ecosystem dynamics. Although soil varies strongly across the landscape, current monitoring capabilities are limited to coarse-scale satellite retrievals few regional in-situ networks. Here, we introduce SMAP-HydroBlocks (SMAP-HB), high-resolution satellite-based surface dataset at an unprecedented 30-m resolution (2015-2019) conterminous United States....

Abstract. Over the past decade, there has been appreciable progress towards modeling water, energy, and carbon cycles at field scales (10–100 m) over continental to global extents in Earth system models (ESMs). One such approach, named HydroBlocks, accomplishes this task while maintaining computational efficiency via Hydrologic Response Units (HRUs), more commonly known as “tiles” ESMs. In these HRUs are learned a hierarchical clustering approach from available high-resolution environmental...

Abstract Soil moisture (SM) spatiotemporal variability critically influences water resources, agriculture, and climate. However, besides site‐specific studies, little is known about how SM varies locally (1–100‐m scale). Consequently, quantifying the its impact on Earth system remains a long‐standing challenge in hydrology. We reveal striking of local‐scale across United States using SMAP‐HydroBlocks — novel satellite‐based surface data set at 30‐m resolution. Results show complex interplay...

Abstract This study assesses the added value of using emerging maps soil properties to improve surface moisture simulations HydroBlocks land model with different hydraulic parameterization schemes. Simulations were run at an hourly 30‐m resolution between 2012 and 2019 evaluated against U.S. Climate Reference Network measurements. The results show that state‐of‐the‐art (POLARIS SoilGrids250m V2.0) accuracy simulated when compared STATSGO‐derived CONUS‐SOIL map. Contemporary pedotransfer...

Abstract. Groundwater is critical in the hydrological cycle, impacting water supply, agriculture, and climate regulation. However, current Land Surface Models (LSMs) often struggle to accurately represent multiple spatial scales of subsurface flow primarily due complexity incorporating sufficient yet efficiently surface heterogeneity, which significantly influences dynamics. Accurately modeling this heterogeneity requires substantial computational resources, making it challenging achieve...

Abstract. We present a new data set aimed at hydrologic studies across North America, with particular focus on facilitating spatially distributed studies. The includes basin outlines, stream observations, meteorological and geospatial for 1426 basins in the United States Canada. To facilitate wide variety of studies, we provide outlines lumped semi-distributed resolution; streamflow observations daily hourly time steps; variables suitable running range models obtained derived from different...

Abstract One of the persistent challenges Land Surface Models (LSMs) is to determine a realistic yet efficient sub‐grid representation heterogeneous landscapes. This particularly important in emulating fine‐scale and nonlinear interactions between water, energy, biogeochemical fluxes at land surface. In LSMs, landscape heterogeneity can be represented using tiling techniques, which partition macroscale grid cells (e.g., 1°) into smaller units or “tiles.” However, there currently no formal...

Abstract. Over the past decade, there has been appreciable progress towards modeling water, energy, and carbon cycles at field-scales (10–100 m) over continental to global extents. One such approach, named HydroBlocks, accomplishes this task while maintaining computational efficiency via sub-grid tiles, or Hydrologic Response Units (HRUs), learned a hierarchical clustering approach from available high-resolution environmental data. However, until now, yet be macroscale river routing that is...

Over recent years, considerable advances have been made in Land Surface Models (LSM) to enhance the representation of small-scale heterogeneity while maintaining reasonable computational efficiency. Such is case HydroBlocks, which employs fine-scale clustering define Hydrologic Response Units (HRUs) or tiles as its core modeling element. These innovations facilitated a better water and energy balances over large-scale domains by capturing local dynamics their signature continental processes....

Abstract Surface fluxes and states can recur remain consistent across various spatial temporal scales, forming space‐time patterns. Quantifying understanding the observed patterns is desirable, as they provide information about dynamics of processes involved. This study introduces empirical spatio‐temporal covariance function a corresponding parametric tools to identify characterize in remotely sensed fields. The method demonstrated using 2 km hourly GOES‐16/17 land surface temperature (LST)...

Land surface temperature (LST) is a crucial state variable determining the interactions between land and atmosphere (i.e., energy, water, carbon fluxes). Accordingly, several hydrological quantities, such as soil moisture content, vegetation water stress, gross primary production, crop yield, correlate strongly with it. Thus, LST constitutes critical in understanding physics of multiple processes. Decades global satellite remotely sensed fields are now available, creating an unprecedented...

<p>Multi-scale spatial heterogeneity over the land surface (meter to km scales) can play a pivotal role in development of clouds and precipitation. To model this process within Earth system models (ESMs; ~100 resolution), sub-grid reduced-order modeling approaches are used. More specifically, state-of-the-art ESMs sub-divide each grid cell into representative clusters (e.g., forest, lakes, grasslands) that learned a-priori from available high-resolution satellite remote sensing...

Space-time patterns of surface fluxes and states have direct implications for boundary layer growth, cloud development, phenology, runoff generation, among other processes. Emerging field-scale resolving land models (the terrestrial component Earth system models), such as HydroBlocks, aim to represent this complexity by modeling the water, energy, biogeochemical cycles at meter-km spatial scales over continental extents. Although there been significant advances in representation...

The Empirical Spatio-Temporal Covariance Function is a flexible tool to describe the spatiotemporal dependence structure of observed fields.• A parametric covariance model can concisely spatio-temporal patterns captured from empirical data.• Multidimensional clustering be used identify areas with similar structures over continental scales.

<p>Representing the physical heterogeneity of land surface in Earth System Models (ESM) remains a persistent challenge due to its relevance represent weather and climate dynamics hydrological cycle accurately. To address this challenge, HydroBlocks Land Surface Model (LSM) [1] uses hierarchical tiling scheme that defines Hydrologic Response Units (HRUs) by clustering high-resolution global environmental data (e.g., 30-m cover, topography, soil properties). The recently...

<p>Soil moisture (SM) varies widely in space and time. This variability influences agriculture, land-atmosphere interactions triggers hazards, such as flooding, landslides, droughts, wildfires. Yet, current observations are limited to a few regional situ measurement networks or coarse-scale satellite retrievals (9–36-km resolution). As result, besides site-specific studies, little is known on how SM locally (1–100-m Consequently, quantifying the...

<p>Emerging field-scale resolving land surface models (LSMs), such as HydroBlocks, aim to model the water, energy, and biogeochemical cycles (e.g., energy partitioning) at 10-100 meter spatial scales over continental extents. However, there has yet be a concerted effort evaluate realism of simulated patterns. This presentation challenges scientific community modeled multi-scale patterns contemporary more critically. Here, we present an approach temperature (a linchpin state...

Earth and Space Science Open Archive Presented WorkOpen AccessYou are viewing the latest version by default [v1]Analysis of hydrological spatial temporal characteristic scales over Contiguous United States using GOES-16 Land Surface Temperature retrievalsAuthorsLauraTorres-RojasiDTylerWatermaniDJiaxuanCaiNathanielChaneyiDSee all authors Laura Torres-RojasiDCorresponding Author• Submitting AuthorDuke UniversityiDhttps://orcid.org/0000-0001-7319-2355view email addressThe was not providedcopy...

<p>The accurate representation of soil properties in the land component Earth system models (land surface models; LSMs) remains a persistent challenge. The emergence state-of-the-art continental-scale digital mapping (DSM) provides unique opportunity to address this weakness (e.g., SoilGrids and POLARIS). However, it unclear whether these data are able improve modeling fluxes states latent heat flux). This presentation addresses question by running evaluating field-scale...

<p>The representation of land surface’s sub-grid heterogeneity in Earth System models remains a persistent challenge. The evolution grid-cell partitioning techniques has evolved from user-defined equally sized tiles (Chen et al., 1997) to structural partition based on vegetation or soil spatial distribution (Melton & Arora, 2014), and finally, advanced clustering techniques, the concept Hydrological Response Units (HRU) (Chaney 2018). These tiling schemes...